diff --git a/.gitattributes b/.gitattributes index 34044a3b410f90befaea33e09c56e261731d7c58..6b705fe1fb5426e9cbf78854cbd780d61cf6dda2 100644 --- a/.gitattributes +++ b/.gitattributes @@ -86,3 +86,13 @@ ads_0_length_6000/trainset.xyz filter=lfs diff=lfs merge=lfs -text ads_0_length_6000/ALLEGRO/results/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text ads_0_length_6000/ALLEGRO/results_/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text ads_16_length_4000/testset.xyz filter=lfs diff=lfs merge=lfs -text +ads_16_length_4000/trainset.xyz filter=lfs diff=lfs merge=lfs -text +ads_16_length_4000/MACE/results/MACE_model_run-123_train.txt filter=lfs diff=lfs merge=lfs -text +ads_32_length_2000/testset.xyz filter=lfs diff=lfs merge=lfs -text +ads_32_length_2000/trainset.xyz filter=lfs diff=lfs merge=lfs -text +ads_0_length_4000/testset.xyz filter=lfs diff=lfs merge=lfs -text +ads_0_length_4000/trainset.xyz filter=lfs diff=lfs merge=lfs -text +ads_0_length_4000/MACE/results/MACE_model_run-123_train.txt filter=lfs diff=lfs merge=lfs -text +ads_8_length_8000/testset.xyz filter=lfs diff=lfs merge=lfs -text +ads_16_length_6000/testset.xyz filter=lfs diff=lfs merge=lfs -text +ads_8_length_8000/ALLEGRO/results/adsorption/metrics_batch_train.csv filter=lfs diff=lfs merge=lfs -text diff --git a/ads_0_length_2000/ALLEGRO/results/adsorption/config.yaml b/ads_0_length_2000/ALLEGRO/results/adsorption/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dbb309d118e0be780f0d79793eeb618227c30b4b --- /dev/null +++ b/ads_0_length_2000/ALLEGRO/results/adsorption/config.yaml @@ -0,0 +1,750 @@ +_jit_bailout_depth: 2 +_jit_fuser: fuser1 +_jit_fusion_strategy: +- !!python/tuple + - DYNAMIC + - 3 +allow_tf32: true +append: true +ase_args: + format: extxyz +avg_num_neighbors: 34.28703787878788 +batch_size: 32 +chemical_symbols: +- H +- C +- N +- Zn +- O +code_commits: {} +dataloader_num_workers: 0 +dataset: ase +dataset_AtomicData_options: + r_max: 5.0 +dataset_file_name: ../trainset.xyz +dataset_key_mapping: + force: forces +dataset_seed: 456 +dataset_statistics_stride: 1 +default_dtype: float64 +device: cuda +e3nn_version: 0.5.4 +early_stopping: null +early_stopping_kwargs: null +edge_eng_mlp_latent_dimensions: +- 128 +edge_eng_mlp_nonlinearity: null +ema_decay: 0.999 +ema_use_num_updates: true +end_of_batch_callbacks: [] +end_of_epoch_callbacks: [] +end_of_train_callbacks: [] +env_embed_mlp_latent_dimensions: [] +env_embed_mlp_nonlinearity: null +env_embed_multiplicity: 16 +equivariance_test: false +exclude_keys: [] +final_callbacks: [] +gpu_oom_offload: false +grad_anomaly_mode: false +init_callbacks: [] +irreps_edge_sh: 1x0e+1x1o+1x2e +l_max: 2 +latent_mlp_latent_dimensions: +- 512 +- 512 +latent_mlp_nonlinearity: silu +latent_resnet: true +learning_rate: 0.005 +log_batch_freq: 100 +log_epoch_freq: 1 +loss_coeffs: + forces: + - 20 + total_energy: + - 1 +lr_scheduler_kwargs: {} +lr_scheduler_name: none +max_epochs: 500 +max_gradient_norm: .inf +metrics_components: +- - forces + - mae + - PerSpecies: false + report_per_component: false +- - total_energy + - mae + - PerAtom: false +- - forces + - rmse + - PerSpecies: false + report_per_component: false +- - total_energy + - rmse + - PerAtom: false +- - total_energy + - mae + - PerAtom: true +- - total_energy + - rmse + - PerAtom: true +metrics_key: validation_loss +model_builders: +- allegro.model.Allegro +- PerSpeciesRescale +- StressForceOutput +- RescaleEnergyEtc +model_debug_mode: false +model_dtype: float32 +n_train: 1500 +n_train_per_epoch: null +n_val: 500 +nequip_version: 0.6.1 +nonscalars_include_parity: true +num_layers: 2 +num_types: 5 +optimizer_kwargs: + amsgrad: false + betas: !!python/tuple + - 0.9 + - 0.999 + capturable: false + differentiable: false + eps: 1.0e-08 + foreach: null + fused: null + maximize: false + weight_decay: 0 +optimizer_name: Adam +parity: o3_full +r_max: 5.0 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@@ -0,0 +1,148 @@ +Torch device: cuda +Number of weights: 1406856 +Number of trainable weights: 1406856 +! Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 727 1.09 705 0.917 1.22 31.1 31.1 0.0883 0.0884 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 6.512 0.005 1.06 1.15e+03 1.17e+03 0.898 1.21 39.5 39.8 0.112 0.113 +Wall time: 6.512737072072923 +! Best model 0 1174.431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 47 56.9 0.996 37 0.869 1.17 7.02 7.12 0.0199 0.0202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 25.1 0.986 5.4 0.879 1.16 2.72 2.72 0.00772 0.00773 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 1 38.096 0.005 1.01 2.02e+04 2.02e+04 0.878 1.18 110 167 0.312 0.474 +! Validation 1 38.096 0.005 0.977 2.71 22.3 0.864 1.16 1.66 1.93 0.00472 0.00547 +Wall time: 38.096726526040584 +! Best model 1 22.253 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 47 28.3 0.987 8.56 0.867 1.16 3.11 3.43 0.00884 0.00974 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 27 0.986 7.33 0.879 1.16 3.17 3.17 0.009 0.00901 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 2 67.110 0.005 0.989 72.2 92 0.867 1.16 7.6 9.97 0.0216 0.0283 +! Validation 2 67.110 0.005 0.977 3.38 22.9 0.864 1.16 1.88 2.15 0.00534 0.00612 +Wall time: 67.11061549978331 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 47 20.4 0.97 1.01 0.863 1.15 0.932 1.18 0.00265 0.00334 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 21 0.985 1.25 0.879 1.16 1.3 1.31 0.0037 0.00372 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 3 96.100 0.005 0.988 1.65 21.4 0.867 1.16 1.21 1.5 0.00343 0.00427 +! Validation 3 96.100 0.005 0.977 1.46 21 0.864 1.16 1.1 1.42 0.00313 0.00402 +Wall time: 96.10013025207445 +! Best model 3 20.996 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 47 20.9 0.969 1.5 0.862 1.15 1.2 1.43 0.00342 0.00407 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 21.4 0.984 1.68 0.879 1.16 1.51 1.52 0.00429 0.00431 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 4 125.114 0.005 0.987 1.13 20.9 0.867 1.16 1 1.25 0.00285 0.00354 +! Validation 4 125.114 0.005 0.976 1.36 20.9 0.863 1.16 1.07 1.36 0.00305 0.00388 +Wall time: 125.11449338681996 +! Best model 4 20.874 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 47 21 1 0.977 0.872 1.17 0.907 1.16 0.00258 0.00329 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 20.9 0.983 1.25 0.878 1.16 1.3 1.31 0.0037 0.00373 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 154.129 0.005 0.986 1.12 20.8 0.866 1.16 0.99 1.24 0.00281 0.00352 +! Validation 5 154.129 0.005 0.975 1.48 21 0.863 1.16 1.11 1.42 0.00315 0.00404 +Wall time: 154.1295640738681 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 47 21 0.998 0.993 0.869 1.17 0.909 1.17 0.00258 0.00332 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 20.7 0.982 1.09 0.878 1.16 1.21 1.22 0.00345 0.00347 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 183.121 0.005 0.985 1.11 20.8 0.866 1.16 0.992 1.23 0.00282 0.00351 +! Validation 6 183.121 0.005 0.974 1.54 21 0.862 1.16 1.13 1.45 0.00321 0.00413 +Wall time: 183.1215311489068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 47 20.8 0.985 1.13 0.867 1.16 0.923 1.24 0.00262 0.00353 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 21 0.981 1.44 0.877 1.16 1.4 1.4 0.00397 0.00399 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 212.118 0.005 0.984 1.12 20.8 0.865 1.16 0.996 1.24 0.00283 0.00352 +! Validation 7 212.118 0.005 0.972 1.4 20.8 0.862 1.16 1.09 1.39 0.00309 0.00394 +Wall time: 212.11811342090368 +! Best model 7 20.849 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 47 20.6 0.968 1.22 0.859 1.15 1.07 1.3 0.00303 0.00368 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 21 0.979 1.45 0.877 1.16 1.4 1.41 0.00398 0.004 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 241.128 0.005 0.983 1.11 20.8 0.865 1.16 0.99 1.23 0.00281 0.0035 +! Validation 8 241.128 0.005 0.971 1.39 20.8 0.861 1.15 1.08 1.38 0.00308 0.00393 +Wall time: 241.12826776411384 +! Best model 8 20.812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 47 20.6 0.972 1.14 0.862 1.15 1.08 1.25 0.00306 0.00355 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 20.6 0.978 1.08 0.876 1.16 1.21 1.22 0.00344 0.00346 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 270.190 0.005 0.981 1.1 20.7 0.864 1.16 0.986 1.23 0.0028 0.00349 +! Validation 9 270.190 0.005 0.97 1.52 20.9 0.861 1.15 1.12 1.44 0.00319 0.0041 +Wall time: 270.190540751908 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse diff --git a/ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_train.csv b/ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_train.csv new file mode 100644 index 0000000000000000000000000000000000000000..b2ed5658ab7240d1e5b3d086ff96d0918fd7d30f --- /dev/null +++ b/ads_0_length_2000/ALLEGRO/results/adsorption/metrics_batch_train.csv @@ -0,0 +1,446 @@ +epoch, batch, loss, loss_f, loss_e, f_mae, f_rmse, e_mae, e_rmse, e/N_mae, e/N_rmse + 1, 1, 1346.8, 1.0706, 1325.4, 0.89721, 1.2121, 42.304, 42.648, 0.12018, 0.12116 + 1, 2, 3.1392e+05, 1.1277, 3.139e+05, 0.94641, 1.244, 655.99, 656.32, 1.8636, 1.8646 + 1, 3, 22190, 1.0104, 22170, 0.88195, 1.1775, 174.36, 174.42, 0.49534, 0.49552 + 1, 4, 50030, 1.1179, 50007, 0.92318, 1.2386, 261.54, 261.96, 0.74302, 0.74421 + 1, 5, 1.3907e+05, 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index 0000000000000000000000000000000000000000..80258ce1ebaca72d2386d0aca33f67068cdb3f04 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/allegro.sh @@ -0,0 +1,40 @@ +#!/bin/bash + +#SBATCH --job-name=a_8k # 作业名称 +#SBATCH --account=PAS2490 # Project ID +#SBATCH --nodes=1 # 节点数 +#SBATCH --ntasks-per-node=1 # 每个节点的任务数 +#SBATCH --cpus-per-task=16 # 每个任务使用的 CPU 核心数 +#SBATCH --gpus-per-node=1 # GPU per node +#SBATCH --mem=200G # 内存限制 +#SBATCH --time=24:00:00 # 作业运行时间限制 + +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=uceckz0@ucl.ac.uk + + + +convert_to_seconds() { + date -d "${1//_/ }" +%s +} +start_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train start time: $start_time" + +source $HOME/miniconda3/etc/profile.d/conda.sh +conda activate allegro_env +rm -rf ./results +nequip-train train_config.yaml --warn-unused +nequip-evaluate \ + --train-dir ./results/adsorption/ \ + --dataset-config ./test_config.yaml \ + --metrics-config ./test_config.yaml + +end_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train end time: $end_time" +start_seconds=$(convert_to_seconds "$start_time") +end_seconds=$(convert_to_seconds "$end_time") +time_difference=$((end_seconds - start_seconds)) +duration_h=$((time_difference / 3600)) +duration_m=$(((time_difference % 3600) / 60)) +duration_s=$((time_difference % 60)) +echo "Training duration: ${duration_h}h ${duration_m}m ${duration_s}s" \ No newline at end of file diff --git a/ads_16_length_8000/ALLEGRO/results/adsorption/config.yaml b/ads_16_length_8000/ALLEGRO/results/adsorption/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a8347372f04998384890259240a4e24e246099a --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/adsorption/config.yaml @@ -0,0 +1,2436 @@ +_jit_bailout_depth: 2 +_jit_fuser: fuser1 +_jit_fusion_strategy: +- !!python/tuple + - DYNAMIC + - 3 +allow_tf32: true +append: true +ase_args: + format: extxyz +avg_num_neighbors: 34.345919333333335 +batch_size: 32 +chemical_symbols: +- H +- C +- N +- Zn +- O +code_commits: {} +dataloader_num_workers: 0 +dataset: ase +dataset_AtomicData_options: + r_max: 5.0 +dataset_file_name: ../trainset.xyz +dataset_key_mapping: + force: forces +dataset_seed: 456 +dataset_statistics_stride: 1 +default_dtype: float64 +device: cuda +e3nn_version: 0.5.4 +early_stopping: null +early_stopping_kwargs: null +edge_eng_mlp_latent_dimensions: +- 128 +edge_eng_mlp_nonlinearity: null +ema_decay: 0.999 +ema_use_num_updates: true +end_of_batch_callbacks: [] +end_of_epoch_callbacks: [] +end_of_train_callbacks: [] +env_embed_mlp_latent_dimensions: [] +env_embed_mlp_nonlinearity: null +env_embed_multiplicity: 16 +equivariance_test: false +exclude_keys: [] +final_callbacks: [] +gpu_oom_offload: false +grad_anomaly_mode: false +init_callbacks: [] +irreps_edge_sh: 1x0e+1x1o+1x2e +l_max: 2 +latent_mlp_latent_dimensions: +- 512 +- 512 +latent_mlp_nonlinearity: silu +latent_resnet: true +learning_rate: 0.005 +log_batch_freq: 100 +log_epoch_freq: 1 +loss_coeffs: + forces: + - 20 + total_energy: + - 1 +lr_scheduler_kwargs: {} +lr_scheduler_name: none +max_epochs: 500 +max_gradient_norm: .inf +metrics_components: +- - forces + - mae + - PerSpecies: false + report_per_component: false +- - total_energy + - mae + - PerAtom: false +- - forces + - rmse + - PerSpecies: false + report_per_component: false +- - total_energy + - rmse + - PerAtom: false +- - total_energy + - mae + - PerAtom: true +- - total_energy + - rmse + - PerAtom: true +metrics_key: validation_loss +model_builders: +- allegro.model.Allegro +- PerSpeciesRescale +- StressForceOutput +- RescaleEnergyEtc +model_debug_mode: false +model_dtype: float32 +n_train: 7500 +n_train_per_epoch: null +n_val: 500 +nequip_version: 0.6.1 +nonscalars_include_parity: true +num_layers: 2 +num_types: 5 +optimizer_kwargs: + amsgrad: false + betas: !!python/tuple + - 0.9 + - 0.999 + capturable: false + differentiable: false + eps: 1.0e-08 + foreach: null + fused: null + maximize: false + weight_decay: 0 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+validation_batch_size: 5 +var_num_neighbors: 4.70023217641662 +verbose: INFO +wandb: false +warn_unused: true diff --git a/ads_16_length_8000/ALLEGRO/results/adsorption/log b/ads_16_length_8000/ALLEGRO/results/adsorption/log new file mode 100644 index 0000000000000000000000000000000000000000..cf967ce9a0ffeb72db5bd7c3b0f3367bc9f2fe96 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/adsorption/log @@ -0,0 +1,8083 @@ +Torch device: cuda +Number of weights: 1406856 +Number of trainable weights: 1406856 +! Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 532 1.1 510 0.924 1.25 26.9 26.9 0.0673 0.0674 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 7.412 0.005 1.07 568 589 0.912 1.23 27.8 28.4 0.0696 0.0711 +Wall time: 7.4122820110060275 +! Best model 0 589.431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 24.8 0.997 4.83 0.884 1.19 2.21 2.62 0.00553 0.00656 + 1 200 26.7 0.997 6.72 0.882 1.19 2.52 3.09 0.0063 0.00773 + 1 235 23.2 0.961 4.03 0.875 1.17 2.06 2.4 0.00515 0.00599 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 31.8 1 11.7 0.888 1.2 4.03 4.08 0.0101 0.0102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 1 154.538 0.005 0.993 5.13e+03 5.15e+03 0.883 1.19 28.3 85.6 0.0707 0.214 +! Validation 1 154.538 0.005 0.981 5.4 25 0.877 1.18 2.3 2.77 0.00574 0.00694 +Wall time: 154.538886888884 +! Best model 1 25.019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 24.7 0.99 4.93 0.881 1.19 1.83 2.65 0.00458 0.00662 + 2 200 26.1 0.993 6.25 0.883 1.19 2.52 2.98 0.00631 0.00746 + 2 235 26.3 1.01 6 0.894 1.2 2.28 2.92 0.00569 0.00731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 25.4 0.998 5.47 0.885 1.19 2.72 2.79 0.00679 0.00698 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 2 298.974 0.005 0.985 5.38 25.1 0.879 1.18 2.24 2.77 0.00559 0.00692 +! Validation 2 298.974 0.005 0.975 5.02 24.5 0.875 1.18 2.05 2.67 0.00512 0.00668 +Wall time: 298.97638173773885 +! Best model 2 24.520 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 23.6 0.989 3.76 0.881 1.19 1.9 2.32 0.00476 0.00579 + 3 200 26 1 5.9 0.888 1.2 1.95 2.9 0.00487 0.00725 + 3 235 27.5 1.01 7.27 0.889 1.2 2.7 3.22 0.00674 0.00805 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 29.7 0.991 9.88 0.882 1.19 3.7 3.75 0.00924 0.00938 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 3 443.940 0.005 0.979 5.18 24.7 0.876 1.18 2.19 2.71 0.00548 0.00678 +! Validation 3 443.940 0.005 0.968 4.67 24 0.872 1.17 2.12 2.58 0.00529 0.00645 +Wall time: 443.9402854051441 +! Best model 3 24.035 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 23.8 0.968 4.4 0.871 1.17 1.99 2.5 0.00498 0.00626 + 4 200 25 0.973 5.58 0.875 1.18 2.22 2.82 0.00555 0.00705 + 4 235 21.9 0.966 2.55 0.871 1.17 1.29 1.91 0.00323 0.00477 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 28.2 0.983 8.58 0.879 1.18 3.44 3.5 0.0086 0.00874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 4 588.365 0.005 0.971 4.79 24.2 0.873 1.18 2.11 2.61 0.00528 0.00653 +! Validation 4 588.365 0.005 0.961 4.45 23.7 0.868 1.17 2.03 2.52 0.00506 0.00629 +Wall time: 588.3658088729717 +! Best model 4 23.668 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 22.1 0.975 2.54 0.874 1.18 1.52 1.9 0.0038 0.00476 + 5 200 24.7 0.973 5.2 0.874 1.18 2.14 2.72 0.00535 0.00681 + 5 235 22.4 0.95 3.42 0.87 1.16 1.9 2.21 0.00476 0.00552 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 24.3 0.975 4.8 0.875 1.18 2.54 2.62 0.00635 0.00654 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 732.702 0.005 0.964 4.7 24 0.869 1.17 2.09 2.59 0.00521 0.00647 +! Validation 5 732.702 0.005 0.953 4.28 23.3 0.864 1.17 1.86 2.47 0.00464 0.00617 +Wall time: 732.7023157807998 +! Best model 5 23.341 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 23.8 0.941 4.96 0.861 1.16 2.2 2.66 0.0055 0.00665 + 6 200 21.9 0.937 3.12 0.858 1.16 1.8 2.11 0.00451 0.00527 + 6 235 21.6 0.968 2.2 0.871 1.17 1.45 1.77 0.00363 0.00442 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 33.1 0.966 13.8 0.871 1.17 4.38 4.43 0.011 0.0111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 877.057 0.005 0.956 4.22 23.3 0.865 1.17 1.97 2.45 0.00491 0.00613 +! Validation 6 877.057 0.005 0.945 5.38 24.3 0.86 1.16 2.33 2.77 0.00583 0.00692 +Wall time: 877.0573468669318 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 25 0.927 6.43 0.853 1.15 2.58 3.03 0.00644 0.00757 + 7 200 21.4 0.942 2.54 0.861 1.16 1.58 1.9 0.00396 0.00475 + 7 235 25.5 0.962 6.21 0.861 1.17 2.4 2.98 0.00599 0.00744 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 21.5 0.957 2.36 0.866 1.17 1.73 1.83 0.00432 0.00458 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 1021.374 0.005 0.947 4.08 23 0.861 1.16 1.93 2.41 0.00483 0.00602 +! Validation 7 1021.374 0.005 0.936 3.91 22.6 0.855 1.15 1.74 2.36 0.00436 0.0059 +Wall time: 1021.3746373481117 +! Best model 7 22.637 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 22.2 0.943 3.34 0.855 1.16 1.79 2.18 0.00447 0.00545 + 8 200 22.4 0.927 3.86 0.849 1.15 2.01 2.34 0.00502 0.00586 + 8 235 22.2 0.906 4.05 0.842 1.14 2.02 2.4 0.00505 0.006 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 20.6 0.947 1.66 0.861 1.16 1.42 1.54 0.00354 0.00384 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 1165.753 0.005 0.938 4.09 22.9 0.856 1.16 1.93 2.42 0.00483 0.00604 +! Validation 8 1165.753 0.005 0.927 4.12 22.7 0.85 1.15 1.77 2.42 0.00443 0.00606 +Wall time: 1165.75320250215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 22.7 0.936 3.96 0.855 1.15 2.15 2.37 0.00537 0.00594 + 9 200 20.6 0.911 2.4 0.843 1.14 1.52 1.85 0.00381 0.00462 + 9 235 22 0.915 3.71 0.843 1.14 1.56 2.3 0.00391 0.00575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 21.3 0.936 2.54 0.855 1.15 1.81 1.9 0.00453 0.00476 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 1310.133 0.005 0.927 3.34 21.9 0.85 1.15 1.74 2.18 0.00436 0.00545 +! Validation 9 1310.133 0.005 0.915 3.03 21.3 0.844 1.14 1.53 2.08 0.00382 0.00519 +Wall time: 1310.1337010888383 +! Best model 9 21.337 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 20.4 0.915 2.15 0.844 1.14 1.46 1.75 0.00366 0.00437 + 10 200 22.3 0.943 3.46 0.854 1.16 1.87 2.22 0.00468 0.00555 + 10 235 20.9 0.94 2.06 0.853 1.16 1.42 1.71 0.00356 0.00428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 34.7 0.918 16.3 0.846 1.14 4.79 4.83 0.012 0.0121 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 10 1454.558 0.005 0.914 2.98 21.3 0.843 1.14 1.63 2.06 0.00409 0.00515 +! Validation 10 1454.558 0.005 0.898 7.87 25.8 0.835 1.13 3.05 3.35 0.00762 0.00837 +Wall time: 1454.5582863050513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 20.7 0.88 3.15 0.827 1.12 1.76 2.12 0.0044 0.00529 + 11 200 18.5 0.859 1.29 0.817 1.11 1.12 1.35 0.00281 0.00338 + 11 235 19.3 0.831 2.67 0.808 1.09 1.64 1.95 0.0041 0.00488 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 19.2 0.867 1.88 0.819 1.11 1.56 1.63 0.0039 0.00409 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 11 1598.970 0.005 0.882 2.38 20 0.827 1.12 1.46 1.84 0.00366 0.0046 +! Validation 11 1598.970 0.005 0.846 1.63 18.6 0.809 1.1 1.12 1.52 0.00281 0.00381 +Wall time: 1598.9706212067977 +! Best model 11 18.553 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 16.9 0.797 0.992 0.786 1.07 0.943 1.19 0.00236 0.00297 + 12 200 16.4 0.751 1.34 0.759 1.03 1.08 1.38 0.0027 0.00345 + 12 235 15.7 0.705 1.59 0.742 1 1.21 1.5 0.00303 0.00376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 15.3 0.732 0.658 0.751 1.02 0.865 0.968 0.00216 0.00242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 12 1743.700 0.005 0.799 2.25 18.2 0.786 1.07 1.41 1.79 0.00353 0.00447 +! Validation 12 1743.700 0.005 0.711 3.49 17.7 0.741 1.01 1.84 2.23 0.00459 0.00557 +Wall time: 1743.7015087939799 +! Best model 12 17.716 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 13.1 0.592 1.23 0.678 0.919 1.06 1.33 0.00266 0.00331 + 13 200 12.6 0.514 2.3 0.627 0.856 1.53 1.81 0.00381 0.00452 + 13 235 13.3 0.483 3.62 0.61 0.83 2.02 2.27 0.00505 0.00568 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 10.6 0.505 0.548 0.617 0.848 0.747 0.883 0.00187 0.00221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 13 1888.258 0.005 0.585 3.23 14.9 0.67 0.913 1.74 2.14 0.00434 0.00536 +! Validation 13 1888.258 0.005 0.495 1.18 11.1 0.616 0.84 0.973 1.3 0.00243 0.00324 +Wall time: 1888.2582933818921 +! Best model 13 11.081 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 10.3 0.447 1.38 0.586 0.798 1.15 1.4 0.00288 0.00351 + 14 200 9.65 0.413 1.38 0.562 0.767 1.2 1.4 0.003 0.00351 + 14 235 8.72 0.399 0.747 0.549 0.754 0.812 1.03 0.00203 0.00258 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 9.46 0.423 0.996 0.561 0.776 1.1 1.19 0.00274 0.00298 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 14 2032.669 0.005 0.448 2.64 11.6 0.585 0.799 1.55 1.94 0.00387 0.00485 +! Validation 14 2032.669 0.005 0.417 1.1 9.43 0.562 0.77 0.954 1.25 0.00238 0.00313 +Wall time: 2032.6695224968717 +! Best model 14 9.433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 13.7 0.397 5.71 0.549 0.752 2.63 2.85 0.00658 0.00713 + 15 200 8.34 0.385 0.644 0.538 0.74 0.78 0.958 0.00195 0.00239 + 15 235 8.39 0.379 0.813 0.538 0.735 0.832 1.08 0.00208 0.00269 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 10.1 0.397 2.2 0.542 0.752 1.71 1.77 0.00426 0.00442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 15 2177.185 0.005 0.393 4.81 12.7 0.546 0.749 2.09 2.62 0.00523 0.00655 +! Validation 15 2177.185 0.005 0.388 6.47 14.2 0.542 0.744 2.74 3.03 0.00686 0.00759 +Wall time: 2177.1854655616917 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 9.95 0.367 2.61 0.528 0.723 1.73 1.93 0.00432 0.00482 + 16 200 8.89 0.351 1.87 0.518 0.708 1.49 1.63 0.00371 0.00408 + 16 235 23 0.347 16.1 0.512 0.703 4.65 4.79 0.0116 0.012 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 11.7 0.355 4.63 0.513 0.711 2.53 2.57 0.00632 0.00642 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 16 2322.069 0.005 0.368 3.97 11.3 0.529 0.724 1.85 2.37 0.00464 0.00592 +! Validation 16 2322.069 0.005 0.351 8.28 15.3 0.515 0.707 3.22 3.43 0.00806 0.00859 +Wall time: 2322.069620097056 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 9.06 0.329 2.48 0.501 0.685 1.64 1.88 0.0041 0.0047 + 17 200 19 0.316 12.7 0.492 0.67 4.17 4.25 0.0104 0.0106 + 17 235 14.1 0.333 7.44 0.504 0.688 3.04 3.26 0.0076 0.00814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 6.84 0.331 0.22 0.495 0.687 0.343 0.56 0.000858 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 17 2466.735 0.005 0.33 3.84 10.4 0.501 0.686 1.79 2.34 0.00448 0.00584 +! Validation 17 2466.735 0.005 0.326 0.945 7.47 0.497 0.682 0.869 1.16 0.00217 0.0029 +Wall time: 2466.7349044638686 +! Best model 17 7.467 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 12.3 0.324 5.84 0.498 0.679 2.73 2.88 0.00683 0.00721 + 18 200 6.69 0.31 0.488 0.485 0.664 0.666 0.834 0.00167 0.00208 + 18 235 24.9 0.3 18.9 0.478 0.654 5.1 5.19 0.0127 0.013 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 48.3 0.306 42.2 0.478 0.66 7.74 7.75 0.0193 0.0194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 18 2611.253 0.005 0.316 5.57 11.9 0.491 0.671 2.23 2.81 0.00558 0.00702 +! Validation 18 2611.253 0.005 0.304 41.8 47.9 0.482 0.658 7.62 7.72 0.0191 0.0193 +Wall time: 2611.2537162578665 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 7.23 0.289 1.45 0.471 0.642 1.2 1.44 0.00299 0.00359 + 19 200 8.9 0.298 2.94 0.479 0.651 1.85 2.05 0.00463 0.00512 + 19 235 21.2 0.296 15.3 0.481 0.649 4.59 4.66 0.0115 0.0117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 6.43 0.296 0.507 0.472 0.65 0.71 0.85 0.00178 0.00212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 19 2755.750 0.005 0.298 6.25 12.2 0.478 0.651 2.3 2.98 0.00575 0.00745 +! Validation 19 2755.750 0.005 0.294 1.12 7 0.475 0.647 0.977 1.26 0.00244 0.00315 +Wall time: 2755.7503659580834 +! Best model 19 7.004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 7.94 0.269 2.56 0.455 0.619 1.75 1.91 0.00437 0.00477 + 20 200 24.7 0.284 19 0.469 0.637 4.95 5.2 0.0124 0.013 + 20 235 6.93 0.296 1.01 0.478 0.649 0.963 1.2 0.00241 0.003 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 10.2 0.287 4.46 0.466 0.639 2.48 2.52 0.0062 0.0063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 20 2901.347 0.005 0.275 5.26 10.8 0.461 0.626 2.06 2.74 0.00514 0.00685 +! Validation 20 2901.347 0.005 0.283 2.94 8.61 0.468 0.635 1.85 2.05 0.00463 0.00512 +Wall time: 2901.3470059768297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 6.65 0.256 1.54 0.445 0.604 1.23 1.48 0.00307 0.0037 + 21 200 6.78 0.253 1.72 0.444 0.6 1.28 1.57 0.0032 0.00391 + 21 235 5.49 0.241 0.678 0.433 0.586 0.716 0.983 0.00179 0.00246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 5.05 0.246 0.125 0.433 0.593 0.378 0.423 0.000944 0.00106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 21 3045.718 0.005 0.256 3.87 8.98 0.445 0.603 1.84 2.35 0.00459 0.00588 +! Validation 21 3045.718 0.005 0.249 1.46 6.44 0.438 0.595 1.06 1.44 0.00264 0.00361 +Wall time: 3045.718507716898 +! Best model 21 6.442 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 15.2 0.241 10.4 0.432 0.585 3.73 3.84 0.00933 0.00961 + 22 200 5.08 0.225 0.584 0.42 0.566 0.721 0.912 0.0018 0.00228 + 22 235 9 0.223 4.54 0.418 0.564 2.53 2.54 0.00632 0.00636 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 5.08 0.228 0.52 0.417 0.57 0.762 0.861 0.0019 0.00215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 22 3190.058 0.005 0.237 4.7 9.44 0.429 0.581 2.05 2.59 0.00512 0.00647 +! Validation 22 3190.058 0.005 0.232 1.64 6.27 0.423 0.574 1.26 1.53 0.00314 0.00382 +Wall time: 3190.058355611749 +! Best model 22 6.273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 30.7 0.23 26.1 0.424 0.572 6.04 6.1 0.0151 0.0152 + 23 200 5.63 0.209 1.45 0.405 0.545 1.23 1.44 0.00306 0.00359 + 23 235 4.88 0.21 0.683 0.405 0.547 0.834 0.986 0.00209 0.00247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 4.62 0.212 0.382 0.402 0.549 0.628 0.738 0.00157 0.00184 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 23 3334.436 0.005 0.225 4.63 9.13 0.419 0.566 1.99 2.57 0.00499 0.00642 +! Validation 23 3334.436 0.005 0.216 0.639 4.97 0.409 0.555 0.762 0.954 0.0019 0.00239 +Wall time: 3334.43658031011 +! Best model 23 4.967 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 5.76 0.213 1.51 0.407 0.55 1.25 1.47 0.00312 0.00366 + 24 200 8.06 0.214 3.78 0.408 0.552 2.26 2.32 0.00564 0.0058 + 24 235 6.41 0.21 2.22 0.407 0.546 1.73 1.78 0.00434 0.00445 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 12.5 0.213 8.28 0.404 0.55 3.41 3.43 0.00853 0.00859 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 24 3478.824 0.005 0.214 5.65 9.94 0.41 0.553 2.22 2.84 0.00556 0.0071 +! Validation 24 3478.824 0.005 0.214 6.42 10.7 0.409 0.552 2.9 3.02 0.00726 0.00756 +Wall time: 3478.8240611427464 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 14.5 0.205 10.4 0.402 0.54 3.75 3.86 0.00936 0.00964 + 25 200 22.9 0.202 18.8 0.398 0.536 5.12 5.18 0.0128 0.0129 + 25 235 4.41 0.205 0.315 0.403 0.54 0.52 0.67 0.0013 0.00167 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 6.08 0.206 1.96 0.399 0.542 1.63 1.67 0.00407 0.00418 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 25 3623.210 0.005 0.204 4.74 8.81 0.4 0.539 2.09 2.6 0.00522 0.0065 +! Validation 25 3623.210 0.005 0.207 1.03 5.16 0.402 0.543 1.04 1.21 0.0026 0.00302 +Wall time: 3623.210521190893 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 18.3 0.195 14.4 0.395 0.528 4.45 4.54 0.0111 0.0113 + 26 200 6.38 0.189 2.59 0.388 0.519 1.77 1.92 0.00443 0.0048 + 26 235 13.9 0.169 10.6 0.368 0.49 3.81 3.88 0.00952 0.0097 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 12.3 0.187 8.57 0.379 0.516 3.47 3.49 0.00868 0.00874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 26 3767.686 0.005 0.192 3.46 7.3 0.389 0.523 1.71 2.21 0.00427 0.00553 +! Validation 26 3767.686 0.005 0.19 13.8 17.6 0.386 0.521 4.34 4.44 0.0109 0.0111 +Wall time: 3767.6863808799535 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 8.3 0.173 4.84 0.371 0.496 2.54 2.63 0.00634 0.00656 + 27 200 5.71 0.185 2.01 0.381 0.513 1.51 1.69 0.00378 0.00423 + 27 235 8 0.181 4.38 0.376 0.508 2.41 2.5 0.00602 0.00624 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 3.62 0.176 0.096 0.369 0.501 0.261 0.37 0.000652 0.000925 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 27 3912.152 0.005 0.182 3.73 7.37 0.379 0.509 1.85 2.3 0.00464 0.00576 +! Validation 27 3912.152 0.005 0.181 1.32 4.94 0.376 0.507 1.06 1.37 0.00264 0.00343 +Wall time: 3912.152584776748 +! Best model 27 4.936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 4.18 0.183 0.52 0.379 0.511 0.674 0.86 0.00169 0.00215 + 28 200 5.54 0.167 2.19 0.364 0.488 1.54 1.77 0.00386 0.00441 + 28 235 4.2 0.173 0.753 0.37 0.496 0.875 1.04 0.00219 0.00259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 4.75 0.186 1.03 0.378 0.514 1.15 1.21 0.00287 0.00303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 28 4057.006 0.005 0.174 3.95 7.43 0.371 0.498 1.9 2.37 0.00474 0.00593 +! Validation 28 4057.006 0.005 0.184 0.575 4.26 0.38 0.512 0.732 0.905 0.00183 0.00226 +Wall time: 4057.0062407511286 +! Best model 28 4.256 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 6.13 0.164 2.86 0.36 0.483 1.89 2.02 0.00473 0.00504 + 29 200 13.8 0.169 10.4 0.367 0.49 3.8 3.85 0.00949 0.00962 + 29 235 3.63 0.154 0.558 0.349 0.468 0.692 0.891 0.00173 0.00223 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 5.17 0.164 1.89 0.356 0.484 1.59 1.64 0.00399 0.0041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 29 4201.412 0.005 0.168 3.18 6.53 0.364 0.489 1.71 2.13 0.00427 0.00532 +! Validation 29 4201.412 0.005 0.167 4.33 7.67 0.362 0.488 2.35 2.48 0.00587 0.00621 +Wall time: 4201.412870106753 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 4.48 0.163 1.22 0.36 0.481 1.17 1.32 0.00293 0.0033 + 30 200 4.82 0.148 1.87 0.341 0.459 1.49 1.63 0.00373 0.00408 + 30 235 3.28 0.151 0.268 0.347 0.463 0.532 0.618 0.00133 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 5.07 0.153 2.02 0.343 0.466 1.65 1.7 0.00413 0.00424 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 30 4345.840 0.005 0.156 2.61 5.73 0.351 0.471 1.54 1.93 0.00385 0.00483 +! Validation 30 4345.840 0.005 0.156 1.08 4.2 0.349 0.471 1.06 1.24 0.00266 0.0031 +Wall time: 4345.840159095824 +! Best model 30 4.200 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 4.56 0.151 1.54 0.345 0.464 1.36 1.48 0.00341 0.0037 + 31 200 4.86 0.149 1.89 0.344 0.461 1.48 1.64 0.00371 0.0041 + 31 235 5.04 0.157 1.91 0.354 0.472 1.46 1.65 0.00365 0.00413 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 4.03 0.155 0.937 0.346 0.47 1.08 1.16 0.0027 0.00289 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 31 4490.282 0.005 0.15 3.48 6.49 0.345 0.462 1.76 2.23 0.00441 0.00557 +! Validation 31 4490.282 0.005 0.155 0.967 4.07 0.349 0.47 0.94 1.17 0.00235 0.00293 +Wall time: 4490.282098467927 +! Best model 31 4.074 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 3.32 0.139 0.538 0.333 0.445 0.722 0.875 0.0018 0.00219 + 32 200 3.61 0.141 0.783 0.334 0.449 0.887 1.06 0.00222 0.00264 + 32 235 3.29 0.129 0.711 0.322 0.429 0.833 1.01 0.00208 0.00252 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 2.84 0.133 0.177 0.32 0.435 0.481 0.502 0.0012 0.00126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 32 4635.896 0.005 0.14 2.29 5.1 0.334 0.447 1.41 1.81 0.00353 0.00452 +! Validation 32 4635.896 0.005 0.138 0.684 3.45 0.33 0.444 0.82 0.987 0.00205 0.00247 +Wall time: 4635.896505605895 +! Best model 32 3.447 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 3.01 0.137 0.267 0.33 0.442 0.499 0.616 0.00125 0.00154 + 33 200 14.2 0.127 11.7 0.317 0.425 4.02 4.08 0.01 0.0102 + 33 235 2.87 0.126 0.35 0.318 0.424 0.584 0.706 0.00146 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 2.66 0.127 0.123 0.314 0.425 0.276 0.418 0.000691 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 33 4780.296 0.005 0.131 2.55 5.17 0.323 0.432 1.57 1.91 0.00392 0.00477 +! Validation 33 4780.296 0.005 0.131 0.448 3.07 0.321 0.432 0.624 0.799 0.00156 0.002 +Wall time: 4780.296226765029 +! Best model 33 3.065 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 6.22 0.178 2.67 0.379 0.503 1.79 1.95 0.00447 0.00487 + 34 200 4.09 0.121 1.68 0.31 0.414 1.36 1.55 0.0034 0.00386 + 34 235 3.32 0.119 0.938 0.308 0.412 1.02 1.16 0.00256 0.00289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 3.48 0.125 0.983 0.311 0.422 1.12 1.18 0.00281 0.00296 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 34 4924.749 0.005 0.133 3.51 6.17 0.325 0.435 1.53 2.24 0.00383 0.0056 +! Validation 34 4924.749 0.005 0.127 2.61 5.15 0.317 0.426 1.79 1.93 0.00448 0.00482 +Wall time: 4924.749737335835 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 4.23 0.11 2.03 0.298 0.396 1.62 1.7 0.00406 0.00425 + 35 200 14.3 0.109 12.1 0.296 0.395 4.1 4.15 0.0102 0.0104 + 35 235 2.7 0.117 0.35 0.304 0.409 0.583 0.707 0.00146 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 4.26 0.107 2.12 0.289 0.391 1.7 1.74 0.00426 0.00434 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 35 5069.297 0.005 0.114 1.6 3.88 0.302 0.403 1.2 1.51 0.003 0.00378 +! Validation 35 5069.297 0.005 0.112 1.21 3.45 0.297 0.399 1.17 1.31 0.00292 0.00328 +Wall time: 5069.297560427804 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 3.12 0.1 1.12 0.284 0.377 1.14 1.27 0.00285 0.00316 + 36 200 2.82 0.104 0.736 0.289 0.386 0.901 1.02 0.00225 0.00256 + 36 235 3.47 0.109 1.28 0.294 0.395 1.22 1.35 0.00305 0.00338 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 2.77 0.105 0.677 0.286 0.386 0.927 0.982 0.00232 0.00245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 36 5213.764 0.005 0.107 2.15 4.3 0.293 0.391 1.36 1.75 0.0034 0.00438 +! Validation 36 5213.764 0.005 0.108 2.86 5.01 0.292 0.391 1.84 2.02 0.0046 0.00504 +Wall time: 5213.764448463917 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 2.79 0.113 0.527 0.301 0.402 0.693 0.866 0.00173 0.00217 + 37 200 2.33 0.0972 0.389 0.278 0.372 0.611 0.744 0.00153 0.00186 + 37 235 2.44 0.0958 0.523 0.279 0.369 0.789 0.863 0.00197 0.00216 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 3.66 0.0928 1.81 0.27 0.364 1.57 1.6 0.00393 0.00401 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 37 5358.695 0.005 0.101 1.86 3.88 0.284 0.378 1.3 1.63 0.00325 0.00408 +! Validation 37 5358.695 0.005 0.0972 1.06 3 0.278 0.372 1.1 1.23 0.00274 0.00307 +Wall time: 5358.695497570094 +! Best model 37 3.002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 2.37 0.0943 0.48 0.275 0.367 0.669 0.827 0.00167 0.00207 + 38 200 2.45 0.0907 0.638 0.27 0.359 0.848 0.954 0.00212 0.00238 + 38 235 2.4 0.102 0.353 0.288 0.382 0.632 0.709 0.00158 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 5.4 0.101 3.38 0.282 0.38 2.17 2.19 0.00542 0.00548 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 38 5503.218 0.005 0.0938 2.05 3.93 0.275 0.365 1.38 1.71 0.00345 0.00428 +! Validation 38 5503.218 0.005 0.104 5.87 7.94 0.288 0.384 2.82 2.89 0.00704 0.00723 +Wall time: 5503.218256849796 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.91 0.0863 0.182 0.264 0.351 0.403 0.509 0.00101 0.00127 + 39 200 2.03 0.0873 0.281 0.266 0.353 0.502 0.633 0.00126 0.00158 + 39 235 1.82 0.0849 0.124 0.261 0.348 0.341 0.42 0.000851 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.78 0.0856 0.0661 0.261 0.349 0.226 0.307 0.000566 0.000767 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 39 5647.682 0.005 0.0903 1.52 3.33 0.27 0.359 1.17 1.47 0.00293 0.00368 +! Validation 39 5647.682 0.005 0.0903 0.835 2.64 0.269 0.359 0.858 1.09 0.00215 0.00273 +Wall time: 5647.682798143011 +! Best model 39 2.641 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 2.42 0.0975 0.469 0.28 0.373 0.683 0.818 0.00171 0.00204 + 40 200 6.56 0.0802 4.96 0.254 0.338 2.6 2.66 0.00649 0.00664 + 40 235 10.1 0.0844 8.45 0.264 0.347 3.43 3.47 0.00858 0.00867 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 10.3 0.0792 8.75 0.251 0.336 3.52 3.53 0.0088 0.00882 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 40 5792.163 0.005 0.0862 1.77 3.49 0.264 0.35 1.26 1.58 0.00314 0.00395 +! Validation 40 5792.163 0.005 0.0843 5.97 7.65 0.26 0.347 2.85 2.92 0.00712 0.00729 +Wall time: 5792.16388477711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 2.29 0.0787 0.714 0.254 0.335 0.894 1.01 0.00224 0.00252 + 41 200 3.82 0.0799 2.22 0.255 0.337 1.62 1.78 0.00405 0.00444 + 41 235 6.89 0.0778 5.33 0.251 0.333 2.69 2.76 0.00673 0.00689 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 3.93 0.072 2.5 0.241 0.32 1.87 1.89 0.00467 0.00471 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 41 5936.873 0.005 0.0842 1.61 3.3 0.261 0.346 1.18 1.51 0.00295 0.00378 +! Validation 41 5936.873 0.005 0.0787 4.78 6.35 0.252 0.335 2.52 2.61 0.0063 0.00652 +Wall time: 5936.873319902923 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 2.13 0.0752 0.628 0.247 0.327 0.837 0.946 0.00209 0.00236 + 42 200 2.09 0.0785 0.518 0.253 0.335 0.724 0.859 0.00181 0.00215 + 42 235 2.06 0.0682 0.69 0.238 0.312 0.904 0.992 0.00226 0.00248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 1.45 0.068 0.0913 0.235 0.311 0.299 0.361 0.000747 0.000902 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 42 6081.377 0.005 0.0769 1.2 2.74 0.25 0.331 1.05 1.31 0.00263 0.00327 +! Validation 42 6081.377 0.005 0.0753 0.896 2.4 0.247 0.328 0.942 1.13 0.00235 0.00282 +Wall time: 6081.377549501136 +! Best model 42 2.402 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 1.74 0.0746 0.25 0.247 0.326 0.499 0.597 0.00125 0.00149 + 43 200 2.76 0.0703 1.36 0.239 0.316 1.3 1.39 0.00326 0.00348 + 43 235 2.09 0.0681 0.733 0.236 0.311 0.881 1.02 0.0022 0.00255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 1.77 0.0674 0.418 0.235 0.31 0.738 0.772 0.00185 0.00193 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 43 6225.941 0.005 0.0729 1.18 2.64 0.244 0.322 1.03 1.3 0.00257 0.00325 +! Validation 43 6225.941 0.005 0.0735 0.321 1.79 0.244 0.323 0.538 0.676 0.00135 0.00169 +Wall time: 6225.941238668747 +! Best model 43 1.790 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 2.7 0.0725 1.25 0.242 0.321 1.18 1.33 0.00295 0.00334 + 44 200 1.65 0.0692 0.271 0.238 0.314 0.503 0.621 0.00126 0.00155 + 44 235 1.85 0.0707 0.434 0.24 0.317 0.702 0.786 0.00175 0.00197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 1.45 0.0638 0.18 0.228 0.301 0.46 0.506 0.00115 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 44 6370.423 0.005 0.072 1.42 2.86 0.242 0.32 1.14 1.42 0.00285 0.00356 +! Validation 44 6370.423 0.005 0.0714 0.437 1.86 0.24 0.319 0.61 0.789 0.00153 0.00197 +Wall time: 6370.422981529031 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 6.73 0.0675 5.38 0.236 0.31 2.72 2.77 0.00681 0.00692 + 45 200 1.9 0.0671 0.559 0.234 0.309 0.733 0.892 0.00183 0.00223 + 45 235 3.48 0.0692 2.09 0.238 0.314 1.63 1.73 0.00407 0.00432 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 7.23 0.0665 5.9 0.232 0.308 2.89 2.9 0.00723 0.00725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 45 6514.891 0.005 0.0686 1.31 2.68 0.237 0.313 1.1 1.37 0.00276 0.00341 +! Validation 45 6514.891 0.005 0.0715 3.99 5.42 0.24 0.319 2.28 2.39 0.00571 0.00596 +Wall time: 6514.891313571949 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 1.99 0.0659 0.667 0.232 0.306 0.804 0.975 0.00201 0.00244 + 46 200 1.7 0.0618 0.465 0.225 0.297 0.715 0.814 0.00179 0.00203 + 46 235 2.56 0.0655 1.25 0.232 0.306 1.28 1.34 0.0032 0.00334 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 1.21 0.0583 0.0478 0.219 0.288 0.207 0.261 0.000518 0.000652 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 46 6659.360 0.005 0.0651 0.952 2.25 0.23 0.304 0.934 1.16 0.00233 0.00291 +! Validation 46 6659.360 0.005 0.0656 0.234 1.55 0.231 0.306 0.442 0.577 0.0011 0.00144 +Wall time: 6659.360179272015 +! Best model 46 1.546 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 1.5 0.0654 0.196 0.232 0.305 0.439 0.528 0.0011 0.00132 + 47 200 1.78 0.0613 0.556 0.223 0.296 0.744 0.89 0.00186 0.00223 + 47 235 2.3 0.056 1.18 0.215 0.282 1.2 1.3 0.00299 0.00325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 1.7 0.0506 0.688 0.205 0.268 0.974 0.99 0.00243 0.00247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 47 6803.825 0.005 0.0628 1.03 2.28 0.226 0.299 0.989 1.21 0.00247 0.00302 +! Validation 47 6803.825 0.005 0.0597 1.84 3.03 0.22 0.292 1.48 1.62 0.0037 0.00405 +Wall time: 6803.8250566208735 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 1.37 0.0599 0.177 0.22 0.292 0.391 0.502 0.000978 0.00125 + 48 200 1.66 0.0587 0.484 0.219 0.289 0.733 0.83 0.00183 0.00207 + 48 235 3.65 0.0542 2.57 0.213 0.278 1.81 1.91 0.00453 0.00478 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 1.9 0.0487 0.924 0.201 0.263 1.14 1.15 0.00284 0.00287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 48 6948.295 0.005 0.0622 1.15 2.39 0.225 0.298 0.979 1.28 0.00245 0.00319 +! Validation 48 6948.295 0.005 0.058 0.619 1.78 0.217 0.287 0.828 0.939 0.00207 0.00235 +Wall time: 6948.295889385976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 1.32 0.0568 0.185 0.217 0.284 0.409 0.513 0.00102 0.00128 + 49 200 1.52 0.0557 0.407 0.213 0.282 0.63 0.762 0.00158 0.0019 + 49 235 1.48 0.0552 0.372 0.213 0.28 0.635 0.728 0.00159 0.00182 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 1.17 0.0479 0.215 0.2 0.261 0.526 0.554 0.00132 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 49 7093.315 0.005 0.0566 0.814 1.95 0.215 0.284 0.871 1.08 0.00218 0.00269 +! Validation 49 7093.315 0.005 0.0566 0.47 1.6 0.215 0.284 0.696 0.819 0.00174 0.00205 +Wall time: 7093.314978809096 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 1.55 0.0547 0.459 0.212 0.279 0.72 0.809 0.0018 0.00202 + 50 200 2.04 0.059 0.864 0.219 0.29 0.986 1.11 0.00247 0.00277 + 50 235 1.76 0.0585 0.588 0.217 0.289 0.763 0.915 0.00191 0.00229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 1.19 0.0469 0.251 0.198 0.258 0.575 0.598 0.00144 0.0015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 50 7237.810 0.005 0.0586 1.31 2.48 0.218 0.289 1.08 1.37 0.0027 0.00341 +! Validation 50 7237.810 0.005 0.0555 0.307 1.42 0.212 0.281 0.546 0.662 0.00137 0.00165 +Wall time: 7237.810591578949 +! Best model 50 1.417 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 4 0.0555 2.89 0.212 0.281 1.9 2.03 0.00474 0.00507 + 51 200 1.49 0.0571 0.346 0.217 0.285 0.552 0.702 0.00138 0.00176 + 51 235 1.16 0.0484 0.195 0.199 0.263 0.406 0.527 0.00101 0.00132 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 0.985 0.0429 0.128 0.19 0.247 0.4 0.427 0.001 0.00107 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 51 7382.288 0.005 0.0542 0.83 1.91 0.21 0.278 0.832 1.09 0.00208 0.00272 +! Validation 51 7382.288 0.005 0.0522 0.244 1.29 0.206 0.273 0.463 0.589 0.00116 0.00147 +Wall time: 7382.288199284114 +! Best model 51 1.287 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 3.95 0.0485 2.98 0.199 0.263 2 2.06 0.00499 0.00515 + 52 200 1.25 0.0483 0.286 0.197 0.262 0.521 0.638 0.0013 0.0016 + 52 235 2.43 0.0503 1.43 0.202 0.268 1.3 1.43 0.00324 0.00356 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.832 0.0409 0.0138 0.185 0.241 0.124 0.14 0.00031 0.000351 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 52 7526.775 0.005 0.0499 0.636 1.63 0.201 0.267 0.752 0.95 0.00188 0.00238 +! Validation 52 7526.775 0.005 0.0494 0.521 1.51 0.2 0.265 0.67 0.862 0.00167 0.00215 +Wall time: 7526.775123720057 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 1.17 0.0489 0.189 0.199 0.264 0.423 0.519 0.00106 0.0013 + 53 200 1.76 0.0502 0.759 0.202 0.267 0.932 1.04 0.00233 0.0026 + 53 235 0.987 0.0451 0.0838 0.19 0.254 0.295 0.345 0.000738 0.000864 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 1.44 0.039 0.66 0.18 0.236 0.964 0.97 0.00241 0.00242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 53 7671.248 0.005 0.0497 0.894 1.89 0.201 0.266 0.917 1.13 0.00229 0.00282 +! Validation 53 7671.248 0.005 0.0481 0.551 1.51 0.197 0.262 0.78 0.886 0.00195 0.00222 +Wall time: 7671.248817464802 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 1.12 0.0465 0.19 0.195 0.257 0.433 0.52 0.00108 0.0013 + 54 200 2.05 0.0439 1.18 0.189 0.25 1.22 1.29 0.00304 0.00323 + 54 235 1.05 0.043 0.192 0.188 0.248 0.411 0.523 0.00103 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 1.11 0.038 0.35 0.178 0.233 0.7 0.706 0.00175 0.00177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 54 7815.731 0.005 0.0462 0.662 1.59 0.194 0.257 0.784 0.972 0.00196 0.00243 +! Validation 54 7815.731 0.005 0.0462 0.383 1.31 0.193 0.257 0.641 0.739 0.0016 0.00185 +Wall time: 7815.731109419838 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.63 0.0433 0.763 0.188 0.248 0.959 1.04 0.0024 0.00261 + 55 200 1.87 0.0474 0.926 0.196 0.26 1.07 1.15 0.00266 0.00287 + 55 235 4 0.0434 3.13 0.188 0.249 2.07 2.11 0.00518 0.00528 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.91 0.0367 1.17 0.175 0.229 1.29 1.29 0.00323 0.00323 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 55 7960.186 0.005 0.044 0.665 1.55 0.189 0.25 0.788 0.969 0.00197 0.00242 +! Validation 55 7960.186 0.005 0.0443 1.18 2.07 0.189 0.251 1.21 1.3 0.00303 0.00325 +Wall time: 7960.186250755098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 0.986 0.0424 0.137 0.186 0.246 0.341 0.442 0.000852 0.00111 + 56 200 1.21 0.0405 0.396 0.181 0.24 0.625 0.751 0.00156 0.00188 + 56 235 1.04 0.0415 0.212 0.184 0.243 0.487 0.55 0.00122 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 2.03 0.0333 1.37 0.167 0.218 1.39 1.4 0.00348 0.00349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 56 8104.646 0.005 0.0439 0.761 1.64 0.188 0.25 0.833 1.04 0.00208 0.00261 +! Validation 56 8104.646 0.005 0.0417 1.02 1.85 0.183 0.244 1.06 1.2 0.00266 0.00301 +Wall time: 8104.6462454730645 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 1.11 0.041 0.286 0.182 0.242 0.483 0.639 0.00121 0.0016 + 57 200 1.2 0.0394 0.417 0.179 0.237 0.672 0.771 0.00168 0.00193 + 57 235 2.16 0.0413 1.33 0.184 0.242 1.19 1.38 0.00298 0.00344 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 6.68 0.038 5.92 0.177 0.233 2.9 2.9 0.00726 0.00726 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 57 8249.428 0.005 0.0409 0.649 1.47 0.182 0.241 0.757 0.961 0.00189 0.0024 +! Validation 57 8249.428 0.005 0.0447 8.83 9.72 0.19 0.252 3.44 3.55 0.00859 0.00886 +Wall time: 8249.428888718132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 2.34 0.0399 1.54 0.18 0.238 1.39 1.48 0.00347 0.0037 + 58 200 0.981 0.0404 0.173 0.181 0.24 0.394 0.496 0.000986 0.00124 + 58 235 0.873 0.0373 0.126 0.174 0.231 0.351 0.424 0.000878 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 1.03 0.0306 0.421 0.16 0.209 0.772 0.774 0.00193 0.00194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 58 8394.051 0.005 0.0412 0.685 1.51 0.182 0.242 0.761 0.989 0.0019 0.00247 +! Validation 58 8394.051 0.005 0.0385 0.385 1.16 0.176 0.234 0.646 0.741 0.00161 0.00185 +Wall time: 8394.051532946061 +! Best model 58 1.155 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 1.12 0.0355 0.414 0.17 0.225 0.616 0.768 0.00154 0.00192 + 59 200 0.961 0.0363 0.236 0.171 0.227 0.509 0.58 0.00127 0.00145 + 59 235 1.21 0.0354 0.5 0.171 0.225 0.752 0.844 0.00188 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.611 0.0288 0.0355 0.155 0.203 0.217 0.225 0.000542 0.000562 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 59 8538.548 0.005 0.0372 0.467 1.21 0.173 0.23 0.658 0.815 0.00164 0.00204 +! Validation 59 8538.548 0.005 0.0362 0.34 1.06 0.17 0.227 0.555 0.696 0.00139 0.00174 +Wall time: 8538.548076983076 +! Best model 59 1.064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.993 0.0337 0.318 0.165 0.219 0.531 0.674 0.00133 0.00168 + 60 200 0.845 0.036 0.124 0.17 0.227 0.309 0.421 0.000772 0.00105 + 60 235 1.24 0.0354 0.534 0.169 0.225 0.76 0.872 0.0019 0.00218 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 2.17 0.029 1.59 0.155 0.203 1.5 1.51 0.00376 0.00376 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 60 8683.009 0.005 0.0366 0.64 1.37 0.172 0.228 0.758 0.955 0.00189 0.00239 +! Validation 60 8683.009 0.005 0.0359 1.38 2.09 0.17 0.226 1.33 1.4 0.00333 0.0035 +Wall time: 8683.008959870785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 1.66 0.0348 0.962 0.167 0.223 0.997 1.17 0.00249 0.00293 + 61 200 0.775 0.0327 0.12 0.163 0.216 0.326 0.414 0.000816 0.00103 + 61 235 1.06 0.0352 0.353 0.168 0.224 0.569 0.709 0.00142 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.965 0.0282 0.4 0.153 0.201 0.753 0.755 0.00188 0.00189 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 61 8827.510 0.005 0.0346 0.475 1.17 0.167 0.222 0.668 0.823 0.00167 0.00206 +! Validation 61 8827.510 0.005 0.0347 0.321 1.01 0.167 0.222 0.581 0.676 0.00145 0.00169 +Wall time: 8827.510361522902 +! Best model 61 1.014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 1.05 0.0341 0.367 0.165 0.221 0.575 0.723 0.00144 0.00181 + 62 200 1.09 0.0336 0.42 0.165 0.219 0.695 0.773 0.00174 0.00193 + 62 235 0.814 0.0333 0.148 0.164 0.218 0.376 0.459 0.00094 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 1.3 0.0266 0.769 0.149 0.194 1.04 1.05 0.00261 0.00262 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 62 8971.918 0.005 0.0355 0.611 1.32 0.169 0.225 0.733 0.934 0.00183 0.00233 +! Validation 62 8971.918 0.005 0.0336 0.587 1.26 0.165 0.219 0.806 0.915 0.00202 0.00229 +Wall time: 8971.917991088703 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 1.36 0.0347 0.664 0.167 0.222 0.887 0.973 0.00222 0.00243 + 63 200 0.751 0.0329 0.0917 0.163 0.217 0.277 0.361 0.000694 0.000903 + 63 235 1.76 0.0348 1.06 0.167 0.223 1.13 1.23 0.00282 0.00307 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 1.41 0.0313 0.781 0.162 0.211 1.05 1.06 0.00263 0.00264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 63 9116.294 0.005 0.0334 0.569 1.24 0.164 0.218 0.731 0.899 0.00183 0.00225 +! Validation 63 9116.294 0.005 0.0383 0.844 1.61 0.178 0.233 0.978 1.1 0.00244 0.00274 +Wall time: 9116.294115031138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 1.12 0.0337 0.447 0.164 0.219 0.697 0.798 0.00174 0.00199 + 64 200 0.668 0.0301 0.0649 0.156 0.207 0.246 0.304 0.000615 0.00076 + 64 235 0.687 0.0293 0.102 0.154 0.204 0.318 0.38 0.000794 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.929 0.0238 0.453 0.14 0.184 0.801 0.803 0.002 0.00201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 64 9260.686 0.005 0.0338 0.51 1.19 0.165 0.22 0.674 0.854 0.00168 0.00213 +! Validation 64 9260.686 0.005 0.0308 0.471 1.09 0.157 0.209 0.672 0.819 0.00168 0.00205 +Wall time: 9260.686161953025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 2.53 0.0365 1.8 0.171 0.228 1.47 1.6 0.00368 0.004 + 65 200 1.11 0.0302 0.509 0.155 0.207 0.767 0.851 0.00192 0.00213 + 65 235 0.997 0.0313 0.371 0.158 0.211 0.615 0.727 0.00154 0.00182 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 2.57 0.0242 2.08 0.141 0.186 1.72 1.72 0.00431 0.00431 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 65 9405.110 0.005 0.0335 0.646 1.32 0.164 0.218 0.752 0.96 0.00188 0.0024 +! Validation 65 9405.110 0.005 0.0309 1.65 2.27 0.157 0.21 1.47 1.53 0.00367 0.00383 +Wall time: 9405.110177647788 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.673 0.028 0.112 0.15 0.2 0.297 0.399 0.000744 0.000998 + 66 200 0.944 0.0284 0.376 0.152 0.201 0.599 0.732 0.0015 0.00183 + 66 235 1.09 0.0275 0.545 0.149 0.198 0.787 0.881 0.00197 0.0022 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 2.01 0.0215 1.58 0.133 0.175 1.5 1.5 0.00375 0.00375 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 66 9549.777 0.005 0.0294 0.308 0.895 0.153 0.205 0.523 0.662 0.00131 0.00165 +! Validation 66 9549.777 0.005 0.028 1.31 1.87 0.149 0.2 1.28 1.37 0.00321 0.00342 +Wall time: 9549.777680072933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 1.07 0.0298 0.478 0.155 0.206 0.675 0.825 0.00169 0.00206 + 67 200 0.864 0.0289 0.287 0.153 0.203 0.525 0.639 0.00131 0.0016 + 67 235 0.914 0.0294 0.326 0.155 0.205 0.621 0.682 0.00155 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.677 0.026 0.157 0.148 0.193 0.468 0.473 0.00117 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 67 9694.160 0.005 0.0288 0.444 1.02 0.152 0.202 0.64 0.796 0.0016 0.00199 +! Validation 67 9694.160 0.005 0.0317 0.21 0.844 0.161 0.213 0.452 0.546 0.00113 0.00137 +Wall time: 9694.160639241803 +! Best model 67 0.844 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.753 0.0277 0.198 0.15 0.199 0.455 0.532 0.00114 0.00133 + 68 200 1.67 0.0317 1.03 0.161 0.212 1.17 1.21 0.00293 0.00303 + 68 235 0.918 0.0287 0.344 0.151 0.202 0.581 0.7 0.00145 0.00175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.524 0.0212 0.1 0.132 0.174 0.371 0.377 0.000928 0.000944 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 68 9838.550 0.005 0.0279 0.369 0.927 0.15 0.199 0.583 0.725 0.00146 0.00181 +! Validation 68 9838.550 0.005 0.0275 0.211 0.76 0.148 0.198 0.443 0.548 0.00111 0.00137 +Wall time: 9838.550173350144 +! Best model 68 0.760 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.648 0.0284 0.0801 0.153 0.201 0.283 0.338 0.000708 0.000844 + 69 200 0.738 0.0298 0.142 0.156 0.206 0.365 0.45 0.000912 0.00112 + 69 235 0.754 0.0298 0.158 0.156 0.206 0.349 0.475 0.000873 0.00119 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 2.54 0.0249 2.04 0.143 0.188 1.7 1.7 0.00426 0.00426 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 69 9983.118 0.005 0.028 0.494 1.05 0.15 0.2 0.664 0.84 0.00166 0.0021 +! Validation 69 9983.118 0.005 0.0306 1.33 1.94 0.157 0.209 1.16 1.38 0.0029 0.00344 +Wall time: 9983.118801401928 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.685 0.0261 0.164 0.144 0.193 0.384 0.483 0.00096 0.00121 + 70 200 0.573 0.0246 0.0802 0.14 0.187 0.267 0.338 0.000667 0.000845 + 70 235 1.13 0.0245 0.641 0.14 0.187 0.868 0.956 0.00217 0.00239 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.423 0.0198 0.0275 0.128 0.168 0.187 0.198 0.000468 0.000495 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 70 10128.297 0.005 0.0261 0.305 0.826 0.144 0.193 0.523 0.658 0.00131 0.00165 +! Validation 70 10128.297 0.005 0.0255 0.179 0.688 0.142 0.19 0.415 0.505 0.00104 0.00126 +Wall time: 10128.297281004023 +! Best model 70 0.688 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 1.37 0.0237 0.898 0.137 0.184 1.08 1.13 0.00271 0.00283 + 71 200 0.624 0.0254 0.115 0.143 0.19 0.335 0.405 0.000839 0.00101 + 71 235 395 1.08 374 0.92 1.24 23 23.1 0.0574 0.0577 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 7.46e+03 0.96 7.44e+03 0.872 1.17 103 103 0.257 0.257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 71 10272.734 0.005 0.0374 14.7 15.5 0.15 0.222 1 4.43 0.0025 0.0111 +! Validation 71 10272.734 0.005 0.938 8.78e+03 8.8e+03 0.859 1.16 111 112 0.278 0.28 +Wall time: 10272.734560063109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 23.3 0.946 4.38 0.864 1.16 1.87 2.5 0.00468 0.00625 + 72 200 21.1 0.907 2.96 0.839 1.14 1.68 2.05 0.00421 0.00513 + 72 235 21.7 0.853 4.65 0.816 1.1 2.35 2.57 0.00587 0.00643 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 21.2 0.893 3.35 0.834 1.13 2.1 2.18 0.00525 0.00546 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 72 10417.165 0.005 0.951 98.4 117 0.862 1.16 4.46 11.9 0.0111 0.0296 +! Validation 72 10417.165 0.005 0.872 2.92 20.4 0.822 1.11 1.55 2.04 0.00389 0.0051 +Wall time: 10417.165217369795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 18.4 0.783 2.72 0.779 1.06 1.72 1.97 0.00431 0.00492 + 73 200 13.4 0.613 1.1 0.689 0.935 0.995 1.25 0.00249 0.00314 + 73 235 12.6 0.508 2.48 0.637 0.851 1.51 1.88 0.00377 0.0047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 11.6 0.545 0.712 0.655 0.881 0.961 1.01 0.0024 0.00252 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 73 10561.595 0.005 0.74 2.32 17.1 0.756 1.03 1.44 1.82 0.0036 0.00454 +! Validation 73 10561.595 0.005 0.532 1.25 11.9 0.648 0.87 1.05 1.33 0.00261 0.00333 +Wall time: 10561.595408943016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 8.21 0.373 0.762 0.549 0.729 0.718 1.04 0.00179 0.0026 + 74 200 7.99 0.297 2.04 0.492 0.651 1.42 1.71 0.00355 0.00427 + 74 235 6.34 0.26 1.15 0.462 0.608 1.09 1.28 0.00273 0.0032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 6.11 0.264 0.82 0.458 0.614 1.08 1.08 0.00269 0.0027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 74 10706.209 0.005 0.369 1.87 9.26 0.543 0.726 1.31 1.63 0.00327 0.00409 +! Validation 74 10706.209 0.005 0.27 0.897 6.29 0.47 0.62 0.926 1.13 0.00231 0.00283 +Wall time: 10706.20972165186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 7.24 0.216 2.91 0.424 0.555 1.77 2.04 0.00443 0.00509 + 75 200 4.78 0.178 1.21 0.384 0.504 1.14 1.31 0.00285 0.00328 + 75 235 3.78 0.164 0.494 0.37 0.484 0.762 0.839 0.0019 0.0021 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 4.97 0.168 1.6 0.367 0.49 1.5 1.51 0.00375 0.00377 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 75 10850.843 0.005 0.21 1.43 5.63 0.416 0.547 1.14 1.43 0.00285 0.00357 +! Validation 75 10850.843 0.005 0.17 4.79 8.19 0.375 0.492 2.35 2.61 0.00587 0.00653 +Wall time: 10850.843317506835 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 4.04 0.138 1.28 0.339 0.443 1.12 1.35 0.00279 0.00338 + 76 200 4.69 0.12 2.3 0.316 0.413 1.64 1.81 0.00411 0.00452 + 76 235 3.65 0.115 1.34 0.309 0.405 1.29 1.38 0.00322 0.00346 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 2.45 0.113 0.188 0.303 0.401 0.489 0.517 0.00122 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 76 10995.260 0.005 0.139 0.972 3.74 0.34 0.444 0.936 1.18 0.00234 0.00294 +! Validation 76 10995.260 0.005 0.118 0.496 2.86 0.314 0.41 0.68 0.841 0.0017 0.0021 +Wall time: 10995.260710535105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 2.62 0.108 0.468 0.3 0.391 0.695 0.817 0.00174 0.00204 + 77 200 3.16 0.0985 1.19 0.286 0.375 1.14 1.3 0.00284 0.00326 + 77 235 2.51 0.0906 0.7 0.277 0.359 0.852 0.998 0.00213 0.0025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 3.22 0.0876 1.47 0.268 0.353 1.44 1.45 0.00359 0.00362 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 77 11139.685 0.005 0.105 0.914 3.02 0.296 0.387 0.921 1.14 0.0023 0.00285 +! Validation 77 11139.685 0.005 0.0955 1.29 3.2 0.282 0.369 1.18 1.36 0.00296 0.00339 +Wall time: 11139.685422561131 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 3.67 0.0852 1.96 0.268 0.348 1.5 1.67 0.00375 0.00418 + 78 200 1.95 0.0805 0.342 0.259 0.339 0.571 0.697 0.00143 0.00174 + 78 235 2.1 0.0819 0.458 0.26 0.342 0.655 0.808 0.00164 0.00202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 1.6 0.0725 0.146 0.245 0.321 0.432 0.456 0.00108 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 78 11284.476 0.005 0.0878 0.801 2.56 0.27 0.354 0.839 1.07 0.0021 0.00267 +! Validation 78 11284.476 0.005 0.0814 0.342 1.97 0.259 0.341 0.569 0.698 0.00142 0.00174 +Wall time: 11284.476392944809 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 1.96 0.0777 0.409 0.253 0.333 0.632 0.764 0.00158 0.00191 + 79 200 1.69 0.0707 0.274 0.242 0.317 0.503 0.625 0.00126 0.00156 + 79 235 2.13 0.0694 0.741 0.239 0.314 0.871 1.03 0.00218 0.00257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 1.95 0.0629 0.695 0.229 0.299 0.986 0.995 0.00247 0.00249 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 79 11429.180 0.005 0.0755 0.704 2.21 0.25 0.328 0.791 1 0.00198 0.0025 +! Validation 79 11429.180 0.005 0.0715 0.671 2.1 0.242 0.319 0.84 0.978 0.0021 0.00244 +Wall time: 11429.180050428025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 3.69 0.0685 2.32 0.237 0.312 1.75 1.82 0.00437 0.00455 + 80 200 5.68 0.064 4.4 0.229 0.302 2.45 2.5 0.00611 0.00626 + 80 235 1.51 0.0646 0.22 0.229 0.303 0.459 0.559 0.00115 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 1.25 0.057 0.114 0.217 0.285 0.387 0.404 0.000966 0.00101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 80 11573.655 0.005 0.0674 0.77 2.12 0.235 0.31 0.851 1.05 0.00213 0.00262 +! Validation 80 11573.655 0.005 0.0658 0.445 1.76 0.231 0.306 0.633 0.796 0.00158 0.00199 +Wall time: 11573.655818609055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 1.57 0.0603 0.361 0.222 0.293 0.57 0.717 0.00143 0.00179 + 81 200 1.41 0.0578 0.254 0.217 0.287 0.521 0.602 0.0013 0.00151 + 81 235 1.55 0.0577 0.394 0.217 0.287 0.652 0.749 0.00163 0.00187 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 1.58 0.0501 0.578 0.203 0.267 0.904 0.907 0.00226 0.00227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 81 11718.214 0.005 0.0605 0.551 1.76 0.222 0.294 0.708 0.886 0.00177 0.00222 +! Validation 81 11718.214 0.005 0.0587 0.473 1.65 0.218 0.289 0.699 0.821 0.00175 0.00205 +Wall time: 11718.214353878051 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 1.38 0.0538 0.305 0.209 0.277 0.564 0.659 0.00141 0.00165 + 82 200 1.25 0.0563 0.122 0.213 0.283 0.342 0.417 0.000856 0.00104 + 82 235 1.26 0.0541 0.177 0.211 0.278 0.381 0.503 0.000952 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.908 0.0442 0.0237 0.191 0.251 0.172 0.184 0.00043 0.00046 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 82 11863.047 0.005 0.055 0.514 1.61 0.211 0.28 0.673 0.856 0.00168 0.00214 +! Validation 82 11863.047 0.005 0.0528 0.247 1.3 0.207 0.274 0.461 0.593 0.00115 0.00148 +Wall time: 11863.047025505919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 1.27 0.0496 0.274 0.201 0.266 0.495 0.624 0.00124 0.00156 + 83 200 1.3 0.0492 0.314 0.2 0.265 0.536 0.669 0.00134 0.00167 + 83 235 1.78 0.0473 0.833 0.197 0.26 0.985 1.09 0.00246 0.00272 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 1.07 0.0407 0.257 0.183 0.241 0.603 0.605 0.00151 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 83 12007.517 0.005 0.0507 0.586 1.6 0.203 0.269 0.733 0.913 0.00183 0.00228 +! Validation 83 12007.517 0.005 0.049 0.632 1.61 0.199 0.264 0.817 0.949 0.00204 0.00237 +Wall time: 12007.517853229772 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 1.21 0.0509 0.19 0.203 0.269 0.412 0.521 0.00103 0.0013 + 84 200 1.1 0.0469 0.164 0.195 0.258 0.408 0.483 0.00102 0.00121 + 84 235 1.21 0.0439 0.327 0.189 0.25 0.582 0.683 0.00145 0.00171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 1.54 0.0371 0.799 0.175 0.23 1.07 1.07 0.00266 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 84 12151.980 0.005 0.0471 0.575 1.52 0.195 0.259 0.72 0.906 0.0018 0.00226 +! Validation 84 12151.980 0.005 0.0453 0.994 1.9 0.191 0.254 1.07 1.19 0.00268 0.00298 +Wall time: 12151.980525293853 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 1.37 0.0424 0.52 0.186 0.246 0.79 0.861 0.00198 0.00215 + 85 200 1.15 0.0426 0.294 0.186 0.246 0.582 0.647 0.00146 0.00162 + 85 235 1.74 0.0436 0.868 0.188 0.249 1.02 1.11 0.00255 0.00278 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 3.14 0.0339 2.46 0.167 0.22 1.87 1.87 0.00468 0.00468 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 85 12296.402 0.005 0.0438 0.565 1.44 0.188 0.25 0.706 0.896 0.00176 0.00224 +! Validation 85 12296.402 0.005 0.0422 2.54 3.38 0.184 0.245 1.83 1.9 0.00458 0.00475 +Wall time: 12296.402141702827 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 1.27 0.0391 0.493 0.178 0.236 0.741 0.838 0.00185 0.00209 + 86 200 0.931 0.0389 0.154 0.178 0.235 0.374 0.468 0.000935 0.00117 + 86 235 1.01 0.0379 0.249 0.177 0.232 0.503 0.595 0.00126 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.702 0.0312 0.0771 0.16 0.211 0.326 0.331 0.000814 0.000829 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 86 12441.204 0.005 0.0402 0.465 1.27 0.181 0.239 0.639 0.814 0.0016 0.00204 +! Validation 86 12441.204 0.005 0.0391 0.601 1.38 0.178 0.236 0.786 0.925 0.00196 0.00231 +Wall time: 12441.204436420929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 1.25 0.0372 0.502 0.174 0.23 0.724 0.845 0.00181 0.00211 + 87 200 0.88 0.0373 0.135 0.174 0.23 0.358 0.438 0.000895 0.00109 + 87 235 0.894 0.0359 0.176 0.172 0.226 0.419 0.501 0.00105 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.676 0.029 0.0957 0.155 0.203 0.361 0.369 0.000902 0.000923 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 87 12585.675 0.005 0.0374 0.5 1.25 0.175 0.231 0.669 0.845 0.00167 0.00211 +! Validation 87 12585.675 0.005 0.0366 0.131 0.863 0.172 0.228 0.342 0.432 0.000855 0.00108 +Wall time: 12585.67505122209 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.89 0.0359 0.171 0.171 0.226 0.403 0.494 0.00101 0.00123 + 88 200 1.67 0.0334 0.998 0.166 0.218 1.12 1.19 0.00281 0.00298 + 88 235 1.81 0.0317 1.18 0.161 0.213 1.23 1.3 0.00308 0.00324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 2.21 0.027 1.67 0.15 0.196 1.54 1.54 0.00385 0.00385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 88 12730.130 0.005 0.0349 0.437 1.14 0.169 0.223 0.626 0.788 0.00157 0.00197 +! Validation 88 12730.130 0.005 0.0344 1.04 1.73 0.167 0.221 1.15 1.22 0.00287 0.00305 +Wall time: 12730.130428154022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.92 0.0353 0.214 0.17 0.224 0.473 0.552 0.00118 0.00138 + 89 200 1.89 0.0356 1.18 0.171 0.225 1.22 1.3 0.00305 0.00324 + 89 235 0.971 0.0324 0.322 0.163 0.215 0.571 0.677 0.00143 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.647 0.0263 0.122 0.148 0.193 0.405 0.416 0.00101 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 89 12874.679 0.005 0.034 0.534 1.21 0.167 0.22 0.709 0.873 0.00177 0.00218 +! Validation 89 12874.679 0.005 0.0337 0.204 0.879 0.166 0.219 0.431 0.539 0.00108 0.00135 +Wall time: 12874.679260071833 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.766 0.0312 0.142 0.16 0.211 0.381 0.449 0.000953 0.00112 + 90 200 0.83 0.0327 0.177 0.163 0.216 0.422 0.502 0.00105 0.00125 + 90 235 0.776 0.0305 0.166 0.16 0.209 0.394 0.486 0.000986 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 4.06 0.0248 3.56 0.144 0.188 2.25 2.25 0.00563 0.00563 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 90 13019.532 0.005 0.0319 0.405 1.04 0.162 0.213 0.606 0.76 0.00152 0.0019 +! Validation 90 13019.532 0.005 0.0321 2.67 3.31 0.162 0.214 1.9 1.95 0.00474 0.00487 +Wall time: 13019.532475110143 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.758 0.0318 0.122 0.162 0.213 0.35 0.417 0.000874 0.00104 + 91 200 0.873 0.0296 0.281 0.156 0.205 0.526 0.633 0.00131 0.00158 + 91 235 0.929 0.0293 0.343 0.156 0.204 0.653 0.699 0.00163 0.00175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.599 0.0238 0.123 0.141 0.184 0.41 0.418 0.00103 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 91 13164.024 0.005 0.0313 0.5 1.13 0.16 0.211 0.685 0.844 0.00171 0.00211 +! Validation 91 13164.024 0.005 0.031 0.132 0.752 0.159 0.21 0.348 0.434 0.00087 0.00109 +Wall time: 13164.024510341696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.861 0.0312 0.237 0.16 0.211 0.474 0.581 0.00119 0.00145 + 92 200 0.714 0.028 0.154 0.151 0.2 0.384 0.469 0.000961 0.00117 + 92 235 0.804 0.0279 0.246 0.151 0.199 0.492 0.592 0.00123 0.00148 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.427 0.0211 0.00451 0.134 0.174 0.076 0.0801 0.00019 0.0002 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 92 13308.496 0.005 0.029 0.314 0.895 0.154 0.203 0.526 0.669 0.00131 0.00167 +! Validation 92 13308.496 0.005 0.0281 0.163 0.726 0.151 0.2 0.382 0.482 0.000956 0.00121 +Wall time: 13308.496760782786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 1 0.0279 0.442 0.151 0.199 0.66 0.794 0.00165 0.00198 + 93 200 0.685 0.0269 0.148 0.148 0.196 0.366 0.46 0.000914 0.00115 + 93 235 0.805 0.0277 0.252 0.151 0.199 0.498 0.599 0.00125 0.0015 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.663 0.0214 0.234 0.134 0.175 0.572 0.578 0.00143 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 93 13452.973 0.005 0.0279 0.42 0.978 0.151 0.199 0.63 0.774 0.00157 0.00194 +! Validation 93 13452.973 0.005 0.0282 0.165 0.73 0.151 0.201 0.392 0.485 0.000979 0.00121 +Wall time: 13452.973826752044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 1.63 0.0271 1.09 0.149 0.196 1.22 1.25 0.00304 0.00311 + 94 200 0.752 0.0249 0.253 0.143 0.188 0.492 0.6 0.00123 0.0015 + 94 235 0.723 0.0259 0.204 0.145 0.192 0.454 0.539 0.00113 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.414 0.0199 0.0166 0.129 0.168 0.132 0.154 0.00033 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 94 13597.448 0.005 0.0273 0.39 0.935 0.149 0.197 0.604 0.746 0.00151 0.00186 +! Validation 94 13597.448 0.005 0.0265 0.109 0.639 0.146 0.194 0.314 0.395 0.000784 0.000986 +Wall time: 13597.448435834143 +! Best model 94 0.639 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.722 0.0269 0.185 0.148 0.196 0.413 0.513 0.00103 0.00128 + 95 200 0.602 0.0258 0.0868 0.145 0.192 0.285 0.352 0.000712 0.000879 + 95 235 0.593 0.0255 0.0831 0.143 0.191 0.302 0.344 0.000756 0.00086 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.887 0.019 0.507 0.126 0.165 0.846 0.85 0.00212 0.00212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 95 13741.995 0.005 0.0255 0.319 0.829 0.144 0.191 0.55 0.675 0.00138 0.00169 +! Validation 95 13741.995 0.005 0.0253 0.262 0.769 0.143 0.19 0.505 0.611 0.00126 0.00153 +Wall time: 13741.995158703998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.651 0.0268 0.116 0.147 0.195 0.327 0.406 0.000817 0.00101 + 96 200 0.735 0.0256 0.224 0.144 0.191 0.449 0.565 0.00112 0.00141 + 96 235 2.32 0.0246 1.83 0.141 0.187 1.57 1.61 0.00392 0.00403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 3.09 0.0184 2.72 0.124 0.162 1.97 1.97 0.00492 0.00492 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 96 13886.472 0.005 0.0247 0.339 0.832 0.142 0.187 0.557 0.691 0.00139 0.00173 +! Validation 96 13886.472 0.005 0.0244 2.18 2.66 0.14 0.187 1.72 1.76 0.0043 0.0044 +Wall time: 13886.472019367851 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 1.69 0.0249 1.19 0.142 0.188 1.25 1.3 0.00314 0.00326 + 97 200 0.545 0.0244 0.0566 0.14 0.187 0.233 0.284 0.000582 0.00071 + 97 235 0.812 0.0237 0.339 0.139 0.184 0.671 0.694 0.00168 0.00174 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.392 0.0186 0.0201 0.125 0.163 0.159 0.169 0.000396 0.000423 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 97 14031.106 0.005 0.0245 0.388 0.878 0.141 0.187 0.61 0.744 0.00152 0.00186 +! Validation 97 14031.106 0.005 0.025 0.0954 0.595 0.142 0.189 0.296 0.369 0.00074 0.000922 +Wall time: 14031.106490117963 +! Best model 97 0.595 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.74 0.0251 0.238 0.143 0.189 0.463 0.582 0.00116 0.00146 + 98 200 0.884 0.0223 0.437 0.135 0.178 0.641 0.789 0.0016 0.00197 + 98 235 0.502 0.022 0.0624 0.134 0.177 0.219 0.298 0.000546 0.000745 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.354 0.0175 0.00346 0.121 0.158 0.0614 0.0702 0.000154 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 98 14175.619 0.005 0.0235 0.318 0.787 0.138 0.183 0.54 0.673 0.00135 0.00168 +! Validation 98 14175.619 0.005 0.0236 0.118 0.59 0.137 0.183 0.327 0.411 0.000816 0.00103 +Wall time: 14175.619454523083 +! Best model 98 0.590 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 1.38 0.025 0.883 0.141 0.189 1.04 1.12 0.0026 0.0028 + 99 200 0.548 0.0214 0.119 0.132 0.175 0.304 0.412 0.00076 0.00103 + 99 235 1.06 0.0201 0.655 0.128 0.169 0.919 0.966 0.0023 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 1.02 0.0163 0.691 0.117 0.153 0.989 0.992 0.00247 0.00248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 99 14320.185 0.005 0.0229 0.309 0.766 0.136 0.18 0.523 0.663 0.00131 0.00166 +! Validation 99 14320.185 0.005 0.0222 0.817 1.26 0.133 0.178 0.991 1.08 0.00248 0.0027 +Wall time: 14320.185446368065 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.889 0.0218 0.453 0.133 0.176 0.738 0.803 0.00185 0.00201 + 100 200 0.539 0.0209 0.12 0.13 0.173 0.37 0.414 0.000925 0.00104 + 100 235 0.532 0.0223 0.0864 0.135 0.178 0.262 0.351 0.000654 0.000877 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.572 0.0168 0.235 0.118 0.155 0.574 0.579 0.00144 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 100 14464.661 0.005 0.0222 0.341 0.785 0.134 0.178 0.568 0.698 0.00142 0.00174 +! Validation 100 14464.661 0.005 0.0225 0.191 0.64 0.134 0.179 0.438 0.522 0.00109 0.0013 +Wall time: 14464.660932763014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.622 0.0213 0.196 0.131 0.174 0.456 0.528 0.00114 0.00132 + 101 200 0.516 0.0211 0.0937 0.131 0.173 0.314 0.365 0.000785 0.000913 + 101 235 1.2 0.0201 0.8 0.128 0.169 1 1.07 0.00251 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.669 0.0171 0.326 0.12 0.156 0.678 0.681 0.00169 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 101 14609.133 0.005 0.0214 0.317 0.746 0.131 0.175 0.531 0.671 0.00133 0.00168 +! Validation 101 14609.133 0.005 0.0225 0.574 1.02 0.134 0.179 0.795 0.904 0.00199 0.00226 +Wall time: 14609.133055957034 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.503 0.0204 0.095 0.128 0.17 0.286 0.368 0.000716 0.00092 + 102 200 0.473 0.0201 0.0713 0.127 0.169 0.26 0.319 0.000651 0.000797 + 102 235 0.79 0.0183 0.425 0.121 0.161 0.724 0.778 0.00181 0.00195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.576 0.0154 0.268 0.113 0.148 0.613 0.618 0.00153 0.00154 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 102 14753.607 0.005 0.0206 0.26 0.671 0.129 0.171 0.486 0.608 0.00121 0.00152 +! Validation 102 14753.607 0.005 0.0206 0.274 0.685 0.128 0.171 0.537 0.624 0.00134 0.00156 +Wall time: 14753.607380301692 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.6 0.0196 0.208 0.126 0.167 0.433 0.545 0.00108 0.00136 + 103 200 0.483 0.0196 0.0908 0.126 0.167 0.284 0.36 0.000711 0.000899 + 103 235 0.442 0.0196 0.0495 0.124 0.167 0.235 0.266 0.000586 0.000664 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.308 0.0147 0.0133 0.111 0.145 0.119 0.138 0.000297 0.000345 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 103 14898.130 0.005 0.0205 0.348 0.759 0.129 0.171 0.558 0.705 0.0014 0.00176 +! Validation 103 14898.130 0.005 0.02 0.132 0.532 0.126 0.169 0.345 0.433 0.000862 0.00108 +Wall time: 14898.129918904044 +! Best model 103 0.532 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.696 0.0199 0.297 0.126 0.168 0.567 0.651 0.00142 0.00163 + 104 200 0.439 0.0188 0.0627 0.124 0.164 0.245 0.299 0.000611 0.000747 + 104 235 0.789 0.024 0.31 0.135 0.185 0.583 0.665 0.00146 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 1.1 0.0151 0.802 0.112 0.146 1.07 1.07 0.00266 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 104 15042.713 0.005 0.0193 0.253 0.638 0.124 0.166 0.482 0.6 0.00121 0.0015 +! Validation 104 15042.713 0.005 0.0201 0.504 0.905 0.127 0.169 0.761 0.847 0.0019 0.00212 +Wall time: 15042.71330748871 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 0.444 0.0186 0.0727 0.123 0.163 0.277 0.322 0.000692 0.000804 + 105 200 0.577 0.0184 0.209 0.122 0.162 0.415 0.546 0.00104 0.00136 + 105 235 0.603 0.0174 0.256 0.119 0.157 0.539 0.604 0.00135 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 1.12 0.014 0.839 0.109 0.141 1.09 1.09 0.00272 0.00273 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 105 15187.159 0.005 0.0188 0.271 0.647 0.123 0.164 0.493 0.621 0.00123 0.00155 +! Validation 105 15187.159 0.005 0.0189 1.09 1.47 0.123 0.164 1.17 1.25 0.00293 0.00311 +Wall time: 15187.159390239976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.634 0.0187 0.26 0.123 0.163 0.517 0.609 0.00129 0.00152 + 106 200 0.417 0.0168 0.0802 0.116 0.155 0.272 0.338 0.000679 0.000845 + 106 235 0.592 0.0178 0.237 0.119 0.159 0.534 0.581 0.00134 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.606 0.0139 0.329 0.107 0.141 0.676 0.685 0.00169 0.00171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 106 15331.792 0.005 0.0183 0.268 0.633 0.121 0.161 0.481 0.618 0.0012 0.00154 +! Validation 106 15331.792 0.005 0.0182 0.574 0.938 0.12 0.161 0.758 0.904 0.0019 0.00226 +Wall time: 15331.792143907864 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.491 0.0177 0.138 0.119 0.159 0.341 0.444 0.000852 0.00111 + 107 200 0.466 0.0179 0.108 0.12 0.16 0.314 0.393 0.000785 0.000983 + 107 235 1.38 0.0177 1.03 0.121 0.159 1.18 1.21 0.00294 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 1.46 0.0136 1.19 0.107 0.139 1.29 1.3 0.00324 0.00325 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 107 15476.231 0.005 0.018 0.323 0.683 0.121 0.16 0.541 0.677 0.00135 0.00169 +! Validation 107 15476.231 0.005 0.0182 1.22 1.59 0.121 0.161 1.27 1.32 0.00316 0.0033 +Wall time: 15476.231334129814 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.396 0.0169 0.0566 0.117 0.155 0.228 0.284 0.00057 0.00071 + 108 200 0.435 0.0168 0.0998 0.116 0.154 0.307 0.377 0.000768 0.000943 + 108 235 1.28 0.0167 0.945 0.117 0.154 1.13 1.16 0.00283 0.0029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.453 0.0138 0.177 0.107 0.14 0.491 0.502 0.00123 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 108 15620.658 0.005 0.0172 0.29 0.635 0.118 0.157 0.508 0.641 0.00127 0.0016 +! Validation 108 15620.658 0.005 0.0182 0.13 0.494 0.121 0.161 0.356 0.43 0.000889 0.00108 +Wall time: 15620.658528014086 +! Best model 108 0.494 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.7 0.0161 0.378 0.115 0.151 0.652 0.734 0.00163 0.00183 + 109 200 0.562 0.0161 0.241 0.113 0.151 0.521 0.586 0.0013 0.00146 + 109 235 0.351 0.0143 0.0653 0.109 0.143 0.258 0.305 0.000646 0.000762 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.773 0.0121 0.53 0.101 0.131 0.861 0.869 0.00215 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 109 15765.098 0.005 0.0165 0.207 0.537 0.115 0.153 0.435 0.544 0.00109 0.00136 +! Validation 109 15765.098 0.005 0.0163 0.324 0.649 0.114 0.152 0.599 0.679 0.0015 0.0017 +Wall time: 15765.098161363974 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.396 0.0145 0.106 0.108 0.144 0.333 0.389 0.000832 0.000972 + 110 200 0.495 0.0149 0.197 0.11 0.146 0.472 0.529 0.00118 0.00132 + 110 235 0.485 0.0155 0.175 0.111 0.149 0.424 0.499 0.00106 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.289 0.0113 0.0635 0.0973 0.127 0.272 0.301 0.000681 0.000752 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 110 15909.631 0.005 0.0155 0.203 0.514 0.112 0.149 0.43 0.538 0.00107 0.00134 +! Validation 110 15909.631 0.005 0.0154 0.0772 0.384 0.111 0.148 0.269 0.332 0.000672 0.000829 +Wall time: 15909.63174845092 +! Best model 110 0.384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.447 0.0157 0.133 0.112 0.15 0.357 0.435 0.000892 0.00109 + 111 200 0.533 0.0149 0.235 0.11 0.146 0.497 0.578 0.00124 0.00145 + 111 235 0.898 0.0166 0.566 0.116 0.154 0.796 0.898 0.00199 0.00225 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.426 0.0128 0.169 0.104 0.135 0.484 0.491 0.00121 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 111 16054.253 0.005 0.0152 0.253 0.557 0.111 0.147 0.483 0.6 0.00121 0.0015 +! Validation 111 16054.253 0.005 0.017 0.171 0.512 0.118 0.156 0.415 0.494 0.00104 0.00123 +Wall time: 16054.253441060893 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 1.16 0.0161 0.837 0.114 0.151 1.02 1.09 0.00255 0.00273 + 112 200 0.357 0.0147 0.0628 0.109 0.145 0.252 0.299 0.000631 0.000748 + 112 235 0.354 0.0142 0.0705 0.108 0.142 0.24 0.317 0.0006 0.000792 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.411 0.012 0.17 0.0999 0.131 0.474 0.493 0.00119 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 112 16198.650 0.005 0.0152 0.261 0.565 0.111 0.147 0.492 0.611 0.00123 0.00153 +! Validation 112 16198.650 0.005 0.0155 0.308 0.618 0.112 0.149 0.592 0.663 0.00148 0.00166 +Wall time: 16198.650671278127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.301 0.0133 0.0357 0.104 0.138 0.18 0.226 0.000449 0.000564 + 113 200 0.623 0.0134 0.355 0.104 0.138 0.667 0.711 0.00167 0.00178 + 113 235 0.464 0.0136 0.192 0.105 0.139 0.417 0.523 0.00104 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.618 0.0114 0.389 0.0988 0.128 0.732 0.745 0.00183 0.00186 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 113 16343.061 0.005 0.0137 0.151 0.425 0.105 0.14 0.371 0.464 0.000928 0.00116 +! Validation 113 16343.061 0.005 0.0147 0.638 0.931 0.109 0.145 0.911 0.953 0.00228 0.00238 +Wall time: 16343.061741454992 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.458 0.0146 0.167 0.109 0.144 0.405 0.487 0.00101 0.00122 + 114 200 0.815 0.0127 0.561 0.102 0.135 0.855 0.894 0.00214 0.00223 + 114 235 0.774 0.0128 0.518 0.102 0.135 0.822 0.859 0.00206 0.00215 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 2.07 0.0116 1.84 0.0986 0.129 1.62 1.62 0.00404 0.00405 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 114 16487.548 0.005 0.0137 0.223 0.497 0.106 0.14 0.453 0.562 0.00113 0.00141 +! Validation 114 16487.548 0.005 0.0153 1.61 1.92 0.111 0.147 1.48 1.51 0.00371 0.00379 +Wall time: 16487.54821286071 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 24.1 0.924 5.61 0.854 1.15 2.32 2.83 0.0058 0.00706 + 115 200 18.8 0.784 3.12 0.783 1.06 1.84 2.11 0.00459 0.00527 + 115 235 16.1 0.717 1.72 0.75 1.01 1.25 1.56 0.00313 0.00391 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 20.9 0.736 6.15 0.758 1.02 2.91 2.96 0.00728 0.0074 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 115 16632.947 0.005 0.834 98.7 115 0.787 1.09 4.73 11.9 0.0118 0.0297 +! Validation 115 16632.947 0.005 0.718 2.58 16.9 0.748 1.01 1.52 1.92 0.0038 0.00479 +Wall time: 16632.947056300938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 9.41 0.426 0.89 0.583 0.779 0.923 1.13 0.00231 0.00281 + 116 200 6.23 0.281 0.619 0.479 0.632 0.701 0.939 0.00175 0.00235 + 116 235 5.49 0.246 0.572 0.448 0.592 0.722 0.902 0.00181 0.00226 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 5.23 0.237 0.488 0.438 0.581 0.802 0.834 0.00201 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 116 16777.420 0.005 0.432 1.39 10 0.58 0.785 1.1 1.41 0.00274 0.00352 +! Validation 116 16777.420 0.005 0.25 0.702 5.69 0.45 0.596 0.794 1 0.00199 0.0025 +Wall time: 16777.420445664786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 4.04 0.167 0.693 0.367 0.488 0.824 0.994 0.00206 0.00248 + 117 200 2.78 0.11 0.582 0.299 0.396 0.724 0.91 0.00181 0.00228 + 117 235 2.54 0.1 0.53 0.285 0.378 0.733 0.869 0.00183 0.00217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 2.41 0.0959 0.489 0.279 0.37 0.833 0.835 0.00208 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 117 16921.889 0.005 0.164 0.563 3.83 0.361 0.483 0.714 0.895 0.00178 0.00224 +! Validation 117 16921.889 0.005 0.106 0.416 2.55 0.292 0.389 0.628 0.77 0.00157 0.00193 +Wall time: 16921.889605091885 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 1.88 0.0806 0.273 0.257 0.339 0.447 0.623 0.00112 0.00156 + 118 200 1.54 0.0643 0.252 0.232 0.303 0.467 0.6 0.00117 0.0015 + 118 235 1.67 0.0621 0.425 0.227 0.297 0.65 0.778 0.00162 0.00195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 1.38 0.0562 0.251 0.217 0.283 0.594 0.598 0.00149 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 118 17066.507 0.005 0.0803 0.447 2.05 0.256 0.338 0.64 0.798 0.0016 0.002 +! Validation 118 17066.507 0.005 0.0646 0.257 1.55 0.23 0.303 0.482 0.605 0.0012 0.00151 +Wall time: 17066.507390832994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 1.28 0.0555 0.169 0.216 0.281 0.435 0.491 0.00109 0.00123 + 119 200 1.52 0.049 0.536 0.201 0.264 0.756 0.874 0.00189 0.00218 + 119 235 1.15 0.0474 0.207 0.198 0.26 0.436 0.543 0.00109 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.921 0.0424 0.0726 0.189 0.246 0.318 0.322 0.000794 0.000804 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 119 17211.052 0.005 0.0551 0.451 1.55 0.214 0.28 0.636 0.803 0.00159 0.00201 +! Validation 119 17211.052 0.005 0.0492 0.201 1.18 0.201 0.265 0.413 0.535 0.00103 0.00134 +Wall time: 17211.052222984843 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 1.23 0.0453 0.322 0.194 0.254 0.556 0.677 0.00139 0.00169 + 120 200 1.42 0.0402 0.614 0.183 0.239 0.82 0.936 0.00205 0.00234 + 120 235 0.967 0.0393 0.181 0.18 0.237 0.413 0.508 0.00103 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.921 0.0336 0.249 0.168 0.219 0.595 0.596 0.00149 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 120 17355.841 0.005 0.0437 0.48 1.35 0.19 0.249 0.677 0.828 0.00169 0.00207 +! Validation 120 17355.841 0.005 0.0397 0.191 0.985 0.181 0.238 0.424 0.521 0.00106 0.0013 +Wall time: 17355.841089469846 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.869 0.0357 0.155 0.171 0.226 0.346 0.469 0.000865 0.00117 + 121 200 0.713 0.0319 0.0749 0.162 0.213 0.25 0.327 0.000625 0.000817 + 121 235 2 0.032 1.36 0.163 0.213 1.37 1.39 0.00343 0.00348 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.632 0.0265 0.103 0.149 0.194 0.382 0.382 0.000956 0.000956 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 121 17500.348 0.005 0.035 0.392 1.09 0.17 0.223 0.591 0.745 0.00148 0.00186 +! Validation 121 17500.348 0.005 0.0324 0.816 1.46 0.163 0.215 0.965 1.08 0.00241 0.0027 +Wall time: 17500.34841020778 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.782 0.0309 0.163 0.159 0.21 0.428 0.483 0.00107 0.00121 + 122 200 1.27 0.0266 0.739 0.147 0.195 0.986 1.03 0.00246 0.00257 + 122 235 0.561 0.0262 0.0371 0.146 0.193 0.211 0.23 0.000527 0.000575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.452 0.0219 0.0139 0.136 0.177 0.139 0.141 0.000347 0.000352 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 122 17644.695 0.005 0.0298 0.509 1.1 0.156 0.206 0.687 0.853 0.00172 0.00213 +! Validation 122 17644.695 0.005 0.0275 0.145 0.696 0.149 0.198 0.361 0.455 0.000901 0.00114 +Wall time: 17644.69530302193 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.721 0.0254 0.214 0.143 0.19 0.466 0.553 0.00117 0.00138 + 123 200 0.602 0.0244 0.115 0.14 0.186 0.333 0.405 0.000833 0.00101 + 123 235 0.511 0.0218 0.0753 0.133 0.176 0.257 0.327 0.000643 0.000819 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.371 0.0185 0.00158 0.124 0.162 0.0415 0.0474 0.000104 0.000118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 123 17789.047 0.005 0.0248 0.244 0.739 0.142 0.188 0.473 0.59 0.00118 0.00148 +! Validation 123 17789.047 0.005 0.0238 0.227 0.703 0.138 0.184 0.459 0.569 0.00115 0.00142 +Wall time: 17789.04762874404 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.564 0.0226 0.112 0.135 0.179 0.334 0.399 0.000834 0.000998 + 124 200 0.525 0.0211 0.103 0.13 0.173 0.304 0.383 0.000759 0.000959 + 124 235 1.09 0.0233 0.625 0.137 0.182 0.878 0.944 0.0022 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 1.37 0.0167 1.04 0.118 0.154 1.21 1.22 0.00304 0.00304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 124 17933.758 0.005 0.0224 0.342 0.789 0.134 0.179 0.559 0.697 0.0014 0.00174 +! Validation 124 17933.758 0.005 0.0219 0.573 1.01 0.132 0.176 0.823 0.904 0.00206 0.00226 +Wall time: 17933.7581955879 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.51 0.02 0.111 0.127 0.169 0.348 0.397 0.000869 0.000993 + 125 200 0.924 0.0211 0.503 0.13 0.173 0.782 0.846 0.00196 0.00212 + 125 235 0.55 0.0219 0.112 0.132 0.177 0.326 0.399 0.000816 0.000998 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.496 0.0156 0.184 0.114 0.149 0.508 0.512 0.00127 0.00128 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 125 18078.112 0.005 0.0207 0.267 0.681 0.129 0.172 0.502 0.617 0.00126 0.00154 +! Validation 125 18078.112 0.005 0.0204 0.15 0.559 0.128 0.171 0.369 0.463 0.000922 0.00116 +Wall time: 18078.11279704701 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.428 0.019 0.0489 0.124 0.164 0.227 0.264 0.000567 0.00066 + 126 200 0.434 0.0187 0.0599 0.123 0.163 0.222 0.292 0.000554 0.00073 + 126 235 0.476 0.0194 0.0884 0.124 0.166 0.292 0.355 0.000731 0.000887 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.342 0.0141 0.0594 0.109 0.142 0.278 0.291 0.000695 0.000727 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 126 18222.676 0.005 0.0193 0.224 0.609 0.124 0.166 0.448 0.565 0.00112 0.00141 +! Validation 126 18222.676 0.005 0.0189 0.31 0.689 0.123 0.164 0.578 0.665 0.00144 0.00166 +Wall time: 18222.676840587985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 1.61 0.0185 1.24 0.121 0.162 1.3 1.33 0.00324 0.00332 + 127 200 0.768 0.019 0.388 0.123 0.165 0.703 0.744 0.00176 0.00186 + 127 235 0.487 0.018 0.127 0.12 0.16 0.286 0.426 0.000715 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.428 0.0139 0.15 0.108 0.141 0.452 0.462 0.00113 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 127 18367.220 0.005 0.0184 0.29 0.659 0.122 0.162 0.519 0.643 0.0013 0.00161 +! Validation 127 18367.220 0.005 0.0184 0.112 0.48 0.122 0.162 0.325 0.4 0.000812 0.000999 +Wall time: 18367.220533271786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.413 0.0175 0.0633 0.118 0.158 0.228 0.3 0.000569 0.00075 + 128 200 1.45 0.0172 1.11 0.118 0.157 1.23 1.26 0.00309 0.00314 + 128 235 0.849 0.0166 0.517 0.117 0.154 0.794 0.858 0.00199 0.00214 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.559 0.0135 0.289 0.107 0.139 0.634 0.642 0.00159 0.00161 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 128 18511.766 0.005 0.0176 0.26 0.612 0.119 0.158 0.486 0.608 0.00121 0.00152 +! Validation 128 18511.766 0.005 0.0179 0.658 1.02 0.12 0.16 0.912 0.969 0.00228 0.00242 +Wall time: 18511.766116563696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.451 0.0166 0.119 0.116 0.154 0.327 0.412 0.000819 0.00103 + 129 200 0.457 0.0178 0.102 0.119 0.159 0.33 0.381 0.000825 0.000952 + 129 235 0.538 0.0166 0.206 0.116 0.154 0.525 0.542 0.00131 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.728 0.0131 0.466 0.105 0.137 0.808 0.815 0.00202 0.00204 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 129 18656.213 0.005 0.0172 0.3 0.645 0.118 0.157 0.524 0.654 0.00131 0.00164 +! Validation 129 18656.213 0.005 0.0178 0.936 1.29 0.119 0.159 1.1 1.15 0.00274 0.00289 +Wall time: 18656.213465429842 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.579 0.0172 0.235 0.118 0.156 0.509 0.579 0.00127 0.00145 + 130 200 0.465 0.0171 0.123 0.118 0.156 0.35 0.418 0.000874 0.00105 + 130 235 0.396 0.0167 0.0607 0.117 0.154 0.239 0.294 0.000597 0.000735 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 1.1 0.0123 0.852 0.102 0.133 1.1 1.1 0.00274 0.00275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 130 18800.662 0.005 0.0167 0.266 0.599 0.116 0.154 0.505 0.616 0.00126 0.00154 +! Validation 130 18800.662 0.005 0.0168 0.698 1.03 0.116 0.155 0.947 0.997 0.00237 0.00249 +Wall time: 18800.662297764793 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.378 0.0149 0.0805 0.109 0.146 0.281 0.339 0.000703 0.000847 + 131 200 0.61 0.0158 0.294 0.113 0.15 0.584 0.647 0.00146 0.00162 + 131 235 0.729 0.0159 0.41 0.112 0.151 0.72 0.765 0.0018 0.00191 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.287 0.0116 0.0547 0.0983 0.129 0.258 0.279 0.000644 0.000698 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 131 18945.104 0.005 0.0161 0.228 0.55 0.114 0.151 0.458 0.569 0.00114 0.00142 +! Validation 131 18945.104 0.005 0.0161 0.0848 0.406 0.113 0.151 0.276 0.348 0.000689 0.000869 +Wall time: 18945.104150644038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.365 0.0155 0.0556 0.111 0.149 0.215 0.281 0.000538 0.000703 + 132 200 0.447 0.0155 0.137 0.112 0.149 0.371 0.442 0.000928 0.00111 + 132 235 0.386 0.0155 0.0751 0.111 0.149 0.281 0.327 0.000702 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.243 0.0117 0.0103 0.0984 0.129 0.103 0.121 0.000258 0.000302 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 132 19089.579 0.005 0.0156 0.253 0.565 0.112 0.149 0.474 0.601 0.00118 0.0015 +! Validation 132 19089.579 0.005 0.0159 0.074 0.392 0.113 0.15 0.26 0.325 0.000651 0.000812 +Wall time: 19089.57900848007 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.384 0.0148 0.0873 0.109 0.145 0.276 0.353 0.000691 0.000882 + 133 200 0.664 0.0144 0.376 0.108 0.143 0.683 0.732 0.00171 0.00183 + 133 235 0.542 0.0147 0.248 0.108 0.145 0.534 0.595 0.00134 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.369 0.011 0.148 0.0959 0.125 0.442 0.46 0.00111 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 133 19234.063 0.005 0.015 0.208 0.508 0.11 0.146 0.437 0.544 0.00109 0.00136 +! Validation 133 19234.063 0.005 0.0152 0.115 0.418 0.11 0.147 0.334 0.404 0.000834 0.00101 +Wall time: 19234.063638100866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.514 0.0165 0.184 0.115 0.153 0.41 0.513 0.00102 0.00128 + 134 200 0.379 0.0164 0.0518 0.115 0.153 0.209 0.272 0.000524 0.000679 + 134 235 0.395 0.0139 0.116 0.106 0.141 0.326 0.407 0.000815 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.273 0.0104 0.0643 0.0936 0.122 0.273 0.303 0.000683 0.000757 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 134 19378.537 0.005 0.0154 0.317 0.626 0.111 0.148 0.522 0.673 0.00131 0.00168 +! Validation 134 19378.537 0.005 0.0149 0.0913 0.388 0.109 0.145 0.285 0.361 0.000713 0.000902 +Wall time: 19378.537290907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.42 0.0151 0.118 0.11 0.147 0.326 0.41 0.000814 0.00103 + 135 200 0.602 0.0139 0.324 0.105 0.141 0.618 0.68 0.00155 0.0017 + 135 235 0.338 0.0135 0.0673 0.104 0.139 0.256 0.31 0.000641 0.000774 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.549 0.0103 0.343 0.0928 0.121 0.688 0.699 0.00172 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 135 19523.114 0.005 0.0141 0.189 0.472 0.107 0.142 0.413 0.52 0.00103 0.0013 +! Validation 135 19523.114 0.005 0.0145 0.277 0.566 0.108 0.143 0.551 0.628 0.00138 0.00157 +Wall time: 19523.114300759975 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.379 0.0135 0.11 0.104 0.139 0.329 0.396 0.000822 0.000989 + 136 200 0.364 0.0135 0.0939 0.104 0.139 0.312 0.366 0.000781 0.000914 + 136 235 0.347 0.0134 0.0793 0.104 0.138 0.272 0.336 0.00068 0.00084 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.525 0.00999 0.325 0.0911 0.119 0.67 0.681 0.00167 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 136 19667.603 0.005 0.0142 0.272 0.557 0.107 0.142 0.47 0.623 0.00118 0.00156 +! Validation 136 19667.603 0.005 0.014 0.212 0.492 0.106 0.141 0.476 0.55 0.00119 0.00138 +Wall time: 19667.602905057836 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.335 0.014 0.054 0.107 0.141 0.23 0.277 0.000575 0.000693 + 137 200 0.668 0.0128 0.411 0.102 0.135 0.73 0.765 0.00182 0.00191 + 137 235 0.447 0.0131 0.185 0.103 0.137 0.437 0.513 0.00109 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.224 0.00936 0.0371 0.0885 0.115 0.203 0.23 0.000507 0.000575 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 137 19812.106 0.005 0.0133 0.182 0.448 0.104 0.138 0.416 0.508 0.00104 0.00127 +! Validation 137 19812.106 0.005 0.0134 0.12 0.387 0.103 0.138 0.34 0.414 0.000849 0.00104 +Wall time: 19812.10645110486 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.369 0.0126 0.118 0.101 0.134 0.357 0.41 0.000893 0.00102 + 138 200 0.495 0.0126 0.243 0.101 0.134 0.532 0.588 0.00133 0.00147 + 138 235 0.354 0.0126 0.102 0.101 0.134 0.309 0.38 0.000773 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.466 0.00911 0.284 0.0873 0.114 0.623 0.636 0.00156 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 138 19956.749 0.005 0.0129 0.193 0.452 0.102 0.136 0.418 0.525 0.00104 0.00131 +! Validation 138 19956.749 0.005 0.013 0.149 0.408 0.102 0.136 0.374 0.461 0.000936 0.00115 +Wall time: 19956.748975795694 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.672 0.0121 0.43 0.0991 0.131 0.749 0.783 0.00187 0.00196 + 139 200 0.328 0.0127 0.0738 0.101 0.135 0.257 0.324 0.000643 0.00081 + 139 235 0.284 0.0117 0.0508 0.0973 0.129 0.234 0.269 0.000584 0.000672 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.912 0.00892 0.734 0.0864 0.113 1.01 1.02 0.00254 0.00256 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 139 20101.230 0.005 0.0124 0.187 0.434 0.1 0.133 0.416 0.516 0.00104 0.00129 +! Validation 139 20101.230 0.005 0.0126 0.503 0.755 0.101 0.134 0.806 0.847 0.00201 0.00212 +Wall time: 20101.23040094273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.359 0.0119 0.122 0.0983 0.13 0.341 0.416 0.000854 0.00104 + 140 200 1.13 0.0121 0.888 0.0988 0.131 1.1 1.12 0.00275 0.00281 + 140 235 0.332 0.0116 0.0997 0.0966 0.129 0.317 0.377 0.000793 0.000942 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.263 0.00844 0.0942 0.0841 0.11 0.344 0.366 0.00086 0.000916 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 140 20245.655 0.005 0.012 0.17 0.411 0.0988 0.131 0.391 0.493 0.000978 0.00123 +! Validation 140 20245.655 0.005 0.012 0.285 0.524 0.0979 0.131 0.572 0.637 0.00143 0.00159 +Wall time: 20245.65508529311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.409 0.0118 0.173 0.0978 0.13 0.459 0.496 0.00115 0.00124 + 141 200 0.651 0.0125 0.4 0.101 0.134 0.709 0.755 0.00177 0.00189 + 141 235 0.313 0.0112 0.0887 0.0951 0.126 0.296 0.355 0.00074 0.000888 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.202 0.00871 0.0281 0.0855 0.111 0.178 0.2 0.000445 0.0005 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 141 20390.059 0.005 0.0122 0.265 0.508 0.0994 0.132 0.493 0.615 0.00123 0.00154 +! Validation 141 20390.059 0.005 0.0124 0.0624 0.311 0.0996 0.133 0.235 0.298 0.000588 0.000745 +Wall time: 20390.059214785695 +! Best model 141 0.311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.286 0.0116 0.0531 0.0963 0.129 0.227 0.275 0.000567 0.000687 + 142 200 0.246 0.0105 0.0364 0.0923 0.122 0.176 0.228 0.000439 0.000569 + 142 235 0.273 0.0113 0.0456 0.0958 0.127 0.214 0.255 0.000536 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.474 0.00819 0.31 0.0828 0.108 0.65 0.665 0.00163 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 142 20534.616 0.005 0.0117 0.16 0.393 0.0972 0.129 0.385 0.478 0.000963 0.0012 +! Validation 142 20534.616 0.005 0.0115 0.254 0.485 0.0961 0.128 0.535 0.602 0.00134 0.00151 +Wall time: 20534.616606795695 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.252 0.0109 0.0333 0.0937 0.125 0.185 0.218 0.000464 0.000544 + 143 200 0.478 0.011 0.259 0.0944 0.125 0.525 0.607 0.00131 0.00152 + 143 235 0.674 0.0136 0.402 0.103 0.139 0.712 0.757 0.00178 0.00189 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.174 0.00787 0.017 0.0811 0.106 0.127 0.156 0.000318 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 143 20679.061 0.005 0.011 0.142 0.362 0.0945 0.125 0.359 0.449 0.000898 0.00112 +! Validation 143 20679.061 0.005 0.0111 0.315 0.537 0.0943 0.126 0.551 0.67 0.00138 0.00167 +Wall time: 20679.06177067384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.372 0.0114 0.145 0.0959 0.127 0.356 0.454 0.000891 0.00114 + 144 200 0.392 0.0106 0.18 0.0922 0.123 0.44 0.506 0.0011 0.00127 + 144 235 0.875 0.0127 0.621 0.102 0.134 0.897 0.941 0.00224 0.00235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.647 0.00887 0.469 0.0864 0.112 0.803 0.818 0.00201 0.00204 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 144 20823.540 0.005 0.0111 0.212 0.434 0.095 0.126 0.437 0.548 0.00109 0.00137 +! Validation 144 20823.540 0.005 0.0121 0.658 0.899 0.0989 0.131 0.931 0.968 0.00233 0.00242 +Wall time: 20823.540859203786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.329 0.0102 0.126 0.0907 0.12 0.361 0.423 0.000902 0.00106 + 145 200 0.305 0.0104 0.0976 0.0919 0.121 0.306 0.373 0.000766 0.000932 + 145 235 0.511 0.0103 0.305 0.092 0.121 0.617 0.659 0.00154 0.00165 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.278 0.00805 0.117 0.0822 0.107 0.385 0.408 0.000962 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 145 20967.905 0.005 0.0107 0.155 0.368 0.0931 0.123 0.369 0.469 0.000921 0.00117 +! Validation 145 20967.905 0.005 0.0111 0.335 0.557 0.0945 0.126 0.641 0.691 0.0016 0.00173 +Wall time: 20967.905220484827 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.259 0.0104 0.0502 0.0923 0.122 0.206 0.268 0.000515 0.000669 + 146 200 0.76 0.0106 0.547 0.0933 0.123 0.857 0.883 0.00214 0.00221 + 146 235 0.38 0.0103 0.175 0.0916 0.121 0.444 0.499 0.00111 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.387 0.00731 0.241 0.0789 0.102 0.573 0.586 0.00143 0.00147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 146 21112.259 0.005 0.0104 0.172 0.38 0.0919 0.122 0.392 0.495 0.000981 0.00124 +! Validation 146 21112.259 0.005 0.0106 0.303 0.514 0.0921 0.123 0.599 0.657 0.0015 0.00164 +Wall time: 21112.259058379102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.286 0.0102 0.0818 0.091 0.121 0.279 0.341 0.000697 0.000853 + 147 200 0.321 0.0109 0.102 0.0941 0.125 0.293 0.382 0.000732 0.000955 + 147 235 0.475 0.0101 0.273 0.0904 0.12 0.549 0.623 0.00137 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.645 0.00719 0.501 0.078 0.101 0.836 0.845 0.00209 0.00211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 147 21256.610 0.005 0.0104 0.185 0.394 0.0922 0.122 0.412 0.512 0.00103 0.00128 +! Validation 147 21256.610 0.005 0.0101 0.315 0.517 0.0898 0.12 0.615 0.67 0.00154 0.00168 +Wall time: 21256.610668350942 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.382 0.00993 0.183 0.0898 0.119 0.448 0.511 0.00112 0.00128 + 148 200 0.42 0.0106 0.208 0.0925 0.123 0.478 0.544 0.0012 0.00136 + 148 235 0.257 0.0103 0.0518 0.0907 0.121 0.225 0.272 0.000562 0.000679 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.476 0.00681 0.339 0.0758 0.0985 0.683 0.695 0.00171 0.00174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 148 21400.960 0.005 0.00985 0.149 0.346 0.0894 0.118 0.375 0.461 0.000936 0.00115 +! Validation 148 21400.960 0.005 0.00977 0.214 0.409 0.0882 0.118 0.495 0.552 0.00124 0.00138 +Wall time: 21400.960659910925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.378 0.0101 0.176 0.0904 0.12 0.455 0.5 0.00114 0.00125 + 149 200 0.274 0.00998 0.0743 0.0895 0.119 0.255 0.325 0.000638 0.000814 + 149 235 0.242 0.0088 0.0658 0.0852 0.112 0.275 0.306 0.000687 0.000766 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.261 0.00714 0.118 0.0779 0.101 0.386 0.411 0.000965 0.00103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 149 21545.541 0.005 0.0098 0.167 0.363 0.0892 0.118 0.393 0.488 0.000984 0.00122 +! Validation 149 21545.541 0.005 0.01 0.214 0.414 0.0896 0.119 0.49 0.552 0.00123 0.00138 +Wall time: 21545.54152773088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.274 0.01 0.073 0.0895 0.12 0.27 0.323 0.000675 0.000806 + 150 200 0.289 0.0092 0.105 0.0863 0.114 0.328 0.387 0.000819 0.000967 + 150 235 0.212 0.00831 0.0457 0.0828 0.109 0.206 0.255 0.000515 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.384 0.00663 0.252 0.0749 0.0972 0.586 0.599 0.00147 0.0015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 150 21690.021 0.005 0.00965 0.151 0.344 0.0885 0.117 0.36 0.464 0.000899 0.00116 +! Validation 150 21690.021 0.005 0.00946 0.213 0.402 0.087 0.116 0.495 0.551 0.00124 0.00138 +Wall time: 21690.021377797704 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.246 0.00896 0.0666 0.0852 0.113 0.243 0.308 0.000607 0.00077 + 151 200 0.547 0.0111 0.325 0.0951 0.126 0.633 0.68 0.00158 0.0017 + 151 235 0.239 0.00929 0.0537 0.0863 0.115 0.219 0.277 0.000548 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.469 0.0069 0.331 0.076 0.0991 0.676 0.686 0.00169 0.00172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 151 21834.489 0.005 0.0101 0.199 0.402 0.0905 0.12 0.411 0.533 0.00103 0.00133 +! Validation 151 21834.489 0.005 0.00976 0.259 0.454 0.0881 0.118 0.553 0.607 0.00138 0.00152 +Wall time: 21834.489188488107 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.286 0.00835 0.119 0.0825 0.109 0.346 0.411 0.000865 0.00103 + 152 200 0.202 0.00801 0.0418 0.0807 0.107 0.197 0.244 0.000492 0.00061 + 152 235 0.503 0.00861 0.33 0.0833 0.111 0.621 0.686 0.00155 0.00172 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.157 0.00691 0.0191 0.0758 0.0992 0.147 0.165 0.000367 0.000413 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 152 21979.153 0.005 0.00879 0.101 0.276 0.0842 0.112 0.302 0.378 0.000754 0.000944 +! Validation 152 21979.153 0.005 0.00952 0.0428 0.233 0.0874 0.116 0.197 0.247 0.000492 0.000618 +Wall time: 21979.15329635609 +! Best model 152 0.233 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.341 0.00858 0.169 0.0837 0.111 0.449 0.491 0.00112 0.00123 + 153 200 0.241 0.00952 0.0509 0.0876 0.116 0.211 0.269 0.000528 0.000673 + 153 235 0.317 0.00802 0.156 0.0808 0.107 0.433 0.472 0.00108 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.168 0.00674 0.0335 0.0756 0.098 0.192 0.218 0.000479 0.000546 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 153 22123.629 0.005 0.00917 0.153 0.337 0.0864 0.114 0.377 0.468 0.000942 0.00117 +! Validation 153 22123.629 0.005 0.00925 0.0528 0.238 0.0862 0.115 0.214 0.274 0.000535 0.000685 +Wall time: 22123.629687495064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.282 0.00839 0.114 0.0821 0.109 0.338 0.403 0.000844 0.00101 + 154 200 0.355 0.0101 0.152 0.0909 0.12 0.377 0.466 0.000942 0.00116 + 154 235 0.198 0.00812 0.0353 0.0813 0.108 0.187 0.224 0.000467 0.000561 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.135 0.0063 0.00904 0.0729 0.0948 0.085 0.113 0.000213 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 154 22267.964 0.005 0.00864 0.131 0.304 0.0835 0.111 0.349 0.433 0.000872 0.00108 +! Validation 154 22267.964 0.005 0.00885 0.0477 0.225 0.0838 0.112 0.205 0.261 0.000514 0.000651 +Wall time: 22267.964301310945 +! Best model 154 0.225 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.203 0.00837 0.0358 0.0821 0.109 0.184 0.226 0.00046 0.000565 + 155 200 0.184 0.00789 0.0264 0.0797 0.106 0.153 0.194 0.000383 0.000485 + 155 235 0.33 0.0101 0.128 0.0919 0.12 0.384 0.427 0.000959 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.149 0.00686 0.0123 0.0769 0.0988 0.119 0.132 0.000298 0.000331 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 155 22412.295 0.005 0.00868 0.141 0.314 0.0838 0.111 0.355 0.448 0.000889 0.00112 +! Validation 155 22412.295 0.005 0.00981 0.09 0.286 0.0895 0.118 0.297 0.358 0.000743 0.000895 +Wall time: 22412.29535155371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.258 0.00824 0.0936 0.0819 0.108 0.307 0.365 0.000768 0.000913 + 156 200 0.461 0.00872 0.287 0.0834 0.111 0.595 0.639 0.00149 0.0016 + 156 235 0.267 0.00821 0.103 0.0821 0.108 0.348 0.383 0.000871 0.000957 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.17 0.00609 0.0486 0.0713 0.0932 0.244 0.263 0.00061 0.000658 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 156 22556.612 0.005 0.00862 0.132 0.304 0.0835 0.111 0.342 0.433 0.000855 0.00108 +! Validation 156 22556.612 0.005 0.00877 0.067 0.242 0.0834 0.112 0.242 0.309 0.000604 0.000773 +Wall time: 22556.61214656895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.189 0.00823 0.0241 0.0819 0.108 0.157 0.185 0.000392 0.000463 + 157 200 0.216 0.0089 0.0379 0.0857 0.113 0.191 0.232 0.000478 0.000581 + 157 235 0.646 0.00904 0.466 0.0857 0.114 0.771 0.814 0.00193 0.00204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.188 0.00703 0.0474 0.0763 0.1 0.243 0.26 0.000606 0.00065 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 157 22701.136 0.005 0.0089 0.143 0.321 0.085 0.113 0.361 0.449 0.000902 0.00112 +! Validation 157 22701.136 0.005 0.00933 0.223 0.409 0.0862 0.115 0.495 0.563 0.00124 0.00141 +Wall time: 22701.13608380407 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.255 0.00788 0.0975 0.079 0.106 0.304 0.373 0.000761 0.000931 + 158 200 0.201 0.00799 0.0407 0.0798 0.107 0.191 0.241 0.000478 0.000602 + 158 235 0.425 0.00838 0.258 0.0831 0.109 0.563 0.606 0.00141 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.294 0.00761 0.142 0.0818 0.104 0.428 0.449 0.00107 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 158 22845.549 0.005 0.00784 0.102 0.259 0.0794 0.106 0.305 0.381 0.000762 0.000952 +! Validation 158 22845.549 0.005 0.0102 0.296 0.499 0.0928 0.12 0.518 0.649 0.0013 0.00162 +Wall time: 22845.549434368964 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.407 0.0119 0.168 0.099 0.13 0.45 0.49 0.00113 0.00122 + 159 200 0.207 0.00793 0.0483 0.0795 0.106 0.226 0.262 0.000566 0.000656 + 159 235 0.37 0.0112 0.146 0.0972 0.127 0.426 0.455 0.00106 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.137 0.00577 0.0216 0.0694 0.0906 0.143 0.176 0.000358 0.000439 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 159 22989.997 0.005 0.00932 0.145 0.332 0.0867 0.115 0.364 0.455 0.00091 0.00114 +! Validation 159 22989.997 0.005 0.00816 0.0609 0.224 0.0805 0.108 0.236 0.295 0.00059 0.000736 +Wall time: 22989.99719727505 +! Best model 159 0.224 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.191 0.00751 0.0409 0.0772 0.103 0.191 0.241 0.000477 0.000604 + 160 200 0.329 0.00795 0.17 0.08 0.106 0.371 0.492 0.000928 0.00123 + 160 235 0.19 0.00695 0.0514 0.0737 0.0995 0.212 0.271 0.00053 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.172 0.0053 0.0663 0.0665 0.0869 0.289 0.307 0.000723 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 160 23134.465 0.005 0.00762 0.0859 0.238 0.0782 0.104 0.281 0.35 0.000702 0.000875 +! Validation 160 23134.465 0.005 0.00749 0.0524 0.202 0.077 0.103 0.224 0.273 0.000559 0.000683 +Wall time: 23134.465148929972 +! Best model 160 0.202 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.183 0.00713 0.0403 0.075 0.101 0.192 0.24 0.000481 0.000599 + 161 200 20.1 0.742 5.29 0.763 1.03 2.03 2.74 0.00508 0.00686 + 161 235 14.3 0.614 2.04 0.694 0.935 1.49 1.7 0.00374 0.00426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 19 0.618 6.59 0.693 0.939 3.02 3.06 0.00755 0.00766 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 161 23279.145 0.005 0.335 94 101 0.363 0.69 3.7 11.6 0.00925 0.029 +! Validation 161 23279.145 0.005 0.61 2.19 14.4 0.688 0.932 1.44 1.76 0.00359 0.00441 +Wall time: 23279.14565075375 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 7.55 0.333 0.882 0.51 0.689 0.811 1.12 0.00203 0.0028 + 162 200 5.45 0.241 0.628 0.436 0.586 0.744 0.946 0.00186 0.00236 + 162 235 6.01 0.23 1.42 0.428 0.572 1.06 1.42 0.00264 0.00355 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 6.13 0.208 1.98 0.401 0.544 1.66 1.68 0.00414 0.0042 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 162 23423.573 0.005 0.345 1.06 7.97 0.515 0.702 0.951 1.23 0.00238 0.00307 +! Validation 162 23423.573 0.005 0.219 0.639 5.02 0.418 0.559 0.781 0.954 0.00195 0.00238 +Wall time: 23423.573570508976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 3.91 0.17 0.502 0.37 0.493 0.698 0.846 0.00175 0.00211 + 163 200 3.14 0.143 0.284 0.341 0.451 0.505 0.636 0.00126 0.00159 + 163 235 3.24 0.132 0.598 0.327 0.434 0.834 0.923 0.00208 0.00231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 3.3 0.128 0.739 0.318 0.427 0.998 1.03 0.0025 0.00257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 163 23568.016 0.005 0.168 0.55 3.91 0.368 0.489 0.703 0.885 0.00176 0.00221 +! Validation 163 23568.016 0.005 0.136 0.354 3.08 0.332 0.44 0.578 0.71 0.00145 0.00178 +Wall time: 23568.01592779206 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 2.84 0.119 0.468 0.312 0.411 0.62 0.817 0.00155 0.00204 + 164 200 2.6 0.109 0.411 0.298 0.395 0.632 0.765 0.00158 0.00191 + 164 235 3.1 0.104 1.01 0.29 0.385 1.1 1.2 0.00274 0.00301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 2.1 0.101 0.0863 0.283 0.379 0.24 0.351 0.000599 0.000877 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 164 23712.705 0.005 0.119 0.467 2.84 0.311 0.411 0.65 0.815 0.00162 0.00204 +! Validation 164 23712.705 0.005 0.108 0.554 2.72 0.296 0.393 0.704 0.888 0.00176 0.00222 +Wall time: 23712.705572926905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 3.31 0.0986 1.34 0.282 0.375 1.27 1.38 0.00318 0.00345 + 165 200 2.98 0.0975 1.03 0.279 0.373 1.07 1.21 0.00266 0.00303 + 165 235 2.55 0.0908 0.739 0.272 0.36 0.908 1.03 0.00227 0.00257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 3.63 0.0845 1.94 0.26 0.347 1.64 1.66 0.0041 0.00415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 165 23857.313 0.005 0.0981 0.491 2.45 0.282 0.374 0.674 0.836 0.00169 0.00209 +! Validation 165 23857.313 0.005 0.0922 0.91 2.75 0.272 0.362 1.01 1.14 0.00252 0.00285 +Wall time: 23857.313687439077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 2.23 0.0852 0.525 0.263 0.348 0.67 0.865 0.00167 0.00216 + 166 200 1.96 0.0815 0.332 0.256 0.341 0.496 0.688 0.00124 0.00172 + 166 235 2.39 0.0889 0.615 0.263 0.356 0.824 0.936 0.00206 0.00234 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 4.31 0.0737 2.84 0.242 0.324 2 2.01 0.00499 0.00502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 166 24002.211 0.005 0.085 0.533 2.23 0.262 0.348 0.7 0.871 0.00175 0.00218 +! Validation 166 24002.211 0.005 0.0813 1.72 3.34 0.254 0.34 1.47 1.56 0.00366 0.00391 +Wall time: 24002.211742019746 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 3.22 0.0778 1.67 0.25 0.333 1.46 1.54 0.00364 0.00385 + 167 200 1.62 0.0735 0.153 0.241 0.323 0.363 0.467 0.000906 0.00117 + 167 235 1.96 0.073 0.5 0.241 0.322 0.717 0.844 0.00179 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 1.34 0.0653 0.0347 0.227 0.305 0.174 0.222 0.000435 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 167 24146.821 0.005 0.0759 0.594 2.11 0.246 0.329 0.742 0.92 0.00185 0.0023 +! Validation 167 24146.821 0.005 0.0733 0.486 1.95 0.241 0.323 0.677 0.832 0.00169 0.00208 +Wall time: 24146.821545765735 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 2.7 0.0683 1.33 0.234 0.312 1.25 1.38 0.00314 0.00344 + 168 200 1.4 0.0654 0.0951 0.229 0.305 0.326 0.368 0.000815 0.00092 + 168 235 2.14 0.0642 0.857 0.227 0.302 0.992 1.11 0.00248 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 1.75 0.0593 0.562 0.217 0.291 0.87 0.895 0.00218 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 168 24291.289 0.005 0.0692 0.615 2 0.235 0.314 0.74 0.936 0.00185 0.00234 +! Validation 168 24291.289 0.005 0.0674 0.476 1.82 0.231 0.31 0.674 0.823 0.00168 0.00206 +Wall time: 24291.289639474824 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 2.95 0.0688 1.58 0.233 0.313 1.4 1.5 0.0035 0.00375 + 169 200 1.8 0.0602 0.598 0.219 0.293 0.813 0.923 0.00203 0.00231 + 169 235 2.62 0.0652 1.31 0.226 0.305 1.25 1.37 0.00313 0.00342 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 4.07 0.0572 2.93 0.212 0.285 2.03 2.04 0.00508 0.00511 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 169 24436.157 0.005 0.0643 0.748 2.03 0.226 0.303 0.826 1.03 0.00207 0.00258 +! Validation 169 24436.157 0.005 0.0649 5.05 6.34 0.226 0.304 2.62 2.68 0.00655 0.0067 +Wall time: 24436.157871528994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 1.53 0.0605 0.319 0.218 0.294 0.545 0.674 0.00136 0.00168 + 170 200 1.35 0.059 0.164 0.216 0.29 0.391 0.484 0.000978 0.00121 + 170 235 3.25 0.0591 2.07 0.215 0.29 1.64 1.72 0.00409 0.00429 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 4.46 0.0529 3.4 0.204 0.275 2.19 2.2 0.00549 0.0055 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 170 24580.560 0.005 0.0611 0.689 1.91 0.22 0.295 0.79 0.988 0.00198 0.00247 +! Validation 170 24580.560 0.005 0.0605 1.97 3.18 0.218 0.294 1.58 1.67 0.00395 0.00418 +Wall time: 24580.560092213098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 1.81 0.0543 0.72 0.208 0.278 0.903 1.01 0.00226 0.00253 + 171 200 2.92 0.0544 1.83 0.207 0.278 1.54 1.62 0.00385 0.00404 + 171 235 1.92 0.056 0.8 0.211 0.282 0.94 1.07 0.00235 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 2.11 0.0495 1.12 0.197 0.266 1.25 1.26 0.00312 0.00315 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 171 24724.960 0.005 0.0567 0.593 1.73 0.212 0.284 0.738 0.919 0.00185 0.0023 +! Validation 171 24724.960 0.005 0.0571 2.25 3.39 0.212 0.285 1.71 1.79 0.00429 0.00447 +Wall time: 24724.96060252469 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 1.28 0.0545 0.192 0.208 0.279 0.458 0.523 0.00114 0.00131 + 172 200 1.6 0.0521 0.557 0.202 0.272 0.79 0.891 0.00197 0.00223 + 172 235 1.2 0.0525 0.147 0.204 0.273 0.418 0.457 0.00105 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 1.02 0.0462 0.0981 0.191 0.257 0.341 0.374 0.000853 0.000934 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 172 24869.413 0.005 0.0546 0.627 1.72 0.208 0.279 0.756 0.946 0.00189 0.00237 +! Validation 172 24869.413 0.005 0.0538 0.656 1.73 0.205 0.277 0.833 0.967 0.00208 0.00242 +Wall time: 24869.413201075047 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 1.3 0.0517 0.261 0.202 0.271 0.498 0.61 0.00125 0.00152 + 173 200 1.83 0.0484 0.865 0.196 0.263 1.03 1.11 0.00257 0.00277 + 173 235 1.37 0.0528 0.311 0.204 0.274 0.539 0.665 0.00135 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.941 0.0435 0.0712 0.185 0.249 0.289 0.318 0.000722 0.000796 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 173 25013.882 0.005 0.0512 0.556 1.58 0.201 0.27 0.707 0.89 0.00177 0.00223 +! Validation 173 25013.882 0.005 0.0509 0.201 1.22 0.199 0.269 0.428 0.535 0.00107 0.00134 +Wall time: 25013.882776924875 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 1.08 0.0488 0.0993 0.196 0.264 0.276 0.376 0.000689 0.00094 + 174 200 1.12 0.0463 0.199 0.192 0.257 0.407 0.533 0.00102 0.00133 + 174 235 1.36 0.0499 0.361 0.198 0.267 0.581 0.717 0.00145 0.00179 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 1.53 0.0427 0.68 0.184 0.247 0.974 0.984 0.00243 0.00246 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 174 25158.634 0.005 0.0495 0.653 1.64 0.197 0.265 0.772 0.965 0.00193 0.00241 +! Validation 174 25158.634 0.005 0.0497 0.382 1.38 0.197 0.266 0.636 0.737 0.00159 0.00184 +Wall time: 25158.634379493073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 1.25 0.0486 0.275 0.196 0.263 0.545 0.626 0.00136 0.00156 + 175 200 1.04 0.046 0.125 0.19 0.256 0.288 0.422 0.000721 0.00105 + 175 235 1.42 0.046 0.497 0.191 0.256 0.71 0.841 0.00178 0.0021 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.945 0.0402 0.142 0.178 0.239 0.437 0.45 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 175 25303.091 0.005 0.0487 0.671 1.65 0.196 0.264 0.79 0.978 0.00197 0.00245 +! Validation 175 25303.091 0.005 0.0475 0.67 1.62 0.193 0.26 0.867 0.977 0.00217 0.00244 +Wall time: 25303.091621663887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 1.08 0.0464 0.154 0.191 0.257 0.368 0.469 0.000921 0.00117 + 176 200 1.47 0.0444 0.581 0.187 0.251 0.8 0.91 0.002 0.00227 + 176 235 1 0.0448 0.107 0.187 0.253 0.296 0.391 0.00074 0.000978 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 1.01 0.0386 0.241 0.175 0.234 0.577 0.586 0.00144 0.00147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 176 25447.539 0.005 0.0464 0.61 1.54 0.191 0.257 0.747 0.933 0.00187 0.00233 +! Validation 176 25447.539 0.005 0.0455 0.17 1.08 0.189 0.255 0.397 0.491 0.000992 0.00123 +Wall time: 25447.53957799403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.972 0.0416 0.141 0.182 0.243 0.358 0.448 0.000895 0.00112 + 177 200 1.13 0.0446 0.238 0.187 0.252 0.485 0.583 0.00121 0.00146 + 177 235 1.06 0.041 0.243 0.18 0.242 0.503 0.589 0.00126 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.781 0.0375 0.032 0.173 0.231 0.192 0.213 0.000481 0.000534 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 177 25591.976 0.005 0.044 0.598 1.48 0.186 0.25 0.749 0.924 0.00187 0.00231 +! Validation 177 25591.976 0.005 0.0442 0.631 1.51 0.186 0.251 0.797 0.948 0.00199 0.00237 +Wall time: 25591.976279752795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 1.28 0.0403 0.47 0.179 0.24 0.708 0.818 0.00177 0.00204 + 178 200 0.975 0.043 0.114 0.185 0.248 0.297 0.402 0.000743 0.00101 + 178 235 1.12 0.0427 0.269 0.184 0.247 0.572 0.619 0.00143 0.00155 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 1.53 0.0355 0.819 0.168 0.225 1.08 1.08 0.00269 0.0027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 178 25736.863 0.005 0.0426 0.558 1.41 0.183 0.246 0.733 0.892 0.00183 0.00223 +! Validation 178 25736.863 0.005 0.0421 0.612 1.45 0.182 0.245 0.833 0.934 0.00208 0.00233 +Wall time: 25736.86346354708 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 1.81 0.0421 0.968 0.182 0.245 1.08 1.17 0.0027 0.00294 + 179 200 1.36 0.0408 0.541 0.179 0.241 0.787 0.878 0.00197 0.0022 + 179 235 1.12 0.0407 0.303 0.181 0.241 0.57 0.657 0.00143 0.00164 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 1.08 0.0339 0.397 0.165 0.22 0.748 0.752 0.00187 0.00188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 179 25881.312 0.005 0.0406 0.53 1.34 0.179 0.24 0.696 0.869 0.00174 0.00217 +! Validation 179 25881.312 0.005 0.0404 0.261 1.07 0.178 0.24 0.511 0.61 0.00128 0.00153 +Wall time: 25881.31231912179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.853 0.0385 0.084 0.175 0.234 0.275 0.346 0.000688 0.000865 + 180 200 1.25 0.0372 0.509 0.171 0.23 0.759 0.852 0.0019 0.00213 + 180 235 1.02 0.0371 0.273 0.172 0.23 0.515 0.624 0.00129 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.983 0.0323 0.336 0.161 0.215 0.689 0.692 0.00172 0.00173 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 180 26026.018 0.005 0.0393 0.542 1.33 0.176 0.237 0.695 0.879 0.00174 0.0022 +! Validation 180 26026.018 0.005 0.0386 0.18 0.952 0.174 0.234 0.406 0.507 0.00101 0.00127 +Wall time: 26026.01848004572 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 1.23 0.0382 0.464 0.174 0.233 0.746 0.813 0.00186 0.00203 + 181 200 1.09 0.0361 0.368 0.169 0.227 0.629 0.724 0.00157 0.00181 + 181 235 0.97 0.0372 0.225 0.172 0.23 0.556 0.567 0.00139 0.00142 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 1.77 0.0308 1.15 0.157 0.21 1.28 1.28 0.0032 0.0032 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 181 26170.600 0.005 0.037 0.5 1.24 0.171 0.23 0.681 0.844 0.0017 0.00211 +! Validation 181 26170.600 0.005 0.0368 0.757 1.49 0.17 0.229 0.96 1.04 0.0024 0.0026 +Wall time: 26170.600215015933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 2.3 0.0355 1.59 0.168 0.225 1.41 1.51 0.00353 0.00376 + 182 200 0.921 0.0363 0.196 0.17 0.227 0.433 0.528 0.00108 0.00132 + 182 235 0.817 0.0341 0.135 0.166 0.22 0.345 0.438 0.000864 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.602 0.0299 0.00351 0.156 0.206 0.0608 0.0707 0.000152 0.000177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 182 26315.089 0.005 0.0363 0.54 1.27 0.17 0.227 0.689 0.878 0.00172 0.00219 +! Validation 182 26315.089 0.005 0.0357 0.19 0.904 0.168 0.226 0.421 0.52 0.00105 0.0013 +Wall time: 26315.088939211797 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 1.35 0.0369 0.614 0.171 0.229 0.873 0.936 0.00218 0.00234 + 183 200 0.776 0.0341 0.0942 0.165 0.22 0.289 0.366 0.000722 0.000916 + 183 235 1.81 0.0337 1.14 0.164 0.219 1.22 1.27 0.00305 0.00319 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 3.66 0.0276 3.11 0.149 0.198 2.11 2.11 0.00526 0.00526 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 183 26459.571 0.005 0.0349 0.517 1.21 0.166 0.223 0.684 0.857 0.00171 0.00214 +! Validation 183 26459.571 0.005 0.0334 2.06 2.73 0.163 0.218 1.67 1.71 0.00418 0.00428 +Wall time: 26459.571079286747 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 1.65 0.0363 0.929 0.169 0.227 1.08 1.15 0.00269 0.00288 + 184 200 1.32 0.0354 0.61 0.168 0.225 0.851 0.932 0.00213 0.00233 + 184 235 0.774 0.031 0.155 0.158 0.21 0.344 0.469 0.000861 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.791 0.0267 0.257 0.148 0.195 0.603 0.605 0.00151 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 184 26604.062 0.005 0.0334 0.524 1.19 0.163 0.218 0.694 0.864 0.00174 0.00216 +! Validation 184 26604.062 0.005 0.0323 0.169 0.816 0.161 0.215 0.391 0.491 0.000977 0.00123 +Wall time: 26604.062740779016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.862 0.0368 0.126 0.173 0.229 0.364 0.423 0.00091 0.00106 + 185 200 1.19 0.0307 0.581 0.156 0.209 0.849 0.91 0.00212 0.00227 + 185 235 0.918 0.0326 0.267 0.161 0.215 0.433 0.616 0.00108 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 2.88 0.0283 2.31 0.151 0.201 1.81 1.81 0.00453 0.00453 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 185 26748.467 0.005 0.0328 0.546 1.2 0.162 0.216 0.662 0.882 0.00165 0.00221 +! Validation 185 26748.467 0.005 0.0337 2.05 2.72 0.164 0.219 1.61 1.71 0.00402 0.00427 +Wall time: 26748.466997777112 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.679 0.0306 0.0676 0.157 0.209 0.258 0.31 0.000645 0.000776 + 186 200 0.936 0.0311 0.314 0.158 0.211 0.621 0.669 0.00155 0.00167 + 186 235 1.15 0.0286 0.578 0.152 0.202 0.854 0.907 0.00214 0.00227 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 1.06 0.0248 0.567 0.143 0.188 0.898 0.899 0.00224 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 186 26892.861 0.005 0.0311 0.52 1.14 0.158 0.21 0.695 0.861 0.00174 0.00215 +! Validation 186 26892.861 0.005 0.03 0.348 0.949 0.155 0.207 0.603 0.704 0.00151 0.00176 +Wall time: 26892.861400740687 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.669 0.0284 0.102 0.151 0.201 0.297 0.381 0.000742 0.000952 + 187 200 1.01 0.0288 0.432 0.153 0.203 0.715 0.784 0.00179 0.00196 + 187 235 1.2 0.0286 0.63 0.152 0.202 0.893 0.947 0.00223 0.00237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.707 0.0246 0.215 0.142 0.187 0.551 0.553 0.00138 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 187 27037.445 0.005 0.0292 0.516 1.1 0.153 0.204 0.683 0.858 0.00171 0.00214 +! Validation 187 27037.445 0.005 0.0299 0.334 0.932 0.155 0.206 0.522 0.69 0.00131 0.00173 +Wall time: 27037.445033539087 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.619 0.0273 0.0741 0.148 0.197 0.273 0.325 0.000681 0.000812 + 188 200 1.41 0.0296 0.816 0.155 0.205 1.04 1.08 0.0026 0.0027 + 188 235 0.597 0.0282 0.0329 0.151 0.2 0.167 0.217 0.000418 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.6 0.0234 0.131 0.139 0.183 0.43 0.432 0.00108 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 188 27181.868 0.005 0.0285 0.524 1.09 0.152 0.202 0.689 0.865 0.00172 0.00216 +! Validation 188 27181.868 0.005 0.0285 0.133 0.703 0.151 0.201 0.338 0.435 0.000845 0.00109 +Wall time: 27181.868871751707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.603 0.0249 0.104 0.142 0.189 0.303 0.384 0.000757 0.000961 + 189 200 0.566 0.0247 0.0727 0.142 0.187 0.248 0.322 0.000619 0.000804 + 189 235 1.14 0.0283 0.573 0.151 0.201 0.855 0.903 0.00214 0.00226 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 3.16 0.0211 2.74 0.132 0.174 1.97 1.97 0.00493 0.00494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 189 27326.269 0.005 0.0266 0.43 0.962 0.147 0.195 0.621 0.782 0.00155 0.00196 +! Validation 189 27326.269 0.005 0.0259 2 2.51 0.144 0.192 1.64 1.69 0.00411 0.00422 +Wall time: 27326.269386835862 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.621 0.0264 0.0924 0.146 0.194 0.284 0.363 0.000709 0.000907 + 190 200 0.584 0.0238 0.107 0.139 0.184 0.318 0.391 0.000795 0.000978 + 190 235 0.601 0.0263 0.0753 0.147 0.194 0.247 0.328 0.000619 0.000819 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.985 0.021 0.565 0.133 0.173 0.895 0.897 0.00224 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 190 27471.032 0.005 0.0246 0.408 0.9 0.141 0.187 0.6 0.763 0.0015 0.00191 +! Validation 190 27471.032 0.005 0.0257 0.449 0.963 0.144 0.191 0.7 0.8 0.00175 0.002 +Wall time: 27471.032235929742 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.602 0.023 0.142 0.136 0.181 0.362 0.451 0.000904 0.00113 + 191 200 0.53 0.022 0.0893 0.133 0.177 0.3 0.357 0.00075 0.000892 + 191 235 1.5 0.0234 1.03 0.138 0.183 1.19 1.21 0.00298 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.536 0.0177 0.182 0.122 0.159 0.509 0.51 0.00127 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 191 27615.503 0.005 0.0228 0.334 0.79 0.136 0.18 0.562 0.688 0.0014 0.00172 +! Validation 191 27615.503 0.005 0.0223 0.63 1.08 0.134 0.178 0.874 0.948 0.00219 0.00237 +Wall time: 27615.503566673025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.753 0.0222 0.31 0.134 0.178 0.56 0.665 0.0014 0.00166 + 192 200 1.71 0.0219 1.27 0.132 0.176 1.3 1.35 0.00325 0.00337 + 192 235 1.11 0.0224 0.66 0.134 0.178 0.763 0.97 0.00191 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 1.37 0.0181 1 0.122 0.161 1.2 1.2 0.00299 0.00299 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 192 27759.993 0.005 0.0223 0.436 0.883 0.134 0.178 0.645 0.788 0.00161 0.00197 +! Validation 192 27759.993 0.005 0.0226 0.462 0.915 0.135 0.18 0.67 0.811 0.00168 0.00203 +Wall time: 27759.993606105912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.53 0.0209 0.111 0.129 0.173 0.323 0.398 0.000807 0.000996 + 193 200 0.867 0.0196 0.474 0.126 0.167 0.774 0.822 0.00193 0.00205 + 193 235 1.22 0.0222 0.779 0.133 0.178 0.905 1.05 0.00226 0.00263 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.351 0.0168 0.0157 0.118 0.155 0.138 0.15 0.000346 0.000374 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 193 27904.538 0.005 0.021 0.34 0.759 0.13 0.173 0.553 0.694 0.00138 0.00174 +! Validation 193 27904.538 0.005 0.0212 0.122 0.546 0.131 0.174 0.315 0.417 0.000789 0.00104 +Wall time: 27904.53875407204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.535 0.0174 0.188 0.119 0.157 0.42 0.517 0.00105 0.00129 + 194 200 0.579 0.018 0.218 0.121 0.16 0.488 0.558 0.00122 0.00139 + 194 235 0.652 0.0185 0.281 0.123 0.163 0.59 0.633 0.00148 0.00158 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 1.8 0.0144 1.52 0.11 0.143 1.47 1.47 0.00367 0.00367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 194 28049.011 0.005 0.0197 0.306 0.699 0.126 0.167 0.532 0.661 0.00133 0.00165 +! Validation 194 28049.011 0.005 0.0187 1.28 1.66 0.123 0.163 1.31 1.35 0.00327 0.00338 +Wall time: 28049.01154657686 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.499 0.0189 0.12 0.124 0.164 0.32 0.413 0.0008 0.00103 + 195 200 0.493 0.0186 0.121 0.123 0.163 0.342 0.415 0.000854 0.00104 + 195 235 0.636 0.0186 0.265 0.123 0.163 0.579 0.615 0.00145 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.359 0.014 0.0789 0.108 0.141 0.331 0.335 0.000828 0.000838 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 195 28193.535 0.005 0.0191 0.34 0.722 0.124 0.165 0.553 0.696 0.00138 0.00174 +! Validation 195 28193.535 0.005 0.0182 0.331 0.695 0.121 0.161 0.618 0.687 0.00155 0.00172 +Wall time: 28193.53502447484 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.444 0.0187 0.0693 0.123 0.163 0.224 0.314 0.00056 0.000786 + 196 200 0.832 0.0184 0.464 0.122 0.162 0.747 0.813 0.00187 0.00203 + 196 235 0.57 0.0181 0.207 0.12 0.161 0.447 0.543 0.00112 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 1.1 0.0133 0.83 0.105 0.138 1.09 1.09 0.00272 0.00272 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 196 28338.097 0.005 0.018 0.307 0.668 0.121 0.16 0.508 0.661 0.00127 0.00165 +! Validation 196 28338.097 0.005 0.0174 0.642 0.99 0.118 0.157 0.898 0.956 0.00225 0.00239 +Wall time: 28338.09699555207 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.51 0.016 0.191 0.114 0.151 0.436 0.521 0.00109 0.0013 + 197 200 0.45 0.0162 0.126 0.114 0.152 0.349 0.424 0.000871 0.00106 + 197 235 0.402 0.0158 0.0867 0.113 0.15 0.314 0.351 0.000784 0.000879 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.64 0.0121 0.399 0.1 0.131 0.752 0.754 0.00188 0.00188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 197 28482.510 0.005 0.0168 0.257 0.594 0.117 0.155 0.466 0.606 0.00116 0.00152 +! Validation 197 28482.510 0.005 0.0159 0.275 0.593 0.113 0.15 0.559 0.626 0.0014 0.00156 +Wall time: 28482.51028347574 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.574 0.0162 0.25 0.114 0.152 0.524 0.596 0.00131 0.00149 + 198 200 0.345 0.0149 0.0481 0.11 0.145 0.21 0.262 0.000525 0.000654 + 198 235 0.458 0.014 0.178 0.106 0.141 0.468 0.503 0.00117 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.444 0.0115 0.213 0.0977 0.128 0.547 0.551 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 198 28627.710 0.005 0.016 0.262 0.581 0.114 0.151 0.48 0.611 0.0012 0.00153 +! Validation 198 28627.710 0.005 0.0153 0.596 0.903 0.111 0.148 0.856 0.922 0.00214 0.0023 +Wall time: 28627.710094919894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.425 0.0147 0.131 0.109 0.145 0.362 0.432 0.000904 0.00108 + 199 200 0.846 0.0134 0.578 0.104 0.138 0.881 0.907 0.0022 0.00227 + 199 235 0.358 0.0146 0.0654 0.109 0.144 0.235 0.305 0.000589 0.000763 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.247 0.0108 0.0318 0.0947 0.124 0.197 0.213 0.000493 0.000532 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 199 28772.101 0.005 0.0147 0.22 0.513 0.109 0.145 0.444 0.56 0.00111 0.0014 +! Validation 199 28772.101 0.005 0.0143 0.0628 0.349 0.107 0.143 0.23 0.299 0.000576 0.000748 +Wall time: 28772.101739841048 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.332 0.0143 0.0462 0.108 0.143 0.211 0.257 0.000528 0.000641 + 200 200 0.279 0.0127 0.0257 0.102 0.134 0.15 0.191 0.000376 0.000479 + 200 235 0.506 0.0134 0.237 0.104 0.138 0.504 0.582 0.00126 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.427 0.00998 0.228 0.0908 0.119 0.565 0.569 0.00141 0.00142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 200 28916.456 0.005 0.0143 0.261 0.547 0.108 0.143 0.473 0.609 0.00118 0.00152 +! Validation 200 28916.456 0.005 0.0134 0.146 0.414 0.104 0.138 0.378 0.457 0.000946 0.00114 +Wall time: 28916.456342815887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.722 0.0156 0.41 0.114 0.149 0.711 0.765 0.00178 0.00191 + 201 200 0.485 0.013 0.225 0.103 0.136 0.48 0.567 0.0012 0.00142 + 201 235 0.39 0.0111 0.169 0.095 0.126 0.458 0.491 0.00115 0.00123 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.37 0.00923 0.186 0.0876 0.115 0.507 0.514 0.00127 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 201 29060.826 0.005 0.0134 0.235 0.503 0.104 0.138 0.456 0.579 0.00114 0.00145 +! Validation 201 29060.826 0.005 0.0125 0.146 0.396 0.1 0.133 0.384 0.456 0.00096 0.00114 +Wall time: 29060.826726787724 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 1.01 0.0158 0.698 0.114 0.15 0.948 0.997 0.00237 0.00249 + 202 200 0.296 0.012 0.0554 0.0994 0.131 0.214 0.281 0.000535 0.000702 + 202 235 1.18 0.0117 0.942 0.0978 0.129 1.12 1.16 0.0028 0.0029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 2.39 0.00926 2.21 0.0874 0.115 1.77 1.77 0.00443 0.00443 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 202 29205.174 0.005 0.0128 0.239 0.495 0.102 0.135 0.457 0.581 0.00114 0.00145 +! Validation 202 29205.174 0.005 0.0126 2.11 2.36 0.101 0.134 1.69 1.73 0.00422 0.00433 +Wall time: 29205.174641112797 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.639 0.0121 0.397 0.099 0.131 0.712 0.752 0.00178 0.00188 + 203 200 0.44 0.0121 0.198 0.0985 0.131 0.435 0.532 0.00109 0.00133 + 203 235 0.848 0.011 0.627 0.0949 0.125 0.916 0.945 0.00229 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.579 0.00879 0.403 0.0855 0.112 0.752 0.758 0.00188 0.00189 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 203 29349.537 0.005 0.0131 0.303 0.564 0.103 0.136 0.523 0.656 0.00131 0.00164 +! Validation 203 29349.537 0.005 0.012 0.274 0.515 0.0981 0.131 0.553 0.625 0.00138 0.00156 +Wall time: 29349.537832372822 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.271 0.0113 0.0455 0.0952 0.127 0.197 0.254 0.000492 0.000636 + 204 200 0.285 0.0119 0.0467 0.0988 0.13 0.204 0.258 0.00051 0.000645 + 204 235 0.244 0.0101 0.0413 0.0906 0.12 0.187 0.243 0.000467 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.188 0.00798 0.0286 0.0813 0.107 0.181 0.202 0.000452 0.000504 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 204 29493.905 0.005 0.0115 0.185 0.415 0.0968 0.128 0.405 0.514 0.00101 0.00128 +! Validation 204 29493.905 0.005 0.011 0.14 0.36 0.0938 0.125 0.382 0.446 0.000955 0.00112 +Wall time: 29493.9053469887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.273 0.0112 0.0481 0.0945 0.127 0.197 0.262 0.000492 0.000655 + 205 200 0.31 0.00962 0.118 0.0885 0.117 0.368 0.41 0.00092 0.00102 + 205 235 1.06 0.00991 0.857 0.0902 0.119 1.06 1.1 0.00264 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.428 0.00743 0.279 0.0784 0.103 0.625 0.631 0.00156 0.00158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 205 29638.244 0.005 0.0105 0.145 0.355 0.0922 0.122 0.36 0.452 0.0009 0.00113 +! Validation 205 29638.244 0.005 0.0103 0.633 0.84 0.0908 0.121 0.873 0.95 0.00218 0.00237 +Wall time: 29638.244379336946 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.261 0.00991 0.0627 0.0891 0.119 0.24 0.299 0.0006 0.000747 + 206 200 0.31 0.00978 0.115 0.0886 0.118 0.303 0.404 0.000757 0.00101 + 206 235 0.448 0.0106 0.236 0.095 0.123 0.525 0.58 0.00131 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.206 0.00839 0.0382 0.0847 0.109 0.214 0.233 0.000536 0.000583 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 206 29782.592 0.005 0.0102 0.169 0.372 0.0908 0.12 0.383 0.49 0.000957 0.00123 +! Validation 206 29782.592 0.005 0.0111 0.165 0.387 0.0955 0.126 0.416 0.486 0.00104 0.00121 +Wall time: 29782.592169892043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.52 0.00983 0.323 0.0889 0.118 0.647 0.679 0.00162 0.0017 + 207 200 0.214 0.00949 0.0238 0.0877 0.116 0.147 0.184 0.000367 0.000461 + 207 235 0.252 0.0104 0.0447 0.0908 0.122 0.193 0.252 0.000484 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.168 0.00727 0.0223 0.0777 0.102 0.148 0.178 0.000369 0.000446 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 207 29926.932 0.005 0.00963 0.164 0.357 0.0884 0.117 0.387 0.484 0.000966 0.00121 +! Validation 207 29926.932 0.005 0.0101 0.0843 0.287 0.0899 0.12 0.291 0.347 0.000729 0.000866 +Wall time: 29926.93198606372 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.247 0.00868 0.0739 0.0835 0.111 0.264 0.325 0.00066 0.000811 + 208 200 0.309 0.0114 0.0818 0.0975 0.127 0.276 0.341 0.000689 0.000853 + 208 235 0.336 0.00948 0.147 0.0879 0.116 0.386 0.458 0.000966 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.495 0.00737 0.347 0.0782 0.102 0.696 0.703 0.00174 0.00176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 208 30071.283 0.005 0.00981 0.205 0.401 0.0892 0.118 0.419 0.541 0.00105 0.00135 +! Validation 208 30071.283 0.005 0.0101 0.253 0.455 0.0899 0.12 0.521 0.6 0.0013 0.0015 +Wall time: 30071.28350604186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.205 0.0092 0.0211 0.0858 0.114 0.14 0.174 0.000351 0.000434 + 209 200 0.234 0.00895 0.0548 0.0849 0.113 0.237 0.279 0.000593 0.000699 + 209 235 0.42 0.0111 0.197 0.0956 0.126 0.373 0.53 0.000932 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.624 0.00817 0.46 0.0821 0.108 0.802 0.81 0.002 0.00202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 209 30215.836 0.005 0.00902 0.153 0.333 0.0853 0.113 0.369 0.466 0.000922 0.00117 +! Validation 209 30215.836 0.005 0.0109 0.511 0.729 0.0938 0.125 0.772 0.853 0.00193 0.00213 +Wall time: 30215.83667997783 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.303 0.00886 0.126 0.0848 0.112 0.373 0.423 0.000932 0.00106 + 210 200 0.199 0.00846 0.0301 0.0831 0.11 0.167 0.207 0.000417 0.000518 + 210 235 0.254 0.0079 0.0959 0.0794 0.106 0.334 0.37 0.000836 0.000924 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.137 0.00635 0.00967 0.0725 0.0951 0.109 0.117 0.000272 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 210 30360.326 0.005 0.0088 0.133 0.309 0.0843 0.112 0.346 0.436 0.000864 0.00109 +! Validation 210 30360.326 0.005 0.00894 0.185 0.364 0.0841 0.113 0.407 0.513 0.00102 0.00128 +Wall time: 30360.326856210828 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.213 0.00906 0.0319 0.0849 0.114 0.168 0.213 0.000419 0.000533 + 211 200 0.231 0.00844 0.0624 0.082 0.11 0.247 0.298 0.000619 0.000746 + 211 235 0.244 0.00806 0.0829 0.081 0.107 0.309 0.344 0.000772 0.000859 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.135 0.00641 0.00667 0.0726 0.0955 0.0733 0.0975 0.000183 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 211 30504.703 0.005 0.00869 0.15 0.324 0.0837 0.111 0.357 0.462 0.000894 0.00116 +! Validation 211 30504.703 0.005 0.00893 0.319 0.497 0.0841 0.113 0.542 0.674 0.00135 0.00169 +Wall time: 30504.70371670602 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 1.35 0.0432 0.484 0.191 0.248 0.707 0.831 0.00177 0.00208 + 212 200 0.314 0.0121 0.0724 0.0994 0.131 0.281 0.321 0.000702 0.000803 + 212 235 0.26 0.0116 0.0284 0.0968 0.128 0.168 0.201 0.000419 0.000503 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.193 0.00866 0.0201 0.0839 0.111 0.152 0.169 0.000381 0.000423 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 212 30649.076 0.005 0.0475 1.88 2.83 0.17 0.26 0.928 1.64 0.00232 0.0041 +! Validation 212 30649.076 0.005 0.0117 0.0878 0.322 0.0967 0.129 0.288 0.354 0.00072 0.000884 +Wall time: 30649.076125403866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.248 0.00973 0.0534 0.0881 0.118 0.233 0.276 0.000583 0.000689 + 213 200 0.221 0.00925 0.0364 0.0859 0.115 0.19 0.228 0.000475 0.00057 + 213 235 0.214 0.00903 0.033 0.0853 0.113 0.141 0.217 0.000352 0.000542 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.139 0.00667 0.00592 0.0737 0.0974 0.0811 0.0918 0.000203 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 213 30793.558 0.005 0.01 0.112 0.313 0.0899 0.12 0.317 0.401 0.000793 0.001 +! Validation 213 30793.558 0.005 0.00943 0.0728 0.261 0.0864 0.116 0.264 0.322 0.00066 0.000805 +Wall time: 30793.558478767984 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.461 0.009 0.281 0.085 0.113 0.576 0.633 0.00144 0.00158 + 214 200 0.198 0.00842 0.0294 0.0822 0.11 0.167 0.205 0.000418 0.000512 + 214 235 0.637 0.00922 0.452 0.0857 0.115 0.766 0.803 0.00192 0.00201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.465 0.00606 0.344 0.0707 0.0929 0.693 0.7 0.00173 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 214 30937.994 0.005 0.00871 0.0955 0.27 0.0836 0.111 0.294 0.367 0.000735 0.000918 +! Validation 214 30937.994 0.005 0.00875 0.663 0.838 0.0833 0.112 0.936 0.972 0.00234 0.00243 +Wall time: 30937.994596284814 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.199 0.0084 0.0306 0.0819 0.109 0.167 0.209 0.000417 0.000522 + 215 200 0.386 0.0078 0.23 0.079 0.105 0.539 0.572 0.00135 0.00143 + 215 235 0.275 0.00824 0.111 0.0795 0.108 0.359 0.397 0.000898 0.000992 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.217 0.0057 0.103 0.0683 0.0901 0.372 0.383 0.000929 0.000957 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 215 31083.392 0.005 0.0084 0.121 0.289 0.0822 0.109 0.331 0.416 0.000828 0.00104 +! Validation 215 31083.392 0.005 0.00832 0.0676 0.234 0.0808 0.109 0.251 0.31 0.000628 0.000776 +Wall time: 31083.392043903936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.197 0.00756 0.0458 0.078 0.104 0.218 0.256 0.000545 0.000639 + 216 200 0.29 0.00692 0.152 0.075 0.0993 0.44 0.465 0.0011 0.00116 + 216 235 0.163 0.00732 0.0169 0.0768 0.102 0.141 0.155 0.000352 0.000388 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.319 0.00543 0.21 0.0667 0.088 0.54 0.547 0.00135 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 216 31227.845 0.005 0.00771 0.0717 0.226 0.0785 0.105 0.255 0.32 0.000637 0.0008 +! Validation 216 31227.845 0.005 0.00792 0.151 0.309 0.0788 0.106 0.405 0.464 0.00101 0.00116 +Wall time: 31227.845250417013 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.207 0.00798 0.0479 0.0799 0.107 0.203 0.261 0.000507 0.000653 + 217 200 0.193 0.00748 0.043 0.0775 0.103 0.193 0.247 0.000482 0.000618 + 217 235 0.188 0.00747 0.0385 0.0774 0.103 0.198 0.234 0.000494 0.000585 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.113 0.0053 0.00691 0.0654 0.0869 0.0783 0.0992 0.000196 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 217 31372.308 0.005 0.00746 0.0878 0.237 0.0772 0.103 0.283 0.354 0.000707 0.000885 +! Validation 217 31372.308 0.005 0.00766 0.0533 0.206 0.0774 0.104 0.224 0.275 0.00056 0.000689 +Wall time: 31372.308386445977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.176 0.00683 0.0391 0.0736 0.0986 0.202 0.236 0.000505 0.00059 + 218 200 0.18 0.00726 0.0351 0.0767 0.102 0.18 0.224 0.00045 0.000559 + 218 235 0.219 0.0086 0.047 0.0817 0.111 0.236 0.259 0.000589 0.000647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.123 0.00517 0.0193 0.065 0.0858 0.149 0.166 0.000373 0.000415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 218 31516.873 0.005 0.00733 0.0975 0.244 0.0765 0.102 0.291 0.373 0.000727 0.000932 +! Validation 218 31516.873 0.005 0.00747 0.0376 0.187 0.0764 0.103 0.179 0.231 0.000447 0.000578 +Wall time: 31516.873147965875 +! Best model 218 0.187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.212 0.00689 0.0741 0.0744 0.0991 0.294 0.325 0.000734 0.000812 + 219 200 0.205 0.00653 0.0743 0.0726 0.0965 0.268 0.325 0.000669 0.000813 + 219 235 0.247 0.00658 0.115 0.0725 0.0968 0.376 0.405 0.000941 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.262 0.00514 0.159 0.0645 0.0856 0.468 0.476 0.00117 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 219 31661.776 0.005 0.00724 0.107 0.252 0.0761 0.102 0.314 0.391 0.000785 0.000977 +! Validation 219 31661.776 0.005 0.00734 0.17 0.317 0.0755 0.102 0.439 0.493 0.0011 0.00123 +Wall time: 31661.776122577954 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.269 0.00686 0.132 0.0739 0.0988 0.392 0.433 0.000981 0.00108 + 220 200 0.18 0.00722 0.0359 0.0752 0.101 0.177 0.226 0.000443 0.000565 + 220 235 0.164 0.0068 0.0279 0.073 0.0984 0.168 0.199 0.000419 0.000499 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.135 0.00518 0.0313 0.0645 0.0859 0.195 0.211 0.000488 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 220 31806.439 0.005 0.00698 0.0983 0.238 0.0746 0.0997 0.297 0.375 0.000743 0.000936 +! Validation 220 31806.439 0.005 0.00747 0.0374 0.187 0.0764 0.103 0.182 0.231 0.000456 0.000577 +Wall time: 31806.439511295874 +! Best model 220 0.187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.226 0.00706 0.0846 0.0754 0.1 0.313 0.347 0.000782 0.000868 + 221 200 0.191 0.00676 0.056 0.0742 0.0981 0.243 0.282 0.000607 0.000706 + 221 235 0.136 0.00586 0.0183 0.0682 0.0914 0.133 0.162 0.000331 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.118 0.00495 0.019 0.0633 0.0839 0.145 0.165 0.000362 0.000412 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 221 31950.848 0.005 0.00684 0.0797 0.216 0.0739 0.0987 0.271 0.337 0.000678 0.000843 +! Validation 221 31950.848 0.005 0.00703 0.0746 0.215 0.074 0.1 0.279 0.326 0.000698 0.000815 +Wall time: 31950.8488442651 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.162 0.00661 0.0303 0.0729 0.097 0.169 0.208 0.000423 0.000519 + 222 200 0.207 0.00697 0.0681 0.0744 0.0996 0.265 0.311 0.000662 0.000779 + 222 235 0.215 0.00698 0.0757 0.0748 0.0997 0.266 0.328 0.000664 0.000821 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.133 0.00496 0.0342 0.0629 0.0841 0.201 0.221 0.000504 0.000551 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 222 32095.269 0.005 0.00676 0.0937 0.229 0.0734 0.0981 0.284 0.365 0.00071 0.000913 +! Validation 222 32095.269 0.005 0.00695 0.076 0.215 0.0735 0.0995 0.284 0.329 0.000709 0.000822 +Wall time: 32095.269019040745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.194 0.00669 0.0605 0.0732 0.0976 0.25 0.294 0.000624 0.000734 + 223 200 0.156 0.00634 0.0293 0.0709 0.095 0.165 0.204 0.000413 0.000511 + 223 235 0.188 0.00729 0.0425 0.0763 0.102 0.183 0.246 0.000458 0.000615 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.275 0.00645 0.146 0.0715 0.0958 0.453 0.456 0.00113 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 223 32239.723 0.005 0.00658 0.0993 0.231 0.0724 0.0968 0.294 0.376 0.000736 0.000941 +! Validation 223 32239.723 0.005 0.00836 0.144 0.312 0.081 0.109 0.406 0.454 0.00101 0.00113 +Wall time: 32239.72354936879 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.188 0.00589 0.0698 0.0689 0.0916 0.288 0.315 0.000721 0.000789 + 224 200 0.156 0.00623 0.031 0.0704 0.0942 0.162 0.21 0.000404 0.000525 + 224 235 0.149 0.00646 0.02 0.0728 0.0959 0.145 0.169 0.000362 0.000422 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.135 0.00489 0.0377 0.0625 0.0835 0.225 0.232 0.000563 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 224 32384.139 0.005 0.00639 0.0832 0.211 0.0713 0.0954 0.274 0.345 0.000685 0.000862 +! Validation 224 32384.139 0.005 0.00681 0.0943 0.23 0.0728 0.0985 0.303 0.367 0.000756 0.000916 +Wall time: 32384.139620163944 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.19 0.00599 0.0702 0.0692 0.0924 0.275 0.316 0.000686 0.00079 + 225 200 0.193 0.00633 0.0665 0.0707 0.095 0.26 0.308 0.000651 0.00077 + 225 235 0.244 0.00642 0.116 0.0716 0.0956 0.359 0.406 0.000897 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.0942 0.00444 0.00542 0.0595 0.0795 0.072 0.0879 0.00018 0.00022 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 225 32528.574 0.005 0.00627 0.0784 0.204 0.0706 0.0945 0.261 0.334 0.000654 0.000835 +! Validation 225 32528.574 0.005 0.0063 0.111 0.237 0.0697 0.0948 0.327 0.398 0.000818 0.000996 +Wall time: 32528.57412370993 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.21 0.00601 0.0894 0.069 0.0926 0.325 0.357 0.000813 0.000892 + 226 200 0.17 0.00687 0.0321 0.074 0.0989 0.174 0.214 0.000434 0.000535 + 226 235 0.147 0.00568 0.0329 0.067 0.09 0.176 0.217 0.000439 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.133 0.0046 0.0413 0.0606 0.081 0.231 0.242 0.000577 0.000606 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 226 32673.027 0.005 0.00682 0.13 0.267 0.0737 0.0986 0.333 0.431 0.000831 0.00108 +! Validation 226 32673.027 0.005 0.00648 0.0635 0.193 0.071 0.0961 0.258 0.301 0.000644 0.000752 +Wall time: 32673.027051643934 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.18 0.00576 0.0651 0.0676 0.0906 0.27 0.305 0.000675 0.000761 + 227 200 0.251 0.00539 0.144 0.0654 0.0876 0.411 0.452 0.00103 0.00113 + 227 235 0.147 0.00621 0.0232 0.0715 0.0941 0.148 0.182 0.000369 0.000455 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.104 0.00496 0.00455 0.0637 0.084 0.0715 0.0805 0.000179 0.000201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 227 32817.437 0.005 0.00602 0.0652 0.186 0.0691 0.0926 0.244 0.305 0.000611 0.000763 +! Validation 227 32817.437 0.005 0.00664 0.0342 0.167 0.0726 0.0973 0.167 0.221 0.000416 0.000551 +Wall time: 32817.43741024472 +! Best model 227 0.167 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.23 0.00639 0.103 0.0709 0.0954 0.327 0.382 0.000818 0.000956 + 228 200 0.203 0.00617 0.0795 0.0694 0.0937 0.292 0.337 0.00073 0.000841 + 228 235 0.164 0.00593 0.0453 0.0677 0.0919 0.199 0.254 0.000498 0.000635 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.114 0.00426 0.0287 0.0586 0.0779 0.193 0.202 0.000483 0.000506 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 228 32961.941 0.005 0.00635 0.0976 0.225 0.071 0.0952 0.292 0.373 0.000729 0.000933 +! Validation 228 32961.941 0.005 0.00603 0.0523 0.173 0.0682 0.0927 0.226 0.273 0.000565 0.000683 +Wall time: 32961.94129371783 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.137 0.0056 0.0254 0.067 0.0893 0.15 0.19 0.000374 0.000476 + 229 200 0.173 0.00567 0.06 0.0667 0.0898 0.206 0.292 0.000515 0.000731 + 229 235 0.258 0.00677 0.123 0.0731 0.0982 0.327 0.418 0.000818 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.34 0.00474 0.245 0.0611 0.0822 0.586 0.591 0.00147 0.00148 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 229 33106.302 0.005 0.00605 0.102 0.223 0.0694 0.0928 0.301 0.381 0.000753 0.000952 +! Validation 229 33106.302 0.005 0.00647 0.26 0.39 0.0709 0.096 0.559 0.609 0.0014 0.00152 +Wall time: 33106.302334988955 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.129 0.00562 0.0167 0.0671 0.0895 0.123 0.154 0.000307 0.000385 + 230 200 0.269 0.00521 0.165 0.0651 0.0862 0.458 0.484 0.00115 0.00121 + 230 235 0.118 0.0053 0.0115 0.0645 0.0869 0.108 0.128 0.000271 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.087 0.00408 0.00538 0.0571 0.0762 0.0774 0.0875 0.000193 0.000219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 230 33250.654 0.005 0.0062 0.0853 0.209 0.0702 0.094 0.275 0.349 0.000687 0.000872 +! Validation 230 33250.654 0.005 0.00569 0.0309 0.145 0.0663 0.0901 0.159 0.21 0.000397 0.000524 +Wall time: 33250.65446929494 +! Best model 230 0.145 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.16 0.00559 0.0482 0.0673 0.0893 0.219 0.262 0.000547 0.000655 + 231 200 0.134 0.0054 0.0258 0.0656 0.0877 0.135 0.192 0.000338 0.000479 + 231 235 0.122 0.00475 0.0274 0.0611 0.0822 0.171 0.198 0.000429 0.000494 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.0789 0.00383 0.00239 0.0553 0.0738 0.0446 0.0584 0.000112 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 231 33394.992 0.005 0.00576 0.0617 0.177 0.0677 0.0906 0.235 0.297 0.000589 0.000742 +! Validation 231 33394.992 0.005 0.00542 0.0408 0.149 0.0645 0.0879 0.197 0.241 0.000492 0.000603 +Wall time: 33394.991961939726 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.136 0.00517 0.0325 0.0639 0.0858 0.178 0.215 0.000445 0.000538 + 232 200 0.131 0.00532 0.0251 0.0626 0.0871 0.136 0.189 0.000339 0.000472 + 232 235 0.591 0.00791 0.433 0.0807 0.106 0.662 0.785 0.00166 0.00196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.282 0.00739 0.135 0.0782 0.103 0.437 0.438 0.00109 0.0011 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 232 33539.448 0.005 0.00653 0.117 0.248 0.0716 0.0964 0.316 0.407 0.000789 0.00102 +! Validation 232 33539.448 0.005 0.00882 0.157 0.334 0.0848 0.112 0.424 0.473 0.00106 0.00118 +Wall time: 33539.4483963931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.279 0.00597 0.159 0.07 0.0922 0.465 0.476 0.00116 0.00119 + 233 200 0.204 0.00525 0.0992 0.0649 0.0865 0.319 0.376 0.000797 0.00094 + 233 235 0.149 0.00502 0.0484 0.0638 0.0846 0.215 0.263 0.000538 0.000656 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.079 0.00381 0.00276 0.055 0.0737 0.0564 0.0627 0.000141 0.000157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 233 33683.998 0.005 0.00605 0.0729 0.194 0.0693 0.0929 0.256 0.322 0.000639 0.000806 +! Validation 233 33683.998 0.005 0.00534 0.0415 0.148 0.0641 0.0872 0.18 0.243 0.000451 0.000608 +Wall time: 33683.99825751176 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.123 0.00508 0.021 0.0639 0.0851 0.14 0.173 0.00035 0.000433 + 234 200 0.173 0.00634 0.0465 0.0711 0.0951 0.21 0.257 0.000525 0.000643 + 234 235 0.132 0.00464 0.0389 0.0614 0.0813 0.184 0.235 0.00046 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.166 0.00416 0.0825 0.0573 0.077 0.34 0.343 0.00085 0.000857 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 234 33828.677 0.005 0.00568 0.0832 0.197 0.0672 0.09 0.265 0.344 0.000663 0.000861 +! Validation 234 33828.677 0.005 0.0056 0.164 0.276 0.066 0.0893 0.403 0.483 0.00101 0.00121 +Wall time: 33828.67690723296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.131 0.00532 0.0242 0.0646 0.087 0.157 0.186 0.000392 0.000465 + 235 200 0.151 0.00509 0.0492 0.0637 0.0852 0.231 0.265 0.000577 0.000662 + 235 235 0.128 0.00512 0.0253 0.0638 0.0854 0.15 0.19 0.000374 0.000475 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0932 0.0046 0.00124 0.0604 0.0809 0.0323 0.0421 8.08e-05 0.000105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 235 33973.134 0.005 0.00547 0.0594 0.169 0.066 0.0883 0.23 0.291 0.000574 0.000728 +! Validation 235 33973.134 0.005 0.00601 0.0324 0.153 0.0685 0.0925 0.176 0.215 0.000441 0.000537 +Wall time: 33973.13487123605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.189 0.00758 0.0372 0.0796 0.104 0.178 0.23 0.000446 0.000575 + 236 200 0.187 0.00489 0.0887 0.0623 0.0835 0.308 0.355 0.000771 0.000889 + 236 235 0.145 0.00485 0.0478 0.0633 0.0831 0.228 0.261 0.00057 0.000652 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.0899 0.00375 0.0148 0.0546 0.0731 0.133 0.145 0.000333 0.000363 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 236 34118.340 0.005 0.0064 0.0893 0.217 0.0703 0.0955 0.268 0.357 0.000671 0.000892 +! Validation 236 34118.340 0.005 0.00522 0.0404 0.145 0.0636 0.0863 0.197 0.24 0.000492 0.0006 +Wall time: 34118.340785505716 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.148 0.00539 0.04 0.0654 0.0876 0.205 0.239 0.000514 0.000597 + 237 200 0.134 0.00486 0.0367 0.0619 0.0832 0.196 0.229 0.00049 0.000572 + 237 235 0.129 0.00511 0.0265 0.0643 0.0853 0.165 0.194 0.000412 0.000486 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.0812 0.00375 0.00629 0.0547 0.0731 0.0728 0.0946 0.000182 0.000237 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 237 34262.686 0.005 0.0056 0.0709 0.183 0.0663 0.0893 0.249 0.318 0.000623 0.000795 +! Validation 237 34262.686 0.005 0.0053 0.028 0.134 0.064 0.0869 0.153 0.2 0.000383 0.0005 +Wall time: 34262.68664267985 +! Best model 237 0.134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.14 0.00518 0.0362 0.0646 0.0859 0.183 0.227 0.000458 0.000568 + 238 200 0.11 0.00479 0.0145 0.0612 0.0826 0.112 0.144 0.00028 0.000359 + 238 235 0.108 0.00456 0.0163 0.0604 0.0806 0.12 0.152 0.0003 0.000381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.0913 0.00354 0.0206 0.0529 0.071 0.164 0.171 0.000409 0.000428 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 238 34407.074 0.005 0.00502 0.0557 0.156 0.0631 0.0846 0.218 0.282 0.000546 0.000705 +! Validation 238 34407.074 0.005 0.00484 0.0502 0.147 0.061 0.083 0.217 0.267 0.000544 0.000668 +Wall time: 34407.07442861609 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.274 0.00734 0.127 0.0769 0.102 0.338 0.426 0.000845 0.00106 + 239 200 0.127 0.0048 0.0311 0.0617 0.0827 0.169 0.21 0.000423 0.000526 + 239 235 0.145 0.00519 0.0414 0.0648 0.086 0.234 0.243 0.000584 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.0797 0.00379 0.00392 0.0548 0.0735 0.0595 0.0747 0.000149 0.000187 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 239 34551.717 0.005 0.00531 0.0742 0.18 0.0647 0.0869 0.254 0.325 0.000636 0.000813 +! Validation 239 34551.717 0.005 0.00501 0.0376 0.138 0.0625 0.0845 0.182 0.231 0.000456 0.000579 +Wall time: 34551.717003651895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.114 0.00439 0.0265 0.0595 0.0791 0.168 0.194 0.00042 0.000486 + 240 200 0.138 0.00448 0.0481 0.0597 0.0799 0.237 0.262 0.000592 0.000655 + 240 235 0.269 0.00473 0.175 0.0613 0.0821 0.478 0.499 0.0012 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.178 0.00362 0.106 0.054 0.0718 0.381 0.388 0.000953 0.000971 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 240 34696.737 0.005 0.00507 0.0666 0.168 0.0635 0.085 0.241 0.307 0.000602 0.000769 +! Validation 240 34696.737 0.005 0.00492 0.116 0.214 0.062 0.0837 0.376 0.406 0.00094 0.00101 +Wall time: 34696.737294198014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.152 0.00438 0.0642 0.0597 0.079 0.274 0.302 0.000685 0.000756 + 241 200 0.143 0.00437 0.0555 0.0582 0.0789 0.251 0.281 0.000627 0.000703 + 241 235 0.123 0.00493 0.0239 0.0624 0.0838 0.148 0.185 0.000371 0.000461 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.084 0.004 0.00394 0.0559 0.0755 0.0663 0.0749 0.000166 0.000187 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 241 34841.269 0.005 0.0046 0.0512 0.143 0.0604 0.081 0.214 0.27 0.000535 0.000675 +! Validation 241 34841.269 0.005 0.00518 0.019 0.123 0.0632 0.0859 0.13 0.164 0.000324 0.000411 +Wall time: 34841.269558385015 +! Best model 241 0.123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 26.2 1.02 5.75 0.897 1.21 2.54 2.86 0.00636 0.00715 + 242 200 10.4 0.428 1.84 0.586 0.781 1.23 1.62 0.00308 0.00405 + 242 235 4.73 0.222 0.292 0.425 0.562 0.557 0.645 0.00139 0.00161 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 6.08 0.232 1.44 0.428 0.575 1.4 1.43 0.00349 0.00359 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 242 34985.853 0.005 0.448 7.53 16.5 0.496 0.799 1.81 3.28 0.00453 0.0082 +! Validation 242 34985.853 0.005 0.224 0.911 5.4 0.427 0.565 0.913 1.14 0.00228 0.00285 +Wall time: 34985.852974404115 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 5.56 0.144 2.68 0.344 0.453 1.74 1.96 0.00436 0.00489 + 243 200 2.36 0.0952 0.46 0.28 0.368 0.64 0.81 0.0016 0.00202 + 243 235 2.5 0.0899 0.702 0.272 0.358 0.847 1 0.00212 0.0025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 3.05 0.0868 1.31 0.267 0.352 1.35 1.37 0.00338 0.00342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 243 35130.646 0.005 0.134 1.19 3.88 0.33 0.437 1.04 1.31 0.0026 0.00326 +! Validation 243 35130.646 0.005 0.0906 1.36 3.17 0.273 0.359 1.25 1.39 0.00313 0.00348 +Wall time: 35130.64674981311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 1.83 0.0738 0.359 0.247 0.324 0.588 0.715 0.00147 0.00179 + 244 200 1.53 0.0625 0.277 0.226 0.298 0.507 0.629 0.00127 0.00157 + 244 235 1.73 0.0598 0.534 0.223 0.292 0.749 0.872 0.00187 0.00218 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 1.64 0.0546 0.551 0.211 0.279 0.877 0.886 0.00219 0.00221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 244 35275.118 0.005 0.0744 0.688 2.18 0.247 0.326 0.797 0.99 0.00199 0.00248 +! Validation 244 35275.118 0.005 0.0589 0.487 1.66 0.22 0.29 0.714 0.833 0.00178 0.00208 +Wall time: 35275.11815839773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 2.13 0.0476 1.17 0.199 0.26 1.2 1.29 0.003 0.00323 + 245 200 0.954 0.0384 0.187 0.177 0.234 0.425 0.516 0.00106 0.00129 + 245 235 3.08 0.0332 2.42 0.165 0.218 1.82 1.86 0.00454 0.00464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.818 0.0339 0.14 0.166 0.22 0.438 0.446 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 245 35419.582 0.005 0.0473 0.617 1.56 0.197 0.26 0.762 0.934 0.00191 0.00234 +! Validation 245 35419.582 0.005 0.038 0.569 1.33 0.175 0.233 0.773 0.9 0.00193 0.00225 +Wall time: 35419.58224955201 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 1.57 0.0328 0.918 0.163 0.216 1.09 1.14 0.00272 0.00286 + 246 200 0.682 0.0265 0.152 0.145 0.194 0.405 0.465 0.00101 0.00116 + 246 235 0.605 0.0249 0.107 0.141 0.188 0.304 0.391 0.00076 0.000977 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 1 0.0213 0.573 0.131 0.174 0.9 0.904 0.00225 0.00226 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 246 35564.041 0.005 0.0321 0.464 1.11 0.16 0.214 0.637 0.814 0.00159 0.00204 +! Validation 246 35564.041 0.005 0.0258 0.55 1.07 0.143 0.192 0.804 0.886 0.00201 0.00221 +Wall time: 35564.04146458581 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.853 0.0226 0.4 0.135 0.18 0.69 0.755 0.00173 0.00189 + 247 200 0.645 0.0207 0.23 0.13 0.172 0.502 0.573 0.00126 0.00143 + 247 235 0.767 0.0225 0.317 0.132 0.179 0.619 0.672 0.00155 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.645 0.0184 0.277 0.12 0.162 0.62 0.629 0.00155 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 247 35708.503 0.005 0.0228 0.317 0.773 0.135 0.18 0.542 0.672 0.00135 0.00168 +! Validation 247 35708.503 0.005 0.0221 0.202 0.644 0.132 0.177 0.443 0.536 0.00111 0.00134 +Wall time: 35708.50294390181 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.503 0.0211 0.0818 0.13 0.173 0.241 0.341 0.000601 0.000853 + 248 200 0.408 0.0174 0.0592 0.118 0.158 0.247 0.291 0.000617 0.000726 + 248 235 0.442 0.0172 0.0982 0.118 0.157 0.298 0.374 0.000745 0.000935 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.773 0.015 0.473 0.108 0.146 0.813 0.82 0.00203 0.00205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 248 35853.009 0.005 0.0199 0.298 0.697 0.126 0.169 0.517 0.653 0.00129 0.00163 +! Validation 248 35853.009 0.005 0.0185 0.348 0.717 0.12 0.162 0.621 0.704 0.00155 0.00176 +Wall time: 35853.00889188284 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.55 0.0166 0.219 0.115 0.154 0.485 0.559 0.00121 0.0014 + 249 200 0.439 0.0165 0.109 0.113 0.153 0.325 0.394 0.000812 0.000985 + 249 235 0.507 0.017 0.167 0.117 0.155 0.393 0.488 0.000983 0.00122 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.44 0.0133 0.174 0.102 0.137 0.487 0.499 0.00122 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 249 35997.417 0.005 0.0169 0.228 0.566 0.116 0.155 0.46 0.57 0.00115 0.00143 +! Validation 249 35997.417 0.005 0.0165 0.15 0.48 0.113 0.153 0.394 0.463 0.000984 0.00116 +Wall time: 35997.41743276082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.785 0.0159 0.468 0.112 0.15 0.76 0.816 0.0019 0.00204 + 250 200 0.376 0.0151 0.0745 0.109 0.147 0.273 0.326 0.000683 0.000814 + 250 235 1.08 0.0138 0.8 0.105 0.14 1.04 1.07 0.0026 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.433 0.0119 0.196 0.096 0.13 0.513 0.528 0.00128 0.00132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 250 36141.842 0.005 0.0153 0.218 0.523 0.11 0.148 0.443 0.555 0.00111 0.00139 +! Validation 250 36141.842 0.005 0.0147 0.328 0.622 0.107 0.145 0.625 0.683 0.00156 0.00171 +Wall time: 36141.8419667501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.625 0.0137 0.351 0.105 0.14 0.664 0.707 0.00166 0.00177 + 251 200 0.678 0.0126 0.426 0.101 0.134 0.752 0.779 0.00188 0.00195 + 251 235 0.361 0.0154 0.0537 0.108 0.148 0.233 0.277 0.000582 0.000692 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.657 0.0111 0.435 0.0927 0.126 0.779 0.787 0.00195 0.00197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 251 36286.378 0.005 0.0139 0.238 0.517 0.105 0.141 0.469 0.583 0.00117 0.00146 +! Validation 251 36286.378 0.005 0.0138 0.26 0.537 0.104 0.14 0.549 0.609 0.00137 0.00152 +Wall time: 36286.37799672084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.386 0.0117 0.151 0.0971 0.129 0.406 0.464 0.00102 0.00116 + 252 200 0.309 0.0113 0.0835 0.0957 0.127 0.309 0.345 0.000771 0.000862 + 252 235 0.503 0.0133 0.236 0.103 0.138 0.533 0.58 0.00133 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.243 0.0108 0.0267 0.0909 0.124 0.15 0.195 0.000375 0.000488 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 252 36431.323 0.005 0.0126 0.197 0.45 0.1 0.134 0.423 0.53 0.00106 0.00133 +! Validation 252 36431.323 0.005 0.0131 0.159 0.422 0.102 0.137 0.407 0.476 0.00102 0.00119 +Wall time: 36431.32355782483 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.305 0.0115 0.0747 0.0956 0.128 0.279 0.326 0.000699 0.000815 + 253 200 0.309 0.0113 0.0826 0.0948 0.127 0.29 0.343 0.000725 0.000858 + 253 235 0.227 0.00983 0.0299 0.0892 0.118 0.189 0.206 0.000472 0.000516 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.21 0.00912 0.0274 0.0838 0.114 0.17 0.198 0.000425 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 253 36575.840 0.005 0.0117 0.184 0.418 0.0966 0.129 0.417 0.513 0.00104 0.00128 +! Validation 253 36575.840 0.005 0.0115 0.0465 0.276 0.0948 0.128 0.203 0.257 0.000507 0.000643 +Wall time: 36575.840185960755 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.269 0.00974 0.0745 0.0891 0.118 0.241 0.326 0.000602 0.000815 + 254 200 0.228 0.00939 0.0401 0.0876 0.116 0.2 0.239 0.0005 0.000597 + 254 235 0.273 0.0118 0.0379 0.0958 0.129 0.183 0.232 0.000458 0.000581 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.208 0.0097 0.0141 0.086 0.118 0.134 0.141 0.000334 0.000354 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 254 36720.275 0.005 0.0105 0.158 0.368 0.0916 0.122 0.371 0.475 0.000927 0.00119 +! Validation 254 36720.275 0.005 0.0118 0.0835 0.319 0.0964 0.129 0.284 0.345 0.000709 0.000862 +Wall time: 36720.27561111003 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.23 0.00949 0.0403 0.0876 0.116 0.182 0.24 0.000455 0.000599 + 255 200 0.276 0.0104 0.0688 0.09 0.122 0.268 0.313 0.00067 0.000783 + 255 235 0.22 0.00912 0.0375 0.0854 0.114 0.201 0.231 0.000503 0.000578 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.17 0.00777 0.015 0.0773 0.105 0.132 0.146 0.000329 0.000365 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 255 36864.768 0.005 0.00985 0.147 0.344 0.0887 0.118 0.36 0.458 0.000899 0.00114 +! Validation 255 36864.768 0.005 0.00978 0.0617 0.257 0.0875 0.118 0.244 0.297 0.000609 0.000741 +Wall time: 36864.76811373094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.199 0.00868 0.025 0.0828 0.111 0.155 0.189 0.000387 0.000471 + 256 200 0.306 0.00865 0.133 0.0832 0.111 0.39 0.436 0.000976 0.00109 + 256 235 0.436 0.00812 0.274 0.0812 0.108 0.613 0.625 0.00153 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.239 0.00713 0.0963 0.074 0.101 0.352 0.37 0.00088 0.000926 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 256 37009.214 0.005 0.00901 0.129 0.31 0.0848 0.113 0.345 0.429 0.000862 0.00107 +! Validation 256 37009.214 0.005 0.00912 0.164 0.346 0.0846 0.114 0.431 0.483 0.00108 0.00121 +Wall time: 37009.21420597285 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.55 0.00828 0.385 0.0816 0.109 0.728 0.74 0.00182 0.00185 + 257 200 0.212 0.00822 0.0479 0.0815 0.108 0.226 0.261 0.000565 0.000653 + 257 235 0.156 0.00727 0.0105 0.0769 0.102 0.107 0.122 0.000267 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.139 0.00645 0.00975 0.0707 0.0959 0.114 0.118 0.000284 0.000295 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 257 37153.664 0.005 0.00848 0.121 0.29 0.0823 0.11 0.331 0.416 0.000828 0.00104 +! Validation 257 37153.664 0.005 0.00849 0.0418 0.212 0.0815 0.11 0.197 0.244 0.000493 0.00061 +Wall time: 37153.66459931806 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.185 0.0078 0.0292 0.0789 0.105 0.155 0.204 0.000387 0.00051 + 258 200 0.22 0.00808 0.0587 0.0813 0.107 0.247 0.289 0.000618 0.000723 + 258 235 0.205 0.00769 0.0513 0.079 0.105 0.239 0.27 0.000597 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.619 0.00659 0.487 0.0712 0.0969 0.822 0.833 0.00205 0.00208 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 258 37298.042 0.005 0.00811 0.154 0.316 0.0804 0.107 0.376 0.468 0.000939 0.00117 +! Validation 258 37298.042 0.005 0.00853 0.756 0.927 0.0818 0.11 1.01 1.04 0.00253 0.0026 +Wall time: 37298.04224750772 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.199 0.00746 0.0503 0.0772 0.103 0.221 0.268 0.000553 0.000669 + 259 200 0.439 0.00744 0.291 0.0772 0.103 0.619 0.643 0.00155 0.00161 + 259 235 0.159 0.0076 0.00738 0.0779 0.104 0.0653 0.103 0.000163 0.000256 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.124 0.00567 0.0109 0.0663 0.0898 0.0889 0.125 0.000222 0.000311 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 259 37442.418 0.005 0.00758 0.0917 0.243 0.0777 0.104 0.285 0.362 0.000713 0.000905 +! Validation 259 37442.418 0.005 0.00755 0.0462 0.197 0.0768 0.104 0.211 0.257 0.000526 0.000641 +Wall time: 37442.41828820575 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.422 0.00744 0.273 0.077 0.103 0.588 0.624 0.00147 0.00156 + 260 200 0.655 0.00803 0.495 0.0803 0.107 0.81 0.84 0.00203 0.0021 + 260 235 0.215 0.00753 0.0645 0.0781 0.104 0.245 0.303 0.000614 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.133 0.00587 0.0158 0.0679 0.0915 0.111 0.15 0.000278 0.000375 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 260 37586.770 0.005 0.00729 0.136 0.282 0.0762 0.102 0.351 0.441 0.000878 0.0011 +! Validation 260 37586.770 0.005 0.0077 0.097 0.251 0.0776 0.105 0.308 0.372 0.00077 0.000929 +Wall time: 37586.77039202675 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.208 0.00748 0.0586 0.0768 0.103 0.206 0.289 0.000515 0.000722 + 261 200 0.174 0.00696 0.0352 0.0747 0.0996 0.171 0.224 0.000428 0.00056 + 261 235 0.184 0.00679 0.0486 0.0738 0.0984 0.218 0.263 0.000544 0.000658 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.2 0.00529 0.0946 0.0644 0.0868 0.344 0.367 0.000861 0.000918 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 261 37731.128 0.005 0.00701 0.108 0.249 0.0747 0.0999 0.309 0.393 0.000772 0.000983 +! Validation 261 37731.128 0.005 0.0071 0.0983 0.24 0.0745 0.101 0.324 0.374 0.000809 0.000935 +Wall time: 37731.12853643112 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.143 0.00633 0.016 0.0717 0.095 0.118 0.151 0.000295 0.000377 + 262 200 0.162 0.00689 0.0245 0.0735 0.0991 0.154 0.187 0.000385 0.000467 + 262 235 0.318 0.00757 0.166 0.0774 0.104 0.468 0.487 0.00117 0.00122 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.183 0.00564 0.07 0.0664 0.0896 0.29 0.316 0.000726 0.000789 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 262 37875.636 0.005 0.0066 0.097 0.229 0.0724 0.0969 0.296 0.371 0.00074 0.000928 +! Validation 262 37875.636 0.005 0.00743 0.0421 0.191 0.0765 0.103 0.194 0.245 0.000486 0.000612 +Wall time: 37875.63676540693 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.145 0.00613 0.0228 0.0697 0.0934 0.143 0.18 0.000358 0.000451 + 263 200 0.129 0.00582 0.013 0.0684 0.091 0.116 0.136 0.000291 0.00034 + 263 235 0.165 0.00564 0.0525 0.0677 0.0897 0.251 0.273 0.000628 0.000684 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.153 0.00466 0.0594 0.0602 0.0814 0.277 0.291 0.000694 0.000728 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 263 38020.163 0.005 0.00645 0.088 0.217 0.0716 0.0958 0.279 0.354 0.000697 0.000886 +! Validation 263 38020.163 0.005 0.00642 0.0802 0.208 0.0706 0.0956 0.294 0.338 0.000735 0.000845 +Wall time: 38020.16378176771 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.133 0.00597 0.0134 0.0691 0.0923 0.114 0.138 0.000285 0.000346 + 264 200 0.134 0.00582 0.018 0.0676 0.0911 0.129 0.16 0.000322 0.0004 + 264 235 0.396 0.00692 0.258 0.0737 0.0993 0.573 0.606 0.00143 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.215 0.00461 0.123 0.0601 0.0811 0.407 0.419 0.00102 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 264 38164.570 0.005 0.00614 0.0896 0.212 0.0698 0.0935 0.283 0.356 0.000707 0.000891 +! Validation 264 38164.570 0.005 0.0064 0.202 0.33 0.0707 0.0955 0.482 0.536 0.0012 0.00134 +Wall time: 38164.57005945174 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.191 0.00605 0.0703 0.0693 0.0928 0.26 0.316 0.000649 0.000791 + 265 200 0.189 0.0056 0.0767 0.0667 0.0893 0.298 0.331 0.000744 0.000827 + 265 235 0.14 0.0052 0.0355 0.0645 0.0861 0.168 0.225 0.000419 0.000562 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.218 0.00423 0.133 0.0582 0.0777 0.422 0.436 0.00106 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 265 38308.956 0.005 0.00592 0.0771 0.196 0.0686 0.0919 0.265 0.332 0.000662 0.000829 +! Validation 265 38308.956 0.005 0.00604 0.129 0.25 0.0686 0.0927 0.389 0.429 0.000972 0.00107 +Wall time: 38308.95599666005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.201 0.006 0.0811 0.0694 0.0925 0.279 0.34 0.000699 0.00085 + 266 200 0.134 0.00581 0.0178 0.0679 0.0909 0.132 0.159 0.000329 0.000399 + 266 235 0.158 0.00637 0.0308 0.0709 0.0953 0.176 0.209 0.00044 0.000524 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.107 0.00416 0.0235 0.0575 0.077 0.148 0.183 0.000371 0.000457 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 266 38453.536 0.005 0.00594 0.107 0.226 0.0687 0.092 0.308 0.39 0.000769 0.000975 +! Validation 266 38453.536 0.005 0.00593 0.0394 0.158 0.0679 0.0919 0.194 0.237 0.000484 0.000592 +Wall time: 38453.536199903116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.155 0.00566 0.0416 0.0671 0.0898 0.192 0.243 0.000479 0.000608 + 267 200 0.216 0.00531 0.11 0.0649 0.087 0.373 0.396 0.000933 0.00099 + 267 235 0.199 0.00513 0.0965 0.0646 0.0855 0.335 0.371 0.000837 0.000927 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.216 0.00413 0.134 0.0568 0.0767 0.428 0.437 0.00107 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 267 38597.770 0.005 0.00556 0.0673 0.179 0.0664 0.089 0.25 0.309 0.000626 0.000774 +! Validation 267 38597.770 0.005 0.0057 0.187 0.301 0.0666 0.0901 0.475 0.516 0.00119 0.00129 +Wall time: 38597.770298619755 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.154 0.00563 0.0414 0.0669 0.0896 0.197 0.243 0.000493 0.000607 + 268 200 0.132 0.00576 0.0168 0.0678 0.0906 0.127 0.155 0.000317 0.000387 + 268 235 0.139 0.00558 0.0274 0.0662 0.0892 0.164 0.197 0.000409 0.000494 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.171 0.00415 0.0885 0.0575 0.0769 0.336 0.355 0.000841 0.000887 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 268 38742.003 0.005 0.00535 0.0666 0.174 0.0651 0.0873 0.247 0.308 0.000618 0.000771 +! Validation 268 38742.003 0.005 0.00582 0.109 0.226 0.0674 0.0911 0.352 0.394 0.000879 0.000986 +Wall time: 38742.003830773756 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.168 0.0051 0.0664 0.064 0.0853 0.26 0.308 0.00065 0.000769 + 269 200 0.195 0.00539 0.0876 0.0654 0.0877 0.3 0.353 0.000751 0.000883 + 269 235 0.176 0.00605 0.0548 0.0711 0.0928 0.253 0.279 0.000634 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.142 0.00514 0.039 0.0656 0.0856 0.194 0.236 0.000486 0.00059 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 269 38886.433 0.005 0.00572 0.12 0.234 0.0674 0.0902 0.327 0.413 0.000817 0.00103 +! Validation 269 38886.433 0.005 0.00682 0.0559 0.192 0.0742 0.0985 0.236 0.282 0.00059 0.000705 +Wall time: 38886.43298922805 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.143 0.00538 0.0354 0.065 0.0875 0.179 0.225 0.000446 0.000561 + 270 200 0.124 0.00507 0.0232 0.0636 0.0849 0.149 0.182 0.000372 0.000454 + 270 235 0.129 0.00541 0.0204 0.0653 0.0878 0.141 0.17 0.000354 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.0842 0.00389 0.00634 0.0555 0.0744 0.0804 0.0951 0.000201 0.000238 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 270 39030.773 0.005 0.00537 0.0779 0.185 0.0653 0.0875 0.266 0.333 0.000665 0.000833 +! Validation 270 39030.773 0.005 0.00542 0.0239 0.132 0.0649 0.0879 0.147 0.185 0.000367 0.000461 +Wall time: 39030.77305335412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.131 0.00545 0.0218 0.0659 0.0881 0.142 0.176 0.000355 0.000441 + 271 200 0.152 0.00548 0.0427 0.0659 0.0883 0.208 0.247 0.000519 0.000617 + 271 235 0.293 0.00617 0.17 0.0699 0.0937 0.474 0.491 0.00119 0.00123 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.196 0.00425 0.111 0.0586 0.0778 0.388 0.398 0.000969 0.000996 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 271 39175.273 0.005 0.00531 0.0819 0.188 0.0649 0.0869 0.263 0.341 0.000657 0.000853 +! Validation 271 39175.273 0.005 0.00565 0.163 0.276 0.0668 0.0897 0.451 0.483 0.00113 0.00121 +Wall time: 39175.27329402603 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.128 0.0049 0.0297 0.0619 0.0835 0.167 0.206 0.000418 0.000514 + 272 200 0.138 0.00473 0.043 0.0618 0.0821 0.226 0.248 0.000564 0.000619 + 272 235 0.118 0.0052 0.014 0.065 0.086 0.119 0.141 0.000297 0.000353 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.129 0.00394 0.0502 0.0571 0.0749 0.256 0.267 0.000641 0.000668 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 272 39319.541 0.005 0.00495 0.0543 0.153 0.0627 0.084 0.218 0.278 0.000545 0.000696 +! Validation 272 39319.541 0.005 0.00551 0.0685 0.179 0.0659 0.0886 0.257 0.312 0.000642 0.000781 +Wall time: 39319.54120127903 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.16 0.00484 0.0634 0.0621 0.083 0.258 0.301 0.000645 0.000751 + 273 200 0.183 0.005 0.0831 0.0636 0.0844 0.311 0.344 0.000778 0.00086 + 273 235 0.165 0.00485 0.0681 0.0624 0.0831 0.289 0.311 0.000721 0.000779 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.292 0.00363 0.22 0.0542 0.0719 0.552 0.559 0.00138 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 273 39463.806 0.005 0.00497 0.0711 0.171 0.0628 0.0842 0.255 0.318 0.000637 0.000796 +! Validation 273 39463.806 0.005 0.00507 0.225 0.326 0.0628 0.085 0.538 0.566 0.00135 0.00142 +Wall time: 39463.8063824391 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.129 0.00493 0.0307 0.0628 0.0838 0.169 0.209 0.000423 0.000523 + 274 200 0.122 0.00493 0.0235 0.0618 0.0838 0.148 0.183 0.000371 0.000458 + 274 235 0.178 0.00575 0.0633 0.0669 0.0905 0.234 0.3 0.000586 0.000751 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0897 0.00347 0.0203 0.0526 0.0703 0.142 0.17 0.000354 0.000425 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 274 39608.083 0.005 0.00486 0.0633 0.161 0.0621 0.0832 0.238 0.3 0.000594 0.000751 +! Validation 274 39608.083 0.005 0.00492 0.0713 0.17 0.0617 0.0837 0.26 0.319 0.000649 0.000797 +Wall time: 39608.08337900881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.116 0.00425 0.0307 0.0579 0.0778 0.179 0.209 0.000447 0.000523 + 275 200 0.203 0.00462 0.111 0.0601 0.0811 0.37 0.397 0.000926 0.000992 + 275 235 0.172 0.00476 0.0771 0.0611 0.0824 0.291 0.331 0.000728 0.000829 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0863 0.00368 0.0127 0.0538 0.0724 0.11 0.135 0.000274 0.000336 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 275 39752.357 0.005 0.00473 0.0499 0.144 0.0612 0.082 0.212 0.266 0.000529 0.000666 +! Validation 275 39752.357 0.005 0.00492 0.0193 0.118 0.0619 0.0837 0.132 0.166 0.000329 0.000414 +Wall time: 39752.35746981902 +! Best model 275 0.118 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.102 0.00453 0.0116 0.0598 0.0804 0.103 0.129 0.000257 0.000322 + 276 200 0.116 0.00423 0.0312 0.0578 0.0777 0.182 0.211 0.000456 0.000527 + 276 235 0.14 0.00454 0.0488 0.0596 0.0804 0.206 0.264 0.000516 0.000659 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.16 0.00346 0.0912 0.0529 0.0702 0.349 0.36 0.000872 0.000901 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 276 39896.651 0.005 0.00468 0.056 0.15 0.0609 0.0816 0.221 0.283 0.000553 0.000706 +! Validation 276 39896.651 0.005 0.00485 0.152 0.249 0.0613 0.0831 0.401 0.465 0.001 0.00116 +Wall time: 39896.650958575774 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.116 0.00454 0.0247 0.0605 0.0804 0.16 0.188 0.000401 0.000469 + 277 200 0.149 0.00556 0.0382 0.0666 0.089 0.203 0.233 0.000508 0.000583 + 277 235 0.108 0.00412 0.0258 0.0575 0.0766 0.165 0.192 0.000412 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.0752 0.00323 0.0106 0.0511 0.0678 0.0848 0.123 0.000212 0.000308 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 277 40041.337 0.005 0.00493 0.0799 0.178 0.0627 0.0838 0.26 0.338 0.000651 0.000844 +! Validation 277 40041.337 0.005 0.00462 0.0408 0.133 0.0598 0.0812 0.201 0.241 0.000502 0.000603 +Wall time: 40041.337317303754 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.12 0.0044 0.0315 0.0587 0.0792 0.18 0.212 0.000451 0.00053 + 278 200 0.225 0.00434 0.139 0.0591 0.0786 0.406 0.444 0.00102 0.00111 + 278 235 0.128 0.00483 0.0314 0.0629 0.083 0.177 0.211 0.000442 0.000528 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0856 0.00398 0.00594 0.0573 0.0753 0.0828 0.092 0.000207 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 278 40185.672 0.005 0.00462 0.0607 0.153 0.0606 0.0811 0.238 0.294 0.000595 0.000736 +! Validation 278 40185.672 0.005 0.00537 0.0303 0.138 0.0649 0.0875 0.155 0.208 0.000387 0.00052 +Wall time: 40185.67262376612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.149 0.00395 0.0698 0.0559 0.075 0.302 0.315 0.000755 0.000789 + 279 200 0.297 0.00476 0.202 0.0621 0.0823 0.496 0.536 0.00124 0.00134 + 279 235 0.233 0.00633 0.107 0.0715 0.0949 0.281 0.39 0.000703 0.000975 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.374 0.00432 0.288 0.0586 0.0784 0.637 0.64 0.00159 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 279 40329.968 0.005 0.00469 0.0677 0.161 0.0611 0.0817 0.252 0.31 0.000631 0.000776 +! Validation 279 40329.968 0.005 0.00542 0.188 0.297 0.0656 0.0879 0.449 0.518 0.00112 0.0013 +Wall time: 40329.968883024994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.179 0.0041 0.0973 0.0571 0.0764 0.343 0.372 0.000856 0.000931 + 280 200 0.128 0.00434 0.0414 0.0585 0.0786 0.192 0.243 0.00048 0.000607 + 280 235 0.121 0.00512 0.0185 0.0642 0.0854 0.129 0.162 0.000322 0.000406 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.0804 0.00363 0.00774 0.0547 0.0719 0.0893 0.105 0.000223 0.000263 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 280 40474.274 0.005 0.00478 0.0636 0.159 0.0617 0.0825 0.233 0.301 0.000583 0.000753 +! Validation 280 40474.274 0.005 0.00493 0.0186 0.117 0.0624 0.0838 0.131 0.163 0.000328 0.000407 +Wall time: 40474.27433515992 +! Best model 280 0.117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.185 0.00405 0.104 0.0566 0.0759 0.358 0.385 0.000894 0.000962 + 281 200 2.84 0.00784 2.68 0.0797 0.106 1.93 1.95 0.00482 0.00488 + 281 235 24 0.931 5.39 0.858 1.15 2.27 2.77 0.00567 0.00693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 29.3 0.89 11.5 0.842 1.13 4.01 4.06 0.01 0.0101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 281 40618.631 0.005 0.115 2.87 5.18 0.16 0.401 0.769 2.02 0.00192 0.00505 +! Validation 281 40618.631 0.005 0.871 4.4 21.8 0.832 1.11 2.08 2.5 0.0052 0.00626 +Wall time: 40618.63141711475 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 2.48 0.101 0.46 0.287 0.379 0.614 0.81 0.00154 0.00202 + 282 200 1.5 0.0459 0.579 0.191 0.256 0.852 0.908 0.00213 0.00227 + 282 235 1.36 0.0424 0.508 0.183 0.246 0.781 0.851 0.00195 0.00213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 2.04 0.0354 1.33 0.167 0.225 1.37 1.38 0.00343 0.00344 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 282 40762.940 0.005 0.169 1.56 4.95 0.326 0.492 1.06 1.49 0.00265 0.00373 +! Validation 282 40762.940 0.005 0.0399 1.01 1.8 0.177 0.238 1.13 1.2 0.00282 0.003 +Wall time: 40762.94065816188 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.754 0.0282 0.189 0.15 0.201 0.451 0.518 0.00113 0.0013 + 283 200 1.29 0.0118 1.06 0.0983 0.129 1.21 1.23 0.00302 0.00307 + 283 235 0.57 0.0115 0.34 0.0969 0.128 0.67 0.696 0.00167 0.00174 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.549 0.00876 0.374 0.0844 0.112 0.729 0.73 0.00182 0.00182 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 283 40907.236 0.005 0.0235 0.372 0.842 0.135 0.183 0.595 0.728 0.00149 0.00182 +! Validation 283 40907.236 0.005 0.0116 0.534 0.767 0.0972 0.129 0.831 0.872 0.00208 0.00218 +Wall time: 40907.236541989725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.208 0.00901 0.0281 0.0855 0.113 0.165 0.2 0.000414 0.0005 + 284 200 0.197 0.00796 0.0381 0.0804 0.107 0.175 0.233 0.000437 0.000582 + 284 235 0.212 0.00815 0.0493 0.0809 0.108 0.215 0.265 0.000538 0.000662 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.177 0.00577 0.0618 0.0675 0.0906 0.289 0.297 0.000723 0.000742 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 284 41051.538 0.005 0.00888 0.131 0.308 0.0848 0.112 0.345 0.432 0.000862 0.00108 +! Validation 284 41051.538 0.005 0.00769 0.0588 0.213 0.0783 0.105 0.234 0.289 0.000584 0.000724 +Wall time: 41051.53833214706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.188 0.00688 0.0502 0.0743 0.099 0.22 0.267 0.000549 0.000668 + 285 200 0.188 0.00616 0.0644 0.0703 0.0937 0.267 0.303 0.000667 0.000757 + 285 235 0.178 0.00666 0.0447 0.0724 0.0974 0.184 0.252 0.00046 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.171 0.00476 0.0761 0.061 0.0823 0.322 0.329 0.000806 0.000823 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 285 41195.833 0.005 0.00666 0.0666 0.2 0.0731 0.0974 0.243 0.308 0.000609 0.000771 +! Validation 285 41195.833 0.005 0.00641 0.0642 0.192 0.0712 0.0956 0.256 0.302 0.00064 0.000756 +Wall time: 41195.83306494774 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.156 0.00618 0.0327 0.0704 0.0938 0.174 0.216 0.000436 0.00054 + 286 200 0.153 0.00549 0.0432 0.0662 0.0884 0.207 0.248 0.000518 0.00062 + 286 235 0.166 0.00554 0.0554 0.0663 0.0888 0.209 0.281 0.000523 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0964 0.00421 0.0122 0.0574 0.0774 0.111 0.132 0.000278 0.00033 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 286 41340.153 0.005 0.00583 0.0704 0.187 0.0682 0.0912 0.245 0.317 0.000614 0.000792 +! Validation 286 41340.153 0.005 0.00575 0.0338 0.149 0.0672 0.0905 0.177 0.22 0.000442 0.000549 +Wall time: 41340.15346198296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.166 0.00566 0.0524 0.0673 0.0898 0.229 0.273 0.000572 0.000683 + 287 200 0.138 0.0048 0.0417 0.0621 0.0827 0.21 0.244 0.000526 0.000609 + 287 235 0.168 0.00504 0.0676 0.0636 0.0848 0.287 0.31 0.000717 0.000776 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.177 0.00393 0.0983 0.0553 0.0748 0.366 0.374 0.000914 0.000936 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 287 41484.821 0.005 0.00535 0.0661 0.173 0.0652 0.0873 0.248 0.307 0.000619 0.000767 +! Validation 287 41484.821 0.005 0.00538 0.0961 0.204 0.0649 0.0875 0.332 0.37 0.000831 0.000925 +Wall time: 41484.82096820604 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.179 0.00498 0.0792 0.063 0.0842 0.301 0.336 0.000751 0.00084 + 288 200 0.106 0.00457 0.0148 0.0603 0.0807 0.102 0.145 0.000256 0.000363 + 288 235 0.158 0.0044 0.0701 0.0599 0.0792 0.288 0.316 0.000719 0.00079 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.133 0.00373 0.0582 0.0541 0.0729 0.281 0.288 0.000703 0.00072 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 288 41629.109 0.005 0.00503 0.0613 0.162 0.0632 0.0847 0.238 0.296 0.000595 0.000739 +! Validation 288 41629.109 0.005 0.00516 0.0888 0.192 0.0635 0.0857 0.315 0.356 0.000788 0.000889 +Wall time: 41629.1091894377 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.3 0.00466 0.207 0.061 0.0814 0.523 0.543 0.00131 0.00136 + 289 200 0.324 0.00475 0.229 0.0617 0.0823 0.55 0.571 0.00137 0.00143 + 289 235 0.217 0.00491 0.119 0.0628 0.0836 0.378 0.411 0.000944 0.00103 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.232 0.00359 0.16 0.053 0.0715 0.47 0.478 0.00118 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 289 41773.640 0.005 0.00483 0.069 0.166 0.0619 0.0829 0.253 0.313 0.000633 0.000783 +! Validation 289 41773.640 0.005 0.005 0.176 0.276 0.0625 0.0844 0.471 0.501 0.00118 0.00125 +Wall time: 41773.64070450887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.129 0.00501 0.0285 0.0632 0.0845 0.173 0.201 0.000433 0.000504 + 290 200 0.121 0.00436 0.0333 0.0589 0.0788 0.184 0.218 0.00046 0.000544 + 290 235 0.143 0.00551 0.0327 0.0651 0.0886 0.178 0.216 0.000445 0.00054 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0771 0.00363 0.00454 0.0528 0.0719 0.067 0.0805 0.000167 0.000201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 290 41918.520 0.005 0.00466 0.052 0.145 0.0607 0.0815 0.219 0.272 0.000548 0.000681 +! Validation 290 41918.520 0.005 0.0049 0.0399 0.138 0.0617 0.0836 0.192 0.238 0.000479 0.000596 +Wall time: 41918.52041292889 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.102 0.00409 0.0204 0.0571 0.0763 0.151 0.17 0.000377 0.000426 + 291 200 0.0976 0.00413 0.0149 0.0574 0.0767 0.117 0.146 0.000294 0.000364 + 291 235 0.129 0.0045 0.0389 0.0592 0.0801 0.199 0.235 0.000498 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0706 0.00335 0.0036 0.0513 0.0691 0.0705 0.0716 0.000176 0.000179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 291 42062.871 0.005 0.0046 0.0769 0.169 0.0603 0.0809 0.256 0.331 0.000639 0.000828 +! Validation 291 42062.871 0.005 0.00467 0.0243 0.118 0.0603 0.0816 0.153 0.186 0.000383 0.000465 +Wall time: 42062.87161427969 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.246 0.00484 0.149 0.062 0.083 0.376 0.46 0.000941 0.00115 + 292 200 0.104 0.00412 0.022 0.0573 0.0766 0.15 0.177 0.000374 0.000442 + 292 235 0.154 0.00494 0.055 0.0624 0.0839 0.26 0.28 0.00065 0.0007 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.22 0.00353 0.149 0.0524 0.0709 0.455 0.461 0.00114 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 292 42207.198 0.005 0.00452 0.0656 0.156 0.0598 0.0803 0.236 0.306 0.000591 0.000764 +! Validation 292 42207.198 0.005 0.00475 0.168 0.263 0.0608 0.0823 0.46 0.489 0.00115 0.00122 +Wall time: 42207.19871415198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.191 0.00427 0.105 0.0579 0.078 0.37 0.388 0.000925 0.000969 + 293 200 0.209 0.00447 0.12 0.0597 0.0798 0.392 0.413 0.00098 0.00103 + 293 235 0.0994 0.00388 0.0218 0.0559 0.0744 0.149 0.176 0.000372 0.000441 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0655 0.00312 0.00308 0.0495 0.0667 0.0555 0.0662 0.000139 0.000166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 293 42351.531 0.005 0.00431 0.0508 0.137 0.0584 0.0784 0.217 0.269 0.000543 0.000673 +! Validation 293 42351.531 0.005 0.00445 0.0243 0.113 0.0587 0.0796 0.151 0.186 0.000378 0.000465 +Wall time: 42351.53167084884 +! Best model 293 0.113 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.135 0.00446 0.0462 0.0591 0.0797 0.22 0.257 0.000549 0.000642 + 294 200 0.107 0.00392 0.0285 0.0553 0.0747 0.166 0.201 0.000416 0.000503 + 294 235 0.117 0.00414 0.0344 0.0574 0.0768 0.19 0.221 0.000474 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.0674 0.00311 0.00521 0.0495 0.0665 0.0728 0.0861 0.000182 0.000215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 294 42496.359 0.005 0.0042 0.0463 0.13 0.0576 0.0774 0.206 0.257 0.000515 0.000642 +! Validation 294 42496.359 0.005 0.0044 0.026 0.114 0.0584 0.0792 0.156 0.192 0.00039 0.000481 +Wall time: 42496.359199489 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.107 0.00415 0.0238 0.0572 0.0769 0.148 0.184 0.000371 0.00046 + 295 200 0.102 0.00383 0.0254 0.055 0.0738 0.163 0.19 0.000409 0.000475 + 295 235 0.0954 0.00386 0.0183 0.0554 0.0741 0.135 0.161 0.000338 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.13 0.00301 0.0703 0.0485 0.0655 0.31 0.317 0.000776 0.000791 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 295 42640.675 0.005 0.00416 0.0495 0.133 0.0573 0.077 0.209 0.266 0.000522 0.000664 +! Validation 295 42640.675 0.005 0.00424 0.0685 0.153 0.0572 0.0777 0.267 0.312 0.000668 0.000781 +Wall time: 42640.67525800504 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.222 0.00426 0.137 0.0582 0.0779 0.417 0.441 0.00104 0.0011 + 296 200 0.182 0.00425 0.0971 0.0581 0.0778 0.328 0.372 0.00082 0.00093 + 296 235 0.105 0.00437 0.0177 0.0587 0.0789 0.129 0.159 0.000322 0.000397 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.109 0.00305 0.0481 0.0491 0.0659 0.254 0.262 0.000634 0.000655 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 296 42785.280 0.005 0.00406 0.0493 0.131 0.0565 0.0761 0.206 0.265 0.000514 0.000663 +! Validation 296 42785.280 0.005 0.00431 0.0462 0.132 0.0577 0.0783 0.213 0.257 0.000532 0.000641 +Wall time: 42785.28020980582 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.115 0.00394 0.0364 0.0556 0.0749 0.183 0.228 0.000458 0.000569 + 297 200 0.189 0.00383 0.112 0.0553 0.0739 0.346 0.4 0.000866 0.000999 + 297 235 0.101 0.00399 0.0208 0.0557 0.0754 0.151 0.172 0.000379 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.062 0.00296 0.0027 0.0483 0.065 0.0615 0.0621 0.000154 0.000155 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 297 42929.603 0.005 0.00406 0.0478 0.129 0.0565 0.076 0.206 0.261 0.000516 0.000653 +! Validation 297 42929.603 0.005 0.00414 0.0266 0.109 0.0565 0.0768 0.156 0.195 0.000389 0.000486 +Wall time: 42929.60297389887 +! Best model 297 0.109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0944 0.0037 0.0204 0.054 0.0726 0.144 0.171 0.000361 0.000427 + 298 200 0.257 0.00438 0.17 0.0593 0.079 0.472 0.492 0.00118 0.00123 + 298 235 0.0853 0.00392 0.00701 0.0557 0.0747 0.0883 0.0999 0.000221 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0657 0.0029 0.00767 0.0479 0.0643 0.0756 0.105 0.000189 0.000261 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 298 43074.141 0.005 0.00407 0.0593 0.141 0.0566 0.0761 0.23 0.291 0.000576 0.000728 +! Validation 298 43074.141 0.005 0.0042 0.0257 0.11 0.0568 0.0773 0.143 0.191 0.000358 0.000478 +Wall time: 43074.14108918188 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0875 0.00373 0.0129 0.0546 0.0729 0.11 0.136 0.000275 0.000339 + 299 200 0.108 0.00414 0.0251 0.0569 0.0768 0.167 0.189 0.000418 0.000473 + 299 235 0.117 0.00379 0.0416 0.0544 0.0735 0.211 0.244 0.000527 0.000609 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0642 0.00291 0.0061 0.0476 0.0643 0.0651 0.0932 0.000163 0.000233 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 299 43218.762 0.005 0.00397 0.0452 0.125 0.056 0.0752 0.202 0.254 0.000505 0.000634 +! Validation 299 43218.762 0.005 0.00405 0.0211 0.102 0.0558 0.0759 0.138 0.173 0.000344 0.000433 +Wall time: 43218.762248031795 +! Best model 299 0.102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.141 0.00445 0.0521 0.0595 0.0796 0.217 0.272 0.000544 0.000681 + 300 200 0.0918 0.00359 0.0199 0.0535 0.0716 0.139 0.168 0.000347 0.000421 + 300 235 0.102 0.00372 0.0276 0.0542 0.0728 0.169 0.198 0.000422 0.000496 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.104 0.00284 0.0476 0.0473 0.0636 0.253 0.26 0.000632 0.000651 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 300 43363.142 0.005 0.00396 0.0568 0.136 0.0558 0.0751 0.223 0.285 0.000557 0.000712 +! Validation 300 43363.142 0.005 0.00401 0.0771 0.157 0.0555 0.0756 0.288 0.331 0.000721 0.000829 +Wall time: 43363.142730733845 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.293 0.00368 0.219 0.0542 0.0724 0.546 0.559 0.00137 0.0014 + 301 200 0.0937 0.00383 0.0172 0.0548 0.0738 0.125 0.157 0.000314 0.000391 + 301 235 0.081 0.0037 0.00704 0.0543 0.0726 0.0764 0.1 0.000191 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.0645 0.00277 0.00901 0.0467 0.0629 0.0945 0.113 0.000236 0.000283 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 301 43507.463 0.005 0.00398 0.0618 0.141 0.0561 0.0754 0.234 0.297 0.000585 0.000742 +! Validation 301 43507.463 0.005 0.00393 0.0165 0.095 0.055 0.0748 0.122 0.153 0.000305 0.000383 +Wall time: 43507.46357901208 +! Best model 301 0.095 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.108 0.00376 0.0324 0.0548 0.0732 0.198 0.215 0.000495 0.000537 + 302 200 0.0947 0.0038 0.0187 0.0544 0.0736 0.125 0.163 0.000312 0.000408 + 302 235 0.0989 0.00399 0.0191 0.056 0.0754 0.126 0.165 0.000315 0.000412 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.055 0.00264 0.00216 0.0455 0.0613 0.0534 0.0555 0.000133 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 302 43651.935 0.005 0.00378 0.0355 0.111 0.0545 0.0734 0.18 0.225 0.000449 0.000563 +! Validation 302 43651.935 0.005 0.00382 0.0198 0.0961 0.0541 0.0737 0.129 0.168 0.000322 0.000419 +Wall time: 43651.9356849147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0969 0.00394 0.0182 0.0554 0.0749 0.13 0.161 0.000326 0.000402 + 303 200 0.627 0.00376 0.552 0.0552 0.0732 0.881 0.887 0.0022 0.00222 + 303 235 0.0883 0.00415 0.00524 0.0577 0.0769 0.0742 0.0864 0.000186 0.000216 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.164 0.00395 0.0848 0.0562 0.075 0.341 0.348 0.000853 0.000869 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 303 43796.233 0.005 0.00424 0.0569 0.142 0.0572 0.0778 0.206 0.285 0.000515 0.000713 +! Validation 303 43796.233 0.005 0.00505 0.0616 0.162 0.0629 0.0848 0.243 0.296 0.000608 0.00074 +Wall time: 43796.2336566248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.115 0.00416 0.0315 0.0574 0.077 0.173 0.212 0.000432 0.000529 + 304 200 0.126 0.00344 0.0577 0.0518 0.07 0.257 0.287 0.000643 0.000717 + 304 235 0.12 0.00382 0.044 0.0546 0.0737 0.218 0.25 0.000544 0.000626 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.153 0.00298 0.0936 0.0485 0.0651 0.361 0.365 0.000903 0.000913 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 304 43940.660 0.005 0.00384 0.0437 0.12 0.055 0.074 0.199 0.249 0.000498 0.000623 +! Validation 304 43940.660 0.005 0.00405 0.0722 0.153 0.0558 0.0759 0.265 0.321 0.000663 0.000802 +Wall time: 43940.660334730055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.131 0.00372 0.0567 0.0538 0.0728 0.235 0.284 0.000587 0.00071 + 305 200 0.13 0.0038 0.054 0.0547 0.0736 0.242 0.277 0.000604 0.000694 + 305 235 0.0941 0.00382 0.0178 0.0547 0.0737 0.136 0.159 0.00034 0.000398 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0606 0.00288 0.00301 0.0474 0.064 0.0599 0.0655 0.00015 0.000164 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 305 44084.903 0.005 0.00387 0.0437 0.121 0.0552 0.0742 0.198 0.25 0.000494 0.000624 +! Validation 305 44084.903 0.005 0.00392 0.0227 0.101 0.055 0.0747 0.145 0.18 0.000362 0.000449 +Wall time: 44084.90300158085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.138 0.00506 0.0369 0.0642 0.0849 0.198 0.229 0.000494 0.000573 + 306 200 0.0949 0.00364 0.0222 0.0536 0.072 0.131 0.178 0.000327 0.000444 + 306 235 0.0918 0.00427 0.00635 0.0588 0.078 0.0756 0.0951 0.000189 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.152 0.00299 0.0924 0.0487 0.0653 0.355 0.363 0.000888 0.000907 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 306 44229.496 0.005 0.0038 0.0463 0.122 0.0548 0.0736 0.2 0.257 0.000499 0.000643 +! Validation 306 44229.496 0.005 0.00405 0.095 0.176 0.0562 0.076 0.334 0.368 0.000835 0.00092 +Wall time: 44229.496123578865 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.133 0.00335 0.0659 0.0516 0.0691 0.271 0.306 0.000677 0.000766 + 307 200 0.0836 0.0035 0.0135 0.0523 0.0707 0.111 0.139 0.000278 0.000347 + 307 235 0.13 0.00382 0.0536 0.0549 0.0738 0.234 0.276 0.000586 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.182 0.00325 0.117 0.0512 0.0681 0.401 0.408 0.001 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 307 44373.847 0.005 0.00372 0.0388 0.113 0.0542 0.0728 0.188 0.235 0.00047 0.000587 +! Validation 307 44373.847 0.005 0.00434 0.0619 0.149 0.0585 0.0786 0.252 0.297 0.00063 0.000743 +Wall time: 44373.8473118688 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.124 0.00435 0.0368 0.0592 0.0787 0.203 0.229 0.000506 0.000572 + 308 200 0.0883 0.00372 0.0139 0.0543 0.0728 0.119 0.141 0.000297 0.000352 + 308 235 0.132 0.00372 0.0575 0.0543 0.0728 0.271 0.286 0.000678 0.000715 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.0635 0.00295 0.00456 0.0485 0.0648 0.0677 0.0806 0.000169 0.000202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 308 44518.200 0.005 0.00389 0.054 0.132 0.0554 0.0744 0.22 0.277 0.00055 0.000693 +! Validation 308 44518.200 0.005 0.0039 0.018 0.0959 0.0552 0.0745 0.127 0.16 0.000319 0.0004 +Wall time: 44518.20062752487 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.0932 0.00363 0.0207 0.0527 0.0719 0.132 0.172 0.000331 0.00043 + 309 200 0.0946 0.00367 0.0212 0.0536 0.0723 0.136 0.174 0.000339 0.000434 + 309 235 0.106 0.00427 0.0204 0.0586 0.078 0.145 0.17 0.000363 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.285 0.00279 0.229 0.0478 0.0631 0.57 0.571 0.00142 0.00143 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 309 44662.648 0.005 0.00357 0.0343 0.106 0.0529 0.0713 0.175 0.221 0.000438 0.000553 +! Validation 309 44662.648 0.005 0.00391 0.197 0.275 0.0552 0.0746 0.45 0.53 0.00113 0.00133 +Wall time: 44662.648325842805 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0956 0.0034 0.0276 0.0516 0.0696 0.163 0.198 0.000408 0.000495 + 310 200 0.0836 0.00348 0.014 0.0521 0.0704 0.117 0.141 0.000292 0.000353 + 310 235 0.136 0.00365 0.0634 0.0536 0.0721 0.254 0.3 0.000635 0.000751 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0761 0.00347 0.00667 0.0524 0.0703 0.0854 0.0975 0.000213 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 310 44806.923 0.005 0.00466 0.0643 0.158 0.0599 0.0815 0.233 0.303 0.000583 0.000756 +! Validation 310 44806.923 0.005 0.00451 0.0658 0.156 0.0592 0.0801 0.246 0.306 0.000615 0.000766 +Wall time: 44806.92299886001 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.0878 0.00338 0.0203 0.0517 0.0693 0.142 0.17 0.000355 0.000425 + 311 200 0.228 0.00336 0.161 0.0515 0.0692 0.445 0.478 0.00111 0.0012 + 311 235 0.12 0.00414 0.0369 0.0581 0.0768 0.197 0.229 0.000492 0.000573 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.251 0.00421 0.167 0.0581 0.0774 0.479 0.487 0.0012 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 311 44951.711 0.005 0.00393 0.0474 0.126 0.0556 0.0748 0.203 0.26 0.000507 0.00065 +! Validation 311 44951.711 0.005 0.00521 0.141 0.245 0.0641 0.0861 0.414 0.448 0.00104 0.00112 +Wall time: 44951.71164222015 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0875 0.00386 0.0103 0.0551 0.0741 0.0974 0.121 0.000243 0.000303 + 312 200 0.0743 0.00338 0.00674 0.05 0.0694 0.0858 0.098 0.000214 0.000245 + 312 235 0.0893 0.00346 0.0202 0.052 0.0702 0.116 0.17 0.000291 0.000424 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0506 0.00236 0.0034 0.0431 0.058 0.0463 0.0696 0.000116 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 312 45096.008 0.005 0.00379 0.0446 0.121 0.0547 0.0735 0.196 0.252 0.000489 0.000631 +! Validation 312 45096.008 0.005 0.00339 0.0166 0.0844 0.0509 0.0695 0.12 0.154 0.000299 0.000384 +Wall time: 45096.008604889736 +! Best model 312 0.084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.112 0.00345 0.0426 0.0515 0.0701 0.213 0.246 0.000532 0.000616 + 313 200 0.116 0.00354 0.0452 0.0532 0.071 0.23 0.254 0.000576 0.000634 + 313 235 0.0678 0.00301 0.0076 0.0485 0.0655 0.0844 0.104 0.000211 0.00026 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0553 0.00254 0.00463 0.0445 0.0601 0.0612 0.0813 0.000153 0.000203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 313 45240.414 0.005 0.00367 0.0395 0.113 0.0538 0.0723 0.187 0.237 0.000467 0.000593 +! Validation 313 45240.414 0.005 0.00355 0.0218 0.0929 0.0521 0.0711 0.146 0.176 0.000366 0.000441 +Wall time: 45240.41398080671 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.174 0.00461 0.0818 0.0622 0.0811 0.306 0.341 0.000765 0.000853 + 314 200 0.0918 0.00338 0.0242 0.0517 0.0694 0.153 0.186 0.000382 0.000465 + 314 235 0.0938 0.00326 0.0285 0.0506 0.0682 0.179 0.202 0.000448 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.0833 0.00269 0.0295 0.0469 0.0619 0.192 0.205 0.000479 0.000512 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 314 45384.717 0.005 0.00402 0.044 0.124 0.0562 0.0757 0.197 0.25 0.000492 0.000626 +! Validation 314 45384.717 0.005 0.00379 0.0253 0.101 0.0547 0.0734 0.152 0.19 0.000379 0.000475 +Wall time: 45384.717338610906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0989 0.00372 0.0244 0.0539 0.0728 0.156 0.186 0.00039 0.000466 + 315 200 0.13 0.00332 0.0639 0.0513 0.0687 0.241 0.302 0.000602 0.000754 + 315 235 0.0992 0.00362 0.0268 0.053 0.0718 0.16 0.195 0.000399 0.000488 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0627 0.00258 0.0112 0.0448 0.0606 0.112 0.126 0.00028 0.000315 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 315 45529.020 0.005 0.00339 0.0285 0.0963 0.0516 0.0695 0.161 0.202 0.000403 0.000504 +! Validation 315 45529.020 0.005 0.0035 0.0182 0.0881 0.0515 0.0706 0.13 0.161 0.000326 0.000403 +Wall time: 45529.02038090583 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0714 0.00307 0.01 0.0489 0.0661 0.0944 0.12 0.000236 0.000299 + 316 200 0.0813 0.00342 0.0128 0.0527 0.0698 0.103 0.135 0.000258 0.000338 + 316 235 0.427 0.00525 0.322 0.0637 0.0864 0.632 0.678 0.00158 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0694 0.0033 0.00333 0.0508 0.0686 0.0614 0.0689 0.000153 0.000172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 316 45673.314 0.005 0.00349 0.045 0.115 0.0524 0.0704 0.198 0.251 0.000494 0.000627 +! Validation 316 45673.314 0.005 0.0042 0.135 0.219 0.0572 0.0774 0.347 0.439 0.000867 0.0011 +Wall time: 45673.314870398026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0932 0.00386 0.0159 0.055 0.0742 0.128 0.151 0.00032 0.000376 + 317 200 0.091 0.00341 0.0228 0.0519 0.0697 0.159 0.18 0.000397 0.00045 + 317 235 0.112 0.00353 0.0414 0.053 0.0709 0.187 0.243 0.000467 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.152 0.00264 0.0997 0.0455 0.0613 0.374 0.377 0.000936 0.000942 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 317 45817.581 0.005 0.00395 0.0519 0.131 0.056 0.075 0.217 0.272 0.000543 0.00068 +! Validation 317 45817.581 0.005 0.00376 0.116 0.191 0.0536 0.0732 0.372 0.406 0.000929 0.00101 +Wall time: 45817.58143334184 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0714 0.0029 0.0133 0.0477 0.0643 0.115 0.138 0.000287 0.000344 + 318 200 0.069 0.00303 0.00848 0.0489 0.0657 0.081 0.11 0.000202 0.000275 + 318 235 0.0732 0.00344 0.00439 0.0519 0.07 0.0656 0.0791 0.000164 0.000198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0923 0.00233 0.0457 0.0426 0.0576 0.253 0.255 0.000633 0.000638 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 318 45961.933 0.005 0.00347 0.0399 0.109 0.0522 0.0703 0.183 0.239 0.000457 0.000597 +! Validation 318 45961.933 0.005 0.00337 0.0684 0.136 0.0508 0.0693 0.262 0.312 0.000656 0.00078 +Wall time: 45961.93343917187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.206 0.00417 0.123 0.0581 0.0771 0.347 0.418 0.000868 0.00105 + 319 200 0.0705 0.00279 0.0148 0.0468 0.063 0.113 0.145 0.000283 0.000363 + 319 235 0.0805 0.00282 0.0241 0.0473 0.0634 0.132 0.185 0.000329 0.000463 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.155 0.00249 0.105 0.0446 0.0596 0.385 0.387 0.000962 0.000967 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 319 46106.290 0.005 0.00389 0.0458 0.123 0.0551 0.0744 0.195 0.255 0.000486 0.000639 +! Validation 319 46106.290 0.005 0.00351 0.0743 0.144 0.0518 0.0707 0.294 0.325 0.000736 0.000813 +Wall time: 46106.290235770866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0648 0.00279 0.00891 0.0469 0.0631 0.0924 0.113 0.000231 0.000282 + 320 200 0.0873 0.00343 0.0186 0.0515 0.0699 0.131 0.163 0.000328 0.000407 + 320 235 0.133 0.00394 0.054 0.0549 0.0749 0.242 0.277 0.000604 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0561 0.00257 0.0046 0.0455 0.0605 0.0637 0.081 0.000159 0.000202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 320 46250.633 0.005 0.00326 0.0359 0.101 0.0506 0.0681 0.176 0.226 0.000441 0.000565 +! Validation 320 46250.633 0.005 0.00367 0.0235 0.0969 0.0533 0.0723 0.148 0.183 0.000369 0.000457 +Wall time: 46250.633740590885 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0723 0.00327 0.00703 0.0507 0.0682 0.0788 0.1 0.000197 0.00025 + 321 200 0.129 0.00434 0.0427 0.0589 0.0786 0.209 0.247 0.000522 0.000616 + 321 235 0.154 0.00362 0.0814 0.0538 0.0718 0.323 0.34 0.000807 0.000851 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0744 0.00254 0.0235 0.0448 0.0602 0.169 0.183 0.000422 0.000458 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 321 46394.849 0.005 0.0034 0.0414 0.109 0.0518 0.0696 0.19 0.243 0.000476 0.000607 +! Validation 321 46394.849 0.005 0.00347 0.0308 0.1 0.0516 0.0703 0.181 0.209 0.000452 0.000523 +Wall time: 46394.849090314936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0997 0.00279 0.0439 0.0467 0.063 0.203 0.25 0.000506 0.000625 + 322 200 0.0833 0.00289 0.0254 0.0474 0.0642 0.166 0.19 0.000414 0.000476 + 322 235 0.0693 0.00312 0.00693 0.0503 0.0666 0.0757 0.0994 0.000189 0.000248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0577 0.00241 0.00949 0.0435 0.0586 0.0942 0.116 0.000235 0.000291 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 322 46539.139 0.005 0.00345 0.0419 0.111 0.0521 0.0702 0.193 0.245 0.000482 0.000611 +! Validation 322 46539.139 0.005 0.00326 0.0336 0.0988 0.05 0.0681 0.186 0.219 0.000466 0.000547 +Wall time: 46539.13911927212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.155 0.00401 0.0746 0.0563 0.0756 0.28 0.326 0.0007 0.000815 + 323 200 0.077 0.00302 0.0166 0.0487 0.0656 0.123 0.154 0.000308 0.000385 + 323 235 0.0708 0.00306 0.00961 0.0482 0.066 0.106 0.117 0.000265 0.000293 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.0656 0.00218 0.022 0.041 0.0557 0.173 0.177 0.000434 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 323 46683.424 0.005 0.00326 0.0354 0.101 0.0506 0.0681 0.177 0.225 0.000442 0.000562 +! Validation 323 46683.424 0.005 0.00306 0.0345 0.0957 0.0482 0.066 0.195 0.222 0.000487 0.000554 +Wall time: 46683.424531791825 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.101 0.00291 0.0434 0.0477 0.0643 0.225 0.249 0.000563 0.000622 + 324 200 0.0701 0.00298 0.0104 0.0487 0.0652 0.094 0.122 0.000235 0.000305 + 324 235 0.0745 0.00316 0.0113 0.0487 0.0671 0.101 0.127 0.000253 0.000318 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0464 0.00219 0.00253 0.0416 0.0559 0.0572 0.0601 0.000143 0.00015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 324 46827.662 0.005 0.00314 0.0292 0.092 0.0497 0.0669 0.161 0.204 0.000403 0.00051 +! Validation 324 46827.662 0.005 0.00316 0.0118 0.075 0.0492 0.0671 0.106 0.129 0.000266 0.000324 +Wall time: 46827.66264419211 +! Best model 324 0.075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 18 0.786 2.29 0.787 1.06 1.45 1.81 0.00364 0.00451 + 325 200 8.08 0.31 1.88 0.511 0.665 1.26 1.64 0.00315 0.00409 + 325 235 4.3 0.193 0.45 0.401 0.524 0.662 0.801 0.00166 0.002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 3.91 0.193 0.0488 0.397 0.524 0.193 0.264 0.000483 0.000659 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 325 46971.935 0.005 0.497 7.75 17.7 0.545 0.842 1.9 3.33 0.00475 0.00832 +! Validation 325 46971.935 0.005 0.196 0.842 4.77 0.402 0.529 0.82 1.1 0.00205 0.00274 +Wall time: 46971.93541740673 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 2.72 0.105 0.615 0.295 0.387 0.755 0.936 0.00189 0.00234 + 326 200 3.71 0.083 2.05 0.261 0.344 1.58 1.71 0.00394 0.00427 + 326 235 3.42 0.0843 1.74 0.263 0.347 1.48 1.57 0.0037 0.00393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 2.06 0.0752 0.556 0.249 0.327 0.868 0.89 0.00217 0.00223 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 326 47116.181 0.005 0.11 0.872 3.07 0.299 0.396 0.889 1.11 0.00222 0.00278 +! Validation 326 47116.181 0.005 0.0827 0.562 2.22 0.26 0.343 0.775 0.894 0.00194 0.00224 +Wall time: 47116.18173437612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 1.76 0.0706 0.345 0.24 0.317 0.568 0.701 0.00142 0.00175 + 327 200 1.67 0.0603 0.46 0.221 0.293 0.688 0.81 0.00172 0.00202 + 327 235 1.62 0.0592 0.437 0.218 0.29 0.675 0.789 0.00169 0.00197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 1.18 0.0518 0.144 0.206 0.272 0.418 0.453 0.00104 0.00113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 327 47260.536 0.005 0.0692 0.619 2 0.237 0.314 0.759 0.939 0.0019 0.00235 +! Validation 327 47260.536 0.005 0.0588 0.256 1.43 0.218 0.289 0.496 0.604 0.00124 0.00151 +Wall time: 47260.53642125381 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 2.94 0.0511 1.91 0.203 0.27 1.61 1.65 0.00402 0.00413 + 328 200 1.03 0.0451 0.13 0.19 0.254 0.349 0.43 0.000873 0.00108 + 328 235 0.911 0.0413 0.0853 0.183 0.243 0.296 0.349 0.000739 0.000871 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.721 0.0348 0.0257 0.168 0.223 0.174 0.191 0.000436 0.000478 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 328 47405.009 0.005 0.0497 0.491 1.49 0.2 0.266 0.669 0.837 0.00167 0.00209 +! Validation 328 47405.009 0.005 0.0406 0.138 0.951 0.181 0.24 0.342 0.444 0.000855 0.00111 +Wall time: 47405.00969097298 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.928 0.0358 0.213 0.169 0.226 0.483 0.55 0.00121 0.00138 + 329 200 0.81 0.0347 0.116 0.165 0.222 0.328 0.407 0.000819 0.00102 + 329 235 0.922 0.033 0.263 0.162 0.217 0.536 0.612 0.00134 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.568 0.0273 0.023 0.147 0.197 0.14 0.181 0.000349 0.000452 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 329 47549.281 0.005 0.0366 0.515 1.25 0.171 0.228 0.691 0.857 0.00173 0.00214 +! Validation 329 47549.281 0.005 0.0324 0.198 0.846 0.159 0.215 0.426 0.531 0.00106 0.00133 +Wall time: 47549.28186490573 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.762 0.0284 0.193 0.149 0.201 0.442 0.525 0.0011 0.00131 + 330 200 0.584 0.0246 0.0927 0.139 0.187 0.315 0.363 0.000786 0.000908 + 330 235 0.553 0.0238 0.0769 0.136 0.184 0.274 0.331 0.000684 0.000828 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.401 0.0197 0.00788 0.125 0.167 0.0968 0.106 0.000242 0.000265 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 330 47693.799 0.005 0.0278 0.303 0.86 0.148 0.199 0.524 0.658 0.00131 0.00165 +! Validation 330 47693.799 0.005 0.0241 0.0831 0.566 0.137 0.185 0.268 0.344 0.000669 0.00086 +Wall time: 47693.79940938577 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.917 0.0218 0.48 0.131 0.176 0.79 0.827 0.00197 0.00207 + 331 200 0.624 0.0195 0.234 0.124 0.167 0.512 0.577 0.00128 0.00144 + 331 235 0.809 0.0193 0.424 0.123 0.166 0.713 0.777 0.00178 0.00194 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.85 0.0161 0.527 0.113 0.152 0.863 0.866 0.00216 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 331 47838.303 0.005 0.0217 0.245 0.678 0.13 0.176 0.473 0.59 0.00118 0.00147 +! Validation 331 47838.303 0.005 0.0204 0.781 1.19 0.126 0.17 1.01 1.06 0.00253 0.00264 +Wall time: 47838.303404938895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.524 0.0177 0.171 0.118 0.159 0.425 0.494 0.00106 0.00123 + 332 200 0.478 0.0189 0.0997 0.122 0.164 0.289 0.377 0.000723 0.000942 + 332 235 0.503 0.0182 0.138 0.119 0.161 0.396 0.444 0.00099 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.33 0.0144 0.0433 0.107 0.143 0.243 0.248 0.000608 0.000621 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 332 47982.634 0.005 0.0191 0.309 0.691 0.122 0.165 0.544 0.664 0.00136 0.00166 +! Validation 332 47982.634 0.005 0.0185 0.207 0.576 0.119 0.162 0.404 0.543 0.00101 0.00136 +Wall time: 47982.634427054785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 1.03 0.0161 0.712 0.112 0.151 0.973 1.01 0.00243 0.00252 + 333 200 0.674 0.0153 0.368 0.11 0.148 0.684 0.724 0.00171 0.00181 + 333 235 0.471 0.0157 0.157 0.111 0.15 0.418 0.472 0.00105 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.257 0.0119 0.0179 0.0975 0.13 0.151 0.159 0.000376 0.000399 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 333 48126.888 0.005 0.0162 0.183 0.507 0.113 0.152 0.414 0.511 0.00104 0.00128 +! Validation 333 48126.888 0.005 0.0157 0.0836 0.397 0.11 0.149 0.277 0.345 0.000691 0.000863 +Wall time: 48126.88822539803 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.446 0.0145 0.156 0.107 0.144 0.37 0.471 0.000924 0.00118 + 334 200 0.325 0.0132 0.0614 0.102 0.137 0.237 0.296 0.000594 0.000739 + 334 235 0.347 0.0129 0.0893 0.101 0.136 0.313 0.357 0.000784 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.405 0.00982 0.209 0.0887 0.118 0.543 0.545 0.00136 0.00136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 334 48271.185 0.005 0.0141 0.16 0.443 0.105 0.142 0.382 0.478 0.000955 0.00119 +! Validation 334 48271.185 0.005 0.0134 0.403 0.67 0.102 0.138 0.713 0.758 0.00178 0.00189 +Wall time: 48271.18573449971 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.28 0.0123 0.0336 0.0987 0.133 0.184 0.219 0.000461 0.000547 + 335 200 0.385 0.0135 0.114 0.101 0.139 0.34 0.403 0.000849 0.00101 + 335 235 0.281 0.0111 0.0595 0.0944 0.126 0.225 0.291 0.000562 0.000728 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.238 0.0088 0.0623 0.0841 0.112 0.293 0.298 0.000732 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 335 48415.538 0.005 0.0125 0.175 0.425 0.0993 0.133 0.395 0.5 0.000987 0.00125 +! Validation 335 48415.538 0.005 0.0121 0.0728 0.315 0.0969 0.131 0.255 0.322 0.000638 0.000805 +Wall time: 48415.5384796178 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.268 0.0103 0.0624 0.0905 0.121 0.249 0.298 0.000623 0.000745 + 336 200 0.527 0.0112 0.303 0.0944 0.126 0.62 0.657 0.00155 0.00164 + 336 235 0.236 0.0104 0.029 0.0914 0.121 0.178 0.203 0.000445 0.000508 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.375 0.00806 0.214 0.0805 0.107 0.55 0.552 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 336 48559.922 0.005 0.0111 0.167 0.388 0.0938 0.126 0.387 0.488 0.000969 0.00122 +! Validation 336 48559.922 0.005 0.0111 0.121 0.343 0.0927 0.126 0.348 0.415 0.000871 0.00104 +Wall time: 48559.92272238713 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.258 0.01 0.0576 0.0897 0.119 0.218 0.286 0.000545 0.000716 + 337 200 0.232 0.0093 0.0465 0.0868 0.115 0.231 0.257 0.000577 0.000644 + 337 235 0.229 0.00921 0.0445 0.0861 0.115 0.187 0.252 0.000467 0.00063 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.397 0.00685 0.26 0.0746 0.0988 0.604 0.609 0.00151 0.00152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 337 48704.182 0.005 0.00981 0.138 0.334 0.0886 0.118 0.357 0.443 0.000894 0.00111 +! Validation 337 48704.182 0.005 0.00968 0.148 0.342 0.0868 0.117 0.399 0.459 0.000996 0.00115 +Wall time: 48704.181996054016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.212 0.00868 0.0379 0.0838 0.111 0.193 0.232 0.000482 0.000581 + 338 200 0.211 0.00761 0.0589 0.0789 0.104 0.239 0.29 0.000598 0.000724 + 338 235 0.391 0.00765 0.238 0.0789 0.104 0.54 0.582 0.00135 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.451 0.00619 0.327 0.0715 0.0939 0.675 0.682 0.00169 0.00171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 338 48848.435 0.005 0.00858 0.114 0.285 0.0831 0.111 0.322 0.402 0.000806 0.001 +! Validation 338 48848.435 0.005 0.00869 0.43 0.604 0.0825 0.111 0.752 0.782 0.00188 0.00196 +Wall time: 48848.43514318904 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.233 0.00753 0.0826 0.0786 0.104 0.302 0.343 0.000755 0.000858 + 339 200 0.212 0.00763 0.0594 0.079 0.104 0.238 0.291 0.000596 0.000727 + 339 235 0.19 0.00728 0.0446 0.0767 0.102 0.235 0.252 0.000588 0.00063 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.116 0.00535 0.00905 0.0664 0.0873 0.0807 0.114 0.000202 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 339 48992.652 0.005 0.00778 0.131 0.286 0.0793 0.105 0.339 0.432 0.000848 0.00108 +! Validation 339 48992.652 0.005 0.00771 0.0707 0.225 0.0772 0.105 0.264 0.317 0.000661 0.000794 +Wall time: 48992.65220658202 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.266 0.0067 0.132 0.074 0.0977 0.41 0.434 0.00103 0.00108 + 340 200 0.171 0.00633 0.0442 0.0719 0.0949 0.222 0.251 0.000555 0.000627 + 340 235 0.207 0.00572 0.0921 0.0685 0.0903 0.331 0.362 0.000828 0.000906 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.136 0.00466 0.0424 0.0621 0.0815 0.228 0.246 0.00057 0.000614 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 340 49136.957 0.005 0.00677 0.0811 0.217 0.074 0.0982 0.27 0.34 0.000674 0.000849 +! Validation 340 49136.957 0.005 0.00687 0.0626 0.2 0.0726 0.0989 0.254 0.299 0.000635 0.000747 +Wall time: 49136.95788027812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.224 0.00595 0.105 0.0698 0.0921 0.356 0.387 0.00089 0.000969 + 341 200 0.158 0.0056 0.0459 0.0673 0.0893 0.223 0.256 0.000558 0.000639 + 341 235 0.257 0.00615 0.134 0.0702 0.0936 0.415 0.437 0.00104 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.094 0.00439 0.00629 0.0602 0.0791 0.0818 0.0946 0.000205 0.000237 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 341 49281.527 0.005 0.0061 0.0914 0.213 0.0702 0.0932 0.292 0.361 0.000729 0.000901 +! Validation 341 49281.527 0.005 0.00639 0.0298 0.158 0.0699 0.0954 0.165 0.206 0.000413 0.000515 +Wall time: 49281.52785938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.235 0.00602 0.115 0.0697 0.0926 0.365 0.405 0.000912 0.00101 + 342 200 0.212 0.00526 0.107 0.065 0.0866 0.346 0.39 0.000865 0.000974 + 342 235 0.183 0.00572 0.069 0.0681 0.0903 0.281 0.314 0.000701 0.000784 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0911 0.00407 0.00963 0.0579 0.0762 0.0797 0.117 0.000199 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 342 49425.843 0.005 0.0057 0.0857 0.2 0.0676 0.0901 0.278 0.349 0.000695 0.000874 +! Validation 342 49425.843 0.005 0.006 0.0733 0.193 0.0675 0.0924 0.277 0.323 0.000693 0.000808 +Wall time: 49425.843414989766 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.311 0.00505 0.21 0.0636 0.0848 0.531 0.547 0.00133 0.00137 + 343 200 0.184 0.0055 0.074 0.0659 0.0885 0.282 0.325 0.000706 0.000812 + 343 235 0.138 0.00467 0.0444 0.0616 0.0816 0.217 0.251 0.000542 0.000628 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.106 0.00368 0.0321 0.0552 0.0724 0.199 0.214 0.000497 0.000534 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 343 49570.152 0.005 0.00523 0.0672 0.172 0.0646 0.0863 0.25 0.31 0.000624 0.000774 +! Validation 343 49570.152 0.005 0.00552 0.0804 0.191 0.0644 0.0887 0.292 0.339 0.00073 0.000846 +Wall time: 49570.15275108302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.117 0.00484 0.0201 0.0626 0.0831 0.142 0.169 0.000356 0.000423 + 344 200 0.178 0.00488 0.0805 0.0626 0.0834 0.306 0.339 0.000764 0.000847 + 344 235 0.709 0.00549 0.599 0.0658 0.0884 0.911 0.923 0.00228 0.00231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.708 0.00356 0.637 0.0536 0.0712 0.95 0.952 0.00237 0.00238 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 344 49714.777 0.005 0.00491 0.0583 0.156 0.0625 0.0836 0.221 0.285 0.000553 0.000712 +! Validation 344 49714.777 0.005 0.00525 0.647 0.752 0.0628 0.0865 0.943 0.96 0.00236 0.0024 +Wall time: 49714.77705103578 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.122 0.00489 0.0241 0.0622 0.0834 0.147 0.185 0.000368 0.000463 + 345 200 0.138 0.00478 0.0429 0.0621 0.0825 0.193 0.247 0.000483 0.000618 + 345 235 0.235 0.00477 0.14 0.0614 0.0824 0.398 0.447 0.000996 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.306 0.00335 0.239 0.0525 0.069 0.578 0.583 0.00144 0.00146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 345 49859.017 0.005 0.0048 0.0818 0.178 0.0618 0.0827 0.265 0.341 0.000663 0.000853 +! Validation 345 49859.017 0.005 0.00521 0.205 0.309 0.0624 0.0861 0.493 0.54 0.00123 0.00135 +Wall time: 49859.01729992498 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.148 0.00485 0.0509 0.0614 0.0831 0.244 0.269 0.00061 0.000673 + 346 200 0.168 0.00429 0.0819 0.0587 0.0782 0.313 0.342 0.000783 0.000854 + 346 235 0.271 0.0049 0.173 0.0631 0.0835 0.461 0.496 0.00115 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.094 0.00333 0.0274 0.0522 0.0689 0.181 0.198 0.000452 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 346 50003.207 0.005 0.00455 0.0649 0.156 0.0601 0.0805 0.243 0.303 0.000607 0.000759 +! Validation 346 50003.207 0.005 0.00507 0.0545 0.156 0.0616 0.085 0.222 0.279 0.000555 0.000696 +Wall time: 50003.20755921677 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.112 0.0047 0.0182 0.0607 0.0818 0.133 0.161 0.000333 0.000403 + 347 200 0.0907 0.0039 0.0126 0.056 0.0746 0.114 0.134 0.000284 0.000335 + 347 235 0.233 0.00439 0.146 0.0588 0.0791 0.44 0.455 0.0011 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.167 0.00328 0.102 0.0512 0.0684 0.375 0.381 0.000937 0.000951 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 347 50147.528 0.005 0.00434 0.0446 0.131 0.0586 0.0786 0.199 0.251 0.000497 0.000629 +! Validation 347 50147.528 0.005 0.00476 0.0813 0.176 0.0596 0.0823 0.302 0.34 0.000755 0.000851 +Wall time: 50147.52809587773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.118 0.00404 0.0367 0.0567 0.0759 0.21 0.229 0.000525 0.000572 + 348 200 0.112 0.00438 0.0247 0.0584 0.0789 0.153 0.188 0.000382 0.000469 + 348 235 0.131 0.00371 0.0565 0.0543 0.0727 0.266 0.284 0.000664 0.000709 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.0649 0.00292 0.00655 0.0485 0.0644 0.0668 0.0966 0.000167 0.000242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 348 50292.100 0.005 0.0042 0.0485 0.132 0.0576 0.0773 0.211 0.263 0.000529 0.000657 +! Validation 348 50292.100 0.005 0.0044 0.0309 0.119 0.0572 0.0791 0.174 0.21 0.000436 0.000525 +Wall time: 50292.10062184697 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.122 0.00392 0.0439 0.056 0.0747 0.217 0.25 0.000543 0.000625 + 349 200 0.108 0.00381 0.0321 0.0549 0.0737 0.177 0.214 0.000444 0.000535 + 349 235 0.1 0.00419 0.0166 0.0572 0.0772 0.115 0.154 0.000288 0.000384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0789 0.00285 0.022 0.0479 0.0637 0.163 0.177 0.000408 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 349 50436.311 0.005 0.00399 0.0349 0.115 0.0561 0.0754 0.181 0.223 0.000452 0.000558 +! Validation 349 50436.311 0.005 0.00425 0.0335 0.118 0.0563 0.0778 0.188 0.218 0.00047 0.000546 +Wall time: 50436.31093759509 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.106 0.00423 0.0213 0.0575 0.0776 0.135 0.174 0.000337 0.000436 + 350 200 0.138 0.00406 0.0564 0.0568 0.0761 0.265 0.283 0.000664 0.000709 + 350 235 0.458 0.0036 0.386 0.0537 0.0716 0.731 0.741 0.00183 0.00185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.214 0.00338 0.147 0.0522 0.0694 0.453 0.457 0.00113 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 350 50580.562 0.005 0.00396 0.0575 0.137 0.0559 0.0751 0.226 0.284 0.000564 0.00071 +! Validation 350 50580.562 0.005 0.00468 0.0942 0.188 0.0596 0.0816 0.323 0.366 0.000808 0.000916 +Wall time: 50580.56228892272 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.109 0.00405 0.028 0.0565 0.076 0.169 0.2 0.000423 0.000499 + 351 200 0.0801 0.00357 0.00876 0.0532 0.0713 0.0889 0.112 0.000222 0.000279 + 351 235 0.0994 0.00326 0.0342 0.0515 0.0682 0.194 0.221 0.000485 0.000551 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0819 0.00322 0.0175 0.0511 0.0677 0.137 0.158 0.000343 0.000395 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 351 50724.984 0.005 0.00394 0.0542 0.133 0.0557 0.0749 0.21 0.278 0.000525 0.000695 +! Validation 351 50724.984 0.005 0.00449 0.0182 0.108 0.0584 0.08 0.125 0.161 0.000313 0.000402 +Wall time: 50724.98471375974 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.191 0.00361 0.119 0.0534 0.0717 0.373 0.411 0.000932 0.00103 + 352 200 0.126 0.00371 0.0516 0.0542 0.0727 0.238 0.271 0.000595 0.000678 + 352 235 0.11 0.00373 0.0349 0.0552 0.0729 0.201 0.223 0.000503 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0703 0.00277 0.0148 0.0472 0.0628 0.131 0.145 0.000326 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 352 50869.284 0.005 0.00382 0.0492 0.126 0.0549 0.0737 0.215 0.265 0.000538 0.000662 +! Validation 352 50869.284 0.005 0.00409 0.0433 0.125 0.0555 0.0763 0.217 0.248 0.000543 0.000621 +Wall time: 50869.28458225308 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.0966 0.00379 0.0207 0.0546 0.0735 0.132 0.172 0.00033 0.00043 + 353 200 0.252 0.00391 0.174 0.0556 0.0746 0.475 0.497 0.00119 0.00124 + 353 235 0.103 0.00347 0.0334 0.0528 0.0703 0.19 0.218 0.000476 0.000545 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.128 0.00293 0.0689 0.0489 0.0646 0.309 0.313 0.000772 0.000783 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 353 51013.592 0.005 0.00366 0.0385 0.112 0.0536 0.0722 0.187 0.234 0.000467 0.000585 +! Validation 353 51013.592 0.005 0.00422 0.0798 0.164 0.0566 0.0775 0.295 0.337 0.000739 0.000843 +Wall time: 51013.592049222905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0775 0.00336 0.0103 0.0514 0.0692 0.101 0.121 0.000252 0.000304 + 354 200 0.0795 0.00333 0.0129 0.0516 0.0689 0.114 0.135 0.000285 0.000338 + 354 235 0.0845 0.00376 0.00936 0.0544 0.0731 0.0985 0.115 0.000246 0.000289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0559 0.00254 0.00503 0.0449 0.0602 0.0609 0.0847 0.000152 0.000212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 354 51157.948 0.005 0.00363 0.0488 0.121 0.0535 0.0719 0.208 0.264 0.000521 0.00066 +! Validation 354 51157.948 0.005 0.00373 0.0181 0.0927 0.0528 0.0729 0.131 0.161 0.000327 0.000402 +Wall time: 51157.948845264036 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0889 0.00344 0.0201 0.0521 0.07 0.146 0.169 0.000364 0.000423 + 355 200 0.127 0.00387 0.0501 0.0553 0.0742 0.234 0.267 0.000584 0.000668 + 355 235 0.0811 0.00341 0.013 0.0519 0.0697 0.12 0.136 0.000299 0.00034 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0823 0.00257 0.0308 0.0452 0.0606 0.196 0.21 0.00049 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 355 51302.254 0.005 0.0036 0.0559 0.128 0.0532 0.0716 0.218 0.282 0.000545 0.000706 +! Validation 355 51302.254 0.005 0.00378 0.0239 0.0996 0.0533 0.0734 0.146 0.185 0.000365 0.000461 +Wall time: 51302.25466386415 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.245 0.00405 0.164 0.0566 0.0759 0.463 0.483 0.00116 0.00121 + 356 200 0.0921 0.00366 0.0189 0.0533 0.0722 0.142 0.164 0.000355 0.00041 + 356 235 0.0646 0.00288 0.007 0.0479 0.0641 0.0812 0.0999 0.000203 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0513 0.00243 0.00275 0.0438 0.0588 0.0601 0.0626 0.00015 0.000157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 356 51446.940 0.005 0.00345 0.0332 0.102 0.0521 0.0701 0.175 0.218 0.000437 0.000544 +! Validation 356 51446.940 0.005 0.00361 0.015 0.0872 0.052 0.0717 0.113 0.146 0.000282 0.000365 +Wall time: 51446.94088063296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0904 0.00339 0.0227 0.0517 0.0695 0.155 0.18 0.000387 0.00045 + 357 200 0.0859 0.00359 0.014 0.0535 0.0715 0.111 0.141 0.000277 0.000354 + 357 235 0.0733 0.00319 0.00952 0.0499 0.0674 0.0994 0.116 0.000248 0.000291 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0602 0.00247 0.0108 0.044 0.0593 0.116 0.124 0.000291 0.000311 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 357 51591.236 0.005 0.00347 0.0459 0.115 0.0523 0.0703 0.208 0.256 0.000519 0.00064 +! Validation 357 51591.236 0.005 0.00353 0.0169 0.0876 0.0516 0.071 0.125 0.155 0.000312 0.000388 +Wall time: 51591.236106197815 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.0759 0.00313 0.0132 0.0499 0.0668 0.111 0.137 0.000279 0.000343 + 358 200 0.117 0.00334 0.05 0.051 0.069 0.246 0.267 0.000616 0.000667 + 358 235 0.107 0.00333 0.04 0.0513 0.0689 0.193 0.239 0.000483 0.000597 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.106 0.00264 0.053 0.0459 0.0614 0.269 0.275 0.000672 0.000687 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 358 51735.527 0.005 0.00335 0.0317 0.0986 0.0513 0.0691 0.168 0.212 0.00042 0.000531 +! Validation 358 51735.527 0.005 0.00379 0.069 0.145 0.0535 0.0734 0.276 0.314 0.000691 0.000784 +Wall time: 51735.527532803826 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0881 0.0038 0.0121 0.0551 0.0736 0.109 0.131 0.000272 0.000329 + 359 200 0.114 0.00329 0.0483 0.0491 0.0685 0.234 0.262 0.000585 0.000656 + 359 235 0.087 0.00333 0.0204 0.051 0.0689 0.153 0.171 0.000384 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0694 0.00242 0.021 0.0435 0.0587 0.166 0.173 0.000415 0.000432 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 359 51879.786 0.005 0.00335 0.0402 0.107 0.0514 0.0691 0.19 0.239 0.000475 0.000599 +! Validation 359 51879.786 0.005 0.00343 0.0239 0.0924 0.051 0.0699 0.146 0.185 0.000366 0.000461 +Wall time: 51879.786420555785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0697 0.00295 0.0107 0.048 0.0648 0.102 0.124 0.000255 0.000309 + 360 200 0.0908 0.00306 0.0296 0.0491 0.066 0.168 0.205 0.000421 0.000514 + 360 235 0.086 0.00315 0.023 0.0503 0.067 0.143 0.181 0.000357 0.000452 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.154 0.00282 0.0971 0.0475 0.0634 0.368 0.372 0.000919 0.00093 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 360 52024.061 0.005 0.00319 0.0297 0.0934 0.05 0.0674 0.16 0.206 0.000401 0.000514 +! Validation 360 52024.061 0.005 0.00388 0.087 0.165 0.0548 0.0743 0.298 0.352 0.000746 0.00088 +Wall time: 52024.06092946371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0711 0.00311 0.00883 0.0497 0.0666 0.0878 0.112 0.00022 0.00028 + 361 200 0.119 0.00357 0.0478 0.0535 0.0713 0.211 0.261 0.000529 0.000653 + 361 235 0.0872 0.00336 0.0201 0.0512 0.0692 0.132 0.169 0.00033 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0566 0.00245 0.00755 0.044 0.0591 0.0835 0.104 0.000209 0.000259 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 361 52168.298 0.005 0.00352 0.0427 0.113 0.0526 0.0708 0.192 0.247 0.000481 0.000617 +! Validation 361 52168.298 0.005 0.00352 0.02 0.0903 0.0515 0.0708 0.127 0.169 0.000317 0.000422 +Wall time: 52168.298276355024 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.076 0.00334 0.00908 0.051 0.069 0.0933 0.114 0.000233 0.000284 + 362 200 0.147 0.00339 0.0796 0.0516 0.0695 0.281 0.337 0.000701 0.000842 + 362 235 0.092 0.00272 0.0376 0.0468 0.0623 0.217 0.231 0.000543 0.000579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0506 0.00249 0.000745 0.0451 0.0596 0.0309 0.0326 7.73e-05 8.15e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 362 52312.520 0.005 0.00337 0.0438 0.111 0.0514 0.0693 0.198 0.25 0.000496 0.000625 +! Validation 362 52312.520 0.005 0.00347 0.032 0.102 0.052 0.0703 0.175 0.214 0.000437 0.000534 +Wall time: 52312.51990353782 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.134 0.00304 0.0736 0.0487 0.0658 0.296 0.324 0.00074 0.000809 + 363 200 0.0728 0.00326 0.00752 0.0504 0.0682 0.0849 0.104 0.000212 0.000259 + 363 235 0.135 0.00385 0.0583 0.0546 0.0741 0.276 0.288 0.000689 0.00072 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.137 0.00266 0.0833 0.0459 0.0616 0.34 0.344 0.00085 0.000861 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 363 52456.702 0.005 0.00336 0.0355 0.103 0.0514 0.0691 0.18 0.225 0.00045 0.000562 +! Validation 363 52456.702 0.005 0.00369 0.0995 0.173 0.0531 0.0725 0.35 0.376 0.000875 0.000941 +Wall time: 52456.70284181088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.331 0.00361 0.259 0.0535 0.0717 0.595 0.608 0.00149 0.00152 + 364 200 0.103 0.00355 0.0319 0.0522 0.0711 0.177 0.213 0.000442 0.000533 + 364 235 0.131 0.0036 0.0585 0.0529 0.0717 0.27 0.289 0.000676 0.000722 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.191 0.00334 0.124 0.0521 0.0689 0.411 0.421 0.00103 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 364 52600.890 0.005 0.0038 0.0517 0.128 0.0545 0.0736 0.213 0.271 0.000533 0.000678 +! Validation 364 52600.890 0.005 0.00435 0.0985 0.185 0.0582 0.0787 0.348 0.375 0.000871 0.000936 +Wall time: 52600.8907357431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.146 0.00399 0.0662 0.0567 0.0754 0.281 0.307 0.000702 0.000768 + 365 200 0.0962 0.00314 0.0334 0.0495 0.0669 0.197 0.218 0.000494 0.000546 + 365 235 0.0708 0.00309 0.00894 0.0492 0.0664 0.0859 0.113 0.000215 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.0776 0.00258 0.0261 0.0451 0.0606 0.185 0.193 0.000462 0.000482 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 365 52745.317 0.005 0.0034 0.0422 0.11 0.0518 0.0696 0.195 0.246 0.000489 0.000614 +! Validation 365 52745.317 0.005 0.00345 0.0221 0.0912 0.0513 0.0701 0.14 0.178 0.00035 0.000444 +Wall time: 52745.31702690991 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.137 0.00344 0.0681 0.0522 0.0701 0.265 0.312 0.000662 0.000779 + 366 200 0.0911 0.00293 0.0326 0.0477 0.0646 0.189 0.215 0.000474 0.000538 + 366 235 0.095 0.00302 0.0347 0.0488 0.0655 0.186 0.222 0.000465 0.000556 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.115 0.00246 0.0658 0.0441 0.0592 0.304 0.306 0.000759 0.000766 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 366 52889.612 0.005 0.00336 0.0398 0.107 0.0513 0.0692 0.183 0.238 0.000457 0.000596 +! Validation 366 52889.612 0.005 0.00345 0.0589 0.128 0.0514 0.0701 0.265 0.29 0.000662 0.000724 +Wall time: 52889.61193024414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.0864 0.003 0.0264 0.0479 0.0654 0.163 0.194 0.000407 0.000484 + 367 200 0.156 0.00343 0.0878 0.0524 0.0699 0.316 0.354 0.000789 0.000884 + 367 235 0.0819 0.00313 0.0194 0.0492 0.0667 0.141 0.166 0.000353 0.000416 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.116 0.00365 0.0433 0.0541 0.0721 0.233 0.248 0.000583 0.000621 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 367 53033.872 0.005 0.00311 0.035 0.0972 0.0494 0.0666 0.172 0.223 0.00043 0.000558 +! Validation 367 53033.872 0.005 0.00448 0.0554 0.145 0.0594 0.0799 0.252 0.281 0.000631 0.000702 +Wall time: 53033.872610896826 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0732 0.00297 0.0138 0.0479 0.0651 0.112 0.14 0.00028 0.000351 + 368 200 0.092 0.00396 0.0127 0.0566 0.0751 0.111 0.135 0.000276 0.000337 + 368 235 0.0839 0.00305 0.023 0.0489 0.0659 0.16 0.181 0.0004 0.000452 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0547 0.00261 0.00239 0.0457 0.061 0.0412 0.0583 0.000103 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 368 53178.144 0.005 0.00367 0.0395 0.113 0.0533 0.0723 0.185 0.237 0.000461 0.000593 +! Validation 368 53178.144 0.005 0.0035 0.047 0.117 0.052 0.0706 0.205 0.259 0.000513 0.000647 +Wall time: 53178.144412810914 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0695 0.00297 0.0101 0.0484 0.0651 0.0986 0.12 0.000246 0.000299 + 369 200 0.141 0.00354 0.0699 0.0531 0.071 0.285 0.316 0.000712 0.000789 + 369 235 0.0562 0.00266 0.00304 0.0454 0.0615 0.0567 0.0658 0.000142 0.000164 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0609 0.00258 0.00926 0.0455 0.0606 0.0907 0.115 0.000227 0.000287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 369 53322.666 0.005 0.00304 0.0344 0.0952 0.0489 0.0658 0.173 0.222 0.000432 0.000554 +! Validation 369 53322.666 0.005 0.00342 0.0282 0.0965 0.0516 0.0698 0.164 0.201 0.000409 0.000501 +Wall time: 53322.66624727612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0747 0.00302 0.0143 0.0488 0.0656 0.121 0.143 0.000302 0.000357 + 370 200 0.0838 0.00311 0.0217 0.0493 0.0665 0.144 0.176 0.000359 0.000439 + 370 235 0.0831 0.00285 0.0261 0.0469 0.0637 0.163 0.193 0.000407 0.000482 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0519 0.00242 0.00351 0.0436 0.0587 0.0576 0.0707 0.000144 0.000177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 370 53467.062 0.005 0.00325 0.0369 0.102 0.0507 0.0681 0.183 0.229 0.000458 0.000574 +! Validation 370 53467.062 0.005 0.00332 0.019 0.0854 0.0504 0.0688 0.133 0.165 0.000332 0.000412 +Wall time: 53467.062609605026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0839 0.00306 0.0228 0.0489 0.066 0.164 0.18 0.00041 0.00045 + 371 200 0.166 0.00332 0.0997 0.0519 0.0688 0.352 0.377 0.000879 0.000942 + 371 235 0.0866 0.0029 0.0287 0.0477 0.0642 0.161 0.202 0.000403 0.000506 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0609 0.00246 0.0117 0.0444 0.0592 0.117 0.129 0.000292 0.000322 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 371 53611.422 0.005 0.00307 0.0333 0.0946 0.049 0.0661 0.171 0.218 0.000427 0.000544 +! Validation 371 53611.422 0.005 0.00353 0.0275 0.098 0.0524 0.0709 0.162 0.198 0.000405 0.000495 +Wall time: 53611.42257788079 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0947 0.00365 0.0216 0.0538 0.0721 0.15 0.176 0.000375 0.000439 + 372 200 0.0584 0.00266 0.00513 0.0458 0.0616 0.0646 0.0854 0.000161 0.000214 + 372 235 0.111 0.00349 0.0411 0.053 0.0706 0.225 0.242 0.000562 0.000605 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0709 0.00321 0.00675 0.0516 0.0676 0.0911 0.0981 0.000228 0.000245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 372 53755.704 0.005 0.00303 0.0301 0.0907 0.0487 0.0657 0.161 0.207 0.000403 0.000518 +! Validation 372 53755.704 0.005 0.00401 0.0246 0.105 0.057 0.0756 0.153 0.187 0.000382 0.000468 +Wall time: 53755.7043740931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.0872 0.00349 0.0175 0.0526 0.0705 0.124 0.158 0.000311 0.000395 + 373 200 0.0813 0.00276 0.0261 0.0464 0.0627 0.171 0.193 0.000427 0.000482 + 373 235 0.0618 0.00287 0.00433 0.0474 0.064 0.0628 0.0785 0.000157 0.000196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.0527 0.00225 0.00759 0.042 0.0567 0.0972 0.104 0.000243 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 373 53900.030 0.005 0.00326 0.0412 0.106 0.0507 0.0681 0.191 0.242 0.000478 0.000606 +! Validation 373 53900.030 0.005 0.00315 0.0196 0.0825 0.049 0.0669 0.13 0.167 0.000326 0.000417 +Wall time: 53900.030432587955 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.105 0.00283 0.0481 0.0477 0.0635 0.236 0.262 0.00059 0.000654 + 374 200 0.0678 0.00277 0.0123 0.0466 0.0628 0.109 0.133 0.000273 0.000331 + 374 235 0.118 0.00375 0.0426 0.0546 0.0731 0.191 0.246 0.000478 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.079 0.00338 0.0114 0.0518 0.0694 0.102 0.128 0.000255 0.000319 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 374 54044.303 0.005 0.00302 0.037 0.0974 0.0487 0.0656 0.182 0.23 0.000454 0.000574 +! Validation 374 54044.303 0.005 0.00407 0.0188 0.1 0.0561 0.0761 0.132 0.163 0.000331 0.000409 +Wall time: 54044.30310849706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.125 0.00307 0.0633 0.049 0.0662 0.271 0.3 0.000676 0.000751 + 375 200 0.241 0.00292 0.182 0.0479 0.0645 0.491 0.51 0.00123 0.00127 + 375 235 0.106 0.00337 0.0389 0.0525 0.0692 0.217 0.235 0.000543 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.0865 0.00236 0.0392 0.044 0.058 0.225 0.236 0.000563 0.000591 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 375 54188.575 0.005 0.00311 0.0438 0.106 0.0495 0.0665 0.197 0.25 0.000493 0.000625 +! Validation 375 54188.575 0.005 0.00328 0.0249 0.0906 0.0513 0.0684 0.156 0.189 0.000389 0.000471 +Wall time: 54188.57570354873 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.142 0.00334 0.075 0.0515 0.069 0.291 0.327 0.000729 0.000817 + 376 200 0.0749 0.00273 0.0203 0.0461 0.0623 0.143 0.17 0.000358 0.000425 + 376 235 0.0678 0.00266 0.0146 0.0449 0.0615 0.123 0.144 0.000307 0.000361 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.0543 0.00191 0.0161 0.0383 0.0521 0.145 0.152 0.000362 0.000379 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 376 54332.999 0.005 0.00318 0.0376 0.101 0.0498 0.0674 0.176 0.232 0.00044 0.000579 +! Validation 376 54332.999 0.005 0.00275 0.0176 0.0726 0.0456 0.0625 0.128 0.159 0.000319 0.000396 +Wall time: 54332.99949862296 +! Best model 376 0.073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0637 0.00273 0.00912 0.0462 0.0624 0.0986 0.114 0.000247 0.000285 + 377 200 0.0657 0.0028 0.00976 0.0472 0.0631 0.095 0.118 0.000237 0.000295 + 377 235 0.11 0.00291 0.0513 0.0475 0.0644 0.234 0.27 0.000585 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0507 0.00215 0.0077 0.0417 0.0554 0.0984 0.105 0.000246 0.000262 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 377 54477.443 0.005 0.00288 0.0279 0.0855 0.0475 0.064 0.155 0.199 0.000388 0.000498 +! Validation 377 54477.443 0.005 0.00299 0.0162 0.0761 0.0483 0.0653 0.122 0.152 0.000304 0.00038 +Wall time: 54477.44320592191 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.114 0.00297 0.0551 0.0479 0.065 0.252 0.28 0.00063 0.000701 + 378 200 0.141 0.00315 0.0784 0.0506 0.067 0.319 0.334 0.000797 0.000836 + 378 235 0.0926 0.00365 0.0195 0.0547 0.0721 0.139 0.167 0.000348 0.000417 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0693 0.00251 0.0191 0.0451 0.0598 0.142 0.165 0.000354 0.000413 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 378 54622.022 0.005 0.00354 0.0511 0.122 0.0521 0.071 0.208 0.27 0.00052 0.000675 +! Validation 378 54622.022 0.005 0.00334 0.0233 0.0901 0.0511 0.069 0.142 0.182 0.000356 0.000455 +Wall time: 54622.0225266451 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0789 0.00263 0.0262 0.0459 0.0612 0.169 0.193 0.000422 0.000483 + 379 200 0.104 0.00367 0.0308 0.0547 0.0723 0.185 0.209 0.000462 0.000524 + 379 235 0.0685 0.00295 0.00944 0.0479 0.0648 0.0884 0.116 0.000221 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0604 0.00196 0.0212 0.039 0.0529 0.171 0.174 0.000429 0.000434 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 379 54766.357 0.005 0.0038 0.0535 0.129 0.0534 0.0736 0.209 0.276 0.000524 0.000691 +! Validation 379 54766.357 0.005 0.00282 0.0277 0.0841 0.0463 0.0634 0.163 0.199 0.000408 0.000497 +Wall time: 54766.35733029386 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0583 0.00253 0.00768 0.0445 0.06 0.0823 0.105 0.000206 0.000261 + 380 200 0.0627 0.0025 0.0127 0.0443 0.0597 0.113 0.134 0.000283 0.000336 + 380 235 0.0805 0.00241 0.0323 0.0433 0.0586 0.193 0.214 0.000483 0.000536 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.043 0.00197 0.00349 0.0394 0.053 0.0531 0.0705 0.000133 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 380 54910.621 0.005 0.00264 0.0158 0.0686 0.0453 0.0613 0.12 0.15 0.0003 0.000375 +! Validation 380 54910.621 0.005 0.00281 0.0123 0.0684 0.0464 0.0632 0.106 0.132 0.000264 0.000331 +Wall time: 54910.621210649144 +! Best model 380 0.068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0612 0.00268 0.00748 0.0455 0.0618 0.0823 0.103 0.000206 0.000258 + 381 200 0.0624 0.00259 0.0106 0.0452 0.0608 0.106 0.123 0.000264 0.000307 + 381 235 0.072 0.00267 0.0186 0.0458 0.0616 0.135 0.163 0.000337 0.000407 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0596 0.00224 0.0148 0.0419 0.0564 0.129 0.145 0.000323 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 381 55054.903 0.005 0.00267 0.0225 0.076 0.0456 0.0617 0.141 0.179 0.000353 0.000448 +! Validation 381 55054.903 0.005 0.00297 0.0135 0.0729 0.0478 0.065 0.11 0.139 0.000276 0.000347 +Wall time: 55054.90339342179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.106 0.0027 0.0522 0.0459 0.062 0.248 0.273 0.00062 0.000682 + 382 200 0.0573 0.00248 0.00762 0.0436 0.0595 0.0912 0.104 0.000228 0.00026 + 382 235 0.156 0.00262 0.103 0.0452 0.0611 0.37 0.383 0.000924 0.000958 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.203 0.00451 0.113 0.0601 0.0802 0.394 0.401 0.000985 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 382 55199.149 0.005 0.00262 0.0222 0.0747 0.0452 0.0611 0.139 0.177 0.000346 0.000443 +! Validation 382 55199.149 0.005 0.00514 0.0795 0.182 0.0638 0.0856 0.284 0.336 0.000711 0.000841 +Wall time: 55199.14978515869 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0611 0.00265 0.0081 0.045 0.0615 0.084 0.107 0.00021 0.000268 + 383 200 0.0882 0.00301 0.0279 0.049 0.0655 0.178 0.199 0.000446 0.000498 + 383 235 0.079 0.00271 0.0247 0.046 0.0622 0.167 0.188 0.000416 0.000469 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0853 0.00227 0.0399 0.0421 0.0569 0.235 0.238 0.000587 0.000596 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 383 55343.457 0.005 0.00295 0.0327 0.0917 0.0481 0.0648 0.172 0.216 0.00043 0.00054 +! Validation 383 55343.457 0.005 0.00302 0.0436 0.104 0.0482 0.0656 0.216 0.249 0.00054 0.000623 +Wall time: 55343.457368700765 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0577 0.00255 0.00659 0.0444 0.0603 0.0816 0.0969 0.000204 0.000242 + 384 200 0.0699 0.00304 0.00901 0.0486 0.0659 0.0912 0.113 0.000228 0.000283 + 384 235 0.0637 0.00283 0.00704 0.0465 0.0635 0.084 0.1 0.00021 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0421 0.00183 0.00564 0.0379 0.051 0.0782 0.0896 0.000195 0.000224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 384 55487.757 0.005 0.0034 0.0478 0.116 0.0511 0.0696 0.199 0.261 0.000498 0.000653 +! Validation 384 55487.757 0.005 0.0027 0.0129 0.0669 0.0454 0.062 0.111 0.136 0.000277 0.000339 +Wall time: 55487.7578345621 +! Best model 384 0.067 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0572 0.00245 0.00816 0.0438 0.0591 0.0851 0.108 0.000213 0.00027 + 385 200 0.0585 0.00245 0.00958 0.0437 0.059 0.0949 0.117 0.000237 0.000292 + 385 235 0.0551 0.00249 0.00531 0.0433 0.0596 0.0762 0.087 0.000191 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0365 0.00178 0.000813 0.037 0.0504 0.0294 0.034 7.35e-05 8.51e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 385 55632.376 0.005 0.0026 0.0197 0.0716 0.045 0.0608 0.133 0.168 0.000332 0.000419 +! Validation 385 55632.376 0.005 0.00251 0.0167 0.0668 0.0435 0.0598 0.123 0.154 0.000308 0.000385 +Wall time: 55632.37656611297 +! Best model 385 0.067 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0558 0.00246 0.00656 0.0433 0.0592 0.0738 0.0967 0.000185 0.000242 + 386 200 0.0599 0.00257 0.00853 0.0445 0.0605 0.0928 0.11 0.000232 0.000276 + 386 235 0.239 0.00261 0.187 0.0453 0.061 0.51 0.516 0.00127 0.00129 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0861 0.00272 0.0316 0.0469 0.0623 0.201 0.212 0.000502 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 386 55776.680 0.005 0.00256 0.0211 0.0722 0.0446 0.0603 0.134 0.171 0.000336 0.000428 +! Validation 386 55776.680 0.005 0.00343 0.0561 0.125 0.0522 0.0699 0.238 0.283 0.000594 0.000707 +Wall time: 55776.68043512991 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.112 0.00275 0.0567 0.0466 0.0626 0.264 0.284 0.000661 0.00071 + 387 200 25 0.0657 23.7 0.21 0.306 5.79 5.81 0.0145 0.0145 + 387 235 25.6 0.998 5.69 0.884 1.19 2.47 2.85 0.00617 0.00712 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 28.6 0.997 8.69 0.886 1.19 3.46 3.52 0.00865 0.0088 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 387 55920.957 0.005 0.139 3.62 6.4 0.166 0.441 0.837 2.27 0.00209 0.00567 +! Validation 387 55920.957 0.005 0.973 4.21 23.7 0.875 1.18 2 2.45 0.00499 0.00612 +Wall time: 55920.95698466478 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 2.29 0.102 0.241 0.291 0.382 0.454 0.586 0.00114 0.00147 + 388 200 3.36 0.0713 1.94 0.242 0.319 1.56 1.66 0.00391 0.00415 + 388 235 1.39 0.0627 0.133 0.227 0.299 0.376 0.435 0.000939 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 1.16 0.0561 0.0362 0.214 0.283 0.183 0.227 0.000457 0.000568 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 388 56065.325 0.005 0.241 1.87 6.7 0.387 0.587 1.15 1.63 0.00286 0.00408 +! Validation 388 56065.325 0.005 0.0625 0.215 1.46 0.225 0.298 0.427 0.553 0.00107 0.00138 +Wall time: 56065.32546728803 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 1.02 0.0446 0.126 0.19 0.252 0.341 0.424 0.000851 0.00106 + 389 200 1.01 0.0408 0.199 0.181 0.241 0.438 0.532 0.00109 0.00133 + 389 235 0.979 0.037 0.239 0.173 0.23 0.549 0.584 0.00137 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.931 0.0338 0.255 0.164 0.219 0.594 0.603 0.00148 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 389 56209.603 0.005 0.0454 0.502 1.41 0.191 0.254 0.678 0.846 0.0017 0.00212 +! Validation 389 56209.603 0.005 0.038 0.469 1.23 0.174 0.233 0.726 0.818 0.00182 0.00204 +Wall time: 56209.60350876907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.595 0.0261 0.0722 0.145 0.193 0.256 0.321 0.000639 0.000802 + 390 200 0.462 0.0194 0.0747 0.126 0.166 0.247 0.326 0.000618 0.000815 + 390 235 0.619 0.0184 0.251 0.122 0.162 0.528 0.598 0.00132 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.354 0.0141 0.0725 0.107 0.142 0.31 0.321 0.000775 0.000803 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 390 56354.773 0.005 0.0253 0.3 0.806 0.142 0.19 0.525 0.654 0.00131 0.00163 +! Validation 390 56354.773 0.005 0.0177 0.103 0.456 0.119 0.159 0.317 0.384 0.000793 0.000959 +Wall time: 56354.77316964185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.532 0.0151 0.229 0.111 0.147 0.496 0.572 0.00124 0.00143 + 391 200 0.264 0.0114 0.0364 0.0965 0.127 0.196 0.228 0.000491 0.000569 + 391 235 0.286 0.0108 0.0691 0.0943 0.124 0.259 0.314 0.000647 0.000784 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.206 0.00863 0.0336 0.0838 0.111 0.216 0.219 0.000539 0.000547 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 391 56499.089 0.005 0.0143 0.236 0.521 0.108 0.143 0.449 0.58 0.00112 0.00145 +! Validation 391 56499.089 0.005 0.0116 0.0514 0.283 0.0963 0.128 0.213 0.271 0.000532 0.000677 +Wall time: 56499.08943786379 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.226 0.00947 0.0367 0.0878 0.116 0.187 0.229 0.000466 0.000571 + 392 200 0.222 0.00882 0.0455 0.0849 0.112 0.212 0.255 0.00053 0.000636 + 392 235 0.229 0.00869 0.0553 0.0841 0.111 0.247 0.281 0.000617 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.207 0.00676 0.0722 0.0738 0.0982 0.319 0.321 0.000798 0.000802 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 392 56643.388 0.005 0.00981 0.151 0.347 0.089 0.118 0.379 0.464 0.000947 0.00116 +! Validation 392 56643.388 0.005 0.00918 0.0636 0.247 0.0852 0.114 0.249 0.301 0.000622 0.000752 +Wall time: 56643.388032217044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.315 0.00895 0.136 0.0855 0.113 0.399 0.44 0.000997 0.0011 + 393 200 0.219 0.0078 0.0628 0.0784 0.105 0.271 0.299 0.000678 0.000748 + 393 235 0.181 0.00773 0.0262 0.0784 0.105 0.171 0.193 0.000428 0.000483 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.14 0.00575 0.0251 0.0678 0.0905 0.186 0.189 0.000466 0.000473 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 393 56787.683 0.005 0.00827 0.139 0.305 0.0814 0.109 0.36 0.446 0.000901 0.00111 +! Validation 393 56787.683 0.005 0.00801 0.0356 0.196 0.0791 0.107 0.182 0.225 0.000456 0.000563 +Wall time: 56787.683030657005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.309 0.00749 0.159 0.0773 0.103 0.43 0.476 0.00107 0.00119 + 394 200 0.283 0.00688 0.145 0.0737 0.099 0.432 0.455 0.00108 0.00114 + 394 235 0.2 0.00728 0.0541 0.0758 0.102 0.24 0.278 0.000601 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.162 0.00513 0.059 0.0636 0.0855 0.287 0.29 0.000718 0.000725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 394 56932.831 0.005 0.00706 0.0862 0.227 0.0749 0.1 0.285 0.351 0.000714 0.000877 +! Validation 394 56932.831 0.005 0.0071 0.158 0.3 0.0743 0.101 0.433 0.475 0.00108 0.00119 +Wall time: 56932.83131431183 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.167 0.00661 0.0345 0.0723 0.097 0.19 0.222 0.000475 0.000554 + 395 200 0.377 0.00635 0.25 0.0706 0.0951 0.584 0.597 0.00146 0.00149 + 395 235 0.172 0.00625 0.0471 0.0701 0.0944 0.209 0.259 0.000521 0.000648 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.132 0.00471 0.0373 0.0602 0.0819 0.226 0.23 0.000566 0.000576 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 395 57077.153 0.005 0.00633 0.0692 0.196 0.0708 0.095 0.248 0.314 0.000621 0.000785 +! Validation 395 57077.153 0.005 0.00651 0.0324 0.163 0.0707 0.0963 0.172 0.215 0.000431 0.000537 +Wall time: 57077.15383552993 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.139 0.0058 0.0226 0.0667 0.0909 0.144 0.18 0.000361 0.000449 + 396 200 0.193 0.00603 0.072 0.0686 0.0927 0.255 0.32 0.000637 0.000801 + 396 235 0.214 0.00567 0.1 0.0668 0.0899 0.352 0.378 0.00088 0.000945 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0987 0.00442 0.0104 0.0582 0.0793 0.109 0.122 0.000273 0.000304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 396 57221.514 0.005 0.00597 0.116 0.235 0.0687 0.0922 0.322 0.407 0.000805 0.00102 +! Validation 396 57221.514 0.005 0.00615 0.0283 0.151 0.0687 0.0936 0.157 0.201 0.000393 0.000502 +Wall time: 57221.514528735075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.16 0.00574 0.0454 0.0668 0.0904 0.216 0.254 0.000539 0.000636 + 397 200 0.13 0.0051 0.0283 0.0635 0.0852 0.167 0.201 0.000417 0.000502 + 397 235 0.187 0.0049 0.0894 0.0628 0.0836 0.321 0.357 0.000802 0.000892 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.109 0.00399 0.0296 0.0553 0.0754 0.199 0.205 0.000496 0.000513 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 397 57365.786 0.005 0.00549 0.0622 0.172 0.0657 0.0884 0.234 0.297 0.000585 0.000744 +! Validation 397 57365.786 0.005 0.00553 0.095 0.206 0.0651 0.0888 0.314 0.368 0.000784 0.00092 +Wall time: 57365.78660275694 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.127 0.00515 0.0236 0.0635 0.0857 0.151 0.183 0.000378 0.000458 + 398 200 0.149 0.00454 0.0584 0.0598 0.0804 0.248 0.289 0.000621 0.000721 + 398 235 0.113 0.00487 0.0159 0.061 0.0833 0.119 0.151 0.000298 0.000376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0802 0.00376 0.00513 0.0535 0.0731 0.066 0.0855 0.000165 0.000214 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 398 57511.251 0.005 0.00509 0.0643 0.166 0.0632 0.0851 0.242 0.303 0.000606 0.000758 +! Validation 398 57511.251 0.005 0.00523 0.023 0.128 0.0632 0.0863 0.144 0.181 0.000359 0.000453 +Wall time: 57511.25102906069 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.238 0.00454 0.148 0.0594 0.0804 0.429 0.458 0.00107 0.00115 + 399 200 0.129 0.00464 0.0362 0.0604 0.0813 0.185 0.227 0.000462 0.000568 + 399 235 0.106 0.00436 0.019 0.0586 0.0788 0.143 0.164 0.000357 0.000411 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0913 0.0036 0.0194 0.0525 0.0716 0.149 0.166 0.000374 0.000416 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 399 57655.516 0.005 0.00484 0.08 0.177 0.0616 0.0831 0.266 0.338 0.000666 0.000845 +! Validation 399 57655.516 0.005 0.00501 0.0256 0.126 0.0619 0.0845 0.153 0.191 0.000382 0.000477 +Wall time: 57655.516035357956 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.136 0.00487 0.039 0.0622 0.0833 0.198 0.236 0.000496 0.00059 + 400 200 0.186 0.00484 0.0888 0.0616 0.083 0.306 0.356 0.000765 0.000889 + 400 235 0.13 0.00514 0.0272 0.0627 0.0856 0.173 0.197 0.000433 0.000492 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.103 0.00341 0.0347 0.0509 0.0697 0.211 0.222 0.000529 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 400 57799.777 0.005 0.00467 0.0764 0.17 0.0605 0.0816 0.265 0.33 0.000664 0.000825 +! Validation 400 57799.777 0.005 0.00473 0.0357 0.13 0.06 0.0821 0.187 0.225 0.000469 0.000563 +Wall time: 57799.77776975185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.103 0.00429 0.0176 0.0576 0.0782 0.126 0.158 0.000315 0.000396 + 401 200 0.155 0.00442 0.0666 0.0588 0.0794 0.26 0.308 0.000651 0.00077 + 401 235 0.327 0.00456 0.236 0.0596 0.0806 0.545 0.58 0.00136 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.142 0.00335 0.0753 0.0503 0.0691 0.32 0.327 0.0008 0.000819 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 401 57944.103 0.005 0.00433 0.0502 0.137 0.0581 0.0785 0.209 0.266 0.000523 0.000665 +! Validation 401 57944.103 0.005 0.00466 0.0622 0.155 0.0597 0.0814 0.261 0.298 0.000652 0.000744 +Wall time: 57944.103223152924 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0957 0.00382 0.0192 0.055 0.0738 0.124 0.166 0.00031 0.000414 + 402 200 0.117 0.00439 0.0294 0.0585 0.0791 0.17 0.205 0.000426 0.000511 + 402 235 0.123 0.00386 0.0454 0.0547 0.0741 0.206 0.254 0.000515 0.000636 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.101 0.00314 0.038 0.0486 0.0669 0.223 0.233 0.000558 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 402 58088.506 0.005 0.00417 0.0526 0.136 0.057 0.0771 0.223 0.274 0.000556 0.000685 +! Validation 402 58088.506 0.005 0.0043 0.0375 0.123 0.0572 0.0782 0.191 0.231 0.000478 0.000578 +Wall time: 58088.50655132998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0943 0.00402 0.0139 0.0558 0.0757 0.112 0.141 0.00028 0.000352 + 403 200 0.141 0.00394 0.0618 0.0559 0.0749 0.266 0.297 0.000666 0.000742 + 403 235 0.115 0.00423 0.03 0.0568 0.0776 0.188 0.207 0.00047 0.000517 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0687 0.00312 0.00622 0.0486 0.0667 0.0712 0.0942 0.000178 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 403 58232.776 0.005 0.00409 0.0812 0.163 0.0565 0.0763 0.26 0.34 0.00065 0.000851 +! Validation 403 58232.776 0.005 0.00437 0.0309 0.118 0.0577 0.0789 0.175 0.21 0.000438 0.000525 +Wall time: 58232.77642524103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0835 0.00365 0.0104 0.0536 0.0721 0.0934 0.122 0.000233 0.000304 + 404 200 0.13 0.00391 0.0514 0.0552 0.0746 0.245 0.271 0.000614 0.000676 + 404 235 0.0852 0.00372 0.0107 0.0542 0.0728 0.104 0.123 0.000259 0.000309 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0712 0.00292 0.0128 0.047 0.0645 0.111 0.135 0.000279 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 404 58377.071 0.005 0.00389 0.0403 0.118 0.055 0.0744 0.192 0.24 0.000479 0.0006 +! Validation 404 58377.071 0.005 0.004 0.0295 0.11 0.0552 0.0755 0.17 0.205 0.000425 0.000512 +Wall time: 58377.07160774106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0837 0.00371 0.00949 0.0533 0.0727 0.0893 0.116 0.000223 0.000291 + 405 200 0.116 0.00391 0.0381 0.0554 0.0747 0.209 0.233 0.000523 0.000583 + 405 235 0.169 0.0035 0.0989 0.0526 0.0706 0.358 0.375 0.000894 0.000939 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0807 0.00278 0.0251 0.0457 0.0629 0.175 0.189 0.000439 0.000473 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 405 58521.360 0.005 0.0038 0.0547 0.131 0.0544 0.0736 0.215 0.279 0.000536 0.000697 +! Validation 405 58521.360 0.005 0.00387 0.0637 0.141 0.0542 0.0743 0.266 0.301 0.000665 0.000753 +Wall time: 58521.36050089775 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.158 0.00369 0.0836 0.0538 0.0725 0.332 0.345 0.000831 0.000863 + 406 200 0.115 0.00325 0.0499 0.0506 0.0681 0.233 0.267 0.000582 0.000667 + 406 235 0.0835 0.00366 0.0102 0.0535 0.0722 0.106 0.121 0.000264 0.000301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.245 0.0029 0.187 0.0467 0.0643 0.512 0.515 0.00128 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 406 58665.694 0.005 0.00365 0.0458 0.119 0.0532 0.0721 0.204 0.256 0.00051 0.000639 +! Validation 406 58665.694 0.005 0.00386 0.114 0.191 0.0542 0.0741 0.37 0.403 0.000924 0.00101 +Wall time: 58665.69401976792 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.152 0.0037 0.0781 0.0535 0.0726 0.322 0.334 0.000806 0.000834 + 407 200 0.0898 0.00372 0.0154 0.0536 0.0728 0.122 0.148 0.000306 0.00037 + 407 235 0.114 0.00412 0.0319 0.0561 0.0766 0.195 0.213 0.000488 0.000533 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0842 0.00271 0.03 0.0455 0.0621 0.191 0.207 0.000477 0.000517 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 407 58809.994 0.005 0.0035 0.0352 0.105 0.0521 0.0706 0.181 0.224 0.000452 0.00056 +! Validation 407 58809.994 0.005 0.00367 0.0388 0.112 0.053 0.0723 0.192 0.235 0.00048 0.000588 +Wall time: 58809.99487568578 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0938 0.00349 0.024 0.0523 0.0705 0.149 0.185 0.000373 0.000462 + 408 200 0.141 0.00414 0.0581 0.0574 0.0768 0.258 0.288 0.000645 0.000719 + 408 235 0.0778 0.00326 0.0126 0.0506 0.0682 0.0975 0.134 0.000244 0.000335 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0641 0.00275 0.0092 0.0459 0.0626 0.093 0.115 0.000233 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 408 58954.367 0.005 0.00351 0.0507 0.121 0.0523 0.0707 0.216 0.269 0.00054 0.000673 +! Validation 408 58954.367 0.005 0.0037 0.0129 0.087 0.0532 0.0726 0.109 0.136 0.000273 0.000339 +Wall time: 58954.36777911894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0916 0.00353 0.021 0.0523 0.0709 0.135 0.173 0.000337 0.000433 + 409 200 0.0768 0.00325 0.0118 0.0503 0.0681 0.101 0.129 0.000253 0.000324 + 409 235 0.184 0.00353 0.113 0.0526 0.0709 0.388 0.402 0.000971 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0744 0.00251 0.0241 0.0438 0.0598 0.18 0.185 0.000449 0.000463 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 409 59098.648 0.005 0.00336 0.0399 0.107 0.0511 0.0692 0.189 0.238 0.000472 0.000594 +! Validation 409 59098.648 0.005 0.0035 0.0389 0.109 0.0517 0.0706 0.2 0.235 0.0005 0.000588 +Wall time: 59098.64880765695 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0836 0.00342 0.0152 0.0516 0.0698 0.129 0.147 0.000322 0.000368 + 410 200 0.114 0.00312 0.0518 0.0493 0.0666 0.257 0.272 0.000643 0.000679 + 410 235 0.0686 0.00311 0.00636 0.0492 0.0666 0.0713 0.0952 0.000178 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.057 0.00252 0.00661 0.0435 0.0599 0.0792 0.0971 0.000198 0.000243 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 410 59243.476 0.005 0.0034 0.0536 0.122 0.0514 0.0696 0.216 0.277 0.00054 0.000691 +! Validation 410 59243.476 0.005 0.00339 0.0182 0.0861 0.0508 0.0695 0.13 0.161 0.000325 0.000402 +Wall time: 59243.47638083901 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0964 0.00331 0.0302 0.0507 0.0686 0.176 0.208 0.000439 0.000519 + 411 200 0.0973 0.00305 0.0364 0.0485 0.0659 0.204 0.228 0.00051 0.000569 + 411 235 0.117 0.00352 0.0465 0.052 0.0708 0.241 0.257 0.000603 0.000644 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.116 0.00306 0.0549 0.0486 0.066 0.26 0.28 0.000651 0.000699 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 411 59387.812 0.005 0.00328 0.0434 0.109 0.0505 0.0683 0.199 0.249 0.000497 0.000621 +! Validation 411 59387.812 0.005 0.00394 0.0869 0.166 0.0553 0.0749 0.288 0.352 0.000719 0.00088 +Wall time: 59387.81281979103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0714 0.00317 0.008 0.0496 0.0672 0.0893 0.107 0.000223 0.000267 + 412 200 0.289 0.00342 0.221 0.0516 0.0698 0.524 0.561 0.00131 0.0014 + 412 235 0.127 0.00317 0.064 0.0498 0.0672 0.292 0.302 0.000731 0.000755 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0559 0.00251 0.00578 0.0436 0.0597 0.0693 0.0907 0.000173 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 412 59532.068 0.005 0.00328 0.0468 0.112 0.0505 0.0683 0.204 0.258 0.00051 0.000645 +! Validation 412 59532.068 0.005 0.00333 0.0341 0.101 0.0506 0.0689 0.185 0.22 0.000461 0.000551 +Wall time: 59532.06840939773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0764 0.00331 0.0101 0.051 0.0687 0.0937 0.12 0.000234 0.0003 + 413 200 0.156 0.0029 0.0983 0.0473 0.0643 0.361 0.374 0.000902 0.000936 + 413 235 0.0731 0.00344 0.0042 0.0526 0.07 0.0571 0.0774 0.000143 0.000193 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0885 0.00306 0.0273 0.0491 0.066 0.189 0.197 0.000472 0.000493 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 413 59676.323 0.005 0.00323 0.0489 0.113 0.0501 0.0678 0.21 0.264 0.000525 0.000661 +! Validation 413 59676.323 0.005 0.00384 0.0351 0.112 0.0552 0.074 0.18 0.224 0.000451 0.000559 +Wall time: 59676.32316221902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0618 0.00282 0.00543 0.0468 0.0634 0.0641 0.0879 0.00016 0.00022 + 414 200 0.107 0.00353 0.036 0.0531 0.0709 0.206 0.227 0.000514 0.000566 + 414 235 0.0718 0.00299 0.0121 0.0486 0.0652 0.11 0.131 0.000274 0.000328 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0621 0.00249 0.0123 0.0436 0.0596 0.123 0.132 0.000308 0.000331 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 414 59820.654 0.005 0.00312 0.0304 0.0929 0.0493 0.0667 0.158 0.208 0.000395 0.000521 +! Validation 414 59820.654 0.005 0.00335 0.044 0.111 0.0508 0.0691 0.2 0.25 0.000499 0.000626 +Wall time: 59820.65451474488 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0619 0.00269 0.00808 0.0458 0.0619 0.0769 0.107 0.000192 0.000268 + 415 200 0.186 0.00424 0.102 0.0577 0.0777 0.312 0.381 0.00078 0.000951 + 415 235 0.0935 0.00333 0.0268 0.0509 0.0689 0.176 0.196 0.00044 0.000489 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0634 0.0023 0.0174 0.0421 0.0573 0.149 0.157 0.000372 0.000393 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 415 59965.025 0.005 0.00318 0.0367 0.1 0.0497 0.0674 0.177 0.229 0.000441 0.000572 +! Validation 415 59965.025 0.005 0.00316 0.023 0.0863 0.0492 0.0671 0.148 0.181 0.00037 0.000453 +Wall time: 59965.02565917978 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0884 0.0027 0.0343 0.0456 0.0621 0.196 0.221 0.00049 0.000553 + 416 200 0.253 0.00784 0.0965 0.0823 0.106 0.303 0.371 0.000757 0.000927 + 416 235 0.0974 0.00348 0.0279 0.0525 0.0704 0.165 0.199 0.000412 0.000498 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0888 0.00256 0.0376 0.0444 0.0604 0.226 0.231 0.000566 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 416 60109.358 0.005 0.00358 0.0528 0.124 0.0519 0.0714 0.193 0.275 0.000483 0.000686 +! Validation 416 60109.358 0.005 0.00351 0.0458 0.116 0.052 0.0707 0.223 0.256 0.000557 0.000639 +Wall time: 60109.35817405488 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0996 0.00271 0.0453 0.0456 0.0622 0.245 0.254 0.000613 0.000635 + 417 200 0.0793 0.00327 0.0139 0.0507 0.0683 0.121 0.141 0.000304 0.000352 + 417 235 0.0777 0.00295 0.0188 0.0472 0.0648 0.138 0.164 0.000344 0.000409 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.121 0.00216 0.0774 0.0405 0.0555 0.326 0.332 0.000814 0.00083 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 417 60253.612 0.005 0.00295 0.0248 0.0838 0.0478 0.0648 0.147 0.188 0.000367 0.00047 +! Validation 417 60253.612 0.005 0.00296 0.0937 0.153 0.0474 0.0649 0.344 0.365 0.000859 0.000913 +Wall time: 60253.61224100785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.068 0.00298 0.00839 0.0478 0.0651 0.082 0.109 0.000205 0.000273 + 418 200 0.0728 0.00261 0.0206 0.0453 0.061 0.155 0.171 0.000387 0.000428 + 418 235 0.0576 0.00254 0.00677 0.0446 0.0601 0.0818 0.0982 0.000204 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.0468 0.00214 0.00399 0.0402 0.0552 0.0549 0.0754 0.000137 0.000188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 418 60397.878 0.005 0.00301 0.0304 0.0906 0.0483 0.0655 0.163 0.208 0.000408 0.000521 +! Validation 418 60397.878 0.005 0.0029 0.0181 0.076 0.047 0.0642 0.127 0.161 0.000317 0.000402 +Wall time: 60397.87869363185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0636 0.0029 0.00557 0.0474 0.0643 0.0772 0.089 0.000193 0.000223 + 419 200 0.0796 0.00342 0.0112 0.0522 0.0698 0.106 0.126 0.000265 0.000315 + 419 235 0.0655 0.0027 0.0116 0.0453 0.062 0.111 0.129 0.000278 0.000321 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0533 0.00211 0.011 0.0402 0.0549 0.116 0.125 0.000289 0.000313 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 419 60542.471 0.005 0.00304 0.0434 0.104 0.0486 0.0658 0.19 0.249 0.000476 0.000623 +! Validation 419 60542.471 0.005 0.0029 0.0212 0.0793 0.047 0.0643 0.145 0.174 0.000362 0.000434 +Wall time: 60542.47181171412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0698 0.00295 0.0108 0.0484 0.0649 0.102 0.124 0.000254 0.00031 + 420 200 0.0781 0.00273 0.0235 0.0462 0.0624 0.151 0.183 0.000377 0.000458 + 420 235 0.0726 0.00311 0.0104 0.0491 0.0666 0.109 0.122 0.000274 0.000304 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.049 0.00217 0.0055 0.0411 0.0557 0.0734 0.0885 0.000183 0.000221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 420 60686.935 0.005 0.00294 0.0314 0.0902 0.0478 0.0647 0.168 0.212 0.000419 0.000529 +! Validation 420 60686.935 0.005 0.00294 0.0189 0.0776 0.0474 0.0647 0.128 0.164 0.000319 0.00041 +Wall time: 60686.93517229799 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0593 0.00268 0.00569 0.0453 0.0618 0.0702 0.09 0.000176 0.000225 + 421 200 0.0876 0.00327 0.0221 0.0508 0.0683 0.149 0.178 0.000373 0.000444 + 421 235 0.054 0.00251 0.0038 0.045 0.0598 0.0564 0.0736 0.000141 0.000184 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0439 0.00209 0.00205 0.0402 0.0546 0.0516 0.054 0.000129 0.000135 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 421 60831.227 0.005 0.00285 0.0308 0.0877 0.047 0.0637 0.164 0.21 0.00041 0.000524 +! Validation 421 60831.227 0.005 0.00288 0.0132 0.0708 0.047 0.064 0.108 0.137 0.00027 0.000343 +Wall time: 60831.227448607795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.163 0.003 0.103 0.0487 0.0654 0.374 0.384 0.000935 0.000959 + 422 200 0.0683 0.00279 0.0125 0.0467 0.0631 0.107 0.133 0.000268 0.000334 + 422 235 0.0699 0.00293 0.0112 0.0476 0.0647 0.106 0.126 0.000264 0.000316 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0548 0.00218 0.0112 0.0405 0.0557 0.122 0.126 0.000304 0.000316 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 422 60975.517 0.005 0.00295 0.0315 0.0904 0.048 0.0648 0.171 0.212 0.000427 0.00053 +! Validation 422 60975.517 0.005 0.00284 0.0131 0.0699 0.0465 0.0636 0.109 0.137 0.000274 0.000342 +Wall time: 60975.51704360312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.322 0.00305 0.261 0.0485 0.0659 0.602 0.61 0.0015 0.00152 + 423 200 0.057 0.00253 0.00631 0.0442 0.0601 0.0785 0.0948 0.000196 0.000237 + 423 235 0.085 0.00303 0.0244 0.0487 0.0657 0.158 0.186 0.000396 0.000466 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0737 0.00223 0.0291 0.0417 0.0564 0.195 0.204 0.000489 0.000509 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 423 61120.435 0.005 0.00291 0.0332 0.0914 0.0475 0.0644 0.169 0.217 0.000423 0.000544 +! Validation 423 61120.435 0.005 0.00291 0.0286 0.0867 0.0475 0.0643 0.168 0.202 0.000421 0.000504 +Wall time: 61120.435109329876 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.0668 0.00277 0.0113 0.0467 0.0629 0.0898 0.127 0.000225 0.000317 + 424 200 0.147 0.00474 0.0523 0.0633 0.0822 0.222 0.273 0.000556 0.000683 + 424 235 0.0827 0.00292 0.0243 0.048 0.0645 0.163 0.186 0.000408 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.219 0.00315 0.156 0.0502 0.067 0.465 0.472 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 424 61264.691 0.005 0.00325 0.0466 0.112 0.0502 0.0681 0.202 0.258 0.000505 0.000644 +! Validation 424 61264.691 0.005 0.00385 0.217 0.294 0.0553 0.074 0.541 0.556 0.00135 0.00139 +Wall time: 61264.691232956015 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.114 0.00258 0.0619 0.045 0.0607 0.29 0.297 0.000725 0.000743 + 425 200 0.0734 0.00259 0.0217 0.0446 0.0607 0.151 0.176 0.000378 0.00044 + 425 235 0.188 0.00281 0.132 0.047 0.0633 0.411 0.433 0.00103 0.00108 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0624 0.00247 0.0129 0.0449 0.0594 0.134 0.136 0.000335 0.000339 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 425 61408.957 0.005 0.00294 0.037 0.0957 0.0477 0.0647 0.183 0.229 0.000458 0.000572 +! Validation 425 61408.957 0.005 0.00331 0.0394 0.106 0.0513 0.0687 0.197 0.237 0.000493 0.000592 +Wall time: 61408.957759634126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.078 0.00265 0.025 0.0453 0.0614 0.155 0.189 0.000387 0.000471 + 426 200 0.0662 0.00289 0.00848 0.047 0.0641 0.0878 0.11 0.000219 0.000275 + 426 235 0.0646 0.00275 0.00969 0.0467 0.0625 0.0918 0.117 0.00023 0.000294 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.042 0.002 0.00197 0.039 0.0534 0.0492 0.0529 0.000123 0.000132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 426 61553.236 0.005 0.00292 0.029 0.0875 0.0478 0.0645 0.16 0.203 0.0004 0.000509 +! Validation 426 61553.236 0.005 0.0027 0.00994 0.064 0.0453 0.0621 0.0954 0.119 0.000238 0.000298 +Wall time: 61553.236784779 +! Best model 426 0.064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0627 0.00269 0.00894 0.0455 0.0619 0.0958 0.113 0.000239 0.000282 + 427 200 0.0847 0.00296 0.0254 0.0483 0.065 0.177 0.19 0.000442 0.000476 + 427 235 0.0549 0.00213 0.0123 0.0403 0.0551 0.112 0.133 0.00028 0.000331 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0484 0.00225 0.0034 0.0414 0.0566 0.0628 0.0696 0.000157 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 427 61697.542 0.005 0.00266 0.023 0.0762 0.0455 0.0615 0.142 0.181 0.000356 0.000453 +! Validation 427 61697.542 0.005 0.00288 0.0112 0.0689 0.047 0.0641 0.101 0.127 0.000252 0.000316 +Wall time: 61697.54266052181 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0674 0.00246 0.0183 0.0436 0.0592 0.133 0.161 0.000332 0.000403 + 428 200 0.146 0.00286 0.0886 0.0474 0.0638 0.331 0.355 0.000828 0.000888 + 428 235 0.0583 0.00249 0.00851 0.0443 0.0595 0.0881 0.11 0.00022 0.000275 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0523 0.00194 0.0134 0.0387 0.0526 0.127 0.138 0.000319 0.000346 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 428 61841.878 0.005 0.00266 0.0308 0.084 0.0456 0.0616 0.165 0.21 0.000413 0.000524 +! Validation 428 61841.878 0.005 0.00266 0.0175 0.0708 0.0451 0.0616 0.13 0.158 0.000324 0.000395 +Wall time: 61841.87881576782 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0647 0.00261 0.0124 0.045 0.061 0.112 0.133 0.00028 0.000333 + 429 200 0.151 0.00333 0.0847 0.0523 0.0689 0.332 0.347 0.00083 0.000869 + 429 235 0.093 0.00243 0.0444 0.0436 0.0589 0.219 0.251 0.000547 0.000629 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.112 0.00215 0.0686 0.0413 0.0553 0.312 0.313 0.000779 0.000782 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 429 61986.146 0.005 0.00264 0.0234 0.0763 0.0454 0.0614 0.146 0.183 0.000365 0.000456 +! Validation 429 61986.146 0.005 0.00285 0.0835 0.141 0.0471 0.0637 0.31 0.345 0.000776 0.000862 +Wall time: 61986.14664041996 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.159 0.00438 0.0718 0.0596 0.079 0.249 0.32 0.000622 0.0008 + 430 200 0.0742 0.00281 0.0179 0.0467 0.0633 0.124 0.16 0.00031 0.0004 + 430 235 0.108 0.00356 0.0365 0.0535 0.0712 0.18 0.228 0.00045 0.00057 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0798 0.0035 0.00987 0.0533 0.0706 0.101 0.119 0.000252 0.000296 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 430 62130.591 0.005 0.00329 0.0468 0.112 0.05 0.0684 0.194 0.258 0.000484 0.000645 +! Validation 430 62130.591 0.005 0.004 0.059 0.139 0.0568 0.0755 0.219 0.29 0.000548 0.000725 +Wall time: 62130.59159749281 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0703 0.00245 0.0213 0.0436 0.059 0.152 0.174 0.000381 0.000436 + 431 200 0.0695 0.00259 0.0177 0.0452 0.0607 0.123 0.159 0.000308 0.000397 + 431 235 0.0914 0.00258 0.0397 0.045 0.0607 0.209 0.238 0.000524 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0583 0.00207 0.0169 0.0408 0.0543 0.149 0.155 0.000372 0.000388 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 431 62275.458 0.005 0.00252 0.0171 0.0676 0.0443 0.06 0.124 0.156 0.000311 0.00039 +! Validation 431 62275.458 0.005 0.00283 0.028 0.0846 0.047 0.0635 0.166 0.2 0.000415 0.000499 +Wall time: 62275.45862667682 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0821 0.00253 0.0316 0.0442 0.06 0.196 0.212 0.00049 0.00053 + 432 200 0.0737 0.00234 0.0269 0.0422 0.0577 0.178 0.196 0.000444 0.000489 + 432 235 0.0544 0.00218 0.0107 0.0413 0.0557 0.107 0.124 0.000266 0.000309 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0397 0.00188 0.00198 0.0378 0.0518 0.0424 0.0532 0.000106 0.000133 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 432 62419.680 0.005 0.00261 0.0294 0.0815 0.045 0.061 0.159 0.205 0.000397 0.000512 +! Validation 432 62419.680 0.005 0.00251 0.0115 0.0617 0.0437 0.0598 0.101 0.128 0.000253 0.00032 +Wall time: 62419.68067802908 +! Best model 432 0.062 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0544 0.00249 0.00467 0.0441 0.0595 0.0678 0.0815 0.00017 0.000204 + 433 200 1.22 0.0381 0.463 0.168 0.233 0.704 0.812 0.00176 0.00203 + 433 235 23.3 0.84 6.51 0.812 1.09 2.54 3.04 0.00635 0.00761 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 18.7 0.863 1.43 0.824 1.11 1.31 1.43 0.00327 0.00357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 433 62564.011 0.005 0.115 7.32 9.62 0.145 0.402 0.894 3.23 0.00223 0.00807 +! Validation 433 62564.011 0.005 0.84 9.12 25.9 0.811 1.09 2.62 3.6 0.00656 0.00901 +Wall time: 62564.01123347413 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 2.95 0.134 0.268 0.334 0.437 0.499 0.618 0.00125 0.00155 + 434 200 1.82 0.079 0.235 0.253 0.336 0.465 0.579 0.00116 0.00145 + 434 235 1.92 0.0723 0.478 0.242 0.321 0.735 0.825 0.00184 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 1.49 0.0663 0.166 0.232 0.307 0.449 0.486 0.00112 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 434 62708.419 0.005 0.228 2.26 6.83 0.39 0.571 1.26 1.8 0.00316 0.00449 +! Validation 434 62708.419 0.005 0.0737 0.322 1.8 0.245 0.324 0.523 0.677 0.00131 0.00169 +Wall time: 62708.41941067297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 1.59 0.0685 0.224 0.236 0.312 0.455 0.565 0.00114 0.00141 + 435 200 1 0.0425 0.156 0.187 0.246 0.374 0.471 0.000936 0.00118 + 435 235 1.5 0.0432 0.639 0.188 0.248 0.666 0.954 0.00167 0.00238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 2.65 0.0355 1.94 0.169 0.225 1.65 1.66 0.00414 0.00415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 435 62853.390 0.005 0.0562 0.654 1.78 0.213 0.283 0.753 0.965 0.00188 0.00241 +! Validation 435 62853.390 0.005 0.0418 0.94 1.77 0.184 0.244 1.07 1.16 0.00268 0.00289 +Wall time: 62853.39012012398 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.778 0.0317 0.144 0.162 0.213 0.388 0.453 0.00097 0.00113 + 436 200 0.685 0.026 0.165 0.147 0.192 0.391 0.485 0.000978 0.00121 + 436 235 0.619 0.0236 0.148 0.139 0.183 0.398 0.459 0.000996 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.541 0.0199 0.144 0.128 0.168 0.438 0.453 0.00109 0.00113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 436 62997.652 0.005 0.0309 0.412 1.03 0.159 0.21 0.619 0.767 0.00155 0.00192 +! Validation 436 62997.652 0.005 0.025 0.691 1.19 0.143 0.189 0.927 0.992 0.00232 0.00248 +Wall time: 62997.65240922384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.989 0.0201 0.587 0.129 0.169 0.873 0.915 0.00218 0.00229 + 437 200 0.543 0.017 0.203 0.118 0.155 0.481 0.538 0.0012 0.00135 + 437 235 0.416 0.0166 0.0847 0.116 0.154 0.309 0.347 0.000773 0.000869 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.895 0.0125 0.645 0.102 0.134 0.951 0.958 0.00238 0.0024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 437 63141.919 0.005 0.0198 0.241 0.636 0.128 0.168 0.471 0.586 0.00118 0.00147 +! Validation 437 63141.919 0.005 0.017 1.17 1.51 0.118 0.156 1.25 1.29 0.00313 0.00322 +Wall time: 63141.919339207 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.526 0.0155 0.217 0.113 0.148 0.497 0.556 0.00124 0.00139 + 438 200 0.364 0.0141 0.0816 0.108 0.142 0.271 0.341 0.000679 0.000852 + 438 235 0.371 0.0155 0.061 0.113 0.148 0.254 0.295 0.000634 0.000737 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.257 0.0101 0.0539 0.0917 0.12 0.246 0.277 0.000616 0.000693 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 438 63286.185 0.005 0.0154 0.287 0.595 0.113 0.148 0.503 0.64 0.00126 0.0016 +! Validation 438 63286.185 0.005 0.014 0.0598 0.34 0.107 0.141 0.222 0.292 0.000556 0.00073 +Wall time: 63286.18561857706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.918 0.0125 0.668 0.101 0.134 0.937 0.976 0.00234 0.00244 + 439 200 0.325 0.0109 0.107 0.0946 0.125 0.332 0.391 0.000829 0.000977 + 439 235 0.408 0.0113 0.182 0.0956 0.127 0.418 0.51 0.00105 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.285 0.00843 0.116 0.0835 0.11 0.385 0.407 0.000963 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 439 63430.633 0.005 0.0123 0.163 0.41 0.1 0.133 0.385 0.481 0.000964 0.0012 +! Validation 439 63430.633 0.005 0.0118 0.241 0.477 0.0976 0.13 0.533 0.586 0.00133 0.00147 +Wall time: 63430.633873270825 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.279 0.0113 0.0524 0.0962 0.127 0.212 0.273 0.000531 0.000683 + 440 200 0.348 0.00964 0.156 0.0889 0.117 0.432 0.471 0.00108 0.00118 + 440 235 0.216 0.00933 0.0293 0.0876 0.115 0.167 0.204 0.000417 0.000511 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.209 0.00735 0.0623 0.078 0.102 0.273 0.298 0.000681 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 440 63574.922 0.005 0.0106 0.182 0.394 0.0929 0.123 0.412 0.51 0.00103 0.00127 +! Validation 440 63574.922 0.005 0.0103 0.0489 0.256 0.0913 0.121 0.21 0.264 0.000524 0.00066 +Wall time: 63574.92273864569 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.272 0.00977 0.0762 0.0885 0.118 0.283 0.329 0.000708 0.000824 + 441 200 0.828 0.00926 0.643 0.0868 0.115 0.928 0.957 0.00232 0.00239 + 441 235 0.249 0.00859 0.0775 0.0829 0.111 0.281 0.332 0.000704 0.000831 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.151 0.00643 0.0221 0.0728 0.0957 0.154 0.177 0.000384 0.000444 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 441 63719.305 0.005 0.00941 0.163 0.351 0.0873 0.116 0.379 0.483 0.000949 0.00121 +! Validation 441 63719.305 0.005 0.00914 0.0388 0.222 0.0854 0.114 0.18 0.235 0.000451 0.000588 +Wall time: 63719.30552006094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.203 0.00874 0.0281 0.0839 0.112 0.14 0.2 0.00035 0.0005 + 442 200 0.702 0.00761 0.55 0.0787 0.104 0.862 0.885 0.00216 0.00221 + 442 235 0.7 0.00856 0.528 0.0822 0.11 0.844 0.868 0.00211 0.00217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.324 0.0058 0.208 0.069 0.0909 0.534 0.544 0.00133 0.00136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 442 63863.554 0.005 0.00835 0.124 0.291 0.082 0.109 0.327 0.419 0.000817 0.00105 +! Validation 442 63863.554 0.005 0.00823 0.158 0.322 0.0808 0.108 0.435 0.474 0.00109 0.00118 +Wall time: 63863.554888284765 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.182 0.00782 0.0258 0.0789 0.106 0.161 0.192 0.000404 0.000479 + 443 200 0.321 0.00763 0.168 0.0783 0.104 0.458 0.49 0.00114 0.00122 + 443 235 0.275 0.00699 0.135 0.0749 0.0998 0.371 0.439 0.000927 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.122 0.00539 0.0142 0.0663 0.0876 0.117 0.142 0.000292 0.000355 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 443 64007.847 0.005 0.00749 0.0961 0.246 0.0775 0.103 0.293 0.37 0.000733 0.000924 +! Validation 443 64007.847 0.005 0.00756 0.0433 0.195 0.0773 0.104 0.198 0.248 0.000494 0.000621 +Wall time: 64007.84702222608 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.196 0.00678 0.06 0.0736 0.0983 0.244 0.292 0.000609 0.000731 + 444 200 0.155 0.00654 0.0243 0.0725 0.0965 0.149 0.186 0.000373 0.000465 + 444 235 0.592 0.00657 0.46 0.0728 0.0968 0.785 0.81 0.00196 0.00202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.375 0.00471 0.28 0.0624 0.0819 0.626 0.632 0.00156 0.00158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 444 64152.142 0.005 0.00682 0.0934 0.23 0.0739 0.0986 0.282 0.363 0.000705 0.000907 +! Validation 444 64152.142 0.005 0.00682 0.47 0.607 0.0732 0.0986 0.785 0.818 0.00196 0.00205 +Wall time: 64152.142751812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.485 0.00662 0.353 0.072 0.0971 0.676 0.709 0.00169 0.00177 + 445 200 0.189 0.00592 0.0703 0.0687 0.0918 0.278 0.316 0.000696 0.000791 + 445 235 0.159 0.00635 0.0323 0.0714 0.0951 0.178 0.215 0.000445 0.000536 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.206 0.00452 0.116 0.0611 0.0802 0.397 0.406 0.000992 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 445 64296.420 0.005 0.00651 0.139 0.27 0.0722 0.0963 0.352 0.446 0.00088 0.00112 +! Validation 445 64296.420 0.005 0.00648 0.0813 0.211 0.0714 0.0961 0.286 0.34 0.000716 0.000851 +Wall time: 64296.4201039481 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.25 0.006 0.13 0.0692 0.0924 0.412 0.431 0.00103 0.00108 + 446 200 0.173 0.00606 0.0518 0.0691 0.0929 0.215 0.272 0.000537 0.000679 + 446 235 0.124 0.0052 0.0201 0.0645 0.086 0.133 0.169 0.000333 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.108 0.00408 0.0259 0.0583 0.0763 0.176 0.192 0.000439 0.00048 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 446 64440.775 0.005 0.00596 0.0866 0.206 0.0689 0.0921 0.283 0.351 0.000708 0.000879 +! Validation 446 64440.775 0.005 0.00592 0.0343 0.153 0.068 0.0919 0.177 0.221 0.000442 0.000553 +Wall time: 64440.77525329171 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.155 0.00524 0.0499 0.0651 0.0864 0.237 0.267 0.000593 0.000666 + 447 200 0.124 0.00554 0.0131 0.0664 0.0888 0.118 0.137 0.000296 0.000342 + 447 235 0.308 0.00525 0.203 0.0647 0.0865 0.524 0.538 0.00131 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.581 0.00391 0.502 0.0569 0.0747 0.843 0.846 0.00211 0.00211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 447 64585.101 0.005 0.00553 0.0778 0.188 0.0663 0.0888 0.268 0.332 0.000669 0.00083 +! Validation 447 64585.101 0.005 0.00561 0.44 0.552 0.0661 0.0894 0.772 0.792 0.00193 0.00198 +Wall time: 64585.10174612282 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.294 0.00503 0.194 0.0633 0.0846 0.47 0.526 0.00118 0.00131 + 448 200 0.125 0.00488 0.0277 0.0625 0.0834 0.173 0.199 0.000433 0.000497 + 448 235 0.14 0.00563 0.0276 0.0663 0.0896 0.164 0.198 0.000411 0.000496 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.133 0.00368 0.0595 0.055 0.0724 0.285 0.291 0.000712 0.000728 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 448 64730.454 0.005 0.00527 0.0876 0.193 0.0647 0.0867 0.283 0.354 0.000708 0.000884 +! Validation 448 64730.454 0.005 0.00536 0.12 0.227 0.0644 0.0874 0.372 0.413 0.000929 0.00103 +Wall time: 64730.454369870946 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.106 0.00461 0.0137 0.0611 0.081 0.114 0.14 0.000285 0.000349 + 449 200 0.137 0.00554 0.0258 0.0661 0.0889 0.15 0.192 0.000376 0.000479 + 449 235 0.247 0.00505 0.146 0.0632 0.0848 0.397 0.456 0.000992 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.0776 0.00368 0.00405 0.0552 0.0724 0.0615 0.076 0.000154 0.00019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 449 64874.663 0.005 0.00505 0.113 0.214 0.0633 0.0848 0.321 0.401 0.000803 0.001 +! Validation 449 64874.663 0.005 0.00525 0.0431 0.148 0.0639 0.0865 0.199 0.248 0.000497 0.000619 +Wall time: 64874.66383775696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.11 0.00494 0.0114 0.0622 0.0839 0.102 0.128 0.000255 0.000319 + 450 200 0.108 0.00449 0.0178 0.0597 0.0799 0.133 0.159 0.000332 0.000399 + 450 235 0.0857 0.00385 0.0087 0.0556 0.074 0.0877 0.111 0.000219 0.000278 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.0871 0.00331 0.0209 0.0521 0.0687 0.161 0.173 0.000403 0.000431 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 450 65018.911 0.005 0.00467 0.0421 0.136 0.0608 0.0816 0.193 0.245 0.000484 0.000613 +! Validation 450 65018.911 0.005 0.00473 0.0293 0.124 0.0604 0.0821 0.169 0.204 0.000423 0.000511 +Wall time: 65018.911819934845 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.106 0.00427 0.0212 0.0584 0.0779 0.146 0.174 0.000364 0.000434 + 451 200 0.11 0.00449 0.0204 0.0598 0.08 0.131 0.17 0.000327 0.000426 + 451 235 0.172 0.00429 0.0865 0.0581 0.0782 0.337 0.351 0.000843 0.000877 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0851 0.00335 0.018 0.052 0.0691 0.151 0.16 0.000377 0.0004 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 451 65163.116 0.005 0.00443 0.0672 0.156 0.0591 0.0794 0.247 0.309 0.000617 0.000773 +! Validation 451 65163.116 0.005 0.00472 0.0767 0.171 0.0603 0.082 0.28 0.331 0.0007 0.000826 +Wall time: 65163.116828526836 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.109 0.00406 0.0279 0.0567 0.0761 0.172 0.199 0.000429 0.000498 + 452 200 0.097 0.00415 0.014 0.0569 0.0769 0.102 0.141 0.000256 0.000354 + 452 235 0.428 0.00403 0.348 0.0569 0.0757 0.686 0.704 0.00171 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.348 0.00309 0.287 0.0501 0.0664 0.636 0.639 0.00159 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 452 65307.614 0.005 0.00424 0.0521 0.137 0.0578 0.0777 0.21 0.27 0.000524 0.000676 +! Validation 452 65307.614 0.005 0.00438 0.315 0.402 0.0582 0.079 0.645 0.669 0.00161 0.00167 +Wall time: 65307.614059988875 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.179 0.00396 0.0993 0.0555 0.0751 0.36 0.376 0.000901 0.00094 + 453 200 0.092 0.00378 0.0165 0.0544 0.0734 0.12 0.153 0.000301 0.000383 + 453 235 0.13 0.00408 0.0486 0.0563 0.0762 0.227 0.263 0.000569 0.000658 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.0659 0.00285 0.00885 0.0479 0.0637 0.0995 0.112 0.000249 0.000281 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 453 65451.876 0.005 0.00406 0.0516 0.133 0.0565 0.076 0.211 0.271 0.000527 0.000678 +! Validation 453 65451.876 0.005 0.00411 0.0253 0.108 0.0561 0.0766 0.15 0.19 0.000374 0.000474 +Wall time: 65451.876196176745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.104 0.00388 0.0262 0.055 0.0744 0.154 0.193 0.000385 0.000483 + 454 200 0.195 0.004 0.115 0.0561 0.0755 0.378 0.405 0.000946 0.00101 + 454 235 0.228 0.00419 0.144 0.0573 0.0772 0.434 0.454 0.00109 0.00113 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.219 0.00288 0.161 0.0483 0.0641 0.476 0.479 0.00119 0.0012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 454 65596.124 0.005 0.00401 0.0891 0.169 0.0562 0.0756 0.286 0.356 0.000715 0.00089 +! Validation 454 65596.124 0.005 0.00411 0.104 0.186 0.0562 0.0765 0.346 0.385 0.000864 0.000963 +Wall time: 65596.12459190795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0851 0.00367 0.0118 0.0533 0.0723 0.102 0.13 0.000255 0.000324 + 455 200 0.111 0.00379 0.0354 0.055 0.0735 0.188 0.225 0.00047 0.000562 + 455 235 0.134 0.0039 0.0563 0.0553 0.0745 0.251 0.283 0.000628 0.000708 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0884 0.00268 0.0347 0.0463 0.0618 0.214 0.222 0.000535 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 455 65740.395 0.005 0.00374 0.0404 0.115 0.0541 0.073 0.188 0.24 0.00047 0.000599 +! Validation 455 65740.395 0.005 0.00388 0.0457 0.123 0.0544 0.0743 0.22 0.255 0.000549 0.000638 +Wall time: 65740.394949432 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.162 0.00352 0.0919 0.053 0.0708 0.331 0.362 0.000829 0.000904 + 456 200 0.106 0.00361 0.034 0.0534 0.0717 0.188 0.22 0.00047 0.00055 + 456 235 0.293 0.00366 0.22 0.0538 0.0722 0.543 0.559 0.00136 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.116 0.00255 0.0654 0.0451 0.0602 0.303 0.305 0.000757 0.000763 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 456 65884.606 0.005 0.00365 0.0484 0.121 0.0535 0.0721 0.205 0.261 0.000514 0.000653 +! Validation 456 65884.606 0.005 0.00372 0.111 0.186 0.0532 0.0728 0.368 0.398 0.00092 0.000995 +Wall time: 65884.60597273894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.25 0.00322 0.185 0.0506 0.0678 0.494 0.514 0.00123 0.00128 + 457 200 0.113 0.00354 0.0426 0.0525 0.071 0.216 0.246 0.00054 0.000616 + 457 235 0.0914 0.00334 0.0245 0.0515 0.069 0.17 0.187 0.000424 0.000467 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.112 0.00253 0.0612 0.0448 0.06 0.291 0.295 0.000728 0.000738 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 457 66028.807 0.005 0.00352 0.0451 0.115 0.0525 0.0708 0.201 0.254 0.000503 0.000634 +! Validation 457 66028.807 0.005 0.00361 0.113 0.185 0.0525 0.0717 0.375 0.4 0.000937 0.001 +Wall time: 66028.80720335478 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.29 0.00379 0.214 0.0548 0.0735 0.545 0.553 0.00136 0.00138 + 458 200 0.0994 0.00367 0.026 0.0534 0.0723 0.152 0.193 0.00038 0.000481 + 458 235 0.278 0.00325 0.213 0.0505 0.068 0.539 0.551 0.00135 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.0846 0.00281 0.0283 0.0472 0.0633 0.196 0.201 0.000489 0.000502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 458 66173.047 0.005 0.00342 0.0499 0.118 0.0517 0.0698 0.213 0.265 0.000533 0.000663 +! Validation 458 66173.047 0.005 0.00384 0.044 0.121 0.0544 0.074 0.206 0.25 0.000516 0.000626 +Wall time: 66173.04704287369 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.114 0.00326 0.0491 0.0512 0.0681 0.239 0.264 0.000598 0.000661 + 459 200 0.12 0.0032 0.0561 0.0501 0.0676 0.265 0.283 0.000663 0.000707 + 459 235 0.22 0.0035 0.15 0.0525 0.0706 0.411 0.462 0.00103 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.087 0.00281 0.0309 0.0471 0.0632 0.198 0.21 0.000495 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 459 66317.237 0.005 0.00338 0.0609 0.128 0.0514 0.0693 0.238 0.294 0.000595 0.000735 +! Validation 459 66317.237 0.005 0.00377 0.103 0.178 0.0538 0.0733 0.341 0.383 0.000851 0.000957 +Wall time: 66317.23732627882 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.0846 0.00305 0.0235 0.0489 0.066 0.152 0.183 0.00038 0.000458 + 460 200 0.0796 0.00305 0.0186 0.0491 0.0659 0.139 0.163 0.000348 0.000407 + 460 235 0.143 0.00334 0.0758 0.0513 0.069 0.29 0.329 0.000724 0.000822 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.147 0.00241 0.0984 0.0436 0.0586 0.37 0.374 0.000925 0.000936 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 460 66461.456 0.005 0.0032 0.0305 0.0945 0.05 0.0675 0.166 0.208 0.000416 0.00052 +! Validation 460 66461.456 0.005 0.00337 0.0898 0.157 0.0508 0.0693 0.327 0.358 0.000818 0.000894 +Wall time: 66461.45627301699 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.205 0.00334 0.138 0.0512 0.0689 0.422 0.444 0.00105 0.00111 + 461 200 0.0827 0.00314 0.0198 0.0494 0.0669 0.145 0.168 0.000362 0.00042 + 461 235 0.0843 0.0032 0.0202 0.0494 0.0676 0.148 0.17 0.00037 0.000424 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.05 0.00221 0.00578 0.0415 0.0561 0.0806 0.0908 0.000201 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 461 66605.757 0.005 0.00326 0.0554 0.121 0.0505 0.0682 0.209 0.281 0.000522 0.000703 +! Validation 461 66605.757 0.005 0.00317 0.0157 0.0792 0.049 0.0672 0.122 0.15 0.000305 0.000374 +Wall time: 66605.7570018419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.0763 0.00303 0.0157 0.0487 0.0657 0.131 0.149 0.000328 0.000373 + 462 200 0.0669 0.00294 0.00815 0.0479 0.0647 0.0858 0.108 0.000214 0.000269 + 462 235 0.128 0.00339 0.0601 0.0519 0.0695 0.2 0.293 0.0005 0.000731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.452 0.00224 0.407 0.0421 0.0565 0.76 0.761 0.0019 0.0019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 462 66749.947 0.005 0.00302 0.0306 0.091 0.0485 0.0656 0.165 0.208 0.000413 0.000521 +! Validation 462 66749.947 0.005 0.00318 0.321 0.385 0.0496 0.0673 0.639 0.676 0.0016 0.00169 +Wall time: 66749.94729287969 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0671 0.00286 0.00991 0.0474 0.0638 0.0924 0.119 0.000231 0.000297 + 463 200 0.0866 0.00294 0.0278 0.0481 0.0647 0.17 0.199 0.000426 0.000497 + 463 235 0.0811 0.00352 0.0106 0.0528 0.0708 0.112 0.123 0.00028 0.000308 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0923 0.00223 0.0477 0.0422 0.0564 0.258 0.261 0.000646 0.000651 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 463 66894.166 0.005 0.00307 0.0492 0.111 0.049 0.0661 0.209 0.265 0.000522 0.000662 +! Validation 463 66894.166 0.005 0.00312 0.0625 0.125 0.0489 0.0667 0.26 0.298 0.000649 0.000746 +Wall time: 66894.166374336 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.0625 0.00287 0.00506 0.0475 0.064 0.0684 0.0849 0.000171 0.000212 + 464 200 0.0807 0.00282 0.0244 0.047 0.0633 0.16 0.186 0.0004 0.000466 + 464 235 0.0638 0.00284 0.00704 0.0466 0.0636 0.0755 0.1 0.000189 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.056 0.00211 0.0137 0.0409 0.0549 0.13 0.14 0.000325 0.000349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 464 67038.915 0.005 0.00296 0.034 0.0933 0.0481 0.065 0.175 0.22 0.000437 0.000551 +! Validation 464 67038.915 0.005 0.00301 0.0237 0.0839 0.0479 0.0655 0.151 0.184 0.000377 0.000459 +Wall time: 67038.91563093895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0655 0.00276 0.0102 0.0464 0.0627 0.1 0.121 0.00025 0.000302 + 465 200 0.0968 0.00289 0.0389 0.0476 0.0642 0.208 0.235 0.000519 0.000588 + 465 235 0.064 0.00278 0.00833 0.0468 0.063 0.0948 0.109 0.000237 0.000272 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.106 0.00214 0.0636 0.0415 0.0553 0.298 0.301 0.000746 0.000752 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 465 67183.119 0.005 0.00284 0.0291 0.0858 0.047 0.0636 0.164 0.204 0.00041 0.000509 +! Validation 465 67183.119 0.005 0.00298 0.0653 0.125 0.048 0.0652 0.283 0.305 0.000707 0.000763 +Wall time: 67183.11910462473 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0675 0.00274 0.0127 0.0461 0.0625 0.12 0.134 0.0003 0.000336 + 466 200 0.085 0.003 0.025 0.0482 0.0654 0.163 0.189 0.000408 0.000472 + 466 235 0.111 0.00278 0.0559 0.0471 0.0629 0.256 0.282 0.000639 0.000706 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.154 0.00206 0.113 0.0401 0.0542 0.398 0.4 0.000995 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 466 67327.322 0.005 0.00281 0.0262 0.0825 0.0468 0.0633 0.152 0.193 0.00038 0.000483 +! Validation 466 67327.322 0.005 0.00292 0.158 0.216 0.047 0.0644 0.456 0.474 0.00114 0.00119 +Wall time: 67327.3226024718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0623 0.00285 0.00526 0.0471 0.0638 0.0758 0.0865 0.000189 0.000216 + 467 200 0.066 0.00271 0.0118 0.0459 0.0621 0.105 0.13 0.000263 0.000324 + 467 235 0.104 0.00244 0.0554 0.0436 0.0589 0.27 0.281 0.000675 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.11 0.00189 0.0718 0.0384 0.0519 0.318 0.32 0.000794 0.0008 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 467 67471.543 0.005 0.00283 0.028 0.0845 0.047 0.0635 0.158 0.199 0.000395 0.000498 +! Validation 467 67471.543 0.005 0.0027 0.0688 0.123 0.0452 0.062 0.283 0.313 0.000708 0.000783 +Wall time: 67471.54387275781 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0718 0.00268 0.0183 0.0455 0.0617 0.139 0.161 0.000348 0.000404 + 468 200 0.0588 0.0025 0.00875 0.0443 0.0597 0.0952 0.112 0.000238 0.000279 + 468 235 0.109 0.00294 0.0504 0.0483 0.0647 0.244 0.268 0.000609 0.00067 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0877 0.0021 0.0457 0.0411 0.0547 0.247 0.255 0.000617 0.000638 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 468 67615.740 0.005 0.00282 0.033 0.0894 0.0469 0.0634 0.171 0.217 0.000427 0.000542 +! Validation 468 67615.740 0.005 0.00293 0.0671 0.126 0.0477 0.0646 0.277 0.309 0.000693 0.000773 +Wall time: 67615.74042699579 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0817 0.00341 0.0136 0.0515 0.0697 0.108 0.139 0.00027 0.000348 + 469 200 0.0867 0.00263 0.0341 0.0448 0.0612 0.196 0.22 0.000491 0.000551 + 469 235 0.16 0.0024 0.112 0.0438 0.0584 0.382 0.4 0.000955 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.123 0.00207 0.0818 0.0405 0.0544 0.336 0.341 0.00084 0.000853 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 469 67759.940 0.005 0.00275 0.0344 0.0893 0.0463 0.0625 0.174 0.221 0.000435 0.000552 +! Validation 469 67759.940 0.005 0.00294 0.0726 0.131 0.0475 0.0647 0.269 0.322 0.000671 0.000804 +Wall time: 67759.9399558669 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.461 0.00275 0.406 0.0466 0.0626 0.752 0.761 0.00188 0.0019 + 470 200 0.0617 0.00273 0.00703 0.046 0.0624 0.085 0.1 0.000212 0.00025 + 470 235 0.0608 0.00244 0.012 0.0439 0.059 0.112 0.131 0.00028 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.0523 0.00197 0.0129 0.0393 0.053 0.124 0.135 0.00031 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 470 67904.142 0.005 0.00556 0.139 0.25 0.0598 0.0891 0.279 0.445 0.000698 0.00111 +! Validation 470 67904.142 0.005 0.00282 0.0297 0.0861 0.0463 0.0634 0.171 0.206 0.000428 0.000514 +Wall time: 67904.14257129002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.061 0.00263 0.00841 0.0453 0.0612 0.082 0.109 0.000205 0.000274 + 471 200 0.0581 0.00261 0.00604 0.0447 0.0609 0.067 0.0927 0.000167 0.000232 + 471 235 0.0524 0.00213 0.00986 0.0408 0.0551 0.107 0.118 0.000268 0.000296 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0394 0.00184 0.00248 0.038 0.0512 0.0498 0.0595 0.000124 0.000149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 471 68048.341 0.005 0.00258 0.0123 0.0639 0.0448 0.0606 0.105 0.132 0.000263 0.000331 +! Validation 471 68048.341 0.005 0.00266 0.0113 0.0644 0.0449 0.0615 0.0962 0.127 0.000241 0.000316 +Wall time: 68048.34092363901 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0681 0.00271 0.014 0.046 0.0621 0.11 0.141 0.000274 0.000353 + 472 200 0.0627 0.00279 0.00695 0.0462 0.063 0.0808 0.0995 0.000202 0.000249 + 472 235 0.0627 0.00245 0.0136 0.0437 0.0591 0.12 0.139 0.000301 0.000348 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0404 0.00183 0.00378 0.0377 0.051 0.0599 0.0734 0.00015 0.000183 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 472 68192.541 0.005 0.00259 0.021 0.0727 0.0449 0.0607 0.132 0.173 0.000331 0.000432 +! Validation 472 68192.541 0.005 0.00261 0.0214 0.0736 0.0446 0.061 0.142 0.174 0.000355 0.000436 +Wall time: 68192.54151186906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0563 0.00246 0.00706 0.0437 0.0592 0.0844 0.1 0.000211 0.000251 + 473 200 0.0563 0.00242 0.00797 0.0433 0.0587 0.0813 0.107 0.000203 0.000266 + 473 235 0.0708 0.00252 0.0205 0.0442 0.0599 0.133 0.171 0.000333 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0763 0.00178 0.0406 0.0374 0.0504 0.236 0.241 0.000589 0.000601 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 473 68336.951 0.005 0.00246 0.0154 0.0645 0.0436 0.0592 0.117 0.148 0.000293 0.00037 +! Validation 473 68336.951 0.005 0.00258 0.052 0.104 0.0443 0.0607 0.247 0.272 0.000617 0.00068 +Wall time: 68336.95123799378 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.064 0.00236 0.0168 0.0425 0.058 0.132 0.155 0.00033 0.000387 + 474 200 0.159 0.00273 0.105 0.0468 0.0623 0.363 0.386 0.000908 0.000965 + 474 235 0.0626 0.00271 0.00837 0.0459 0.0621 0.0817 0.109 0.000204 0.000273 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0541 0.00246 0.0049 0.0443 0.0592 0.0806 0.0835 0.000202 0.000209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 474 68481.203 0.005 0.00256 0.025 0.0761 0.0446 0.0603 0.143 0.189 0.000359 0.000472 +! Validation 474 68481.203 0.005 0.00327 0.0151 0.0804 0.0503 0.0682 0.119 0.147 0.000298 0.000366 +Wall time: 68481.20381666906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0608 0.00241 0.0126 0.0431 0.0586 0.115 0.134 0.000289 0.000335 + 475 200 0.0642 0.00247 0.0149 0.0442 0.0593 0.129 0.145 0.000322 0.000364 + 475 235 0.0558 0.00249 0.00598 0.0441 0.0596 0.0686 0.0923 0.000172 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0377 0.0018 0.0017 0.0375 0.0506 0.0479 0.0492 0.00012 0.000123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 475 68625.462 0.005 0.00253 0.0224 0.0729 0.0443 0.06 0.142 0.179 0.000355 0.000447 +! Validation 475 68625.462 0.005 0.00251 0.0208 0.071 0.0437 0.0598 0.137 0.172 0.000342 0.00043 +Wall time: 68625.46240947396 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.072 0.00276 0.0167 0.0469 0.0627 0.126 0.154 0.000315 0.000386 + 476 200 0.0855 0.00247 0.0361 0.0438 0.0593 0.215 0.227 0.000536 0.000567 + 476 235 0.0657 0.00274 0.0109 0.0467 0.0625 0.1 0.125 0.00025 0.000312 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0412 0.00187 0.00367 0.0386 0.0517 0.0585 0.0723 0.000146 0.000181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 476 68769.726 0.005 0.00253 0.0225 0.073 0.0443 0.06 0.142 0.179 0.000355 0.000447 +! Validation 476 68769.726 0.005 0.00264 0.0144 0.0672 0.045 0.0613 0.118 0.143 0.000295 0.000358 +Wall time: 68769.7262204988 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0647 0.00234 0.018 0.0425 0.0577 0.137 0.16 0.000342 0.0004 + 477 200 0.0761 0.00325 0.0111 0.0511 0.068 0.0994 0.126 0.000248 0.000315 + 477 235 0.358 0.0029 0.3 0.0484 0.0643 0.591 0.653 0.00148 0.00163 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0923 0.00273 0.0377 0.047 0.0624 0.219 0.232 0.000549 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 477 68915.286 0.005 0.003 0.0371 0.097 0.0483 0.0653 0.178 0.228 0.000446 0.000569 +! Validation 477 68915.286 0.005 0.00357 0.0786 0.15 0.0528 0.0713 0.285 0.335 0.000713 0.000836 +Wall time: 68915.28634444671 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0621 0.00241 0.0138 0.0431 0.0586 0.108 0.14 0.000269 0.000351 + 478 200 0.0577 0.00251 0.00743 0.0441 0.0598 0.0774 0.103 0.000193 0.000257 + 478 235 0.0974 0.00245 0.0483 0.0444 0.0591 0.247 0.262 0.000618 0.000656 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.203 0.00489 0.105 0.064 0.0835 0.38 0.387 0.00095 0.000968 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 478 69059.543 0.005 0.00261 0.0321 0.0842 0.0451 0.061 0.168 0.214 0.000419 0.000534 +! Validation 478 69059.543 0.005 0.00546 0.373 0.482 0.0673 0.0882 0.653 0.729 0.00163 0.00182 +Wall time: 69059.54294471908 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0564 0.00249 0.00654 0.0441 0.0596 0.0781 0.0966 0.000195 0.000241 + 479 200 0.0576 0.00227 0.0122 0.0418 0.0569 0.11 0.132 0.000275 0.000329 + 479 235 0.0557 0.0022 0.0116 0.0419 0.056 0.115 0.129 0.000287 0.000321 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0539 0.00182 0.0176 0.038 0.0509 0.146 0.158 0.000365 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 479 69203.813 0.005 0.00301 0.0351 0.0954 0.048 0.0655 0.169 0.224 0.000423 0.00056 +! Validation 479 69203.813 0.005 0.00262 0.0174 0.0697 0.0448 0.061 0.134 0.158 0.000335 0.000394 +Wall time: 69203.81318051182 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0802 0.00212 0.0377 0.0407 0.055 0.208 0.232 0.00052 0.00058 + 480 200 0.0804 0.00232 0.0339 0.0422 0.0575 0.207 0.22 0.000517 0.00055 + 480 235 0.0859 0.003 0.0258 0.0493 0.0654 0.173 0.192 0.000432 0.000479 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.092 0.00217 0.0486 0.0416 0.0556 0.255 0.263 0.000637 0.000658 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 480 69347.964 0.005 0.00233 0.0141 0.0607 0.0425 0.0576 0.112 0.142 0.000279 0.000354 +! Validation 480 69347.964 0.005 0.0029 0.0994 0.157 0.0476 0.0643 0.34 0.376 0.000849 0.000941 +Wall time: 69347.96416059881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.346 0.00567 0.233 0.0661 0.0898 0.517 0.576 0.00129 0.00144 + 481 200 20.4 0.749 5.43 0.773 1.03 2.44 2.78 0.00609 0.00695 + 481 235 7.13 0.325 0.629 0.507 0.68 0.841 0.947 0.0021 0.00237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 10.2 0.312 4 0.491 0.667 2.35 2.39 0.00586 0.00597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 481 69492.158 0.005 0.37 11.7 19.1 0.398 0.726 1.81 4.09 0.00452 0.0102 +! Validation 481 69492.158 0.005 0.307 1.4 7.54 0.489 0.661 1.09 1.41 0.00271 0.00353 +Wall time: 69492.158293874 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 2.85 0.113 0.592 0.305 0.401 0.71 0.918 0.00178 0.0023 + 482 200 1.65 0.072 0.208 0.244 0.32 0.47 0.545 0.00117 0.00136 + 482 235 1.5 0.0642 0.211 0.23 0.302 0.444 0.548 0.00111 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 1.33 0.056 0.212 0.215 0.282 0.517 0.549 0.00129 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 482 69636.351 0.005 0.121 0.895 3.32 0.309 0.416 0.877 1.13 0.00219 0.00283 +! Validation 482 69636.351 0.005 0.0625 0.209 1.46 0.226 0.298 0.449 0.546 0.00112 0.00136 +Wall time: 69636.35155477002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.995 0.0446 0.103 0.191 0.252 0.294 0.384 0.000735 0.00096 + 483 200 0.761 0.0332 0.098 0.164 0.217 0.292 0.374 0.000731 0.000934 + 483 235 0.979 0.0301 0.378 0.156 0.207 0.64 0.733 0.0016 0.00183 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.691 0.0256 0.179 0.146 0.191 0.496 0.505 0.00124 0.00126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 483 69780.556 0.005 0.0439 0.4 1.28 0.188 0.25 0.606 0.755 0.00152 0.00189 +! Validation 483 69780.556 0.005 0.031 0.141 0.76 0.158 0.21 0.371 0.448 0.000928 0.00112 +Wall time: 69780.55679070204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.612 0.0247 0.117 0.141 0.188 0.32 0.409 0.0008 0.00102 + 484 200 0.48 0.0205 0.0704 0.129 0.171 0.25 0.317 0.000624 0.000792 + 484 235 0.392 0.019 0.0119 0.125 0.165 0.113 0.13 0.000281 0.000325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.367 0.0155 0.0576 0.113 0.148 0.285 0.286 0.000712 0.000716 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 484 69924.838 0.005 0.0242 0.315 0.799 0.14 0.186 0.532 0.671 0.00133 0.00168 +! Validation 484 69924.838 0.005 0.0193 0.0757 0.461 0.125 0.166 0.264 0.328 0.000661 0.000821 +Wall time: 69924.83884157473 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.381 0.0165 0.0506 0.116 0.153 0.214 0.268 0.000535 0.000671 + 485 200 0.394 0.0154 0.085 0.112 0.148 0.282 0.348 0.000706 0.00087 + 485 235 0.321 0.0139 0.0426 0.107 0.141 0.207 0.246 0.000517 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.236 0.0117 0.00203 0.098 0.129 0.0469 0.0538 0.000117 0.000134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 485 70069.435 0.005 0.0166 0.231 0.563 0.116 0.154 0.451 0.574 0.00113 0.00144 +! Validation 485 70069.435 0.005 0.0146 0.0528 0.345 0.109 0.144 0.218 0.274 0.000546 0.000686 +Wall time: 70069.43581336504 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.448 0.0129 0.19 0.103 0.136 0.441 0.52 0.0011 0.0013 + 486 200 0.315 0.0123 0.0683 0.0996 0.132 0.263 0.312 0.000656 0.00078 + 486 235 0.411 0.011 0.192 0.0947 0.125 0.478 0.523 0.0012 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.891 0.00929 0.705 0.0868 0.115 1 1 0.0025 0.00251 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 486 70213.859 0.005 0.0127 0.128 0.381 0.101 0.134 0.341 0.427 0.000851 0.00107 +! Validation 486 70213.859 0.005 0.0116 0.797 1.03 0.0969 0.129 1.04 1.07 0.0026 0.00266 +Wall time: 70213.85905050905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.252 0.0105 0.043 0.0924 0.122 0.19 0.248 0.000474 0.000619 + 487 200 0.248 0.00958 0.0561 0.0885 0.117 0.222 0.283 0.000555 0.000707 + 487 235 0.252 0.0106 0.0406 0.0919 0.123 0.186 0.24 0.000466 0.000601 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.18 0.00754 0.0295 0.0784 0.104 0.2 0.205 0.000499 0.000512 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 487 70358.137 0.005 0.0104 0.149 0.357 0.0919 0.122 0.368 0.461 0.00092 0.00115 +! Validation 487 70358.137 0.005 0.00966 0.0417 0.235 0.0881 0.117 0.196 0.244 0.00049 0.000609 +Wall time: 70358.13747901795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.265 0.00867 0.092 0.0839 0.111 0.31 0.362 0.000774 0.000905 + 488 200 0.275 0.00848 0.105 0.0828 0.11 0.355 0.388 0.000887 0.000969 + 488 235 0.223 0.00735 0.0762 0.0774 0.102 0.275 0.329 0.000687 0.000824 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.16 0.00635 0.0326 0.0721 0.0951 0.207 0.215 0.000517 0.000538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 488 70502.431 0.005 0.00869 0.126 0.3 0.084 0.111 0.345 0.424 0.000862 0.00106 +! Validation 488 70502.431 0.005 0.00838 0.0645 0.232 0.082 0.109 0.253 0.303 0.000633 0.000758 +Wall time: 70502.43097038288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.172 0.00753 0.0216 0.0782 0.104 0.141 0.176 0.000353 0.000439 + 489 200 0.166 0.00702 0.0255 0.0755 0.1 0.149 0.191 0.000372 0.000477 + 489 235 0.214 0.00699 0.0747 0.0753 0.0998 0.24 0.326 0.000599 0.000816 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.152 0.00565 0.0389 0.0678 0.0897 0.23 0.236 0.000574 0.000589 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 489 70646.937 0.005 0.00751 0.101 0.251 0.0779 0.103 0.302 0.379 0.000755 0.000948 +! Validation 489 70646.937 0.005 0.0074 0.195 0.343 0.0768 0.103 0.44 0.527 0.0011 0.00132 +Wall time: 70646.93766419403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.17 0.00659 0.038 0.0732 0.0969 0.2 0.233 0.0005 0.000582 + 490 200 0.259 0.00605 0.138 0.0698 0.0928 0.42 0.443 0.00105 0.00111 + 490 235 0.529 0.00719 0.385 0.076 0.101 0.72 0.741 0.0018 0.00185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.121 0.00535 0.0144 0.0659 0.0873 0.117 0.143 0.000292 0.000358 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 490 70791.164 0.005 0.00667 0.129 0.262 0.0734 0.0975 0.347 0.427 0.000867 0.00107 +! Validation 490 70791.164 0.005 0.00701 0.0498 0.19 0.0749 0.0999 0.217 0.266 0.000543 0.000666 +Wall time: 70791.16395996371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.135 0.00595 0.0157 0.0691 0.0921 0.13 0.149 0.000326 0.000374 + 491 200 0.12 0.00521 0.0156 0.065 0.0862 0.121 0.149 0.000303 0.000373 + 491 235 0.188 0.00597 0.0689 0.0687 0.0922 0.276 0.313 0.00069 0.000783 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.198 0.00444 0.109 0.0601 0.0795 0.388 0.395 0.000971 0.000987 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 491 70935.395 0.005 0.00605 0.0801 0.201 0.0698 0.0929 0.267 0.338 0.000667 0.000845 +! Validation 491 70935.395 0.005 0.00591 0.139 0.257 0.0683 0.0917 0.411 0.445 0.00103 0.00111 +Wall time: 70935.39544789493 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.138 0.00564 0.025 0.0668 0.0896 0.156 0.189 0.000391 0.000472 + 492 200 0.187 0.00545 0.0776 0.0655 0.0881 0.295 0.332 0.000738 0.000831 + 492 235 0.118 0.00545 0.00941 0.0661 0.0881 0.098 0.116 0.000245 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0886 0.00415 0.00552 0.0576 0.0769 0.0644 0.0887 0.000161 0.000222 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 492 71079.622 0.005 0.00538 0.0734 0.181 0.0656 0.0876 0.257 0.324 0.000642 0.000809 +! Validation 492 71079.622 0.005 0.00554 0.0298 0.141 0.066 0.0888 0.164 0.206 0.000411 0.000515 +Wall time: 71079.6228546109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.194 0.005 0.0945 0.0632 0.0844 0.332 0.367 0.000829 0.000917 + 493 200 0.159 0.00454 0.0685 0.0604 0.0804 0.294 0.312 0.000736 0.000781 + 493 235 0.213 0.00485 0.116 0.0631 0.0831 0.354 0.406 0.000885 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.109 0.00373 0.0348 0.0546 0.0729 0.214 0.223 0.000534 0.000557 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 493 71223.909 0.005 0.00499 0.0731 0.173 0.0631 0.0843 0.261 0.322 0.000653 0.000806 +! Validation 493 71223.909 0.005 0.00504 0.0333 0.134 0.0629 0.0848 0.178 0.218 0.000445 0.000545 +Wall time: 71223.90901631303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.127 0.00476 0.0316 0.0617 0.0824 0.181 0.212 0.000453 0.000531 + 494 200 0.13 0.00428 0.0439 0.0584 0.0781 0.219 0.25 0.000549 0.000625 + 494 235 0.116 0.00434 0.0286 0.0584 0.0787 0.164 0.202 0.000411 0.000505 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0925 0.00342 0.0241 0.0521 0.0698 0.174 0.185 0.000436 0.000463 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 494 71368.063 0.005 0.00464 0.0658 0.159 0.0608 0.0813 0.246 0.306 0.000615 0.000766 +! Validation 494 71368.063 0.005 0.00466 0.0629 0.156 0.0604 0.0815 0.255 0.299 0.000638 0.000748 +Wall time: 71368.06302877888 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0915 0.0042 0.00754 0.0579 0.0773 0.0852 0.104 0.000213 0.000259 + 495 200 0.24 0.00443 0.151 0.0593 0.0794 0.449 0.464 0.00112 0.00116 + 495 235 0.119 0.00415 0.0363 0.0574 0.0769 0.191 0.227 0.000477 0.000569 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.093 0.00328 0.0274 0.051 0.0684 0.186 0.198 0.000466 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 495 71512.212 0.005 0.00441 0.0874 0.176 0.0592 0.0793 0.285 0.353 0.000712 0.000883 +! Validation 495 71512.212 0.005 0.00444 0.0405 0.129 0.0589 0.0796 0.206 0.24 0.000515 0.0006 +Wall time: 71512.21250345279 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.107 0.00426 0.0218 0.0578 0.0779 0.149 0.176 0.000374 0.00044 + 496 200 0.114 0.00414 0.0306 0.0574 0.0768 0.164 0.209 0.000411 0.000522 + 496 235 0.125 0.00388 0.0471 0.0551 0.0744 0.246 0.259 0.000615 0.000647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.145 0.00309 0.0833 0.0494 0.0663 0.341 0.344 0.000852 0.000861 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 496 71656.377 0.005 0.00409 0.046 0.128 0.0569 0.0763 0.204 0.256 0.00051 0.00064 +! Validation 496 71656.377 0.005 0.00415 0.0951 0.178 0.0568 0.0769 0.343 0.368 0.000857 0.00092 +Wall time: 71656.37764628977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.1 0.00375 0.0254 0.0544 0.0731 0.166 0.19 0.000415 0.000475 + 497 200 0.119 0.00365 0.0457 0.0539 0.0721 0.234 0.255 0.000584 0.000638 + 497 235 0.0971 0.0036 0.0251 0.0536 0.0716 0.149 0.189 0.000372 0.000473 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0634 0.00291 0.00532 0.0477 0.0643 0.0696 0.087 0.000174 0.000218 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 497 71800.531 0.005 0.00389 0.0519 0.13 0.0555 0.0744 0.215 0.272 0.000537 0.00068 +! Validation 497 71800.531 0.005 0.00393 0.0209 0.0995 0.0552 0.0748 0.137 0.173 0.000341 0.000431 +Wall time: 71800.53124801302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.244 0.00361 0.172 0.0538 0.0717 0.479 0.495 0.0012 0.00124 + 498 200 0.143 0.0038 0.0673 0.0546 0.0736 0.276 0.31 0.00069 0.000774 + 498 235 0.112 0.00413 0.0289 0.0565 0.0767 0.191 0.203 0.000478 0.000507 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0997 0.00298 0.0401 0.0486 0.0652 0.234 0.239 0.000584 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 498 71944.723 0.005 0.00372 0.0669 0.141 0.0542 0.0728 0.249 0.309 0.000623 0.000772 +! Validation 498 71944.723 0.005 0.004 0.0524 0.132 0.0559 0.0755 0.242 0.273 0.000604 0.000683 +Wall time: 71944.72359607369 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0874 0.00357 0.016 0.0528 0.0713 0.119 0.151 0.000297 0.000377 + 499 200 0.0878 0.00345 0.0187 0.0522 0.0701 0.132 0.163 0.000329 0.000408 + 499 235 0.0772 0.00309 0.0155 0.0497 0.0663 0.118 0.149 0.000296 0.000372 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0549 0.00264 0.00201 0.0454 0.0614 0.0399 0.0535 9.98e-05 0.000134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 499 72088.960 0.005 0.00354 0.03 0.101 0.0528 0.0711 0.164 0.207 0.000411 0.000517 +! Validation 499 72088.960 0.005 0.00358 0.0118 0.0835 0.0525 0.0714 0.101 0.13 0.000253 0.000325 +Wall time: 72088.95992531907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.0714 0.00332 0.00496 0.0514 0.0688 0.0673 0.0841 0.000168 0.00021 + 500 200 0.0809 0.00326 0.0156 0.0509 0.0682 0.118 0.149 0.000296 0.000372 + 500 235 0.0932 0.00345 0.0242 0.0524 0.0701 0.163 0.186 0.000407 0.000464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.0555 0.00264 0.00275 0.0455 0.0613 0.0407 0.0626 0.000102 0.000156 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 500 72233.116 0.005 0.0035 0.064 0.134 0.0525 0.0706 0.219 0.302 0.000547 0.000755 +! Validation 500 72233.116 0.005 0.0035 0.0136 0.0837 0.052 0.0706 0.107 0.139 0.000268 0.000349 +Wall time: 72233.11625261512 +! Stop training: max epochs +Wall time: 72233.15702054976 +Cumulative wall time: 72233.15702054976 diff --git a/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_batch_val.csv b/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_batch_val.csv new file mode 100644 index 0000000000000000000000000000000000000000..4ad71ce4e3d57255a59333ee91273f84c94a26ba --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_batch_val.csv @@ -0,0 +1,50101 @@ +epoch, batch, loss, loss_f, loss_e, f_mae, f_rmse, e_mae, e_rmse, e/N_mae, e/N_rmse + 0, 1, 645.53, 1.0563, 624.4, 0.92303, 1.2267, 29.821, 29.824, 0.074552, 0.074561 + 0, 2, 596.08, 1.0655, 574.77, 0.9264, 1.232, 28.608, 28.614, 0.071519, 0.071536 + 0, 3, 587.3, 1.126, 564.78, 0.94388, 1.2665, 28.356, 28.365, 0.07089, 0.070912 + 0, 4, 575.39, 1.1261, 552.87, 0.93925, 1.2666, 28.042, 28.064, 0.070105, 0.07016 + 0, 5, 517.68, 1.2018, 493.65, 0.96542, 1.3084, 26.501, 26.518, 0.066252, 0.066296 + 0, 6, 568.72, 1.1773, 545.18, 0.9498, 1.295, 27.856, 27.868, 0.069641, 0.06967 + 0, 7, 544.34, 1.1617, 521.1, 0.9493, 1.2864, 27.244, 27.246, 0.068109, 0.068115 + 0, 8, 572.12, 1.1471, 549.18, 0.93472, 1.2783, 27.96, 27.97, 0.069901, 0.069926 + 0, 9, 580.53, 1.1524, 557.48, 0.9433, 1.2813, 28.179, 28.181, 0.070447, 0.070452 + 0, 10, 521.57, 1.1916, 497.73, 0.95893, 1.3029, 26.626, 26.628, 0.066566, 0.06657 + 0, 11, 554.41, 1.1607, 531.19, 0.94972, 1.2859, 27.503, 27.508, 0.068757, 0.068771 + 0, 12, 816.87, 1.0467, 795.94, 0.89417, 1.2211, 33.577, 33.673, 0.083943, 0.084182 + 0, 13, 887.31, 1.0444, 866.42, 0.88979, 1.2197, 35.124, 35.132, 0.087809, 0.08783 + 0, 14, 891.07, 1.0611, 869.85, 0.89657, 1.2295, 35.197, 35.201, 0.087993, 0.088003 + 0, 15, 907.47, 1.0344, 886.78, 0.88666, 1.2139, 35.54, 35.542, 0.088851, 0.088856 + 0, 16, 882.09, 1.0842, 860.4, 0.91366, 1.2428, 35.005, 35.01, 0.087514, 0.087524 + 0, 17, 903.53, 1.0543, 882.44, 0.89922, 1.2255, 35.452, 35.455, 0.088629, 0.088638 + 0, 18, 872.74, 1.0476, 851.79, 0.90862, 1.2216, 34.826, 34.834, 0.087064, 0.087085 + 0, 19, 809.43, 1.097, 787.49, 0.92442, 1.2501, 33.485, 33.493, 0.083713, 0.083734 + 0, 20, 825.95, 1.0901, 804.14, 0.91098, 1.2462, 33.838, 33.846, 0.084596, 0.084614 + 0, 21, 804.77, 1.0876, 783.02, 0.91341, 1.2447, 33.387, 33.398, 0.083468, 0.083496 + 0, 22, 764.67, 1.0971, 742.72, 0.91943, 1.2502, 32.525, 32.528, 0.081313, 0.081319 + 0, 23, 740.42, 1.1201, 718.02, 0.92127, 1.2632, 31.978, 31.982, 0.079946, 0.079955 + 0, 24, 710.21, 1.0566, 689.08, 0.91022, 1.2269, 31.326, 31.331, 0.078315, 0.078327 + 0, 25, 723.58, 1.1106, 701.36, 0.92081, 1.2578, 31.599, 31.609, 0.078998, 0.079022 + 0, 26, 808.15, 1.0825, 786.5, 0.90455, 1.2418, 33.448, 33.472, 0.083621, 0.083681 + 0, 27, 817.57, 1.0739, 796.09, 0.9115, 1.2368, 33.668, 33.676, 0.084169, 0.08419 + 0, 28, 828.67, 1.1419, 805.84, 0.93384, 1.2754, 33.877, 33.881, 0.084693, 0.084703 + 0, 29, 810.12, 1.1806, 786.51, 0.9383, 1.2968, 33.463, 33.473, 0.083657, 0.083681 + 0, 30, 692.84, 1.1607, 669.63, 0.941, 1.2859, 30.694, 30.886, 0.076734, 0.077214 + 0, 31, 387.46, 1.0614, 366.23, 0.92077, 1.2296, 22.807, 22.841, 0.057017, 0.057103 + 0, 32, 460.43, 0.96755, 441.08, 0.8773, 1.174, 25.054, 25.067, 0.062635, 0.062667 + 0, 33, 505.59, 0.99669, 485.66, 0.88126, 1.1916, 26.293, 26.303, 0.065733, 0.065757 + 0, 34, 516.73, 1.016, 496.4, 0.89416, 1.2031, 26.589, 26.592, 0.066474, 0.066481 + 0, 35, 487.41, 1.0524, 466.36, 0.90744, 1.2244, 25.766, 25.775, 0.064414, 0.064437 + 0, 36, 478.38, 1.0519, 457.34, 0.90523, 1.2241, 25.512, 25.524, 0.063781, 0.063811 + 0, 37, 453.67, 1.0407, 432.85, 0.89918, 1.2176, 24.828, 24.832, 0.062071, 0.062079 + 0, 38, 467.37, 0.97811, 447.81, 0.88217, 1.1804, 25.253, 25.257, 0.063132, 0.063143 + 0, 39, 412.44, 1.0366, 391.71, 0.89443, 1.2152, 23.614, 23.622, 0.059035, 0.059055 + 0, 40, 389.99, 1.0648, 368.7, 0.90575, 1.2316, 22.898, 22.918, 0.057245, 0.057295 + 0, 41, 380.94, 1.0711, 359.51, 0.91166, 1.2353, 22.623, 22.631, 0.056557, 0.056576 + 0, 42, 372.06, 1.0467, 351.13, 0.90701, 1.2211, 22.359, 22.365, 0.055897, 0.055913 + 0, 43, 337.8, 1.0698, 316.4, 0.91483, 1.2345, 21.225, 21.23, 0.053063, 0.053076 + 0, 44, 364.51, 1.0593, 343.33, 0.90507, 1.2284, 22.109, 22.115, 0.055273, 0.055288 + 0, 45, 337.33, 1.0735, 315.86, 0.9156, 1.2366, 21.205, 21.212, 0.053011, 0.05303 + 0, 46, 352.58, 1.07, 331.18, 0.91469, 1.2346, 21.698, 21.721, 0.054245, 0.054301 + 0, 47, 582.92, 1.0704, 561.51, 0.91246, 1.2348, 27.709, 28.282, 0.069272, 0.070706 + 0, 48, 694.18, 1.0761, 672.66, 0.92218, 1.2381, 30.921, 30.955, 0.077302, 0.077388 + 0, 49, 769.48, 1.0197, 749.09, 0.89293, 1.2052, 32.636, 32.667, 0.08159, 0.081667 + 0, 50, 696.8, 1.0401, 676, 0.9017, 1.2173, 31.02, 31.032, 0.077551, 0.07758 + 0, 51, 632.99, 1.0783, 611.43, 0.92062, 1.2394, 29.496, 29.513, 0.07374, 0.073782 + 0, 52, 664.77, 1.0373, 644.03, 0.8931, 1.2156, 30.273, 30.289, 0.075682, 0.075723 + 0, 53, 715.34, 1.0461, 694.42, 0.90018, 1.2208, 31.435, 31.452, 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0.000547, 0.000755, 0.0035037, 0.013642, 0.083716, 0.052, 0.0706, 0.107, 0.139, 0.000268, 0.000349 diff --git a/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_initialization.csv b/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_initialization.csv new file mode 100644 index 0000000000000000000000000000000000000000..86e612648c5ddc392f256f6b06cfdfa21bc5c5b9 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/adsorption/metrics_initialization.csv @@ -0,0 +1,2 @@ +epoch, wall, LR,validation_loss_f,validation_loss_e,validation_loss,validation_f_mae,validation_f_rmse,validation_e_mae,validation_e_rmse,validation_e/N_mae,validation_e/N_rmse + 0, 7.412, 0.005, 1.0688, 568.06, 589.43, 0.912, 1.23, 27.8, 28.4, 0.0696, 0.0711 diff --git a/ads_16_length_8000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml b/ads_16_length_8000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..906b5b5aa67bc70a044b209bbfe73545d7dce375 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml @@ -0,0 +1,13 @@ +AtomicData_options: + r_max: 5.0 +ase_args: + format: extxyz +dtype: torch.float64 +file_name: ../trainset.xyz +include_frames: null +include_keys: null +key_mapping: + force: forces +nequip_version: 0.6.1 +root: results +url: null diff --git a/ads_16_length_8000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml b/ads_16_length_8000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..40b87d4c1e16bd79aa0ed8dfc8a9327facf04bad --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml @@ -0,0 +1,13 @@ +AtomicData_options: + r_max: 5.0 +ase_args: + format: extxyz +dtype: torch.float64 +file_name: ../testset.xyz +include_frames: null +include_keys: null +key_mapping: + force: forces +nequip_version: 0.6.1 +root: results +url: null diff --git a/ads_16_length_8000/ALLEGRO/slurm-254471.out b/ads_16_length_8000/ALLEGRO/slurm-254471.out new file mode 100644 index 0000000000000000000000000000000000000000..db52e31e4fc8d7af83d340c9210d7dc8da9ebf81 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/slurm-254471.out @@ -0,0 +1,32 @@ +Train start time: 2024-12-07_06:57:52 +Torch device: cuda +Processing dataset... +Loaded data: Batch(atomic_numbers=[2400000, 1], batch=[2400000], cell=[6000, 3, 3], edge_cell_shift=[82543766, 3], edge_index=[2, 82543766], forces=[2400000, 3], pbc=[6000, 3], pos=[2400000, 3], ptr=[6001], total_energy=[6000, 1]) + processed data size: ~3295.80 MB +Cached processed data to disk +Done! +Successfully loaded the data set of type ASEDataset(6000)... +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/bin/nequip-train", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 94, in main + trainer = fresh_start(config) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 219, in fresh_start + trainer.set_dataset(dataset, validation_dataset) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/train/trainer.py", line 1217, in set_dataset + raise ValueError( +ValueError: too little data for training and validation. please reduce n_train and n_val +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/bin/nequip-evaluate", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/evaluate.py", line 196, in main + trainer = torch.load( + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 1319, in load + with _open_file_like(f, "rb") as opened_file: + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 659, in _open_file_like + return _open_file(name_or_buffer, mode) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 640, in __init__ + super().__init__(open(name, mode)) +FileNotFoundError: [Errno 2] No such file or directory: 'results/adsorption/trainer.pth' +Train end time: 2024-12-07_07:01:16 +Training duration: 0h 3m 24s diff --git a/ads_16_length_8000/ALLEGRO/slurm-254606.out b/ads_16_length_8000/ALLEGRO/slurm-254606.out new file mode 100644 index 0000000000000000000000000000000000000000..6123caf2b8d888ff41601eb3c4b97d321b8f16ae --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/slurm-254606.out @@ -0,0 +1,32 @@ +Train start time: 2024-12-07_18:16:33 +Torch device: cuda +Processing dataset... +Loaded data: Batch(atomic_numbers=[2400000, 1], batch=[2400000], cell=[6000, 3, 3], edge_cell_shift=[82543766, 3], edge_index=[2, 82543766], forces=[2400000, 3], pbc=[6000, 3], pos=[2400000, 3], ptr=[6001], total_energy=[6000, 1]) + processed data size: ~3295.80 MB +Cached processed data to disk +Done! +Successfully loaded the data set of type ASEDataset(6000)... +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/bin/nequip-train", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 94, in main + trainer = fresh_start(config) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 219, in fresh_start + trainer.set_dataset(dataset, validation_dataset) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/train/trainer.py", line 1217, in set_dataset + raise ValueError( +ValueError: too little data for training and validation. please reduce n_train and n_val +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/bin/nequip-evaluate", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/nequip/scripts/evaluate.py", line 196, in main + trainer = torch.load( + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 1319, in load + with _open_file_like(f, "rb") as opened_file: + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 659, in _open_file_like + return _open_file(name_or_buffer, mode) + File "/users/PAS2490/marcusshen/miniconda3/envs/allegro_env/lib/python3.9/site-packages/torch/serialization.py", line 640, in __init__ + super().__init__(open(name, mode)) +FileNotFoundError: [Errno 2] No such file or directory: 'results/adsorption/trainer.pth' +Train end time: 2024-12-07_18:19:33 +Training duration: 0h 3m 0s diff --git a/ads_16_length_8000/ALLEGRO/slurm-257528.out b/ads_16_length_8000/ALLEGRO/slurm-257528.out new file mode 100644 index 0000000000000000000000000000000000000000..facb04943414071764d27afd40a1dad9d3209b81 --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/slurm-257528.out @@ -0,0 +1,8121 @@ +Train start time: 2024-12-08_10:34:06 +Torch device: cuda +Processing dataset... +Loaded data: Batch(atomic_numbers=[3200000, 1], batch=[3200000], cell=[8000, 3, 3], edge_cell_shift=[110025848, 3], edge_index=[2, 110025848], forces=[3200000, 3], pbc=[8000, 3], pos=[3200000, 3], ptr=[8001], total_energy=[8000, 1]) + processed data size: ~4393.16 MB +Cached processed data to disk +Done! +Successfully loaded the data set of type ASEDataset(8000)... +Replace string dataset_per_atom_total_energy_mean to -347.4158314169961 +Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -347.415831]. +Replace string dataset_forces_rms to 1.1935424919787043 +Initially outputs are globally scaled by: 1.1935424919787043, total_energy are globally shifted by None. +Successfully built the network... +Number of weights: 1406856 +Number of trainable weights: 1406856 +! Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 532 1.1 510 0.924 1.25 26.9 26.9 0.0673 0.0674 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 7.412 0.005 1.07 568 589 0.912 1.23 27.8 28.4 0.0696 0.0711 +Wall time: 7.4122820110060275 +! Best model 0 589.431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 24.8 0.997 4.83 0.884 1.19 2.21 2.62 0.00553 0.00656 + 1 200 26.7 0.997 6.72 0.882 1.19 2.52 3.09 0.0063 0.00773 + 1 235 23.2 0.961 4.03 0.875 1.17 2.06 2.4 0.00515 0.00599 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 31.8 1 11.7 0.888 1.2 4.03 4.08 0.0101 0.0102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 1 154.538 0.005 0.993 5.13e+03 5.15e+03 0.883 1.19 28.3 85.6 0.0707 0.214 +! Validation 1 154.538 0.005 0.981 5.4 25 0.877 1.18 2.3 2.77 0.00574 0.00694 +Wall time: 154.538886888884 +! Best model 1 25.019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 24.7 0.99 4.93 0.881 1.19 1.83 2.65 0.00458 0.00662 + 2 200 26.1 0.993 6.25 0.883 1.19 2.52 2.98 0.00631 0.00746 + 2 235 26.3 1.01 6 0.894 1.2 2.28 2.92 0.00569 0.00731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 25.4 0.998 5.47 0.885 1.19 2.72 2.79 0.00679 0.00698 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 2 298.974 0.005 0.985 5.38 25.1 0.879 1.18 2.24 2.77 0.00559 0.00692 +! Validation 2 298.974 0.005 0.975 5.02 24.5 0.875 1.18 2.05 2.67 0.00512 0.00668 +Wall time: 298.97638173773885 +! Best model 2 24.520 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 23.6 0.989 3.76 0.881 1.19 1.9 2.32 0.00476 0.00579 + 3 200 26 1 5.9 0.888 1.2 1.95 2.9 0.00487 0.00725 + 3 235 27.5 1.01 7.27 0.889 1.2 2.7 3.22 0.00674 0.00805 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 29.7 0.991 9.88 0.882 1.19 3.7 3.75 0.00924 0.00938 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 3 443.940 0.005 0.979 5.18 24.7 0.876 1.18 2.19 2.71 0.00548 0.00678 +! Validation 3 443.940 0.005 0.968 4.67 24 0.872 1.17 2.12 2.58 0.00529 0.00645 +Wall time: 443.9402854051441 +! Best model 3 24.035 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 23.8 0.968 4.4 0.871 1.17 1.99 2.5 0.00498 0.00626 + 4 200 25 0.973 5.58 0.875 1.18 2.22 2.82 0.00555 0.00705 + 4 235 21.9 0.966 2.55 0.871 1.17 1.29 1.91 0.00323 0.00477 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 28.2 0.983 8.58 0.879 1.18 3.44 3.5 0.0086 0.00874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 4 588.365 0.005 0.971 4.79 24.2 0.873 1.18 2.11 2.61 0.00528 0.00653 +! Validation 4 588.365 0.005 0.961 4.45 23.7 0.868 1.17 2.03 2.52 0.00506 0.00629 +Wall time: 588.3658088729717 +! Best model 4 23.668 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 22.1 0.975 2.54 0.874 1.18 1.52 1.9 0.0038 0.00476 + 5 200 24.7 0.973 5.2 0.874 1.18 2.14 2.72 0.00535 0.00681 + 5 235 22.4 0.95 3.42 0.87 1.16 1.9 2.21 0.00476 0.00552 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 24.3 0.975 4.8 0.875 1.18 2.54 2.62 0.00635 0.00654 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 732.702 0.005 0.964 4.7 24 0.869 1.17 2.09 2.59 0.00521 0.00647 +! Validation 5 732.702 0.005 0.953 4.28 23.3 0.864 1.17 1.86 2.47 0.00464 0.00617 +Wall time: 732.7023157807998 +! Best model 5 23.341 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 23.8 0.941 4.96 0.861 1.16 2.2 2.66 0.0055 0.00665 + 6 200 21.9 0.937 3.12 0.858 1.16 1.8 2.11 0.00451 0.00527 + 6 235 21.6 0.968 2.2 0.871 1.17 1.45 1.77 0.00363 0.00442 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 33.1 0.966 13.8 0.871 1.17 4.38 4.43 0.011 0.0111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 877.057 0.005 0.956 4.22 23.3 0.865 1.17 1.97 2.45 0.00491 0.00613 +! Validation 6 877.057 0.005 0.945 5.38 24.3 0.86 1.16 2.33 2.77 0.00583 0.00692 +Wall time: 877.0573468669318 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 25 0.927 6.43 0.853 1.15 2.58 3.03 0.00644 0.00757 + 7 200 21.4 0.942 2.54 0.861 1.16 1.58 1.9 0.00396 0.00475 + 7 235 25.5 0.962 6.21 0.861 1.17 2.4 2.98 0.00599 0.00744 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 21.5 0.957 2.36 0.866 1.17 1.73 1.83 0.00432 0.00458 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 1021.374 0.005 0.947 4.08 23 0.861 1.16 1.93 2.41 0.00483 0.00602 +! Validation 7 1021.374 0.005 0.936 3.91 22.6 0.855 1.15 1.74 2.36 0.00436 0.0059 +Wall time: 1021.3746373481117 +! Best model 7 22.637 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 22.2 0.943 3.34 0.855 1.16 1.79 2.18 0.00447 0.00545 + 8 200 22.4 0.927 3.86 0.849 1.15 2.01 2.34 0.00502 0.00586 + 8 235 22.2 0.906 4.05 0.842 1.14 2.02 2.4 0.00505 0.006 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 20.6 0.947 1.66 0.861 1.16 1.42 1.54 0.00354 0.00384 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 1165.753 0.005 0.938 4.09 22.9 0.856 1.16 1.93 2.42 0.00483 0.00604 +! Validation 8 1165.753 0.005 0.927 4.12 22.7 0.85 1.15 1.77 2.42 0.00443 0.00606 +Wall time: 1165.75320250215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 22.7 0.936 3.96 0.855 1.15 2.15 2.37 0.00537 0.00594 + 9 200 20.6 0.911 2.4 0.843 1.14 1.52 1.85 0.00381 0.00462 + 9 235 22 0.915 3.71 0.843 1.14 1.56 2.3 0.00391 0.00575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 21.3 0.936 2.54 0.855 1.15 1.81 1.9 0.00453 0.00476 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 1310.133 0.005 0.927 3.34 21.9 0.85 1.15 1.74 2.18 0.00436 0.00545 +! Validation 9 1310.133 0.005 0.915 3.03 21.3 0.844 1.14 1.53 2.08 0.00382 0.00519 +Wall time: 1310.1337010888383 +! Best model 9 21.337 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 20.4 0.915 2.15 0.844 1.14 1.46 1.75 0.00366 0.00437 + 10 200 22.3 0.943 3.46 0.854 1.16 1.87 2.22 0.00468 0.00555 + 10 235 20.9 0.94 2.06 0.853 1.16 1.42 1.71 0.00356 0.00428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 34.7 0.918 16.3 0.846 1.14 4.79 4.83 0.012 0.0121 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 10 1454.558 0.005 0.914 2.98 21.3 0.843 1.14 1.63 2.06 0.00409 0.00515 +! Validation 10 1454.558 0.005 0.898 7.87 25.8 0.835 1.13 3.05 3.35 0.00762 0.00837 +Wall time: 1454.5582863050513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 20.7 0.88 3.15 0.827 1.12 1.76 2.12 0.0044 0.00529 + 11 200 18.5 0.859 1.29 0.817 1.11 1.12 1.35 0.00281 0.00338 + 11 235 19.3 0.831 2.67 0.808 1.09 1.64 1.95 0.0041 0.00488 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 19.2 0.867 1.88 0.819 1.11 1.56 1.63 0.0039 0.00409 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 11 1598.970 0.005 0.882 2.38 20 0.827 1.12 1.46 1.84 0.00366 0.0046 +! Validation 11 1598.970 0.005 0.846 1.63 18.6 0.809 1.1 1.12 1.52 0.00281 0.00381 +Wall time: 1598.9706212067977 +! Best model 11 18.553 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 16.9 0.797 0.992 0.786 1.07 0.943 1.19 0.00236 0.00297 + 12 200 16.4 0.751 1.34 0.759 1.03 1.08 1.38 0.0027 0.00345 + 12 235 15.7 0.705 1.59 0.742 1 1.21 1.5 0.00303 0.00376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 15.3 0.732 0.658 0.751 1.02 0.865 0.968 0.00216 0.00242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 12 1743.700 0.005 0.799 2.25 18.2 0.786 1.07 1.41 1.79 0.00353 0.00447 +! Validation 12 1743.700 0.005 0.711 3.49 17.7 0.741 1.01 1.84 2.23 0.00459 0.00557 +Wall time: 1743.7015087939799 +! Best model 12 17.716 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 13.1 0.592 1.23 0.678 0.919 1.06 1.33 0.00266 0.00331 + 13 200 12.6 0.514 2.3 0.627 0.856 1.53 1.81 0.00381 0.00452 + 13 235 13.3 0.483 3.62 0.61 0.83 2.02 2.27 0.00505 0.00568 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 10.6 0.505 0.548 0.617 0.848 0.747 0.883 0.00187 0.00221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 13 1888.258 0.005 0.585 3.23 14.9 0.67 0.913 1.74 2.14 0.00434 0.00536 +! Validation 13 1888.258 0.005 0.495 1.18 11.1 0.616 0.84 0.973 1.3 0.00243 0.00324 +Wall time: 1888.2582933818921 +! Best model 13 11.081 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 10.3 0.447 1.38 0.586 0.798 1.15 1.4 0.00288 0.00351 + 14 200 9.65 0.413 1.38 0.562 0.767 1.2 1.4 0.003 0.00351 + 14 235 8.72 0.399 0.747 0.549 0.754 0.812 1.03 0.00203 0.00258 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 9.46 0.423 0.996 0.561 0.776 1.1 1.19 0.00274 0.00298 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 14 2032.669 0.005 0.448 2.64 11.6 0.585 0.799 1.55 1.94 0.00387 0.00485 +! Validation 14 2032.669 0.005 0.417 1.1 9.43 0.562 0.77 0.954 1.25 0.00238 0.00313 +Wall time: 2032.6695224968717 +! Best model 14 9.433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 13.7 0.397 5.71 0.549 0.752 2.63 2.85 0.00658 0.00713 + 15 200 8.34 0.385 0.644 0.538 0.74 0.78 0.958 0.00195 0.00239 + 15 235 8.39 0.379 0.813 0.538 0.735 0.832 1.08 0.00208 0.00269 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 10.1 0.397 2.2 0.542 0.752 1.71 1.77 0.00426 0.00442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 15 2177.185 0.005 0.393 4.81 12.7 0.546 0.749 2.09 2.62 0.00523 0.00655 +! Validation 15 2177.185 0.005 0.388 6.47 14.2 0.542 0.744 2.74 3.03 0.00686 0.00759 +Wall time: 2177.1854655616917 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 9.95 0.367 2.61 0.528 0.723 1.73 1.93 0.00432 0.00482 + 16 200 8.89 0.351 1.87 0.518 0.708 1.49 1.63 0.00371 0.00408 + 16 235 23 0.347 16.1 0.512 0.703 4.65 4.79 0.0116 0.012 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 11.7 0.355 4.63 0.513 0.711 2.53 2.57 0.00632 0.00642 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 16 2322.069 0.005 0.368 3.97 11.3 0.529 0.724 1.85 2.37 0.00464 0.00592 +! Validation 16 2322.069 0.005 0.351 8.28 15.3 0.515 0.707 3.22 3.43 0.00806 0.00859 +Wall time: 2322.069620097056 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 9.06 0.329 2.48 0.501 0.685 1.64 1.88 0.0041 0.0047 + 17 200 19 0.316 12.7 0.492 0.67 4.17 4.25 0.0104 0.0106 + 17 235 14.1 0.333 7.44 0.504 0.688 3.04 3.26 0.0076 0.00814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 6.84 0.331 0.22 0.495 0.687 0.343 0.56 0.000858 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 17 2466.735 0.005 0.33 3.84 10.4 0.501 0.686 1.79 2.34 0.00448 0.00584 +! Validation 17 2466.735 0.005 0.326 0.945 7.47 0.497 0.682 0.869 1.16 0.00217 0.0029 +Wall time: 2466.7349044638686 +! Best model 17 7.467 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 12.3 0.324 5.84 0.498 0.679 2.73 2.88 0.00683 0.00721 + 18 200 6.69 0.31 0.488 0.485 0.664 0.666 0.834 0.00167 0.00208 + 18 235 24.9 0.3 18.9 0.478 0.654 5.1 5.19 0.0127 0.013 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 48.3 0.306 42.2 0.478 0.66 7.74 7.75 0.0193 0.0194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 18 2611.253 0.005 0.316 5.57 11.9 0.491 0.671 2.23 2.81 0.00558 0.00702 +! Validation 18 2611.253 0.005 0.304 41.8 47.9 0.482 0.658 7.62 7.72 0.0191 0.0193 +Wall time: 2611.2537162578665 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 7.23 0.289 1.45 0.471 0.642 1.2 1.44 0.00299 0.00359 + 19 200 8.9 0.298 2.94 0.479 0.651 1.85 2.05 0.00463 0.00512 + 19 235 21.2 0.296 15.3 0.481 0.649 4.59 4.66 0.0115 0.0117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 6.43 0.296 0.507 0.472 0.65 0.71 0.85 0.00178 0.00212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 19 2755.750 0.005 0.298 6.25 12.2 0.478 0.651 2.3 2.98 0.00575 0.00745 +! Validation 19 2755.750 0.005 0.294 1.12 7 0.475 0.647 0.977 1.26 0.00244 0.00315 +Wall time: 2755.7503659580834 +! Best model 19 7.004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 7.94 0.269 2.56 0.455 0.619 1.75 1.91 0.00437 0.00477 + 20 200 24.7 0.284 19 0.469 0.637 4.95 5.2 0.0124 0.013 + 20 235 6.93 0.296 1.01 0.478 0.649 0.963 1.2 0.00241 0.003 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 10.2 0.287 4.46 0.466 0.639 2.48 2.52 0.0062 0.0063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 20 2901.347 0.005 0.275 5.26 10.8 0.461 0.626 2.06 2.74 0.00514 0.00685 +! Validation 20 2901.347 0.005 0.283 2.94 8.61 0.468 0.635 1.85 2.05 0.00463 0.00512 +Wall time: 2901.3470059768297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 6.65 0.256 1.54 0.445 0.604 1.23 1.48 0.00307 0.0037 + 21 200 6.78 0.253 1.72 0.444 0.6 1.28 1.57 0.0032 0.00391 + 21 235 5.49 0.241 0.678 0.433 0.586 0.716 0.983 0.00179 0.00246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 5.05 0.246 0.125 0.433 0.593 0.378 0.423 0.000944 0.00106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 21 3045.718 0.005 0.256 3.87 8.98 0.445 0.603 1.84 2.35 0.00459 0.00588 +! Validation 21 3045.718 0.005 0.249 1.46 6.44 0.438 0.595 1.06 1.44 0.00264 0.00361 +Wall time: 3045.718507716898 +! Best model 21 6.442 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 15.2 0.241 10.4 0.432 0.585 3.73 3.84 0.00933 0.00961 + 22 200 5.08 0.225 0.584 0.42 0.566 0.721 0.912 0.0018 0.00228 + 22 235 9 0.223 4.54 0.418 0.564 2.53 2.54 0.00632 0.00636 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 5.08 0.228 0.52 0.417 0.57 0.762 0.861 0.0019 0.00215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 22 3190.058 0.005 0.237 4.7 9.44 0.429 0.581 2.05 2.59 0.00512 0.00647 +! Validation 22 3190.058 0.005 0.232 1.64 6.27 0.423 0.574 1.26 1.53 0.00314 0.00382 +Wall time: 3190.058355611749 +! Best model 22 6.273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 30.7 0.23 26.1 0.424 0.572 6.04 6.1 0.0151 0.0152 + 23 200 5.63 0.209 1.45 0.405 0.545 1.23 1.44 0.00306 0.00359 + 23 235 4.88 0.21 0.683 0.405 0.547 0.834 0.986 0.00209 0.00247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 4.62 0.212 0.382 0.402 0.549 0.628 0.738 0.00157 0.00184 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 23 3334.436 0.005 0.225 4.63 9.13 0.419 0.566 1.99 2.57 0.00499 0.00642 +! Validation 23 3334.436 0.005 0.216 0.639 4.97 0.409 0.555 0.762 0.954 0.0019 0.00239 +Wall time: 3334.43658031011 +! Best model 23 4.967 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 5.76 0.213 1.51 0.407 0.55 1.25 1.47 0.00312 0.00366 + 24 200 8.06 0.214 3.78 0.408 0.552 2.26 2.32 0.00564 0.0058 + 24 235 6.41 0.21 2.22 0.407 0.546 1.73 1.78 0.00434 0.00445 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 12.5 0.213 8.28 0.404 0.55 3.41 3.43 0.00853 0.00859 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 24 3478.824 0.005 0.214 5.65 9.94 0.41 0.553 2.22 2.84 0.00556 0.0071 +! Validation 24 3478.824 0.005 0.214 6.42 10.7 0.409 0.552 2.9 3.02 0.00726 0.00756 +Wall time: 3478.8240611427464 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 14.5 0.205 10.4 0.402 0.54 3.75 3.86 0.00936 0.00964 + 25 200 22.9 0.202 18.8 0.398 0.536 5.12 5.18 0.0128 0.0129 + 25 235 4.41 0.205 0.315 0.403 0.54 0.52 0.67 0.0013 0.00167 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 6.08 0.206 1.96 0.399 0.542 1.63 1.67 0.00407 0.00418 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 25 3623.210 0.005 0.204 4.74 8.81 0.4 0.539 2.09 2.6 0.00522 0.0065 +! Validation 25 3623.210 0.005 0.207 1.03 5.16 0.402 0.543 1.04 1.21 0.0026 0.00302 +Wall time: 3623.210521190893 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 18.3 0.195 14.4 0.395 0.528 4.45 4.54 0.0111 0.0113 + 26 200 6.38 0.189 2.59 0.388 0.519 1.77 1.92 0.00443 0.0048 + 26 235 13.9 0.169 10.6 0.368 0.49 3.81 3.88 0.00952 0.0097 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 12.3 0.187 8.57 0.379 0.516 3.47 3.49 0.00868 0.00874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 26 3767.686 0.005 0.192 3.46 7.3 0.389 0.523 1.71 2.21 0.00427 0.00553 +! Validation 26 3767.686 0.005 0.19 13.8 17.6 0.386 0.521 4.34 4.44 0.0109 0.0111 +Wall time: 3767.6863808799535 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 8.3 0.173 4.84 0.371 0.496 2.54 2.63 0.00634 0.00656 + 27 200 5.71 0.185 2.01 0.381 0.513 1.51 1.69 0.00378 0.00423 + 27 235 8 0.181 4.38 0.376 0.508 2.41 2.5 0.00602 0.00624 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 3.62 0.176 0.096 0.369 0.501 0.261 0.37 0.000652 0.000925 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 27 3912.152 0.005 0.182 3.73 7.37 0.379 0.509 1.85 2.3 0.00464 0.00576 +! Validation 27 3912.152 0.005 0.181 1.32 4.94 0.376 0.507 1.06 1.37 0.00264 0.00343 +Wall time: 3912.152584776748 +! Best model 27 4.936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 4.18 0.183 0.52 0.379 0.511 0.674 0.86 0.00169 0.00215 + 28 200 5.54 0.167 2.19 0.364 0.488 1.54 1.77 0.00386 0.00441 + 28 235 4.2 0.173 0.753 0.37 0.496 0.875 1.04 0.00219 0.00259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 4.75 0.186 1.03 0.378 0.514 1.15 1.21 0.00287 0.00303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 28 4057.006 0.005 0.174 3.95 7.43 0.371 0.498 1.9 2.37 0.00474 0.00593 +! Validation 28 4057.006 0.005 0.184 0.575 4.26 0.38 0.512 0.732 0.905 0.00183 0.00226 +Wall time: 4057.0062407511286 +! Best model 28 4.256 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 6.13 0.164 2.86 0.36 0.483 1.89 2.02 0.00473 0.00504 + 29 200 13.8 0.169 10.4 0.367 0.49 3.8 3.85 0.00949 0.00962 + 29 235 3.63 0.154 0.558 0.349 0.468 0.692 0.891 0.00173 0.00223 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 5.17 0.164 1.89 0.356 0.484 1.59 1.64 0.00399 0.0041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 29 4201.412 0.005 0.168 3.18 6.53 0.364 0.489 1.71 2.13 0.00427 0.00532 +! Validation 29 4201.412 0.005 0.167 4.33 7.67 0.362 0.488 2.35 2.48 0.00587 0.00621 +Wall time: 4201.412870106753 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 4.48 0.163 1.22 0.36 0.481 1.17 1.32 0.00293 0.0033 + 30 200 4.82 0.148 1.87 0.341 0.459 1.49 1.63 0.00373 0.00408 + 30 235 3.28 0.151 0.268 0.347 0.463 0.532 0.618 0.00133 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 5.07 0.153 2.02 0.343 0.466 1.65 1.7 0.00413 0.00424 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 30 4345.840 0.005 0.156 2.61 5.73 0.351 0.471 1.54 1.93 0.00385 0.00483 +! Validation 30 4345.840 0.005 0.156 1.08 4.2 0.349 0.471 1.06 1.24 0.00266 0.0031 +Wall time: 4345.840159095824 +! Best model 30 4.200 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 4.56 0.151 1.54 0.345 0.464 1.36 1.48 0.00341 0.0037 + 31 200 4.86 0.149 1.89 0.344 0.461 1.48 1.64 0.00371 0.0041 + 31 235 5.04 0.157 1.91 0.354 0.472 1.46 1.65 0.00365 0.00413 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 4.03 0.155 0.937 0.346 0.47 1.08 1.16 0.0027 0.00289 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 31 4490.282 0.005 0.15 3.48 6.49 0.345 0.462 1.76 2.23 0.00441 0.00557 +! Validation 31 4490.282 0.005 0.155 0.967 4.07 0.349 0.47 0.94 1.17 0.00235 0.00293 +Wall time: 4490.282098467927 +! Best model 31 4.074 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 3.32 0.139 0.538 0.333 0.445 0.722 0.875 0.0018 0.00219 + 32 200 3.61 0.141 0.783 0.334 0.449 0.887 1.06 0.00222 0.00264 + 32 235 3.29 0.129 0.711 0.322 0.429 0.833 1.01 0.00208 0.00252 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 2.84 0.133 0.177 0.32 0.435 0.481 0.502 0.0012 0.00126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 32 4635.896 0.005 0.14 2.29 5.1 0.334 0.447 1.41 1.81 0.00353 0.00452 +! Validation 32 4635.896 0.005 0.138 0.684 3.45 0.33 0.444 0.82 0.987 0.00205 0.00247 +Wall time: 4635.896505605895 +! Best model 32 3.447 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 3.01 0.137 0.267 0.33 0.442 0.499 0.616 0.00125 0.00154 + 33 200 14.2 0.127 11.7 0.317 0.425 4.02 4.08 0.01 0.0102 + 33 235 2.87 0.126 0.35 0.318 0.424 0.584 0.706 0.00146 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 2.66 0.127 0.123 0.314 0.425 0.276 0.418 0.000691 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 33 4780.296 0.005 0.131 2.55 5.17 0.323 0.432 1.57 1.91 0.00392 0.00477 +! Validation 33 4780.296 0.005 0.131 0.448 3.07 0.321 0.432 0.624 0.799 0.00156 0.002 +Wall time: 4780.296226765029 +! Best model 33 3.065 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 6.22 0.178 2.67 0.379 0.503 1.79 1.95 0.00447 0.00487 + 34 200 4.09 0.121 1.68 0.31 0.414 1.36 1.55 0.0034 0.00386 + 34 235 3.32 0.119 0.938 0.308 0.412 1.02 1.16 0.00256 0.00289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 3.48 0.125 0.983 0.311 0.422 1.12 1.18 0.00281 0.00296 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 34 4924.749 0.005 0.133 3.51 6.17 0.325 0.435 1.53 2.24 0.00383 0.0056 +! Validation 34 4924.749 0.005 0.127 2.61 5.15 0.317 0.426 1.79 1.93 0.00448 0.00482 +Wall time: 4924.749737335835 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 4.23 0.11 2.03 0.298 0.396 1.62 1.7 0.00406 0.00425 + 35 200 14.3 0.109 12.1 0.296 0.395 4.1 4.15 0.0102 0.0104 + 35 235 2.7 0.117 0.35 0.304 0.409 0.583 0.707 0.00146 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 4.26 0.107 2.12 0.289 0.391 1.7 1.74 0.00426 0.00434 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 35 5069.297 0.005 0.114 1.6 3.88 0.302 0.403 1.2 1.51 0.003 0.00378 +! Validation 35 5069.297 0.005 0.112 1.21 3.45 0.297 0.399 1.17 1.31 0.00292 0.00328 +Wall time: 5069.297560427804 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 3.12 0.1 1.12 0.284 0.377 1.14 1.27 0.00285 0.00316 + 36 200 2.82 0.104 0.736 0.289 0.386 0.901 1.02 0.00225 0.00256 + 36 235 3.47 0.109 1.28 0.294 0.395 1.22 1.35 0.00305 0.00338 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 2.77 0.105 0.677 0.286 0.386 0.927 0.982 0.00232 0.00245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 36 5213.764 0.005 0.107 2.15 4.3 0.293 0.391 1.36 1.75 0.0034 0.00438 +! Validation 36 5213.764 0.005 0.108 2.86 5.01 0.292 0.391 1.84 2.02 0.0046 0.00504 +Wall time: 5213.764448463917 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 2.79 0.113 0.527 0.301 0.402 0.693 0.866 0.00173 0.00217 + 37 200 2.33 0.0972 0.389 0.278 0.372 0.611 0.744 0.00153 0.00186 + 37 235 2.44 0.0958 0.523 0.279 0.369 0.789 0.863 0.00197 0.00216 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 3.66 0.0928 1.81 0.27 0.364 1.57 1.6 0.00393 0.00401 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 37 5358.695 0.005 0.101 1.86 3.88 0.284 0.378 1.3 1.63 0.00325 0.00408 +! Validation 37 5358.695 0.005 0.0972 1.06 3 0.278 0.372 1.1 1.23 0.00274 0.00307 +Wall time: 5358.695497570094 +! Best model 37 3.002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 2.37 0.0943 0.48 0.275 0.367 0.669 0.827 0.00167 0.00207 + 38 200 2.45 0.0907 0.638 0.27 0.359 0.848 0.954 0.00212 0.00238 + 38 235 2.4 0.102 0.353 0.288 0.382 0.632 0.709 0.00158 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 5.4 0.101 3.38 0.282 0.38 2.17 2.19 0.00542 0.00548 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 38 5503.218 0.005 0.0938 2.05 3.93 0.275 0.365 1.38 1.71 0.00345 0.00428 +! Validation 38 5503.218 0.005 0.104 5.87 7.94 0.288 0.384 2.82 2.89 0.00704 0.00723 +Wall time: 5503.218256849796 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.91 0.0863 0.182 0.264 0.351 0.403 0.509 0.00101 0.00127 + 39 200 2.03 0.0873 0.281 0.266 0.353 0.502 0.633 0.00126 0.00158 + 39 235 1.82 0.0849 0.124 0.261 0.348 0.341 0.42 0.000851 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.78 0.0856 0.0661 0.261 0.349 0.226 0.307 0.000566 0.000767 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 39 5647.682 0.005 0.0903 1.52 3.33 0.27 0.359 1.17 1.47 0.00293 0.00368 +! Validation 39 5647.682 0.005 0.0903 0.835 2.64 0.269 0.359 0.858 1.09 0.00215 0.00273 +Wall time: 5647.682798143011 +! Best model 39 2.641 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 2.42 0.0975 0.469 0.28 0.373 0.683 0.818 0.00171 0.00204 + 40 200 6.56 0.0802 4.96 0.254 0.338 2.6 2.66 0.00649 0.00664 + 40 235 10.1 0.0844 8.45 0.264 0.347 3.43 3.47 0.00858 0.00867 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 10.3 0.0792 8.75 0.251 0.336 3.52 3.53 0.0088 0.00882 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 40 5792.163 0.005 0.0862 1.77 3.49 0.264 0.35 1.26 1.58 0.00314 0.00395 +! Validation 40 5792.163 0.005 0.0843 5.97 7.65 0.26 0.347 2.85 2.92 0.00712 0.00729 +Wall time: 5792.16388477711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 2.29 0.0787 0.714 0.254 0.335 0.894 1.01 0.00224 0.00252 + 41 200 3.82 0.0799 2.22 0.255 0.337 1.62 1.78 0.00405 0.00444 + 41 235 6.89 0.0778 5.33 0.251 0.333 2.69 2.76 0.00673 0.00689 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 3.93 0.072 2.5 0.241 0.32 1.87 1.89 0.00467 0.00471 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 41 5936.873 0.005 0.0842 1.61 3.3 0.261 0.346 1.18 1.51 0.00295 0.00378 +! Validation 41 5936.873 0.005 0.0787 4.78 6.35 0.252 0.335 2.52 2.61 0.0063 0.00652 +Wall time: 5936.873319902923 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 2.13 0.0752 0.628 0.247 0.327 0.837 0.946 0.00209 0.00236 + 42 200 2.09 0.0785 0.518 0.253 0.335 0.724 0.859 0.00181 0.00215 + 42 235 2.06 0.0682 0.69 0.238 0.312 0.904 0.992 0.00226 0.00248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 1.45 0.068 0.0913 0.235 0.311 0.299 0.361 0.000747 0.000902 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 42 6081.377 0.005 0.0769 1.2 2.74 0.25 0.331 1.05 1.31 0.00263 0.00327 +! Validation 42 6081.377 0.005 0.0753 0.896 2.4 0.247 0.328 0.942 1.13 0.00235 0.00282 +Wall time: 6081.377549501136 +! Best model 42 2.402 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 1.74 0.0746 0.25 0.247 0.326 0.499 0.597 0.00125 0.00149 + 43 200 2.76 0.0703 1.36 0.239 0.316 1.3 1.39 0.00326 0.00348 + 43 235 2.09 0.0681 0.733 0.236 0.311 0.881 1.02 0.0022 0.00255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 1.77 0.0674 0.418 0.235 0.31 0.738 0.772 0.00185 0.00193 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 43 6225.941 0.005 0.0729 1.18 2.64 0.244 0.322 1.03 1.3 0.00257 0.00325 +! Validation 43 6225.941 0.005 0.0735 0.321 1.79 0.244 0.323 0.538 0.676 0.00135 0.00169 +Wall time: 6225.941238668747 +! Best model 43 1.790 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 2.7 0.0725 1.25 0.242 0.321 1.18 1.33 0.00295 0.00334 + 44 200 1.65 0.0692 0.271 0.238 0.314 0.503 0.621 0.00126 0.00155 + 44 235 1.85 0.0707 0.434 0.24 0.317 0.702 0.786 0.00175 0.00197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 1.45 0.0638 0.18 0.228 0.301 0.46 0.506 0.00115 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 44 6370.423 0.005 0.072 1.42 2.86 0.242 0.32 1.14 1.42 0.00285 0.00356 +! Validation 44 6370.423 0.005 0.0714 0.437 1.86 0.24 0.319 0.61 0.789 0.00153 0.00197 +Wall time: 6370.422981529031 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 6.73 0.0675 5.38 0.236 0.31 2.72 2.77 0.00681 0.00692 + 45 200 1.9 0.0671 0.559 0.234 0.309 0.733 0.892 0.00183 0.00223 + 45 235 3.48 0.0692 2.09 0.238 0.314 1.63 1.73 0.00407 0.00432 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 7.23 0.0665 5.9 0.232 0.308 2.89 2.9 0.00723 0.00725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 45 6514.891 0.005 0.0686 1.31 2.68 0.237 0.313 1.1 1.37 0.00276 0.00341 +! Validation 45 6514.891 0.005 0.0715 3.99 5.42 0.24 0.319 2.28 2.39 0.00571 0.00596 +Wall time: 6514.891313571949 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 1.99 0.0659 0.667 0.232 0.306 0.804 0.975 0.00201 0.00244 + 46 200 1.7 0.0618 0.465 0.225 0.297 0.715 0.814 0.00179 0.00203 + 46 235 2.56 0.0655 1.25 0.232 0.306 1.28 1.34 0.0032 0.00334 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 1.21 0.0583 0.0478 0.219 0.288 0.207 0.261 0.000518 0.000652 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 46 6659.360 0.005 0.0651 0.952 2.25 0.23 0.304 0.934 1.16 0.00233 0.00291 +! Validation 46 6659.360 0.005 0.0656 0.234 1.55 0.231 0.306 0.442 0.577 0.0011 0.00144 +Wall time: 6659.360179272015 +! Best model 46 1.546 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 1.5 0.0654 0.196 0.232 0.305 0.439 0.528 0.0011 0.00132 + 47 200 1.78 0.0613 0.556 0.223 0.296 0.744 0.89 0.00186 0.00223 + 47 235 2.3 0.056 1.18 0.215 0.282 1.2 1.3 0.00299 0.00325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 1.7 0.0506 0.688 0.205 0.268 0.974 0.99 0.00243 0.00247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 47 6803.825 0.005 0.0628 1.03 2.28 0.226 0.299 0.989 1.21 0.00247 0.00302 +! Validation 47 6803.825 0.005 0.0597 1.84 3.03 0.22 0.292 1.48 1.62 0.0037 0.00405 +Wall time: 6803.8250566208735 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 1.37 0.0599 0.177 0.22 0.292 0.391 0.502 0.000978 0.00125 + 48 200 1.66 0.0587 0.484 0.219 0.289 0.733 0.83 0.00183 0.00207 + 48 235 3.65 0.0542 2.57 0.213 0.278 1.81 1.91 0.00453 0.00478 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 1.9 0.0487 0.924 0.201 0.263 1.14 1.15 0.00284 0.00287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 48 6948.295 0.005 0.0622 1.15 2.39 0.225 0.298 0.979 1.28 0.00245 0.00319 +! Validation 48 6948.295 0.005 0.058 0.619 1.78 0.217 0.287 0.828 0.939 0.00207 0.00235 +Wall time: 6948.295889385976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 1.32 0.0568 0.185 0.217 0.284 0.409 0.513 0.00102 0.00128 + 49 200 1.52 0.0557 0.407 0.213 0.282 0.63 0.762 0.00158 0.0019 + 49 235 1.48 0.0552 0.372 0.213 0.28 0.635 0.728 0.00159 0.00182 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 1.17 0.0479 0.215 0.2 0.261 0.526 0.554 0.00132 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 49 7093.315 0.005 0.0566 0.814 1.95 0.215 0.284 0.871 1.08 0.00218 0.00269 +! Validation 49 7093.315 0.005 0.0566 0.47 1.6 0.215 0.284 0.696 0.819 0.00174 0.00205 +Wall time: 7093.314978809096 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 1.55 0.0547 0.459 0.212 0.279 0.72 0.809 0.0018 0.00202 + 50 200 2.04 0.059 0.864 0.219 0.29 0.986 1.11 0.00247 0.00277 + 50 235 1.76 0.0585 0.588 0.217 0.289 0.763 0.915 0.00191 0.00229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 1.19 0.0469 0.251 0.198 0.258 0.575 0.598 0.00144 0.0015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 50 7237.810 0.005 0.0586 1.31 2.48 0.218 0.289 1.08 1.37 0.0027 0.00341 +! Validation 50 7237.810 0.005 0.0555 0.307 1.42 0.212 0.281 0.546 0.662 0.00137 0.00165 +Wall time: 7237.810591578949 +! Best model 50 1.417 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 4 0.0555 2.89 0.212 0.281 1.9 2.03 0.00474 0.00507 + 51 200 1.49 0.0571 0.346 0.217 0.285 0.552 0.702 0.00138 0.00176 + 51 235 1.16 0.0484 0.195 0.199 0.263 0.406 0.527 0.00101 0.00132 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 0.985 0.0429 0.128 0.19 0.247 0.4 0.427 0.001 0.00107 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 51 7382.288 0.005 0.0542 0.83 1.91 0.21 0.278 0.832 1.09 0.00208 0.00272 +! Validation 51 7382.288 0.005 0.0522 0.244 1.29 0.206 0.273 0.463 0.589 0.00116 0.00147 +Wall time: 7382.288199284114 +! Best model 51 1.287 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 3.95 0.0485 2.98 0.199 0.263 2 2.06 0.00499 0.00515 + 52 200 1.25 0.0483 0.286 0.197 0.262 0.521 0.638 0.0013 0.0016 + 52 235 2.43 0.0503 1.43 0.202 0.268 1.3 1.43 0.00324 0.00356 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.832 0.0409 0.0138 0.185 0.241 0.124 0.14 0.00031 0.000351 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 52 7526.775 0.005 0.0499 0.636 1.63 0.201 0.267 0.752 0.95 0.00188 0.00238 +! Validation 52 7526.775 0.005 0.0494 0.521 1.51 0.2 0.265 0.67 0.862 0.00167 0.00215 +Wall time: 7526.775123720057 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 1.17 0.0489 0.189 0.199 0.264 0.423 0.519 0.00106 0.0013 + 53 200 1.76 0.0502 0.759 0.202 0.267 0.932 1.04 0.00233 0.0026 + 53 235 0.987 0.0451 0.0838 0.19 0.254 0.295 0.345 0.000738 0.000864 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 1.44 0.039 0.66 0.18 0.236 0.964 0.97 0.00241 0.00242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 53 7671.248 0.005 0.0497 0.894 1.89 0.201 0.266 0.917 1.13 0.00229 0.00282 +! Validation 53 7671.248 0.005 0.0481 0.551 1.51 0.197 0.262 0.78 0.886 0.00195 0.00222 +Wall time: 7671.248817464802 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 1.12 0.0465 0.19 0.195 0.257 0.433 0.52 0.00108 0.0013 + 54 200 2.05 0.0439 1.18 0.189 0.25 1.22 1.29 0.00304 0.00323 + 54 235 1.05 0.043 0.192 0.188 0.248 0.411 0.523 0.00103 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 1.11 0.038 0.35 0.178 0.233 0.7 0.706 0.00175 0.00177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 54 7815.731 0.005 0.0462 0.662 1.59 0.194 0.257 0.784 0.972 0.00196 0.00243 +! Validation 54 7815.731 0.005 0.0462 0.383 1.31 0.193 0.257 0.641 0.739 0.0016 0.00185 +Wall time: 7815.731109419838 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.63 0.0433 0.763 0.188 0.248 0.959 1.04 0.0024 0.00261 + 55 200 1.87 0.0474 0.926 0.196 0.26 1.07 1.15 0.00266 0.00287 + 55 235 4 0.0434 3.13 0.188 0.249 2.07 2.11 0.00518 0.00528 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.91 0.0367 1.17 0.175 0.229 1.29 1.29 0.00323 0.00323 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 55 7960.186 0.005 0.044 0.665 1.55 0.189 0.25 0.788 0.969 0.00197 0.00242 +! Validation 55 7960.186 0.005 0.0443 1.18 2.07 0.189 0.251 1.21 1.3 0.00303 0.00325 +Wall time: 7960.186250755098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 0.986 0.0424 0.137 0.186 0.246 0.341 0.442 0.000852 0.00111 + 56 200 1.21 0.0405 0.396 0.181 0.24 0.625 0.751 0.00156 0.00188 + 56 235 1.04 0.0415 0.212 0.184 0.243 0.487 0.55 0.00122 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 2.03 0.0333 1.37 0.167 0.218 1.39 1.4 0.00348 0.00349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 56 8104.646 0.005 0.0439 0.761 1.64 0.188 0.25 0.833 1.04 0.00208 0.00261 +! Validation 56 8104.646 0.005 0.0417 1.02 1.85 0.183 0.244 1.06 1.2 0.00266 0.00301 +Wall time: 8104.6462454730645 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 1.11 0.041 0.286 0.182 0.242 0.483 0.639 0.00121 0.0016 + 57 200 1.2 0.0394 0.417 0.179 0.237 0.672 0.771 0.00168 0.00193 + 57 235 2.16 0.0413 1.33 0.184 0.242 1.19 1.38 0.00298 0.00344 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 6.68 0.038 5.92 0.177 0.233 2.9 2.9 0.00726 0.00726 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 57 8249.428 0.005 0.0409 0.649 1.47 0.182 0.241 0.757 0.961 0.00189 0.0024 +! Validation 57 8249.428 0.005 0.0447 8.83 9.72 0.19 0.252 3.44 3.55 0.00859 0.00886 +Wall time: 8249.428888718132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 2.34 0.0399 1.54 0.18 0.238 1.39 1.48 0.00347 0.0037 + 58 200 0.981 0.0404 0.173 0.181 0.24 0.394 0.496 0.000986 0.00124 + 58 235 0.873 0.0373 0.126 0.174 0.231 0.351 0.424 0.000878 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 1.03 0.0306 0.421 0.16 0.209 0.772 0.774 0.00193 0.00194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 58 8394.051 0.005 0.0412 0.685 1.51 0.182 0.242 0.761 0.989 0.0019 0.00247 +! Validation 58 8394.051 0.005 0.0385 0.385 1.16 0.176 0.234 0.646 0.741 0.00161 0.00185 +Wall time: 8394.051532946061 +! Best model 58 1.155 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 1.12 0.0355 0.414 0.17 0.225 0.616 0.768 0.00154 0.00192 + 59 200 0.961 0.0363 0.236 0.171 0.227 0.509 0.58 0.00127 0.00145 + 59 235 1.21 0.0354 0.5 0.171 0.225 0.752 0.844 0.00188 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.611 0.0288 0.0355 0.155 0.203 0.217 0.225 0.000542 0.000562 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 59 8538.548 0.005 0.0372 0.467 1.21 0.173 0.23 0.658 0.815 0.00164 0.00204 +! Validation 59 8538.548 0.005 0.0362 0.34 1.06 0.17 0.227 0.555 0.696 0.00139 0.00174 +Wall time: 8538.548076983076 +! Best model 59 1.064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.993 0.0337 0.318 0.165 0.219 0.531 0.674 0.00133 0.00168 + 60 200 0.845 0.036 0.124 0.17 0.227 0.309 0.421 0.000772 0.00105 + 60 235 1.24 0.0354 0.534 0.169 0.225 0.76 0.872 0.0019 0.00218 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 2.17 0.029 1.59 0.155 0.203 1.5 1.51 0.00376 0.00376 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 60 8683.009 0.005 0.0366 0.64 1.37 0.172 0.228 0.758 0.955 0.00189 0.00239 +! Validation 60 8683.009 0.005 0.0359 1.38 2.09 0.17 0.226 1.33 1.4 0.00333 0.0035 +Wall time: 8683.008959870785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 1.66 0.0348 0.962 0.167 0.223 0.997 1.17 0.00249 0.00293 + 61 200 0.775 0.0327 0.12 0.163 0.216 0.326 0.414 0.000816 0.00103 + 61 235 1.06 0.0352 0.353 0.168 0.224 0.569 0.709 0.00142 0.00177 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.965 0.0282 0.4 0.153 0.201 0.753 0.755 0.00188 0.00189 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 61 8827.510 0.005 0.0346 0.475 1.17 0.167 0.222 0.668 0.823 0.00167 0.00206 +! Validation 61 8827.510 0.005 0.0347 0.321 1.01 0.167 0.222 0.581 0.676 0.00145 0.00169 +Wall time: 8827.510361522902 +! Best model 61 1.014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 1.05 0.0341 0.367 0.165 0.221 0.575 0.723 0.00144 0.00181 + 62 200 1.09 0.0336 0.42 0.165 0.219 0.695 0.773 0.00174 0.00193 + 62 235 0.814 0.0333 0.148 0.164 0.218 0.376 0.459 0.00094 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 1.3 0.0266 0.769 0.149 0.194 1.04 1.05 0.00261 0.00262 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 62 8971.918 0.005 0.0355 0.611 1.32 0.169 0.225 0.733 0.934 0.00183 0.00233 +! Validation 62 8971.918 0.005 0.0336 0.587 1.26 0.165 0.219 0.806 0.915 0.00202 0.00229 +Wall time: 8971.917991088703 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 1.36 0.0347 0.664 0.167 0.222 0.887 0.973 0.00222 0.00243 + 63 200 0.751 0.0329 0.0917 0.163 0.217 0.277 0.361 0.000694 0.000903 + 63 235 1.76 0.0348 1.06 0.167 0.223 1.13 1.23 0.00282 0.00307 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 1.41 0.0313 0.781 0.162 0.211 1.05 1.06 0.00263 0.00264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 63 9116.294 0.005 0.0334 0.569 1.24 0.164 0.218 0.731 0.899 0.00183 0.00225 +! Validation 63 9116.294 0.005 0.0383 0.844 1.61 0.178 0.233 0.978 1.1 0.00244 0.00274 +Wall time: 9116.294115031138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 1.12 0.0337 0.447 0.164 0.219 0.697 0.798 0.00174 0.00199 + 64 200 0.668 0.0301 0.0649 0.156 0.207 0.246 0.304 0.000615 0.00076 + 64 235 0.687 0.0293 0.102 0.154 0.204 0.318 0.38 0.000794 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.929 0.0238 0.453 0.14 0.184 0.801 0.803 0.002 0.00201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 64 9260.686 0.005 0.0338 0.51 1.19 0.165 0.22 0.674 0.854 0.00168 0.00213 +! Validation 64 9260.686 0.005 0.0308 0.471 1.09 0.157 0.209 0.672 0.819 0.00168 0.00205 +Wall time: 9260.686161953025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 2.53 0.0365 1.8 0.171 0.228 1.47 1.6 0.00368 0.004 + 65 200 1.11 0.0302 0.509 0.155 0.207 0.767 0.851 0.00192 0.00213 + 65 235 0.997 0.0313 0.371 0.158 0.211 0.615 0.727 0.00154 0.00182 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 2.57 0.0242 2.08 0.141 0.186 1.72 1.72 0.00431 0.00431 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 65 9405.110 0.005 0.0335 0.646 1.32 0.164 0.218 0.752 0.96 0.00188 0.0024 +! Validation 65 9405.110 0.005 0.0309 1.65 2.27 0.157 0.21 1.47 1.53 0.00367 0.00383 +Wall time: 9405.110177647788 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.673 0.028 0.112 0.15 0.2 0.297 0.399 0.000744 0.000998 + 66 200 0.944 0.0284 0.376 0.152 0.201 0.599 0.732 0.0015 0.00183 + 66 235 1.09 0.0275 0.545 0.149 0.198 0.787 0.881 0.00197 0.0022 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 2.01 0.0215 1.58 0.133 0.175 1.5 1.5 0.00375 0.00375 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 66 9549.777 0.005 0.0294 0.308 0.895 0.153 0.205 0.523 0.662 0.00131 0.00165 +! Validation 66 9549.777 0.005 0.028 1.31 1.87 0.149 0.2 1.28 1.37 0.00321 0.00342 +Wall time: 9549.777680072933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 1.07 0.0298 0.478 0.155 0.206 0.675 0.825 0.00169 0.00206 + 67 200 0.864 0.0289 0.287 0.153 0.203 0.525 0.639 0.00131 0.0016 + 67 235 0.914 0.0294 0.326 0.155 0.205 0.621 0.682 0.00155 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.677 0.026 0.157 0.148 0.193 0.468 0.473 0.00117 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 67 9694.160 0.005 0.0288 0.444 1.02 0.152 0.202 0.64 0.796 0.0016 0.00199 +! Validation 67 9694.160 0.005 0.0317 0.21 0.844 0.161 0.213 0.452 0.546 0.00113 0.00137 +Wall time: 9694.160639241803 +! Best model 67 0.844 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.753 0.0277 0.198 0.15 0.199 0.455 0.532 0.00114 0.00133 + 68 200 1.67 0.0317 1.03 0.161 0.212 1.17 1.21 0.00293 0.00303 + 68 235 0.918 0.0287 0.344 0.151 0.202 0.581 0.7 0.00145 0.00175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.524 0.0212 0.1 0.132 0.174 0.371 0.377 0.000928 0.000944 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 68 9838.550 0.005 0.0279 0.369 0.927 0.15 0.199 0.583 0.725 0.00146 0.00181 +! Validation 68 9838.550 0.005 0.0275 0.211 0.76 0.148 0.198 0.443 0.548 0.00111 0.00137 +Wall time: 9838.550173350144 +! Best model 68 0.760 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.648 0.0284 0.0801 0.153 0.201 0.283 0.338 0.000708 0.000844 + 69 200 0.738 0.0298 0.142 0.156 0.206 0.365 0.45 0.000912 0.00112 + 69 235 0.754 0.0298 0.158 0.156 0.206 0.349 0.475 0.000873 0.00119 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 2.54 0.0249 2.04 0.143 0.188 1.7 1.7 0.00426 0.00426 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 69 9983.118 0.005 0.028 0.494 1.05 0.15 0.2 0.664 0.84 0.00166 0.0021 +! Validation 69 9983.118 0.005 0.0306 1.33 1.94 0.157 0.209 1.16 1.38 0.0029 0.00344 +Wall time: 9983.118801401928 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.685 0.0261 0.164 0.144 0.193 0.384 0.483 0.00096 0.00121 + 70 200 0.573 0.0246 0.0802 0.14 0.187 0.267 0.338 0.000667 0.000845 + 70 235 1.13 0.0245 0.641 0.14 0.187 0.868 0.956 0.00217 0.00239 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.423 0.0198 0.0275 0.128 0.168 0.187 0.198 0.000468 0.000495 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 70 10128.297 0.005 0.0261 0.305 0.826 0.144 0.193 0.523 0.658 0.00131 0.00165 +! Validation 70 10128.297 0.005 0.0255 0.179 0.688 0.142 0.19 0.415 0.505 0.00104 0.00126 +Wall time: 10128.297281004023 +! Best model 70 0.688 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 1.37 0.0237 0.898 0.137 0.184 1.08 1.13 0.00271 0.00283 + 71 200 0.624 0.0254 0.115 0.143 0.19 0.335 0.405 0.000839 0.00101 + 71 235 395 1.08 374 0.92 1.24 23 23.1 0.0574 0.0577 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 7.46e+03 0.96 7.44e+03 0.872 1.17 103 103 0.257 0.257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 71 10272.734 0.005 0.0374 14.7 15.5 0.15 0.222 1 4.43 0.0025 0.0111 +! Validation 71 10272.734 0.005 0.938 8.78e+03 8.8e+03 0.859 1.16 111 112 0.278 0.28 +Wall time: 10272.734560063109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 23.3 0.946 4.38 0.864 1.16 1.87 2.5 0.00468 0.00625 + 72 200 21.1 0.907 2.96 0.839 1.14 1.68 2.05 0.00421 0.00513 + 72 235 21.7 0.853 4.65 0.816 1.1 2.35 2.57 0.00587 0.00643 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 21.2 0.893 3.35 0.834 1.13 2.1 2.18 0.00525 0.00546 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 72 10417.165 0.005 0.951 98.4 117 0.862 1.16 4.46 11.9 0.0111 0.0296 +! Validation 72 10417.165 0.005 0.872 2.92 20.4 0.822 1.11 1.55 2.04 0.00389 0.0051 +Wall time: 10417.165217369795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 18.4 0.783 2.72 0.779 1.06 1.72 1.97 0.00431 0.00492 + 73 200 13.4 0.613 1.1 0.689 0.935 0.995 1.25 0.00249 0.00314 + 73 235 12.6 0.508 2.48 0.637 0.851 1.51 1.88 0.00377 0.0047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 11.6 0.545 0.712 0.655 0.881 0.961 1.01 0.0024 0.00252 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 73 10561.595 0.005 0.74 2.32 17.1 0.756 1.03 1.44 1.82 0.0036 0.00454 +! Validation 73 10561.595 0.005 0.532 1.25 11.9 0.648 0.87 1.05 1.33 0.00261 0.00333 +Wall time: 10561.595408943016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 8.21 0.373 0.762 0.549 0.729 0.718 1.04 0.00179 0.0026 + 74 200 7.99 0.297 2.04 0.492 0.651 1.42 1.71 0.00355 0.00427 + 74 235 6.34 0.26 1.15 0.462 0.608 1.09 1.28 0.00273 0.0032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 6.11 0.264 0.82 0.458 0.614 1.08 1.08 0.00269 0.0027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 74 10706.209 0.005 0.369 1.87 9.26 0.543 0.726 1.31 1.63 0.00327 0.00409 +! Validation 74 10706.209 0.005 0.27 0.897 6.29 0.47 0.62 0.926 1.13 0.00231 0.00283 +Wall time: 10706.20972165186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 7.24 0.216 2.91 0.424 0.555 1.77 2.04 0.00443 0.00509 + 75 200 4.78 0.178 1.21 0.384 0.504 1.14 1.31 0.00285 0.00328 + 75 235 3.78 0.164 0.494 0.37 0.484 0.762 0.839 0.0019 0.0021 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 4.97 0.168 1.6 0.367 0.49 1.5 1.51 0.00375 0.00377 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 75 10850.843 0.005 0.21 1.43 5.63 0.416 0.547 1.14 1.43 0.00285 0.00357 +! Validation 75 10850.843 0.005 0.17 4.79 8.19 0.375 0.492 2.35 2.61 0.00587 0.00653 +Wall time: 10850.843317506835 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 4.04 0.138 1.28 0.339 0.443 1.12 1.35 0.00279 0.00338 + 76 200 4.69 0.12 2.3 0.316 0.413 1.64 1.81 0.00411 0.00452 + 76 235 3.65 0.115 1.34 0.309 0.405 1.29 1.38 0.00322 0.00346 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 2.45 0.113 0.188 0.303 0.401 0.489 0.517 0.00122 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 76 10995.260 0.005 0.139 0.972 3.74 0.34 0.444 0.936 1.18 0.00234 0.00294 +! Validation 76 10995.260 0.005 0.118 0.496 2.86 0.314 0.41 0.68 0.841 0.0017 0.0021 +Wall time: 10995.260710535105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 2.62 0.108 0.468 0.3 0.391 0.695 0.817 0.00174 0.00204 + 77 200 3.16 0.0985 1.19 0.286 0.375 1.14 1.3 0.00284 0.00326 + 77 235 2.51 0.0906 0.7 0.277 0.359 0.852 0.998 0.00213 0.0025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 3.22 0.0876 1.47 0.268 0.353 1.44 1.45 0.00359 0.00362 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 77 11139.685 0.005 0.105 0.914 3.02 0.296 0.387 0.921 1.14 0.0023 0.00285 +! Validation 77 11139.685 0.005 0.0955 1.29 3.2 0.282 0.369 1.18 1.36 0.00296 0.00339 +Wall time: 11139.685422561131 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 3.67 0.0852 1.96 0.268 0.348 1.5 1.67 0.00375 0.00418 + 78 200 1.95 0.0805 0.342 0.259 0.339 0.571 0.697 0.00143 0.00174 + 78 235 2.1 0.0819 0.458 0.26 0.342 0.655 0.808 0.00164 0.00202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 1.6 0.0725 0.146 0.245 0.321 0.432 0.456 0.00108 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 78 11284.476 0.005 0.0878 0.801 2.56 0.27 0.354 0.839 1.07 0.0021 0.00267 +! Validation 78 11284.476 0.005 0.0814 0.342 1.97 0.259 0.341 0.569 0.698 0.00142 0.00174 +Wall time: 11284.476392944809 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 1.96 0.0777 0.409 0.253 0.333 0.632 0.764 0.00158 0.00191 + 79 200 1.69 0.0707 0.274 0.242 0.317 0.503 0.625 0.00126 0.00156 + 79 235 2.13 0.0694 0.741 0.239 0.314 0.871 1.03 0.00218 0.00257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 1.95 0.0629 0.695 0.229 0.299 0.986 0.995 0.00247 0.00249 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 79 11429.180 0.005 0.0755 0.704 2.21 0.25 0.328 0.791 1 0.00198 0.0025 +! Validation 79 11429.180 0.005 0.0715 0.671 2.1 0.242 0.319 0.84 0.978 0.0021 0.00244 +Wall time: 11429.180050428025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 3.69 0.0685 2.32 0.237 0.312 1.75 1.82 0.00437 0.00455 + 80 200 5.68 0.064 4.4 0.229 0.302 2.45 2.5 0.00611 0.00626 + 80 235 1.51 0.0646 0.22 0.229 0.303 0.459 0.559 0.00115 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 1.25 0.057 0.114 0.217 0.285 0.387 0.404 0.000966 0.00101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 80 11573.655 0.005 0.0674 0.77 2.12 0.235 0.31 0.851 1.05 0.00213 0.00262 +! Validation 80 11573.655 0.005 0.0658 0.445 1.76 0.231 0.306 0.633 0.796 0.00158 0.00199 +Wall time: 11573.655818609055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 1.57 0.0603 0.361 0.222 0.293 0.57 0.717 0.00143 0.00179 + 81 200 1.41 0.0578 0.254 0.217 0.287 0.521 0.602 0.0013 0.00151 + 81 235 1.55 0.0577 0.394 0.217 0.287 0.652 0.749 0.00163 0.00187 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 1.58 0.0501 0.578 0.203 0.267 0.904 0.907 0.00226 0.00227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 81 11718.214 0.005 0.0605 0.551 1.76 0.222 0.294 0.708 0.886 0.00177 0.00222 +! Validation 81 11718.214 0.005 0.0587 0.473 1.65 0.218 0.289 0.699 0.821 0.00175 0.00205 +Wall time: 11718.214353878051 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 1.38 0.0538 0.305 0.209 0.277 0.564 0.659 0.00141 0.00165 + 82 200 1.25 0.0563 0.122 0.213 0.283 0.342 0.417 0.000856 0.00104 + 82 235 1.26 0.0541 0.177 0.211 0.278 0.381 0.503 0.000952 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.908 0.0442 0.0237 0.191 0.251 0.172 0.184 0.00043 0.00046 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 82 11863.047 0.005 0.055 0.514 1.61 0.211 0.28 0.673 0.856 0.00168 0.00214 +! Validation 82 11863.047 0.005 0.0528 0.247 1.3 0.207 0.274 0.461 0.593 0.00115 0.00148 +Wall time: 11863.047025505919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 1.27 0.0496 0.274 0.201 0.266 0.495 0.624 0.00124 0.00156 + 83 200 1.3 0.0492 0.314 0.2 0.265 0.536 0.669 0.00134 0.00167 + 83 235 1.78 0.0473 0.833 0.197 0.26 0.985 1.09 0.00246 0.00272 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 1.07 0.0407 0.257 0.183 0.241 0.603 0.605 0.00151 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 83 12007.517 0.005 0.0507 0.586 1.6 0.203 0.269 0.733 0.913 0.00183 0.00228 +! Validation 83 12007.517 0.005 0.049 0.632 1.61 0.199 0.264 0.817 0.949 0.00204 0.00237 +Wall time: 12007.517853229772 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 1.21 0.0509 0.19 0.203 0.269 0.412 0.521 0.00103 0.0013 + 84 200 1.1 0.0469 0.164 0.195 0.258 0.408 0.483 0.00102 0.00121 + 84 235 1.21 0.0439 0.327 0.189 0.25 0.582 0.683 0.00145 0.00171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 1.54 0.0371 0.799 0.175 0.23 1.07 1.07 0.00266 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 84 12151.980 0.005 0.0471 0.575 1.52 0.195 0.259 0.72 0.906 0.0018 0.00226 +! Validation 84 12151.980 0.005 0.0453 0.994 1.9 0.191 0.254 1.07 1.19 0.00268 0.00298 +Wall time: 12151.980525293853 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 1.37 0.0424 0.52 0.186 0.246 0.79 0.861 0.00198 0.00215 + 85 200 1.15 0.0426 0.294 0.186 0.246 0.582 0.647 0.00146 0.00162 + 85 235 1.74 0.0436 0.868 0.188 0.249 1.02 1.11 0.00255 0.00278 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 3.14 0.0339 2.46 0.167 0.22 1.87 1.87 0.00468 0.00468 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 85 12296.402 0.005 0.0438 0.565 1.44 0.188 0.25 0.706 0.896 0.00176 0.00224 +! Validation 85 12296.402 0.005 0.0422 2.54 3.38 0.184 0.245 1.83 1.9 0.00458 0.00475 +Wall time: 12296.402141702827 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 1.27 0.0391 0.493 0.178 0.236 0.741 0.838 0.00185 0.00209 + 86 200 0.931 0.0389 0.154 0.178 0.235 0.374 0.468 0.000935 0.00117 + 86 235 1.01 0.0379 0.249 0.177 0.232 0.503 0.595 0.00126 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.702 0.0312 0.0771 0.16 0.211 0.326 0.331 0.000814 0.000829 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 86 12441.204 0.005 0.0402 0.465 1.27 0.181 0.239 0.639 0.814 0.0016 0.00204 +! Validation 86 12441.204 0.005 0.0391 0.601 1.38 0.178 0.236 0.786 0.925 0.00196 0.00231 +Wall time: 12441.204436420929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 1.25 0.0372 0.502 0.174 0.23 0.724 0.845 0.00181 0.00211 + 87 200 0.88 0.0373 0.135 0.174 0.23 0.358 0.438 0.000895 0.00109 + 87 235 0.894 0.0359 0.176 0.172 0.226 0.419 0.501 0.00105 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.676 0.029 0.0957 0.155 0.203 0.361 0.369 0.000902 0.000923 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 87 12585.675 0.005 0.0374 0.5 1.25 0.175 0.231 0.669 0.845 0.00167 0.00211 +! Validation 87 12585.675 0.005 0.0366 0.131 0.863 0.172 0.228 0.342 0.432 0.000855 0.00108 +Wall time: 12585.67505122209 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.89 0.0359 0.171 0.171 0.226 0.403 0.494 0.00101 0.00123 + 88 200 1.67 0.0334 0.998 0.166 0.218 1.12 1.19 0.00281 0.00298 + 88 235 1.81 0.0317 1.18 0.161 0.213 1.23 1.3 0.00308 0.00324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 2.21 0.027 1.67 0.15 0.196 1.54 1.54 0.00385 0.00385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 88 12730.130 0.005 0.0349 0.437 1.14 0.169 0.223 0.626 0.788 0.00157 0.00197 +! Validation 88 12730.130 0.005 0.0344 1.04 1.73 0.167 0.221 1.15 1.22 0.00287 0.00305 +Wall time: 12730.130428154022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.92 0.0353 0.214 0.17 0.224 0.473 0.552 0.00118 0.00138 + 89 200 1.89 0.0356 1.18 0.171 0.225 1.22 1.3 0.00305 0.00324 + 89 235 0.971 0.0324 0.322 0.163 0.215 0.571 0.677 0.00143 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.647 0.0263 0.122 0.148 0.193 0.405 0.416 0.00101 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 89 12874.679 0.005 0.034 0.534 1.21 0.167 0.22 0.709 0.873 0.00177 0.00218 +! Validation 89 12874.679 0.005 0.0337 0.204 0.879 0.166 0.219 0.431 0.539 0.00108 0.00135 +Wall time: 12874.679260071833 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.766 0.0312 0.142 0.16 0.211 0.381 0.449 0.000953 0.00112 + 90 200 0.83 0.0327 0.177 0.163 0.216 0.422 0.502 0.00105 0.00125 + 90 235 0.776 0.0305 0.166 0.16 0.209 0.394 0.486 0.000986 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 4.06 0.0248 3.56 0.144 0.188 2.25 2.25 0.00563 0.00563 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 90 13019.532 0.005 0.0319 0.405 1.04 0.162 0.213 0.606 0.76 0.00152 0.0019 +! Validation 90 13019.532 0.005 0.0321 2.67 3.31 0.162 0.214 1.9 1.95 0.00474 0.00487 +Wall time: 13019.532475110143 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.758 0.0318 0.122 0.162 0.213 0.35 0.417 0.000874 0.00104 + 91 200 0.873 0.0296 0.281 0.156 0.205 0.526 0.633 0.00131 0.00158 + 91 235 0.929 0.0293 0.343 0.156 0.204 0.653 0.699 0.00163 0.00175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.599 0.0238 0.123 0.141 0.184 0.41 0.418 0.00103 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 91 13164.024 0.005 0.0313 0.5 1.13 0.16 0.211 0.685 0.844 0.00171 0.00211 +! Validation 91 13164.024 0.005 0.031 0.132 0.752 0.159 0.21 0.348 0.434 0.00087 0.00109 +Wall time: 13164.024510341696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.861 0.0312 0.237 0.16 0.211 0.474 0.581 0.00119 0.00145 + 92 200 0.714 0.028 0.154 0.151 0.2 0.384 0.469 0.000961 0.00117 + 92 235 0.804 0.0279 0.246 0.151 0.199 0.492 0.592 0.00123 0.00148 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.427 0.0211 0.00451 0.134 0.174 0.076 0.0801 0.00019 0.0002 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 92 13308.496 0.005 0.029 0.314 0.895 0.154 0.203 0.526 0.669 0.00131 0.00167 +! Validation 92 13308.496 0.005 0.0281 0.163 0.726 0.151 0.2 0.382 0.482 0.000956 0.00121 +Wall time: 13308.496760782786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 1 0.0279 0.442 0.151 0.199 0.66 0.794 0.00165 0.00198 + 93 200 0.685 0.0269 0.148 0.148 0.196 0.366 0.46 0.000914 0.00115 + 93 235 0.805 0.0277 0.252 0.151 0.199 0.498 0.599 0.00125 0.0015 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.663 0.0214 0.234 0.134 0.175 0.572 0.578 0.00143 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 93 13452.973 0.005 0.0279 0.42 0.978 0.151 0.199 0.63 0.774 0.00157 0.00194 +! Validation 93 13452.973 0.005 0.0282 0.165 0.73 0.151 0.201 0.392 0.485 0.000979 0.00121 +Wall time: 13452.973826752044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 1.63 0.0271 1.09 0.149 0.196 1.22 1.25 0.00304 0.00311 + 94 200 0.752 0.0249 0.253 0.143 0.188 0.492 0.6 0.00123 0.0015 + 94 235 0.723 0.0259 0.204 0.145 0.192 0.454 0.539 0.00113 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.414 0.0199 0.0166 0.129 0.168 0.132 0.154 0.00033 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 94 13597.448 0.005 0.0273 0.39 0.935 0.149 0.197 0.604 0.746 0.00151 0.00186 +! Validation 94 13597.448 0.005 0.0265 0.109 0.639 0.146 0.194 0.314 0.395 0.000784 0.000986 +Wall time: 13597.448435834143 +! Best model 94 0.639 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.722 0.0269 0.185 0.148 0.196 0.413 0.513 0.00103 0.00128 + 95 200 0.602 0.0258 0.0868 0.145 0.192 0.285 0.352 0.000712 0.000879 + 95 235 0.593 0.0255 0.0831 0.143 0.191 0.302 0.344 0.000756 0.00086 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.887 0.019 0.507 0.126 0.165 0.846 0.85 0.00212 0.00212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 95 13741.995 0.005 0.0255 0.319 0.829 0.144 0.191 0.55 0.675 0.00138 0.00169 +! Validation 95 13741.995 0.005 0.0253 0.262 0.769 0.143 0.19 0.505 0.611 0.00126 0.00153 +Wall time: 13741.995158703998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.651 0.0268 0.116 0.147 0.195 0.327 0.406 0.000817 0.00101 + 96 200 0.735 0.0256 0.224 0.144 0.191 0.449 0.565 0.00112 0.00141 + 96 235 2.32 0.0246 1.83 0.141 0.187 1.57 1.61 0.00392 0.00403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 3.09 0.0184 2.72 0.124 0.162 1.97 1.97 0.00492 0.00492 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 96 13886.472 0.005 0.0247 0.339 0.832 0.142 0.187 0.557 0.691 0.00139 0.00173 +! Validation 96 13886.472 0.005 0.0244 2.18 2.66 0.14 0.187 1.72 1.76 0.0043 0.0044 +Wall time: 13886.472019367851 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 1.69 0.0249 1.19 0.142 0.188 1.25 1.3 0.00314 0.00326 + 97 200 0.545 0.0244 0.0566 0.14 0.187 0.233 0.284 0.000582 0.00071 + 97 235 0.812 0.0237 0.339 0.139 0.184 0.671 0.694 0.00168 0.00174 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.392 0.0186 0.0201 0.125 0.163 0.159 0.169 0.000396 0.000423 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 97 14031.106 0.005 0.0245 0.388 0.878 0.141 0.187 0.61 0.744 0.00152 0.00186 +! Validation 97 14031.106 0.005 0.025 0.0954 0.595 0.142 0.189 0.296 0.369 0.00074 0.000922 +Wall time: 14031.106490117963 +! Best model 97 0.595 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.74 0.0251 0.238 0.143 0.189 0.463 0.582 0.00116 0.00146 + 98 200 0.884 0.0223 0.437 0.135 0.178 0.641 0.789 0.0016 0.00197 + 98 235 0.502 0.022 0.0624 0.134 0.177 0.219 0.298 0.000546 0.000745 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.354 0.0175 0.00346 0.121 0.158 0.0614 0.0702 0.000154 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 98 14175.619 0.005 0.0235 0.318 0.787 0.138 0.183 0.54 0.673 0.00135 0.00168 +! Validation 98 14175.619 0.005 0.0236 0.118 0.59 0.137 0.183 0.327 0.411 0.000816 0.00103 +Wall time: 14175.619454523083 +! Best model 98 0.590 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 1.38 0.025 0.883 0.141 0.189 1.04 1.12 0.0026 0.0028 + 99 200 0.548 0.0214 0.119 0.132 0.175 0.304 0.412 0.00076 0.00103 + 99 235 1.06 0.0201 0.655 0.128 0.169 0.919 0.966 0.0023 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 1.02 0.0163 0.691 0.117 0.153 0.989 0.992 0.00247 0.00248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 99 14320.185 0.005 0.0229 0.309 0.766 0.136 0.18 0.523 0.663 0.00131 0.00166 +! Validation 99 14320.185 0.005 0.0222 0.817 1.26 0.133 0.178 0.991 1.08 0.00248 0.0027 +Wall time: 14320.185446368065 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.889 0.0218 0.453 0.133 0.176 0.738 0.803 0.00185 0.00201 + 100 200 0.539 0.0209 0.12 0.13 0.173 0.37 0.414 0.000925 0.00104 + 100 235 0.532 0.0223 0.0864 0.135 0.178 0.262 0.351 0.000654 0.000877 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.572 0.0168 0.235 0.118 0.155 0.574 0.579 0.00144 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 100 14464.661 0.005 0.0222 0.341 0.785 0.134 0.178 0.568 0.698 0.00142 0.00174 +! Validation 100 14464.661 0.005 0.0225 0.191 0.64 0.134 0.179 0.438 0.522 0.00109 0.0013 +Wall time: 14464.660932763014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.622 0.0213 0.196 0.131 0.174 0.456 0.528 0.00114 0.00132 + 101 200 0.516 0.0211 0.0937 0.131 0.173 0.314 0.365 0.000785 0.000913 + 101 235 1.2 0.0201 0.8 0.128 0.169 1 1.07 0.00251 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.669 0.0171 0.326 0.12 0.156 0.678 0.681 0.00169 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 101 14609.133 0.005 0.0214 0.317 0.746 0.131 0.175 0.531 0.671 0.00133 0.00168 +! Validation 101 14609.133 0.005 0.0225 0.574 1.02 0.134 0.179 0.795 0.904 0.00199 0.00226 +Wall time: 14609.133055957034 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.503 0.0204 0.095 0.128 0.17 0.286 0.368 0.000716 0.00092 + 102 200 0.473 0.0201 0.0713 0.127 0.169 0.26 0.319 0.000651 0.000797 + 102 235 0.79 0.0183 0.425 0.121 0.161 0.724 0.778 0.00181 0.00195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.576 0.0154 0.268 0.113 0.148 0.613 0.618 0.00153 0.00154 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 102 14753.607 0.005 0.0206 0.26 0.671 0.129 0.171 0.486 0.608 0.00121 0.00152 +! Validation 102 14753.607 0.005 0.0206 0.274 0.685 0.128 0.171 0.537 0.624 0.00134 0.00156 +Wall time: 14753.607380301692 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.6 0.0196 0.208 0.126 0.167 0.433 0.545 0.00108 0.00136 + 103 200 0.483 0.0196 0.0908 0.126 0.167 0.284 0.36 0.000711 0.000899 + 103 235 0.442 0.0196 0.0495 0.124 0.167 0.235 0.266 0.000586 0.000664 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.308 0.0147 0.0133 0.111 0.145 0.119 0.138 0.000297 0.000345 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 103 14898.130 0.005 0.0205 0.348 0.759 0.129 0.171 0.558 0.705 0.0014 0.00176 +! Validation 103 14898.130 0.005 0.02 0.132 0.532 0.126 0.169 0.345 0.433 0.000862 0.00108 +Wall time: 14898.129918904044 +! Best model 103 0.532 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.696 0.0199 0.297 0.126 0.168 0.567 0.651 0.00142 0.00163 + 104 200 0.439 0.0188 0.0627 0.124 0.164 0.245 0.299 0.000611 0.000747 + 104 235 0.789 0.024 0.31 0.135 0.185 0.583 0.665 0.00146 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 1.1 0.0151 0.802 0.112 0.146 1.07 1.07 0.00266 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 104 15042.713 0.005 0.0193 0.253 0.638 0.124 0.166 0.482 0.6 0.00121 0.0015 +! Validation 104 15042.713 0.005 0.0201 0.504 0.905 0.127 0.169 0.761 0.847 0.0019 0.00212 +Wall time: 15042.71330748871 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 0.444 0.0186 0.0727 0.123 0.163 0.277 0.322 0.000692 0.000804 + 105 200 0.577 0.0184 0.209 0.122 0.162 0.415 0.546 0.00104 0.00136 + 105 235 0.603 0.0174 0.256 0.119 0.157 0.539 0.604 0.00135 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 1.12 0.014 0.839 0.109 0.141 1.09 1.09 0.00272 0.00273 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 105 15187.159 0.005 0.0188 0.271 0.647 0.123 0.164 0.493 0.621 0.00123 0.00155 +! Validation 105 15187.159 0.005 0.0189 1.09 1.47 0.123 0.164 1.17 1.25 0.00293 0.00311 +Wall time: 15187.159390239976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.634 0.0187 0.26 0.123 0.163 0.517 0.609 0.00129 0.00152 + 106 200 0.417 0.0168 0.0802 0.116 0.155 0.272 0.338 0.000679 0.000845 + 106 235 0.592 0.0178 0.237 0.119 0.159 0.534 0.581 0.00134 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.606 0.0139 0.329 0.107 0.141 0.676 0.685 0.00169 0.00171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 106 15331.792 0.005 0.0183 0.268 0.633 0.121 0.161 0.481 0.618 0.0012 0.00154 +! Validation 106 15331.792 0.005 0.0182 0.574 0.938 0.12 0.161 0.758 0.904 0.0019 0.00226 +Wall time: 15331.792143907864 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.491 0.0177 0.138 0.119 0.159 0.341 0.444 0.000852 0.00111 + 107 200 0.466 0.0179 0.108 0.12 0.16 0.314 0.393 0.000785 0.000983 + 107 235 1.38 0.0177 1.03 0.121 0.159 1.18 1.21 0.00294 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 1.46 0.0136 1.19 0.107 0.139 1.29 1.3 0.00324 0.00325 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 107 15476.231 0.005 0.018 0.323 0.683 0.121 0.16 0.541 0.677 0.00135 0.00169 +! Validation 107 15476.231 0.005 0.0182 1.22 1.59 0.121 0.161 1.27 1.32 0.00316 0.0033 +Wall time: 15476.231334129814 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.396 0.0169 0.0566 0.117 0.155 0.228 0.284 0.00057 0.00071 + 108 200 0.435 0.0168 0.0998 0.116 0.154 0.307 0.377 0.000768 0.000943 + 108 235 1.28 0.0167 0.945 0.117 0.154 1.13 1.16 0.00283 0.0029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.453 0.0138 0.177 0.107 0.14 0.491 0.502 0.00123 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 108 15620.658 0.005 0.0172 0.29 0.635 0.118 0.157 0.508 0.641 0.00127 0.0016 +! Validation 108 15620.658 0.005 0.0182 0.13 0.494 0.121 0.161 0.356 0.43 0.000889 0.00108 +Wall time: 15620.658528014086 +! Best model 108 0.494 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.7 0.0161 0.378 0.115 0.151 0.652 0.734 0.00163 0.00183 + 109 200 0.562 0.0161 0.241 0.113 0.151 0.521 0.586 0.0013 0.00146 + 109 235 0.351 0.0143 0.0653 0.109 0.143 0.258 0.305 0.000646 0.000762 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.773 0.0121 0.53 0.101 0.131 0.861 0.869 0.00215 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 109 15765.098 0.005 0.0165 0.207 0.537 0.115 0.153 0.435 0.544 0.00109 0.00136 +! Validation 109 15765.098 0.005 0.0163 0.324 0.649 0.114 0.152 0.599 0.679 0.0015 0.0017 +Wall time: 15765.098161363974 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.396 0.0145 0.106 0.108 0.144 0.333 0.389 0.000832 0.000972 + 110 200 0.495 0.0149 0.197 0.11 0.146 0.472 0.529 0.00118 0.00132 + 110 235 0.485 0.0155 0.175 0.111 0.149 0.424 0.499 0.00106 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.289 0.0113 0.0635 0.0973 0.127 0.272 0.301 0.000681 0.000752 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 110 15909.631 0.005 0.0155 0.203 0.514 0.112 0.149 0.43 0.538 0.00107 0.00134 +! Validation 110 15909.631 0.005 0.0154 0.0772 0.384 0.111 0.148 0.269 0.332 0.000672 0.000829 +Wall time: 15909.63174845092 +! Best model 110 0.384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.447 0.0157 0.133 0.112 0.15 0.357 0.435 0.000892 0.00109 + 111 200 0.533 0.0149 0.235 0.11 0.146 0.497 0.578 0.00124 0.00145 + 111 235 0.898 0.0166 0.566 0.116 0.154 0.796 0.898 0.00199 0.00225 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.426 0.0128 0.169 0.104 0.135 0.484 0.491 0.00121 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 111 16054.253 0.005 0.0152 0.253 0.557 0.111 0.147 0.483 0.6 0.00121 0.0015 +! Validation 111 16054.253 0.005 0.017 0.171 0.512 0.118 0.156 0.415 0.494 0.00104 0.00123 +Wall time: 16054.253441060893 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 1.16 0.0161 0.837 0.114 0.151 1.02 1.09 0.00255 0.00273 + 112 200 0.357 0.0147 0.0628 0.109 0.145 0.252 0.299 0.000631 0.000748 + 112 235 0.354 0.0142 0.0705 0.108 0.142 0.24 0.317 0.0006 0.000792 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.411 0.012 0.17 0.0999 0.131 0.474 0.493 0.00119 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 112 16198.650 0.005 0.0152 0.261 0.565 0.111 0.147 0.492 0.611 0.00123 0.00153 +! Validation 112 16198.650 0.005 0.0155 0.308 0.618 0.112 0.149 0.592 0.663 0.00148 0.00166 +Wall time: 16198.650671278127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.301 0.0133 0.0357 0.104 0.138 0.18 0.226 0.000449 0.000564 + 113 200 0.623 0.0134 0.355 0.104 0.138 0.667 0.711 0.00167 0.00178 + 113 235 0.464 0.0136 0.192 0.105 0.139 0.417 0.523 0.00104 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.618 0.0114 0.389 0.0988 0.128 0.732 0.745 0.00183 0.00186 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 113 16343.061 0.005 0.0137 0.151 0.425 0.105 0.14 0.371 0.464 0.000928 0.00116 +! Validation 113 16343.061 0.005 0.0147 0.638 0.931 0.109 0.145 0.911 0.953 0.00228 0.00238 +Wall time: 16343.061741454992 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.458 0.0146 0.167 0.109 0.144 0.405 0.487 0.00101 0.00122 + 114 200 0.815 0.0127 0.561 0.102 0.135 0.855 0.894 0.00214 0.00223 + 114 235 0.774 0.0128 0.518 0.102 0.135 0.822 0.859 0.00206 0.00215 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 2.07 0.0116 1.84 0.0986 0.129 1.62 1.62 0.00404 0.00405 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 114 16487.548 0.005 0.0137 0.223 0.497 0.106 0.14 0.453 0.562 0.00113 0.00141 +! Validation 114 16487.548 0.005 0.0153 1.61 1.92 0.111 0.147 1.48 1.51 0.00371 0.00379 +Wall time: 16487.54821286071 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 24.1 0.924 5.61 0.854 1.15 2.32 2.83 0.0058 0.00706 + 115 200 18.8 0.784 3.12 0.783 1.06 1.84 2.11 0.00459 0.00527 + 115 235 16.1 0.717 1.72 0.75 1.01 1.25 1.56 0.00313 0.00391 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 20.9 0.736 6.15 0.758 1.02 2.91 2.96 0.00728 0.0074 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 115 16632.947 0.005 0.834 98.7 115 0.787 1.09 4.73 11.9 0.0118 0.0297 +! Validation 115 16632.947 0.005 0.718 2.58 16.9 0.748 1.01 1.52 1.92 0.0038 0.00479 +Wall time: 16632.947056300938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 9.41 0.426 0.89 0.583 0.779 0.923 1.13 0.00231 0.00281 + 116 200 6.23 0.281 0.619 0.479 0.632 0.701 0.939 0.00175 0.00235 + 116 235 5.49 0.246 0.572 0.448 0.592 0.722 0.902 0.00181 0.00226 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 5.23 0.237 0.488 0.438 0.581 0.802 0.834 0.00201 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 116 16777.420 0.005 0.432 1.39 10 0.58 0.785 1.1 1.41 0.00274 0.00352 +! Validation 116 16777.420 0.005 0.25 0.702 5.69 0.45 0.596 0.794 1 0.00199 0.0025 +Wall time: 16777.420445664786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 4.04 0.167 0.693 0.367 0.488 0.824 0.994 0.00206 0.00248 + 117 200 2.78 0.11 0.582 0.299 0.396 0.724 0.91 0.00181 0.00228 + 117 235 2.54 0.1 0.53 0.285 0.378 0.733 0.869 0.00183 0.00217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 2.41 0.0959 0.489 0.279 0.37 0.833 0.835 0.00208 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 117 16921.889 0.005 0.164 0.563 3.83 0.361 0.483 0.714 0.895 0.00178 0.00224 +! Validation 117 16921.889 0.005 0.106 0.416 2.55 0.292 0.389 0.628 0.77 0.00157 0.00193 +Wall time: 16921.889605091885 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 1.88 0.0806 0.273 0.257 0.339 0.447 0.623 0.00112 0.00156 + 118 200 1.54 0.0643 0.252 0.232 0.303 0.467 0.6 0.00117 0.0015 + 118 235 1.67 0.0621 0.425 0.227 0.297 0.65 0.778 0.00162 0.00195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 1.38 0.0562 0.251 0.217 0.283 0.594 0.598 0.00149 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 118 17066.507 0.005 0.0803 0.447 2.05 0.256 0.338 0.64 0.798 0.0016 0.002 +! Validation 118 17066.507 0.005 0.0646 0.257 1.55 0.23 0.303 0.482 0.605 0.0012 0.00151 +Wall time: 17066.507390832994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 1.28 0.0555 0.169 0.216 0.281 0.435 0.491 0.00109 0.00123 + 119 200 1.52 0.049 0.536 0.201 0.264 0.756 0.874 0.00189 0.00218 + 119 235 1.15 0.0474 0.207 0.198 0.26 0.436 0.543 0.00109 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.921 0.0424 0.0726 0.189 0.246 0.318 0.322 0.000794 0.000804 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 119 17211.052 0.005 0.0551 0.451 1.55 0.214 0.28 0.636 0.803 0.00159 0.00201 +! Validation 119 17211.052 0.005 0.0492 0.201 1.18 0.201 0.265 0.413 0.535 0.00103 0.00134 +Wall time: 17211.052222984843 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 1.23 0.0453 0.322 0.194 0.254 0.556 0.677 0.00139 0.00169 + 120 200 1.42 0.0402 0.614 0.183 0.239 0.82 0.936 0.00205 0.00234 + 120 235 0.967 0.0393 0.181 0.18 0.237 0.413 0.508 0.00103 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.921 0.0336 0.249 0.168 0.219 0.595 0.596 0.00149 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 120 17355.841 0.005 0.0437 0.48 1.35 0.19 0.249 0.677 0.828 0.00169 0.00207 +! Validation 120 17355.841 0.005 0.0397 0.191 0.985 0.181 0.238 0.424 0.521 0.00106 0.0013 +Wall time: 17355.841089469846 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.869 0.0357 0.155 0.171 0.226 0.346 0.469 0.000865 0.00117 + 121 200 0.713 0.0319 0.0749 0.162 0.213 0.25 0.327 0.000625 0.000817 + 121 235 2 0.032 1.36 0.163 0.213 1.37 1.39 0.00343 0.00348 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.632 0.0265 0.103 0.149 0.194 0.382 0.382 0.000956 0.000956 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 121 17500.348 0.005 0.035 0.392 1.09 0.17 0.223 0.591 0.745 0.00148 0.00186 +! Validation 121 17500.348 0.005 0.0324 0.816 1.46 0.163 0.215 0.965 1.08 0.00241 0.0027 +Wall time: 17500.34841020778 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.782 0.0309 0.163 0.159 0.21 0.428 0.483 0.00107 0.00121 + 122 200 1.27 0.0266 0.739 0.147 0.195 0.986 1.03 0.00246 0.00257 + 122 235 0.561 0.0262 0.0371 0.146 0.193 0.211 0.23 0.000527 0.000575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.452 0.0219 0.0139 0.136 0.177 0.139 0.141 0.000347 0.000352 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 122 17644.695 0.005 0.0298 0.509 1.1 0.156 0.206 0.687 0.853 0.00172 0.00213 +! Validation 122 17644.695 0.005 0.0275 0.145 0.696 0.149 0.198 0.361 0.455 0.000901 0.00114 +Wall time: 17644.69530302193 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.721 0.0254 0.214 0.143 0.19 0.466 0.553 0.00117 0.00138 + 123 200 0.602 0.0244 0.115 0.14 0.186 0.333 0.405 0.000833 0.00101 + 123 235 0.511 0.0218 0.0753 0.133 0.176 0.257 0.327 0.000643 0.000819 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.371 0.0185 0.00158 0.124 0.162 0.0415 0.0474 0.000104 0.000118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 123 17789.047 0.005 0.0248 0.244 0.739 0.142 0.188 0.473 0.59 0.00118 0.00148 +! Validation 123 17789.047 0.005 0.0238 0.227 0.703 0.138 0.184 0.459 0.569 0.00115 0.00142 +Wall time: 17789.04762874404 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.564 0.0226 0.112 0.135 0.179 0.334 0.399 0.000834 0.000998 + 124 200 0.525 0.0211 0.103 0.13 0.173 0.304 0.383 0.000759 0.000959 + 124 235 1.09 0.0233 0.625 0.137 0.182 0.878 0.944 0.0022 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 1.37 0.0167 1.04 0.118 0.154 1.21 1.22 0.00304 0.00304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 124 17933.758 0.005 0.0224 0.342 0.789 0.134 0.179 0.559 0.697 0.0014 0.00174 +! Validation 124 17933.758 0.005 0.0219 0.573 1.01 0.132 0.176 0.823 0.904 0.00206 0.00226 +Wall time: 17933.7581955879 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.51 0.02 0.111 0.127 0.169 0.348 0.397 0.000869 0.000993 + 125 200 0.924 0.0211 0.503 0.13 0.173 0.782 0.846 0.00196 0.00212 + 125 235 0.55 0.0219 0.112 0.132 0.177 0.326 0.399 0.000816 0.000998 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.496 0.0156 0.184 0.114 0.149 0.508 0.512 0.00127 0.00128 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 125 18078.112 0.005 0.0207 0.267 0.681 0.129 0.172 0.502 0.617 0.00126 0.00154 +! Validation 125 18078.112 0.005 0.0204 0.15 0.559 0.128 0.171 0.369 0.463 0.000922 0.00116 +Wall time: 18078.11279704701 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.428 0.019 0.0489 0.124 0.164 0.227 0.264 0.000567 0.00066 + 126 200 0.434 0.0187 0.0599 0.123 0.163 0.222 0.292 0.000554 0.00073 + 126 235 0.476 0.0194 0.0884 0.124 0.166 0.292 0.355 0.000731 0.000887 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.342 0.0141 0.0594 0.109 0.142 0.278 0.291 0.000695 0.000727 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 126 18222.676 0.005 0.0193 0.224 0.609 0.124 0.166 0.448 0.565 0.00112 0.00141 +! Validation 126 18222.676 0.005 0.0189 0.31 0.689 0.123 0.164 0.578 0.665 0.00144 0.00166 +Wall time: 18222.676840587985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 1.61 0.0185 1.24 0.121 0.162 1.3 1.33 0.00324 0.00332 + 127 200 0.768 0.019 0.388 0.123 0.165 0.703 0.744 0.00176 0.00186 + 127 235 0.487 0.018 0.127 0.12 0.16 0.286 0.426 0.000715 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.428 0.0139 0.15 0.108 0.141 0.452 0.462 0.00113 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 127 18367.220 0.005 0.0184 0.29 0.659 0.122 0.162 0.519 0.643 0.0013 0.00161 +! Validation 127 18367.220 0.005 0.0184 0.112 0.48 0.122 0.162 0.325 0.4 0.000812 0.000999 +Wall time: 18367.220533271786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.413 0.0175 0.0633 0.118 0.158 0.228 0.3 0.000569 0.00075 + 128 200 1.45 0.0172 1.11 0.118 0.157 1.23 1.26 0.00309 0.00314 + 128 235 0.849 0.0166 0.517 0.117 0.154 0.794 0.858 0.00199 0.00214 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.559 0.0135 0.289 0.107 0.139 0.634 0.642 0.00159 0.00161 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 128 18511.766 0.005 0.0176 0.26 0.612 0.119 0.158 0.486 0.608 0.00121 0.00152 +! Validation 128 18511.766 0.005 0.0179 0.658 1.02 0.12 0.16 0.912 0.969 0.00228 0.00242 +Wall time: 18511.766116563696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.451 0.0166 0.119 0.116 0.154 0.327 0.412 0.000819 0.00103 + 129 200 0.457 0.0178 0.102 0.119 0.159 0.33 0.381 0.000825 0.000952 + 129 235 0.538 0.0166 0.206 0.116 0.154 0.525 0.542 0.00131 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.728 0.0131 0.466 0.105 0.137 0.808 0.815 0.00202 0.00204 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 129 18656.213 0.005 0.0172 0.3 0.645 0.118 0.157 0.524 0.654 0.00131 0.00164 +! Validation 129 18656.213 0.005 0.0178 0.936 1.29 0.119 0.159 1.1 1.15 0.00274 0.00289 +Wall time: 18656.213465429842 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.579 0.0172 0.235 0.118 0.156 0.509 0.579 0.00127 0.00145 + 130 200 0.465 0.0171 0.123 0.118 0.156 0.35 0.418 0.000874 0.00105 + 130 235 0.396 0.0167 0.0607 0.117 0.154 0.239 0.294 0.000597 0.000735 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 1.1 0.0123 0.852 0.102 0.133 1.1 1.1 0.00274 0.00275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 130 18800.662 0.005 0.0167 0.266 0.599 0.116 0.154 0.505 0.616 0.00126 0.00154 +! Validation 130 18800.662 0.005 0.0168 0.698 1.03 0.116 0.155 0.947 0.997 0.00237 0.00249 +Wall time: 18800.662297764793 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.378 0.0149 0.0805 0.109 0.146 0.281 0.339 0.000703 0.000847 + 131 200 0.61 0.0158 0.294 0.113 0.15 0.584 0.647 0.00146 0.00162 + 131 235 0.729 0.0159 0.41 0.112 0.151 0.72 0.765 0.0018 0.00191 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.287 0.0116 0.0547 0.0983 0.129 0.258 0.279 0.000644 0.000698 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 131 18945.104 0.005 0.0161 0.228 0.55 0.114 0.151 0.458 0.569 0.00114 0.00142 +! Validation 131 18945.104 0.005 0.0161 0.0848 0.406 0.113 0.151 0.276 0.348 0.000689 0.000869 +Wall time: 18945.104150644038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.365 0.0155 0.0556 0.111 0.149 0.215 0.281 0.000538 0.000703 + 132 200 0.447 0.0155 0.137 0.112 0.149 0.371 0.442 0.000928 0.00111 + 132 235 0.386 0.0155 0.0751 0.111 0.149 0.281 0.327 0.000702 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.243 0.0117 0.0103 0.0984 0.129 0.103 0.121 0.000258 0.000302 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 132 19089.579 0.005 0.0156 0.253 0.565 0.112 0.149 0.474 0.601 0.00118 0.0015 +! Validation 132 19089.579 0.005 0.0159 0.074 0.392 0.113 0.15 0.26 0.325 0.000651 0.000812 +Wall time: 19089.57900848007 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.384 0.0148 0.0873 0.109 0.145 0.276 0.353 0.000691 0.000882 + 133 200 0.664 0.0144 0.376 0.108 0.143 0.683 0.732 0.00171 0.00183 + 133 235 0.542 0.0147 0.248 0.108 0.145 0.534 0.595 0.00134 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.369 0.011 0.148 0.0959 0.125 0.442 0.46 0.00111 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 133 19234.063 0.005 0.015 0.208 0.508 0.11 0.146 0.437 0.544 0.00109 0.00136 +! Validation 133 19234.063 0.005 0.0152 0.115 0.418 0.11 0.147 0.334 0.404 0.000834 0.00101 +Wall time: 19234.063638100866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.514 0.0165 0.184 0.115 0.153 0.41 0.513 0.00102 0.00128 + 134 200 0.379 0.0164 0.0518 0.115 0.153 0.209 0.272 0.000524 0.000679 + 134 235 0.395 0.0139 0.116 0.106 0.141 0.326 0.407 0.000815 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.273 0.0104 0.0643 0.0936 0.122 0.273 0.303 0.000683 0.000757 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 134 19378.537 0.005 0.0154 0.317 0.626 0.111 0.148 0.522 0.673 0.00131 0.00168 +! Validation 134 19378.537 0.005 0.0149 0.0913 0.388 0.109 0.145 0.285 0.361 0.000713 0.000902 +Wall time: 19378.537290907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.42 0.0151 0.118 0.11 0.147 0.326 0.41 0.000814 0.00103 + 135 200 0.602 0.0139 0.324 0.105 0.141 0.618 0.68 0.00155 0.0017 + 135 235 0.338 0.0135 0.0673 0.104 0.139 0.256 0.31 0.000641 0.000774 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.549 0.0103 0.343 0.0928 0.121 0.688 0.699 0.00172 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 135 19523.114 0.005 0.0141 0.189 0.472 0.107 0.142 0.413 0.52 0.00103 0.0013 +! Validation 135 19523.114 0.005 0.0145 0.277 0.566 0.108 0.143 0.551 0.628 0.00138 0.00157 +Wall time: 19523.114300759975 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.379 0.0135 0.11 0.104 0.139 0.329 0.396 0.000822 0.000989 + 136 200 0.364 0.0135 0.0939 0.104 0.139 0.312 0.366 0.000781 0.000914 + 136 235 0.347 0.0134 0.0793 0.104 0.138 0.272 0.336 0.00068 0.00084 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.525 0.00999 0.325 0.0911 0.119 0.67 0.681 0.00167 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 136 19667.603 0.005 0.0142 0.272 0.557 0.107 0.142 0.47 0.623 0.00118 0.00156 +! Validation 136 19667.603 0.005 0.014 0.212 0.492 0.106 0.141 0.476 0.55 0.00119 0.00138 +Wall time: 19667.602905057836 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.335 0.014 0.054 0.107 0.141 0.23 0.277 0.000575 0.000693 + 137 200 0.668 0.0128 0.411 0.102 0.135 0.73 0.765 0.00182 0.00191 + 137 235 0.447 0.0131 0.185 0.103 0.137 0.437 0.513 0.00109 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.224 0.00936 0.0371 0.0885 0.115 0.203 0.23 0.000507 0.000575 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 137 19812.106 0.005 0.0133 0.182 0.448 0.104 0.138 0.416 0.508 0.00104 0.00127 +! Validation 137 19812.106 0.005 0.0134 0.12 0.387 0.103 0.138 0.34 0.414 0.000849 0.00104 +Wall time: 19812.10645110486 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.369 0.0126 0.118 0.101 0.134 0.357 0.41 0.000893 0.00102 + 138 200 0.495 0.0126 0.243 0.101 0.134 0.532 0.588 0.00133 0.00147 + 138 235 0.354 0.0126 0.102 0.101 0.134 0.309 0.38 0.000773 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.466 0.00911 0.284 0.0873 0.114 0.623 0.636 0.00156 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 138 19956.749 0.005 0.0129 0.193 0.452 0.102 0.136 0.418 0.525 0.00104 0.00131 +! Validation 138 19956.749 0.005 0.013 0.149 0.408 0.102 0.136 0.374 0.461 0.000936 0.00115 +Wall time: 19956.748975795694 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.672 0.0121 0.43 0.0991 0.131 0.749 0.783 0.00187 0.00196 + 139 200 0.328 0.0127 0.0738 0.101 0.135 0.257 0.324 0.000643 0.00081 + 139 235 0.284 0.0117 0.0508 0.0973 0.129 0.234 0.269 0.000584 0.000672 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.912 0.00892 0.734 0.0864 0.113 1.01 1.02 0.00254 0.00256 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 139 20101.230 0.005 0.0124 0.187 0.434 0.1 0.133 0.416 0.516 0.00104 0.00129 +! Validation 139 20101.230 0.005 0.0126 0.503 0.755 0.101 0.134 0.806 0.847 0.00201 0.00212 +Wall time: 20101.23040094273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.359 0.0119 0.122 0.0983 0.13 0.341 0.416 0.000854 0.00104 + 140 200 1.13 0.0121 0.888 0.0988 0.131 1.1 1.12 0.00275 0.00281 + 140 235 0.332 0.0116 0.0997 0.0966 0.129 0.317 0.377 0.000793 0.000942 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.263 0.00844 0.0942 0.0841 0.11 0.344 0.366 0.00086 0.000916 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 140 20245.655 0.005 0.012 0.17 0.411 0.0988 0.131 0.391 0.493 0.000978 0.00123 +! Validation 140 20245.655 0.005 0.012 0.285 0.524 0.0979 0.131 0.572 0.637 0.00143 0.00159 +Wall time: 20245.65508529311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.409 0.0118 0.173 0.0978 0.13 0.459 0.496 0.00115 0.00124 + 141 200 0.651 0.0125 0.4 0.101 0.134 0.709 0.755 0.00177 0.00189 + 141 235 0.313 0.0112 0.0887 0.0951 0.126 0.296 0.355 0.00074 0.000888 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.202 0.00871 0.0281 0.0855 0.111 0.178 0.2 0.000445 0.0005 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 141 20390.059 0.005 0.0122 0.265 0.508 0.0994 0.132 0.493 0.615 0.00123 0.00154 +! Validation 141 20390.059 0.005 0.0124 0.0624 0.311 0.0996 0.133 0.235 0.298 0.000588 0.000745 +Wall time: 20390.059214785695 +! Best model 141 0.311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.286 0.0116 0.0531 0.0963 0.129 0.227 0.275 0.000567 0.000687 + 142 200 0.246 0.0105 0.0364 0.0923 0.122 0.176 0.228 0.000439 0.000569 + 142 235 0.273 0.0113 0.0456 0.0958 0.127 0.214 0.255 0.000536 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.474 0.00819 0.31 0.0828 0.108 0.65 0.665 0.00163 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 142 20534.616 0.005 0.0117 0.16 0.393 0.0972 0.129 0.385 0.478 0.000963 0.0012 +! Validation 142 20534.616 0.005 0.0115 0.254 0.485 0.0961 0.128 0.535 0.602 0.00134 0.00151 +Wall time: 20534.616606795695 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.252 0.0109 0.0333 0.0937 0.125 0.185 0.218 0.000464 0.000544 + 143 200 0.478 0.011 0.259 0.0944 0.125 0.525 0.607 0.00131 0.00152 + 143 235 0.674 0.0136 0.402 0.103 0.139 0.712 0.757 0.00178 0.00189 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.174 0.00787 0.017 0.0811 0.106 0.127 0.156 0.000318 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 143 20679.061 0.005 0.011 0.142 0.362 0.0945 0.125 0.359 0.449 0.000898 0.00112 +! Validation 143 20679.061 0.005 0.0111 0.315 0.537 0.0943 0.126 0.551 0.67 0.00138 0.00167 +Wall time: 20679.06177067384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.372 0.0114 0.145 0.0959 0.127 0.356 0.454 0.000891 0.00114 + 144 200 0.392 0.0106 0.18 0.0922 0.123 0.44 0.506 0.0011 0.00127 + 144 235 0.875 0.0127 0.621 0.102 0.134 0.897 0.941 0.00224 0.00235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.647 0.00887 0.469 0.0864 0.112 0.803 0.818 0.00201 0.00204 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 144 20823.540 0.005 0.0111 0.212 0.434 0.095 0.126 0.437 0.548 0.00109 0.00137 +! Validation 144 20823.540 0.005 0.0121 0.658 0.899 0.0989 0.131 0.931 0.968 0.00233 0.00242 +Wall time: 20823.540859203786 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.329 0.0102 0.126 0.0907 0.12 0.361 0.423 0.000902 0.00106 + 145 200 0.305 0.0104 0.0976 0.0919 0.121 0.306 0.373 0.000766 0.000932 + 145 235 0.511 0.0103 0.305 0.092 0.121 0.617 0.659 0.00154 0.00165 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.278 0.00805 0.117 0.0822 0.107 0.385 0.408 0.000962 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 145 20967.905 0.005 0.0107 0.155 0.368 0.0931 0.123 0.369 0.469 0.000921 0.00117 +! Validation 145 20967.905 0.005 0.0111 0.335 0.557 0.0945 0.126 0.641 0.691 0.0016 0.00173 +Wall time: 20967.905220484827 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.259 0.0104 0.0502 0.0923 0.122 0.206 0.268 0.000515 0.000669 + 146 200 0.76 0.0106 0.547 0.0933 0.123 0.857 0.883 0.00214 0.00221 + 146 235 0.38 0.0103 0.175 0.0916 0.121 0.444 0.499 0.00111 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.387 0.00731 0.241 0.0789 0.102 0.573 0.586 0.00143 0.00147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 146 21112.259 0.005 0.0104 0.172 0.38 0.0919 0.122 0.392 0.495 0.000981 0.00124 +! Validation 146 21112.259 0.005 0.0106 0.303 0.514 0.0921 0.123 0.599 0.657 0.0015 0.00164 +Wall time: 21112.259058379102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.286 0.0102 0.0818 0.091 0.121 0.279 0.341 0.000697 0.000853 + 147 200 0.321 0.0109 0.102 0.0941 0.125 0.293 0.382 0.000732 0.000955 + 147 235 0.475 0.0101 0.273 0.0904 0.12 0.549 0.623 0.00137 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.645 0.00719 0.501 0.078 0.101 0.836 0.845 0.00209 0.00211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 147 21256.610 0.005 0.0104 0.185 0.394 0.0922 0.122 0.412 0.512 0.00103 0.00128 +! Validation 147 21256.610 0.005 0.0101 0.315 0.517 0.0898 0.12 0.615 0.67 0.00154 0.00168 +Wall time: 21256.610668350942 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.382 0.00993 0.183 0.0898 0.119 0.448 0.511 0.00112 0.00128 + 148 200 0.42 0.0106 0.208 0.0925 0.123 0.478 0.544 0.0012 0.00136 + 148 235 0.257 0.0103 0.0518 0.0907 0.121 0.225 0.272 0.000562 0.000679 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.476 0.00681 0.339 0.0758 0.0985 0.683 0.695 0.00171 0.00174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 148 21400.960 0.005 0.00985 0.149 0.346 0.0894 0.118 0.375 0.461 0.000936 0.00115 +! Validation 148 21400.960 0.005 0.00977 0.214 0.409 0.0882 0.118 0.495 0.552 0.00124 0.00138 +Wall time: 21400.960659910925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.378 0.0101 0.176 0.0904 0.12 0.455 0.5 0.00114 0.00125 + 149 200 0.274 0.00998 0.0743 0.0895 0.119 0.255 0.325 0.000638 0.000814 + 149 235 0.242 0.0088 0.0658 0.0852 0.112 0.275 0.306 0.000687 0.000766 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.261 0.00714 0.118 0.0779 0.101 0.386 0.411 0.000965 0.00103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 149 21545.541 0.005 0.0098 0.167 0.363 0.0892 0.118 0.393 0.488 0.000984 0.00122 +! Validation 149 21545.541 0.005 0.01 0.214 0.414 0.0896 0.119 0.49 0.552 0.00123 0.00138 +Wall time: 21545.54152773088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.274 0.01 0.073 0.0895 0.12 0.27 0.323 0.000675 0.000806 + 150 200 0.289 0.0092 0.105 0.0863 0.114 0.328 0.387 0.000819 0.000967 + 150 235 0.212 0.00831 0.0457 0.0828 0.109 0.206 0.255 0.000515 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.384 0.00663 0.252 0.0749 0.0972 0.586 0.599 0.00147 0.0015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 150 21690.021 0.005 0.00965 0.151 0.344 0.0885 0.117 0.36 0.464 0.000899 0.00116 +! Validation 150 21690.021 0.005 0.00946 0.213 0.402 0.087 0.116 0.495 0.551 0.00124 0.00138 +Wall time: 21690.021377797704 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.246 0.00896 0.0666 0.0852 0.113 0.243 0.308 0.000607 0.00077 + 151 200 0.547 0.0111 0.325 0.0951 0.126 0.633 0.68 0.00158 0.0017 + 151 235 0.239 0.00929 0.0537 0.0863 0.115 0.219 0.277 0.000548 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.469 0.0069 0.331 0.076 0.0991 0.676 0.686 0.00169 0.00172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 151 21834.489 0.005 0.0101 0.199 0.402 0.0905 0.12 0.411 0.533 0.00103 0.00133 +! Validation 151 21834.489 0.005 0.00976 0.259 0.454 0.0881 0.118 0.553 0.607 0.00138 0.00152 +Wall time: 21834.489188488107 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.286 0.00835 0.119 0.0825 0.109 0.346 0.411 0.000865 0.00103 + 152 200 0.202 0.00801 0.0418 0.0807 0.107 0.197 0.244 0.000492 0.00061 + 152 235 0.503 0.00861 0.33 0.0833 0.111 0.621 0.686 0.00155 0.00172 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.157 0.00691 0.0191 0.0758 0.0992 0.147 0.165 0.000367 0.000413 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 152 21979.153 0.005 0.00879 0.101 0.276 0.0842 0.112 0.302 0.378 0.000754 0.000944 +! Validation 152 21979.153 0.005 0.00952 0.0428 0.233 0.0874 0.116 0.197 0.247 0.000492 0.000618 +Wall time: 21979.15329635609 +! Best model 152 0.233 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.341 0.00858 0.169 0.0837 0.111 0.449 0.491 0.00112 0.00123 + 153 200 0.241 0.00952 0.0509 0.0876 0.116 0.211 0.269 0.000528 0.000673 + 153 235 0.317 0.00802 0.156 0.0808 0.107 0.433 0.472 0.00108 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.168 0.00674 0.0335 0.0756 0.098 0.192 0.218 0.000479 0.000546 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 153 22123.629 0.005 0.00917 0.153 0.337 0.0864 0.114 0.377 0.468 0.000942 0.00117 +! Validation 153 22123.629 0.005 0.00925 0.0528 0.238 0.0862 0.115 0.214 0.274 0.000535 0.000685 +Wall time: 22123.629687495064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.282 0.00839 0.114 0.0821 0.109 0.338 0.403 0.000844 0.00101 + 154 200 0.355 0.0101 0.152 0.0909 0.12 0.377 0.466 0.000942 0.00116 + 154 235 0.198 0.00812 0.0353 0.0813 0.108 0.187 0.224 0.000467 0.000561 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.135 0.0063 0.00904 0.0729 0.0948 0.085 0.113 0.000213 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 154 22267.964 0.005 0.00864 0.131 0.304 0.0835 0.111 0.349 0.433 0.000872 0.00108 +! Validation 154 22267.964 0.005 0.00885 0.0477 0.225 0.0838 0.112 0.205 0.261 0.000514 0.000651 +Wall time: 22267.964301310945 +! Best model 154 0.225 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.203 0.00837 0.0358 0.0821 0.109 0.184 0.226 0.00046 0.000565 + 155 200 0.184 0.00789 0.0264 0.0797 0.106 0.153 0.194 0.000383 0.000485 + 155 235 0.33 0.0101 0.128 0.0919 0.12 0.384 0.427 0.000959 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.149 0.00686 0.0123 0.0769 0.0988 0.119 0.132 0.000298 0.000331 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 155 22412.295 0.005 0.00868 0.141 0.314 0.0838 0.111 0.355 0.448 0.000889 0.00112 +! Validation 155 22412.295 0.005 0.00981 0.09 0.286 0.0895 0.118 0.297 0.358 0.000743 0.000895 +Wall time: 22412.29535155371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.258 0.00824 0.0936 0.0819 0.108 0.307 0.365 0.000768 0.000913 + 156 200 0.461 0.00872 0.287 0.0834 0.111 0.595 0.639 0.00149 0.0016 + 156 235 0.267 0.00821 0.103 0.0821 0.108 0.348 0.383 0.000871 0.000957 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.17 0.00609 0.0486 0.0713 0.0932 0.244 0.263 0.00061 0.000658 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 156 22556.612 0.005 0.00862 0.132 0.304 0.0835 0.111 0.342 0.433 0.000855 0.00108 +! Validation 156 22556.612 0.005 0.00877 0.067 0.242 0.0834 0.112 0.242 0.309 0.000604 0.000773 +Wall time: 22556.61214656895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.189 0.00823 0.0241 0.0819 0.108 0.157 0.185 0.000392 0.000463 + 157 200 0.216 0.0089 0.0379 0.0857 0.113 0.191 0.232 0.000478 0.000581 + 157 235 0.646 0.00904 0.466 0.0857 0.114 0.771 0.814 0.00193 0.00204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.188 0.00703 0.0474 0.0763 0.1 0.243 0.26 0.000606 0.00065 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 157 22701.136 0.005 0.0089 0.143 0.321 0.085 0.113 0.361 0.449 0.000902 0.00112 +! Validation 157 22701.136 0.005 0.00933 0.223 0.409 0.0862 0.115 0.495 0.563 0.00124 0.00141 +Wall time: 22701.13608380407 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.255 0.00788 0.0975 0.079 0.106 0.304 0.373 0.000761 0.000931 + 158 200 0.201 0.00799 0.0407 0.0798 0.107 0.191 0.241 0.000478 0.000602 + 158 235 0.425 0.00838 0.258 0.0831 0.109 0.563 0.606 0.00141 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.294 0.00761 0.142 0.0818 0.104 0.428 0.449 0.00107 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 158 22845.549 0.005 0.00784 0.102 0.259 0.0794 0.106 0.305 0.381 0.000762 0.000952 +! Validation 158 22845.549 0.005 0.0102 0.296 0.499 0.0928 0.12 0.518 0.649 0.0013 0.00162 +Wall time: 22845.549434368964 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.407 0.0119 0.168 0.099 0.13 0.45 0.49 0.00113 0.00122 + 159 200 0.207 0.00793 0.0483 0.0795 0.106 0.226 0.262 0.000566 0.000656 + 159 235 0.37 0.0112 0.146 0.0972 0.127 0.426 0.455 0.00106 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.137 0.00577 0.0216 0.0694 0.0906 0.143 0.176 0.000358 0.000439 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 159 22989.997 0.005 0.00932 0.145 0.332 0.0867 0.115 0.364 0.455 0.00091 0.00114 +! Validation 159 22989.997 0.005 0.00816 0.0609 0.224 0.0805 0.108 0.236 0.295 0.00059 0.000736 +Wall time: 22989.99719727505 +! Best model 159 0.224 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.191 0.00751 0.0409 0.0772 0.103 0.191 0.241 0.000477 0.000604 + 160 200 0.329 0.00795 0.17 0.08 0.106 0.371 0.492 0.000928 0.00123 + 160 235 0.19 0.00695 0.0514 0.0737 0.0995 0.212 0.271 0.00053 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.172 0.0053 0.0663 0.0665 0.0869 0.289 0.307 0.000723 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 160 23134.465 0.005 0.00762 0.0859 0.238 0.0782 0.104 0.281 0.35 0.000702 0.000875 +! Validation 160 23134.465 0.005 0.00749 0.0524 0.202 0.077 0.103 0.224 0.273 0.000559 0.000683 +Wall time: 23134.465148929972 +! Best model 160 0.202 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.183 0.00713 0.0403 0.075 0.101 0.192 0.24 0.000481 0.000599 + 161 200 20.1 0.742 5.29 0.763 1.03 2.03 2.74 0.00508 0.00686 + 161 235 14.3 0.614 2.04 0.694 0.935 1.49 1.7 0.00374 0.00426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 19 0.618 6.59 0.693 0.939 3.02 3.06 0.00755 0.00766 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 161 23279.145 0.005 0.335 94 101 0.363 0.69 3.7 11.6 0.00925 0.029 +! Validation 161 23279.145 0.005 0.61 2.19 14.4 0.688 0.932 1.44 1.76 0.00359 0.00441 +Wall time: 23279.14565075375 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 7.55 0.333 0.882 0.51 0.689 0.811 1.12 0.00203 0.0028 + 162 200 5.45 0.241 0.628 0.436 0.586 0.744 0.946 0.00186 0.00236 + 162 235 6.01 0.23 1.42 0.428 0.572 1.06 1.42 0.00264 0.00355 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 6.13 0.208 1.98 0.401 0.544 1.66 1.68 0.00414 0.0042 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 162 23423.573 0.005 0.345 1.06 7.97 0.515 0.702 0.951 1.23 0.00238 0.00307 +! Validation 162 23423.573 0.005 0.219 0.639 5.02 0.418 0.559 0.781 0.954 0.00195 0.00238 +Wall time: 23423.573570508976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 3.91 0.17 0.502 0.37 0.493 0.698 0.846 0.00175 0.00211 + 163 200 3.14 0.143 0.284 0.341 0.451 0.505 0.636 0.00126 0.00159 + 163 235 3.24 0.132 0.598 0.327 0.434 0.834 0.923 0.00208 0.00231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 3.3 0.128 0.739 0.318 0.427 0.998 1.03 0.0025 0.00257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 163 23568.016 0.005 0.168 0.55 3.91 0.368 0.489 0.703 0.885 0.00176 0.00221 +! Validation 163 23568.016 0.005 0.136 0.354 3.08 0.332 0.44 0.578 0.71 0.00145 0.00178 +Wall time: 23568.01592779206 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 2.84 0.119 0.468 0.312 0.411 0.62 0.817 0.00155 0.00204 + 164 200 2.6 0.109 0.411 0.298 0.395 0.632 0.765 0.00158 0.00191 + 164 235 3.1 0.104 1.01 0.29 0.385 1.1 1.2 0.00274 0.00301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 2.1 0.101 0.0863 0.283 0.379 0.24 0.351 0.000599 0.000877 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 164 23712.705 0.005 0.119 0.467 2.84 0.311 0.411 0.65 0.815 0.00162 0.00204 +! Validation 164 23712.705 0.005 0.108 0.554 2.72 0.296 0.393 0.704 0.888 0.00176 0.00222 +Wall time: 23712.705572926905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 3.31 0.0986 1.34 0.282 0.375 1.27 1.38 0.00318 0.00345 + 165 200 2.98 0.0975 1.03 0.279 0.373 1.07 1.21 0.00266 0.00303 + 165 235 2.55 0.0908 0.739 0.272 0.36 0.908 1.03 0.00227 0.00257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 3.63 0.0845 1.94 0.26 0.347 1.64 1.66 0.0041 0.00415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 165 23857.313 0.005 0.0981 0.491 2.45 0.282 0.374 0.674 0.836 0.00169 0.00209 +! Validation 165 23857.313 0.005 0.0922 0.91 2.75 0.272 0.362 1.01 1.14 0.00252 0.00285 +Wall time: 23857.313687439077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 2.23 0.0852 0.525 0.263 0.348 0.67 0.865 0.00167 0.00216 + 166 200 1.96 0.0815 0.332 0.256 0.341 0.496 0.688 0.00124 0.00172 + 166 235 2.39 0.0889 0.615 0.263 0.356 0.824 0.936 0.00206 0.00234 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 4.31 0.0737 2.84 0.242 0.324 2 2.01 0.00499 0.00502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 166 24002.211 0.005 0.085 0.533 2.23 0.262 0.348 0.7 0.871 0.00175 0.00218 +! Validation 166 24002.211 0.005 0.0813 1.72 3.34 0.254 0.34 1.47 1.56 0.00366 0.00391 +Wall time: 24002.211742019746 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 3.22 0.0778 1.67 0.25 0.333 1.46 1.54 0.00364 0.00385 + 167 200 1.62 0.0735 0.153 0.241 0.323 0.363 0.467 0.000906 0.00117 + 167 235 1.96 0.073 0.5 0.241 0.322 0.717 0.844 0.00179 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 1.34 0.0653 0.0347 0.227 0.305 0.174 0.222 0.000435 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 167 24146.821 0.005 0.0759 0.594 2.11 0.246 0.329 0.742 0.92 0.00185 0.0023 +! Validation 167 24146.821 0.005 0.0733 0.486 1.95 0.241 0.323 0.677 0.832 0.00169 0.00208 +Wall time: 24146.821545765735 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 2.7 0.0683 1.33 0.234 0.312 1.25 1.38 0.00314 0.00344 + 168 200 1.4 0.0654 0.0951 0.229 0.305 0.326 0.368 0.000815 0.00092 + 168 235 2.14 0.0642 0.857 0.227 0.302 0.992 1.11 0.00248 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 1.75 0.0593 0.562 0.217 0.291 0.87 0.895 0.00218 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 168 24291.289 0.005 0.0692 0.615 2 0.235 0.314 0.74 0.936 0.00185 0.00234 +! Validation 168 24291.289 0.005 0.0674 0.476 1.82 0.231 0.31 0.674 0.823 0.00168 0.00206 +Wall time: 24291.289639474824 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 2.95 0.0688 1.58 0.233 0.313 1.4 1.5 0.0035 0.00375 + 169 200 1.8 0.0602 0.598 0.219 0.293 0.813 0.923 0.00203 0.00231 + 169 235 2.62 0.0652 1.31 0.226 0.305 1.25 1.37 0.00313 0.00342 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 4.07 0.0572 2.93 0.212 0.285 2.03 2.04 0.00508 0.00511 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 169 24436.157 0.005 0.0643 0.748 2.03 0.226 0.303 0.826 1.03 0.00207 0.00258 +! Validation 169 24436.157 0.005 0.0649 5.05 6.34 0.226 0.304 2.62 2.68 0.00655 0.0067 +Wall time: 24436.157871528994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 1.53 0.0605 0.319 0.218 0.294 0.545 0.674 0.00136 0.00168 + 170 200 1.35 0.059 0.164 0.216 0.29 0.391 0.484 0.000978 0.00121 + 170 235 3.25 0.0591 2.07 0.215 0.29 1.64 1.72 0.00409 0.00429 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 4.46 0.0529 3.4 0.204 0.275 2.19 2.2 0.00549 0.0055 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 170 24580.560 0.005 0.0611 0.689 1.91 0.22 0.295 0.79 0.988 0.00198 0.00247 +! Validation 170 24580.560 0.005 0.0605 1.97 3.18 0.218 0.294 1.58 1.67 0.00395 0.00418 +Wall time: 24580.560092213098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 1.81 0.0543 0.72 0.208 0.278 0.903 1.01 0.00226 0.00253 + 171 200 2.92 0.0544 1.83 0.207 0.278 1.54 1.62 0.00385 0.00404 + 171 235 1.92 0.056 0.8 0.211 0.282 0.94 1.07 0.00235 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 2.11 0.0495 1.12 0.197 0.266 1.25 1.26 0.00312 0.00315 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 171 24724.960 0.005 0.0567 0.593 1.73 0.212 0.284 0.738 0.919 0.00185 0.0023 +! Validation 171 24724.960 0.005 0.0571 2.25 3.39 0.212 0.285 1.71 1.79 0.00429 0.00447 +Wall time: 24724.96060252469 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 1.28 0.0545 0.192 0.208 0.279 0.458 0.523 0.00114 0.00131 + 172 200 1.6 0.0521 0.557 0.202 0.272 0.79 0.891 0.00197 0.00223 + 172 235 1.2 0.0525 0.147 0.204 0.273 0.418 0.457 0.00105 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 1.02 0.0462 0.0981 0.191 0.257 0.341 0.374 0.000853 0.000934 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 172 24869.413 0.005 0.0546 0.627 1.72 0.208 0.279 0.756 0.946 0.00189 0.00237 +! Validation 172 24869.413 0.005 0.0538 0.656 1.73 0.205 0.277 0.833 0.967 0.00208 0.00242 +Wall time: 24869.413201075047 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 1.3 0.0517 0.261 0.202 0.271 0.498 0.61 0.00125 0.00152 + 173 200 1.83 0.0484 0.865 0.196 0.263 1.03 1.11 0.00257 0.00277 + 173 235 1.37 0.0528 0.311 0.204 0.274 0.539 0.665 0.00135 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.941 0.0435 0.0712 0.185 0.249 0.289 0.318 0.000722 0.000796 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 173 25013.882 0.005 0.0512 0.556 1.58 0.201 0.27 0.707 0.89 0.00177 0.00223 +! Validation 173 25013.882 0.005 0.0509 0.201 1.22 0.199 0.269 0.428 0.535 0.00107 0.00134 +Wall time: 25013.882776924875 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 1.08 0.0488 0.0993 0.196 0.264 0.276 0.376 0.000689 0.00094 + 174 200 1.12 0.0463 0.199 0.192 0.257 0.407 0.533 0.00102 0.00133 + 174 235 1.36 0.0499 0.361 0.198 0.267 0.581 0.717 0.00145 0.00179 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 1.53 0.0427 0.68 0.184 0.247 0.974 0.984 0.00243 0.00246 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 174 25158.634 0.005 0.0495 0.653 1.64 0.197 0.265 0.772 0.965 0.00193 0.00241 +! Validation 174 25158.634 0.005 0.0497 0.382 1.38 0.197 0.266 0.636 0.737 0.00159 0.00184 +Wall time: 25158.634379493073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 1.25 0.0486 0.275 0.196 0.263 0.545 0.626 0.00136 0.00156 + 175 200 1.04 0.046 0.125 0.19 0.256 0.288 0.422 0.000721 0.00105 + 175 235 1.42 0.046 0.497 0.191 0.256 0.71 0.841 0.00178 0.0021 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.945 0.0402 0.142 0.178 0.239 0.437 0.45 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 175 25303.091 0.005 0.0487 0.671 1.65 0.196 0.264 0.79 0.978 0.00197 0.00245 +! Validation 175 25303.091 0.005 0.0475 0.67 1.62 0.193 0.26 0.867 0.977 0.00217 0.00244 +Wall time: 25303.091621663887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 1.08 0.0464 0.154 0.191 0.257 0.368 0.469 0.000921 0.00117 + 176 200 1.47 0.0444 0.581 0.187 0.251 0.8 0.91 0.002 0.00227 + 176 235 1 0.0448 0.107 0.187 0.253 0.296 0.391 0.00074 0.000978 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 1.01 0.0386 0.241 0.175 0.234 0.577 0.586 0.00144 0.00147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 176 25447.539 0.005 0.0464 0.61 1.54 0.191 0.257 0.747 0.933 0.00187 0.00233 +! Validation 176 25447.539 0.005 0.0455 0.17 1.08 0.189 0.255 0.397 0.491 0.000992 0.00123 +Wall time: 25447.53957799403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.972 0.0416 0.141 0.182 0.243 0.358 0.448 0.000895 0.00112 + 177 200 1.13 0.0446 0.238 0.187 0.252 0.485 0.583 0.00121 0.00146 + 177 235 1.06 0.041 0.243 0.18 0.242 0.503 0.589 0.00126 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.781 0.0375 0.032 0.173 0.231 0.192 0.213 0.000481 0.000534 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 177 25591.976 0.005 0.044 0.598 1.48 0.186 0.25 0.749 0.924 0.00187 0.00231 +! Validation 177 25591.976 0.005 0.0442 0.631 1.51 0.186 0.251 0.797 0.948 0.00199 0.00237 +Wall time: 25591.976279752795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 1.28 0.0403 0.47 0.179 0.24 0.708 0.818 0.00177 0.00204 + 178 200 0.975 0.043 0.114 0.185 0.248 0.297 0.402 0.000743 0.00101 + 178 235 1.12 0.0427 0.269 0.184 0.247 0.572 0.619 0.00143 0.00155 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 1.53 0.0355 0.819 0.168 0.225 1.08 1.08 0.00269 0.0027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 178 25736.863 0.005 0.0426 0.558 1.41 0.183 0.246 0.733 0.892 0.00183 0.00223 +! Validation 178 25736.863 0.005 0.0421 0.612 1.45 0.182 0.245 0.833 0.934 0.00208 0.00233 +Wall time: 25736.86346354708 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 1.81 0.0421 0.968 0.182 0.245 1.08 1.17 0.0027 0.00294 + 179 200 1.36 0.0408 0.541 0.179 0.241 0.787 0.878 0.00197 0.0022 + 179 235 1.12 0.0407 0.303 0.181 0.241 0.57 0.657 0.00143 0.00164 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 1.08 0.0339 0.397 0.165 0.22 0.748 0.752 0.00187 0.00188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 179 25881.312 0.005 0.0406 0.53 1.34 0.179 0.24 0.696 0.869 0.00174 0.00217 +! Validation 179 25881.312 0.005 0.0404 0.261 1.07 0.178 0.24 0.511 0.61 0.00128 0.00153 +Wall time: 25881.31231912179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.853 0.0385 0.084 0.175 0.234 0.275 0.346 0.000688 0.000865 + 180 200 1.25 0.0372 0.509 0.171 0.23 0.759 0.852 0.0019 0.00213 + 180 235 1.02 0.0371 0.273 0.172 0.23 0.515 0.624 0.00129 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.983 0.0323 0.336 0.161 0.215 0.689 0.692 0.00172 0.00173 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 180 26026.018 0.005 0.0393 0.542 1.33 0.176 0.237 0.695 0.879 0.00174 0.0022 +! Validation 180 26026.018 0.005 0.0386 0.18 0.952 0.174 0.234 0.406 0.507 0.00101 0.00127 +Wall time: 26026.01848004572 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 1.23 0.0382 0.464 0.174 0.233 0.746 0.813 0.00186 0.00203 + 181 200 1.09 0.0361 0.368 0.169 0.227 0.629 0.724 0.00157 0.00181 + 181 235 0.97 0.0372 0.225 0.172 0.23 0.556 0.567 0.00139 0.00142 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 1.77 0.0308 1.15 0.157 0.21 1.28 1.28 0.0032 0.0032 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 181 26170.600 0.005 0.037 0.5 1.24 0.171 0.23 0.681 0.844 0.0017 0.00211 +! Validation 181 26170.600 0.005 0.0368 0.757 1.49 0.17 0.229 0.96 1.04 0.0024 0.0026 +Wall time: 26170.600215015933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 2.3 0.0355 1.59 0.168 0.225 1.41 1.51 0.00353 0.00376 + 182 200 0.921 0.0363 0.196 0.17 0.227 0.433 0.528 0.00108 0.00132 + 182 235 0.817 0.0341 0.135 0.166 0.22 0.345 0.438 0.000864 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.602 0.0299 0.00351 0.156 0.206 0.0608 0.0707 0.000152 0.000177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 182 26315.089 0.005 0.0363 0.54 1.27 0.17 0.227 0.689 0.878 0.00172 0.00219 +! Validation 182 26315.089 0.005 0.0357 0.19 0.904 0.168 0.226 0.421 0.52 0.00105 0.0013 +Wall time: 26315.088939211797 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 1.35 0.0369 0.614 0.171 0.229 0.873 0.936 0.00218 0.00234 + 183 200 0.776 0.0341 0.0942 0.165 0.22 0.289 0.366 0.000722 0.000916 + 183 235 1.81 0.0337 1.14 0.164 0.219 1.22 1.27 0.00305 0.00319 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 3.66 0.0276 3.11 0.149 0.198 2.11 2.11 0.00526 0.00526 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 183 26459.571 0.005 0.0349 0.517 1.21 0.166 0.223 0.684 0.857 0.00171 0.00214 +! Validation 183 26459.571 0.005 0.0334 2.06 2.73 0.163 0.218 1.67 1.71 0.00418 0.00428 +Wall time: 26459.571079286747 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 1.65 0.0363 0.929 0.169 0.227 1.08 1.15 0.00269 0.00288 + 184 200 1.32 0.0354 0.61 0.168 0.225 0.851 0.932 0.00213 0.00233 + 184 235 0.774 0.031 0.155 0.158 0.21 0.344 0.469 0.000861 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.791 0.0267 0.257 0.148 0.195 0.603 0.605 0.00151 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 184 26604.062 0.005 0.0334 0.524 1.19 0.163 0.218 0.694 0.864 0.00174 0.00216 +! Validation 184 26604.062 0.005 0.0323 0.169 0.816 0.161 0.215 0.391 0.491 0.000977 0.00123 +Wall time: 26604.062740779016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.862 0.0368 0.126 0.173 0.229 0.364 0.423 0.00091 0.00106 + 185 200 1.19 0.0307 0.581 0.156 0.209 0.849 0.91 0.00212 0.00227 + 185 235 0.918 0.0326 0.267 0.161 0.215 0.433 0.616 0.00108 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 2.88 0.0283 2.31 0.151 0.201 1.81 1.81 0.00453 0.00453 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 185 26748.467 0.005 0.0328 0.546 1.2 0.162 0.216 0.662 0.882 0.00165 0.00221 +! Validation 185 26748.467 0.005 0.0337 2.05 2.72 0.164 0.219 1.61 1.71 0.00402 0.00427 +Wall time: 26748.466997777112 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.679 0.0306 0.0676 0.157 0.209 0.258 0.31 0.000645 0.000776 + 186 200 0.936 0.0311 0.314 0.158 0.211 0.621 0.669 0.00155 0.00167 + 186 235 1.15 0.0286 0.578 0.152 0.202 0.854 0.907 0.00214 0.00227 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 1.06 0.0248 0.567 0.143 0.188 0.898 0.899 0.00224 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 186 26892.861 0.005 0.0311 0.52 1.14 0.158 0.21 0.695 0.861 0.00174 0.00215 +! Validation 186 26892.861 0.005 0.03 0.348 0.949 0.155 0.207 0.603 0.704 0.00151 0.00176 +Wall time: 26892.861400740687 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.669 0.0284 0.102 0.151 0.201 0.297 0.381 0.000742 0.000952 + 187 200 1.01 0.0288 0.432 0.153 0.203 0.715 0.784 0.00179 0.00196 + 187 235 1.2 0.0286 0.63 0.152 0.202 0.893 0.947 0.00223 0.00237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.707 0.0246 0.215 0.142 0.187 0.551 0.553 0.00138 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 187 27037.445 0.005 0.0292 0.516 1.1 0.153 0.204 0.683 0.858 0.00171 0.00214 +! Validation 187 27037.445 0.005 0.0299 0.334 0.932 0.155 0.206 0.522 0.69 0.00131 0.00173 +Wall time: 27037.445033539087 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.619 0.0273 0.0741 0.148 0.197 0.273 0.325 0.000681 0.000812 + 188 200 1.41 0.0296 0.816 0.155 0.205 1.04 1.08 0.0026 0.0027 + 188 235 0.597 0.0282 0.0329 0.151 0.2 0.167 0.217 0.000418 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.6 0.0234 0.131 0.139 0.183 0.43 0.432 0.00108 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 188 27181.868 0.005 0.0285 0.524 1.09 0.152 0.202 0.689 0.865 0.00172 0.00216 +! Validation 188 27181.868 0.005 0.0285 0.133 0.703 0.151 0.201 0.338 0.435 0.000845 0.00109 +Wall time: 27181.868871751707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.603 0.0249 0.104 0.142 0.189 0.303 0.384 0.000757 0.000961 + 189 200 0.566 0.0247 0.0727 0.142 0.187 0.248 0.322 0.000619 0.000804 + 189 235 1.14 0.0283 0.573 0.151 0.201 0.855 0.903 0.00214 0.00226 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 3.16 0.0211 2.74 0.132 0.174 1.97 1.97 0.00493 0.00494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 189 27326.269 0.005 0.0266 0.43 0.962 0.147 0.195 0.621 0.782 0.00155 0.00196 +! Validation 189 27326.269 0.005 0.0259 2 2.51 0.144 0.192 1.64 1.69 0.00411 0.00422 +Wall time: 27326.269386835862 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.621 0.0264 0.0924 0.146 0.194 0.284 0.363 0.000709 0.000907 + 190 200 0.584 0.0238 0.107 0.139 0.184 0.318 0.391 0.000795 0.000978 + 190 235 0.601 0.0263 0.0753 0.147 0.194 0.247 0.328 0.000619 0.000819 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.985 0.021 0.565 0.133 0.173 0.895 0.897 0.00224 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 190 27471.032 0.005 0.0246 0.408 0.9 0.141 0.187 0.6 0.763 0.0015 0.00191 +! Validation 190 27471.032 0.005 0.0257 0.449 0.963 0.144 0.191 0.7 0.8 0.00175 0.002 +Wall time: 27471.032235929742 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.602 0.023 0.142 0.136 0.181 0.362 0.451 0.000904 0.00113 + 191 200 0.53 0.022 0.0893 0.133 0.177 0.3 0.357 0.00075 0.000892 + 191 235 1.5 0.0234 1.03 0.138 0.183 1.19 1.21 0.00298 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.536 0.0177 0.182 0.122 0.159 0.509 0.51 0.00127 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 191 27615.503 0.005 0.0228 0.334 0.79 0.136 0.18 0.562 0.688 0.0014 0.00172 +! Validation 191 27615.503 0.005 0.0223 0.63 1.08 0.134 0.178 0.874 0.948 0.00219 0.00237 +Wall time: 27615.503566673025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.753 0.0222 0.31 0.134 0.178 0.56 0.665 0.0014 0.00166 + 192 200 1.71 0.0219 1.27 0.132 0.176 1.3 1.35 0.00325 0.00337 + 192 235 1.11 0.0224 0.66 0.134 0.178 0.763 0.97 0.00191 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 1.37 0.0181 1 0.122 0.161 1.2 1.2 0.00299 0.00299 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 192 27759.993 0.005 0.0223 0.436 0.883 0.134 0.178 0.645 0.788 0.00161 0.00197 +! Validation 192 27759.993 0.005 0.0226 0.462 0.915 0.135 0.18 0.67 0.811 0.00168 0.00203 +Wall time: 27759.993606105912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.53 0.0209 0.111 0.129 0.173 0.323 0.398 0.000807 0.000996 + 193 200 0.867 0.0196 0.474 0.126 0.167 0.774 0.822 0.00193 0.00205 + 193 235 1.22 0.0222 0.779 0.133 0.178 0.905 1.05 0.00226 0.00263 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.351 0.0168 0.0157 0.118 0.155 0.138 0.15 0.000346 0.000374 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 193 27904.538 0.005 0.021 0.34 0.759 0.13 0.173 0.553 0.694 0.00138 0.00174 +! Validation 193 27904.538 0.005 0.0212 0.122 0.546 0.131 0.174 0.315 0.417 0.000789 0.00104 +Wall time: 27904.53875407204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.535 0.0174 0.188 0.119 0.157 0.42 0.517 0.00105 0.00129 + 194 200 0.579 0.018 0.218 0.121 0.16 0.488 0.558 0.00122 0.00139 + 194 235 0.652 0.0185 0.281 0.123 0.163 0.59 0.633 0.00148 0.00158 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 1.8 0.0144 1.52 0.11 0.143 1.47 1.47 0.00367 0.00367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 194 28049.011 0.005 0.0197 0.306 0.699 0.126 0.167 0.532 0.661 0.00133 0.00165 +! Validation 194 28049.011 0.005 0.0187 1.28 1.66 0.123 0.163 1.31 1.35 0.00327 0.00338 +Wall time: 28049.01154657686 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.499 0.0189 0.12 0.124 0.164 0.32 0.413 0.0008 0.00103 + 195 200 0.493 0.0186 0.121 0.123 0.163 0.342 0.415 0.000854 0.00104 + 195 235 0.636 0.0186 0.265 0.123 0.163 0.579 0.615 0.00145 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.359 0.014 0.0789 0.108 0.141 0.331 0.335 0.000828 0.000838 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 195 28193.535 0.005 0.0191 0.34 0.722 0.124 0.165 0.553 0.696 0.00138 0.00174 +! Validation 195 28193.535 0.005 0.0182 0.331 0.695 0.121 0.161 0.618 0.687 0.00155 0.00172 +Wall time: 28193.53502447484 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.444 0.0187 0.0693 0.123 0.163 0.224 0.314 0.00056 0.000786 + 196 200 0.832 0.0184 0.464 0.122 0.162 0.747 0.813 0.00187 0.00203 + 196 235 0.57 0.0181 0.207 0.12 0.161 0.447 0.543 0.00112 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 1.1 0.0133 0.83 0.105 0.138 1.09 1.09 0.00272 0.00272 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 196 28338.097 0.005 0.018 0.307 0.668 0.121 0.16 0.508 0.661 0.00127 0.00165 +! Validation 196 28338.097 0.005 0.0174 0.642 0.99 0.118 0.157 0.898 0.956 0.00225 0.00239 +Wall time: 28338.09699555207 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.51 0.016 0.191 0.114 0.151 0.436 0.521 0.00109 0.0013 + 197 200 0.45 0.0162 0.126 0.114 0.152 0.349 0.424 0.000871 0.00106 + 197 235 0.402 0.0158 0.0867 0.113 0.15 0.314 0.351 0.000784 0.000879 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.64 0.0121 0.399 0.1 0.131 0.752 0.754 0.00188 0.00188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 197 28482.510 0.005 0.0168 0.257 0.594 0.117 0.155 0.466 0.606 0.00116 0.00152 +! Validation 197 28482.510 0.005 0.0159 0.275 0.593 0.113 0.15 0.559 0.626 0.0014 0.00156 +Wall time: 28482.51028347574 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.574 0.0162 0.25 0.114 0.152 0.524 0.596 0.00131 0.00149 + 198 200 0.345 0.0149 0.0481 0.11 0.145 0.21 0.262 0.000525 0.000654 + 198 235 0.458 0.014 0.178 0.106 0.141 0.468 0.503 0.00117 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.444 0.0115 0.213 0.0977 0.128 0.547 0.551 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 198 28627.710 0.005 0.016 0.262 0.581 0.114 0.151 0.48 0.611 0.0012 0.00153 +! Validation 198 28627.710 0.005 0.0153 0.596 0.903 0.111 0.148 0.856 0.922 0.00214 0.0023 +Wall time: 28627.710094919894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.425 0.0147 0.131 0.109 0.145 0.362 0.432 0.000904 0.00108 + 199 200 0.846 0.0134 0.578 0.104 0.138 0.881 0.907 0.0022 0.00227 + 199 235 0.358 0.0146 0.0654 0.109 0.144 0.235 0.305 0.000589 0.000763 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.247 0.0108 0.0318 0.0947 0.124 0.197 0.213 0.000493 0.000532 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 199 28772.101 0.005 0.0147 0.22 0.513 0.109 0.145 0.444 0.56 0.00111 0.0014 +! Validation 199 28772.101 0.005 0.0143 0.0628 0.349 0.107 0.143 0.23 0.299 0.000576 0.000748 +Wall time: 28772.101739841048 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.332 0.0143 0.0462 0.108 0.143 0.211 0.257 0.000528 0.000641 + 200 200 0.279 0.0127 0.0257 0.102 0.134 0.15 0.191 0.000376 0.000479 + 200 235 0.506 0.0134 0.237 0.104 0.138 0.504 0.582 0.00126 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.427 0.00998 0.228 0.0908 0.119 0.565 0.569 0.00141 0.00142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 200 28916.456 0.005 0.0143 0.261 0.547 0.108 0.143 0.473 0.609 0.00118 0.00152 +! Validation 200 28916.456 0.005 0.0134 0.146 0.414 0.104 0.138 0.378 0.457 0.000946 0.00114 +Wall time: 28916.456342815887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.722 0.0156 0.41 0.114 0.149 0.711 0.765 0.00178 0.00191 + 201 200 0.485 0.013 0.225 0.103 0.136 0.48 0.567 0.0012 0.00142 + 201 235 0.39 0.0111 0.169 0.095 0.126 0.458 0.491 0.00115 0.00123 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.37 0.00923 0.186 0.0876 0.115 0.507 0.514 0.00127 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 201 29060.826 0.005 0.0134 0.235 0.503 0.104 0.138 0.456 0.579 0.00114 0.00145 +! Validation 201 29060.826 0.005 0.0125 0.146 0.396 0.1 0.133 0.384 0.456 0.00096 0.00114 +Wall time: 29060.826726787724 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 1.01 0.0158 0.698 0.114 0.15 0.948 0.997 0.00237 0.00249 + 202 200 0.296 0.012 0.0554 0.0994 0.131 0.214 0.281 0.000535 0.000702 + 202 235 1.18 0.0117 0.942 0.0978 0.129 1.12 1.16 0.0028 0.0029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 2.39 0.00926 2.21 0.0874 0.115 1.77 1.77 0.00443 0.00443 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 202 29205.174 0.005 0.0128 0.239 0.495 0.102 0.135 0.457 0.581 0.00114 0.00145 +! Validation 202 29205.174 0.005 0.0126 2.11 2.36 0.101 0.134 1.69 1.73 0.00422 0.00433 +Wall time: 29205.174641112797 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.639 0.0121 0.397 0.099 0.131 0.712 0.752 0.00178 0.00188 + 203 200 0.44 0.0121 0.198 0.0985 0.131 0.435 0.532 0.00109 0.00133 + 203 235 0.848 0.011 0.627 0.0949 0.125 0.916 0.945 0.00229 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.579 0.00879 0.403 0.0855 0.112 0.752 0.758 0.00188 0.00189 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 203 29349.537 0.005 0.0131 0.303 0.564 0.103 0.136 0.523 0.656 0.00131 0.00164 +! Validation 203 29349.537 0.005 0.012 0.274 0.515 0.0981 0.131 0.553 0.625 0.00138 0.00156 +Wall time: 29349.537832372822 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.271 0.0113 0.0455 0.0952 0.127 0.197 0.254 0.000492 0.000636 + 204 200 0.285 0.0119 0.0467 0.0988 0.13 0.204 0.258 0.00051 0.000645 + 204 235 0.244 0.0101 0.0413 0.0906 0.12 0.187 0.243 0.000467 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.188 0.00798 0.0286 0.0813 0.107 0.181 0.202 0.000452 0.000504 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 204 29493.905 0.005 0.0115 0.185 0.415 0.0968 0.128 0.405 0.514 0.00101 0.00128 +! Validation 204 29493.905 0.005 0.011 0.14 0.36 0.0938 0.125 0.382 0.446 0.000955 0.00112 +Wall time: 29493.9053469887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.273 0.0112 0.0481 0.0945 0.127 0.197 0.262 0.000492 0.000655 + 205 200 0.31 0.00962 0.118 0.0885 0.117 0.368 0.41 0.00092 0.00102 + 205 235 1.06 0.00991 0.857 0.0902 0.119 1.06 1.1 0.00264 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.428 0.00743 0.279 0.0784 0.103 0.625 0.631 0.00156 0.00158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 205 29638.244 0.005 0.0105 0.145 0.355 0.0922 0.122 0.36 0.452 0.0009 0.00113 +! Validation 205 29638.244 0.005 0.0103 0.633 0.84 0.0908 0.121 0.873 0.95 0.00218 0.00237 +Wall time: 29638.244379336946 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.261 0.00991 0.0627 0.0891 0.119 0.24 0.299 0.0006 0.000747 + 206 200 0.31 0.00978 0.115 0.0886 0.118 0.303 0.404 0.000757 0.00101 + 206 235 0.448 0.0106 0.236 0.095 0.123 0.525 0.58 0.00131 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.206 0.00839 0.0382 0.0847 0.109 0.214 0.233 0.000536 0.000583 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 206 29782.592 0.005 0.0102 0.169 0.372 0.0908 0.12 0.383 0.49 0.000957 0.00123 +! Validation 206 29782.592 0.005 0.0111 0.165 0.387 0.0955 0.126 0.416 0.486 0.00104 0.00121 +Wall time: 29782.592169892043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.52 0.00983 0.323 0.0889 0.118 0.647 0.679 0.00162 0.0017 + 207 200 0.214 0.00949 0.0238 0.0877 0.116 0.147 0.184 0.000367 0.000461 + 207 235 0.252 0.0104 0.0447 0.0908 0.122 0.193 0.252 0.000484 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.168 0.00727 0.0223 0.0777 0.102 0.148 0.178 0.000369 0.000446 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 207 29926.932 0.005 0.00963 0.164 0.357 0.0884 0.117 0.387 0.484 0.000966 0.00121 +! Validation 207 29926.932 0.005 0.0101 0.0843 0.287 0.0899 0.12 0.291 0.347 0.000729 0.000866 +Wall time: 29926.93198606372 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.247 0.00868 0.0739 0.0835 0.111 0.264 0.325 0.00066 0.000811 + 208 200 0.309 0.0114 0.0818 0.0975 0.127 0.276 0.341 0.000689 0.000853 + 208 235 0.336 0.00948 0.147 0.0879 0.116 0.386 0.458 0.000966 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.495 0.00737 0.347 0.0782 0.102 0.696 0.703 0.00174 0.00176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 208 30071.283 0.005 0.00981 0.205 0.401 0.0892 0.118 0.419 0.541 0.00105 0.00135 +! Validation 208 30071.283 0.005 0.0101 0.253 0.455 0.0899 0.12 0.521 0.6 0.0013 0.0015 +Wall time: 30071.28350604186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.205 0.0092 0.0211 0.0858 0.114 0.14 0.174 0.000351 0.000434 + 209 200 0.234 0.00895 0.0548 0.0849 0.113 0.237 0.279 0.000593 0.000699 + 209 235 0.42 0.0111 0.197 0.0956 0.126 0.373 0.53 0.000932 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.624 0.00817 0.46 0.0821 0.108 0.802 0.81 0.002 0.00202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 209 30215.836 0.005 0.00902 0.153 0.333 0.0853 0.113 0.369 0.466 0.000922 0.00117 +! Validation 209 30215.836 0.005 0.0109 0.511 0.729 0.0938 0.125 0.772 0.853 0.00193 0.00213 +Wall time: 30215.83667997783 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.303 0.00886 0.126 0.0848 0.112 0.373 0.423 0.000932 0.00106 + 210 200 0.199 0.00846 0.0301 0.0831 0.11 0.167 0.207 0.000417 0.000518 + 210 235 0.254 0.0079 0.0959 0.0794 0.106 0.334 0.37 0.000836 0.000924 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.137 0.00635 0.00967 0.0725 0.0951 0.109 0.117 0.000272 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 210 30360.326 0.005 0.0088 0.133 0.309 0.0843 0.112 0.346 0.436 0.000864 0.00109 +! Validation 210 30360.326 0.005 0.00894 0.185 0.364 0.0841 0.113 0.407 0.513 0.00102 0.00128 +Wall time: 30360.326856210828 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.213 0.00906 0.0319 0.0849 0.114 0.168 0.213 0.000419 0.000533 + 211 200 0.231 0.00844 0.0624 0.082 0.11 0.247 0.298 0.000619 0.000746 + 211 235 0.244 0.00806 0.0829 0.081 0.107 0.309 0.344 0.000772 0.000859 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.135 0.00641 0.00667 0.0726 0.0955 0.0733 0.0975 0.000183 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 211 30504.703 0.005 0.00869 0.15 0.324 0.0837 0.111 0.357 0.462 0.000894 0.00116 +! Validation 211 30504.703 0.005 0.00893 0.319 0.497 0.0841 0.113 0.542 0.674 0.00135 0.00169 +Wall time: 30504.70371670602 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 1.35 0.0432 0.484 0.191 0.248 0.707 0.831 0.00177 0.00208 + 212 200 0.314 0.0121 0.0724 0.0994 0.131 0.281 0.321 0.000702 0.000803 + 212 235 0.26 0.0116 0.0284 0.0968 0.128 0.168 0.201 0.000419 0.000503 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.193 0.00866 0.0201 0.0839 0.111 0.152 0.169 0.000381 0.000423 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 212 30649.076 0.005 0.0475 1.88 2.83 0.17 0.26 0.928 1.64 0.00232 0.0041 +! Validation 212 30649.076 0.005 0.0117 0.0878 0.322 0.0967 0.129 0.288 0.354 0.00072 0.000884 +Wall time: 30649.076125403866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.248 0.00973 0.0534 0.0881 0.118 0.233 0.276 0.000583 0.000689 + 213 200 0.221 0.00925 0.0364 0.0859 0.115 0.19 0.228 0.000475 0.00057 + 213 235 0.214 0.00903 0.033 0.0853 0.113 0.141 0.217 0.000352 0.000542 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.139 0.00667 0.00592 0.0737 0.0974 0.0811 0.0918 0.000203 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 213 30793.558 0.005 0.01 0.112 0.313 0.0899 0.12 0.317 0.401 0.000793 0.001 +! Validation 213 30793.558 0.005 0.00943 0.0728 0.261 0.0864 0.116 0.264 0.322 0.00066 0.000805 +Wall time: 30793.558478767984 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.461 0.009 0.281 0.085 0.113 0.576 0.633 0.00144 0.00158 + 214 200 0.198 0.00842 0.0294 0.0822 0.11 0.167 0.205 0.000418 0.000512 + 214 235 0.637 0.00922 0.452 0.0857 0.115 0.766 0.803 0.00192 0.00201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.465 0.00606 0.344 0.0707 0.0929 0.693 0.7 0.00173 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 214 30937.994 0.005 0.00871 0.0955 0.27 0.0836 0.111 0.294 0.367 0.000735 0.000918 +! Validation 214 30937.994 0.005 0.00875 0.663 0.838 0.0833 0.112 0.936 0.972 0.00234 0.00243 +Wall time: 30937.994596284814 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.199 0.0084 0.0306 0.0819 0.109 0.167 0.209 0.000417 0.000522 + 215 200 0.386 0.0078 0.23 0.079 0.105 0.539 0.572 0.00135 0.00143 + 215 235 0.275 0.00824 0.111 0.0795 0.108 0.359 0.397 0.000898 0.000992 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.217 0.0057 0.103 0.0683 0.0901 0.372 0.383 0.000929 0.000957 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 215 31083.392 0.005 0.0084 0.121 0.289 0.0822 0.109 0.331 0.416 0.000828 0.00104 +! Validation 215 31083.392 0.005 0.00832 0.0676 0.234 0.0808 0.109 0.251 0.31 0.000628 0.000776 +Wall time: 31083.392043903936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.197 0.00756 0.0458 0.078 0.104 0.218 0.256 0.000545 0.000639 + 216 200 0.29 0.00692 0.152 0.075 0.0993 0.44 0.465 0.0011 0.00116 + 216 235 0.163 0.00732 0.0169 0.0768 0.102 0.141 0.155 0.000352 0.000388 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.319 0.00543 0.21 0.0667 0.088 0.54 0.547 0.00135 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 216 31227.845 0.005 0.00771 0.0717 0.226 0.0785 0.105 0.255 0.32 0.000637 0.0008 +! Validation 216 31227.845 0.005 0.00792 0.151 0.309 0.0788 0.106 0.405 0.464 0.00101 0.00116 +Wall time: 31227.845250417013 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.207 0.00798 0.0479 0.0799 0.107 0.203 0.261 0.000507 0.000653 + 217 200 0.193 0.00748 0.043 0.0775 0.103 0.193 0.247 0.000482 0.000618 + 217 235 0.188 0.00747 0.0385 0.0774 0.103 0.198 0.234 0.000494 0.000585 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.113 0.0053 0.00691 0.0654 0.0869 0.0783 0.0992 0.000196 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 217 31372.308 0.005 0.00746 0.0878 0.237 0.0772 0.103 0.283 0.354 0.000707 0.000885 +! Validation 217 31372.308 0.005 0.00766 0.0533 0.206 0.0774 0.104 0.224 0.275 0.00056 0.000689 +Wall time: 31372.308386445977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.176 0.00683 0.0391 0.0736 0.0986 0.202 0.236 0.000505 0.00059 + 218 200 0.18 0.00726 0.0351 0.0767 0.102 0.18 0.224 0.00045 0.000559 + 218 235 0.219 0.0086 0.047 0.0817 0.111 0.236 0.259 0.000589 0.000647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.123 0.00517 0.0193 0.065 0.0858 0.149 0.166 0.000373 0.000415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 218 31516.873 0.005 0.00733 0.0975 0.244 0.0765 0.102 0.291 0.373 0.000727 0.000932 +! Validation 218 31516.873 0.005 0.00747 0.0376 0.187 0.0764 0.103 0.179 0.231 0.000447 0.000578 +Wall time: 31516.873147965875 +! Best model 218 0.187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.212 0.00689 0.0741 0.0744 0.0991 0.294 0.325 0.000734 0.000812 + 219 200 0.205 0.00653 0.0743 0.0726 0.0965 0.268 0.325 0.000669 0.000813 + 219 235 0.247 0.00658 0.115 0.0725 0.0968 0.376 0.405 0.000941 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.262 0.00514 0.159 0.0645 0.0856 0.468 0.476 0.00117 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 219 31661.776 0.005 0.00724 0.107 0.252 0.0761 0.102 0.314 0.391 0.000785 0.000977 +! Validation 219 31661.776 0.005 0.00734 0.17 0.317 0.0755 0.102 0.439 0.493 0.0011 0.00123 +Wall time: 31661.776122577954 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.269 0.00686 0.132 0.0739 0.0988 0.392 0.433 0.000981 0.00108 + 220 200 0.18 0.00722 0.0359 0.0752 0.101 0.177 0.226 0.000443 0.000565 + 220 235 0.164 0.0068 0.0279 0.073 0.0984 0.168 0.199 0.000419 0.000499 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.135 0.00518 0.0313 0.0645 0.0859 0.195 0.211 0.000488 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 220 31806.439 0.005 0.00698 0.0983 0.238 0.0746 0.0997 0.297 0.375 0.000743 0.000936 +! Validation 220 31806.439 0.005 0.00747 0.0374 0.187 0.0764 0.103 0.182 0.231 0.000456 0.000577 +Wall time: 31806.439511295874 +! Best model 220 0.187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.226 0.00706 0.0846 0.0754 0.1 0.313 0.347 0.000782 0.000868 + 221 200 0.191 0.00676 0.056 0.0742 0.0981 0.243 0.282 0.000607 0.000706 + 221 235 0.136 0.00586 0.0183 0.0682 0.0914 0.133 0.162 0.000331 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.118 0.00495 0.019 0.0633 0.0839 0.145 0.165 0.000362 0.000412 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 221 31950.848 0.005 0.00684 0.0797 0.216 0.0739 0.0987 0.271 0.337 0.000678 0.000843 +! Validation 221 31950.848 0.005 0.00703 0.0746 0.215 0.074 0.1 0.279 0.326 0.000698 0.000815 +Wall time: 31950.8488442651 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.162 0.00661 0.0303 0.0729 0.097 0.169 0.208 0.000423 0.000519 + 222 200 0.207 0.00697 0.0681 0.0744 0.0996 0.265 0.311 0.000662 0.000779 + 222 235 0.215 0.00698 0.0757 0.0748 0.0997 0.266 0.328 0.000664 0.000821 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.133 0.00496 0.0342 0.0629 0.0841 0.201 0.221 0.000504 0.000551 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 222 32095.269 0.005 0.00676 0.0937 0.229 0.0734 0.0981 0.284 0.365 0.00071 0.000913 +! Validation 222 32095.269 0.005 0.00695 0.076 0.215 0.0735 0.0995 0.284 0.329 0.000709 0.000822 +Wall time: 32095.269019040745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.194 0.00669 0.0605 0.0732 0.0976 0.25 0.294 0.000624 0.000734 + 223 200 0.156 0.00634 0.0293 0.0709 0.095 0.165 0.204 0.000413 0.000511 + 223 235 0.188 0.00729 0.0425 0.0763 0.102 0.183 0.246 0.000458 0.000615 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.275 0.00645 0.146 0.0715 0.0958 0.453 0.456 0.00113 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 223 32239.723 0.005 0.00658 0.0993 0.231 0.0724 0.0968 0.294 0.376 0.000736 0.000941 +! Validation 223 32239.723 0.005 0.00836 0.144 0.312 0.081 0.109 0.406 0.454 0.00101 0.00113 +Wall time: 32239.72354936879 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.188 0.00589 0.0698 0.0689 0.0916 0.288 0.315 0.000721 0.000789 + 224 200 0.156 0.00623 0.031 0.0704 0.0942 0.162 0.21 0.000404 0.000525 + 224 235 0.149 0.00646 0.02 0.0728 0.0959 0.145 0.169 0.000362 0.000422 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.135 0.00489 0.0377 0.0625 0.0835 0.225 0.232 0.000563 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 224 32384.139 0.005 0.00639 0.0832 0.211 0.0713 0.0954 0.274 0.345 0.000685 0.000862 +! Validation 224 32384.139 0.005 0.00681 0.0943 0.23 0.0728 0.0985 0.303 0.367 0.000756 0.000916 +Wall time: 32384.139620163944 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.19 0.00599 0.0702 0.0692 0.0924 0.275 0.316 0.000686 0.00079 + 225 200 0.193 0.00633 0.0665 0.0707 0.095 0.26 0.308 0.000651 0.00077 + 225 235 0.244 0.00642 0.116 0.0716 0.0956 0.359 0.406 0.000897 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.0942 0.00444 0.00542 0.0595 0.0795 0.072 0.0879 0.00018 0.00022 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 225 32528.574 0.005 0.00627 0.0784 0.204 0.0706 0.0945 0.261 0.334 0.000654 0.000835 +! Validation 225 32528.574 0.005 0.0063 0.111 0.237 0.0697 0.0948 0.327 0.398 0.000818 0.000996 +Wall time: 32528.57412370993 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.21 0.00601 0.0894 0.069 0.0926 0.325 0.357 0.000813 0.000892 + 226 200 0.17 0.00687 0.0321 0.074 0.0989 0.174 0.214 0.000434 0.000535 + 226 235 0.147 0.00568 0.0329 0.067 0.09 0.176 0.217 0.000439 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.133 0.0046 0.0413 0.0606 0.081 0.231 0.242 0.000577 0.000606 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 226 32673.027 0.005 0.00682 0.13 0.267 0.0737 0.0986 0.333 0.431 0.000831 0.00108 +! Validation 226 32673.027 0.005 0.00648 0.0635 0.193 0.071 0.0961 0.258 0.301 0.000644 0.000752 +Wall time: 32673.027051643934 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.18 0.00576 0.0651 0.0676 0.0906 0.27 0.305 0.000675 0.000761 + 227 200 0.251 0.00539 0.144 0.0654 0.0876 0.411 0.452 0.00103 0.00113 + 227 235 0.147 0.00621 0.0232 0.0715 0.0941 0.148 0.182 0.000369 0.000455 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.104 0.00496 0.00455 0.0637 0.084 0.0715 0.0805 0.000179 0.000201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 227 32817.437 0.005 0.00602 0.0652 0.186 0.0691 0.0926 0.244 0.305 0.000611 0.000763 +! Validation 227 32817.437 0.005 0.00664 0.0342 0.167 0.0726 0.0973 0.167 0.221 0.000416 0.000551 +Wall time: 32817.43741024472 +! Best model 227 0.167 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.23 0.00639 0.103 0.0709 0.0954 0.327 0.382 0.000818 0.000956 + 228 200 0.203 0.00617 0.0795 0.0694 0.0937 0.292 0.337 0.00073 0.000841 + 228 235 0.164 0.00593 0.0453 0.0677 0.0919 0.199 0.254 0.000498 0.000635 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.114 0.00426 0.0287 0.0586 0.0779 0.193 0.202 0.000483 0.000506 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 228 32961.941 0.005 0.00635 0.0976 0.225 0.071 0.0952 0.292 0.373 0.000729 0.000933 +! Validation 228 32961.941 0.005 0.00603 0.0523 0.173 0.0682 0.0927 0.226 0.273 0.000565 0.000683 +Wall time: 32961.94129371783 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.137 0.0056 0.0254 0.067 0.0893 0.15 0.19 0.000374 0.000476 + 229 200 0.173 0.00567 0.06 0.0667 0.0898 0.206 0.292 0.000515 0.000731 + 229 235 0.258 0.00677 0.123 0.0731 0.0982 0.327 0.418 0.000818 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.34 0.00474 0.245 0.0611 0.0822 0.586 0.591 0.00147 0.00148 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 229 33106.302 0.005 0.00605 0.102 0.223 0.0694 0.0928 0.301 0.381 0.000753 0.000952 +! Validation 229 33106.302 0.005 0.00647 0.26 0.39 0.0709 0.096 0.559 0.609 0.0014 0.00152 +Wall time: 33106.302334988955 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.129 0.00562 0.0167 0.0671 0.0895 0.123 0.154 0.000307 0.000385 + 230 200 0.269 0.00521 0.165 0.0651 0.0862 0.458 0.484 0.00115 0.00121 + 230 235 0.118 0.0053 0.0115 0.0645 0.0869 0.108 0.128 0.000271 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.087 0.00408 0.00538 0.0571 0.0762 0.0774 0.0875 0.000193 0.000219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 230 33250.654 0.005 0.0062 0.0853 0.209 0.0702 0.094 0.275 0.349 0.000687 0.000872 +! Validation 230 33250.654 0.005 0.00569 0.0309 0.145 0.0663 0.0901 0.159 0.21 0.000397 0.000524 +Wall time: 33250.65446929494 +! Best model 230 0.145 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.16 0.00559 0.0482 0.0673 0.0893 0.219 0.262 0.000547 0.000655 + 231 200 0.134 0.0054 0.0258 0.0656 0.0877 0.135 0.192 0.000338 0.000479 + 231 235 0.122 0.00475 0.0274 0.0611 0.0822 0.171 0.198 0.000429 0.000494 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.0789 0.00383 0.00239 0.0553 0.0738 0.0446 0.0584 0.000112 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 231 33394.992 0.005 0.00576 0.0617 0.177 0.0677 0.0906 0.235 0.297 0.000589 0.000742 +! Validation 231 33394.992 0.005 0.00542 0.0408 0.149 0.0645 0.0879 0.197 0.241 0.000492 0.000603 +Wall time: 33394.991961939726 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.136 0.00517 0.0325 0.0639 0.0858 0.178 0.215 0.000445 0.000538 + 232 200 0.131 0.00532 0.0251 0.0626 0.0871 0.136 0.189 0.000339 0.000472 + 232 235 0.591 0.00791 0.433 0.0807 0.106 0.662 0.785 0.00166 0.00196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.282 0.00739 0.135 0.0782 0.103 0.437 0.438 0.00109 0.0011 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 232 33539.448 0.005 0.00653 0.117 0.248 0.0716 0.0964 0.316 0.407 0.000789 0.00102 +! Validation 232 33539.448 0.005 0.00882 0.157 0.334 0.0848 0.112 0.424 0.473 0.00106 0.00118 +Wall time: 33539.4483963931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.279 0.00597 0.159 0.07 0.0922 0.465 0.476 0.00116 0.00119 + 233 200 0.204 0.00525 0.0992 0.0649 0.0865 0.319 0.376 0.000797 0.00094 + 233 235 0.149 0.00502 0.0484 0.0638 0.0846 0.215 0.263 0.000538 0.000656 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.079 0.00381 0.00276 0.055 0.0737 0.0564 0.0627 0.000141 0.000157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 233 33683.998 0.005 0.00605 0.0729 0.194 0.0693 0.0929 0.256 0.322 0.000639 0.000806 +! Validation 233 33683.998 0.005 0.00534 0.0415 0.148 0.0641 0.0872 0.18 0.243 0.000451 0.000608 +Wall time: 33683.99825751176 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.123 0.00508 0.021 0.0639 0.0851 0.14 0.173 0.00035 0.000433 + 234 200 0.173 0.00634 0.0465 0.0711 0.0951 0.21 0.257 0.000525 0.000643 + 234 235 0.132 0.00464 0.0389 0.0614 0.0813 0.184 0.235 0.00046 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.166 0.00416 0.0825 0.0573 0.077 0.34 0.343 0.00085 0.000857 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 234 33828.677 0.005 0.00568 0.0832 0.197 0.0672 0.09 0.265 0.344 0.000663 0.000861 +! Validation 234 33828.677 0.005 0.0056 0.164 0.276 0.066 0.0893 0.403 0.483 0.00101 0.00121 +Wall time: 33828.67690723296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.131 0.00532 0.0242 0.0646 0.087 0.157 0.186 0.000392 0.000465 + 235 200 0.151 0.00509 0.0492 0.0637 0.0852 0.231 0.265 0.000577 0.000662 + 235 235 0.128 0.00512 0.0253 0.0638 0.0854 0.15 0.19 0.000374 0.000475 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0932 0.0046 0.00124 0.0604 0.0809 0.0323 0.0421 8.08e-05 0.000105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 235 33973.134 0.005 0.00547 0.0594 0.169 0.066 0.0883 0.23 0.291 0.000574 0.000728 +! Validation 235 33973.134 0.005 0.00601 0.0324 0.153 0.0685 0.0925 0.176 0.215 0.000441 0.000537 +Wall time: 33973.13487123605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.189 0.00758 0.0372 0.0796 0.104 0.178 0.23 0.000446 0.000575 + 236 200 0.187 0.00489 0.0887 0.0623 0.0835 0.308 0.355 0.000771 0.000889 + 236 235 0.145 0.00485 0.0478 0.0633 0.0831 0.228 0.261 0.00057 0.000652 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.0899 0.00375 0.0148 0.0546 0.0731 0.133 0.145 0.000333 0.000363 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 236 34118.340 0.005 0.0064 0.0893 0.217 0.0703 0.0955 0.268 0.357 0.000671 0.000892 +! Validation 236 34118.340 0.005 0.00522 0.0404 0.145 0.0636 0.0863 0.197 0.24 0.000492 0.0006 +Wall time: 34118.340785505716 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.148 0.00539 0.04 0.0654 0.0876 0.205 0.239 0.000514 0.000597 + 237 200 0.134 0.00486 0.0367 0.0619 0.0832 0.196 0.229 0.00049 0.000572 + 237 235 0.129 0.00511 0.0265 0.0643 0.0853 0.165 0.194 0.000412 0.000486 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.0812 0.00375 0.00629 0.0547 0.0731 0.0728 0.0946 0.000182 0.000237 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 237 34262.686 0.005 0.0056 0.0709 0.183 0.0663 0.0893 0.249 0.318 0.000623 0.000795 +! Validation 237 34262.686 0.005 0.0053 0.028 0.134 0.064 0.0869 0.153 0.2 0.000383 0.0005 +Wall time: 34262.68664267985 +! Best model 237 0.134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.14 0.00518 0.0362 0.0646 0.0859 0.183 0.227 0.000458 0.000568 + 238 200 0.11 0.00479 0.0145 0.0612 0.0826 0.112 0.144 0.00028 0.000359 + 238 235 0.108 0.00456 0.0163 0.0604 0.0806 0.12 0.152 0.0003 0.000381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.0913 0.00354 0.0206 0.0529 0.071 0.164 0.171 0.000409 0.000428 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 238 34407.074 0.005 0.00502 0.0557 0.156 0.0631 0.0846 0.218 0.282 0.000546 0.000705 +! Validation 238 34407.074 0.005 0.00484 0.0502 0.147 0.061 0.083 0.217 0.267 0.000544 0.000668 +Wall time: 34407.07442861609 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.274 0.00734 0.127 0.0769 0.102 0.338 0.426 0.000845 0.00106 + 239 200 0.127 0.0048 0.0311 0.0617 0.0827 0.169 0.21 0.000423 0.000526 + 239 235 0.145 0.00519 0.0414 0.0648 0.086 0.234 0.243 0.000584 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.0797 0.00379 0.00392 0.0548 0.0735 0.0595 0.0747 0.000149 0.000187 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 239 34551.717 0.005 0.00531 0.0742 0.18 0.0647 0.0869 0.254 0.325 0.000636 0.000813 +! Validation 239 34551.717 0.005 0.00501 0.0376 0.138 0.0625 0.0845 0.182 0.231 0.000456 0.000579 +Wall time: 34551.717003651895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.114 0.00439 0.0265 0.0595 0.0791 0.168 0.194 0.00042 0.000486 + 240 200 0.138 0.00448 0.0481 0.0597 0.0799 0.237 0.262 0.000592 0.000655 + 240 235 0.269 0.00473 0.175 0.0613 0.0821 0.478 0.499 0.0012 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.178 0.00362 0.106 0.054 0.0718 0.381 0.388 0.000953 0.000971 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 240 34696.737 0.005 0.00507 0.0666 0.168 0.0635 0.085 0.241 0.307 0.000602 0.000769 +! Validation 240 34696.737 0.005 0.00492 0.116 0.214 0.062 0.0837 0.376 0.406 0.00094 0.00101 +Wall time: 34696.737294198014 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.152 0.00438 0.0642 0.0597 0.079 0.274 0.302 0.000685 0.000756 + 241 200 0.143 0.00437 0.0555 0.0582 0.0789 0.251 0.281 0.000627 0.000703 + 241 235 0.123 0.00493 0.0239 0.0624 0.0838 0.148 0.185 0.000371 0.000461 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.084 0.004 0.00394 0.0559 0.0755 0.0663 0.0749 0.000166 0.000187 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 241 34841.269 0.005 0.0046 0.0512 0.143 0.0604 0.081 0.214 0.27 0.000535 0.000675 +! Validation 241 34841.269 0.005 0.00518 0.019 0.123 0.0632 0.0859 0.13 0.164 0.000324 0.000411 +Wall time: 34841.269558385015 +! Best model 241 0.123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 26.2 1.02 5.75 0.897 1.21 2.54 2.86 0.00636 0.00715 + 242 200 10.4 0.428 1.84 0.586 0.781 1.23 1.62 0.00308 0.00405 + 242 235 4.73 0.222 0.292 0.425 0.562 0.557 0.645 0.00139 0.00161 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 6.08 0.232 1.44 0.428 0.575 1.4 1.43 0.00349 0.00359 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 242 34985.853 0.005 0.448 7.53 16.5 0.496 0.799 1.81 3.28 0.00453 0.0082 +! Validation 242 34985.853 0.005 0.224 0.911 5.4 0.427 0.565 0.913 1.14 0.00228 0.00285 +Wall time: 34985.852974404115 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 5.56 0.144 2.68 0.344 0.453 1.74 1.96 0.00436 0.00489 + 243 200 2.36 0.0952 0.46 0.28 0.368 0.64 0.81 0.0016 0.00202 + 243 235 2.5 0.0899 0.702 0.272 0.358 0.847 1 0.00212 0.0025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 3.05 0.0868 1.31 0.267 0.352 1.35 1.37 0.00338 0.00342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 243 35130.646 0.005 0.134 1.19 3.88 0.33 0.437 1.04 1.31 0.0026 0.00326 +! Validation 243 35130.646 0.005 0.0906 1.36 3.17 0.273 0.359 1.25 1.39 0.00313 0.00348 +Wall time: 35130.64674981311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 1.83 0.0738 0.359 0.247 0.324 0.588 0.715 0.00147 0.00179 + 244 200 1.53 0.0625 0.277 0.226 0.298 0.507 0.629 0.00127 0.00157 + 244 235 1.73 0.0598 0.534 0.223 0.292 0.749 0.872 0.00187 0.00218 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 1.64 0.0546 0.551 0.211 0.279 0.877 0.886 0.00219 0.00221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 244 35275.118 0.005 0.0744 0.688 2.18 0.247 0.326 0.797 0.99 0.00199 0.00248 +! Validation 244 35275.118 0.005 0.0589 0.487 1.66 0.22 0.29 0.714 0.833 0.00178 0.00208 +Wall time: 35275.11815839773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 2.13 0.0476 1.17 0.199 0.26 1.2 1.29 0.003 0.00323 + 245 200 0.954 0.0384 0.187 0.177 0.234 0.425 0.516 0.00106 0.00129 + 245 235 3.08 0.0332 2.42 0.165 0.218 1.82 1.86 0.00454 0.00464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.818 0.0339 0.14 0.166 0.22 0.438 0.446 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 245 35419.582 0.005 0.0473 0.617 1.56 0.197 0.26 0.762 0.934 0.00191 0.00234 +! Validation 245 35419.582 0.005 0.038 0.569 1.33 0.175 0.233 0.773 0.9 0.00193 0.00225 +Wall time: 35419.58224955201 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 1.57 0.0328 0.918 0.163 0.216 1.09 1.14 0.00272 0.00286 + 246 200 0.682 0.0265 0.152 0.145 0.194 0.405 0.465 0.00101 0.00116 + 246 235 0.605 0.0249 0.107 0.141 0.188 0.304 0.391 0.00076 0.000977 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 1 0.0213 0.573 0.131 0.174 0.9 0.904 0.00225 0.00226 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 246 35564.041 0.005 0.0321 0.464 1.11 0.16 0.214 0.637 0.814 0.00159 0.00204 +! Validation 246 35564.041 0.005 0.0258 0.55 1.07 0.143 0.192 0.804 0.886 0.00201 0.00221 +Wall time: 35564.04146458581 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.853 0.0226 0.4 0.135 0.18 0.69 0.755 0.00173 0.00189 + 247 200 0.645 0.0207 0.23 0.13 0.172 0.502 0.573 0.00126 0.00143 + 247 235 0.767 0.0225 0.317 0.132 0.179 0.619 0.672 0.00155 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.645 0.0184 0.277 0.12 0.162 0.62 0.629 0.00155 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 247 35708.503 0.005 0.0228 0.317 0.773 0.135 0.18 0.542 0.672 0.00135 0.00168 +! Validation 247 35708.503 0.005 0.0221 0.202 0.644 0.132 0.177 0.443 0.536 0.00111 0.00134 +Wall time: 35708.50294390181 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.503 0.0211 0.0818 0.13 0.173 0.241 0.341 0.000601 0.000853 + 248 200 0.408 0.0174 0.0592 0.118 0.158 0.247 0.291 0.000617 0.000726 + 248 235 0.442 0.0172 0.0982 0.118 0.157 0.298 0.374 0.000745 0.000935 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.773 0.015 0.473 0.108 0.146 0.813 0.82 0.00203 0.00205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 248 35853.009 0.005 0.0199 0.298 0.697 0.126 0.169 0.517 0.653 0.00129 0.00163 +! Validation 248 35853.009 0.005 0.0185 0.348 0.717 0.12 0.162 0.621 0.704 0.00155 0.00176 +Wall time: 35853.00889188284 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.55 0.0166 0.219 0.115 0.154 0.485 0.559 0.00121 0.0014 + 249 200 0.439 0.0165 0.109 0.113 0.153 0.325 0.394 0.000812 0.000985 + 249 235 0.507 0.017 0.167 0.117 0.155 0.393 0.488 0.000983 0.00122 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.44 0.0133 0.174 0.102 0.137 0.487 0.499 0.00122 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 249 35997.417 0.005 0.0169 0.228 0.566 0.116 0.155 0.46 0.57 0.00115 0.00143 +! Validation 249 35997.417 0.005 0.0165 0.15 0.48 0.113 0.153 0.394 0.463 0.000984 0.00116 +Wall time: 35997.41743276082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.785 0.0159 0.468 0.112 0.15 0.76 0.816 0.0019 0.00204 + 250 200 0.376 0.0151 0.0745 0.109 0.147 0.273 0.326 0.000683 0.000814 + 250 235 1.08 0.0138 0.8 0.105 0.14 1.04 1.07 0.0026 0.00267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.433 0.0119 0.196 0.096 0.13 0.513 0.528 0.00128 0.00132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 250 36141.842 0.005 0.0153 0.218 0.523 0.11 0.148 0.443 0.555 0.00111 0.00139 +! Validation 250 36141.842 0.005 0.0147 0.328 0.622 0.107 0.145 0.625 0.683 0.00156 0.00171 +Wall time: 36141.8419667501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.625 0.0137 0.351 0.105 0.14 0.664 0.707 0.00166 0.00177 + 251 200 0.678 0.0126 0.426 0.101 0.134 0.752 0.779 0.00188 0.00195 + 251 235 0.361 0.0154 0.0537 0.108 0.148 0.233 0.277 0.000582 0.000692 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.657 0.0111 0.435 0.0927 0.126 0.779 0.787 0.00195 0.00197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 251 36286.378 0.005 0.0139 0.238 0.517 0.105 0.141 0.469 0.583 0.00117 0.00146 +! Validation 251 36286.378 0.005 0.0138 0.26 0.537 0.104 0.14 0.549 0.609 0.00137 0.00152 +Wall time: 36286.37799672084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.386 0.0117 0.151 0.0971 0.129 0.406 0.464 0.00102 0.00116 + 252 200 0.309 0.0113 0.0835 0.0957 0.127 0.309 0.345 0.000771 0.000862 + 252 235 0.503 0.0133 0.236 0.103 0.138 0.533 0.58 0.00133 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.243 0.0108 0.0267 0.0909 0.124 0.15 0.195 0.000375 0.000488 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 252 36431.323 0.005 0.0126 0.197 0.45 0.1 0.134 0.423 0.53 0.00106 0.00133 +! Validation 252 36431.323 0.005 0.0131 0.159 0.422 0.102 0.137 0.407 0.476 0.00102 0.00119 +Wall time: 36431.32355782483 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.305 0.0115 0.0747 0.0956 0.128 0.279 0.326 0.000699 0.000815 + 253 200 0.309 0.0113 0.0826 0.0948 0.127 0.29 0.343 0.000725 0.000858 + 253 235 0.227 0.00983 0.0299 0.0892 0.118 0.189 0.206 0.000472 0.000516 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.21 0.00912 0.0274 0.0838 0.114 0.17 0.198 0.000425 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 253 36575.840 0.005 0.0117 0.184 0.418 0.0966 0.129 0.417 0.513 0.00104 0.00128 +! Validation 253 36575.840 0.005 0.0115 0.0465 0.276 0.0948 0.128 0.203 0.257 0.000507 0.000643 +Wall time: 36575.840185960755 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.269 0.00974 0.0745 0.0891 0.118 0.241 0.326 0.000602 0.000815 + 254 200 0.228 0.00939 0.0401 0.0876 0.116 0.2 0.239 0.0005 0.000597 + 254 235 0.273 0.0118 0.0379 0.0958 0.129 0.183 0.232 0.000458 0.000581 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.208 0.0097 0.0141 0.086 0.118 0.134 0.141 0.000334 0.000354 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 254 36720.275 0.005 0.0105 0.158 0.368 0.0916 0.122 0.371 0.475 0.000927 0.00119 +! Validation 254 36720.275 0.005 0.0118 0.0835 0.319 0.0964 0.129 0.284 0.345 0.000709 0.000862 +Wall time: 36720.27561111003 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.23 0.00949 0.0403 0.0876 0.116 0.182 0.24 0.000455 0.000599 + 255 200 0.276 0.0104 0.0688 0.09 0.122 0.268 0.313 0.00067 0.000783 + 255 235 0.22 0.00912 0.0375 0.0854 0.114 0.201 0.231 0.000503 0.000578 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.17 0.00777 0.015 0.0773 0.105 0.132 0.146 0.000329 0.000365 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 255 36864.768 0.005 0.00985 0.147 0.344 0.0887 0.118 0.36 0.458 0.000899 0.00114 +! Validation 255 36864.768 0.005 0.00978 0.0617 0.257 0.0875 0.118 0.244 0.297 0.000609 0.000741 +Wall time: 36864.76811373094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.199 0.00868 0.025 0.0828 0.111 0.155 0.189 0.000387 0.000471 + 256 200 0.306 0.00865 0.133 0.0832 0.111 0.39 0.436 0.000976 0.00109 + 256 235 0.436 0.00812 0.274 0.0812 0.108 0.613 0.625 0.00153 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.239 0.00713 0.0963 0.074 0.101 0.352 0.37 0.00088 0.000926 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 256 37009.214 0.005 0.00901 0.129 0.31 0.0848 0.113 0.345 0.429 0.000862 0.00107 +! Validation 256 37009.214 0.005 0.00912 0.164 0.346 0.0846 0.114 0.431 0.483 0.00108 0.00121 +Wall time: 37009.21420597285 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.55 0.00828 0.385 0.0816 0.109 0.728 0.74 0.00182 0.00185 + 257 200 0.212 0.00822 0.0479 0.0815 0.108 0.226 0.261 0.000565 0.000653 + 257 235 0.156 0.00727 0.0105 0.0769 0.102 0.107 0.122 0.000267 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.139 0.00645 0.00975 0.0707 0.0959 0.114 0.118 0.000284 0.000295 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 257 37153.664 0.005 0.00848 0.121 0.29 0.0823 0.11 0.331 0.416 0.000828 0.00104 +! Validation 257 37153.664 0.005 0.00849 0.0418 0.212 0.0815 0.11 0.197 0.244 0.000493 0.00061 +Wall time: 37153.66459931806 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.185 0.0078 0.0292 0.0789 0.105 0.155 0.204 0.000387 0.00051 + 258 200 0.22 0.00808 0.0587 0.0813 0.107 0.247 0.289 0.000618 0.000723 + 258 235 0.205 0.00769 0.0513 0.079 0.105 0.239 0.27 0.000597 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.619 0.00659 0.487 0.0712 0.0969 0.822 0.833 0.00205 0.00208 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 258 37298.042 0.005 0.00811 0.154 0.316 0.0804 0.107 0.376 0.468 0.000939 0.00117 +! Validation 258 37298.042 0.005 0.00853 0.756 0.927 0.0818 0.11 1.01 1.04 0.00253 0.0026 +Wall time: 37298.04224750772 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.199 0.00746 0.0503 0.0772 0.103 0.221 0.268 0.000553 0.000669 + 259 200 0.439 0.00744 0.291 0.0772 0.103 0.619 0.643 0.00155 0.00161 + 259 235 0.159 0.0076 0.00738 0.0779 0.104 0.0653 0.103 0.000163 0.000256 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.124 0.00567 0.0109 0.0663 0.0898 0.0889 0.125 0.000222 0.000311 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 259 37442.418 0.005 0.00758 0.0917 0.243 0.0777 0.104 0.285 0.362 0.000713 0.000905 +! Validation 259 37442.418 0.005 0.00755 0.0462 0.197 0.0768 0.104 0.211 0.257 0.000526 0.000641 +Wall time: 37442.41828820575 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.422 0.00744 0.273 0.077 0.103 0.588 0.624 0.00147 0.00156 + 260 200 0.655 0.00803 0.495 0.0803 0.107 0.81 0.84 0.00203 0.0021 + 260 235 0.215 0.00753 0.0645 0.0781 0.104 0.245 0.303 0.000614 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.133 0.00587 0.0158 0.0679 0.0915 0.111 0.15 0.000278 0.000375 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 260 37586.770 0.005 0.00729 0.136 0.282 0.0762 0.102 0.351 0.441 0.000878 0.0011 +! Validation 260 37586.770 0.005 0.0077 0.097 0.251 0.0776 0.105 0.308 0.372 0.00077 0.000929 +Wall time: 37586.77039202675 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.208 0.00748 0.0586 0.0768 0.103 0.206 0.289 0.000515 0.000722 + 261 200 0.174 0.00696 0.0352 0.0747 0.0996 0.171 0.224 0.000428 0.00056 + 261 235 0.184 0.00679 0.0486 0.0738 0.0984 0.218 0.263 0.000544 0.000658 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.2 0.00529 0.0946 0.0644 0.0868 0.344 0.367 0.000861 0.000918 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 261 37731.128 0.005 0.00701 0.108 0.249 0.0747 0.0999 0.309 0.393 0.000772 0.000983 +! Validation 261 37731.128 0.005 0.0071 0.0983 0.24 0.0745 0.101 0.324 0.374 0.000809 0.000935 +Wall time: 37731.12853643112 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.143 0.00633 0.016 0.0717 0.095 0.118 0.151 0.000295 0.000377 + 262 200 0.162 0.00689 0.0245 0.0735 0.0991 0.154 0.187 0.000385 0.000467 + 262 235 0.318 0.00757 0.166 0.0774 0.104 0.468 0.487 0.00117 0.00122 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.183 0.00564 0.07 0.0664 0.0896 0.29 0.316 0.000726 0.000789 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 262 37875.636 0.005 0.0066 0.097 0.229 0.0724 0.0969 0.296 0.371 0.00074 0.000928 +! Validation 262 37875.636 0.005 0.00743 0.0421 0.191 0.0765 0.103 0.194 0.245 0.000486 0.000612 +Wall time: 37875.63676540693 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.145 0.00613 0.0228 0.0697 0.0934 0.143 0.18 0.000358 0.000451 + 263 200 0.129 0.00582 0.013 0.0684 0.091 0.116 0.136 0.000291 0.00034 + 263 235 0.165 0.00564 0.0525 0.0677 0.0897 0.251 0.273 0.000628 0.000684 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.153 0.00466 0.0594 0.0602 0.0814 0.277 0.291 0.000694 0.000728 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 263 38020.163 0.005 0.00645 0.088 0.217 0.0716 0.0958 0.279 0.354 0.000697 0.000886 +! Validation 263 38020.163 0.005 0.00642 0.0802 0.208 0.0706 0.0956 0.294 0.338 0.000735 0.000845 +Wall time: 38020.16378176771 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.133 0.00597 0.0134 0.0691 0.0923 0.114 0.138 0.000285 0.000346 + 264 200 0.134 0.00582 0.018 0.0676 0.0911 0.129 0.16 0.000322 0.0004 + 264 235 0.396 0.00692 0.258 0.0737 0.0993 0.573 0.606 0.00143 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.215 0.00461 0.123 0.0601 0.0811 0.407 0.419 0.00102 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 264 38164.570 0.005 0.00614 0.0896 0.212 0.0698 0.0935 0.283 0.356 0.000707 0.000891 +! Validation 264 38164.570 0.005 0.0064 0.202 0.33 0.0707 0.0955 0.482 0.536 0.0012 0.00134 +Wall time: 38164.57005945174 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.191 0.00605 0.0703 0.0693 0.0928 0.26 0.316 0.000649 0.000791 + 265 200 0.189 0.0056 0.0767 0.0667 0.0893 0.298 0.331 0.000744 0.000827 + 265 235 0.14 0.0052 0.0355 0.0645 0.0861 0.168 0.225 0.000419 0.000562 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.218 0.00423 0.133 0.0582 0.0777 0.422 0.436 0.00106 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 265 38308.956 0.005 0.00592 0.0771 0.196 0.0686 0.0919 0.265 0.332 0.000662 0.000829 +! Validation 265 38308.956 0.005 0.00604 0.129 0.25 0.0686 0.0927 0.389 0.429 0.000972 0.00107 +Wall time: 38308.95599666005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.201 0.006 0.0811 0.0694 0.0925 0.279 0.34 0.000699 0.00085 + 266 200 0.134 0.00581 0.0178 0.0679 0.0909 0.132 0.159 0.000329 0.000399 + 266 235 0.158 0.00637 0.0308 0.0709 0.0953 0.176 0.209 0.00044 0.000524 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.107 0.00416 0.0235 0.0575 0.077 0.148 0.183 0.000371 0.000457 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 266 38453.536 0.005 0.00594 0.107 0.226 0.0687 0.092 0.308 0.39 0.000769 0.000975 +! Validation 266 38453.536 0.005 0.00593 0.0394 0.158 0.0679 0.0919 0.194 0.237 0.000484 0.000592 +Wall time: 38453.536199903116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.155 0.00566 0.0416 0.0671 0.0898 0.192 0.243 0.000479 0.000608 + 267 200 0.216 0.00531 0.11 0.0649 0.087 0.373 0.396 0.000933 0.00099 + 267 235 0.199 0.00513 0.0965 0.0646 0.0855 0.335 0.371 0.000837 0.000927 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.216 0.00413 0.134 0.0568 0.0767 0.428 0.437 0.00107 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 267 38597.770 0.005 0.00556 0.0673 0.179 0.0664 0.089 0.25 0.309 0.000626 0.000774 +! Validation 267 38597.770 0.005 0.0057 0.187 0.301 0.0666 0.0901 0.475 0.516 0.00119 0.00129 +Wall time: 38597.770298619755 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.154 0.00563 0.0414 0.0669 0.0896 0.197 0.243 0.000493 0.000607 + 268 200 0.132 0.00576 0.0168 0.0678 0.0906 0.127 0.155 0.000317 0.000387 + 268 235 0.139 0.00558 0.0274 0.0662 0.0892 0.164 0.197 0.000409 0.000494 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.171 0.00415 0.0885 0.0575 0.0769 0.336 0.355 0.000841 0.000887 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 268 38742.003 0.005 0.00535 0.0666 0.174 0.0651 0.0873 0.247 0.308 0.000618 0.000771 +! Validation 268 38742.003 0.005 0.00582 0.109 0.226 0.0674 0.0911 0.352 0.394 0.000879 0.000986 +Wall time: 38742.003830773756 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.168 0.0051 0.0664 0.064 0.0853 0.26 0.308 0.00065 0.000769 + 269 200 0.195 0.00539 0.0876 0.0654 0.0877 0.3 0.353 0.000751 0.000883 + 269 235 0.176 0.00605 0.0548 0.0711 0.0928 0.253 0.279 0.000634 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.142 0.00514 0.039 0.0656 0.0856 0.194 0.236 0.000486 0.00059 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 269 38886.433 0.005 0.00572 0.12 0.234 0.0674 0.0902 0.327 0.413 0.000817 0.00103 +! Validation 269 38886.433 0.005 0.00682 0.0559 0.192 0.0742 0.0985 0.236 0.282 0.00059 0.000705 +Wall time: 38886.43298922805 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.143 0.00538 0.0354 0.065 0.0875 0.179 0.225 0.000446 0.000561 + 270 200 0.124 0.00507 0.0232 0.0636 0.0849 0.149 0.182 0.000372 0.000454 + 270 235 0.129 0.00541 0.0204 0.0653 0.0878 0.141 0.17 0.000354 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.0842 0.00389 0.00634 0.0555 0.0744 0.0804 0.0951 0.000201 0.000238 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 270 39030.773 0.005 0.00537 0.0779 0.185 0.0653 0.0875 0.266 0.333 0.000665 0.000833 +! Validation 270 39030.773 0.005 0.00542 0.0239 0.132 0.0649 0.0879 0.147 0.185 0.000367 0.000461 +Wall time: 39030.77305335412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.131 0.00545 0.0218 0.0659 0.0881 0.142 0.176 0.000355 0.000441 + 271 200 0.152 0.00548 0.0427 0.0659 0.0883 0.208 0.247 0.000519 0.000617 + 271 235 0.293 0.00617 0.17 0.0699 0.0937 0.474 0.491 0.00119 0.00123 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.196 0.00425 0.111 0.0586 0.0778 0.388 0.398 0.000969 0.000996 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 271 39175.273 0.005 0.00531 0.0819 0.188 0.0649 0.0869 0.263 0.341 0.000657 0.000853 +! Validation 271 39175.273 0.005 0.00565 0.163 0.276 0.0668 0.0897 0.451 0.483 0.00113 0.00121 +Wall time: 39175.27329402603 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.128 0.0049 0.0297 0.0619 0.0835 0.167 0.206 0.000418 0.000514 + 272 200 0.138 0.00473 0.043 0.0618 0.0821 0.226 0.248 0.000564 0.000619 + 272 235 0.118 0.0052 0.014 0.065 0.086 0.119 0.141 0.000297 0.000353 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.129 0.00394 0.0502 0.0571 0.0749 0.256 0.267 0.000641 0.000668 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 272 39319.541 0.005 0.00495 0.0543 0.153 0.0627 0.084 0.218 0.278 0.000545 0.000696 +! Validation 272 39319.541 0.005 0.00551 0.0685 0.179 0.0659 0.0886 0.257 0.312 0.000642 0.000781 +Wall time: 39319.54120127903 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.16 0.00484 0.0634 0.0621 0.083 0.258 0.301 0.000645 0.000751 + 273 200 0.183 0.005 0.0831 0.0636 0.0844 0.311 0.344 0.000778 0.00086 + 273 235 0.165 0.00485 0.0681 0.0624 0.0831 0.289 0.311 0.000721 0.000779 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.292 0.00363 0.22 0.0542 0.0719 0.552 0.559 0.00138 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 273 39463.806 0.005 0.00497 0.0711 0.171 0.0628 0.0842 0.255 0.318 0.000637 0.000796 +! Validation 273 39463.806 0.005 0.00507 0.225 0.326 0.0628 0.085 0.538 0.566 0.00135 0.00142 +Wall time: 39463.8063824391 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.129 0.00493 0.0307 0.0628 0.0838 0.169 0.209 0.000423 0.000523 + 274 200 0.122 0.00493 0.0235 0.0618 0.0838 0.148 0.183 0.000371 0.000458 + 274 235 0.178 0.00575 0.0633 0.0669 0.0905 0.234 0.3 0.000586 0.000751 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0897 0.00347 0.0203 0.0526 0.0703 0.142 0.17 0.000354 0.000425 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 274 39608.083 0.005 0.00486 0.0633 0.161 0.0621 0.0832 0.238 0.3 0.000594 0.000751 +! Validation 274 39608.083 0.005 0.00492 0.0713 0.17 0.0617 0.0837 0.26 0.319 0.000649 0.000797 +Wall time: 39608.08337900881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.116 0.00425 0.0307 0.0579 0.0778 0.179 0.209 0.000447 0.000523 + 275 200 0.203 0.00462 0.111 0.0601 0.0811 0.37 0.397 0.000926 0.000992 + 275 235 0.172 0.00476 0.0771 0.0611 0.0824 0.291 0.331 0.000728 0.000829 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0863 0.00368 0.0127 0.0538 0.0724 0.11 0.135 0.000274 0.000336 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 275 39752.357 0.005 0.00473 0.0499 0.144 0.0612 0.082 0.212 0.266 0.000529 0.000666 +! Validation 275 39752.357 0.005 0.00492 0.0193 0.118 0.0619 0.0837 0.132 0.166 0.000329 0.000414 +Wall time: 39752.35746981902 +! Best model 275 0.118 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.102 0.00453 0.0116 0.0598 0.0804 0.103 0.129 0.000257 0.000322 + 276 200 0.116 0.00423 0.0312 0.0578 0.0777 0.182 0.211 0.000456 0.000527 + 276 235 0.14 0.00454 0.0488 0.0596 0.0804 0.206 0.264 0.000516 0.000659 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.16 0.00346 0.0912 0.0529 0.0702 0.349 0.36 0.000872 0.000901 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 276 39896.651 0.005 0.00468 0.056 0.15 0.0609 0.0816 0.221 0.283 0.000553 0.000706 +! Validation 276 39896.651 0.005 0.00485 0.152 0.249 0.0613 0.0831 0.401 0.465 0.001 0.00116 +Wall time: 39896.650958575774 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.116 0.00454 0.0247 0.0605 0.0804 0.16 0.188 0.000401 0.000469 + 277 200 0.149 0.00556 0.0382 0.0666 0.089 0.203 0.233 0.000508 0.000583 + 277 235 0.108 0.00412 0.0258 0.0575 0.0766 0.165 0.192 0.000412 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.0752 0.00323 0.0106 0.0511 0.0678 0.0848 0.123 0.000212 0.000308 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 277 40041.337 0.005 0.00493 0.0799 0.178 0.0627 0.0838 0.26 0.338 0.000651 0.000844 +! Validation 277 40041.337 0.005 0.00462 0.0408 0.133 0.0598 0.0812 0.201 0.241 0.000502 0.000603 +Wall time: 40041.337317303754 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.12 0.0044 0.0315 0.0587 0.0792 0.18 0.212 0.000451 0.00053 + 278 200 0.225 0.00434 0.139 0.0591 0.0786 0.406 0.444 0.00102 0.00111 + 278 235 0.128 0.00483 0.0314 0.0629 0.083 0.177 0.211 0.000442 0.000528 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0856 0.00398 0.00594 0.0573 0.0753 0.0828 0.092 0.000207 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 278 40185.672 0.005 0.00462 0.0607 0.153 0.0606 0.0811 0.238 0.294 0.000595 0.000736 +! Validation 278 40185.672 0.005 0.00537 0.0303 0.138 0.0649 0.0875 0.155 0.208 0.000387 0.00052 +Wall time: 40185.67262376612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.149 0.00395 0.0698 0.0559 0.075 0.302 0.315 0.000755 0.000789 + 279 200 0.297 0.00476 0.202 0.0621 0.0823 0.496 0.536 0.00124 0.00134 + 279 235 0.233 0.00633 0.107 0.0715 0.0949 0.281 0.39 0.000703 0.000975 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.374 0.00432 0.288 0.0586 0.0784 0.637 0.64 0.00159 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 279 40329.968 0.005 0.00469 0.0677 0.161 0.0611 0.0817 0.252 0.31 0.000631 0.000776 +! Validation 279 40329.968 0.005 0.00542 0.188 0.297 0.0656 0.0879 0.449 0.518 0.00112 0.0013 +Wall time: 40329.968883024994 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.179 0.0041 0.0973 0.0571 0.0764 0.343 0.372 0.000856 0.000931 + 280 200 0.128 0.00434 0.0414 0.0585 0.0786 0.192 0.243 0.00048 0.000607 + 280 235 0.121 0.00512 0.0185 0.0642 0.0854 0.129 0.162 0.000322 0.000406 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.0804 0.00363 0.00774 0.0547 0.0719 0.0893 0.105 0.000223 0.000263 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 280 40474.274 0.005 0.00478 0.0636 0.159 0.0617 0.0825 0.233 0.301 0.000583 0.000753 +! Validation 280 40474.274 0.005 0.00493 0.0186 0.117 0.0624 0.0838 0.131 0.163 0.000328 0.000407 +Wall time: 40474.27433515992 +! Best model 280 0.117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.185 0.00405 0.104 0.0566 0.0759 0.358 0.385 0.000894 0.000962 + 281 200 2.84 0.00784 2.68 0.0797 0.106 1.93 1.95 0.00482 0.00488 + 281 235 24 0.931 5.39 0.858 1.15 2.27 2.77 0.00567 0.00693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 29.3 0.89 11.5 0.842 1.13 4.01 4.06 0.01 0.0101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 281 40618.631 0.005 0.115 2.87 5.18 0.16 0.401 0.769 2.02 0.00192 0.00505 +! Validation 281 40618.631 0.005 0.871 4.4 21.8 0.832 1.11 2.08 2.5 0.0052 0.00626 +Wall time: 40618.63141711475 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 2.48 0.101 0.46 0.287 0.379 0.614 0.81 0.00154 0.00202 + 282 200 1.5 0.0459 0.579 0.191 0.256 0.852 0.908 0.00213 0.00227 + 282 235 1.36 0.0424 0.508 0.183 0.246 0.781 0.851 0.00195 0.00213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 2.04 0.0354 1.33 0.167 0.225 1.37 1.38 0.00343 0.00344 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 282 40762.940 0.005 0.169 1.56 4.95 0.326 0.492 1.06 1.49 0.00265 0.00373 +! Validation 282 40762.940 0.005 0.0399 1.01 1.8 0.177 0.238 1.13 1.2 0.00282 0.003 +Wall time: 40762.94065816188 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.754 0.0282 0.189 0.15 0.201 0.451 0.518 0.00113 0.0013 + 283 200 1.29 0.0118 1.06 0.0983 0.129 1.21 1.23 0.00302 0.00307 + 283 235 0.57 0.0115 0.34 0.0969 0.128 0.67 0.696 0.00167 0.00174 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.549 0.00876 0.374 0.0844 0.112 0.729 0.73 0.00182 0.00182 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 283 40907.236 0.005 0.0235 0.372 0.842 0.135 0.183 0.595 0.728 0.00149 0.00182 +! Validation 283 40907.236 0.005 0.0116 0.534 0.767 0.0972 0.129 0.831 0.872 0.00208 0.00218 +Wall time: 40907.236541989725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.208 0.00901 0.0281 0.0855 0.113 0.165 0.2 0.000414 0.0005 + 284 200 0.197 0.00796 0.0381 0.0804 0.107 0.175 0.233 0.000437 0.000582 + 284 235 0.212 0.00815 0.0493 0.0809 0.108 0.215 0.265 0.000538 0.000662 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.177 0.00577 0.0618 0.0675 0.0906 0.289 0.297 0.000723 0.000742 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 284 41051.538 0.005 0.00888 0.131 0.308 0.0848 0.112 0.345 0.432 0.000862 0.00108 +! Validation 284 41051.538 0.005 0.00769 0.0588 0.213 0.0783 0.105 0.234 0.289 0.000584 0.000724 +Wall time: 41051.53833214706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.188 0.00688 0.0502 0.0743 0.099 0.22 0.267 0.000549 0.000668 + 285 200 0.188 0.00616 0.0644 0.0703 0.0937 0.267 0.303 0.000667 0.000757 + 285 235 0.178 0.00666 0.0447 0.0724 0.0974 0.184 0.252 0.00046 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.171 0.00476 0.0761 0.061 0.0823 0.322 0.329 0.000806 0.000823 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 285 41195.833 0.005 0.00666 0.0666 0.2 0.0731 0.0974 0.243 0.308 0.000609 0.000771 +! Validation 285 41195.833 0.005 0.00641 0.0642 0.192 0.0712 0.0956 0.256 0.302 0.00064 0.000756 +Wall time: 41195.83306494774 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.156 0.00618 0.0327 0.0704 0.0938 0.174 0.216 0.000436 0.00054 + 286 200 0.153 0.00549 0.0432 0.0662 0.0884 0.207 0.248 0.000518 0.00062 + 286 235 0.166 0.00554 0.0554 0.0663 0.0888 0.209 0.281 0.000523 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0964 0.00421 0.0122 0.0574 0.0774 0.111 0.132 0.000278 0.00033 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 286 41340.153 0.005 0.00583 0.0704 0.187 0.0682 0.0912 0.245 0.317 0.000614 0.000792 +! Validation 286 41340.153 0.005 0.00575 0.0338 0.149 0.0672 0.0905 0.177 0.22 0.000442 0.000549 +Wall time: 41340.15346198296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.166 0.00566 0.0524 0.0673 0.0898 0.229 0.273 0.000572 0.000683 + 287 200 0.138 0.0048 0.0417 0.0621 0.0827 0.21 0.244 0.000526 0.000609 + 287 235 0.168 0.00504 0.0676 0.0636 0.0848 0.287 0.31 0.000717 0.000776 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.177 0.00393 0.0983 0.0553 0.0748 0.366 0.374 0.000914 0.000936 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 287 41484.821 0.005 0.00535 0.0661 0.173 0.0652 0.0873 0.248 0.307 0.000619 0.000767 +! Validation 287 41484.821 0.005 0.00538 0.0961 0.204 0.0649 0.0875 0.332 0.37 0.000831 0.000925 +Wall time: 41484.82096820604 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.179 0.00498 0.0792 0.063 0.0842 0.301 0.336 0.000751 0.00084 + 288 200 0.106 0.00457 0.0148 0.0603 0.0807 0.102 0.145 0.000256 0.000363 + 288 235 0.158 0.0044 0.0701 0.0599 0.0792 0.288 0.316 0.000719 0.00079 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.133 0.00373 0.0582 0.0541 0.0729 0.281 0.288 0.000703 0.00072 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 288 41629.109 0.005 0.00503 0.0613 0.162 0.0632 0.0847 0.238 0.296 0.000595 0.000739 +! Validation 288 41629.109 0.005 0.00516 0.0888 0.192 0.0635 0.0857 0.315 0.356 0.000788 0.000889 +Wall time: 41629.1091894377 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.3 0.00466 0.207 0.061 0.0814 0.523 0.543 0.00131 0.00136 + 289 200 0.324 0.00475 0.229 0.0617 0.0823 0.55 0.571 0.00137 0.00143 + 289 235 0.217 0.00491 0.119 0.0628 0.0836 0.378 0.411 0.000944 0.00103 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.232 0.00359 0.16 0.053 0.0715 0.47 0.478 0.00118 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 289 41773.640 0.005 0.00483 0.069 0.166 0.0619 0.0829 0.253 0.313 0.000633 0.000783 +! Validation 289 41773.640 0.005 0.005 0.176 0.276 0.0625 0.0844 0.471 0.501 0.00118 0.00125 +Wall time: 41773.64070450887 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.129 0.00501 0.0285 0.0632 0.0845 0.173 0.201 0.000433 0.000504 + 290 200 0.121 0.00436 0.0333 0.0589 0.0788 0.184 0.218 0.00046 0.000544 + 290 235 0.143 0.00551 0.0327 0.0651 0.0886 0.178 0.216 0.000445 0.00054 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0771 0.00363 0.00454 0.0528 0.0719 0.067 0.0805 0.000167 0.000201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 290 41918.520 0.005 0.00466 0.052 0.145 0.0607 0.0815 0.219 0.272 0.000548 0.000681 +! Validation 290 41918.520 0.005 0.0049 0.0399 0.138 0.0617 0.0836 0.192 0.238 0.000479 0.000596 +Wall time: 41918.52041292889 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.102 0.00409 0.0204 0.0571 0.0763 0.151 0.17 0.000377 0.000426 + 291 200 0.0976 0.00413 0.0149 0.0574 0.0767 0.117 0.146 0.000294 0.000364 + 291 235 0.129 0.0045 0.0389 0.0592 0.0801 0.199 0.235 0.000498 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0706 0.00335 0.0036 0.0513 0.0691 0.0705 0.0716 0.000176 0.000179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 291 42062.871 0.005 0.0046 0.0769 0.169 0.0603 0.0809 0.256 0.331 0.000639 0.000828 +! Validation 291 42062.871 0.005 0.00467 0.0243 0.118 0.0603 0.0816 0.153 0.186 0.000383 0.000465 +Wall time: 42062.87161427969 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.246 0.00484 0.149 0.062 0.083 0.376 0.46 0.000941 0.00115 + 292 200 0.104 0.00412 0.022 0.0573 0.0766 0.15 0.177 0.000374 0.000442 + 292 235 0.154 0.00494 0.055 0.0624 0.0839 0.26 0.28 0.00065 0.0007 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.22 0.00353 0.149 0.0524 0.0709 0.455 0.461 0.00114 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 292 42207.198 0.005 0.00452 0.0656 0.156 0.0598 0.0803 0.236 0.306 0.000591 0.000764 +! Validation 292 42207.198 0.005 0.00475 0.168 0.263 0.0608 0.0823 0.46 0.489 0.00115 0.00122 +Wall time: 42207.19871415198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.191 0.00427 0.105 0.0579 0.078 0.37 0.388 0.000925 0.000969 + 293 200 0.209 0.00447 0.12 0.0597 0.0798 0.392 0.413 0.00098 0.00103 + 293 235 0.0994 0.00388 0.0218 0.0559 0.0744 0.149 0.176 0.000372 0.000441 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0655 0.00312 0.00308 0.0495 0.0667 0.0555 0.0662 0.000139 0.000166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 293 42351.531 0.005 0.00431 0.0508 0.137 0.0584 0.0784 0.217 0.269 0.000543 0.000673 +! Validation 293 42351.531 0.005 0.00445 0.0243 0.113 0.0587 0.0796 0.151 0.186 0.000378 0.000465 +Wall time: 42351.53167084884 +! Best model 293 0.113 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.135 0.00446 0.0462 0.0591 0.0797 0.22 0.257 0.000549 0.000642 + 294 200 0.107 0.00392 0.0285 0.0553 0.0747 0.166 0.201 0.000416 0.000503 + 294 235 0.117 0.00414 0.0344 0.0574 0.0768 0.19 0.221 0.000474 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.0674 0.00311 0.00521 0.0495 0.0665 0.0728 0.0861 0.000182 0.000215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 294 42496.359 0.005 0.0042 0.0463 0.13 0.0576 0.0774 0.206 0.257 0.000515 0.000642 +! Validation 294 42496.359 0.005 0.0044 0.026 0.114 0.0584 0.0792 0.156 0.192 0.00039 0.000481 +Wall time: 42496.359199489 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.107 0.00415 0.0238 0.0572 0.0769 0.148 0.184 0.000371 0.00046 + 295 200 0.102 0.00383 0.0254 0.055 0.0738 0.163 0.19 0.000409 0.000475 + 295 235 0.0954 0.00386 0.0183 0.0554 0.0741 0.135 0.161 0.000338 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.13 0.00301 0.0703 0.0485 0.0655 0.31 0.317 0.000776 0.000791 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 295 42640.675 0.005 0.00416 0.0495 0.133 0.0573 0.077 0.209 0.266 0.000522 0.000664 +! Validation 295 42640.675 0.005 0.00424 0.0685 0.153 0.0572 0.0777 0.267 0.312 0.000668 0.000781 +Wall time: 42640.67525800504 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.222 0.00426 0.137 0.0582 0.0779 0.417 0.441 0.00104 0.0011 + 296 200 0.182 0.00425 0.0971 0.0581 0.0778 0.328 0.372 0.00082 0.00093 + 296 235 0.105 0.00437 0.0177 0.0587 0.0789 0.129 0.159 0.000322 0.000397 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.109 0.00305 0.0481 0.0491 0.0659 0.254 0.262 0.000634 0.000655 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 296 42785.280 0.005 0.00406 0.0493 0.131 0.0565 0.0761 0.206 0.265 0.000514 0.000663 +! Validation 296 42785.280 0.005 0.00431 0.0462 0.132 0.0577 0.0783 0.213 0.257 0.000532 0.000641 +Wall time: 42785.28020980582 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.115 0.00394 0.0364 0.0556 0.0749 0.183 0.228 0.000458 0.000569 + 297 200 0.189 0.00383 0.112 0.0553 0.0739 0.346 0.4 0.000866 0.000999 + 297 235 0.101 0.00399 0.0208 0.0557 0.0754 0.151 0.172 0.000379 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.062 0.00296 0.0027 0.0483 0.065 0.0615 0.0621 0.000154 0.000155 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 297 42929.603 0.005 0.00406 0.0478 0.129 0.0565 0.076 0.206 0.261 0.000516 0.000653 +! Validation 297 42929.603 0.005 0.00414 0.0266 0.109 0.0565 0.0768 0.156 0.195 0.000389 0.000486 +Wall time: 42929.60297389887 +! Best model 297 0.109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0944 0.0037 0.0204 0.054 0.0726 0.144 0.171 0.000361 0.000427 + 298 200 0.257 0.00438 0.17 0.0593 0.079 0.472 0.492 0.00118 0.00123 + 298 235 0.0853 0.00392 0.00701 0.0557 0.0747 0.0883 0.0999 0.000221 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0657 0.0029 0.00767 0.0479 0.0643 0.0756 0.105 0.000189 0.000261 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 298 43074.141 0.005 0.00407 0.0593 0.141 0.0566 0.0761 0.23 0.291 0.000576 0.000728 +! Validation 298 43074.141 0.005 0.0042 0.0257 0.11 0.0568 0.0773 0.143 0.191 0.000358 0.000478 +Wall time: 43074.14108918188 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0875 0.00373 0.0129 0.0546 0.0729 0.11 0.136 0.000275 0.000339 + 299 200 0.108 0.00414 0.0251 0.0569 0.0768 0.167 0.189 0.000418 0.000473 + 299 235 0.117 0.00379 0.0416 0.0544 0.0735 0.211 0.244 0.000527 0.000609 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0642 0.00291 0.0061 0.0476 0.0643 0.0651 0.0932 0.000163 0.000233 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 299 43218.762 0.005 0.00397 0.0452 0.125 0.056 0.0752 0.202 0.254 0.000505 0.000634 +! Validation 299 43218.762 0.005 0.00405 0.0211 0.102 0.0558 0.0759 0.138 0.173 0.000344 0.000433 +Wall time: 43218.762248031795 +! Best model 299 0.102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.141 0.00445 0.0521 0.0595 0.0796 0.217 0.272 0.000544 0.000681 + 300 200 0.0918 0.00359 0.0199 0.0535 0.0716 0.139 0.168 0.000347 0.000421 + 300 235 0.102 0.00372 0.0276 0.0542 0.0728 0.169 0.198 0.000422 0.000496 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.104 0.00284 0.0476 0.0473 0.0636 0.253 0.26 0.000632 0.000651 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 300 43363.142 0.005 0.00396 0.0568 0.136 0.0558 0.0751 0.223 0.285 0.000557 0.000712 +! Validation 300 43363.142 0.005 0.00401 0.0771 0.157 0.0555 0.0756 0.288 0.331 0.000721 0.000829 +Wall time: 43363.142730733845 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.293 0.00368 0.219 0.0542 0.0724 0.546 0.559 0.00137 0.0014 + 301 200 0.0937 0.00383 0.0172 0.0548 0.0738 0.125 0.157 0.000314 0.000391 + 301 235 0.081 0.0037 0.00704 0.0543 0.0726 0.0764 0.1 0.000191 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.0645 0.00277 0.00901 0.0467 0.0629 0.0945 0.113 0.000236 0.000283 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 301 43507.463 0.005 0.00398 0.0618 0.141 0.0561 0.0754 0.234 0.297 0.000585 0.000742 +! Validation 301 43507.463 0.005 0.00393 0.0165 0.095 0.055 0.0748 0.122 0.153 0.000305 0.000383 +Wall time: 43507.46357901208 +! Best model 301 0.095 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.108 0.00376 0.0324 0.0548 0.0732 0.198 0.215 0.000495 0.000537 + 302 200 0.0947 0.0038 0.0187 0.0544 0.0736 0.125 0.163 0.000312 0.000408 + 302 235 0.0989 0.00399 0.0191 0.056 0.0754 0.126 0.165 0.000315 0.000412 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.055 0.00264 0.00216 0.0455 0.0613 0.0534 0.0555 0.000133 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 302 43651.935 0.005 0.00378 0.0355 0.111 0.0545 0.0734 0.18 0.225 0.000449 0.000563 +! Validation 302 43651.935 0.005 0.00382 0.0198 0.0961 0.0541 0.0737 0.129 0.168 0.000322 0.000419 +Wall time: 43651.9356849147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0969 0.00394 0.0182 0.0554 0.0749 0.13 0.161 0.000326 0.000402 + 303 200 0.627 0.00376 0.552 0.0552 0.0732 0.881 0.887 0.0022 0.00222 + 303 235 0.0883 0.00415 0.00524 0.0577 0.0769 0.0742 0.0864 0.000186 0.000216 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.164 0.00395 0.0848 0.0562 0.075 0.341 0.348 0.000853 0.000869 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 303 43796.233 0.005 0.00424 0.0569 0.142 0.0572 0.0778 0.206 0.285 0.000515 0.000713 +! Validation 303 43796.233 0.005 0.00505 0.0616 0.162 0.0629 0.0848 0.243 0.296 0.000608 0.00074 +Wall time: 43796.2336566248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.115 0.00416 0.0315 0.0574 0.077 0.173 0.212 0.000432 0.000529 + 304 200 0.126 0.00344 0.0577 0.0518 0.07 0.257 0.287 0.000643 0.000717 + 304 235 0.12 0.00382 0.044 0.0546 0.0737 0.218 0.25 0.000544 0.000626 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.153 0.00298 0.0936 0.0485 0.0651 0.361 0.365 0.000903 0.000913 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 304 43940.660 0.005 0.00384 0.0437 0.12 0.055 0.074 0.199 0.249 0.000498 0.000623 +! Validation 304 43940.660 0.005 0.00405 0.0722 0.153 0.0558 0.0759 0.265 0.321 0.000663 0.000802 +Wall time: 43940.660334730055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.131 0.00372 0.0567 0.0538 0.0728 0.235 0.284 0.000587 0.00071 + 305 200 0.13 0.0038 0.054 0.0547 0.0736 0.242 0.277 0.000604 0.000694 + 305 235 0.0941 0.00382 0.0178 0.0547 0.0737 0.136 0.159 0.00034 0.000398 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0606 0.00288 0.00301 0.0474 0.064 0.0599 0.0655 0.00015 0.000164 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 305 44084.903 0.005 0.00387 0.0437 0.121 0.0552 0.0742 0.198 0.25 0.000494 0.000624 +! Validation 305 44084.903 0.005 0.00392 0.0227 0.101 0.055 0.0747 0.145 0.18 0.000362 0.000449 +Wall time: 44084.90300158085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.138 0.00506 0.0369 0.0642 0.0849 0.198 0.229 0.000494 0.000573 + 306 200 0.0949 0.00364 0.0222 0.0536 0.072 0.131 0.178 0.000327 0.000444 + 306 235 0.0918 0.00427 0.00635 0.0588 0.078 0.0756 0.0951 0.000189 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.152 0.00299 0.0924 0.0487 0.0653 0.355 0.363 0.000888 0.000907 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 306 44229.496 0.005 0.0038 0.0463 0.122 0.0548 0.0736 0.2 0.257 0.000499 0.000643 +! Validation 306 44229.496 0.005 0.00405 0.095 0.176 0.0562 0.076 0.334 0.368 0.000835 0.00092 +Wall time: 44229.496123578865 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.133 0.00335 0.0659 0.0516 0.0691 0.271 0.306 0.000677 0.000766 + 307 200 0.0836 0.0035 0.0135 0.0523 0.0707 0.111 0.139 0.000278 0.000347 + 307 235 0.13 0.00382 0.0536 0.0549 0.0738 0.234 0.276 0.000586 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.182 0.00325 0.117 0.0512 0.0681 0.401 0.408 0.001 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 307 44373.847 0.005 0.00372 0.0388 0.113 0.0542 0.0728 0.188 0.235 0.00047 0.000587 +! Validation 307 44373.847 0.005 0.00434 0.0619 0.149 0.0585 0.0786 0.252 0.297 0.00063 0.000743 +Wall time: 44373.8473118688 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.124 0.00435 0.0368 0.0592 0.0787 0.203 0.229 0.000506 0.000572 + 308 200 0.0883 0.00372 0.0139 0.0543 0.0728 0.119 0.141 0.000297 0.000352 + 308 235 0.132 0.00372 0.0575 0.0543 0.0728 0.271 0.286 0.000678 0.000715 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.0635 0.00295 0.00456 0.0485 0.0648 0.0677 0.0806 0.000169 0.000202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 308 44518.200 0.005 0.00389 0.054 0.132 0.0554 0.0744 0.22 0.277 0.00055 0.000693 +! Validation 308 44518.200 0.005 0.0039 0.018 0.0959 0.0552 0.0745 0.127 0.16 0.000319 0.0004 +Wall time: 44518.20062752487 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.0932 0.00363 0.0207 0.0527 0.0719 0.132 0.172 0.000331 0.00043 + 309 200 0.0946 0.00367 0.0212 0.0536 0.0723 0.136 0.174 0.000339 0.000434 + 309 235 0.106 0.00427 0.0204 0.0586 0.078 0.145 0.17 0.000363 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.285 0.00279 0.229 0.0478 0.0631 0.57 0.571 0.00142 0.00143 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 309 44662.648 0.005 0.00357 0.0343 0.106 0.0529 0.0713 0.175 0.221 0.000438 0.000553 +! Validation 309 44662.648 0.005 0.00391 0.197 0.275 0.0552 0.0746 0.45 0.53 0.00113 0.00133 +Wall time: 44662.648325842805 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0956 0.0034 0.0276 0.0516 0.0696 0.163 0.198 0.000408 0.000495 + 310 200 0.0836 0.00348 0.014 0.0521 0.0704 0.117 0.141 0.000292 0.000353 + 310 235 0.136 0.00365 0.0634 0.0536 0.0721 0.254 0.3 0.000635 0.000751 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0761 0.00347 0.00667 0.0524 0.0703 0.0854 0.0975 0.000213 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 310 44806.923 0.005 0.00466 0.0643 0.158 0.0599 0.0815 0.233 0.303 0.000583 0.000756 +! Validation 310 44806.923 0.005 0.00451 0.0658 0.156 0.0592 0.0801 0.246 0.306 0.000615 0.000766 +Wall time: 44806.92299886001 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.0878 0.00338 0.0203 0.0517 0.0693 0.142 0.17 0.000355 0.000425 + 311 200 0.228 0.00336 0.161 0.0515 0.0692 0.445 0.478 0.00111 0.0012 + 311 235 0.12 0.00414 0.0369 0.0581 0.0768 0.197 0.229 0.000492 0.000573 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.251 0.00421 0.167 0.0581 0.0774 0.479 0.487 0.0012 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 311 44951.711 0.005 0.00393 0.0474 0.126 0.0556 0.0748 0.203 0.26 0.000507 0.00065 +! Validation 311 44951.711 0.005 0.00521 0.141 0.245 0.0641 0.0861 0.414 0.448 0.00104 0.00112 +Wall time: 44951.71164222015 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0875 0.00386 0.0103 0.0551 0.0741 0.0974 0.121 0.000243 0.000303 + 312 200 0.0743 0.00338 0.00674 0.05 0.0694 0.0858 0.098 0.000214 0.000245 + 312 235 0.0893 0.00346 0.0202 0.052 0.0702 0.116 0.17 0.000291 0.000424 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0506 0.00236 0.0034 0.0431 0.058 0.0463 0.0696 0.000116 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 312 45096.008 0.005 0.00379 0.0446 0.121 0.0547 0.0735 0.196 0.252 0.000489 0.000631 +! Validation 312 45096.008 0.005 0.00339 0.0166 0.0844 0.0509 0.0695 0.12 0.154 0.000299 0.000384 +Wall time: 45096.008604889736 +! Best model 312 0.084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.112 0.00345 0.0426 0.0515 0.0701 0.213 0.246 0.000532 0.000616 + 313 200 0.116 0.00354 0.0452 0.0532 0.071 0.23 0.254 0.000576 0.000634 + 313 235 0.0678 0.00301 0.0076 0.0485 0.0655 0.0844 0.104 0.000211 0.00026 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0553 0.00254 0.00463 0.0445 0.0601 0.0612 0.0813 0.000153 0.000203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 313 45240.414 0.005 0.00367 0.0395 0.113 0.0538 0.0723 0.187 0.237 0.000467 0.000593 +! Validation 313 45240.414 0.005 0.00355 0.0218 0.0929 0.0521 0.0711 0.146 0.176 0.000366 0.000441 +Wall time: 45240.41398080671 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.174 0.00461 0.0818 0.0622 0.0811 0.306 0.341 0.000765 0.000853 + 314 200 0.0918 0.00338 0.0242 0.0517 0.0694 0.153 0.186 0.000382 0.000465 + 314 235 0.0938 0.00326 0.0285 0.0506 0.0682 0.179 0.202 0.000448 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.0833 0.00269 0.0295 0.0469 0.0619 0.192 0.205 0.000479 0.000512 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 314 45384.717 0.005 0.00402 0.044 0.124 0.0562 0.0757 0.197 0.25 0.000492 0.000626 +! Validation 314 45384.717 0.005 0.00379 0.0253 0.101 0.0547 0.0734 0.152 0.19 0.000379 0.000475 +Wall time: 45384.717338610906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0989 0.00372 0.0244 0.0539 0.0728 0.156 0.186 0.00039 0.000466 + 315 200 0.13 0.00332 0.0639 0.0513 0.0687 0.241 0.302 0.000602 0.000754 + 315 235 0.0992 0.00362 0.0268 0.053 0.0718 0.16 0.195 0.000399 0.000488 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0627 0.00258 0.0112 0.0448 0.0606 0.112 0.126 0.00028 0.000315 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 315 45529.020 0.005 0.00339 0.0285 0.0963 0.0516 0.0695 0.161 0.202 0.000403 0.000504 +! Validation 315 45529.020 0.005 0.0035 0.0182 0.0881 0.0515 0.0706 0.13 0.161 0.000326 0.000403 +Wall time: 45529.02038090583 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0714 0.00307 0.01 0.0489 0.0661 0.0944 0.12 0.000236 0.000299 + 316 200 0.0813 0.00342 0.0128 0.0527 0.0698 0.103 0.135 0.000258 0.000338 + 316 235 0.427 0.00525 0.322 0.0637 0.0864 0.632 0.678 0.00158 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0694 0.0033 0.00333 0.0508 0.0686 0.0614 0.0689 0.000153 0.000172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 316 45673.314 0.005 0.00349 0.045 0.115 0.0524 0.0704 0.198 0.251 0.000494 0.000627 +! Validation 316 45673.314 0.005 0.0042 0.135 0.219 0.0572 0.0774 0.347 0.439 0.000867 0.0011 +Wall time: 45673.314870398026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0932 0.00386 0.0159 0.055 0.0742 0.128 0.151 0.00032 0.000376 + 317 200 0.091 0.00341 0.0228 0.0519 0.0697 0.159 0.18 0.000397 0.00045 + 317 235 0.112 0.00353 0.0414 0.053 0.0709 0.187 0.243 0.000467 0.000607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.152 0.00264 0.0997 0.0455 0.0613 0.374 0.377 0.000936 0.000942 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 317 45817.581 0.005 0.00395 0.0519 0.131 0.056 0.075 0.217 0.272 0.000543 0.00068 +! Validation 317 45817.581 0.005 0.00376 0.116 0.191 0.0536 0.0732 0.372 0.406 0.000929 0.00101 +Wall time: 45817.58143334184 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0714 0.0029 0.0133 0.0477 0.0643 0.115 0.138 0.000287 0.000344 + 318 200 0.069 0.00303 0.00848 0.0489 0.0657 0.081 0.11 0.000202 0.000275 + 318 235 0.0732 0.00344 0.00439 0.0519 0.07 0.0656 0.0791 0.000164 0.000198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0923 0.00233 0.0457 0.0426 0.0576 0.253 0.255 0.000633 0.000638 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 318 45961.933 0.005 0.00347 0.0399 0.109 0.0522 0.0703 0.183 0.239 0.000457 0.000597 +! Validation 318 45961.933 0.005 0.00337 0.0684 0.136 0.0508 0.0693 0.262 0.312 0.000656 0.00078 +Wall time: 45961.93343917187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.206 0.00417 0.123 0.0581 0.0771 0.347 0.418 0.000868 0.00105 + 319 200 0.0705 0.00279 0.0148 0.0468 0.063 0.113 0.145 0.000283 0.000363 + 319 235 0.0805 0.00282 0.0241 0.0473 0.0634 0.132 0.185 0.000329 0.000463 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.155 0.00249 0.105 0.0446 0.0596 0.385 0.387 0.000962 0.000967 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 319 46106.290 0.005 0.00389 0.0458 0.123 0.0551 0.0744 0.195 0.255 0.000486 0.000639 +! Validation 319 46106.290 0.005 0.00351 0.0743 0.144 0.0518 0.0707 0.294 0.325 0.000736 0.000813 +Wall time: 46106.290235770866 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0648 0.00279 0.00891 0.0469 0.0631 0.0924 0.113 0.000231 0.000282 + 320 200 0.0873 0.00343 0.0186 0.0515 0.0699 0.131 0.163 0.000328 0.000407 + 320 235 0.133 0.00394 0.054 0.0549 0.0749 0.242 0.277 0.000604 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0561 0.00257 0.0046 0.0455 0.0605 0.0637 0.081 0.000159 0.000202 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 320 46250.633 0.005 0.00326 0.0359 0.101 0.0506 0.0681 0.176 0.226 0.000441 0.000565 +! Validation 320 46250.633 0.005 0.00367 0.0235 0.0969 0.0533 0.0723 0.148 0.183 0.000369 0.000457 +Wall time: 46250.633740590885 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0723 0.00327 0.00703 0.0507 0.0682 0.0788 0.1 0.000197 0.00025 + 321 200 0.129 0.00434 0.0427 0.0589 0.0786 0.209 0.247 0.000522 0.000616 + 321 235 0.154 0.00362 0.0814 0.0538 0.0718 0.323 0.34 0.000807 0.000851 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0744 0.00254 0.0235 0.0448 0.0602 0.169 0.183 0.000422 0.000458 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 321 46394.849 0.005 0.0034 0.0414 0.109 0.0518 0.0696 0.19 0.243 0.000476 0.000607 +! Validation 321 46394.849 0.005 0.00347 0.0308 0.1 0.0516 0.0703 0.181 0.209 0.000452 0.000523 +Wall time: 46394.849090314936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0997 0.00279 0.0439 0.0467 0.063 0.203 0.25 0.000506 0.000625 + 322 200 0.0833 0.00289 0.0254 0.0474 0.0642 0.166 0.19 0.000414 0.000476 + 322 235 0.0693 0.00312 0.00693 0.0503 0.0666 0.0757 0.0994 0.000189 0.000248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0577 0.00241 0.00949 0.0435 0.0586 0.0942 0.116 0.000235 0.000291 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 322 46539.139 0.005 0.00345 0.0419 0.111 0.0521 0.0702 0.193 0.245 0.000482 0.000611 +! Validation 322 46539.139 0.005 0.00326 0.0336 0.0988 0.05 0.0681 0.186 0.219 0.000466 0.000547 +Wall time: 46539.13911927212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.155 0.00401 0.0746 0.0563 0.0756 0.28 0.326 0.0007 0.000815 + 323 200 0.077 0.00302 0.0166 0.0487 0.0656 0.123 0.154 0.000308 0.000385 + 323 235 0.0708 0.00306 0.00961 0.0482 0.066 0.106 0.117 0.000265 0.000293 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.0656 0.00218 0.022 0.041 0.0557 0.173 0.177 0.000434 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 323 46683.424 0.005 0.00326 0.0354 0.101 0.0506 0.0681 0.177 0.225 0.000442 0.000562 +! Validation 323 46683.424 0.005 0.00306 0.0345 0.0957 0.0482 0.066 0.195 0.222 0.000487 0.000554 +Wall time: 46683.424531791825 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.101 0.00291 0.0434 0.0477 0.0643 0.225 0.249 0.000563 0.000622 + 324 200 0.0701 0.00298 0.0104 0.0487 0.0652 0.094 0.122 0.000235 0.000305 + 324 235 0.0745 0.00316 0.0113 0.0487 0.0671 0.101 0.127 0.000253 0.000318 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0464 0.00219 0.00253 0.0416 0.0559 0.0572 0.0601 0.000143 0.00015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 324 46827.662 0.005 0.00314 0.0292 0.092 0.0497 0.0669 0.161 0.204 0.000403 0.00051 +! Validation 324 46827.662 0.005 0.00316 0.0118 0.075 0.0492 0.0671 0.106 0.129 0.000266 0.000324 +Wall time: 46827.66264419211 +! Best model 324 0.075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 18 0.786 2.29 0.787 1.06 1.45 1.81 0.00364 0.00451 + 325 200 8.08 0.31 1.88 0.511 0.665 1.26 1.64 0.00315 0.00409 + 325 235 4.3 0.193 0.45 0.401 0.524 0.662 0.801 0.00166 0.002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 3.91 0.193 0.0488 0.397 0.524 0.193 0.264 0.000483 0.000659 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 325 46971.935 0.005 0.497 7.75 17.7 0.545 0.842 1.9 3.33 0.00475 0.00832 +! Validation 325 46971.935 0.005 0.196 0.842 4.77 0.402 0.529 0.82 1.1 0.00205 0.00274 +Wall time: 46971.93541740673 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 2.72 0.105 0.615 0.295 0.387 0.755 0.936 0.00189 0.00234 + 326 200 3.71 0.083 2.05 0.261 0.344 1.58 1.71 0.00394 0.00427 + 326 235 3.42 0.0843 1.74 0.263 0.347 1.48 1.57 0.0037 0.00393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 2.06 0.0752 0.556 0.249 0.327 0.868 0.89 0.00217 0.00223 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 326 47116.181 0.005 0.11 0.872 3.07 0.299 0.396 0.889 1.11 0.00222 0.00278 +! Validation 326 47116.181 0.005 0.0827 0.562 2.22 0.26 0.343 0.775 0.894 0.00194 0.00224 +Wall time: 47116.18173437612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 1.76 0.0706 0.345 0.24 0.317 0.568 0.701 0.00142 0.00175 + 327 200 1.67 0.0603 0.46 0.221 0.293 0.688 0.81 0.00172 0.00202 + 327 235 1.62 0.0592 0.437 0.218 0.29 0.675 0.789 0.00169 0.00197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 1.18 0.0518 0.144 0.206 0.272 0.418 0.453 0.00104 0.00113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 327 47260.536 0.005 0.0692 0.619 2 0.237 0.314 0.759 0.939 0.0019 0.00235 +! Validation 327 47260.536 0.005 0.0588 0.256 1.43 0.218 0.289 0.496 0.604 0.00124 0.00151 +Wall time: 47260.53642125381 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 2.94 0.0511 1.91 0.203 0.27 1.61 1.65 0.00402 0.00413 + 328 200 1.03 0.0451 0.13 0.19 0.254 0.349 0.43 0.000873 0.00108 + 328 235 0.911 0.0413 0.0853 0.183 0.243 0.296 0.349 0.000739 0.000871 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.721 0.0348 0.0257 0.168 0.223 0.174 0.191 0.000436 0.000478 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 328 47405.009 0.005 0.0497 0.491 1.49 0.2 0.266 0.669 0.837 0.00167 0.00209 +! Validation 328 47405.009 0.005 0.0406 0.138 0.951 0.181 0.24 0.342 0.444 0.000855 0.00111 +Wall time: 47405.00969097298 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.928 0.0358 0.213 0.169 0.226 0.483 0.55 0.00121 0.00138 + 329 200 0.81 0.0347 0.116 0.165 0.222 0.328 0.407 0.000819 0.00102 + 329 235 0.922 0.033 0.263 0.162 0.217 0.536 0.612 0.00134 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.568 0.0273 0.023 0.147 0.197 0.14 0.181 0.000349 0.000452 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 329 47549.281 0.005 0.0366 0.515 1.25 0.171 0.228 0.691 0.857 0.00173 0.00214 +! Validation 329 47549.281 0.005 0.0324 0.198 0.846 0.159 0.215 0.426 0.531 0.00106 0.00133 +Wall time: 47549.28186490573 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.762 0.0284 0.193 0.149 0.201 0.442 0.525 0.0011 0.00131 + 330 200 0.584 0.0246 0.0927 0.139 0.187 0.315 0.363 0.000786 0.000908 + 330 235 0.553 0.0238 0.0769 0.136 0.184 0.274 0.331 0.000684 0.000828 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.401 0.0197 0.00788 0.125 0.167 0.0968 0.106 0.000242 0.000265 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 330 47693.799 0.005 0.0278 0.303 0.86 0.148 0.199 0.524 0.658 0.00131 0.00165 +! Validation 330 47693.799 0.005 0.0241 0.0831 0.566 0.137 0.185 0.268 0.344 0.000669 0.00086 +Wall time: 47693.79940938577 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.917 0.0218 0.48 0.131 0.176 0.79 0.827 0.00197 0.00207 + 331 200 0.624 0.0195 0.234 0.124 0.167 0.512 0.577 0.00128 0.00144 + 331 235 0.809 0.0193 0.424 0.123 0.166 0.713 0.777 0.00178 0.00194 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.85 0.0161 0.527 0.113 0.152 0.863 0.866 0.00216 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 331 47838.303 0.005 0.0217 0.245 0.678 0.13 0.176 0.473 0.59 0.00118 0.00147 +! Validation 331 47838.303 0.005 0.0204 0.781 1.19 0.126 0.17 1.01 1.06 0.00253 0.00264 +Wall time: 47838.303404938895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.524 0.0177 0.171 0.118 0.159 0.425 0.494 0.00106 0.00123 + 332 200 0.478 0.0189 0.0997 0.122 0.164 0.289 0.377 0.000723 0.000942 + 332 235 0.503 0.0182 0.138 0.119 0.161 0.396 0.444 0.00099 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.33 0.0144 0.0433 0.107 0.143 0.243 0.248 0.000608 0.000621 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 332 47982.634 0.005 0.0191 0.309 0.691 0.122 0.165 0.544 0.664 0.00136 0.00166 +! Validation 332 47982.634 0.005 0.0185 0.207 0.576 0.119 0.162 0.404 0.543 0.00101 0.00136 +Wall time: 47982.634427054785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 1.03 0.0161 0.712 0.112 0.151 0.973 1.01 0.00243 0.00252 + 333 200 0.674 0.0153 0.368 0.11 0.148 0.684 0.724 0.00171 0.00181 + 333 235 0.471 0.0157 0.157 0.111 0.15 0.418 0.472 0.00105 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.257 0.0119 0.0179 0.0975 0.13 0.151 0.159 0.000376 0.000399 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 333 48126.888 0.005 0.0162 0.183 0.507 0.113 0.152 0.414 0.511 0.00104 0.00128 +! Validation 333 48126.888 0.005 0.0157 0.0836 0.397 0.11 0.149 0.277 0.345 0.000691 0.000863 +Wall time: 48126.88822539803 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.446 0.0145 0.156 0.107 0.144 0.37 0.471 0.000924 0.00118 + 334 200 0.325 0.0132 0.0614 0.102 0.137 0.237 0.296 0.000594 0.000739 + 334 235 0.347 0.0129 0.0893 0.101 0.136 0.313 0.357 0.000784 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.405 0.00982 0.209 0.0887 0.118 0.543 0.545 0.00136 0.00136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 334 48271.185 0.005 0.0141 0.16 0.443 0.105 0.142 0.382 0.478 0.000955 0.00119 +! Validation 334 48271.185 0.005 0.0134 0.403 0.67 0.102 0.138 0.713 0.758 0.00178 0.00189 +Wall time: 48271.18573449971 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.28 0.0123 0.0336 0.0987 0.133 0.184 0.219 0.000461 0.000547 + 335 200 0.385 0.0135 0.114 0.101 0.139 0.34 0.403 0.000849 0.00101 + 335 235 0.281 0.0111 0.0595 0.0944 0.126 0.225 0.291 0.000562 0.000728 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.238 0.0088 0.0623 0.0841 0.112 0.293 0.298 0.000732 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 335 48415.538 0.005 0.0125 0.175 0.425 0.0993 0.133 0.395 0.5 0.000987 0.00125 +! Validation 335 48415.538 0.005 0.0121 0.0728 0.315 0.0969 0.131 0.255 0.322 0.000638 0.000805 +Wall time: 48415.5384796178 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.268 0.0103 0.0624 0.0905 0.121 0.249 0.298 0.000623 0.000745 + 336 200 0.527 0.0112 0.303 0.0944 0.126 0.62 0.657 0.00155 0.00164 + 336 235 0.236 0.0104 0.029 0.0914 0.121 0.178 0.203 0.000445 0.000508 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.375 0.00806 0.214 0.0805 0.107 0.55 0.552 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 336 48559.922 0.005 0.0111 0.167 0.388 0.0938 0.126 0.387 0.488 0.000969 0.00122 +! Validation 336 48559.922 0.005 0.0111 0.121 0.343 0.0927 0.126 0.348 0.415 0.000871 0.00104 +Wall time: 48559.92272238713 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.258 0.01 0.0576 0.0897 0.119 0.218 0.286 0.000545 0.000716 + 337 200 0.232 0.0093 0.0465 0.0868 0.115 0.231 0.257 0.000577 0.000644 + 337 235 0.229 0.00921 0.0445 0.0861 0.115 0.187 0.252 0.000467 0.00063 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.397 0.00685 0.26 0.0746 0.0988 0.604 0.609 0.00151 0.00152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 337 48704.182 0.005 0.00981 0.138 0.334 0.0886 0.118 0.357 0.443 0.000894 0.00111 +! Validation 337 48704.182 0.005 0.00968 0.148 0.342 0.0868 0.117 0.399 0.459 0.000996 0.00115 +Wall time: 48704.181996054016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.212 0.00868 0.0379 0.0838 0.111 0.193 0.232 0.000482 0.000581 + 338 200 0.211 0.00761 0.0589 0.0789 0.104 0.239 0.29 0.000598 0.000724 + 338 235 0.391 0.00765 0.238 0.0789 0.104 0.54 0.582 0.00135 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.451 0.00619 0.327 0.0715 0.0939 0.675 0.682 0.00169 0.00171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 338 48848.435 0.005 0.00858 0.114 0.285 0.0831 0.111 0.322 0.402 0.000806 0.001 +! Validation 338 48848.435 0.005 0.00869 0.43 0.604 0.0825 0.111 0.752 0.782 0.00188 0.00196 +Wall time: 48848.43514318904 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.233 0.00753 0.0826 0.0786 0.104 0.302 0.343 0.000755 0.000858 + 339 200 0.212 0.00763 0.0594 0.079 0.104 0.238 0.291 0.000596 0.000727 + 339 235 0.19 0.00728 0.0446 0.0767 0.102 0.235 0.252 0.000588 0.00063 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.116 0.00535 0.00905 0.0664 0.0873 0.0807 0.114 0.000202 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 339 48992.652 0.005 0.00778 0.131 0.286 0.0793 0.105 0.339 0.432 0.000848 0.00108 +! Validation 339 48992.652 0.005 0.00771 0.0707 0.225 0.0772 0.105 0.264 0.317 0.000661 0.000794 +Wall time: 48992.65220658202 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.266 0.0067 0.132 0.074 0.0977 0.41 0.434 0.00103 0.00108 + 340 200 0.171 0.00633 0.0442 0.0719 0.0949 0.222 0.251 0.000555 0.000627 + 340 235 0.207 0.00572 0.0921 0.0685 0.0903 0.331 0.362 0.000828 0.000906 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.136 0.00466 0.0424 0.0621 0.0815 0.228 0.246 0.00057 0.000614 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 340 49136.957 0.005 0.00677 0.0811 0.217 0.074 0.0982 0.27 0.34 0.000674 0.000849 +! Validation 340 49136.957 0.005 0.00687 0.0626 0.2 0.0726 0.0989 0.254 0.299 0.000635 0.000747 +Wall time: 49136.95788027812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.224 0.00595 0.105 0.0698 0.0921 0.356 0.387 0.00089 0.000969 + 341 200 0.158 0.0056 0.0459 0.0673 0.0893 0.223 0.256 0.000558 0.000639 + 341 235 0.257 0.00615 0.134 0.0702 0.0936 0.415 0.437 0.00104 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.094 0.00439 0.00629 0.0602 0.0791 0.0818 0.0946 0.000205 0.000237 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 341 49281.527 0.005 0.0061 0.0914 0.213 0.0702 0.0932 0.292 0.361 0.000729 0.000901 +! Validation 341 49281.527 0.005 0.00639 0.0298 0.158 0.0699 0.0954 0.165 0.206 0.000413 0.000515 +Wall time: 49281.52785938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.235 0.00602 0.115 0.0697 0.0926 0.365 0.405 0.000912 0.00101 + 342 200 0.212 0.00526 0.107 0.065 0.0866 0.346 0.39 0.000865 0.000974 + 342 235 0.183 0.00572 0.069 0.0681 0.0903 0.281 0.314 0.000701 0.000784 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0911 0.00407 0.00963 0.0579 0.0762 0.0797 0.117 0.000199 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 342 49425.843 0.005 0.0057 0.0857 0.2 0.0676 0.0901 0.278 0.349 0.000695 0.000874 +! Validation 342 49425.843 0.005 0.006 0.0733 0.193 0.0675 0.0924 0.277 0.323 0.000693 0.000808 +Wall time: 49425.843414989766 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.311 0.00505 0.21 0.0636 0.0848 0.531 0.547 0.00133 0.00137 + 343 200 0.184 0.0055 0.074 0.0659 0.0885 0.282 0.325 0.000706 0.000812 + 343 235 0.138 0.00467 0.0444 0.0616 0.0816 0.217 0.251 0.000542 0.000628 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.106 0.00368 0.0321 0.0552 0.0724 0.199 0.214 0.000497 0.000534 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 343 49570.152 0.005 0.00523 0.0672 0.172 0.0646 0.0863 0.25 0.31 0.000624 0.000774 +! Validation 343 49570.152 0.005 0.00552 0.0804 0.191 0.0644 0.0887 0.292 0.339 0.00073 0.000846 +Wall time: 49570.15275108302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.117 0.00484 0.0201 0.0626 0.0831 0.142 0.169 0.000356 0.000423 + 344 200 0.178 0.00488 0.0805 0.0626 0.0834 0.306 0.339 0.000764 0.000847 + 344 235 0.709 0.00549 0.599 0.0658 0.0884 0.911 0.923 0.00228 0.00231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.708 0.00356 0.637 0.0536 0.0712 0.95 0.952 0.00237 0.00238 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 344 49714.777 0.005 0.00491 0.0583 0.156 0.0625 0.0836 0.221 0.285 0.000553 0.000712 +! Validation 344 49714.777 0.005 0.00525 0.647 0.752 0.0628 0.0865 0.943 0.96 0.00236 0.0024 +Wall time: 49714.77705103578 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.122 0.00489 0.0241 0.0622 0.0834 0.147 0.185 0.000368 0.000463 + 345 200 0.138 0.00478 0.0429 0.0621 0.0825 0.193 0.247 0.000483 0.000618 + 345 235 0.235 0.00477 0.14 0.0614 0.0824 0.398 0.447 0.000996 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.306 0.00335 0.239 0.0525 0.069 0.578 0.583 0.00144 0.00146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 345 49859.017 0.005 0.0048 0.0818 0.178 0.0618 0.0827 0.265 0.341 0.000663 0.000853 +! Validation 345 49859.017 0.005 0.00521 0.205 0.309 0.0624 0.0861 0.493 0.54 0.00123 0.00135 +Wall time: 49859.01729992498 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.148 0.00485 0.0509 0.0614 0.0831 0.244 0.269 0.00061 0.000673 + 346 200 0.168 0.00429 0.0819 0.0587 0.0782 0.313 0.342 0.000783 0.000854 + 346 235 0.271 0.0049 0.173 0.0631 0.0835 0.461 0.496 0.00115 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.094 0.00333 0.0274 0.0522 0.0689 0.181 0.198 0.000452 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 346 50003.207 0.005 0.00455 0.0649 0.156 0.0601 0.0805 0.243 0.303 0.000607 0.000759 +! Validation 346 50003.207 0.005 0.00507 0.0545 0.156 0.0616 0.085 0.222 0.279 0.000555 0.000696 +Wall time: 50003.20755921677 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.112 0.0047 0.0182 0.0607 0.0818 0.133 0.161 0.000333 0.000403 + 347 200 0.0907 0.0039 0.0126 0.056 0.0746 0.114 0.134 0.000284 0.000335 + 347 235 0.233 0.00439 0.146 0.0588 0.0791 0.44 0.455 0.0011 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.167 0.00328 0.102 0.0512 0.0684 0.375 0.381 0.000937 0.000951 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 347 50147.528 0.005 0.00434 0.0446 0.131 0.0586 0.0786 0.199 0.251 0.000497 0.000629 +! Validation 347 50147.528 0.005 0.00476 0.0813 0.176 0.0596 0.0823 0.302 0.34 0.000755 0.000851 +Wall time: 50147.52809587773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.118 0.00404 0.0367 0.0567 0.0759 0.21 0.229 0.000525 0.000572 + 348 200 0.112 0.00438 0.0247 0.0584 0.0789 0.153 0.188 0.000382 0.000469 + 348 235 0.131 0.00371 0.0565 0.0543 0.0727 0.266 0.284 0.000664 0.000709 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.0649 0.00292 0.00655 0.0485 0.0644 0.0668 0.0966 0.000167 0.000242 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 348 50292.100 0.005 0.0042 0.0485 0.132 0.0576 0.0773 0.211 0.263 0.000529 0.000657 +! Validation 348 50292.100 0.005 0.0044 0.0309 0.119 0.0572 0.0791 0.174 0.21 0.000436 0.000525 +Wall time: 50292.10062184697 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.122 0.00392 0.0439 0.056 0.0747 0.217 0.25 0.000543 0.000625 + 349 200 0.108 0.00381 0.0321 0.0549 0.0737 0.177 0.214 0.000444 0.000535 + 349 235 0.1 0.00419 0.0166 0.0572 0.0772 0.115 0.154 0.000288 0.000384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0789 0.00285 0.022 0.0479 0.0637 0.163 0.177 0.000408 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 349 50436.311 0.005 0.00399 0.0349 0.115 0.0561 0.0754 0.181 0.223 0.000452 0.000558 +! Validation 349 50436.311 0.005 0.00425 0.0335 0.118 0.0563 0.0778 0.188 0.218 0.00047 0.000546 +Wall time: 50436.31093759509 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.106 0.00423 0.0213 0.0575 0.0776 0.135 0.174 0.000337 0.000436 + 350 200 0.138 0.00406 0.0564 0.0568 0.0761 0.265 0.283 0.000664 0.000709 + 350 235 0.458 0.0036 0.386 0.0537 0.0716 0.731 0.741 0.00183 0.00185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.214 0.00338 0.147 0.0522 0.0694 0.453 0.457 0.00113 0.00114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 350 50580.562 0.005 0.00396 0.0575 0.137 0.0559 0.0751 0.226 0.284 0.000564 0.00071 +! Validation 350 50580.562 0.005 0.00468 0.0942 0.188 0.0596 0.0816 0.323 0.366 0.000808 0.000916 +Wall time: 50580.56228892272 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.109 0.00405 0.028 0.0565 0.076 0.169 0.2 0.000423 0.000499 + 351 200 0.0801 0.00357 0.00876 0.0532 0.0713 0.0889 0.112 0.000222 0.000279 + 351 235 0.0994 0.00326 0.0342 0.0515 0.0682 0.194 0.221 0.000485 0.000551 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0819 0.00322 0.0175 0.0511 0.0677 0.137 0.158 0.000343 0.000395 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 351 50724.984 0.005 0.00394 0.0542 0.133 0.0557 0.0749 0.21 0.278 0.000525 0.000695 +! Validation 351 50724.984 0.005 0.00449 0.0182 0.108 0.0584 0.08 0.125 0.161 0.000313 0.000402 +Wall time: 50724.98471375974 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.191 0.00361 0.119 0.0534 0.0717 0.373 0.411 0.000932 0.00103 + 352 200 0.126 0.00371 0.0516 0.0542 0.0727 0.238 0.271 0.000595 0.000678 + 352 235 0.11 0.00373 0.0349 0.0552 0.0729 0.201 0.223 0.000503 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0703 0.00277 0.0148 0.0472 0.0628 0.131 0.145 0.000326 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 352 50869.284 0.005 0.00382 0.0492 0.126 0.0549 0.0737 0.215 0.265 0.000538 0.000662 +! Validation 352 50869.284 0.005 0.00409 0.0433 0.125 0.0555 0.0763 0.217 0.248 0.000543 0.000621 +Wall time: 50869.28458225308 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.0966 0.00379 0.0207 0.0546 0.0735 0.132 0.172 0.00033 0.00043 + 353 200 0.252 0.00391 0.174 0.0556 0.0746 0.475 0.497 0.00119 0.00124 + 353 235 0.103 0.00347 0.0334 0.0528 0.0703 0.19 0.218 0.000476 0.000545 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.128 0.00293 0.0689 0.0489 0.0646 0.309 0.313 0.000772 0.000783 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 353 51013.592 0.005 0.00366 0.0385 0.112 0.0536 0.0722 0.187 0.234 0.000467 0.000585 +! Validation 353 51013.592 0.005 0.00422 0.0798 0.164 0.0566 0.0775 0.295 0.337 0.000739 0.000843 +Wall time: 51013.592049222905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0775 0.00336 0.0103 0.0514 0.0692 0.101 0.121 0.000252 0.000304 + 354 200 0.0795 0.00333 0.0129 0.0516 0.0689 0.114 0.135 0.000285 0.000338 + 354 235 0.0845 0.00376 0.00936 0.0544 0.0731 0.0985 0.115 0.000246 0.000289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0559 0.00254 0.00503 0.0449 0.0602 0.0609 0.0847 0.000152 0.000212 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 354 51157.948 0.005 0.00363 0.0488 0.121 0.0535 0.0719 0.208 0.264 0.000521 0.00066 +! Validation 354 51157.948 0.005 0.00373 0.0181 0.0927 0.0528 0.0729 0.131 0.161 0.000327 0.000402 +Wall time: 51157.948845264036 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0889 0.00344 0.0201 0.0521 0.07 0.146 0.169 0.000364 0.000423 + 355 200 0.127 0.00387 0.0501 0.0553 0.0742 0.234 0.267 0.000584 0.000668 + 355 235 0.0811 0.00341 0.013 0.0519 0.0697 0.12 0.136 0.000299 0.00034 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0823 0.00257 0.0308 0.0452 0.0606 0.196 0.21 0.00049 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 355 51302.254 0.005 0.0036 0.0559 0.128 0.0532 0.0716 0.218 0.282 0.000545 0.000706 +! Validation 355 51302.254 0.005 0.00378 0.0239 0.0996 0.0533 0.0734 0.146 0.185 0.000365 0.000461 +Wall time: 51302.25466386415 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.245 0.00405 0.164 0.0566 0.0759 0.463 0.483 0.00116 0.00121 + 356 200 0.0921 0.00366 0.0189 0.0533 0.0722 0.142 0.164 0.000355 0.00041 + 356 235 0.0646 0.00288 0.007 0.0479 0.0641 0.0812 0.0999 0.000203 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0513 0.00243 0.00275 0.0438 0.0588 0.0601 0.0626 0.00015 0.000157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 356 51446.940 0.005 0.00345 0.0332 0.102 0.0521 0.0701 0.175 0.218 0.000437 0.000544 +! Validation 356 51446.940 0.005 0.00361 0.015 0.0872 0.052 0.0717 0.113 0.146 0.000282 0.000365 +Wall time: 51446.94088063296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0904 0.00339 0.0227 0.0517 0.0695 0.155 0.18 0.000387 0.00045 + 357 200 0.0859 0.00359 0.014 0.0535 0.0715 0.111 0.141 0.000277 0.000354 + 357 235 0.0733 0.00319 0.00952 0.0499 0.0674 0.0994 0.116 0.000248 0.000291 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0602 0.00247 0.0108 0.044 0.0593 0.116 0.124 0.000291 0.000311 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 357 51591.236 0.005 0.00347 0.0459 0.115 0.0523 0.0703 0.208 0.256 0.000519 0.00064 +! Validation 357 51591.236 0.005 0.00353 0.0169 0.0876 0.0516 0.071 0.125 0.155 0.000312 0.000388 +Wall time: 51591.236106197815 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.0759 0.00313 0.0132 0.0499 0.0668 0.111 0.137 0.000279 0.000343 + 358 200 0.117 0.00334 0.05 0.051 0.069 0.246 0.267 0.000616 0.000667 + 358 235 0.107 0.00333 0.04 0.0513 0.0689 0.193 0.239 0.000483 0.000597 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.106 0.00264 0.053 0.0459 0.0614 0.269 0.275 0.000672 0.000687 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 358 51735.527 0.005 0.00335 0.0317 0.0986 0.0513 0.0691 0.168 0.212 0.00042 0.000531 +! Validation 358 51735.527 0.005 0.00379 0.069 0.145 0.0535 0.0734 0.276 0.314 0.000691 0.000784 +Wall time: 51735.527532803826 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0881 0.0038 0.0121 0.0551 0.0736 0.109 0.131 0.000272 0.000329 + 359 200 0.114 0.00329 0.0483 0.0491 0.0685 0.234 0.262 0.000585 0.000656 + 359 235 0.087 0.00333 0.0204 0.051 0.0689 0.153 0.171 0.000384 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0694 0.00242 0.021 0.0435 0.0587 0.166 0.173 0.000415 0.000432 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 359 51879.786 0.005 0.00335 0.0402 0.107 0.0514 0.0691 0.19 0.239 0.000475 0.000599 +! Validation 359 51879.786 0.005 0.00343 0.0239 0.0924 0.051 0.0699 0.146 0.185 0.000366 0.000461 +Wall time: 51879.786420555785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0697 0.00295 0.0107 0.048 0.0648 0.102 0.124 0.000255 0.000309 + 360 200 0.0908 0.00306 0.0296 0.0491 0.066 0.168 0.205 0.000421 0.000514 + 360 235 0.086 0.00315 0.023 0.0503 0.067 0.143 0.181 0.000357 0.000452 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.154 0.00282 0.0971 0.0475 0.0634 0.368 0.372 0.000919 0.00093 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 360 52024.061 0.005 0.00319 0.0297 0.0934 0.05 0.0674 0.16 0.206 0.000401 0.000514 +! Validation 360 52024.061 0.005 0.00388 0.087 0.165 0.0548 0.0743 0.298 0.352 0.000746 0.00088 +Wall time: 52024.06092946371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0711 0.00311 0.00883 0.0497 0.0666 0.0878 0.112 0.00022 0.00028 + 361 200 0.119 0.00357 0.0478 0.0535 0.0713 0.211 0.261 0.000529 0.000653 + 361 235 0.0872 0.00336 0.0201 0.0512 0.0692 0.132 0.169 0.00033 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0566 0.00245 0.00755 0.044 0.0591 0.0835 0.104 0.000209 0.000259 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 361 52168.298 0.005 0.00352 0.0427 0.113 0.0526 0.0708 0.192 0.247 0.000481 0.000617 +! Validation 361 52168.298 0.005 0.00352 0.02 0.0903 0.0515 0.0708 0.127 0.169 0.000317 0.000422 +Wall time: 52168.298276355024 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.076 0.00334 0.00908 0.051 0.069 0.0933 0.114 0.000233 0.000284 + 362 200 0.147 0.00339 0.0796 0.0516 0.0695 0.281 0.337 0.000701 0.000842 + 362 235 0.092 0.00272 0.0376 0.0468 0.0623 0.217 0.231 0.000543 0.000579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0506 0.00249 0.000745 0.0451 0.0596 0.0309 0.0326 7.73e-05 8.15e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 362 52312.520 0.005 0.00337 0.0438 0.111 0.0514 0.0693 0.198 0.25 0.000496 0.000625 +! Validation 362 52312.520 0.005 0.00347 0.032 0.102 0.052 0.0703 0.175 0.214 0.000437 0.000534 +Wall time: 52312.51990353782 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.134 0.00304 0.0736 0.0487 0.0658 0.296 0.324 0.00074 0.000809 + 363 200 0.0728 0.00326 0.00752 0.0504 0.0682 0.0849 0.104 0.000212 0.000259 + 363 235 0.135 0.00385 0.0583 0.0546 0.0741 0.276 0.288 0.000689 0.00072 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.137 0.00266 0.0833 0.0459 0.0616 0.34 0.344 0.00085 0.000861 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 363 52456.702 0.005 0.00336 0.0355 0.103 0.0514 0.0691 0.18 0.225 0.00045 0.000562 +! Validation 363 52456.702 0.005 0.00369 0.0995 0.173 0.0531 0.0725 0.35 0.376 0.000875 0.000941 +Wall time: 52456.70284181088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.331 0.00361 0.259 0.0535 0.0717 0.595 0.608 0.00149 0.00152 + 364 200 0.103 0.00355 0.0319 0.0522 0.0711 0.177 0.213 0.000442 0.000533 + 364 235 0.131 0.0036 0.0585 0.0529 0.0717 0.27 0.289 0.000676 0.000722 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.191 0.00334 0.124 0.0521 0.0689 0.411 0.421 0.00103 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 364 52600.890 0.005 0.0038 0.0517 0.128 0.0545 0.0736 0.213 0.271 0.000533 0.000678 +! Validation 364 52600.890 0.005 0.00435 0.0985 0.185 0.0582 0.0787 0.348 0.375 0.000871 0.000936 +Wall time: 52600.8907357431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.146 0.00399 0.0662 0.0567 0.0754 0.281 0.307 0.000702 0.000768 + 365 200 0.0962 0.00314 0.0334 0.0495 0.0669 0.197 0.218 0.000494 0.000546 + 365 235 0.0708 0.00309 0.00894 0.0492 0.0664 0.0859 0.113 0.000215 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.0776 0.00258 0.0261 0.0451 0.0606 0.185 0.193 0.000462 0.000482 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 365 52745.317 0.005 0.0034 0.0422 0.11 0.0518 0.0696 0.195 0.246 0.000489 0.000614 +! Validation 365 52745.317 0.005 0.00345 0.0221 0.0912 0.0513 0.0701 0.14 0.178 0.00035 0.000444 +Wall time: 52745.31702690991 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.137 0.00344 0.0681 0.0522 0.0701 0.265 0.312 0.000662 0.000779 + 366 200 0.0911 0.00293 0.0326 0.0477 0.0646 0.189 0.215 0.000474 0.000538 + 366 235 0.095 0.00302 0.0347 0.0488 0.0655 0.186 0.222 0.000465 0.000556 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.115 0.00246 0.0658 0.0441 0.0592 0.304 0.306 0.000759 0.000766 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 366 52889.612 0.005 0.00336 0.0398 0.107 0.0513 0.0692 0.183 0.238 0.000457 0.000596 +! Validation 366 52889.612 0.005 0.00345 0.0589 0.128 0.0514 0.0701 0.265 0.29 0.000662 0.000724 +Wall time: 52889.61193024414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.0864 0.003 0.0264 0.0479 0.0654 0.163 0.194 0.000407 0.000484 + 367 200 0.156 0.00343 0.0878 0.0524 0.0699 0.316 0.354 0.000789 0.000884 + 367 235 0.0819 0.00313 0.0194 0.0492 0.0667 0.141 0.166 0.000353 0.000416 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.116 0.00365 0.0433 0.0541 0.0721 0.233 0.248 0.000583 0.000621 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 367 53033.872 0.005 0.00311 0.035 0.0972 0.0494 0.0666 0.172 0.223 0.00043 0.000558 +! Validation 367 53033.872 0.005 0.00448 0.0554 0.145 0.0594 0.0799 0.252 0.281 0.000631 0.000702 +Wall time: 53033.872610896826 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0732 0.00297 0.0138 0.0479 0.0651 0.112 0.14 0.00028 0.000351 + 368 200 0.092 0.00396 0.0127 0.0566 0.0751 0.111 0.135 0.000276 0.000337 + 368 235 0.0839 0.00305 0.023 0.0489 0.0659 0.16 0.181 0.0004 0.000452 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0547 0.00261 0.00239 0.0457 0.061 0.0412 0.0583 0.000103 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 368 53178.144 0.005 0.00367 0.0395 0.113 0.0533 0.0723 0.185 0.237 0.000461 0.000593 +! Validation 368 53178.144 0.005 0.0035 0.047 0.117 0.052 0.0706 0.205 0.259 0.000513 0.000647 +Wall time: 53178.144412810914 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0695 0.00297 0.0101 0.0484 0.0651 0.0986 0.12 0.000246 0.000299 + 369 200 0.141 0.00354 0.0699 0.0531 0.071 0.285 0.316 0.000712 0.000789 + 369 235 0.0562 0.00266 0.00304 0.0454 0.0615 0.0567 0.0658 0.000142 0.000164 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0609 0.00258 0.00926 0.0455 0.0606 0.0907 0.115 0.000227 0.000287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 369 53322.666 0.005 0.00304 0.0344 0.0952 0.0489 0.0658 0.173 0.222 0.000432 0.000554 +! Validation 369 53322.666 0.005 0.00342 0.0282 0.0965 0.0516 0.0698 0.164 0.201 0.000409 0.000501 +Wall time: 53322.66624727612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0747 0.00302 0.0143 0.0488 0.0656 0.121 0.143 0.000302 0.000357 + 370 200 0.0838 0.00311 0.0217 0.0493 0.0665 0.144 0.176 0.000359 0.000439 + 370 235 0.0831 0.00285 0.0261 0.0469 0.0637 0.163 0.193 0.000407 0.000482 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0519 0.00242 0.00351 0.0436 0.0587 0.0576 0.0707 0.000144 0.000177 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 370 53467.062 0.005 0.00325 0.0369 0.102 0.0507 0.0681 0.183 0.229 0.000458 0.000574 +! Validation 370 53467.062 0.005 0.00332 0.019 0.0854 0.0504 0.0688 0.133 0.165 0.000332 0.000412 +Wall time: 53467.062609605026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0839 0.00306 0.0228 0.0489 0.066 0.164 0.18 0.00041 0.00045 + 371 200 0.166 0.00332 0.0997 0.0519 0.0688 0.352 0.377 0.000879 0.000942 + 371 235 0.0866 0.0029 0.0287 0.0477 0.0642 0.161 0.202 0.000403 0.000506 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0609 0.00246 0.0117 0.0444 0.0592 0.117 0.129 0.000292 0.000322 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 371 53611.422 0.005 0.00307 0.0333 0.0946 0.049 0.0661 0.171 0.218 0.000427 0.000544 +! Validation 371 53611.422 0.005 0.00353 0.0275 0.098 0.0524 0.0709 0.162 0.198 0.000405 0.000495 +Wall time: 53611.42257788079 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0947 0.00365 0.0216 0.0538 0.0721 0.15 0.176 0.000375 0.000439 + 372 200 0.0584 0.00266 0.00513 0.0458 0.0616 0.0646 0.0854 0.000161 0.000214 + 372 235 0.111 0.00349 0.0411 0.053 0.0706 0.225 0.242 0.000562 0.000605 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0709 0.00321 0.00675 0.0516 0.0676 0.0911 0.0981 0.000228 0.000245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 372 53755.704 0.005 0.00303 0.0301 0.0907 0.0487 0.0657 0.161 0.207 0.000403 0.000518 +! Validation 372 53755.704 0.005 0.00401 0.0246 0.105 0.057 0.0756 0.153 0.187 0.000382 0.000468 +Wall time: 53755.7043740931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.0872 0.00349 0.0175 0.0526 0.0705 0.124 0.158 0.000311 0.000395 + 373 200 0.0813 0.00276 0.0261 0.0464 0.0627 0.171 0.193 0.000427 0.000482 + 373 235 0.0618 0.00287 0.00433 0.0474 0.064 0.0628 0.0785 0.000157 0.000196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.0527 0.00225 0.00759 0.042 0.0567 0.0972 0.104 0.000243 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 373 53900.030 0.005 0.00326 0.0412 0.106 0.0507 0.0681 0.191 0.242 0.000478 0.000606 +! Validation 373 53900.030 0.005 0.00315 0.0196 0.0825 0.049 0.0669 0.13 0.167 0.000326 0.000417 +Wall time: 53900.030432587955 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.105 0.00283 0.0481 0.0477 0.0635 0.236 0.262 0.00059 0.000654 + 374 200 0.0678 0.00277 0.0123 0.0466 0.0628 0.109 0.133 0.000273 0.000331 + 374 235 0.118 0.00375 0.0426 0.0546 0.0731 0.191 0.246 0.000478 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.079 0.00338 0.0114 0.0518 0.0694 0.102 0.128 0.000255 0.000319 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 374 54044.303 0.005 0.00302 0.037 0.0974 0.0487 0.0656 0.182 0.23 0.000454 0.000574 +! Validation 374 54044.303 0.005 0.00407 0.0188 0.1 0.0561 0.0761 0.132 0.163 0.000331 0.000409 +Wall time: 54044.30310849706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.125 0.00307 0.0633 0.049 0.0662 0.271 0.3 0.000676 0.000751 + 375 200 0.241 0.00292 0.182 0.0479 0.0645 0.491 0.51 0.00123 0.00127 + 375 235 0.106 0.00337 0.0389 0.0525 0.0692 0.217 0.235 0.000543 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.0865 0.00236 0.0392 0.044 0.058 0.225 0.236 0.000563 0.000591 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 375 54188.575 0.005 0.00311 0.0438 0.106 0.0495 0.0665 0.197 0.25 0.000493 0.000625 +! Validation 375 54188.575 0.005 0.00328 0.0249 0.0906 0.0513 0.0684 0.156 0.189 0.000389 0.000471 +Wall time: 54188.57570354873 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.142 0.00334 0.075 0.0515 0.069 0.291 0.327 0.000729 0.000817 + 376 200 0.0749 0.00273 0.0203 0.0461 0.0623 0.143 0.17 0.000358 0.000425 + 376 235 0.0678 0.00266 0.0146 0.0449 0.0615 0.123 0.144 0.000307 0.000361 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.0543 0.00191 0.0161 0.0383 0.0521 0.145 0.152 0.000362 0.000379 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 376 54332.999 0.005 0.00318 0.0376 0.101 0.0498 0.0674 0.176 0.232 0.00044 0.000579 +! Validation 376 54332.999 0.005 0.00275 0.0176 0.0726 0.0456 0.0625 0.128 0.159 0.000319 0.000396 +Wall time: 54332.99949862296 +! Best model 376 0.073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0637 0.00273 0.00912 0.0462 0.0624 0.0986 0.114 0.000247 0.000285 + 377 200 0.0657 0.0028 0.00976 0.0472 0.0631 0.095 0.118 0.000237 0.000295 + 377 235 0.11 0.00291 0.0513 0.0475 0.0644 0.234 0.27 0.000585 0.000676 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0507 0.00215 0.0077 0.0417 0.0554 0.0984 0.105 0.000246 0.000262 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 377 54477.443 0.005 0.00288 0.0279 0.0855 0.0475 0.064 0.155 0.199 0.000388 0.000498 +! Validation 377 54477.443 0.005 0.00299 0.0162 0.0761 0.0483 0.0653 0.122 0.152 0.000304 0.00038 +Wall time: 54477.44320592191 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.114 0.00297 0.0551 0.0479 0.065 0.252 0.28 0.00063 0.000701 + 378 200 0.141 0.00315 0.0784 0.0506 0.067 0.319 0.334 0.000797 0.000836 + 378 235 0.0926 0.00365 0.0195 0.0547 0.0721 0.139 0.167 0.000348 0.000417 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0693 0.00251 0.0191 0.0451 0.0598 0.142 0.165 0.000354 0.000413 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 378 54622.022 0.005 0.00354 0.0511 0.122 0.0521 0.071 0.208 0.27 0.00052 0.000675 +! Validation 378 54622.022 0.005 0.00334 0.0233 0.0901 0.0511 0.069 0.142 0.182 0.000356 0.000455 +Wall time: 54622.0225266451 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0789 0.00263 0.0262 0.0459 0.0612 0.169 0.193 0.000422 0.000483 + 379 200 0.104 0.00367 0.0308 0.0547 0.0723 0.185 0.209 0.000462 0.000524 + 379 235 0.0685 0.00295 0.00944 0.0479 0.0648 0.0884 0.116 0.000221 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0604 0.00196 0.0212 0.039 0.0529 0.171 0.174 0.000429 0.000434 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 379 54766.357 0.005 0.0038 0.0535 0.129 0.0534 0.0736 0.209 0.276 0.000524 0.000691 +! Validation 379 54766.357 0.005 0.00282 0.0277 0.0841 0.0463 0.0634 0.163 0.199 0.000408 0.000497 +Wall time: 54766.35733029386 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0583 0.00253 0.00768 0.0445 0.06 0.0823 0.105 0.000206 0.000261 + 380 200 0.0627 0.0025 0.0127 0.0443 0.0597 0.113 0.134 0.000283 0.000336 + 380 235 0.0805 0.00241 0.0323 0.0433 0.0586 0.193 0.214 0.000483 0.000536 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.043 0.00197 0.00349 0.0394 0.053 0.0531 0.0705 0.000133 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 380 54910.621 0.005 0.00264 0.0158 0.0686 0.0453 0.0613 0.12 0.15 0.0003 0.000375 +! Validation 380 54910.621 0.005 0.00281 0.0123 0.0684 0.0464 0.0632 0.106 0.132 0.000264 0.000331 +Wall time: 54910.621210649144 +! Best model 380 0.068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0612 0.00268 0.00748 0.0455 0.0618 0.0823 0.103 0.000206 0.000258 + 381 200 0.0624 0.00259 0.0106 0.0452 0.0608 0.106 0.123 0.000264 0.000307 + 381 235 0.072 0.00267 0.0186 0.0458 0.0616 0.135 0.163 0.000337 0.000407 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0596 0.00224 0.0148 0.0419 0.0564 0.129 0.145 0.000323 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 381 55054.903 0.005 0.00267 0.0225 0.076 0.0456 0.0617 0.141 0.179 0.000353 0.000448 +! Validation 381 55054.903 0.005 0.00297 0.0135 0.0729 0.0478 0.065 0.11 0.139 0.000276 0.000347 +Wall time: 55054.90339342179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.106 0.0027 0.0522 0.0459 0.062 0.248 0.273 0.00062 0.000682 + 382 200 0.0573 0.00248 0.00762 0.0436 0.0595 0.0912 0.104 0.000228 0.00026 + 382 235 0.156 0.00262 0.103 0.0452 0.0611 0.37 0.383 0.000924 0.000958 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.203 0.00451 0.113 0.0601 0.0802 0.394 0.401 0.000985 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 382 55199.149 0.005 0.00262 0.0222 0.0747 0.0452 0.0611 0.139 0.177 0.000346 0.000443 +! Validation 382 55199.149 0.005 0.00514 0.0795 0.182 0.0638 0.0856 0.284 0.336 0.000711 0.000841 +Wall time: 55199.14978515869 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0611 0.00265 0.0081 0.045 0.0615 0.084 0.107 0.00021 0.000268 + 383 200 0.0882 0.00301 0.0279 0.049 0.0655 0.178 0.199 0.000446 0.000498 + 383 235 0.079 0.00271 0.0247 0.046 0.0622 0.167 0.188 0.000416 0.000469 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0853 0.00227 0.0399 0.0421 0.0569 0.235 0.238 0.000587 0.000596 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 383 55343.457 0.005 0.00295 0.0327 0.0917 0.0481 0.0648 0.172 0.216 0.00043 0.00054 +! Validation 383 55343.457 0.005 0.00302 0.0436 0.104 0.0482 0.0656 0.216 0.249 0.00054 0.000623 +Wall time: 55343.457368700765 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0577 0.00255 0.00659 0.0444 0.0603 0.0816 0.0969 0.000204 0.000242 + 384 200 0.0699 0.00304 0.00901 0.0486 0.0659 0.0912 0.113 0.000228 0.000283 + 384 235 0.0637 0.00283 0.00704 0.0465 0.0635 0.084 0.1 0.00021 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0421 0.00183 0.00564 0.0379 0.051 0.0782 0.0896 0.000195 0.000224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 384 55487.757 0.005 0.0034 0.0478 0.116 0.0511 0.0696 0.199 0.261 0.000498 0.000653 +! Validation 384 55487.757 0.005 0.0027 0.0129 0.0669 0.0454 0.062 0.111 0.136 0.000277 0.000339 +Wall time: 55487.7578345621 +! Best model 384 0.067 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0572 0.00245 0.00816 0.0438 0.0591 0.0851 0.108 0.000213 0.00027 + 385 200 0.0585 0.00245 0.00958 0.0437 0.059 0.0949 0.117 0.000237 0.000292 + 385 235 0.0551 0.00249 0.00531 0.0433 0.0596 0.0762 0.087 0.000191 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0365 0.00178 0.000813 0.037 0.0504 0.0294 0.034 7.35e-05 8.51e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 385 55632.376 0.005 0.0026 0.0197 0.0716 0.045 0.0608 0.133 0.168 0.000332 0.000419 +! Validation 385 55632.376 0.005 0.00251 0.0167 0.0668 0.0435 0.0598 0.123 0.154 0.000308 0.000385 +Wall time: 55632.37656611297 +! Best model 385 0.067 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0558 0.00246 0.00656 0.0433 0.0592 0.0738 0.0967 0.000185 0.000242 + 386 200 0.0599 0.00257 0.00853 0.0445 0.0605 0.0928 0.11 0.000232 0.000276 + 386 235 0.239 0.00261 0.187 0.0453 0.061 0.51 0.516 0.00127 0.00129 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0861 0.00272 0.0316 0.0469 0.0623 0.201 0.212 0.000502 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 386 55776.680 0.005 0.00256 0.0211 0.0722 0.0446 0.0603 0.134 0.171 0.000336 0.000428 +! Validation 386 55776.680 0.005 0.00343 0.0561 0.125 0.0522 0.0699 0.238 0.283 0.000594 0.000707 +Wall time: 55776.68043512991 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.112 0.00275 0.0567 0.0466 0.0626 0.264 0.284 0.000661 0.00071 + 387 200 25 0.0657 23.7 0.21 0.306 5.79 5.81 0.0145 0.0145 + 387 235 25.6 0.998 5.69 0.884 1.19 2.47 2.85 0.00617 0.00712 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 28.6 0.997 8.69 0.886 1.19 3.46 3.52 0.00865 0.0088 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 387 55920.957 0.005 0.139 3.62 6.4 0.166 0.441 0.837 2.27 0.00209 0.00567 +! Validation 387 55920.957 0.005 0.973 4.21 23.7 0.875 1.18 2 2.45 0.00499 0.00612 +Wall time: 55920.95698466478 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 2.29 0.102 0.241 0.291 0.382 0.454 0.586 0.00114 0.00147 + 388 200 3.36 0.0713 1.94 0.242 0.319 1.56 1.66 0.00391 0.00415 + 388 235 1.39 0.0627 0.133 0.227 0.299 0.376 0.435 0.000939 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 1.16 0.0561 0.0362 0.214 0.283 0.183 0.227 0.000457 0.000568 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 388 56065.325 0.005 0.241 1.87 6.7 0.387 0.587 1.15 1.63 0.00286 0.00408 +! Validation 388 56065.325 0.005 0.0625 0.215 1.46 0.225 0.298 0.427 0.553 0.00107 0.00138 +Wall time: 56065.32546728803 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 1.02 0.0446 0.126 0.19 0.252 0.341 0.424 0.000851 0.00106 + 389 200 1.01 0.0408 0.199 0.181 0.241 0.438 0.532 0.00109 0.00133 + 389 235 0.979 0.037 0.239 0.173 0.23 0.549 0.584 0.00137 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.931 0.0338 0.255 0.164 0.219 0.594 0.603 0.00148 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 389 56209.603 0.005 0.0454 0.502 1.41 0.191 0.254 0.678 0.846 0.0017 0.00212 +! Validation 389 56209.603 0.005 0.038 0.469 1.23 0.174 0.233 0.726 0.818 0.00182 0.00204 +Wall time: 56209.60350876907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.595 0.0261 0.0722 0.145 0.193 0.256 0.321 0.000639 0.000802 + 390 200 0.462 0.0194 0.0747 0.126 0.166 0.247 0.326 0.000618 0.000815 + 390 235 0.619 0.0184 0.251 0.122 0.162 0.528 0.598 0.00132 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.354 0.0141 0.0725 0.107 0.142 0.31 0.321 0.000775 0.000803 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 390 56354.773 0.005 0.0253 0.3 0.806 0.142 0.19 0.525 0.654 0.00131 0.00163 +! Validation 390 56354.773 0.005 0.0177 0.103 0.456 0.119 0.159 0.317 0.384 0.000793 0.000959 +Wall time: 56354.77316964185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.532 0.0151 0.229 0.111 0.147 0.496 0.572 0.00124 0.00143 + 391 200 0.264 0.0114 0.0364 0.0965 0.127 0.196 0.228 0.000491 0.000569 + 391 235 0.286 0.0108 0.0691 0.0943 0.124 0.259 0.314 0.000647 0.000784 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.206 0.00863 0.0336 0.0838 0.111 0.216 0.219 0.000539 0.000547 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 391 56499.089 0.005 0.0143 0.236 0.521 0.108 0.143 0.449 0.58 0.00112 0.00145 +! Validation 391 56499.089 0.005 0.0116 0.0514 0.283 0.0963 0.128 0.213 0.271 0.000532 0.000677 +Wall time: 56499.08943786379 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.226 0.00947 0.0367 0.0878 0.116 0.187 0.229 0.000466 0.000571 + 392 200 0.222 0.00882 0.0455 0.0849 0.112 0.212 0.255 0.00053 0.000636 + 392 235 0.229 0.00869 0.0553 0.0841 0.111 0.247 0.281 0.000617 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.207 0.00676 0.0722 0.0738 0.0982 0.319 0.321 0.000798 0.000802 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 392 56643.388 0.005 0.00981 0.151 0.347 0.089 0.118 0.379 0.464 0.000947 0.00116 +! Validation 392 56643.388 0.005 0.00918 0.0636 0.247 0.0852 0.114 0.249 0.301 0.000622 0.000752 +Wall time: 56643.388032217044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.315 0.00895 0.136 0.0855 0.113 0.399 0.44 0.000997 0.0011 + 393 200 0.219 0.0078 0.0628 0.0784 0.105 0.271 0.299 0.000678 0.000748 + 393 235 0.181 0.00773 0.0262 0.0784 0.105 0.171 0.193 0.000428 0.000483 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.14 0.00575 0.0251 0.0678 0.0905 0.186 0.189 0.000466 0.000473 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 393 56787.683 0.005 0.00827 0.139 0.305 0.0814 0.109 0.36 0.446 0.000901 0.00111 +! Validation 393 56787.683 0.005 0.00801 0.0356 0.196 0.0791 0.107 0.182 0.225 0.000456 0.000563 +Wall time: 56787.683030657005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.309 0.00749 0.159 0.0773 0.103 0.43 0.476 0.00107 0.00119 + 394 200 0.283 0.00688 0.145 0.0737 0.099 0.432 0.455 0.00108 0.00114 + 394 235 0.2 0.00728 0.0541 0.0758 0.102 0.24 0.278 0.000601 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.162 0.00513 0.059 0.0636 0.0855 0.287 0.29 0.000718 0.000725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 394 56932.831 0.005 0.00706 0.0862 0.227 0.0749 0.1 0.285 0.351 0.000714 0.000877 +! Validation 394 56932.831 0.005 0.0071 0.158 0.3 0.0743 0.101 0.433 0.475 0.00108 0.00119 +Wall time: 56932.83131431183 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.167 0.00661 0.0345 0.0723 0.097 0.19 0.222 0.000475 0.000554 + 395 200 0.377 0.00635 0.25 0.0706 0.0951 0.584 0.597 0.00146 0.00149 + 395 235 0.172 0.00625 0.0471 0.0701 0.0944 0.209 0.259 0.000521 0.000648 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.132 0.00471 0.0373 0.0602 0.0819 0.226 0.23 0.000566 0.000576 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 395 57077.153 0.005 0.00633 0.0692 0.196 0.0708 0.095 0.248 0.314 0.000621 0.000785 +! Validation 395 57077.153 0.005 0.00651 0.0324 0.163 0.0707 0.0963 0.172 0.215 0.000431 0.000537 +Wall time: 57077.15383552993 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.139 0.0058 0.0226 0.0667 0.0909 0.144 0.18 0.000361 0.000449 + 396 200 0.193 0.00603 0.072 0.0686 0.0927 0.255 0.32 0.000637 0.000801 + 396 235 0.214 0.00567 0.1 0.0668 0.0899 0.352 0.378 0.00088 0.000945 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0987 0.00442 0.0104 0.0582 0.0793 0.109 0.122 0.000273 0.000304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 396 57221.514 0.005 0.00597 0.116 0.235 0.0687 0.0922 0.322 0.407 0.000805 0.00102 +! Validation 396 57221.514 0.005 0.00615 0.0283 0.151 0.0687 0.0936 0.157 0.201 0.000393 0.000502 +Wall time: 57221.514528735075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.16 0.00574 0.0454 0.0668 0.0904 0.216 0.254 0.000539 0.000636 + 397 200 0.13 0.0051 0.0283 0.0635 0.0852 0.167 0.201 0.000417 0.000502 + 397 235 0.187 0.0049 0.0894 0.0628 0.0836 0.321 0.357 0.000802 0.000892 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.109 0.00399 0.0296 0.0553 0.0754 0.199 0.205 0.000496 0.000513 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 397 57365.786 0.005 0.00549 0.0622 0.172 0.0657 0.0884 0.234 0.297 0.000585 0.000744 +! Validation 397 57365.786 0.005 0.00553 0.095 0.206 0.0651 0.0888 0.314 0.368 0.000784 0.00092 +Wall time: 57365.78660275694 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.127 0.00515 0.0236 0.0635 0.0857 0.151 0.183 0.000378 0.000458 + 398 200 0.149 0.00454 0.0584 0.0598 0.0804 0.248 0.289 0.000621 0.000721 + 398 235 0.113 0.00487 0.0159 0.061 0.0833 0.119 0.151 0.000298 0.000376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0802 0.00376 0.00513 0.0535 0.0731 0.066 0.0855 0.000165 0.000214 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 398 57511.251 0.005 0.00509 0.0643 0.166 0.0632 0.0851 0.242 0.303 0.000606 0.000758 +! Validation 398 57511.251 0.005 0.00523 0.023 0.128 0.0632 0.0863 0.144 0.181 0.000359 0.000453 +Wall time: 57511.25102906069 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.238 0.00454 0.148 0.0594 0.0804 0.429 0.458 0.00107 0.00115 + 399 200 0.129 0.00464 0.0362 0.0604 0.0813 0.185 0.227 0.000462 0.000568 + 399 235 0.106 0.00436 0.019 0.0586 0.0788 0.143 0.164 0.000357 0.000411 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0913 0.0036 0.0194 0.0525 0.0716 0.149 0.166 0.000374 0.000416 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 399 57655.516 0.005 0.00484 0.08 0.177 0.0616 0.0831 0.266 0.338 0.000666 0.000845 +! Validation 399 57655.516 0.005 0.00501 0.0256 0.126 0.0619 0.0845 0.153 0.191 0.000382 0.000477 +Wall time: 57655.516035357956 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.136 0.00487 0.039 0.0622 0.0833 0.198 0.236 0.000496 0.00059 + 400 200 0.186 0.00484 0.0888 0.0616 0.083 0.306 0.356 0.000765 0.000889 + 400 235 0.13 0.00514 0.0272 0.0627 0.0856 0.173 0.197 0.000433 0.000492 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.103 0.00341 0.0347 0.0509 0.0697 0.211 0.222 0.000529 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 400 57799.777 0.005 0.00467 0.0764 0.17 0.0605 0.0816 0.265 0.33 0.000664 0.000825 +! Validation 400 57799.777 0.005 0.00473 0.0357 0.13 0.06 0.0821 0.187 0.225 0.000469 0.000563 +Wall time: 57799.77776975185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.103 0.00429 0.0176 0.0576 0.0782 0.126 0.158 0.000315 0.000396 + 401 200 0.155 0.00442 0.0666 0.0588 0.0794 0.26 0.308 0.000651 0.00077 + 401 235 0.327 0.00456 0.236 0.0596 0.0806 0.545 0.58 0.00136 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.142 0.00335 0.0753 0.0503 0.0691 0.32 0.327 0.0008 0.000819 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 401 57944.103 0.005 0.00433 0.0502 0.137 0.0581 0.0785 0.209 0.266 0.000523 0.000665 +! Validation 401 57944.103 0.005 0.00466 0.0622 0.155 0.0597 0.0814 0.261 0.298 0.000652 0.000744 +Wall time: 57944.103223152924 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0957 0.00382 0.0192 0.055 0.0738 0.124 0.166 0.00031 0.000414 + 402 200 0.117 0.00439 0.0294 0.0585 0.0791 0.17 0.205 0.000426 0.000511 + 402 235 0.123 0.00386 0.0454 0.0547 0.0741 0.206 0.254 0.000515 0.000636 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.101 0.00314 0.038 0.0486 0.0669 0.223 0.233 0.000558 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 402 58088.506 0.005 0.00417 0.0526 0.136 0.057 0.0771 0.223 0.274 0.000556 0.000685 +! Validation 402 58088.506 0.005 0.0043 0.0375 0.123 0.0572 0.0782 0.191 0.231 0.000478 0.000578 +Wall time: 58088.50655132998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0943 0.00402 0.0139 0.0558 0.0757 0.112 0.141 0.00028 0.000352 + 403 200 0.141 0.00394 0.0618 0.0559 0.0749 0.266 0.297 0.000666 0.000742 + 403 235 0.115 0.00423 0.03 0.0568 0.0776 0.188 0.207 0.00047 0.000517 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0687 0.00312 0.00622 0.0486 0.0667 0.0712 0.0942 0.000178 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 403 58232.776 0.005 0.00409 0.0812 0.163 0.0565 0.0763 0.26 0.34 0.00065 0.000851 +! Validation 403 58232.776 0.005 0.00437 0.0309 0.118 0.0577 0.0789 0.175 0.21 0.000438 0.000525 +Wall time: 58232.77642524103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0835 0.00365 0.0104 0.0536 0.0721 0.0934 0.122 0.000233 0.000304 + 404 200 0.13 0.00391 0.0514 0.0552 0.0746 0.245 0.271 0.000614 0.000676 + 404 235 0.0852 0.00372 0.0107 0.0542 0.0728 0.104 0.123 0.000259 0.000309 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0712 0.00292 0.0128 0.047 0.0645 0.111 0.135 0.000279 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 404 58377.071 0.005 0.00389 0.0403 0.118 0.055 0.0744 0.192 0.24 0.000479 0.0006 +! Validation 404 58377.071 0.005 0.004 0.0295 0.11 0.0552 0.0755 0.17 0.205 0.000425 0.000512 +Wall time: 58377.07160774106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0837 0.00371 0.00949 0.0533 0.0727 0.0893 0.116 0.000223 0.000291 + 405 200 0.116 0.00391 0.0381 0.0554 0.0747 0.209 0.233 0.000523 0.000583 + 405 235 0.169 0.0035 0.0989 0.0526 0.0706 0.358 0.375 0.000894 0.000939 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0807 0.00278 0.0251 0.0457 0.0629 0.175 0.189 0.000439 0.000473 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 405 58521.360 0.005 0.0038 0.0547 0.131 0.0544 0.0736 0.215 0.279 0.000536 0.000697 +! Validation 405 58521.360 0.005 0.00387 0.0637 0.141 0.0542 0.0743 0.266 0.301 0.000665 0.000753 +Wall time: 58521.36050089775 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.158 0.00369 0.0836 0.0538 0.0725 0.332 0.345 0.000831 0.000863 + 406 200 0.115 0.00325 0.0499 0.0506 0.0681 0.233 0.267 0.000582 0.000667 + 406 235 0.0835 0.00366 0.0102 0.0535 0.0722 0.106 0.121 0.000264 0.000301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.245 0.0029 0.187 0.0467 0.0643 0.512 0.515 0.00128 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 406 58665.694 0.005 0.00365 0.0458 0.119 0.0532 0.0721 0.204 0.256 0.00051 0.000639 +! Validation 406 58665.694 0.005 0.00386 0.114 0.191 0.0542 0.0741 0.37 0.403 0.000924 0.00101 +Wall time: 58665.69401976792 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.152 0.0037 0.0781 0.0535 0.0726 0.322 0.334 0.000806 0.000834 + 407 200 0.0898 0.00372 0.0154 0.0536 0.0728 0.122 0.148 0.000306 0.00037 + 407 235 0.114 0.00412 0.0319 0.0561 0.0766 0.195 0.213 0.000488 0.000533 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0842 0.00271 0.03 0.0455 0.0621 0.191 0.207 0.000477 0.000517 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 407 58809.994 0.005 0.0035 0.0352 0.105 0.0521 0.0706 0.181 0.224 0.000452 0.00056 +! Validation 407 58809.994 0.005 0.00367 0.0388 0.112 0.053 0.0723 0.192 0.235 0.00048 0.000588 +Wall time: 58809.99487568578 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0938 0.00349 0.024 0.0523 0.0705 0.149 0.185 0.000373 0.000462 + 408 200 0.141 0.00414 0.0581 0.0574 0.0768 0.258 0.288 0.000645 0.000719 + 408 235 0.0778 0.00326 0.0126 0.0506 0.0682 0.0975 0.134 0.000244 0.000335 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0641 0.00275 0.0092 0.0459 0.0626 0.093 0.115 0.000233 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 408 58954.367 0.005 0.00351 0.0507 0.121 0.0523 0.0707 0.216 0.269 0.00054 0.000673 +! Validation 408 58954.367 0.005 0.0037 0.0129 0.087 0.0532 0.0726 0.109 0.136 0.000273 0.000339 +Wall time: 58954.36777911894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0916 0.00353 0.021 0.0523 0.0709 0.135 0.173 0.000337 0.000433 + 409 200 0.0768 0.00325 0.0118 0.0503 0.0681 0.101 0.129 0.000253 0.000324 + 409 235 0.184 0.00353 0.113 0.0526 0.0709 0.388 0.402 0.000971 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0744 0.00251 0.0241 0.0438 0.0598 0.18 0.185 0.000449 0.000463 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 409 59098.648 0.005 0.00336 0.0399 0.107 0.0511 0.0692 0.189 0.238 0.000472 0.000594 +! Validation 409 59098.648 0.005 0.0035 0.0389 0.109 0.0517 0.0706 0.2 0.235 0.0005 0.000588 +Wall time: 59098.64880765695 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0836 0.00342 0.0152 0.0516 0.0698 0.129 0.147 0.000322 0.000368 + 410 200 0.114 0.00312 0.0518 0.0493 0.0666 0.257 0.272 0.000643 0.000679 + 410 235 0.0686 0.00311 0.00636 0.0492 0.0666 0.0713 0.0952 0.000178 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.057 0.00252 0.00661 0.0435 0.0599 0.0792 0.0971 0.000198 0.000243 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 410 59243.476 0.005 0.0034 0.0536 0.122 0.0514 0.0696 0.216 0.277 0.00054 0.000691 +! Validation 410 59243.476 0.005 0.00339 0.0182 0.0861 0.0508 0.0695 0.13 0.161 0.000325 0.000402 +Wall time: 59243.47638083901 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0964 0.00331 0.0302 0.0507 0.0686 0.176 0.208 0.000439 0.000519 + 411 200 0.0973 0.00305 0.0364 0.0485 0.0659 0.204 0.228 0.00051 0.000569 + 411 235 0.117 0.00352 0.0465 0.052 0.0708 0.241 0.257 0.000603 0.000644 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.116 0.00306 0.0549 0.0486 0.066 0.26 0.28 0.000651 0.000699 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 411 59387.812 0.005 0.00328 0.0434 0.109 0.0505 0.0683 0.199 0.249 0.000497 0.000621 +! Validation 411 59387.812 0.005 0.00394 0.0869 0.166 0.0553 0.0749 0.288 0.352 0.000719 0.00088 +Wall time: 59387.81281979103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0714 0.00317 0.008 0.0496 0.0672 0.0893 0.107 0.000223 0.000267 + 412 200 0.289 0.00342 0.221 0.0516 0.0698 0.524 0.561 0.00131 0.0014 + 412 235 0.127 0.00317 0.064 0.0498 0.0672 0.292 0.302 0.000731 0.000755 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0559 0.00251 0.00578 0.0436 0.0597 0.0693 0.0907 0.000173 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 412 59532.068 0.005 0.00328 0.0468 0.112 0.0505 0.0683 0.204 0.258 0.00051 0.000645 +! Validation 412 59532.068 0.005 0.00333 0.0341 0.101 0.0506 0.0689 0.185 0.22 0.000461 0.000551 +Wall time: 59532.06840939773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0764 0.00331 0.0101 0.051 0.0687 0.0937 0.12 0.000234 0.0003 + 413 200 0.156 0.0029 0.0983 0.0473 0.0643 0.361 0.374 0.000902 0.000936 + 413 235 0.0731 0.00344 0.0042 0.0526 0.07 0.0571 0.0774 0.000143 0.000193 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0885 0.00306 0.0273 0.0491 0.066 0.189 0.197 0.000472 0.000493 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 413 59676.323 0.005 0.00323 0.0489 0.113 0.0501 0.0678 0.21 0.264 0.000525 0.000661 +! Validation 413 59676.323 0.005 0.00384 0.0351 0.112 0.0552 0.074 0.18 0.224 0.000451 0.000559 +Wall time: 59676.32316221902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0618 0.00282 0.00543 0.0468 0.0634 0.0641 0.0879 0.00016 0.00022 + 414 200 0.107 0.00353 0.036 0.0531 0.0709 0.206 0.227 0.000514 0.000566 + 414 235 0.0718 0.00299 0.0121 0.0486 0.0652 0.11 0.131 0.000274 0.000328 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0621 0.00249 0.0123 0.0436 0.0596 0.123 0.132 0.000308 0.000331 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 414 59820.654 0.005 0.00312 0.0304 0.0929 0.0493 0.0667 0.158 0.208 0.000395 0.000521 +! Validation 414 59820.654 0.005 0.00335 0.044 0.111 0.0508 0.0691 0.2 0.25 0.000499 0.000626 +Wall time: 59820.65451474488 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0619 0.00269 0.00808 0.0458 0.0619 0.0769 0.107 0.000192 0.000268 + 415 200 0.186 0.00424 0.102 0.0577 0.0777 0.312 0.381 0.00078 0.000951 + 415 235 0.0935 0.00333 0.0268 0.0509 0.0689 0.176 0.196 0.00044 0.000489 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0634 0.0023 0.0174 0.0421 0.0573 0.149 0.157 0.000372 0.000393 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 415 59965.025 0.005 0.00318 0.0367 0.1 0.0497 0.0674 0.177 0.229 0.000441 0.000572 +! Validation 415 59965.025 0.005 0.00316 0.023 0.0863 0.0492 0.0671 0.148 0.181 0.00037 0.000453 +Wall time: 59965.02565917978 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0884 0.0027 0.0343 0.0456 0.0621 0.196 0.221 0.00049 0.000553 + 416 200 0.253 0.00784 0.0965 0.0823 0.106 0.303 0.371 0.000757 0.000927 + 416 235 0.0974 0.00348 0.0279 0.0525 0.0704 0.165 0.199 0.000412 0.000498 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0888 0.00256 0.0376 0.0444 0.0604 0.226 0.231 0.000566 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 416 60109.358 0.005 0.00358 0.0528 0.124 0.0519 0.0714 0.193 0.275 0.000483 0.000686 +! Validation 416 60109.358 0.005 0.00351 0.0458 0.116 0.052 0.0707 0.223 0.256 0.000557 0.000639 +Wall time: 60109.35817405488 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0996 0.00271 0.0453 0.0456 0.0622 0.245 0.254 0.000613 0.000635 + 417 200 0.0793 0.00327 0.0139 0.0507 0.0683 0.121 0.141 0.000304 0.000352 + 417 235 0.0777 0.00295 0.0188 0.0472 0.0648 0.138 0.164 0.000344 0.000409 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.121 0.00216 0.0774 0.0405 0.0555 0.326 0.332 0.000814 0.00083 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 417 60253.612 0.005 0.00295 0.0248 0.0838 0.0478 0.0648 0.147 0.188 0.000367 0.00047 +! Validation 417 60253.612 0.005 0.00296 0.0937 0.153 0.0474 0.0649 0.344 0.365 0.000859 0.000913 +Wall time: 60253.61224100785 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.068 0.00298 0.00839 0.0478 0.0651 0.082 0.109 0.000205 0.000273 + 418 200 0.0728 0.00261 0.0206 0.0453 0.061 0.155 0.171 0.000387 0.000428 + 418 235 0.0576 0.00254 0.00677 0.0446 0.0601 0.0818 0.0982 0.000204 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.0468 0.00214 0.00399 0.0402 0.0552 0.0549 0.0754 0.000137 0.000188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 418 60397.878 0.005 0.00301 0.0304 0.0906 0.0483 0.0655 0.163 0.208 0.000408 0.000521 +! Validation 418 60397.878 0.005 0.0029 0.0181 0.076 0.047 0.0642 0.127 0.161 0.000317 0.000402 +Wall time: 60397.87869363185 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0636 0.0029 0.00557 0.0474 0.0643 0.0772 0.089 0.000193 0.000223 + 419 200 0.0796 0.00342 0.0112 0.0522 0.0698 0.106 0.126 0.000265 0.000315 + 419 235 0.0655 0.0027 0.0116 0.0453 0.062 0.111 0.129 0.000278 0.000321 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0533 0.00211 0.011 0.0402 0.0549 0.116 0.125 0.000289 0.000313 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 419 60542.471 0.005 0.00304 0.0434 0.104 0.0486 0.0658 0.19 0.249 0.000476 0.000623 +! Validation 419 60542.471 0.005 0.0029 0.0212 0.0793 0.047 0.0643 0.145 0.174 0.000362 0.000434 +Wall time: 60542.47181171412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0698 0.00295 0.0108 0.0484 0.0649 0.102 0.124 0.000254 0.00031 + 420 200 0.0781 0.00273 0.0235 0.0462 0.0624 0.151 0.183 0.000377 0.000458 + 420 235 0.0726 0.00311 0.0104 0.0491 0.0666 0.109 0.122 0.000274 0.000304 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.049 0.00217 0.0055 0.0411 0.0557 0.0734 0.0885 0.000183 0.000221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 420 60686.935 0.005 0.00294 0.0314 0.0902 0.0478 0.0647 0.168 0.212 0.000419 0.000529 +! Validation 420 60686.935 0.005 0.00294 0.0189 0.0776 0.0474 0.0647 0.128 0.164 0.000319 0.00041 +Wall time: 60686.93517229799 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0593 0.00268 0.00569 0.0453 0.0618 0.0702 0.09 0.000176 0.000225 + 421 200 0.0876 0.00327 0.0221 0.0508 0.0683 0.149 0.178 0.000373 0.000444 + 421 235 0.054 0.00251 0.0038 0.045 0.0598 0.0564 0.0736 0.000141 0.000184 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0439 0.00209 0.00205 0.0402 0.0546 0.0516 0.054 0.000129 0.000135 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 421 60831.227 0.005 0.00285 0.0308 0.0877 0.047 0.0637 0.164 0.21 0.00041 0.000524 +! Validation 421 60831.227 0.005 0.00288 0.0132 0.0708 0.047 0.064 0.108 0.137 0.00027 0.000343 +Wall time: 60831.227448607795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.163 0.003 0.103 0.0487 0.0654 0.374 0.384 0.000935 0.000959 + 422 200 0.0683 0.00279 0.0125 0.0467 0.0631 0.107 0.133 0.000268 0.000334 + 422 235 0.0699 0.00293 0.0112 0.0476 0.0647 0.106 0.126 0.000264 0.000316 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0548 0.00218 0.0112 0.0405 0.0557 0.122 0.126 0.000304 0.000316 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 422 60975.517 0.005 0.00295 0.0315 0.0904 0.048 0.0648 0.171 0.212 0.000427 0.00053 +! Validation 422 60975.517 0.005 0.00284 0.0131 0.0699 0.0465 0.0636 0.109 0.137 0.000274 0.000342 +Wall time: 60975.51704360312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.322 0.00305 0.261 0.0485 0.0659 0.602 0.61 0.0015 0.00152 + 423 200 0.057 0.00253 0.00631 0.0442 0.0601 0.0785 0.0948 0.000196 0.000237 + 423 235 0.085 0.00303 0.0244 0.0487 0.0657 0.158 0.186 0.000396 0.000466 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0737 0.00223 0.0291 0.0417 0.0564 0.195 0.204 0.000489 0.000509 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 423 61120.435 0.005 0.00291 0.0332 0.0914 0.0475 0.0644 0.169 0.217 0.000423 0.000544 +! Validation 423 61120.435 0.005 0.00291 0.0286 0.0867 0.0475 0.0643 0.168 0.202 0.000421 0.000504 +Wall time: 61120.435109329876 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.0668 0.00277 0.0113 0.0467 0.0629 0.0898 0.127 0.000225 0.000317 + 424 200 0.147 0.00474 0.0523 0.0633 0.0822 0.222 0.273 0.000556 0.000683 + 424 235 0.0827 0.00292 0.0243 0.048 0.0645 0.163 0.186 0.000408 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.219 0.00315 0.156 0.0502 0.067 0.465 0.472 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 424 61264.691 0.005 0.00325 0.0466 0.112 0.0502 0.0681 0.202 0.258 0.000505 0.000644 +! Validation 424 61264.691 0.005 0.00385 0.217 0.294 0.0553 0.074 0.541 0.556 0.00135 0.00139 +Wall time: 61264.691232956015 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.114 0.00258 0.0619 0.045 0.0607 0.29 0.297 0.000725 0.000743 + 425 200 0.0734 0.00259 0.0217 0.0446 0.0607 0.151 0.176 0.000378 0.00044 + 425 235 0.188 0.00281 0.132 0.047 0.0633 0.411 0.433 0.00103 0.00108 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0624 0.00247 0.0129 0.0449 0.0594 0.134 0.136 0.000335 0.000339 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 425 61408.957 0.005 0.00294 0.037 0.0957 0.0477 0.0647 0.183 0.229 0.000458 0.000572 +! Validation 425 61408.957 0.005 0.00331 0.0394 0.106 0.0513 0.0687 0.197 0.237 0.000493 0.000592 +Wall time: 61408.957759634126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.078 0.00265 0.025 0.0453 0.0614 0.155 0.189 0.000387 0.000471 + 426 200 0.0662 0.00289 0.00848 0.047 0.0641 0.0878 0.11 0.000219 0.000275 + 426 235 0.0646 0.00275 0.00969 0.0467 0.0625 0.0918 0.117 0.00023 0.000294 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.042 0.002 0.00197 0.039 0.0534 0.0492 0.0529 0.000123 0.000132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 426 61553.236 0.005 0.00292 0.029 0.0875 0.0478 0.0645 0.16 0.203 0.0004 0.000509 +! Validation 426 61553.236 0.005 0.0027 0.00994 0.064 0.0453 0.0621 0.0954 0.119 0.000238 0.000298 +Wall time: 61553.236784779 +! Best model 426 0.064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0627 0.00269 0.00894 0.0455 0.0619 0.0958 0.113 0.000239 0.000282 + 427 200 0.0847 0.00296 0.0254 0.0483 0.065 0.177 0.19 0.000442 0.000476 + 427 235 0.0549 0.00213 0.0123 0.0403 0.0551 0.112 0.133 0.00028 0.000331 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0484 0.00225 0.0034 0.0414 0.0566 0.0628 0.0696 0.000157 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 427 61697.542 0.005 0.00266 0.023 0.0762 0.0455 0.0615 0.142 0.181 0.000356 0.000453 +! Validation 427 61697.542 0.005 0.00288 0.0112 0.0689 0.047 0.0641 0.101 0.127 0.000252 0.000316 +Wall time: 61697.54266052181 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0674 0.00246 0.0183 0.0436 0.0592 0.133 0.161 0.000332 0.000403 + 428 200 0.146 0.00286 0.0886 0.0474 0.0638 0.331 0.355 0.000828 0.000888 + 428 235 0.0583 0.00249 0.00851 0.0443 0.0595 0.0881 0.11 0.00022 0.000275 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0523 0.00194 0.0134 0.0387 0.0526 0.127 0.138 0.000319 0.000346 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 428 61841.878 0.005 0.00266 0.0308 0.084 0.0456 0.0616 0.165 0.21 0.000413 0.000524 +! Validation 428 61841.878 0.005 0.00266 0.0175 0.0708 0.0451 0.0616 0.13 0.158 0.000324 0.000395 +Wall time: 61841.87881576782 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0647 0.00261 0.0124 0.045 0.061 0.112 0.133 0.00028 0.000333 + 429 200 0.151 0.00333 0.0847 0.0523 0.0689 0.332 0.347 0.00083 0.000869 + 429 235 0.093 0.00243 0.0444 0.0436 0.0589 0.219 0.251 0.000547 0.000629 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.112 0.00215 0.0686 0.0413 0.0553 0.312 0.313 0.000779 0.000782 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 429 61986.146 0.005 0.00264 0.0234 0.0763 0.0454 0.0614 0.146 0.183 0.000365 0.000456 +! Validation 429 61986.146 0.005 0.00285 0.0835 0.141 0.0471 0.0637 0.31 0.345 0.000776 0.000862 +Wall time: 61986.14664041996 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.159 0.00438 0.0718 0.0596 0.079 0.249 0.32 0.000622 0.0008 + 430 200 0.0742 0.00281 0.0179 0.0467 0.0633 0.124 0.16 0.00031 0.0004 + 430 235 0.108 0.00356 0.0365 0.0535 0.0712 0.18 0.228 0.00045 0.00057 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0798 0.0035 0.00987 0.0533 0.0706 0.101 0.119 0.000252 0.000296 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 430 62130.591 0.005 0.00329 0.0468 0.112 0.05 0.0684 0.194 0.258 0.000484 0.000645 +! Validation 430 62130.591 0.005 0.004 0.059 0.139 0.0568 0.0755 0.219 0.29 0.000548 0.000725 +Wall time: 62130.59159749281 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0703 0.00245 0.0213 0.0436 0.059 0.152 0.174 0.000381 0.000436 + 431 200 0.0695 0.00259 0.0177 0.0452 0.0607 0.123 0.159 0.000308 0.000397 + 431 235 0.0914 0.00258 0.0397 0.045 0.0607 0.209 0.238 0.000524 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0583 0.00207 0.0169 0.0408 0.0543 0.149 0.155 0.000372 0.000388 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 431 62275.458 0.005 0.00252 0.0171 0.0676 0.0443 0.06 0.124 0.156 0.000311 0.00039 +! Validation 431 62275.458 0.005 0.00283 0.028 0.0846 0.047 0.0635 0.166 0.2 0.000415 0.000499 +Wall time: 62275.45862667682 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0821 0.00253 0.0316 0.0442 0.06 0.196 0.212 0.00049 0.00053 + 432 200 0.0737 0.00234 0.0269 0.0422 0.0577 0.178 0.196 0.000444 0.000489 + 432 235 0.0544 0.00218 0.0107 0.0413 0.0557 0.107 0.124 0.000266 0.000309 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0397 0.00188 0.00198 0.0378 0.0518 0.0424 0.0532 0.000106 0.000133 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 432 62419.680 0.005 0.00261 0.0294 0.0815 0.045 0.061 0.159 0.205 0.000397 0.000512 +! Validation 432 62419.680 0.005 0.00251 0.0115 0.0617 0.0437 0.0598 0.101 0.128 0.000253 0.00032 +Wall time: 62419.68067802908 +! Best model 432 0.062 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0544 0.00249 0.00467 0.0441 0.0595 0.0678 0.0815 0.00017 0.000204 + 433 200 1.22 0.0381 0.463 0.168 0.233 0.704 0.812 0.00176 0.00203 + 433 235 23.3 0.84 6.51 0.812 1.09 2.54 3.04 0.00635 0.00761 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 18.7 0.863 1.43 0.824 1.11 1.31 1.43 0.00327 0.00357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 433 62564.011 0.005 0.115 7.32 9.62 0.145 0.402 0.894 3.23 0.00223 0.00807 +! Validation 433 62564.011 0.005 0.84 9.12 25.9 0.811 1.09 2.62 3.6 0.00656 0.00901 +Wall time: 62564.01123347413 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 2.95 0.134 0.268 0.334 0.437 0.499 0.618 0.00125 0.00155 + 434 200 1.82 0.079 0.235 0.253 0.336 0.465 0.579 0.00116 0.00145 + 434 235 1.92 0.0723 0.478 0.242 0.321 0.735 0.825 0.00184 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 1.49 0.0663 0.166 0.232 0.307 0.449 0.486 0.00112 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 434 62708.419 0.005 0.228 2.26 6.83 0.39 0.571 1.26 1.8 0.00316 0.00449 +! Validation 434 62708.419 0.005 0.0737 0.322 1.8 0.245 0.324 0.523 0.677 0.00131 0.00169 +Wall time: 62708.41941067297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 1.59 0.0685 0.224 0.236 0.312 0.455 0.565 0.00114 0.00141 + 435 200 1 0.0425 0.156 0.187 0.246 0.374 0.471 0.000936 0.00118 + 435 235 1.5 0.0432 0.639 0.188 0.248 0.666 0.954 0.00167 0.00238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 2.65 0.0355 1.94 0.169 0.225 1.65 1.66 0.00414 0.00415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 435 62853.390 0.005 0.0562 0.654 1.78 0.213 0.283 0.753 0.965 0.00188 0.00241 +! Validation 435 62853.390 0.005 0.0418 0.94 1.77 0.184 0.244 1.07 1.16 0.00268 0.00289 +Wall time: 62853.39012012398 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.778 0.0317 0.144 0.162 0.213 0.388 0.453 0.00097 0.00113 + 436 200 0.685 0.026 0.165 0.147 0.192 0.391 0.485 0.000978 0.00121 + 436 235 0.619 0.0236 0.148 0.139 0.183 0.398 0.459 0.000996 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.541 0.0199 0.144 0.128 0.168 0.438 0.453 0.00109 0.00113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 436 62997.652 0.005 0.0309 0.412 1.03 0.159 0.21 0.619 0.767 0.00155 0.00192 +! Validation 436 62997.652 0.005 0.025 0.691 1.19 0.143 0.189 0.927 0.992 0.00232 0.00248 +Wall time: 62997.65240922384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.989 0.0201 0.587 0.129 0.169 0.873 0.915 0.00218 0.00229 + 437 200 0.543 0.017 0.203 0.118 0.155 0.481 0.538 0.0012 0.00135 + 437 235 0.416 0.0166 0.0847 0.116 0.154 0.309 0.347 0.000773 0.000869 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.895 0.0125 0.645 0.102 0.134 0.951 0.958 0.00238 0.0024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 437 63141.919 0.005 0.0198 0.241 0.636 0.128 0.168 0.471 0.586 0.00118 0.00147 +! Validation 437 63141.919 0.005 0.017 1.17 1.51 0.118 0.156 1.25 1.29 0.00313 0.00322 +Wall time: 63141.919339207 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.526 0.0155 0.217 0.113 0.148 0.497 0.556 0.00124 0.00139 + 438 200 0.364 0.0141 0.0816 0.108 0.142 0.271 0.341 0.000679 0.000852 + 438 235 0.371 0.0155 0.061 0.113 0.148 0.254 0.295 0.000634 0.000737 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.257 0.0101 0.0539 0.0917 0.12 0.246 0.277 0.000616 0.000693 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 438 63286.185 0.005 0.0154 0.287 0.595 0.113 0.148 0.503 0.64 0.00126 0.0016 +! Validation 438 63286.185 0.005 0.014 0.0598 0.34 0.107 0.141 0.222 0.292 0.000556 0.00073 +Wall time: 63286.18561857706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.918 0.0125 0.668 0.101 0.134 0.937 0.976 0.00234 0.00244 + 439 200 0.325 0.0109 0.107 0.0946 0.125 0.332 0.391 0.000829 0.000977 + 439 235 0.408 0.0113 0.182 0.0956 0.127 0.418 0.51 0.00105 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.285 0.00843 0.116 0.0835 0.11 0.385 0.407 0.000963 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 439 63430.633 0.005 0.0123 0.163 0.41 0.1 0.133 0.385 0.481 0.000964 0.0012 +! Validation 439 63430.633 0.005 0.0118 0.241 0.477 0.0976 0.13 0.533 0.586 0.00133 0.00147 +Wall time: 63430.633873270825 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.279 0.0113 0.0524 0.0962 0.127 0.212 0.273 0.000531 0.000683 + 440 200 0.348 0.00964 0.156 0.0889 0.117 0.432 0.471 0.00108 0.00118 + 440 235 0.216 0.00933 0.0293 0.0876 0.115 0.167 0.204 0.000417 0.000511 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.209 0.00735 0.0623 0.078 0.102 0.273 0.298 0.000681 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 440 63574.922 0.005 0.0106 0.182 0.394 0.0929 0.123 0.412 0.51 0.00103 0.00127 +! Validation 440 63574.922 0.005 0.0103 0.0489 0.256 0.0913 0.121 0.21 0.264 0.000524 0.00066 +Wall time: 63574.92273864569 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.272 0.00977 0.0762 0.0885 0.118 0.283 0.329 0.000708 0.000824 + 441 200 0.828 0.00926 0.643 0.0868 0.115 0.928 0.957 0.00232 0.00239 + 441 235 0.249 0.00859 0.0775 0.0829 0.111 0.281 0.332 0.000704 0.000831 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.151 0.00643 0.0221 0.0728 0.0957 0.154 0.177 0.000384 0.000444 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 441 63719.305 0.005 0.00941 0.163 0.351 0.0873 0.116 0.379 0.483 0.000949 0.00121 +! Validation 441 63719.305 0.005 0.00914 0.0388 0.222 0.0854 0.114 0.18 0.235 0.000451 0.000588 +Wall time: 63719.30552006094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.203 0.00874 0.0281 0.0839 0.112 0.14 0.2 0.00035 0.0005 + 442 200 0.702 0.00761 0.55 0.0787 0.104 0.862 0.885 0.00216 0.00221 + 442 235 0.7 0.00856 0.528 0.0822 0.11 0.844 0.868 0.00211 0.00217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.324 0.0058 0.208 0.069 0.0909 0.534 0.544 0.00133 0.00136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 442 63863.554 0.005 0.00835 0.124 0.291 0.082 0.109 0.327 0.419 0.000817 0.00105 +! Validation 442 63863.554 0.005 0.00823 0.158 0.322 0.0808 0.108 0.435 0.474 0.00109 0.00118 +Wall time: 63863.554888284765 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.182 0.00782 0.0258 0.0789 0.106 0.161 0.192 0.000404 0.000479 + 443 200 0.321 0.00763 0.168 0.0783 0.104 0.458 0.49 0.00114 0.00122 + 443 235 0.275 0.00699 0.135 0.0749 0.0998 0.371 0.439 0.000927 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.122 0.00539 0.0142 0.0663 0.0876 0.117 0.142 0.000292 0.000355 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 443 64007.847 0.005 0.00749 0.0961 0.246 0.0775 0.103 0.293 0.37 0.000733 0.000924 +! Validation 443 64007.847 0.005 0.00756 0.0433 0.195 0.0773 0.104 0.198 0.248 0.000494 0.000621 +Wall time: 64007.84702222608 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.196 0.00678 0.06 0.0736 0.0983 0.244 0.292 0.000609 0.000731 + 444 200 0.155 0.00654 0.0243 0.0725 0.0965 0.149 0.186 0.000373 0.000465 + 444 235 0.592 0.00657 0.46 0.0728 0.0968 0.785 0.81 0.00196 0.00202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.375 0.00471 0.28 0.0624 0.0819 0.626 0.632 0.00156 0.00158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 444 64152.142 0.005 0.00682 0.0934 0.23 0.0739 0.0986 0.282 0.363 0.000705 0.000907 +! Validation 444 64152.142 0.005 0.00682 0.47 0.607 0.0732 0.0986 0.785 0.818 0.00196 0.00205 +Wall time: 64152.142751812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.485 0.00662 0.353 0.072 0.0971 0.676 0.709 0.00169 0.00177 + 445 200 0.189 0.00592 0.0703 0.0687 0.0918 0.278 0.316 0.000696 0.000791 + 445 235 0.159 0.00635 0.0323 0.0714 0.0951 0.178 0.215 0.000445 0.000536 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.206 0.00452 0.116 0.0611 0.0802 0.397 0.406 0.000992 0.00102 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 445 64296.420 0.005 0.00651 0.139 0.27 0.0722 0.0963 0.352 0.446 0.00088 0.00112 +! Validation 445 64296.420 0.005 0.00648 0.0813 0.211 0.0714 0.0961 0.286 0.34 0.000716 0.000851 +Wall time: 64296.4201039481 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.25 0.006 0.13 0.0692 0.0924 0.412 0.431 0.00103 0.00108 + 446 200 0.173 0.00606 0.0518 0.0691 0.0929 0.215 0.272 0.000537 0.000679 + 446 235 0.124 0.0052 0.0201 0.0645 0.086 0.133 0.169 0.000333 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.108 0.00408 0.0259 0.0583 0.0763 0.176 0.192 0.000439 0.00048 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 446 64440.775 0.005 0.00596 0.0866 0.206 0.0689 0.0921 0.283 0.351 0.000708 0.000879 +! Validation 446 64440.775 0.005 0.00592 0.0343 0.153 0.068 0.0919 0.177 0.221 0.000442 0.000553 +Wall time: 64440.77525329171 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.155 0.00524 0.0499 0.0651 0.0864 0.237 0.267 0.000593 0.000666 + 447 200 0.124 0.00554 0.0131 0.0664 0.0888 0.118 0.137 0.000296 0.000342 + 447 235 0.308 0.00525 0.203 0.0647 0.0865 0.524 0.538 0.00131 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.581 0.00391 0.502 0.0569 0.0747 0.843 0.846 0.00211 0.00211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 447 64585.101 0.005 0.00553 0.0778 0.188 0.0663 0.0888 0.268 0.332 0.000669 0.00083 +! Validation 447 64585.101 0.005 0.00561 0.44 0.552 0.0661 0.0894 0.772 0.792 0.00193 0.00198 +Wall time: 64585.10174612282 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.294 0.00503 0.194 0.0633 0.0846 0.47 0.526 0.00118 0.00131 + 448 200 0.125 0.00488 0.0277 0.0625 0.0834 0.173 0.199 0.000433 0.000497 + 448 235 0.14 0.00563 0.0276 0.0663 0.0896 0.164 0.198 0.000411 0.000496 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.133 0.00368 0.0595 0.055 0.0724 0.285 0.291 0.000712 0.000728 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 448 64730.454 0.005 0.00527 0.0876 0.193 0.0647 0.0867 0.283 0.354 0.000708 0.000884 +! Validation 448 64730.454 0.005 0.00536 0.12 0.227 0.0644 0.0874 0.372 0.413 0.000929 0.00103 +Wall time: 64730.454369870946 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.106 0.00461 0.0137 0.0611 0.081 0.114 0.14 0.000285 0.000349 + 449 200 0.137 0.00554 0.0258 0.0661 0.0889 0.15 0.192 0.000376 0.000479 + 449 235 0.247 0.00505 0.146 0.0632 0.0848 0.397 0.456 0.000992 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.0776 0.00368 0.00405 0.0552 0.0724 0.0615 0.076 0.000154 0.00019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 449 64874.663 0.005 0.00505 0.113 0.214 0.0633 0.0848 0.321 0.401 0.000803 0.001 +! Validation 449 64874.663 0.005 0.00525 0.0431 0.148 0.0639 0.0865 0.199 0.248 0.000497 0.000619 +Wall time: 64874.66383775696 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.11 0.00494 0.0114 0.0622 0.0839 0.102 0.128 0.000255 0.000319 + 450 200 0.108 0.00449 0.0178 0.0597 0.0799 0.133 0.159 0.000332 0.000399 + 450 235 0.0857 0.00385 0.0087 0.0556 0.074 0.0877 0.111 0.000219 0.000278 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.0871 0.00331 0.0209 0.0521 0.0687 0.161 0.173 0.000403 0.000431 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 450 65018.911 0.005 0.00467 0.0421 0.136 0.0608 0.0816 0.193 0.245 0.000484 0.000613 +! Validation 450 65018.911 0.005 0.00473 0.0293 0.124 0.0604 0.0821 0.169 0.204 0.000423 0.000511 +Wall time: 65018.911819934845 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.106 0.00427 0.0212 0.0584 0.0779 0.146 0.174 0.000364 0.000434 + 451 200 0.11 0.00449 0.0204 0.0598 0.08 0.131 0.17 0.000327 0.000426 + 451 235 0.172 0.00429 0.0865 0.0581 0.0782 0.337 0.351 0.000843 0.000877 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0851 0.00335 0.018 0.052 0.0691 0.151 0.16 0.000377 0.0004 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 451 65163.116 0.005 0.00443 0.0672 0.156 0.0591 0.0794 0.247 0.309 0.000617 0.000773 +! Validation 451 65163.116 0.005 0.00472 0.0767 0.171 0.0603 0.082 0.28 0.331 0.0007 0.000826 +Wall time: 65163.116828526836 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.109 0.00406 0.0279 0.0567 0.0761 0.172 0.199 0.000429 0.000498 + 452 200 0.097 0.00415 0.014 0.0569 0.0769 0.102 0.141 0.000256 0.000354 + 452 235 0.428 0.00403 0.348 0.0569 0.0757 0.686 0.704 0.00171 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.348 0.00309 0.287 0.0501 0.0664 0.636 0.639 0.00159 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 452 65307.614 0.005 0.00424 0.0521 0.137 0.0578 0.0777 0.21 0.27 0.000524 0.000676 +! Validation 452 65307.614 0.005 0.00438 0.315 0.402 0.0582 0.079 0.645 0.669 0.00161 0.00167 +Wall time: 65307.614059988875 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.179 0.00396 0.0993 0.0555 0.0751 0.36 0.376 0.000901 0.00094 + 453 200 0.092 0.00378 0.0165 0.0544 0.0734 0.12 0.153 0.000301 0.000383 + 453 235 0.13 0.00408 0.0486 0.0563 0.0762 0.227 0.263 0.000569 0.000658 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.0659 0.00285 0.00885 0.0479 0.0637 0.0995 0.112 0.000249 0.000281 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 453 65451.876 0.005 0.00406 0.0516 0.133 0.0565 0.076 0.211 0.271 0.000527 0.000678 +! Validation 453 65451.876 0.005 0.00411 0.0253 0.108 0.0561 0.0766 0.15 0.19 0.000374 0.000474 +Wall time: 65451.876196176745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.104 0.00388 0.0262 0.055 0.0744 0.154 0.193 0.000385 0.000483 + 454 200 0.195 0.004 0.115 0.0561 0.0755 0.378 0.405 0.000946 0.00101 + 454 235 0.228 0.00419 0.144 0.0573 0.0772 0.434 0.454 0.00109 0.00113 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.219 0.00288 0.161 0.0483 0.0641 0.476 0.479 0.00119 0.0012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 454 65596.124 0.005 0.00401 0.0891 0.169 0.0562 0.0756 0.286 0.356 0.000715 0.00089 +! Validation 454 65596.124 0.005 0.00411 0.104 0.186 0.0562 0.0765 0.346 0.385 0.000864 0.000963 +Wall time: 65596.12459190795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0851 0.00367 0.0118 0.0533 0.0723 0.102 0.13 0.000255 0.000324 + 455 200 0.111 0.00379 0.0354 0.055 0.0735 0.188 0.225 0.00047 0.000562 + 455 235 0.134 0.0039 0.0563 0.0553 0.0745 0.251 0.283 0.000628 0.000708 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0884 0.00268 0.0347 0.0463 0.0618 0.214 0.222 0.000535 0.000556 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 455 65740.395 0.005 0.00374 0.0404 0.115 0.0541 0.073 0.188 0.24 0.00047 0.000599 +! Validation 455 65740.395 0.005 0.00388 0.0457 0.123 0.0544 0.0743 0.22 0.255 0.000549 0.000638 +Wall time: 65740.394949432 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.162 0.00352 0.0919 0.053 0.0708 0.331 0.362 0.000829 0.000904 + 456 200 0.106 0.00361 0.034 0.0534 0.0717 0.188 0.22 0.00047 0.00055 + 456 235 0.293 0.00366 0.22 0.0538 0.0722 0.543 0.559 0.00136 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.116 0.00255 0.0654 0.0451 0.0602 0.303 0.305 0.000757 0.000763 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 456 65884.606 0.005 0.00365 0.0484 0.121 0.0535 0.0721 0.205 0.261 0.000514 0.000653 +! Validation 456 65884.606 0.005 0.00372 0.111 0.186 0.0532 0.0728 0.368 0.398 0.00092 0.000995 +Wall time: 65884.60597273894 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.25 0.00322 0.185 0.0506 0.0678 0.494 0.514 0.00123 0.00128 + 457 200 0.113 0.00354 0.0426 0.0525 0.071 0.216 0.246 0.00054 0.000616 + 457 235 0.0914 0.00334 0.0245 0.0515 0.069 0.17 0.187 0.000424 0.000467 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.112 0.00253 0.0612 0.0448 0.06 0.291 0.295 0.000728 0.000738 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 457 66028.807 0.005 0.00352 0.0451 0.115 0.0525 0.0708 0.201 0.254 0.000503 0.000634 +! Validation 457 66028.807 0.005 0.00361 0.113 0.185 0.0525 0.0717 0.375 0.4 0.000937 0.001 +Wall time: 66028.80720335478 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.29 0.00379 0.214 0.0548 0.0735 0.545 0.553 0.00136 0.00138 + 458 200 0.0994 0.00367 0.026 0.0534 0.0723 0.152 0.193 0.00038 0.000481 + 458 235 0.278 0.00325 0.213 0.0505 0.068 0.539 0.551 0.00135 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.0846 0.00281 0.0283 0.0472 0.0633 0.196 0.201 0.000489 0.000502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 458 66173.047 0.005 0.00342 0.0499 0.118 0.0517 0.0698 0.213 0.265 0.000533 0.000663 +! Validation 458 66173.047 0.005 0.00384 0.044 0.121 0.0544 0.074 0.206 0.25 0.000516 0.000626 +Wall time: 66173.04704287369 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.114 0.00326 0.0491 0.0512 0.0681 0.239 0.264 0.000598 0.000661 + 459 200 0.12 0.0032 0.0561 0.0501 0.0676 0.265 0.283 0.000663 0.000707 + 459 235 0.22 0.0035 0.15 0.0525 0.0706 0.411 0.462 0.00103 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.087 0.00281 0.0309 0.0471 0.0632 0.198 0.21 0.000495 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 459 66317.237 0.005 0.00338 0.0609 0.128 0.0514 0.0693 0.238 0.294 0.000595 0.000735 +! Validation 459 66317.237 0.005 0.00377 0.103 0.178 0.0538 0.0733 0.341 0.383 0.000851 0.000957 +Wall time: 66317.23732627882 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.0846 0.00305 0.0235 0.0489 0.066 0.152 0.183 0.00038 0.000458 + 460 200 0.0796 0.00305 0.0186 0.0491 0.0659 0.139 0.163 0.000348 0.000407 + 460 235 0.143 0.00334 0.0758 0.0513 0.069 0.29 0.329 0.000724 0.000822 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.147 0.00241 0.0984 0.0436 0.0586 0.37 0.374 0.000925 0.000936 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 460 66461.456 0.005 0.0032 0.0305 0.0945 0.05 0.0675 0.166 0.208 0.000416 0.00052 +! Validation 460 66461.456 0.005 0.00337 0.0898 0.157 0.0508 0.0693 0.327 0.358 0.000818 0.000894 +Wall time: 66461.45627301699 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.205 0.00334 0.138 0.0512 0.0689 0.422 0.444 0.00105 0.00111 + 461 200 0.0827 0.00314 0.0198 0.0494 0.0669 0.145 0.168 0.000362 0.00042 + 461 235 0.0843 0.0032 0.0202 0.0494 0.0676 0.148 0.17 0.00037 0.000424 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.05 0.00221 0.00578 0.0415 0.0561 0.0806 0.0908 0.000201 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 461 66605.757 0.005 0.00326 0.0554 0.121 0.0505 0.0682 0.209 0.281 0.000522 0.000703 +! Validation 461 66605.757 0.005 0.00317 0.0157 0.0792 0.049 0.0672 0.122 0.15 0.000305 0.000374 +Wall time: 66605.7570018419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.0763 0.00303 0.0157 0.0487 0.0657 0.131 0.149 0.000328 0.000373 + 462 200 0.0669 0.00294 0.00815 0.0479 0.0647 0.0858 0.108 0.000214 0.000269 + 462 235 0.128 0.00339 0.0601 0.0519 0.0695 0.2 0.293 0.0005 0.000731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.452 0.00224 0.407 0.0421 0.0565 0.76 0.761 0.0019 0.0019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 462 66749.947 0.005 0.00302 0.0306 0.091 0.0485 0.0656 0.165 0.208 0.000413 0.000521 +! Validation 462 66749.947 0.005 0.00318 0.321 0.385 0.0496 0.0673 0.639 0.676 0.0016 0.00169 +Wall time: 66749.94729287969 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0671 0.00286 0.00991 0.0474 0.0638 0.0924 0.119 0.000231 0.000297 + 463 200 0.0866 0.00294 0.0278 0.0481 0.0647 0.17 0.199 0.000426 0.000497 + 463 235 0.0811 0.00352 0.0106 0.0528 0.0708 0.112 0.123 0.00028 0.000308 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0923 0.00223 0.0477 0.0422 0.0564 0.258 0.261 0.000646 0.000651 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 463 66894.166 0.005 0.00307 0.0492 0.111 0.049 0.0661 0.209 0.265 0.000522 0.000662 +! Validation 463 66894.166 0.005 0.00312 0.0625 0.125 0.0489 0.0667 0.26 0.298 0.000649 0.000746 +Wall time: 66894.166374336 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.0625 0.00287 0.00506 0.0475 0.064 0.0684 0.0849 0.000171 0.000212 + 464 200 0.0807 0.00282 0.0244 0.047 0.0633 0.16 0.186 0.0004 0.000466 + 464 235 0.0638 0.00284 0.00704 0.0466 0.0636 0.0755 0.1 0.000189 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.056 0.00211 0.0137 0.0409 0.0549 0.13 0.14 0.000325 0.000349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 464 67038.915 0.005 0.00296 0.034 0.0933 0.0481 0.065 0.175 0.22 0.000437 0.000551 +! Validation 464 67038.915 0.005 0.00301 0.0237 0.0839 0.0479 0.0655 0.151 0.184 0.000377 0.000459 +Wall time: 67038.91563093895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0655 0.00276 0.0102 0.0464 0.0627 0.1 0.121 0.00025 0.000302 + 465 200 0.0968 0.00289 0.0389 0.0476 0.0642 0.208 0.235 0.000519 0.000588 + 465 235 0.064 0.00278 0.00833 0.0468 0.063 0.0948 0.109 0.000237 0.000272 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.106 0.00214 0.0636 0.0415 0.0553 0.298 0.301 0.000746 0.000752 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 465 67183.119 0.005 0.00284 0.0291 0.0858 0.047 0.0636 0.164 0.204 0.00041 0.000509 +! Validation 465 67183.119 0.005 0.00298 0.0653 0.125 0.048 0.0652 0.283 0.305 0.000707 0.000763 +Wall time: 67183.11910462473 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0675 0.00274 0.0127 0.0461 0.0625 0.12 0.134 0.0003 0.000336 + 466 200 0.085 0.003 0.025 0.0482 0.0654 0.163 0.189 0.000408 0.000472 + 466 235 0.111 0.00278 0.0559 0.0471 0.0629 0.256 0.282 0.000639 0.000706 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.154 0.00206 0.113 0.0401 0.0542 0.398 0.4 0.000995 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 466 67327.322 0.005 0.00281 0.0262 0.0825 0.0468 0.0633 0.152 0.193 0.00038 0.000483 +! Validation 466 67327.322 0.005 0.00292 0.158 0.216 0.047 0.0644 0.456 0.474 0.00114 0.00119 +Wall time: 67327.3226024718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0623 0.00285 0.00526 0.0471 0.0638 0.0758 0.0865 0.000189 0.000216 + 467 200 0.066 0.00271 0.0118 0.0459 0.0621 0.105 0.13 0.000263 0.000324 + 467 235 0.104 0.00244 0.0554 0.0436 0.0589 0.27 0.281 0.000675 0.000702 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.11 0.00189 0.0718 0.0384 0.0519 0.318 0.32 0.000794 0.0008 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 467 67471.543 0.005 0.00283 0.028 0.0845 0.047 0.0635 0.158 0.199 0.000395 0.000498 +! Validation 467 67471.543 0.005 0.0027 0.0688 0.123 0.0452 0.062 0.283 0.313 0.000708 0.000783 +Wall time: 67471.54387275781 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0718 0.00268 0.0183 0.0455 0.0617 0.139 0.161 0.000348 0.000404 + 468 200 0.0588 0.0025 0.00875 0.0443 0.0597 0.0952 0.112 0.000238 0.000279 + 468 235 0.109 0.00294 0.0504 0.0483 0.0647 0.244 0.268 0.000609 0.00067 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0877 0.0021 0.0457 0.0411 0.0547 0.247 0.255 0.000617 0.000638 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 468 67615.740 0.005 0.00282 0.033 0.0894 0.0469 0.0634 0.171 0.217 0.000427 0.000542 +! Validation 468 67615.740 0.005 0.00293 0.0671 0.126 0.0477 0.0646 0.277 0.309 0.000693 0.000773 +Wall time: 67615.74042699579 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0817 0.00341 0.0136 0.0515 0.0697 0.108 0.139 0.00027 0.000348 + 469 200 0.0867 0.00263 0.0341 0.0448 0.0612 0.196 0.22 0.000491 0.000551 + 469 235 0.16 0.0024 0.112 0.0438 0.0584 0.382 0.4 0.000955 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.123 0.00207 0.0818 0.0405 0.0544 0.336 0.341 0.00084 0.000853 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 469 67759.940 0.005 0.00275 0.0344 0.0893 0.0463 0.0625 0.174 0.221 0.000435 0.000552 +! Validation 469 67759.940 0.005 0.00294 0.0726 0.131 0.0475 0.0647 0.269 0.322 0.000671 0.000804 +Wall time: 67759.9399558669 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.461 0.00275 0.406 0.0466 0.0626 0.752 0.761 0.00188 0.0019 + 470 200 0.0617 0.00273 0.00703 0.046 0.0624 0.085 0.1 0.000212 0.00025 + 470 235 0.0608 0.00244 0.012 0.0439 0.059 0.112 0.131 0.00028 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.0523 0.00197 0.0129 0.0393 0.053 0.124 0.135 0.00031 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 470 67904.142 0.005 0.00556 0.139 0.25 0.0598 0.0891 0.279 0.445 0.000698 0.00111 +! Validation 470 67904.142 0.005 0.00282 0.0297 0.0861 0.0463 0.0634 0.171 0.206 0.000428 0.000514 +Wall time: 67904.14257129002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.061 0.00263 0.00841 0.0453 0.0612 0.082 0.109 0.000205 0.000274 + 471 200 0.0581 0.00261 0.00604 0.0447 0.0609 0.067 0.0927 0.000167 0.000232 + 471 235 0.0524 0.00213 0.00986 0.0408 0.0551 0.107 0.118 0.000268 0.000296 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0394 0.00184 0.00248 0.038 0.0512 0.0498 0.0595 0.000124 0.000149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 471 68048.341 0.005 0.00258 0.0123 0.0639 0.0448 0.0606 0.105 0.132 0.000263 0.000331 +! Validation 471 68048.341 0.005 0.00266 0.0113 0.0644 0.0449 0.0615 0.0962 0.127 0.000241 0.000316 +Wall time: 68048.34092363901 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0681 0.00271 0.014 0.046 0.0621 0.11 0.141 0.000274 0.000353 + 472 200 0.0627 0.00279 0.00695 0.0462 0.063 0.0808 0.0995 0.000202 0.000249 + 472 235 0.0627 0.00245 0.0136 0.0437 0.0591 0.12 0.139 0.000301 0.000348 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0404 0.00183 0.00378 0.0377 0.051 0.0599 0.0734 0.00015 0.000183 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 472 68192.541 0.005 0.00259 0.021 0.0727 0.0449 0.0607 0.132 0.173 0.000331 0.000432 +! Validation 472 68192.541 0.005 0.00261 0.0214 0.0736 0.0446 0.061 0.142 0.174 0.000355 0.000436 +Wall time: 68192.54151186906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0563 0.00246 0.00706 0.0437 0.0592 0.0844 0.1 0.000211 0.000251 + 473 200 0.0563 0.00242 0.00797 0.0433 0.0587 0.0813 0.107 0.000203 0.000266 + 473 235 0.0708 0.00252 0.0205 0.0442 0.0599 0.133 0.171 0.000333 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0763 0.00178 0.0406 0.0374 0.0504 0.236 0.241 0.000589 0.000601 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 473 68336.951 0.005 0.00246 0.0154 0.0645 0.0436 0.0592 0.117 0.148 0.000293 0.00037 +! Validation 473 68336.951 0.005 0.00258 0.052 0.104 0.0443 0.0607 0.247 0.272 0.000617 0.00068 +Wall time: 68336.95123799378 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.064 0.00236 0.0168 0.0425 0.058 0.132 0.155 0.00033 0.000387 + 474 200 0.159 0.00273 0.105 0.0468 0.0623 0.363 0.386 0.000908 0.000965 + 474 235 0.0626 0.00271 0.00837 0.0459 0.0621 0.0817 0.109 0.000204 0.000273 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0541 0.00246 0.0049 0.0443 0.0592 0.0806 0.0835 0.000202 0.000209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 474 68481.203 0.005 0.00256 0.025 0.0761 0.0446 0.0603 0.143 0.189 0.000359 0.000472 +! Validation 474 68481.203 0.005 0.00327 0.0151 0.0804 0.0503 0.0682 0.119 0.147 0.000298 0.000366 +Wall time: 68481.20381666906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0608 0.00241 0.0126 0.0431 0.0586 0.115 0.134 0.000289 0.000335 + 475 200 0.0642 0.00247 0.0149 0.0442 0.0593 0.129 0.145 0.000322 0.000364 + 475 235 0.0558 0.00249 0.00598 0.0441 0.0596 0.0686 0.0923 0.000172 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0377 0.0018 0.0017 0.0375 0.0506 0.0479 0.0492 0.00012 0.000123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 475 68625.462 0.005 0.00253 0.0224 0.0729 0.0443 0.06 0.142 0.179 0.000355 0.000447 +! Validation 475 68625.462 0.005 0.00251 0.0208 0.071 0.0437 0.0598 0.137 0.172 0.000342 0.00043 +Wall time: 68625.46240947396 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.072 0.00276 0.0167 0.0469 0.0627 0.126 0.154 0.000315 0.000386 + 476 200 0.0855 0.00247 0.0361 0.0438 0.0593 0.215 0.227 0.000536 0.000567 + 476 235 0.0657 0.00274 0.0109 0.0467 0.0625 0.1 0.125 0.00025 0.000312 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0412 0.00187 0.00367 0.0386 0.0517 0.0585 0.0723 0.000146 0.000181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 476 68769.726 0.005 0.00253 0.0225 0.073 0.0443 0.06 0.142 0.179 0.000355 0.000447 +! Validation 476 68769.726 0.005 0.00264 0.0144 0.0672 0.045 0.0613 0.118 0.143 0.000295 0.000358 +Wall time: 68769.7262204988 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0647 0.00234 0.018 0.0425 0.0577 0.137 0.16 0.000342 0.0004 + 477 200 0.0761 0.00325 0.0111 0.0511 0.068 0.0994 0.126 0.000248 0.000315 + 477 235 0.358 0.0029 0.3 0.0484 0.0643 0.591 0.653 0.00148 0.00163 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0923 0.00273 0.0377 0.047 0.0624 0.219 0.232 0.000549 0.000579 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 477 68915.286 0.005 0.003 0.0371 0.097 0.0483 0.0653 0.178 0.228 0.000446 0.000569 +! Validation 477 68915.286 0.005 0.00357 0.0786 0.15 0.0528 0.0713 0.285 0.335 0.000713 0.000836 +Wall time: 68915.28634444671 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0621 0.00241 0.0138 0.0431 0.0586 0.108 0.14 0.000269 0.000351 + 478 200 0.0577 0.00251 0.00743 0.0441 0.0598 0.0774 0.103 0.000193 0.000257 + 478 235 0.0974 0.00245 0.0483 0.0444 0.0591 0.247 0.262 0.000618 0.000656 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.203 0.00489 0.105 0.064 0.0835 0.38 0.387 0.00095 0.000968 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 478 69059.543 0.005 0.00261 0.0321 0.0842 0.0451 0.061 0.168 0.214 0.000419 0.000534 +! Validation 478 69059.543 0.005 0.00546 0.373 0.482 0.0673 0.0882 0.653 0.729 0.00163 0.00182 +Wall time: 69059.54294471908 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0564 0.00249 0.00654 0.0441 0.0596 0.0781 0.0966 0.000195 0.000241 + 479 200 0.0576 0.00227 0.0122 0.0418 0.0569 0.11 0.132 0.000275 0.000329 + 479 235 0.0557 0.0022 0.0116 0.0419 0.056 0.115 0.129 0.000287 0.000321 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0539 0.00182 0.0176 0.038 0.0509 0.146 0.158 0.000365 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 479 69203.813 0.005 0.00301 0.0351 0.0954 0.048 0.0655 0.169 0.224 0.000423 0.00056 +! Validation 479 69203.813 0.005 0.00262 0.0174 0.0697 0.0448 0.061 0.134 0.158 0.000335 0.000394 +Wall time: 69203.81318051182 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0802 0.00212 0.0377 0.0407 0.055 0.208 0.232 0.00052 0.00058 + 480 200 0.0804 0.00232 0.0339 0.0422 0.0575 0.207 0.22 0.000517 0.00055 + 480 235 0.0859 0.003 0.0258 0.0493 0.0654 0.173 0.192 0.000432 0.000479 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.092 0.00217 0.0486 0.0416 0.0556 0.255 0.263 0.000637 0.000658 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 480 69347.964 0.005 0.00233 0.0141 0.0607 0.0425 0.0576 0.112 0.142 0.000279 0.000354 +! Validation 480 69347.964 0.005 0.0029 0.0994 0.157 0.0476 0.0643 0.34 0.376 0.000849 0.000941 +Wall time: 69347.96416059881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.346 0.00567 0.233 0.0661 0.0898 0.517 0.576 0.00129 0.00144 + 481 200 20.4 0.749 5.43 0.773 1.03 2.44 2.78 0.00609 0.00695 + 481 235 7.13 0.325 0.629 0.507 0.68 0.841 0.947 0.0021 0.00237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 10.2 0.312 4 0.491 0.667 2.35 2.39 0.00586 0.00597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 481 69492.158 0.005 0.37 11.7 19.1 0.398 0.726 1.81 4.09 0.00452 0.0102 +! Validation 481 69492.158 0.005 0.307 1.4 7.54 0.489 0.661 1.09 1.41 0.00271 0.00353 +Wall time: 69492.158293874 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 2.85 0.113 0.592 0.305 0.401 0.71 0.918 0.00178 0.0023 + 482 200 1.65 0.072 0.208 0.244 0.32 0.47 0.545 0.00117 0.00136 + 482 235 1.5 0.0642 0.211 0.23 0.302 0.444 0.548 0.00111 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 1.33 0.056 0.212 0.215 0.282 0.517 0.549 0.00129 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 482 69636.351 0.005 0.121 0.895 3.32 0.309 0.416 0.877 1.13 0.00219 0.00283 +! Validation 482 69636.351 0.005 0.0625 0.209 1.46 0.226 0.298 0.449 0.546 0.00112 0.00136 +Wall time: 69636.35155477002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.995 0.0446 0.103 0.191 0.252 0.294 0.384 0.000735 0.00096 + 483 200 0.761 0.0332 0.098 0.164 0.217 0.292 0.374 0.000731 0.000934 + 483 235 0.979 0.0301 0.378 0.156 0.207 0.64 0.733 0.0016 0.00183 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.691 0.0256 0.179 0.146 0.191 0.496 0.505 0.00124 0.00126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 483 69780.556 0.005 0.0439 0.4 1.28 0.188 0.25 0.606 0.755 0.00152 0.00189 +! Validation 483 69780.556 0.005 0.031 0.141 0.76 0.158 0.21 0.371 0.448 0.000928 0.00112 +Wall time: 69780.55679070204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.612 0.0247 0.117 0.141 0.188 0.32 0.409 0.0008 0.00102 + 484 200 0.48 0.0205 0.0704 0.129 0.171 0.25 0.317 0.000624 0.000792 + 484 235 0.392 0.019 0.0119 0.125 0.165 0.113 0.13 0.000281 0.000325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.367 0.0155 0.0576 0.113 0.148 0.285 0.286 0.000712 0.000716 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 484 69924.838 0.005 0.0242 0.315 0.799 0.14 0.186 0.532 0.671 0.00133 0.00168 +! Validation 484 69924.838 0.005 0.0193 0.0757 0.461 0.125 0.166 0.264 0.328 0.000661 0.000821 +Wall time: 69924.83884157473 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.381 0.0165 0.0506 0.116 0.153 0.214 0.268 0.000535 0.000671 + 485 200 0.394 0.0154 0.085 0.112 0.148 0.282 0.348 0.000706 0.00087 + 485 235 0.321 0.0139 0.0426 0.107 0.141 0.207 0.246 0.000517 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.236 0.0117 0.00203 0.098 0.129 0.0469 0.0538 0.000117 0.000134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 485 70069.435 0.005 0.0166 0.231 0.563 0.116 0.154 0.451 0.574 0.00113 0.00144 +! Validation 485 70069.435 0.005 0.0146 0.0528 0.345 0.109 0.144 0.218 0.274 0.000546 0.000686 +Wall time: 70069.43581336504 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.448 0.0129 0.19 0.103 0.136 0.441 0.52 0.0011 0.0013 + 486 200 0.315 0.0123 0.0683 0.0996 0.132 0.263 0.312 0.000656 0.00078 + 486 235 0.411 0.011 0.192 0.0947 0.125 0.478 0.523 0.0012 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.891 0.00929 0.705 0.0868 0.115 1 1 0.0025 0.00251 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 486 70213.859 0.005 0.0127 0.128 0.381 0.101 0.134 0.341 0.427 0.000851 0.00107 +! Validation 486 70213.859 0.005 0.0116 0.797 1.03 0.0969 0.129 1.04 1.07 0.0026 0.00266 +Wall time: 70213.85905050905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.252 0.0105 0.043 0.0924 0.122 0.19 0.248 0.000474 0.000619 + 487 200 0.248 0.00958 0.0561 0.0885 0.117 0.222 0.283 0.000555 0.000707 + 487 235 0.252 0.0106 0.0406 0.0919 0.123 0.186 0.24 0.000466 0.000601 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.18 0.00754 0.0295 0.0784 0.104 0.2 0.205 0.000499 0.000512 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 487 70358.137 0.005 0.0104 0.149 0.357 0.0919 0.122 0.368 0.461 0.00092 0.00115 +! Validation 487 70358.137 0.005 0.00966 0.0417 0.235 0.0881 0.117 0.196 0.244 0.00049 0.000609 +Wall time: 70358.13747901795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.265 0.00867 0.092 0.0839 0.111 0.31 0.362 0.000774 0.000905 + 488 200 0.275 0.00848 0.105 0.0828 0.11 0.355 0.388 0.000887 0.000969 + 488 235 0.223 0.00735 0.0762 0.0774 0.102 0.275 0.329 0.000687 0.000824 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.16 0.00635 0.0326 0.0721 0.0951 0.207 0.215 0.000517 0.000538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 488 70502.431 0.005 0.00869 0.126 0.3 0.084 0.111 0.345 0.424 0.000862 0.00106 +! Validation 488 70502.431 0.005 0.00838 0.0645 0.232 0.082 0.109 0.253 0.303 0.000633 0.000758 +Wall time: 70502.43097038288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.172 0.00753 0.0216 0.0782 0.104 0.141 0.176 0.000353 0.000439 + 489 200 0.166 0.00702 0.0255 0.0755 0.1 0.149 0.191 0.000372 0.000477 + 489 235 0.214 0.00699 0.0747 0.0753 0.0998 0.24 0.326 0.000599 0.000816 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.152 0.00565 0.0389 0.0678 0.0897 0.23 0.236 0.000574 0.000589 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 489 70646.937 0.005 0.00751 0.101 0.251 0.0779 0.103 0.302 0.379 0.000755 0.000948 +! Validation 489 70646.937 0.005 0.0074 0.195 0.343 0.0768 0.103 0.44 0.527 0.0011 0.00132 +Wall time: 70646.93766419403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.17 0.00659 0.038 0.0732 0.0969 0.2 0.233 0.0005 0.000582 + 490 200 0.259 0.00605 0.138 0.0698 0.0928 0.42 0.443 0.00105 0.00111 + 490 235 0.529 0.00719 0.385 0.076 0.101 0.72 0.741 0.0018 0.00185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.121 0.00535 0.0144 0.0659 0.0873 0.117 0.143 0.000292 0.000358 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 490 70791.164 0.005 0.00667 0.129 0.262 0.0734 0.0975 0.347 0.427 0.000867 0.00107 +! Validation 490 70791.164 0.005 0.00701 0.0498 0.19 0.0749 0.0999 0.217 0.266 0.000543 0.000666 +Wall time: 70791.16395996371 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.135 0.00595 0.0157 0.0691 0.0921 0.13 0.149 0.000326 0.000374 + 491 200 0.12 0.00521 0.0156 0.065 0.0862 0.121 0.149 0.000303 0.000373 + 491 235 0.188 0.00597 0.0689 0.0687 0.0922 0.276 0.313 0.00069 0.000783 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.198 0.00444 0.109 0.0601 0.0795 0.388 0.395 0.000971 0.000987 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 491 70935.395 0.005 0.00605 0.0801 0.201 0.0698 0.0929 0.267 0.338 0.000667 0.000845 +! Validation 491 70935.395 0.005 0.00591 0.139 0.257 0.0683 0.0917 0.411 0.445 0.00103 0.00111 +Wall time: 70935.39544789493 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.138 0.00564 0.025 0.0668 0.0896 0.156 0.189 0.000391 0.000472 + 492 200 0.187 0.00545 0.0776 0.0655 0.0881 0.295 0.332 0.000738 0.000831 + 492 235 0.118 0.00545 0.00941 0.0661 0.0881 0.098 0.116 0.000245 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0886 0.00415 0.00552 0.0576 0.0769 0.0644 0.0887 0.000161 0.000222 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 492 71079.622 0.005 0.00538 0.0734 0.181 0.0656 0.0876 0.257 0.324 0.000642 0.000809 +! Validation 492 71079.622 0.005 0.00554 0.0298 0.141 0.066 0.0888 0.164 0.206 0.000411 0.000515 +Wall time: 71079.6228546109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.194 0.005 0.0945 0.0632 0.0844 0.332 0.367 0.000829 0.000917 + 493 200 0.159 0.00454 0.0685 0.0604 0.0804 0.294 0.312 0.000736 0.000781 + 493 235 0.213 0.00485 0.116 0.0631 0.0831 0.354 0.406 0.000885 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.109 0.00373 0.0348 0.0546 0.0729 0.214 0.223 0.000534 0.000557 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 493 71223.909 0.005 0.00499 0.0731 0.173 0.0631 0.0843 0.261 0.322 0.000653 0.000806 +! Validation 493 71223.909 0.005 0.00504 0.0333 0.134 0.0629 0.0848 0.178 0.218 0.000445 0.000545 +Wall time: 71223.90901631303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.127 0.00476 0.0316 0.0617 0.0824 0.181 0.212 0.000453 0.000531 + 494 200 0.13 0.00428 0.0439 0.0584 0.0781 0.219 0.25 0.000549 0.000625 + 494 235 0.116 0.00434 0.0286 0.0584 0.0787 0.164 0.202 0.000411 0.000505 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0925 0.00342 0.0241 0.0521 0.0698 0.174 0.185 0.000436 0.000463 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 494 71368.063 0.005 0.00464 0.0658 0.159 0.0608 0.0813 0.246 0.306 0.000615 0.000766 +! Validation 494 71368.063 0.005 0.00466 0.0629 0.156 0.0604 0.0815 0.255 0.299 0.000638 0.000748 +Wall time: 71368.06302877888 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0915 0.0042 0.00754 0.0579 0.0773 0.0852 0.104 0.000213 0.000259 + 495 200 0.24 0.00443 0.151 0.0593 0.0794 0.449 0.464 0.00112 0.00116 + 495 235 0.119 0.00415 0.0363 0.0574 0.0769 0.191 0.227 0.000477 0.000569 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.093 0.00328 0.0274 0.051 0.0684 0.186 0.198 0.000466 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 495 71512.212 0.005 0.00441 0.0874 0.176 0.0592 0.0793 0.285 0.353 0.000712 0.000883 +! Validation 495 71512.212 0.005 0.00444 0.0405 0.129 0.0589 0.0796 0.206 0.24 0.000515 0.0006 +Wall time: 71512.21250345279 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.107 0.00426 0.0218 0.0578 0.0779 0.149 0.176 0.000374 0.00044 + 496 200 0.114 0.00414 0.0306 0.0574 0.0768 0.164 0.209 0.000411 0.000522 + 496 235 0.125 0.00388 0.0471 0.0551 0.0744 0.246 0.259 0.000615 0.000647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.145 0.00309 0.0833 0.0494 0.0663 0.341 0.344 0.000852 0.000861 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 496 71656.377 0.005 0.00409 0.046 0.128 0.0569 0.0763 0.204 0.256 0.00051 0.00064 +! Validation 496 71656.377 0.005 0.00415 0.0951 0.178 0.0568 0.0769 0.343 0.368 0.000857 0.00092 +Wall time: 71656.37764628977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.1 0.00375 0.0254 0.0544 0.0731 0.166 0.19 0.000415 0.000475 + 497 200 0.119 0.00365 0.0457 0.0539 0.0721 0.234 0.255 0.000584 0.000638 + 497 235 0.0971 0.0036 0.0251 0.0536 0.0716 0.149 0.189 0.000372 0.000473 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0634 0.00291 0.00532 0.0477 0.0643 0.0696 0.087 0.000174 0.000218 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 497 71800.531 0.005 0.00389 0.0519 0.13 0.0555 0.0744 0.215 0.272 0.000537 0.00068 +! Validation 497 71800.531 0.005 0.00393 0.0209 0.0995 0.0552 0.0748 0.137 0.173 0.000341 0.000431 +Wall time: 71800.53124801302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.244 0.00361 0.172 0.0538 0.0717 0.479 0.495 0.0012 0.00124 + 498 200 0.143 0.0038 0.0673 0.0546 0.0736 0.276 0.31 0.00069 0.000774 + 498 235 0.112 0.00413 0.0289 0.0565 0.0767 0.191 0.203 0.000478 0.000507 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0997 0.00298 0.0401 0.0486 0.0652 0.234 0.239 0.000584 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 498 71944.723 0.005 0.00372 0.0669 0.141 0.0542 0.0728 0.249 0.309 0.000623 0.000772 +! Validation 498 71944.723 0.005 0.004 0.0524 0.132 0.0559 0.0755 0.242 0.273 0.000604 0.000683 +Wall time: 71944.72359607369 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0874 0.00357 0.016 0.0528 0.0713 0.119 0.151 0.000297 0.000377 + 499 200 0.0878 0.00345 0.0187 0.0522 0.0701 0.132 0.163 0.000329 0.000408 + 499 235 0.0772 0.00309 0.0155 0.0497 0.0663 0.118 0.149 0.000296 0.000372 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0549 0.00264 0.00201 0.0454 0.0614 0.0399 0.0535 9.98e-05 0.000134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 499 72088.960 0.005 0.00354 0.03 0.101 0.0528 0.0711 0.164 0.207 0.000411 0.000517 +! Validation 499 72088.960 0.005 0.00358 0.0118 0.0835 0.0525 0.0714 0.101 0.13 0.000253 0.000325 +Wall time: 72088.95992531907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.0714 0.00332 0.00496 0.0514 0.0688 0.0673 0.0841 0.000168 0.00021 + 500 200 0.0809 0.00326 0.0156 0.0509 0.0682 0.118 0.149 0.000296 0.000372 + 500 235 0.0932 0.00345 0.0242 0.0524 0.0701 0.163 0.186 0.000407 0.000464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.0555 0.00264 0.00275 0.0455 0.0613 0.0407 0.0626 0.000102 0.000156 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 500 72233.116 0.005 0.0035 0.064 0.134 0.0525 0.0706 0.219 0.302 0.000547 0.000755 +! Validation 500 72233.116 0.005 0.0035 0.0136 0.0837 0.052 0.0706 0.107 0.139 0.000268 0.000349 +Wall time: 72233.11625261512 +! Stop training: max epochs +Wall time: 72233.15702054976 +Cumulative wall time: 72233.15702054976 +Using device: cuda +Please note that _all_ machine learning models running on CUDA hardware are generally somewhat nondeterministic and that this can manifest in small, generally unimportant variation in the final test errors. +Loading model... + loaded model +Loading dataset... +Processing dataset... +Done! +Loaded dataset specified in test_config.yaml. +Using all frames from the specified test dataset, yielding a test set size of 500 frames. +Starting... + +--- Final result: --- + f_mae = 0.044299 + f_rmse = 0.059587 + e_mae = 0.091577 + e_rmse = 0.118932 + e/N_mae = 0.000229 + e/N_rmse = 0.000297 + f_mae = 0.044299 + f_rmse = 0.059587 + e_mae = 0.091577 + e_rmse = 0.118932 + e/N_mae = 0.000229 + e/N_rmse = 0.000297 +Train end time: 2024-12-09_06:42:25 +Training duration: 20h 8m 19s diff --git a/ads_16_length_8000/ALLEGRO/test_config.yaml b/ads_16_length_8000/ALLEGRO/test_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..884dc8444702d34a5c56eff240e10413d17e885c --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/test_config.yaml @@ -0,0 +1,63 @@ +default_dtype: float64 +dataset_file_name: ../testset.xyz +n_train: 0 +n_val: 500 + +root: ./results/ +run_name: adsorption +seed: 123 +dataset_seed: 456 +append: true +model_builders: + - allegro.model.Allegro + - PerSpeciesRescale + - StressForceOutput + - RescaleEnergyEtc +r_max: 5.0 +l_max: 2 +parity: o3_full # allowed: o3_full, o3_restricted, so3 +num_layers: 2 +env_embed_multiplicity: 16 +two_body_latent_mlp_latent_dimensions: [128, 256, 512] +two_body_latent_mlp_nonlinearity: silu +latent_mlp_latent_dimensions: [512, 512] +latent_mlp_nonlinearity: silu +latent_resnet: true +env_embed_mlp_latent_dimensions: [] +env_embed_mlp_nonlinearity: null +edge_eng_mlp_latent_dimensions: [128] +edge_eng_mlp_nonlinearity: null + +dataset: ase +dataset_key_mapping: + force: forces +ase_args: + format: extxyz +chemical_symbols: + - H + - C + - N + - Zn + - O + +metrics_components: + - - forces + - mae + - PerSpecies: False + report_per_component: False + - - total_energy + - mae + - PerAtom: False + - - forces + - rmse + - PerSpecies: False + report_per_component: False + - - total_energy + - rmse + - PerAtom: False + - - total_energy + - mae + - PerAtom: True + - - total_energy + - rmse + - PerAtom: True \ No newline at end of file diff --git a/ads_16_length_8000/ALLEGRO/train_config.yaml b/ads_16_length_8000/ALLEGRO/train_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b8099ccdbab280ea644a4795384ae20a36e7779d --- /dev/null +++ b/ads_16_length_8000/ALLEGRO/train_config.yaml @@ -0,0 +1,78 @@ +default_dtype: float64 +dataset_file_name: ../trainset.xyz +n_train: 7500 +n_val: 500 +batch_size: 32 +max_epochs: 500 +train_val_split: sequential + +root: ./results/ +run_name: adsorption +seed: 123 +dataset_seed: 456 +append: true +model_builders: + - allegro.model.Allegro + - PerSpeciesRescale + - StressForceOutput + - RescaleEnergyEtc +r_max: 5.0 +l_max: 2 +parity: o3_full # allowed: o3_full, o3_restricted, so3 +num_layers: 2 +env_embed_multiplicity: 16 +two_body_latent_mlp_latent_dimensions: [128, 256, 512] +two_body_latent_mlp_nonlinearity: silu +latent_mlp_latent_dimensions: [512, 512] +latent_mlp_nonlinearity: silu +latent_resnet: true +env_embed_mlp_latent_dimensions: [] +env_embed_mlp_nonlinearity: null +edge_eng_mlp_latent_dimensions: [128] +edge_eng_mlp_nonlinearity: null + +dataset: ase +dataset_key_mapping: + force: forces +ase_args: + format: extxyz +chemical_symbols: + - H + - C + - N + - Zn + - O + + +wandb: false +learning_rate: 0.005 + +loss_coeffs: + forces: + - 20 + total_energy: + - 1 + +metrics_components: + - - forces + - mae + - PerSpecies: False + report_per_component: False + - - total_energy + - mae + - PerAtom: False + - - forces + - rmse + - PerSpecies: False + report_per_component: False + - - total_energy + - rmse + - PerAtom: False + - - total_energy + - mae + - PerAtom: True + - - total_energy + - rmse + - PerAtom: True + +optimizer_name: Adam \ No newline at end of file diff --git a/ads_16_length_8000/MACE/logs/MACE_model_run-123.log b/ads_16_length_8000/MACE/logs/MACE_model_run-123.log new file mode 100644 index 0000000000000000000000000000000000000000..4179747b5a388563f6dd92d73f8b7a6e031e8c64 --- /dev/null +++ b/ads_16_length_8000/MACE/logs/MACE_model_run-123.log @@ -0,0 +1,590 @@ +2024-12-08 10:27:04.088 INFO: ===========VERIFYING SETTINGS=========== +2024-12-08 10:27:04.088 INFO: MACE version: 0.3.9 +2024-12-08 10:27:04.251 INFO: CUDA version: 12.4, CUDA device: 0 +2024-12-08 10:27:04.360 INFO: ===========LOADING INPUT DATA=========== +2024-12-08 10:27:04.361 INFO: Using heads: ['default'] +2024-12-08 10:27:04.361 INFO: ============= Processing head default =========== +2024-12-08 10:27:14.213 INFO: Training set [8000 configs, 0 energy, 9600000 forces] loaded from '../trainset.xyz' +2024-12-08 10:27:14.216 INFO: Using random 6% of training set for validation with indices saved in: ./valid_indices_123.txt +2024-12-08 10:27:14.217 INFO: Validaton set contains 500 configurations [0 energy, 600000 forces] +2024-12-08 10:27:14.834 INFO: Test set (500 configs) loaded from '../testset.xyz': +2024-12-08 10:27:14.834 INFO: Default_Default: 500 configs, 0 energy, 600000 forces +2024-12-08 10:27:14.837 INFO: Total number of configurations: train=7500, valid=500, tests=[Default_Default: 500], +2024-12-08 10:27:15.150 INFO: Atomic Numbers used: [1, 6, 7, 8, 30] +2024-12-08 10:27:15.150 INFO: Isolated Atomic Energies (E0s) not in training file, using command line argument +2024-12-08 10:27:15.151 INFO: Atomic Energies used (z: eV) for head default: {1: -13.663181292231226, 6: -1029.2809654211628, 7: -1484.1187695035828, 8: -2042.0330099956639, 30: -500.123456789} +2024-12-08 10:30:32.377 INFO: Computing average number of neighbors +2024-12-08 10:30:57.864 INFO: Average number of neighbors: 34.390905333333336 +2024-12-08 10:30:57.866 INFO: During training the following quantities will be reported: energy, forces +2024-12-08 10:30:57.866 INFO: ===========MODEL DETAILS=========== +2024-12-08 10:31:09.971 WARNING: Standard deviation of the scaling is zero, Changing to no scaling +2024-12-08 10:31:09.973 INFO: Building model +2024-12-08 10:31:09.973 INFO: Message passing with 128 channels and max_L=1 (128x0e + 128x1o) +2024-12-08 10:31:09.973 INFO: 2 layers, each with correlation order: 3 (body order: 4) and spherical harmonics up to: l=3 +2024-12-08 10:31:09.974 INFO: 8 radial and 5 basis functions +2024-12-08 10:31:09.974 INFO: Radial cutoff: 5.0 A (total receptive field for each atom: 10.0 A) +2024-12-08 10:31:09.974 INFO: Distance transform for radial basis functions: None +2024-12-08 10:31:09.974 INFO: Hidden irreps: 128x0e + 128x1o +2024-12-08 10:31:11.311 INFO: Total number of parameters: 945168 +2024-12-08 10:31:11.311 INFO: +2024-12-08 10:31:11.312 INFO: ===========OPTIMIZER INFORMATION=========== +2024-12-08 10:31:11.312 INFO: Using ADAM as parameter optimizer +2024-12-08 10:31:11.312 INFO: Batch size: 10 +2024-12-08 10:31:11.312 INFO: Using Exponential Moving Average with decay: 0.99 +2024-12-08 10:31:11.312 INFO: Number of gradient updates: 375000 +2024-12-08 10:31:11.312 INFO: Learning rate: 0.0001, weight decay: 5e-07 +2024-12-08 10:31:11.312 INFO: WeightedEnergyForcesLoss(energy_weight=1.000, forces_weight=100.000) +2024-12-08 10:31:11.313 INFO: Stage Two (after 375 epochs) with loss function: WeightedEnergyForcesLoss(energy_weight=1000.000, forces_weight=100.000), with energy weight : 1000.0, forces weight : 100.0 and learning rate : 0.001 +2024-12-08 10:31:11.411 WARNING: Cannot find checkpoint with tag 'MACE_model_run-123' in './checkpoints' +2024-12-08 10:31:11.416 INFO: Using gradient clipping with tolerance=10.000 +2024-12-08 10:31:11.416 INFO: +2024-12-08 10:31:11.416 INFO: ===========TRAINING=========== +2024-12-08 10:31:11.416 INFO: Started training, reporting errors on validation set +2024-12-08 10:31:11.416 INFO: Loss metrics on validation set +2024-12-08 10:31:30.817 INFO: Initial: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.59 meV, RMSE_F= 8.00 meV / A +2024-12-08 10:43:28.626 INFO: Epoch 0: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.81 meV, RMSE_F= 6.93 meV / A +2024-12-08 10:55:24.333 INFO: Epoch 1: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.04 meV, RMSE_F= 5.86 meV / A +2024-12-08 11:07:20.041 INFO: Epoch 2: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.25 meV, RMSE_F= 4.86 meV / A +2024-12-08 11:19:16.210 INFO: Epoch 3: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.43 meV, RMSE_F= 3.98 meV / A +2024-12-08 11:31:11.991 INFO: Epoch 4: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.58 meV, RMSE_F= 3.25 meV / A +2024-12-08 11:43:06.856 INFO: Epoch 5: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.70 meV, RMSE_F= 2.65 meV / A +2024-12-08 11:55:01.165 INFO: Epoch 6: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.80 meV, RMSE_F= 2.16 meV / A +2024-12-08 12:06:56.362 INFO: Epoch 7: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.87 meV, RMSE_F= 1.76 meV / A +2024-12-08 12:18:50.358 INFO: Epoch 8: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.93 meV, RMSE_F= 1.44 meV / A +2024-12-08 12:30:46.020 INFO: Epoch 9: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.97 meV, RMSE_F= 1.18 meV / A +2024-12-08 12:42:40.820 INFO: Epoch 10: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.00 meV, RMSE_F= 0.96 meV / A +2024-12-08 12:54:35.332 INFO: Epoch 11: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.03 meV, RMSE_F= 0.79 meV / A +2024-12-08 13:06:30.753 INFO: Epoch 12: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.05 meV, RMSE_F= 0.65 meV / A +2024-12-08 13:18:26.112 INFO: Epoch 13: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.06 meV, RMSE_F= 0.53 meV / A +2024-12-08 13:30:20.810 INFO: Epoch 14: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.07 meV, RMSE_F= 0.44 meV / A +2024-12-08 13:42:15.196 INFO: Epoch 15: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.36 meV / A +2024-12-08 13:54:09.819 INFO: Epoch 16: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.30 meV / A +2024-12-08 14:06:05.537 INFO: Epoch 17: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.25 meV / A +2024-12-08 14:18:01.007 INFO: Epoch 18: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.21 meV / A +2024-12-08 14:29:56.653 INFO: Epoch 19: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.17 meV / A +2024-12-08 14:41:52.358 INFO: Epoch 20: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.14 meV / A +2024-12-08 14:53:48.513 INFO: Epoch 21: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.12 meV / A +2024-12-08 15:05:43.359 INFO: Epoch 22: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.10 meV / A +2024-12-08 15:17:38.974 INFO: Epoch 23: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.08 meV / A +2024-12-08 15:29:34.686 INFO: Epoch 24: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.07 meV / A +2024-12-08 15:41:29.597 INFO: Epoch 25: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.06 meV / A +2024-12-08 15:53:25.294 INFO: Epoch 26: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.05 meV / A +2024-12-08 16:05:20.961 INFO: Epoch 27: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:17:16.607 INFO: Epoch 28: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:29:12.261 INFO: Epoch 29: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:41:07.925 INFO: Epoch 30: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:53:02.602 INFO: Epoch 31: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 17:04:57.046 INFO: Epoch 32: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:16:51.740 INFO: Epoch 33: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:28:46.123 INFO: Epoch 34: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:40:42.071 INFO: Epoch 35: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:52:37.361 INFO: Epoch 36: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 18:04:33.026 INFO: Epoch 37: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:16:28.769 INFO: Epoch 38: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:28:23.730 INFO: Epoch 39: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:40:18.114 INFO: Epoch 40: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:52:13.648 INFO: Epoch 41: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:04:09.270 INFO: Epoch 42: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:16:05.154 INFO: Epoch 43: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:27:59.796 INFO: Epoch 44: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:39:55.525 INFO: Epoch 45: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:51:51.177 INFO: Epoch 46: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:03:45.570 INFO: Epoch 47: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:15:41.084 INFO: Epoch 48: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:27:36.946 INFO: Epoch 49: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:39:32.505 INFO: Epoch 50: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:51:28.227 INFO: Epoch 51: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:03:23.063 INFO: Epoch 52: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:15:18.938 INFO: Epoch 53: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:27:15.045 INFO: Epoch 54: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:39:09.591 INFO: Epoch 55: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:51:05.492 INFO: Epoch 56: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:02:59.714 INFO: Epoch 57: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:14:55.585 INFO: Epoch 58: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:26:51.102 INFO: Epoch 59: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:38:45.753 INFO: Epoch 60: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:50:41.405 INFO: Epoch 61: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:02:37.139 INFO: Epoch 62: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:14:32.989 INFO: Epoch 63: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:26:28.808 INFO: Epoch 64: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:38:23.213 INFO: Epoch 65: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:50:19.277 INFO: Epoch 66: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:02:15.317 INFO: Epoch 67: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:14:11.434 INFO: Epoch 68: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:26:07.195 INFO: Epoch 69: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:38:01.588 INFO: Epoch 70: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:49:55.998 INFO: Epoch 71: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:01:50.464 INFO: Epoch 72: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:13:44.950 INFO: Epoch 73: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:25:39.761 INFO: Epoch 74: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:37:34.518 INFO: Epoch 75: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:49:29.934 INFO: Epoch 76: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:01:24.386 INFO: Epoch 77: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:13:18.984 INFO: Epoch 78: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:25:13.768 INFO: Epoch 79: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:37:08.653 INFO: Epoch 80: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:49:04.292 INFO: Epoch 81: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:01:00.252 INFO: Epoch 82: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:12:54.966 INFO: Epoch 83: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:24:50.005 INFO: Epoch 84: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:36:44.612 INFO: Epoch 85: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:48:39.873 INFO: Epoch 86: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:00:34.728 INFO: Epoch 87: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:12:30.321 INFO: Epoch 88: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:24:25.177 INFO: Epoch 89: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:36:19.942 INFO: Epoch 90: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:48:15.858 INFO: Epoch 91: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:00:10.664 INFO: Epoch 92: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:12:05.266 INFO: Epoch 93: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:24:01.304 INFO: Epoch 94: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:35:57.308 INFO: Epoch 95: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:47:52.114 INFO: Epoch 96: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:59:48.009 INFO: Epoch 97: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:11:43.926 INFO: Epoch 98: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:23:39.755 INFO: Epoch 99: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:35:35.255 INFO: Epoch 100: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:47:31.170 INFO: Epoch 101: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:59:27.024 INFO: Epoch 102: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:11:23.070 INFO: Epoch 103: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:23:17.336 INFO: Epoch 104: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:35:13.214 INFO: Epoch 105: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:47:09.766 INFO: Epoch 106: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:59:05.856 INFO: Epoch 107: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:11:02.067 INFO: Epoch 108: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:22:58.239 INFO: Epoch 109: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:34:54.232 INFO: Epoch 110: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:46:48.791 INFO: Epoch 111: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:58:44.589 INFO: Epoch 112: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:10:40.363 INFO: Epoch 113: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:22:36.216 INFO: Epoch 114: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:34:32.085 INFO: Epoch 115: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:46:27.706 INFO: Epoch 116: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:58:23.697 INFO: Epoch 117: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:10:19.309 INFO: Epoch 118: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:22:13.647 INFO: Epoch 119: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:34:09.444 INFO: Epoch 120: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:46:04.236 INFO: Epoch 121: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:58:00.456 INFO: Epoch 122: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:09:56.544 INFO: Epoch 123: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:21:52.460 INFO: Epoch 124: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:33:48.452 INFO: Epoch 125: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:45:43.100 INFO: Epoch 126: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:57:39.277 INFO: Epoch 127: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:09:35.273 INFO: Epoch 128: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:21:31.020 INFO: Epoch 129: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:33:26.694 INFO: Epoch 130: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:45:21.532 INFO: Epoch 131: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:57:16.909 INFO: Epoch 132: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:09:11.244 INFO: Epoch 133: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:21:07.247 INFO: Epoch 134: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:33:02.393 INFO: Epoch 135: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:44:56.950 INFO: Epoch 136: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:56:51.236 INFO: Epoch 137: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:08:46.769 INFO: Epoch 138: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:20:42.559 INFO: Epoch 139: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:32:38.679 INFO: Epoch 140: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:44:33.998 INFO: Epoch 141: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:56:29.747 INFO: Epoch 142: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:08:24.058 INFO: Epoch 143: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:20:19.888 INFO: Epoch 144: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:32:14.869 INFO: Epoch 145: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:44:10.713 INFO: Epoch 146: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:56:06.378 INFO: Epoch 147: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:08:02.365 INFO: Epoch 148: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:19:58.283 INFO: Epoch 149: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:31:52.524 INFO: Epoch 150: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:43:46.959 INFO: Epoch 151: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:55:41.448 INFO: Epoch 152: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:07:36.941 INFO: Epoch 153: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:19:32.118 INFO: Epoch 154: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:31:26.638 INFO: Epoch 155: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:43:21.528 INFO: Epoch 156: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:55:17.216 INFO: Epoch 157: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:07:13.149 INFO: Epoch 158: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:19:09.055 INFO: Epoch 159: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:31:05.099 INFO: Epoch 160: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:43:00.978 INFO: Epoch 161: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:54:57.010 INFO: Epoch 162: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:06:50.934 INFO: Epoch 163: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:18:46.637 INFO: Epoch 164: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:30:42.231 INFO: Epoch 165: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:42:37.914 INFO: Epoch 166: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:54:32.246 INFO: Epoch 167: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:06:27.867 INFO: Epoch 168: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:18:23.413 INFO: Epoch 169: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:30:18.183 INFO: Epoch 170: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:42:12.599 INFO: Epoch 171: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:54:06.951 INFO: Epoch 172: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:06:01.673 INFO: Epoch 173: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:17:55.924 INFO: Epoch 174: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:29:50.044 INFO: Epoch 175: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:41:45.049 INFO: Epoch 176: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:53:40.899 INFO: Epoch 177: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:05:35.776 INFO: Epoch 178: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:17:31.020 INFO: Epoch 179: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:29:25.430 INFO: Epoch 180: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:41:19.621 INFO: Epoch 181: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:53:14.971 INFO: Epoch 182: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:05:09.267 INFO: Epoch 183: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:17:05.126 INFO: Epoch 184: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:28:59.637 INFO: Epoch 185: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:40:55.474 INFO: Epoch 186: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:52:51.380 INFO: Epoch 187: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:04:47.228 INFO: Epoch 188: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:16:42.851 INFO: Epoch 189: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:28:38.552 INFO: Epoch 190: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:40:34.291 INFO: Epoch 191: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:52:30.056 INFO: Epoch 192: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:04:25.410 INFO: Epoch 193: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:16:19.643 INFO: Epoch 194: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:28:14.878 INFO: Epoch 195: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:40:10.368 INFO: Epoch 196: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:52:05.628 INFO: Epoch 197: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:04:00.778 INFO: Epoch 198: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:15:56.390 INFO: Epoch 199: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:27:52.148 INFO: Epoch 200: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:39:47.606 INFO: Epoch 201: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:51:41.996 INFO: Epoch 202: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:03:37.665 INFO: Epoch 203: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:15:31.950 INFO: Epoch 204: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:27:26.433 INFO: Epoch 205: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:39:22.356 INFO: Epoch 206: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:51:18.392 INFO: Epoch 207: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:03:14.075 INFO: Epoch 208: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:15:10.049 INFO: Epoch 209: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:27:04.899 INFO: Epoch 210: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:39:00.783 INFO: Epoch 211: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:50:55.489 INFO: Epoch 212: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:02:49.689 INFO: Epoch 213: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:14:45.240 INFO: Epoch 214: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:26:39.838 INFO: Epoch 215: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:38:35.501 INFO: Epoch 216: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:50:30.052 INFO: Epoch 217: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:02:24.517 INFO: Epoch 218: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:14:18.570 INFO: Epoch 219: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:26:13.901 INFO: Epoch 220: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:38:08.213 INFO: Epoch 221: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:50:02.661 INFO: Epoch 222: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:01:58.533 INFO: Epoch 223: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:13:54.322 INFO: Epoch 224: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:25:48.965 INFO: Epoch 225: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:37:43.391 INFO: Epoch 226: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:49:37.894 INFO: Epoch 227: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:01:33.418 INFO: Epoch 228: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:13:27.632 INFO: Epoch 229: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:25:22.885 INFO: Epoch 230: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:37:18.015 INFO: Epoch 231: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:49:12.793 INFO: Epoch 232: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:01:07.307 INFO: Epoch 233: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:13:03.175 INFO: Epoch 234: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:24:59.223 INFO: Epoch 235: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:36:54.239 INFO: Epoch 236: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:48:50.157 INFO: Epoch 237: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:00:44.812 INFO: Epoch 238: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:12:40.580 INFO: Epoch 239: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:24:36.577 INFO: Epoch 240: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:36:31.786 INFO: Epoch 241: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:48:27.805 INFO: Epoch 242: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:00:23.771 INFO: Epoch 243: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:12:19.466 INFO: Epoch 244: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:24:14.560 INFO: Epoch 245: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:36:09.015 INFO: Epoch 246: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:48:03.884 INFO: Epoch 247: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:59:59.919 INFO: Epoch 248: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:11:55.799 INFO: Epoch 249: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:23:51.018 INFO: Epoch 250: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:35:46.975 INFO: Epoch 251: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:47:42.845 INFO: Epoch 252: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:59:37.534 INFO: Epoch 253: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:11:32.204 INFO: Epoch 254: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:23:28.131 INFO: Epoch 255: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:35:23.078 INFO: Epoch 256: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:47:19.199 INFO: Epoch 257: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:59:15.363 INFO: Epoch 258: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:11:11.490 INFO: Epoch 259: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:23:05.967 INFO: Epoch 260: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:35:00.533 INFO: Epoch 261: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:46:54.970 INFO: Epoch 262: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:58:49.732 INFO: Epoch 263: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:10:45.906 INFO: Epoch 264: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:22:42.249 INFO: Epoch 265: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:34:38.257 INFO: Epoch 266: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:46:34.251 INFO: Epoch 267: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:58:29.051 INFO: Epoch 268: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:10:25.133 INFO: Epoch 269: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:22:20.231 INFO: Epoch 270: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:34:15.149 INFO: Epoch 271: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:46:10.635 INFO: Epoch 272: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:58:05.279 INFO: Epoch 273: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:10:01.505 INFO: Epoch 274: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:21:57.648 INFO: Epoch 275: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:33:53.978 INFO: Epoch 276: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:45:49.756 INFO: Epoch 277: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:57:44.828 INFO: Epoch 278: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:09:40.943 INFO: Epoch 279: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:21:36.478 INFO: Epoch 280: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:33:32.323 INFO: Epoch 281: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:45:28.483 INFO: Epoch 282: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:57:24.715 INFO: Epoch 283: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:09:20.895 INFO: Epoch 284: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:21:17.034 INFO: Epoch 285: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:33:13.103 INFO: Epoch 286: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:45:08.280 INFO: Epoch 287: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:57:04.237 INFO: Epoch 288: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:08:59.195 INFO: Epoch 289: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:20:54.913 INFO: Epoch 290: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:32:50.979 INFO: Epoch 291: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:44:46.484 INFO: Epoch 292: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:56:41.983 INFO: Epoch 293: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:08:37.460 INFO: Epoch 294: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:20:33.870 INFO: Epoch 295: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:32:29.680 INFO: Epoch 296: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:44:25.832 INFO: Epoch 297: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:56:20.853 INFO: Epoch 298: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:08:16.750 INFO: Epoch 299: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:20:12.433 INFO: Epoch 300: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:32:08.522 INFO: Epoch 301: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:44:03.243 INFO: Epoch 302: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:55:58.890 INFO: Epoch 303: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:07:54.926 INFO: Epoch 304: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:19:50.900 INFO: Epoch 305: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:31:45.740 INFO: Epoch 306: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:43:41.048 INFO: Epoch 307: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:55:36.787 INFO: Epoch 308: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:07:31.244 INFO: Epoch 309: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:19:27.111 INFO: Epoch 310: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:31:21.678 INFO: Epoch 311: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:43:17.745 INFO: Epoch 312: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:55:12.267 INFO: Epoch 313: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:07:07.731 INFO: Epoch 314: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:19:03.615 INFO: Epoch 315: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:30:58.174 INFO: Epoch 316: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:42:52.805 INFO: Epoch 317: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:54:48.978 INFO: Epoch 318: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:06:43.606 INFO: Epoch 319: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:18:39.721 INFO: Epoch 320: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:30:34.259 INFO: Epoch 321: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:42:29.325 INFO: Epoch 322: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:54:24.772 INFO: Epoch 323: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:06:20.497 INFO: Epoch 324: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:18:15.937 INFO: Epoch 325: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:30:11.892 INFO: Epoch 326: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:42:07.172 INFO: Epoch 327: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:54:02.531 INFO: Epoch 328: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:05:58.271 INFO: Epoch 329: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:17:52.760 INFO: Epoch 330: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:29:47.873 INFO: Epoch 331: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:41:42.319 INFO: Epoch 332: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:53:37.290 INFO: Epoch 333: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:05:33.474 INFO: Epoch 334: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:17:29.446 INFO: Epoch 335: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:29:25.437 INFO: Epoch 336: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:41:21.785 INFO: Epoch 337: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:53:17.973 INFO: Epoch 338: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:05:12.479 INFO: Epoch 339: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:17:06.952 INFO: Epoch 340: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:29:02.964 INFO: Epoch 341: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:40:57.533 INFO: Epoch 342: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:52:52.026 INFO: Epoch 343: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:04:47.944 INFO: Epoch 344: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:16:44.328 INFO: Epoch 345: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:28:40.385 INFO: Epoch 346: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:40:36.110 INFO: Epoch 347: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:52:32.080 INFO: Epoch 348: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:04:28.075 INFO: Epoch 349: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:16:24.417 INFO: Epoch 350: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:28:20.381 INFO: Epoch 351: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:40:15.843 INFO: Epoch 352: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:52:10.309 INFO: Epoch 353: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:04:05.831 INFO: Epoch 354: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:16:00.698 INFO: Epoch 355: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:27:56.307 INFO: Epoch 356: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:39:51.849 INFO: Epoch 357: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:51:47.267 INFO: Epoch 358: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:03:42.795 INFO: Epoch 359: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:15:38.943 INFO: Epoch 360: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:27:35.120 INFO: Epoch 361: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:39:31.186 INFO: Epoch 362: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:51:25.968 INFO: Epoch 363: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:03:21.984 INFO: Epoch 364: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:15:18.545 INFO: Epoch 365: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:27:13.772 INFO: Epoch 366: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:39:09.617 INFO: Epoch 367: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:51:05.646 INFO: Epoch 368: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:03:00.493 INFO: Epoch 369: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:14:54.868 INFO: Epoch 370: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:26:50.754 INFO: Epoch 371: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:38:46.519 INFO: Epoch 372: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:50:43.315 INFO: Epoch 373: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.939 INFO: Epoch 374: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.940 INFO: Changing loss based on Stage Two Weights +2024-12-11 13:14:33.194 INFO: Epoch 375: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:26:29.020 INFO: Epoch 376: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:38:23.794 INFO: Epoch 377: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:50:19.535 INFO: Epoch 378: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:02:15.204 INFO: Epoch 379: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:14:10.327 INFO: Epoch 380: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:26:05.510 INFO: Epoch 381: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:38:01.439 INFO: Epoch 382: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:49:56.048 INFO: Epoch 383: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:01:51.347 INFO: Epoch 384: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:13:45.633 INFO: Epoch 385: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:25:40.499 INFO: Epoch 386: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:37:36.110 INFO: Epoch 387: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:49:30.718 INFO: Epoch 388: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:01:25.225 INFO: Epoch 389: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:13:20.954 INFO: Epoch 390: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:25:16.513 INFO: Epoch 391: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:37:11.872 INFO: Epoch 392: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:49:07.412 INFO: Epoch 393: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:01:03.197 INFO: Epoch 394: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:12:58.369 INFO: Epoch 395: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:24:54.031 INFO: Epoch 396: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:36:48.891 INFO: Epoch 397: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:48:44.528 INFO: Epoch 398: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:00:39.247 INFO: Epoch 399: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:12:34.874 INFO: Epoch 400: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:24:30.006 INFO: Epoch 401: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:36:24.850 INFO: Epoch 402: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:48:19.214 INFO: Epoch 403: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:00:13.313 INFO: Epoch 404: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:12:09.002 INFO: Epoch 405: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:24:03.380 INFO: Epoch 406: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:35:58.179 INFO: Epoch 407: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:47:52.307 INFO: Epoch 408: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:59:47.012 INFO: Epoch 409: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:11:41.308 INFO: Epoch 410: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:23:36.968 INFO: Epoch 411: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:35:31.946 INFO: Epoch 412: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:47:27.753 INFO: Epoch 413: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:59:23.042 INFO: Epoch 414: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:11:18.783 INFO: Epoch 415: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:23:13.289 INFO: Epoch 416: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:35:09.165 INFO: Epoch 417: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:47:04.044 INFO: Epoch 418: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:59:00.084 INFO: Epoch 419: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:10:54.856 INFO: Epoch 420: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:22:49.777 INFO: Epoch 421: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:34:45.349 INFO: Epoch 422: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:46:41.212 INFO: Epoch 423: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:58:36.201 INFO: Epoch 424: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:10:30.588 INFO: Epoch 425: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:22:26.463 INFO: Epoch 426: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:34:20.665 INFO: Epoch 427: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:46:14.983 INFO: Epoch 428: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:58:10.355 INFO: Epoch 429: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:10:05.891 INFO: Epoch 430: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:22:01.697 INFO: Epoch 431: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:33:56.776 INFO: Epoch 432: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:45:52.555 INFO: Epoch 433: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:57:47.702 INFO: Epoch 434: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:09:42.965 INFO: Epoch 435: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:21:37.852 INFO: Epoch 436: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:33:32.371 INFO: Epoch 437: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:45:28.261 INFO: Epoch 438: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:57:23.224 INFO: Epoch 439: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:09:18.250 INFO: Epoch 440: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:21:12.860 INFO: Epoch 441: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:33:08.768 INFO: Epoch 442: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:45:03.156 INFO: Epoch 443: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:56:57.936 INFO: Epoch 444: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:08:52.879 INFO: Epoch 445: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:20:47.714 INFO: Epoch 446: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:32:43.567 INFO: Epoch 447: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:44:39.520 INFO: Epoch 448: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:56:35.643 INFO: Epoch 449: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:08:30.160 INFO: Epoch 450: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:20:25.858 INFO: Epoch 451: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:32:21.608 INFO: Epoch 452: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:44:17.563 INFO: Epoch 453: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:56:11.764 INFO: Epoch 454: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:08:07.183 INFO: Epoch 455: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:20:03.056 INFO: Epoch 456: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:31:58.887 INFO: Epoch 457: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:43:54.344 INFO: Epoch 458: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:55:49.903 INFO: Epoch 459: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:07:45.294 INFO: Epoch 460: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:19:41.162 INFO: Epoch 461: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:31:36.277 INFO: Epoch 462: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:43:30.875 INFO: Epoch 463: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:55:25.307 INFO: Epoch 464: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:07:20.807 INFO: Epoch 465: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:19:14.752 INFO: Epoch 466: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:31:10.036 INFO: Epoch 467: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:43:04.418 INFO: Epoch 468: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:54:59.764 INFO: Epoch 469: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:06:55.514 INFO: Epoch 470: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:18:50.829 INFO: Epoch 471: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:30:44.814 INFO: Epoch 472: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:42:39.933 INFO: Epoch 473: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:54:35.589 INFO: Epoch 474: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:06:30.907 INFO: Epoch 475: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:18:26.439 INFO: Epoch 476: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:30:21.470 INFO: Epoch 477: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:42:15.962 INFO: Epoch 478: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:54:11.244 INFO: Epoch 479: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:06:06.781 INFO: Epoch 480: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:18:02.568 INFO: Epoch 481: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:29:58.016 INFO: Epoch 482: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:41:53.982 INFO: Epoch 483: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:53:49.866 INFO: Epoch 484: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:05:45.533 INFO: Epoch 485: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:17:41.308 INFO: Epoch 486: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:29:36.139 INFO: Epoch 487: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:41:31.906 INFO: Epoch 488: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:53:26.445 INFO: Epoch 489: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:05:22.451 INFO: Epoch 490: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:17:16.848 INFO: Epoch 491: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:29:12.518 INFO: Epoch 492: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:41:08.396 INFO: Epoch 493: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:53:04.225 INFO: Epoch 494: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:04:59.969 INFO: Epoch 495: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:16:55.385 INFO: Epoch 496: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:28:50.189 INFO: Epoch 497: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:40:44.721 INFO: Epoch 498: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.296 INFO: Epoch 499: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.297 INFO: Training complete +2024-12-12 13:52:40.297 INFO: +2024-12-12 13:52:40.297 INFO: ===========RESULTS=========== +2024-12-12 13:52:40.297 INFO: Computing metrics for training, validation, and test sets +2024-12-12 13:52:52.999 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-0.pt +2024-12-12 13:52:53.078 INFO: Loaded Stage one model from epoch 0 for evaluation +2024-12-12 13:52:53.079 INFO: Evaluating train_default ... +2024-12-12 13:56:45.053 INFO: Evaluating valid_default ... +2024-12-12 13:57:02.036 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176669.8 | 6.9 | 693943104.62 | +| valid_default | 1176669.8 | 6.9 | 693187632.22 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:02.036 INFO: Evaluating Default_Default ... +2024-12-12 13:57:19.559 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176669.8 | 7.0 | 697278235.90 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:19.560 INFO: Saving model to checkpoints/MACE_model_run-123.model +2024-12-12 13:57:19.747 INFO: Compiling model, saving metadata to MACE_model_compiled.model +2024-12-12 13:57:20.459 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-375_swa.pt +2024-12-12 13:57:20.576 INFO: Loaded Stage two model from epoch 375 for evaluation +2024-12-12 13:57:20.576 INFO: Evaluating train_default ... +2024-12-12 14:01:12.309 INFO: Evaluating valid_default ... +2024-12-12 14:01:29.292 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176671.1 | 0.0 | 2.07 | +| valid_default | 1176671.1 | 0.0 | 2.07 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:29.293 INFO: Evaluating Default_Default ... +2024-12-12 14:01:46.864 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176671.1 | 0.0 | 2.07 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:46.864 INFO: Saving model to checkpoints/MACE_model_run-123_stagetwo.model +2024-12-12 14:01:47.056 INFO: Compiling model, saving metadata MACE_model_stagetwo_compiled.model +2024-12-12 14:01:47.690 INFO: Done diff --git a/ads_16_length_8000/MACE/logs/MACE_model_run-123_debug.log b/ads_16_length_8000/MACE/logs/MACE_model_run-123_debug.log new file mode 100644 index 0000000000000000000000000000000000000000..993b4de838e88e1b2408f0471a21717b7356f338 --- /dev/null +++ b/ads_16_length_8000/MACE/logs/MACE_model_run-123_debug.log @@ -0,0 +1,691 @@ +2024-12-08 10:27:04.088 INFO: ===========VERIFYING SETTINGS=========== +2024-12-08 10:27:04.088 INFO: MACE version: 0.3.9 +2024-12-08 10:27:04.088 DEBUG: Configuration: Namespace(config=None, name='MACE_model', seed=123, work_dir='.', log_dir='./logs', model_dir='.', checkpoints_dir='./checkpoints', results_dir='./results', downloads_dir='./downloads', device='cuda', default_dtype='float64', distributed=False, log_level='INFO', error_table='PerAtomRMSE', model='MACE', r_max=5.0, radial_type='bessel', num_radial_basis=8, num_cutoff_basis=5, pair_repulsion=False, distance_transform='None', interaction='RealAgnosticResidualInteractionBlock', interaction_first='RealAgnosticResidualInteractionBlock', max_ell=3, correlation=3, num_interactions=2, MLP_irreps='16x0e', radial_MLP='[64, 64, 64]', hidden_irreps='128x0e + 128x1o', num_channels=128, max_L=1, gate='silu', scaling='rms_forces_scaling', avg_num_neighbors=1, compute_avg_num_neighbors=True, compute_stress=False, compute_forces=True, train_file='../trainset.xyz', valid_file=None, valid_fraction=0.0625, test_file='../testset.xyz', test_dir=None, multi_processed_test=False, num_workers=16, pin_memory=True, atomic_numbers=None, mean=None, std=None, statistics_file=None, E0s='{1:-13.663181292231226, 6:-1029.2809654211628, 7:-1484.1187695035828, 8:-2042.0330099956639, 30:-500.123456789}', foundation_filter_elements=True, heads=None, multiheads_finetuning=True, foundation_head=None, weight_pt_head=1.0, num_samples_pt=1000, subselect_pt='random', pt_train_file=None, pt_valid_file=None, keep_isolated_atoms=False, energy_key='REF_energy', forces_key='REF_forces', virials_key='REF_virials', stress_key='REF_stress', dipole_key='REF_dipole', charges_key='REF_charges', loss='weighted', forces_weight=100.0, swa_forces_weight=100.0, energy_weight=1.0, swa_energy_weight=1000.0, virials_weight=1.0, swa_virials_weight=10.0, stress_weight=1.0, swa_stress_weight=10.0, dipole_weight=1.0, swa_dipole_weight=1.0, config_type_weights='{"Default":1.0}', huber_delta=0.01, optimizer='adam', beta=0.9, batch_size=10, valid_batch_size=10, lr=0.0001, swa_lr=0.001, weight_decay=5e-07, amsgrad=True, scheduler='ReduceLROnPlateau', lr_factor=0.8, scheduler_patience=50, lr_scheduler_gamma=0.9993, swa=True, start_swa=375, ema=True, ema_decay=0.99, max_num_epochs=500, patience=2048, foundation_model=None, foundation_model_readout=True, eval_interval=1, keep_checkpoints=False, save_all_checkpoints=False, restart_latest=True, save_cpu=False, clip_grad=10.0, enable_cueq=False, wandb=False, wandb_dir=None, wandb_project='', wandb_entity='', wandb_name='', wandb_log_hypers=['num_channels', 'max_L', 'correlation', 'lr', 'swa_lr', 'weight_decay', 'batch_size', 'max_num_epochs', 'start_swa', 'energy_weight', 'forces_weight']) +2024-12-08 10:27:04.251 INFO: CUDA version: 12.4, CUDA device: 0 +2024-12-08 10:27:04.340 DEBUG: Popen(['git', 'version'], cwd=/fs/scratch/PAS2490/CMAME/aimd/ads_16_length_8000/MACE, stdin=None, shell=False, universal_newlines=False) +2024-12-08 10:27:04.349 DEBUG: Popen(['git', 'version'], cwd=/fs/scratch/PAS2490/CMAME/aimd/ads_16_length_8000/MACE, stdin=None, shell=False, universal_newlines=False) +2024-12-08 10:27:04.360 DEBUG: Error accessing Git repository: /fs/scratch/PAS2490/CMAME/aimd/ads_16_length_8000/MACE +2024-12-08 10:27:04.360 INFO: ===========LOADING INPUT DATA=========== +2024-12-08 10:27:04.361 INFO: Using heads: ['default'] +2024-12-08 10:27:04.361 INFO: ============= Processing head default =========== +2024-12-08 10:27:14.213 INFO: Training set [8000 configs, 0 energy, 9600000 forces] loaded from '../trainset.xyz' +2024-12-08 10:27:14.216 INFO: Using random 6% of training set for validation with indices saved in: ./valid_indices_123.txt +2024-12-08 10:27:14.217 INFO: Validaton set contains 500 configurations [0 energy, 600000 forces] +2024-12-08 10:27:14.834 INFO: Test set (500 configs) loaded from '../testset.xyz': +2024-12-08 10:27:14.834 INFO: Default_Default: 500 configs, 0 energy, 600000 forces +2024-12-08 10:27:14.837 INFO: Total number of configurations: train=7500, valid=500, tests=[Default_Default: 500], +2024-12-08 10:27:15.150 INFO: Atomic Numbers used: [1, 6, 7, 8, 30] +2024-12-08 10:27:15.150 INFO: Isolated Atomic Energies (E0s) not in training file, using command line argument +2024-12-08 10:27:15.151 INFO: Atomic Energies used (z: eV) for head default: {1: -13.663181292231226, 6: -1029.2809654211628, 7: -1484.1187695035828, 8: -2042.0330099956639, 30: -500.123456789} +2024-12-08 10:30:32.377 INFO: Computing average number of neighbors +2024-12-08 10:30:57.864 INFO: Average number of neighbors: 34.390905333333336 +2024-12-08 10:30:57.866 INFO: During training the following quantities will be reported: energy, forces +2024-12-08 10:30:57.866 INFO: ===========MODEL DETAILS=========== +2024-12-08 10:31:09.971 WARNING: Standard deviation of the scaling is zero, Changing to no scaling +2024-12-08 10:31:09.973 INFO: Building model +2024-12-08 10:31:09.973 INFO: Message passing with 128 channels and max_L=1 (128x0e + 128x1o) +2024-12-08 10:31:09.973 INFO: 2 layers, each with correlation order: 3 (body order: 4) and spherical harmonics up to: l=3 +2024-12-08 10:31:09.974 INFO: 8 radial and 5 basis functions +2024-12-08 10:31:09.974 INFO: Radial cutoff: 5.0 A (total receptive field for each atom: 10.0 A) +2024-12-08 10:31:09.974 INFO: Distance transform for radial basis functions: None +2024-12-08 10:31:09.974 INFO: Hidden irreps: 128x0e + 128x1o +2024-12-08 10:31:11.310 DEBUG: ScaleShiftMACE( + (node_embedding): LinearNodeEmbeddingBlock( + (linear): Linear(5x0e -> 128x0e | 640 weights) + ) + (radial_embedding): RadialEmbeddingBlock( + (bessel_fn): BesselBasis(r_max=5.0, num_basis=8, trainable=False) + (cutoff_fn): PolynomialCutoff(p=5.0, r_max=5.0) + ) + (spherical_harmonics): SphericalHarmonics() + (atomic_energies_fn): AtomicEnergiesBlock(energies=[[-13.6632, -1029.2810, -1484.1188, -2042.0330, -500.1235]]) + (interactions): ModuleList( + (0): RealAgnosticInteractionBlock( + (linear_up): Linear(128x0e -> 128x0e | 16384 weights) + (conv_tp): TensorProduct(128x0e x 1x0e+1x1o+1x2e+1x3o -> 128x0e+128x1o+128x2e+128x3o | 512 paths | 512 weights) + (conv_tp_weights): FullyConnectedNet[8, 64, 64, 64, 512] + (linear): Linear(128x0e+128x1o+128x2e+128x3o -> 128x0e+128x1o+128x2e+128x3o | 65536 weights) + (skip_tp): FullyConnectedTensorProduct(128x0e+128x1o+128x2e+128x3o x 5x0e -> 128x0e+128x1o+128x2e+128x3o | 327680 paths | 327680 weights) + (reshape): reshape_irreps() + ) + (1): RealAgnosticResidualInteractionBlock( + (linear_up): Linear(128x0e+128x1o -> 128x0e+128x1o | 32768 weights) + (conv_tp): TensorProduct(128x0e+128x1o x 1x0e+1x1o+1x2e+1x3o -> 256x0e+384x1o+384x2e+256x3o | 1280 paths | 1280 weights) + (conv_tp_weights): FullyConnectedNet[8, 64, 64, 64, 1280] + (linear): Linear(128x0e+128x0e+128x1o+128x1o+128x1o+128x2e+128x2e+128x2e+128x3o+128x3o -> 128x0e+128x1o+128x2e+128x3o | 163840 weights) + (skip_tp): FullyConnectedTensorProduct(128x0e+128x1o x 5x0e -> 128x0e | 81920 paths | 81920 weights) + (reshape): reshape_irreps() + ) + ) + (products): ModuleList( + (0): EquivariantProductBasisBlock( + (symmetric_contractions): SymmetricContraction( + (contractions): ModuleList( + (0): Contraction( + (contractions_weighting): ModuleList( + (0-1): 2 x GraphModule() + ) + (contractions_features): ModuleList( + (0-1): 2 x GraphModule() + ) + (weights): ParameterList( + (0): Parameter containing: [torch.float64 of size 5x4x128 (cuda:0)] + (1): Parameter containing: [torch.float64 of size 5x1x128 (cuda:0)] + ) + (graph_opt_main): GraphModule() + ) + (1): Contraction( + (contractions_weighting): ModuleList( + (0-1): 2 x GraphModule() + ) + (contractions_features): ModuleList( + (0-1): 2 x GraphModule() + ) + (weights): ParameterList( + (0): Parameter containing: [torch.float64 of size 5x6x128 (cuda:0)] + (1): Parameter containing: [torch.float64 of size 5x1x128 (cuda:0)] + ) + (graph_opt_main): GraphModule() + ) + ) + ) + (linear): Linear(128x0e+128x1o -> 128x0e+128x1o | 32768 weights) + ) + (1): EquivariantProductBasisBlock( + (symmetric_contractions): SymmetricContraction( + (contractions): ModuleList( + (0): Contraction( + (contractions_weighting): ModuleList( + (0-1): 2 x GraphModule() + ) + (contractions_features): ModuleList( + (0-1): 2 x GraphModule() + ) + (weights): ParameterList( + (0): Parameter containing: [torch.float64 of size 5x4x128 (cuda:0)] + (1): Parameter containing: [torch.float64 of size 5x1x128 (cuda:0)] + ) + (graph_opt_main): GraphModule() + ) + ) + ) + (linear): Linear(128x0e -> 128x0e | 16384 weights) + ) + ) + (readouts): ModuleList( + (0): LinearReadoutBlock( + (linear): Linear(128x0e+128x1o -> 1x0e | 128 weights) + ) + (1): NonLinearReadoutBlock( + (linear_1): Linear(128x0e -> 16x0e | 2048 weights) + (non_linearity): Activation [x] (16x0e -> 16x0e) + (linear_2): Linear(16x0e -> 1x0e | 16 weights) + ) + ) + (scale_shift): ScaleShiftBlock(scale=1.0000, shift=0.0000) +) +2024-12-08 10:31:11.311 INFO: Total number of parameters: 945168 +2024-12-08 10:31:11.311 INFO: +2024-12-08 10:31:11.312 INFO: ===========OPTIMIZER INFORMATION=========== +2024-12-08 10:31:11.312 INFO: Using ADAM as parameter optimizer +2024-12-08 10:31:11.312 INFO: Batch size: 10 +2024-12-08 10:31:11.312 INFO: Using Exponential Moving Average with decay: 0.99 +2024-12-08 10:31:11.312 INFO: Number of gradient updates: 375000 +2024-12-08 10:31:11.312 INFO: Learning rate: 0.0001, weight decay: 5e-07 +2024-12-08 10:31:11.312 INFO: WeightedEnergyForcesLoss(energy_weight=1.000, forces_weight=100.000) +2024-12-08 10:31:11.313 INFO: Stage Two (after 375 epochs) with loss function: WeightedEnergyForcesLoss(energy_weight=1000.000, forces_weight=100.000), with energy weight : 1000.0, forces weight : 100.0 and learning rate : 0.001 +2024-12-08 10:31:11.411 WARNING: Cannot find checkpoint with tag 'MACE_model_run-123' in './checkpoints' +2024-12-08 10:31:11.416 INFO: Using gradient clipping with tolerance=10.000 +2024-12-08 10:31:11.416 INFO: +2024-12-08 10:31:11.416 INFO: ===========TRAINING=========== +2024-12-08 10:31:11.416 INFO: Started training, reporting errors on validation set +2024-12-08 10:31:11.416 INFO: Loss metrics on validation set +2024-12-08 10:31:30.817 INFO: Initial: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.59 meV, RMSE_F= 8.00 meV / A +2024-12-08 10:43:28.626 INFO: Epoch 0: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.81 meV, RMSE_F= 6.93 meV / A +2024-12-08 10:43:28.628 DEBUG: Saving checkpoint: ./checkpoints/MACE_model_run-123_epoch-0.pt +2024-12-08 10:55:24.333 INFO: Epoch 1: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.04 meV, RMSE_F= 5.86 meV / A +2024-12-08 11:07:20.041 INFO: Epoch 2: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.25 meV, RMSE_F= 4.86 meV / A +2024-12-08 11:19:16.210 INFO: Epoch 3: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.43 meV, RMSE_F= 3.98 meV / A +2024-12-08 11:31:11.991 INFO: Epoch 4: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.58 meV, RMSE_F= 3.25 meV / A +2024-12-08 11:43:06.856 INFO: Epoch 5: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.70 meV, RMSE_F= 2.65 meV / A +2024-12-08 11:55:01.165 INFO: Epoch 6: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.80 meV, RMSE_F= 2.16 meV / A +2024-12-08 12:06:56.362 INFO: Epoch 7: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.87 meV, RMSE_F= 1.76 meV / A +2024-12-08 12:18:50.358 INFO: Epoch 8: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.93 meV, RMSE_F= 1.44 meV / A +2024-12-08 12:30:46.020 INFO: Epoch 9: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.97 meV, RMSE_F= 1.18 meV / A +2024-12-08 12:42:40.820 INFO: Epoch 10: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.00 meV, RMSE_F= 0.96 meV / A +2024-12-08 12:54:35.332 INFO: Epoch 11: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.03 meV, RMSE_F= 0.79 meV / A +2024-12-08 13:06:30.753 INFO: Epoch 12: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.05 meV, RMSE_F= 0.65 meV / A +2024-12-08 13:18:26.112 INFO: Epoch 13: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.06 meV, RMSE_F= 0.53 meV / A +2024-12-08 13:30:20.810 INFO: Epoch 14: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.07 meV, RMSE_F= 0.44 meV / A +2024-12-08 13:42:15.196 INFO: Epoch 15: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.36 meV / A +2024-12-08 13:54:09.819 INFO: Epoch 16: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.30 meV / A +2024-12-08 14:06:05.537 INFO: Epoch 17: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.25 meV / A +2024-12-08 14:18:01.007 INFO: Epoch 18: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.21 meV / A +2024-12-08 14:29:56.653 INFO: Epoch 19: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.17 meV / A +2024-12-08 14:41:52.358 INFO: Epoch 20: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.14 meV / A +2024-12-08 14:53:48.513 INFO: Epoch 21: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.12 meV / A +2024-12-08 15:05:43.359 INFO: Epoch 22: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.10 meV / A +2024-12-08 15:17:38.974 INFO: Epoch 23: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.08 meV / A +2024-12-08 15:29:34.686 INFO: Epoch 24: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.07 meV / A +2024-12-08 15:41:29.597 INFO: Epoch 25: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.06 meV / A +2024-12-08 15:53:25.294 INFO: Epoch 26: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.05 meV / A +2024-12-08 16:05:20.961 INFO: Epoch 27: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:17:16.607 INFO: Epoch 28: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:29:12.261 INFO: Epoch 29: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:41:07.925 INFO: Epoch 30: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:53:02.602 INFO: Epoch 31: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 17:04:57.046 INFO: Epoch 32: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:16:51.740 INFO: Epoch 33: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:28:46.123 INFO: Epoch 34: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:40:42.071 INFO: Epoch 35: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:52:37.361 INFO: Epoch 36: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 18:04:33.026 INFO: Epoch 37: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:16:28.769 INFO: Epoch 38: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:28:23.730 INFO: Epoch 39: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:40:18.114 INFO: Epoch 40: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:52:13.648 INFO: Epoch 41: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:04:09.270 INFO: Epoch 42: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:16:05.154 INFO: Epoch 43: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:27:59.796 INFO: Epoch 44: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:39:55.525 INFO: Epoch 45: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:51:51.177 INFO: Epoch 46: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:03:45.570 INFO: Epoch 47: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:15:41.084 INFO: Epoch 48: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:27:36.946 INFO: Epoch 49: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:39:32.505 INFO: Epoch 50: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:51:28.227 INFO: Epoch 51: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:03:23.063 INFO: Epoch 52: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:15:18.938 INFO: Epoch 53: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:27:15.045 INFO: Epoch 54: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:39:09.591 INFO: Epoch 55: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:51:05.492 INFO: Epoch 56: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:02:59.714 INFO: Epoch 57: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:14:55.585 INFO: Epoch 58: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:26:51.102 INFO: Epoch 59: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:38:45.753 INFO: Epoch 60: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:50:41.405 INFO: Epoch 61: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:02:37.139 INFO: Epoch 62: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:14:32.989 INFO: Epoch 63: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:26:28.808 INFO: Epoch 64: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:38:23.213 INFO: Epoch 65: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:50:19.277 INFO: Epoch 66: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:02:15.317 INFO: Epoch 67: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:14:11.434 INFO: Epoch 68: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:26:07.195 INFO: Epoch 69: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:38:01.588 INFO: Epoch 70: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:49:55.998 INFO: Epoch 71: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:01:50.464 INFO: Epoch 72: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:13:44.950 INFO: Epoch 73: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:25:39.761 INFO: Epoch 74: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:37:34.518 INFO: Epoch 75: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:49:29.934 INFO: Epoch 76: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:01:24.386 INFO: Epoch 77: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:13:18.984 INFO: Epoch 78: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:25:13.768 INFO: Epoch 79: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:37:08.653 INFO: Epoch 80: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:49:04.292 INFO: Epoch 81: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:01:00.252 INFO: Epoch 82: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:12:54.966 INFO: Epoch 83: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:24:50.005 INFO: Epoch 84: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:36:44.612 INFO: Epoch 85: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:48:39.873 INFO: Epoch 86: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:00:34.728 INFO: Epoch 87: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:12:30.321 INFO: Epoch 88: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:24:25.177 INFO: Epoch 89: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:36:19.942 INFO: Epoch 90: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:48:15.858 INFO: Epoch 91: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:00:10.664 INFO: Epoch 92: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:12:05.266 INFO: Epoch 93: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:24:01.304 INFO: Epoch 94: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:35:57.308 INFO: Epoch 95: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:47:52.114 INFO: Epoch 96: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:59:48.009 INFO: Epoch 97: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:11:43.926 INFO: Epoch 98: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:23:39.755 INFO: Epoch 99: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:35:35.255 INFO: Epoch 100: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:47:31.170 INFO: Epoch 101: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:59:27.024 INFO: Epoch 102: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:11:23.070 INFO: Epoch 103: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:23:17.336 INFO: Epoch 104: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:35:13.214 INFO: Epoch 105: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:47:09.766 INFO: Epoch 106: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:59:05.856 INFO: Epoch 107: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:11:02.067 INFO: Epoch 108: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:22:58.239 INFO: Epoch 109: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:34:54.232 INFO: Epoch 110: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:46:48.791 INFO: Epoch 111: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:58:44.589 INFO: Epoch 112: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:10:40.363 INFO: Epoch 113: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:22:36.216 INFO: Epoch 114: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:34:32.085 INFO: Epoch 115: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:46:27.706 INFO: Epoch 116: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:58:23.697 INFO: Epoch 117: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:10:19.309 INFO: Epoch 118: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:22:13.647 INFO: Epoch 119: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:34:09.444 INFO: Epoch 120: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:46:04.236 INFO: Epoch 121: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:58:00.456 INFO: Epoch 122: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:09:56.544 INFO: Epoch 123: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:21:52.460 INFO: Epoch 124: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:33:48.452 INFO: Epoch 125: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:45:43.100 INFO: Epoch 126: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:57:39.277 INFO: Epoch 127: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:09:35.273 INFO: Epoch 128: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:21:31.020 INFO: Epoch 129: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:33:26.694 INFO: Epoch 130: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:45:21.532 INFO: Epoch 131: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:57:16.909 INFO: Epoch 132: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:09:11.244 INFO: Epoch 133: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:21:07.247 INFO: Epoch 134: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:33:02.393 INFO: Epoch 135: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:44:56.950 INFO: Epoch 136: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:56:51.236 INFO: Epoch 137: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:08:46.769 INFO: Epoch 138: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:20:42.559 INFO: Epoch 139: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:32:38.679 INFO: Epoch 140: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:44:33.998 INFO: Epoch 141: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:56:29.747 INFO: Epoch 142: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:08:24.058 INFO: Epoch 143: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:20:19.888 INFO: Epoch 144: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:32:14.869 INFO: Epoch 145: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:44:10.713 INFO: Epoch 146: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:56:06.378 INFO: Epoch 147: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:08:02.365 INFO: Epoch 148: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:19:58.283 INFO: Epoch 149: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:31:52.524 INFO: Epoch 150: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:43:46.959 INFO: Epoch 151: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:55:41.448 INFO: Epoch 152: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:07:36.941 INFO: Epoch 153: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:19:32.118 INFO: Epoch 154: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:31:26.638 INFO: Epoch 155: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:43:21.528 INFO: Epoch 156: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:55:17.216 INFO: Epoch 157: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:07:13.149 INFO: Epoch 158: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:19:09.055 INFO: Epoch 159: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:31:05.099 INFO: Epoch 160: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:43:00.978 INFO: Epoch 161: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:54:57.010 INFO: Epoch 162: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:06:50.934 INFO: Epoch 163: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:18:46.637 INFO: Epoch 164: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:30:42.231 INFO: Epoch 165: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:42:37.914 INFO: Epoch 166: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:54:32.246 INFO: Epoch 167: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:06:27.867 INFO: Epoch 168: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:18:23.413 INFO: Epoch 169: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:30:18.183 INFO: Epoch 170: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:42:12.599 INFO: Epoch 171: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:54:06.951 INFO: Epoch 172: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:06:01.673 INFO: Epoch 173: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:17:55.924 INFO: Epoch 174: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:29:50.044 INFO: Epoch 175: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:41:45.049 INFO: Epoch 176: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:53:40.899 INFO: Epoch 177: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:05:35.776 INFO: Epoch 178: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:17:31.020 INFO: Epoch 179: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:29:25.430 INFO: Epoch 180: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:41:19.621 INFO: Epoch 181: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:53:14.971 INFO: Epoch 182: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:05:09.267 INFO: Epoch 183: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:17:05.126 INFO: Epoch 184: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:28:59.637 INFO: Epoch 185: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:40:55.474 INFO: Epoch 186: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:52:51.380 INFO: Epoch 187: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:04:47.228 INFO: Epoch 188: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:16:42.851 INFO: Epoch 189: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:28:38.552 INFO: Epoch 190: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:40:34.291 INFO: Epoch 191: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:52:30.056 INFO: Epoch 192: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:04:25.410 INFO: Epoch 193: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:16:19.643 INFO: Epoch 194: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:28:14.878 INFO: Epoch 195: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:40:10.368 INFO: Epoch 196: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:52:05.628 INFO: Epoch 197: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:04:00.778 INFO: Epoch 198: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:15:56.390 INFO: Epoch 199: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:27:52.148 INFO: Epoch 200: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:39:47.606 INFO: Epoch 201: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:51:41.996 INFO: Epoch 202: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:03:37.665 INFO: Epoch 203: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:15:31.950 INFO: Epoch 204: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:27:26.433 INFO: Epoch 205: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:39:22.356 INFO: Epoch 206: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:51:18.392 INFO: Epoch 207: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:03:14.075 INFO: Epoch 208: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:15:10.049 INFO: Epoch 209: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:27:04.899 INFO: Epoch 210: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:39:00.783 INFO: Epoch 211: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:50:55.489 INFO: Epoch 212: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:02:49.689 INFO: Epoch 213: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:14:45.240 INFO: Epoch 214: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:26:39.838 INFO: Epoch 215: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:38:35.501 INFO: Epoch 216: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:50:30.052 INFO: Epoch 217: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:02:24.517 INFO: Epoch 218: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:14:18.570 INFO: Epoch 219: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:26:13.901 INFO: Epoch 220: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:38:08.213 INFO: Epoch 221: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:50:02.661 INFO: Epoch 222: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:01:58.533 INFO: Epoch 223: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:13:54.322 INFO: Epoch 224: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:25:48.965 INFO: Epoch 225: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:37:43.391 INFO: Epoch 226: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:49:37.894 INFO: Epoch 227: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:01:33.418 INFO: Epoch 228: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:13:27.632 INFO: Epoch 229: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:25:22.885 INFO: Epoch 230: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:37:18.015 INFO: Epoch 231: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:49:12.793 INFO: Epoch 232: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:01:07.307 INFO: Epoch 233: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:13:03.175 INFO: Epoch 234: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:24:59.223 INFO: Epoch 235: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:36:54.239 INFO: Epoch 236: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:48:50.157 INFO: Epoch 237: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:00:44.812 INFO: Epoch 238: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:12:40.580 INFO: Epoch 239: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:24:36.577 INFO: Epoch 240: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:36:31.786 INFO: Epoch 241: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:48:27.805 INFO: Epoch 242: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:00:23.771 INFO: Epoch 243: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:12:19.466 INFO: Epoch 244: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:24:14.560 INFO: Epoch 245: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:36:09.015 INFO: Epoch 246: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:48:03.884 INFO: Epoch 247: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:59:59.919 INFO: Epoch 248: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:11:55.799 INFO: Epoch 249: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:23:51.018 INFO: Epoch 250: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:35:46.975 INFO: Epoch 251: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:47:42.845 INFO: Epoch 252: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:59:37.534 INFO: Epoch 253: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:11:32.204 INFO: Epoch 254: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:23:28.131 INFO: Epoch 255: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:35:23.078 INFO: Epoch 256: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:47:19.199 INFO: Epoch 257: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:59:15.363 INFO: Epoch 258: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:11:11.490 INFO: Epoch 259: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:23:05.967 INFO: Epoch 260: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:35:00.533 INFO: Epoch 261: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:46:54.970 INFO: Epoch 262: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:58:49.732 INFO: Epoch 263: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:10:45.906 INFO: Epoch 264: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:22:42.249 INFO: Epoch 265: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:34:38.257 INFO: Epoch 266: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:46:34.251 INFO: Epoch 267: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:58:29.051 INFO: Epoch 268: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:10:25.133 INFO: Epoch 269: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:22:20.231 INFO: Epoch 270: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:34:15.149 INFO: Epoch 271: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:46:10.635 INFO: Epoch 272: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:58:05.279 INFO: Epoch 273: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:10:01.505 INFO: Epoch 274: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:21:57.648 INFO: Epoch 275: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:33:53.978 INFO: Epoch 276: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:45:49.756 INFO: Epoch 277: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:57:44.828 INFO: Epoch 278: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:09:40.943 INFO: Epoch 279: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:21:36.478 INFO: Epoch 280: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:33:32.323 INFO: Epoch 281: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:45:28.483 INFO: Epoch 282: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:57:24.715 INFO: Epoch 283: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:09:20.895 INFO: Epoch 284: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:21:17.034 INFO: Epoch 285: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:33:13.103 INFO: Epoch 286: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:45:08.280 INFO: Epoch 287: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:57:04.237 INFO: Epoch 288: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:08:59.195 INFO: Epoch 289: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:20:54.913 INFO: Epoch 290: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:32:50.979 INFO: Epoch 291: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:44:46.484 INFO: Epoch 292: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:56:41.983 INFO: Epoch 293: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:08:37.460 INFO: Epoch 294: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:20:33.870 INFO: Epoch 295: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:32:29.680 INFO: Epoch 296: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:44:25.832 INFO: Epoch 297: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:56:20.853 INFO: Epoch 298: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:08:16.750 INFO: Epoch 299: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:20:12.433 INFO: Epoch 300: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:32:08.522 INFO: Epoch 301: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:44:03.243 INFO: Epoch 302: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:55:58.890 INFO: Epoch 303: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:07:54.926 INFO: Epoch 304: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:19:50.900 INFO: Epoch 305: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:31:45.740 INFO: Epoch 306: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:43:41.048 INFO: Epoch 307: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:55:36.787 INFO: Epoch 308: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:07:31.244 INFO: Epoch 309: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:19:27.111 INFO: Epoch 310: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:31:21.678 INFO: Epoch 311: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:43:17.745 INFO: Epoch 312: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:55:12.267 INFO: Epoch 313: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:07:07.731 INFO: Epoch 314: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:19:03.615 INFO: Epoch 315: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:30:58.174 INFO: Epoch 316: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:42:52.805 INFO: Epoch 317: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:54:48.978 INFO: Epoch 318: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:06:43.606 INFO: Epoch 319: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:18:39.721 INFO: Epoch 320: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:30:34.259 INFO: Epoch 321: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:42:29.325 INFO: Epoch 322: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:54:24.772 INFO: Epoch 323: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:06:20.497 INFO: Epoch 324: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:18:15.937 INFO: Epoch 325: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:30:11.892 INFO: Epoch 326: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:42:07.172 INFO: Epoch 327: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:54:02.531 INFO: Epoch 328: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:05:58.271 INFO: Epoch 329: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:17:52.760 INFO: Epoch 330: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:29:47.873 INFO: Epoch 331: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:41:42.319 INFO: Epoch 332: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:53:37.290 INFO: Epoch 333: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:05:33.474 INFO: Epoch 334: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:17:29.446 INFO: Epoch 335: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:29:25.437 INFO: Epoch 336: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:41:21.785 INFO: Epoch 337: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:53:17.973 INFO: Epoch 338: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:05:12.479 INFO: Epoch 339: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:17:06.952 INFO: Epoch 340: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:29:02.964 INFO: Epoch 341: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:40:57.533 INFO: Epoch 342: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:52:52.026 INFO: Epoch 343: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:04:47.944 INFO: Epoch 344: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:16:44.328 INFO: Epoch 345: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:28:40.385 INFO: Epoch 346: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:40:36.110 INFO: Epoch 347: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:52:32.080 INFO: Epoch 348: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:04:28.075 INFO: Epoch 349: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:16:24.417 INFO: Epoch 350: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:28:20.381 INFO: Epoch 351: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:40:15.843 INFO: Epoch 352: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:52:10.309 INFO: Epoch 353: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:04:05.831 INFO: Epoch 354: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:16:00.698 INFO: Epoch 355: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:27:56.307 INFO: Epoch 356: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:39:51.849 INFO: Epoch 357: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:51:47.267 INFO: Epoch 358: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:03:42.795 INFO: Epoch 359: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:15:38.943 INFO: Epoch 360: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:27:35.120 INFO: Epoch 361: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:39:31.186 INFO: Epoch 362: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:51:25.968 INFO: Epoch 363: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:03:21.984 INFO: Epoch 364: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:15:18.545 INFO: Epoch 365: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:27:13.772 INFO: Epoch 366: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:39:09.617 INFO: Epoch 367: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:51:05.646 INFO: Epoch 368: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:03:00.493 INFO: Epoch 369: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:14:54.868 INFO: Epoch 370: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:26:50.754 INFO: Epoch 371: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:38:46.519 INFO: Epoch 372: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:50:43.315 INFO: Epoch 373: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.939 INFO: Epoch 374: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.940 INFO: Changing loss based on Stage Two Weights +2024-12-11 13:14:33.194 INFO: Epoch 375: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:14:33.196 DEBUG: Saving checkpoint: ./checkpoints/MACE_model_run-123_epoch-375_swa.pt +2024-12-11 13:26:29.020 INFO: Epoch 376: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:38:23.794 INFO: Epoch 377: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:50:19.535 INFO: Epoch 378: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:02:15.204 INFO: Epoch 379: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:14:10.327 INFO: Epoch 380: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:26:05.510 INFO: Epoch 381: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:38:01.439 INFO: Epoch 382: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:49:56.048 INFO: Epoch 383: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:01:51.347 INFO: Epoch 384: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:13:45.633 INFO: Epoch 385: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:25:40.499 INFO: Epoch 386: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:37:36.110 INFO: Epoch 387: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:49:30.718 INFO: Epoch 388: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:01:25.225 INFO: Epoch 389: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:13:20.954 INFO: Epoch 390: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:25:16.513 INFO: Epoch 391: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:37:11.872 INFO: Epoch 392: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:49:07.412 INFO: Epoch 393: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:01:03.197 INFO: Epoch 394: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:12:58.369 INFO: Epoch 395: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:24:54.031 INFO: Epoch 396: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:36:48.891 INFO: Epoch 397: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:48:44.528 INFO: Epoch 398: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:00:39.247 INFO: Epoch 399: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:12:34.874 INFO: Epoch 400: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:24:30.006 INFO: Epoch 401: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:36:24.850 INFO: Epoch 402: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:48:19.214 INFO: Epoch 403: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:00:13.313 INFO: Epoch 404: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:12:09.002 INFO: Epoch 405: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:24:03.380 INFO: Epoch 406: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:35:58.179 INFO: Epoch 407: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:47:52.307 INFO: Epoch 408: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:59:47.012 INFO: Epoch 409: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:11:41.308 INFO: Epoch 410: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:23:36.968 INFO: Epoch 411: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:35:31.946 INFO: Epoch 412: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:47:27.753 INFO: Epoch 413: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:59:23.042 INFO: Epoch 414: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:11:18.783 INFO: Epoch 415: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:23:13.289 INFO: Epoch 416: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:35:09.165 INFO: Epoch 417: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:47:04.044 INFO: Epoch 418: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:59:00.084 INFO: Epoch 419: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:10:54.856 INFO: Epoch 420: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:22:49.777 INFO: Epoch 421: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:34:45.349 INFO: Epoch 422: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:46:41.212 INFO: Epoch 423: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:58:36.201 INFO: Epoch 424: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:10:30.588 INFO: Epoch 425: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:22:26.463 INFO: Epoch 426: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:34:20.665 INFO: Epoch 427: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:46:14.983 INFO: Epoch 428: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:58:10.355 INFO: Epoch 429: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:10:05.891 INFO: Epoch 430: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:22:01.697 INFO: Epoch 431: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:33:56.776 INFO: Epoch 432: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:45:52.555 INFO: Epoch 433: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:57:47.702 INFO: Epoch 434: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:09:42.965 INFO: Epoch 435: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:21:37.852 INFO: Epoch 436: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:33:32.371 INFO: Epoch 437: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:45:28.261 INFO: Epoch 438: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:57:23.224 INFO: Epoch 439: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:09:18.250 INFO: Epoch 440: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:21:12.860 INFO: Epoch 441: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:33:08.768 INFO: Epoch 442: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:45:03.156 INFO: Epoch 443: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:56:57.936 INFO: Epoch 444: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:08:52.879 INFO: Epoch 445: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:20:47.714 INFO: Epoch 446: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:32:43.567 INFO: Epoch 447: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:44:39.520 INFO: Epoch 448: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:56:35.643 INFO: Epoch 449: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:08:30.160 INFO: Epoch 450: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:20:25.858 INFO: Epoch 451: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:32:21.608 INFO: Epoch 452: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:44:17.563 INFO: Epoch 453: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:56:11.764 INFO: Epoch 454: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:08:07.183 INFO: Epoch 455: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:20:03.056 INFO: Epoch 456: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:31:58.887 INFO: Epoch 457: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:43:54.344 INFO: Epoch 458: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:55:49.903 INFO: Epoch 459: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:07:45.294 INFO: Epoch 460: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:19:41.162 INFO: Epoch 461: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:31:36.277 INFO: Epoch 462: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:43:30.875 INFO: Epoch 463: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:55:25.307 INFO: Epoch 464: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:07:20.807 INFO: Epoch 465: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:19:14.752 INFO: Epoch 466: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:31:10.036 INFO: Epoch 467: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:43:04.418 INFO: Epoch 468: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:54:59.764 INFO: Epoch 469: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:06:55.514 INFO: Epoch 470: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:18:50.829 INFO: Epoch 471: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:30:44.814 INFO: Epoch 472: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:42:39.933 INFO: Epoch 473: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:54:35.589 INFO: Epoch 474: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:06:30.907 INFO: Epoch 475: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:18:26.439 INFO: Epoch 476: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:30:21.470 INFO: Epoch 477: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:42:15.962 INFO: Epoch 478: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:54:11.244 INFO: Epoch 479: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:06:06.781 INFO: Epoch 480: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:18:02.568 INFO: Epoch 481: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:29:58.016 INFO: Epoch 482: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:41:53.982 INFO: Epoch 483: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:53:49.866 INFO: Epoch 484: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:05:45.533 INFO: Epoch 485: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:17:41.308 INFO: Epoch 486: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:29:36.139 INFO: Epoch 487: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:41:31.906 INFO: Epoch 488: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:53:26.445 INFO: Epoch 489: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:05:22.451 INFO: Epoch 490: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:17:16.848 INFO: Epoch 491: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:29:12.518 INFO: Epoch 492: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:41:08.396 INFO: Epoch 493: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:53:04.225 INFO: Epoch 494: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:04:59.969 INFO: Epoch 495: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:16:55.385 INFO: Epoch 496: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:28:50.189 INFO: Epoch 497: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:40:44.721 INFO: Epoch 498: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.296 INFO: Epoch 499: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.297 INFO: Training complete +2024-12-12 13:52:40.297 INFO: +2024-12-12 13:52:40.297 INFO: ===========RESULTS=========== +2024-12-12 13:52:40.297 INFO: Computing metrics for training, validation, and test sets +2024-12-12 13:52:52.999 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-0.pt +2024-12-12 13:52:53.078 INFO: Loaded Stage one model from epoch 0 for evaluation +2024-12-12 13:52:53.079 INFO: Evaluating train_default ... +2024-12-12 13:56:45.053 INFO: Evaluating valid_default ... +2024-12-12 13:57:02.036 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176669.8 | 6.9 | 693943104.62 | +| valid_default | 1176669.8 | 6.9 | 693187632.22 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:02.036 INFO: Evaluating Default_Default ... +2024-12-12 13:57:19.559 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176669.8 | 7.0 | 697278235.90 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:19.560 INFO: Saving model to checkpoints/MACE_model_run-123.model +2024-12-12 13:57:19.747 INFO: Compiling model, saving metadata to MACE_model_compiled.model +2024-12-12 13:57:20.459 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-375_swa.pt +2024-12-12 13:57:20.576 INFO: Loaded Stage two model from epoch 375 for evaluation +2024-12-12 13:57:20.576 INFO: Evaluating train_default ... +2024-12-12 14:01:12.309 INFO: Evaluating valid_default ... +2024-12-12 14:01:29.292 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176671.1 | 0.0 | 2.07 | +| valid_default | 1176671.1 | 0.0 | 2.07 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:29.293 INFO: Evaluating Default_Default ... +2024-12-12 14:01:46.864 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176671.1 | 0.0 | 2.07 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:46.864 INFO: Saving model to checkpoints/MACE_model_run-123_stagetwo.model +2024-12-12 14:01:47.056 INFO: Compiling model, saving metadata MACE_model_stagetwo_compiled.model +2024-12-12 14:01:47.690 INFO: Done diff --git a/ads_16_length_8000/MACE/mace.sh b/ads_16_length_8000/MACE/mace.sh new file mode 100644 index 0000000000000000000000000000000000000000..a37c671217a00b7c2e47a974c881c56af2b2d116 --- /dev/null +++ b/ads_16_length_8000/MACE/mace.sh @@ -0,0 +1,69 @@ +#!/bin/bash + +#SBATCH --job-name=m_16_8k # 作业名称 +#SBATCH --account=PAS2490 # Project ID +#SBATCH --nodes=1 # 节点数 +#SBATCH --ntasks-per-node=1 # 每个节点的任务数 +#SBATCH --cpus-per-task=16 # 每个任务使用的 CPU 核心数 +#SBATCH --gpus-per-node=1 # GPU per node +#SBATCH --mem=200G # 内存限制 +#SBATCH --time=167:00:00 # 作业运行时间限制 + +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=uceckz0@ucl.ac.uk + + +source $HOME/miniconda3/etc/profile.d/conda.sh +conda activate mace_env + + + +convert_to_seconds() { + date -d "${1//_/ }" +%s +} + +start_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train start time: $start_time" + + + + +mace_run_train \ + --name="MACE_model" \ + --train_file="../trainset.xyz" \ + --num_workers=16 \ + --valid_fraction=0.0625 \ + --test_file="../testset.xyz" \ + --config_type_weights='{"Default":1.0}' \ + --E0s='{1:-13.663181292231226, 6:-1029.2809654211628, 7:-1484.1187695035828, 8:-2042.0330099956639, 30:-500.123456789}' \ + --lr=0.0001 \ + --model="MACE" \ + --hidden_irreps='128x0e + 128x1o' \ + --r_max=5.0 \ + --batch_size=10 \ + --max_num_epochs=500 \ + --swa \ + --ema \ + --ema_decay=0.99 \ + --amsgrad \ + --restart_latest \ + --device=cuda \ + + + + + +end_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train end time: $end_time" + +start_seconds=$(convert_to_seconds "$start_time") +end_seconds=$(convert_to_seconds "$end_time") +time_difference=$((end_seconds - start_seconds)) + +echo "Training duration: $time_difference seconds" + +duration_h=$((time_difference / 3600)) +duration_m=$(((time_difference % 3600) / 60)) +duration_s=$((time_difference % 60)) + +echo "Training duration: ${duration_h}h ${duration_m}m ${duration_s}s" \ No newline at end of file diff --git a/ads_16_length_8000/MACE/slurm-257522.out b/ads_16_length_8000/MACE/slurm-257522.out new file mode 100644 index 0000000000000000000000000000000000000000..5f3256252eb8534c458c1c4c507a7e4be7715fc7 --- /dev/null +++ b/ads_16_length_8000/MACE/slurm-257522.out @@ -0,0 +1,626 @@ +Train start time: 2024-12-08_10:26:56 +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/e3nn/o3/_wigner.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + _Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(__file__), 'constants.pt')) +2024-12-08 10:27:04.088 INFO: ===========VERIFYING SETTINGS=========== +2024-12-08 10:27:04.088 INFO: MACE version: 0.3.9 +2024-12-08 10:27:04.251 INFO: CUDA version: 12.4, CUDA device: 0 +2024-12-08 10:27:04.360 INFO: ===========LOADING INPUT DATA=========== +2024-12-08 10:27:04.361 INFO: Using heads: ['default'] +2024-12-08 10:27:04.361 INFO: ============= Processing head default =========== +2024-12-08 10:27:14.213 INFO: Training set [8000 configs, 0 energy, 9600000 forces] loaded from '../trainset.xyz' +2024-12-08 10:27:14.216 INFO: Using random 6% of training set for validation with indices saved in: ./valid_indices_123.txt +2024-12-08 10:27:14.217 INFO: Validaton set contains 500 configurations [0 energy, 600000 forces] +2024-12-08 10:27:14.834 INFO: Test set (500 configs) loaded from '../testset.xyz': +2024-12-08 10:27:14.834 INFO: Default_Default: 500 configs, 0 energy, 600000 forces +2024-12-08 10:27:14.837 INFO: Total number of configurations: train=7500, valid=500, tests=[Default_Default: 500], +2024-12-08 10:27:15.150 INFO: Atomic Numbers used: [1, 6, 7, 8, 30] +2024-12-08 10:27:15.150 INFO: Isolated Atomic Energies (E0s) not in training file, using command line argument +2024-12-08 10:27:15.151 INFO: Atomic Energies used (z: eV) for head default: {1: -13.663181292231226, 6: -1029.2809654211628, 7: -1484.1187695035828, 8: -2042.0330099956639, 30: -500.123456789} +2024-12-08 10:30:32.377 INFO: Computing average number of neighbors +2024-12-08 10:30:57.864 INFO: Average number of neighbors: 34.390905333333336 +2024-12-08 10:30:57.866 INFO: During training the following quantities will be reported: energy, forces +2024-12-08 10:30:57.866 INFO: ===========MODEL DETAILS=========== +2024-12-08 10:31:09.971 WARNING: Standard deviation of the scaling is zero, Changing to no scaling +2024-12-08 10:31:09.973 INFO: Building model +2024-12-08 10:31:09.973 INFO: Message passing with 128 channels and max_L=1 (128x0e + 128x1o) +2024-12-08 10:31:09.973 INFO: 2 layers, each with correlation order: 3 (body order: 4) and spherical harmonics up to: l=3 +2024-12-08 10:31:09.974 INFO: 8 radial and 5 basis functions +2024-12-08 10:31:09.974 INFO: Radial cutoff: 5.0 A (total receptive field for each atom: 10.0 A) +2024-12-08 10:31:09.974 INFO: Distance transform for radial basis functions: None +2024-12-08 10:31:09.974 INFO: Hidden irreps: 128x0e + 128x1o +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`. + warnings.warn( +2024-12-08 10:31:11.311 INFO: Total number of parameters: 945168 +2024-12-08 10:31:11.311 INFO: +2024-12-08 10:31:11.312 INFO: ===========OPTIMIZER INFORMATION=========== +2024-12-08 10:31:11.312 INFO: Using ADAM as parameter optimizer +2024-12-08 10:31:11.312 INFO: Batch size: 10 +2024-12-08 10:31:11.312 INFO: Using Exponential Moving Average with decay: 0.99 +2024-12-08 10:31:11.312 INFO: Number of gradient updates: 375000 +2024-12-08 10:31:11.312 INFO: Learning rate: 0.0001, weight decay: 5e-07 +2024-12-08 10:31:11.312 INFO: WeightedEnergyForcesLoss(energy_weight=1.000, forces_weight=100.000) +2024-12-08 10:31:11.313 INFO: Stage Two (after 375 epochs) with loss function: WeightedEnergyForcesLoss(energy_weight=1000.000, forces_weight=100.000), with energy weight : 1000.0, forces weight : 100.0 and learning rate : 0.001 +2024-12-08 10:31:11.411 WARNING: Cannot find checkpoint with tag 'MACE_model_run-123' in './checkpoints' +2024-12-08 10:31:11.416 INFO: Using gradient clipping with tolerance=10.000 +2024-12-08 10:31:11.416 INFO: +2024-12-08 10:31:11.416 INFO: ===========TRAINING=========== +2024-12-08 10:31:11.416 INFO: Started training, reporting errors on validation set +2024-12-08 10:31:11.416 INFO: Loss metrics on validation set +2024-12-08 10:31:30.817 INFO: Initial: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.59 meV, RMSE_F= 8.00 meV / A +2024-12-08 10:43:28.626 INFO: Epoch 0: head: default, loss=0.00000000, RMSE_E_per_atom=1176669.81 meV, RMSE_F= 6.93 meV / A +2024-12-08 10:55:24.333 INFO: Epoch 1: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.04 meV, RMSE_F= 5.86 meV / A +2024-12-08 11:07:20.041 INFO: Epoch 2: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.25 meV, RMSE_F= 4.86 meV / A +2024-12-08 11:19:16.210 INFO: Epoch 3: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.43 meV, RMSE_F= 3.98 meV / A +2024-12-08 11:31:11.991 INFO: Epoch 4: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.58 meV, RMSE_F= 3.25 meV / A +2024-12-08 11:43:06.856 INFO: Epoch 5: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.70 meV, RMSE_F= 2.65 meV / A +2024-12-08 11:55:01.165 INFO: Epoch 6: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.80 meV, RMSE_F= 2.16 meV / A +2024-12-08 12:06:56.362 INFO: Epoch 7: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.87 meV, RMSE_F= 1.76 meV / A +2024-12-08 12:18:50.358 INFO: Epoch 8: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.93 meV, RMSE_F= 1.44 meV / A +2024-12-08 12:30:46.020 INFO: Epoch 9: head: default, loss=0.00000000, RMSE_E_per_atom=1176670.97 meV, RMSE_F= 1.18 meV / A +2024-12-08 12:42:40.820 INFO: Epoch 10: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.00 meV, RMSE_F= 0.96 meV / A +2024-12-08 12:54:35.332 INFO: Epoch 11: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.03 meV, RMSE_F= 0.79 meV / A +2024-12-08 13:06:30.753 INFO: Epoch 12: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.05 meV, RMSE_F= 0.65 meV / A +2024-12-08 13:18:26.112 INFO: Epoch 13: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.06 meV, RMSE_F= 0.53 meV / A +2024-12-08 13:30:20.810 INFO: Epoch 14: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.07 meV, RMSE_F= 0.44 meV / A +2024-12-08 13:42:15.196 INFO: Epoch 15: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.36 meV / A +2024-12-08 13:54:09.819 INFO: Epoch 16: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.30 meV / A +2024-12-08 14:06:05.537 INFO: Epoch 17: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.25 meV / A +2024-12-08 14:18:01.007 INFO: Epoch 18: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.21 meV / A +2024-12-08 14:29:56.653 INFO: Epoch 19: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.17 meV / A +2024-12-08 14:41:52.358 INFO: Epoch 20: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.14 meV / A +2024-12-08 14:53:48.513 INFO: Epoch 21: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.12 meV / A +2024-12-08 15:05:43.359 INFO: Epoch 22: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.10 meV / A +2024-12-08 15:17:38.974 INFO: Epoch 23: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.08 meV / A +2024-12-08 15:29:34.686 INFO: Epoch 24: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.07 meV / A +2024-12-08 15:41:29.597 INFO: Epoch 25: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.06 meV / A +2024-12-08 15:53:25.294 INFO: Epoch 26: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.05 meV / A +2024-12-08 16:05:20.961 INFO: Epoch 27: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:17:16.607 INFO: Epoch 28: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.04 meV / A +2024-12-08 16:29:12.261 INFO: Epoch 29: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:41:07.925 INFO: Epoch 30: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 16:53:02.602 INFO: Epoch 31: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.03 meV / A +2024-12-08 17:04:57.046 INFO: Epoch 32: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:16:51.740 INFO: Epoch 33: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:28:46.123 INFO: Epoch 34: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:40:42.071 INFO: Epoch 35: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 17:52:37.361 INFO: Epoch 36: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.02 meV / A +2024-12-08 18:04:33.026 INFO: Epoch 37: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:16:28.769 INFO: Epoch 38: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.09 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:28:23.730 INFO: Epoch 39: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:40:18.114 INFO: Epoch 40: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 18:52:13.648 INFO: Epoch 41: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:04:09.270 INFO: Epoch 42: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:16:05.154 INFO: Epoch 43: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:27:59.796 INFO: Epoch 44: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:39:55.525 INFO: Epoch 45: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 19:51:51.177 INFO: Epoch 46: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:03:45.570 INFO: Epoch 47: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:15:41.084 INFO: Epoch 48: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:27:36.946 INFO: Epoch 49: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:39:32.505 INFO: Epoch 50: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 20:51:28.227 INFO: Epoch 51: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:03:23.063 INFO: Epoch 52: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.01 meV / A +2024-12-08 21:15:18.938 INFO: Epoch 53: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:27:15.045 INFO: Epoch 54: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:39:09.591 INFO: Epoch 55: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 21:51:05.492 INFO: Epoch 56: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:02:59.714 INFO: Epoch 57: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:14:55.585 INFO: Epoch 58: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:26:51.102 INFO: Epoch 59: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:38:45.753 INFO: Epoch 60: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 22:50:41.405 INFO: Epoch 61: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:02:37.139 INFO: Epoch 62: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:14:32.989 INFO: Epoch 63: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:26:28.808 INFO: Epoch 64: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:38:23.213 INFO: Epoch 65: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-08 23:50:19.277 INFO: Epoch 66: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:02:15.317 INFO: Epoch 67: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:14:11.434 INFO: Epoch 68: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:26:07.195 INFO: Epoch 69: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:38:01.588 INFO: Epoch 70: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 00:49:55.998 INFO: Epoch 71: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:01:50.464 INFO: Epoch 72: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:13:44.950 INFO: Epoch 73: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:25:39.761 INFO: Epoch 74: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:37:34.518 INFO: Epoch 75: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 01:49:29.934 INFO: Epoch 76: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:01:24.386 INFO: Epoch 77: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:13:18.984 INFO: Epoch 78: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:25:13.768 INFO: Epoch 79: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:37:08.653 INFO: Epoch 80: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 02:49:04.292 INFO: Epoch 81: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:01:00.252 INFO: Epoch 82: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:12:54.966 INFO: Epoch 83: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:24:50.005 INFO: Epoch 84: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:36:44.612 INFO: Epoch 85: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 03:48:39.873 INFO: Epoch 86: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:00:34.728 INFO: Epoch 87: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:12:30.321 INFO: Epoch 88: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:24:25.177 INFO: Epoch 89: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:36:19.942 INFO: Epoch 90: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 04:48:15.858 INFO: Epoch 91: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:00:10.664 INFO: Epoch 92: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:12:05.266 INFO: Epoch 93: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:24:01.304 INFO: Epoch 94: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:35:57.308 INFO: Epoch 95: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:47:52.114 INFO: Epoch 96: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 05:59:48.009 INFO: Epoch 97: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:11:43.926 INFO: Epoch 98: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:23:39.755 INFO: Epoch 99: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:35:35.255 INFO: Epoch 100: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:47:31.170 INFO: Epoch 101: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 06:59:27.024 INFO: Epoch 102: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:11:23.070 INFO: Epoch 103: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:23:17.336 INFO: Epoch 104: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:35:13.214 INFO: Epoch 105: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:47:09.766 INFO: Epoch 106: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 07:59:05.856 INFO: Epoch 107: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:11:02.067 INFO: Epoch 108: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:22:58.239 INFO: Epoch 109: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:34:54.232 INFO: Epoch 110: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:46:48.791 INFO: Epoch 111: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 08:58:44.589 INFO: Epoch 112: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:10:40.363 INFO: Epoch 113: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:22:36.216 INFO: Epoch 114: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:34:32.085 INFO: Epoch 115: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:46:27.706 INFO: Epoch 116: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 09:58:23.697 INFO: Epoch 117: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:10:19.309 INFO: Epoch 118: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:22:13.647 INFO: Epoch 119: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:34:09.444 INFO: Epoch 120: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:46:04.236 INFO: Epoch 121: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 10:58:00.456 INFO: Epoch 122: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:09:56.544 INFO: Epoch 123: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:21:52.460 INFO: Epoch 124: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:33:48.452 INFO: Epoch 125: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:45:43.100 INFO: Epoch 126: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 11:57:39.277 INFO: Epoch 127: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:09:35.273 INFO: Epoch 128: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:21:31.020 INFO: Epoch 129: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:33:26.694 INFO: Epoch 130: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:45:21.532 INFO: Epoch 131: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 12:57:16.909 INFO: Epoch 132: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:09:11.244 INFO: Epoch 133: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:21:07.247 INFO: Epoch 134: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:33:02.393 INFO: Epoch 135: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:44:56.950 INFO: Epoch 136: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 13:56:51.236 INFO: Epoch 137: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:08:46.769 INFO: Epoch 138: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:20:42.559 INFO: Epoch 139: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:32:38.679 INFO: Epoch 140: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:44:33.998 INFO: Epoch 141: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 14:56:29.747 INFO: Epoch 142: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:08:24.058 INFO: Epoch 143: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:20:19.888 INFO: Epoch 144: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:32:14.869 INFO: Epoch 145: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:44:10.713 INFO: Epoch 146: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 15:56:06.378 INFO: Epoch 147: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:08:02.365 INFO: Epoch 148: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:19:58.283 INFO: Epoch 149: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:31:52.524 INFO: Epoch 150: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:43:46.959 INFO: Epoch 151: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 16:55:41.448 INFO: Epoch 152: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:07:36.941 INFO: Epoch 153: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:19:32.118 INFO: Epoch 154: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:31:26.638 INFO: Epoch 155: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:43:21.528 INFO: Epoch 156: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 17:55:17.216 INFO: Epoch 157: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:07:13.149 INFO: Epoch 158: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:19:09.055 INFO: Epoch 159: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:31:05.099 INFO: Epoch 160: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:43:00.978 INFO: Epoch 161: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 18:54:57.010 INFO: Epoch 162: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:06:50.934 INFO: Epoch 163: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:18:46.637 INFO: Epoch 164: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:30:42.231 INFO: Epoch 165: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:42:37.914 INFO: Epoch 166: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 19:54:32.246 INFO: Epoch 167: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:06:27.867 INFO: Epoch 168: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:18:23.413 INFO: Epoch 169: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:30:18.183 INFO: Epoch 170: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:42:12.599 INFO: Epoch 171: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 20:54:06.951 INFO: Epoch 172: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:06:01.673 INFO: Epoch 173: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:17:55.924 INFO: Epoch 174: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:29:50.044 INFO: Epoch 175: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:41:45.049 INFO: Epoch 176: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 21:53:40.899 INFO: Epoch 177: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:05:35.776 INFO: Epoch 178: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:17:31.020 INFO: Epoch 179: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:29:25.430 INFO: Epoch 180: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:41:19.621 INFO: Epoch 181: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 22:53:14.971 INFO: Epoch 182: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:05:09.267 INFO: Epoch 183: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:17:05.126 INFO: Epoch 184: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:28:59.637 INFO: Epoch 185: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:40:55.474 INFO: Epoch 186: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-09 23:52:51.380 INFO: Epoch 187: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:04:47.228 INFO: Epoch 188: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:16:42.851 INFO: Epoch 189: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:28:38.552 INFO: Epoch 190: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:40:34.291 INFO: Epoch 191: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 00:52:30.056 INFO: Epoch 192: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:04:25.410 INFO: Epoch 193: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:16:19.643 INFO: Epoch 194: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:28:14.878 INFO: Epoch 195: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:40:10.368 INFO: Epoch 196: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 01:52:05.628 INFO: Epoch 197: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:04:00.778 INFO: Epoch 198: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:15:56.390 INFO: Epoch 199: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:27:52.148 INFO: Epoch 200: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:39:47.606 INFO: Epoch 201: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 02:51:41.996 INFO: Epoch 202: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:03:37.665 INFO: Epoch 203: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:15:31.950 INFO: Epoch 204: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:27:26.433 INFO: Epoch 205: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:39:22.356 INFO: Epoch 206: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 03:51:18.392 INFO: Epoch 207: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:03:14.075 INFO: Epoch 208: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:15:10.049 INFO: Epoch 209: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:27:04.899 INFO: Epoch 210: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:39:00.783 INFO: Epoch 211: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 04:50:55.489 INFO: Epoch 212: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:02:49.689 INFO: Epoch 213: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:14:45.240 INFO: Epoch 214: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:26:39.838 INFO: Epoch 215: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:38:35.501 INFO: Epoch 216: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 05:50:30.052 INFO: Epoch 217: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:02:24.517 INFO: Epoch 218: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:14:18.570 INFO: Epoch 219: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:26:13.901 INFO: Epoch 220: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:38:08.213 INFO: Epoch 221: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 06:50:02.661 INFO: Epoch 222: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:01:58.533 INFO: Epoch 223: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:13:54.322 INFO: Epoch 224: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:25:48.965 INFO: Epoch 225: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:37:43.391 INFO: Epoch 226: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 07:49:37.894 INFO: Epoch 227: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:01:33.418 INFO: Epoch 228: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:13:27.632 INFO: Epoch 229: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:25:22.885 INFO: Epoch 230: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:37:18.015 INFO: Epoch 231: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 08:49:12.793 INFO: Epoch 232: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:01:07.307 INFO: Epoch 233: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:13:03.175 INFO: Epoch 234: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:24:59.223 INFO: Epoch 235: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:36:54.239 INFO: Epoch 236: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 09:48:50.157 INFO: Epoch 237: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:00:44.812 INFO: Epoch 238: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:12:40.580 INFO: Epoch 239: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:24:36.577 INFO: Epoch 240: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:36:31.786 INFO: Epoch 241: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 10:48:27.805 INFO: Epoch 242: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:00:23.771 INFO: Epoch 243: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:12:19.466 INFO: Epoch 244: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:24:14.560 INFO: Epoch 245: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:36:09.015 INFO: Epoch 246: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:48:03.884 INFO: Epoch 247: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 11:59:59.919 INFO: Epoch 248: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:11:55.799 INFO: Epoch 249: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:23:51.018 INFO: Epoch 250: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:35:46.975 INFO: Epoch 251: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:47:42.845 INFO: Epoch 252: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 12:59:37.534 INFO: Epoch 253: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:11:32.204 INFO: Epoch 254: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:23:28.131 INFO: Epoch 255: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:35:23.078 INFO: Epoch 256: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:47:19.199 INFO: Epoch 257: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 13:59:15.363 INFO: Epoch 258: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:11:11.490 INFO: Epoch 259: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:23:05.967 INFO: Epoch 260: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:35:00.533 INFO: Epoch 261: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:46:54.970 INFO: Epoch 262: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 14:58:49.732 INFO: Epoch 263: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:10:45.906 INFO: Epoch 264: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:22:42.249 INFO: Epoch 265: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:34:38.257 INFO: Epoch 266: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:46:34.251 INFO: Epoch 267: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 15:58:29.051 INFO: Epoch 268: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:10:25.133 INFO: Epoch 269: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:22:20.231 INFO: Epoch 270: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:34:15.149 INFO: Epoch 271: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:46:10.635 INFO: Epoch 272: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 16:58:05.279 INFO: Epoch 273: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:10:01.505 INFO: Epoch 274: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:21:57.648 INFO: Epoch 275: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:33:53.978 INFO: Epoch 276: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:45:49.756 INFO: Epoch 277: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 17:57:44.828 INFO: Epoch 278: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:09:40.943 INFO: Epoch 279: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:21:36.478 INFO: Epoch 280: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:33:32.323 INFO: Epoch 281: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:45:28.483 INFO: Epoch 282: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 18:57:24.715 INFO: Epoch 283: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:09:20.895 INFO: Epoch 284: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:21:17.034 INFO: Epoch 285: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:33:13.103 INFO: Epoch 286: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:45:08.280 INFO: Epoch 287: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 19:57:04.237 INFO: Epoch 288: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:08:59.195 INFO: Epoch 289: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:20:54.913 INFO: Epoch 290: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:32:50.979 INFO: Epoch 291: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:44:46.484 INFO: Epoch 292: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 20:56:41.983 INFO: Epoch 293: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:08:37.460 INFO: Epoch 294: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:20:33.870 INFO: Epoch 295: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:32:29.680 INFO: Epoch 296: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:44:25.832 INFO: Epoch 297: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 21:56:20.853 INFO: Epoch 298: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:08:16.750 INFO: Epoch 299: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:20:12.433 INFO: Epoch 300: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:32:08.522 INFO: Epoch 301: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:44:03.243 INFO: Epoch 302: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 22:55:58.890 INFO: Epoch 303: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:07:54.926 INFO: Epoch 304: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:19:50.900 INFO: Epoch 305: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:31:45.740 INFO: Epoch 306: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:43:41.048 INFO: Epoch 307: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-10 23:55:36.787 INFO: Epoch 308: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:07:31.244 INFO: Epoch 309: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:19:27.111 INFO: Epoch 310: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:31:21.678 INFO: Epoch 311: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:43:17.745 INFO: Epoch 312: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 00:55:12.267 INFO: Epoch 313: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:07:07.731 INFO: Epoch 314: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:19:03.615 INFO: Epoch 315: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:30:58.174 INFO: Epoch 316: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:42:52.805 INFO: Epoch 317: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 01:54:48.978 INFO: Epoch 318: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:06:43.606 INFO: Epoch 319: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:18:39.721 INFO: Epoch 320: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:30:34.259 INFO: Epoch 321: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:42:29.325 INFO: Epoch 322: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 02:54:24.772 INFO: Epoch 323: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:06:20.497 INFO: Epoch 324: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:18:15.937 INFO: Epoch 325: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:30:11.892 INFO: Epoch 326: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:42:07.172 INFO: Epoch 327: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 03:54:02.531 INFO: Epoch 328: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:05:58.271 INFO: Epoch 329: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:17:52.760 INFO: Epoch 330: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:29:47.873 INFO: Epoch 331: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:41:42.319 INFO: Epoch 332: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 04:53:37.290 INFO: Epoch 333: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:05:33.474 INFO: Epoch 334: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:17:29.446 INFO: Epoch 335: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:29:25.437 INFO: Epoch 336: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:41:21.785 INFO: Epoch 337: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 05:53:17.973 INFO: Epoch 338: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:05:12.479 INFO: Epoch 339: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:17:06.952 INFO: Epoch 340: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:29:02.964 INFO: Epoch 341: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:40:57.533 INFO: Epoch 342: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 06:52:52.026 INFO: Epoch 343: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:04:47.944 INFO: Epoch 344: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:16:44.328 INFO: Epoch 345: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:28:40.385 INFO: Epoch 346: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:40:36.110 INFO: Epoch 347: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 07:52:32.080 INFO: Epoch 348: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:04:28.075 INFO: Epoch 349: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:16:24.417 INFO: Epoch 350: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:28:20.381 INFO: Epoch 351: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:40:15.843 INFO: Epoch 352: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 08:52:10.309 INFO: Epoch 353: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:04:05.831 INFO: Epoch 354: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:16:00.698 INFO: Epoch 355: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:27:56.307 INFO: Epoch 356: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:39:51.849 INFO: Epoch 357: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 09:51:47.267 INFO: Epoch 358: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:03:42.795 INFO: Epoch 359: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:15:38.943 INFO: Epoch 360: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:27:35.120 INFO: Epoch 361: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:39:31.186 INFO: Epoch 362: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 10:51:25.968 INFO: Epoch 363: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:03:21.984 INFO: Epoch 364: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:15:18.545 INFO: Epoch 365: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:27:13.772 INFO: Epoch 366: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:39:09.617 INFO: Epoch 367: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 11:51:05.646 INFO: Epoch 368: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:03:00.493 INFO: Epoch 369: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:14:54.868 INFO: Epoch 370: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:26:50.754 INFO: Epoch 371: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:38:46.519 INFO: Epoch 372: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 12:50:43.315 INFO: Epoch 373: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.939 INFO: Epoch 374: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:02:38.940 INFO: Changing loss based on Stage Two Weights +2024-12-11 13:14:33.194 INFO: Epoch 375: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:26:29.020 INFO: Epoch 376: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:38:23.794 INFO: Epoch 377: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 13:50:19.535 INFO: Epoch 378: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:02:15.204 INFO: Epoch 379: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:14:10.327 INFO: Epoch 380: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:26:05.510 INFO: Epoch 381: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:38:01.439 INFO: Epoch 382: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 14:49:56.048 INFO: Epoch 383: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:01:51.347 INFO: Epoch 384: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:13:45.633 INFO: Epoch 385: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:25:40.499 INFO: Epoch 386: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:37:36.110 INFO: Epoch 387: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 15:49:30.718 INFO: Epoch 388: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:01:25.225 INFO: Epoch 389: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:13:20.954 INFO: Epoch 390: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:25:16.513 INFO: Epoch 391: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:37:11.872 INFO: Epoch 392: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 16:49:07.412 INFO: Epoch 393: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:01:03.197 INFO: Epoch 394: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:12:58.369 INFO: Epoch 395: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:24:54.031 INFO: Epoch 396: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:36:48.891 INFO: Epoch 397: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 17:48:44.528 INFO: Epoch 398: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:00:39.247 INFO: Epoch 399: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:12:34.874 INFO: Epoch 400: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:24:30.006 INFO: Epoch 401: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:36:24.850 INFO: Epoch 402: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 18:48:19.214 INFO: Epoch 403: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:00:13.313 INFO: Epoch 404: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:12:09.002 INFO: Epoch 405: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:24:03.380 INFO: Epoch 406: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:35:58.179 INFO: Epoch 407: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:47:52.307 INFO: Epoch 408: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 19:59:47.012 INFO: Epoch 409: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:11:41.308 INFO: Epoch 410: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:23:36.968 INFO: Epoch 411: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:35:31.946 INFO: Epoch 412: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:47:27.753 INFO: Epoch 413: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 20:59:23.042 INFO: Epoch 414: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:11:18.783 INFO: Epoch 415: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:23:13.289 INFO: Epoch 416: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:35:09.165 INFO: Epoch 417: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:47:04.044 INFO: Epoch 418: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 21:59:00.084 INFO: Epoch 419: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:10:54.856 INFO: Epoch 420: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:22:49.777 INFO: Epoch 421: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:34:45.349 INFO: Epoch 422: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:46:41.212 INFO: Epoch 423: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 22:58:36.201 INFO: Epoch 424: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:10:30.588 INFO: Epoch 425: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:22:26.463 INFO: Epoch 426: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:34:20.665 INFO: Epoch 427: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:46:14.983 INFO: Epoch 428: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-11 23:58:10.355 INFO: Epoch 429: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:10:05.891 INFO: Epoch 430: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:22:01.697 INFO: Epoch 431: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:33:56.776 INFO: Epoch 432: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:45:52.555 INFO: Epoch 433: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 00:57:47.702 INFO: Epoch 434: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:09:42.965 INFO: Epoch 435: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:21:37.852 INFO: Epoch 436: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:33:32.371 INFO: Epoch 437: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:45:28.261 INFO: Epoch 438: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 01:57:23.224 INFO: Epoch 439: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:09:18.250 INFO: Epoch 440: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:21:12.860 INFO: Epoch 441: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:33:08.768 INFO: Epoch 442: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:45:03.156 INFO: Epoch 443: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 02:56:57.936 INFO: Epoch 444: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:08:52.879 INFO: Epoch 445: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:20:47.714 INFO: Epoch 446: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:32:43.567 INFO: Epoch 447: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:44:39.520 INFO: Epoch 448: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 03:56:35.643 INFO: Epoch 449: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:08:30.160 INFO: Epoch 450: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:20:25.858 INFO: Epoch 451: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:32:21.608 INFO: Epoch 452: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:44:17.563 INFO: Epoch 453: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 04:56:11.764 INFO: Epoch 454: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:08:07.183 INFO: Epoch 455: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:20:03.056 INFO: Epoch 456: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:31:58.887 INFO: Epoch 457: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:43:54.344 INFO: Epoch 458: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 05:55:49.903 INFO: Epoch 459: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:07:45.294 INFO: Epoch 460: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:19:41.162 INFO: Epoch 461: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:31:36.277 INFO: Epoch 462: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:43:30.875 INFO: Epoch 463: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 06:55:25.307 INFO: Epoch 464: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:07:20.807 INFO: Epoch 465: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:19:14.752 INFO: Epoch 466: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:31:10.036 INFO: Epoch 467: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:43:04.418 INFO: Epoch 468: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 07:54:59.764 INFO: Epoch 469: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:06:55.514 INFO: Epoch 470: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:18:50.829 INFO: Epoch 471: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:30:44.814 INFO: Epoch 472: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:42:39.933 INFO: Epoch 473: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 08:54:35.589 INFO: Epoch 474: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:06:30.907 INFO: Epoch 475: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:18:26.439 INFO: Epoch 476: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:30:21.470 INFO: Epoch 477: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:42:15.962 INFO: Epoch 478: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 09:54:11.244 INFO: Epoch 479: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:06:06.781 INFO: Epoch 480: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:18:02.568 INFO: Epoch 481: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:29:58.016 INFO: Epoch 482: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:41:53.982 INFO: Epoch 483: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 10:53:49.866 INFO: Epoch 484: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:05:45.533 INFO: Epoch 485: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:17:41.308 INFO: Epoch 486: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:29:36.139 INFO: Epoch 487: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:41:31.906 INFO: Epoch 488: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 11:53:26.445 INFO: Epoch 489: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:05:22.451 INFO: Epoch 490: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:17:16.848 INFO: Epoch 491: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:29:12.518 INFO: Epoch 492: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:41:08.396 INFO: Epoch 493: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 12:53:04.225 INFO: Epoch 494: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:04:59.969 INFO: Epoch 495: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:16:55.385 INFO: Epoch 496: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:28:50.189 INFO: Epoch 497: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:40:44.721 INFO: Epoch 498: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.296 INFO: Epoch 499: head: default, loss=0.00000000, RMSE_E_per_atom=1176671.08 meV, RMSE_F= 0.00 meV / A +2024-12-12 13:52:40.297 INFO: Training complete +2024-12-12 13:52:40.297 INFO: +2024-12-12 13:52:40.297 INFO: ===========RESULTS=========== +2024-12-12 13:52:40.297 INFO: Computing metrics for training, validation, and test sets +2024-12-12 13:52:52.999 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-0.pt +/users/PAS2490/marcusshen/miniconda3/envs/mace_env/lib/python3.10/site-packages/mace/tools/checkpoint.py:187: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. + torch.load(f=checkpoint_info.path, map_location=device), +2024-12-12 13:52:53.078 INFO: Loaded Stage one model from epoch 0 for evaluation +2024-12-12 13:52:53.079 INFO: Evaluating train_default ... +2024-12-12 13:56:45.053 INFO: Evaluating valid_default ... +2024-12-12 13:57:02.036 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176669.8 | 6.9 | 693943104.62 | +| valid_default | 1176669.8 | 6.9 | 693187632.22 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:02.036 INFO: Evaluating Default_Default ... +2024-12-12 13:57:19.559 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176669.8 | 7.0 | 697278235.90 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 13:57:19.560 INFO: Saving model to checkpoints/MACE_model_run-123.model +2024-12-12 13:57:19.747 INFO: Compiling model, saving metadata to MACE_model_compiled.model +2024-12-12 13:57:20.459 INFO: Loading checkpoint: ./checkpoints/MACE_model_run-123_epoch-375_swa.pt +2024-12-12 13:57:20.576 INFO: Loaded Stage two model from epoch 375 for evaluation +2024-12-12 13:57:20.576 INFO: Evaluating train_default ... +2024-12-12 14:01:12.309 INFO: Evaluating valid_default ... +2024-12-12 14:01:29.292 INFO: Error-table on TRAIN and VALID: ++---------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++---------------+---------------------+------------------+-------------------+ +| train_default | 1176671.1 | 0.0 | 2.07 | +| valid_default | 1176671.1 | 0.0 | 2.07 | ++---------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:29.293 INFO: Evaluating Default_Default ... +2024-12-12 14:01:46.864 INFO: Error-table on TEST: ++-----------------+---------------------+------------------+-------------------+ +| config_type | RMSE E / meV / atom | RMSE F / meV / A | relative F RMSE % | ++-----------------+---------------------+------------------+-------------------+ +| Default_Default | 1176671.1 | 0.0 | 2.07 | ++-----------------+---------------------+------------------+-------------------+ +2024-12-12 14:01:46.864 INFO: Saving model to checkpoints/MACE_model_run-123_stagetwo.model +2024-12-12 14:01:47.056 INFO: Compiling model, saving metadata MACE_model_stagetwo_compiled.model +2024-12-12 14:01:47.690 INFO: Done +Train end time: 2024-12-12_14:01:50 +Training duration: 358494 seconds +Training duration: 99h 34m 54s diff --git a/ads_16_length_8000/MACE/valid_indices_123.txt b/ads_16_length_8000/MACE/valid_indices_123.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0700e90cea445e6c56cae4b20d855ebd6df30f7 --- /dev/null +++ b/ads_16_length_8000/MACE/valid_indices_123.txt @@ -0,0 +1,500 @@ +1636 +6891 +1535 +1398 +7267 +1208 +2363 +6387 +3369 +7695 +7775 +2070 +5946 +882 +4652 +5235 +290 +3203 +7759 +3773 +7098 +7557 +6087 +547 +3188 +2122 +3220 +6817 +4802 +1718 +1526 +7472 +2959 +1530 +2294 +201 +6695 +5033 +2985 +7880 +5532 +124 +6312 +4079 +7682 +6673 +3308 +4994 +756 +2101 +2103 +7813 +4853 +3052 +5005 +4603 +6636 +3068 +794 +6783 +5436 +7571 +1224 +2906 +1657 +3450 +1103 +2194 +7688 +5450 +6390 +6824 +4319 +5265 +2328 +7450 +3757 +5043 +970 +6350 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+#SBATCH --time=24:00:00 # 作业运行时间限制 + +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=uceckz0@ucl.ac.uk + +convert_to_seconds() { + date -d "${1//_/ }" +%s +} +start_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train start time: $start_time" + + +source $HOME/miniconda3/etc/profile.d/conda.sh +conda activate nequip_env +rm -rf ./results +nequip-train train_config.yaml --warn-unused +nequip-evaluate \ + --train-dir ./results/adsorption/ \ + --dataset-config ./test_config.yaml \ + --metrics-config ./test_config.yaml + +end_time=$(date +%Y-%m-%d_%H:%M:%S) +echo "Train end time: $end_time" +start_seconds=$(convert_to_seconds "$start_time") +end_seconds=$(convert_to_seconds "$end_time") +time_difference=$((end_seconds - start_seconds)) +duration_h=$((time_difference / 3600)) +duration_m=$(((time_difference % 3600) / 60)) +duration_s=$((time_difference % 60)) +echo "Training duration: ${duration_h}h ${duration_m}m ${duration_s}s" \ No newline at end of file diff --git a/ads_16_length_8000/NEQUIP/results/adsorption/config.yaml b/ads_16_length_8000/NEQUIP/results/adsorption/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c4015353afea69d51f6dc686f5dd3cb6120fb732 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/results/adsorption/config.yaml @@ -0,0 +1,2425 @@ +_jit_bailout_depth: 2 +_jit_fuser: fuser1 +_jit_fusion_strategy: +- !!python/tuple + - DYNAMIC + - 3 +allow_tf32: true +append: true +ase_args: + format: extxyz +avg_num_neighbors: 34.345919333333335 +batch_size: 64 +chemical_embedding_irreps_out: 64x0e +chemical_symbols: +- H +- C +- N +- Zn +- O +code_commits: {} +conv_to_output_hidden_irreps_out: 32x0e +dataloader_num_workers: 0 +dataset: ase +dataset_AtomicData_options: + r_max: 5.0 +dataset_file_name: ../trainset.xyz +dataset_key_mapping: + force: forces +dataset_seed: 456 +dataset_statistics_stride: 1 +default_dtype: float64 +device: cuda +e3nn_version: 0.5.4 +early_stopping: null +early_stopping_kwargs: null +ema_decay: 0.999 +ema_use_num_updates: true +end_of_batch_callbacks: [] +end_of_epoch_callbacks: [] +end_of_train_callbacks: [] +equivariance_test: false +exclude_keys: [] +feature_irreps_hidden: 64x0e+64x1e +final_callbacks: [] +gpu_oom_offload: false +grad_anomaly_mode: false +init_callbacks: [] +irreps_edge_sh: 1x0e+1x1e +l_max: 1 +learning_rate: 0.005 +log_batch_freq: 100 +log_epoch_freq: 1 +loss_coeffs: + forces: + - 20 + total_energy: + - 1 +lr_scheduler_kwargs: {} +lr_scheduler_name: none +max_epochs: 500 +max_gradient_norm: .inf +metrics_components: +- - forces + - mae + - PerSpecies: false + report_per_component: false +- - total_energy + - mae + - PerAtom: false +- - forces + - rmse + - PerSpecies: false + report_per_component: false +- - total_energy + - rmse + - PerAtom: false +- - total_energy + - mae + - PerAtom: true +- - total_energy + - rmse + - PerAtom: true +metrics_key: validation_loss +model_builders: +- SimpleIrrepsConfig +- EnergyModel +- PerSpeciesRescale +- StressForceOutput +- RescaleEnergyEtc +model_debug_mode: false +model_dtype: float32 +n_train: 7500 +n_train_per_epoch: null +n_val: 500 +nequip_version: 0.6.1 +num_basis: 8 +num_features: 64 +num_layers: 4 +num_types: 5 +optimizer_kwargs: + amsgrad: false + betas: !!python/tuple + - 0.9 + - 0.999 + capturable: false + differentiable: false + eps: 1.0e-08 + foreach: null + fused: null + maximize: false + weight_decay: 0 +optimizer_name: Adam +parity: false +r_max: 5.0 +report_init_validation: true +root: ./results/ +run_name: adsorption +save_checkpoint_freq: -1 +save_ema_checkpoint_freq: -1 +seed: 123 +shuffle: true +start_of_epoch_callbacks: [] +tensorboard: false +torch_version: !!python/object/new:torch.torch_version.TorchVersion +- 2.5.1+cu124 +train_idcs: !!python/object/apply:torch._utils._rebuild_tensor_v2 +- !!python/object/apply:torch.storage._load_from_bytes + - !!binary | + gAKKCmz8nEb5IGqoUBkugAJN6QMugAJ9cQAoWBAAAABwcm90b2NvbF92ZXJzaW9ucQFN6QNYDQAA + 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Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 28.7 1.02 8.37 0.896 1.2 3.39 3.45 0.00848 0.00863 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 6.358 0.005 0.993 4.42 24.3 0.884 1.19 2.02 2.51 0.00506 0.00628 +Wall time: 6.3580403393134475 +! Best model 0 24.288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 24.7 0.996 4.77 0.884 1.19 2.06 2.61 0.00514 0.00652 + 1 118 22.4 0.972 2.93 0.881 1.18 1.57 2.04 0.00391 0.00511 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 29 1.01 8.7 0.894 1.2 3.46 3.52 0.00866 0.0088 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 1 174.389 0.005 0.999 4.88 24.9 0.887 1.19 2.12 2.64 0.00531 0.0066 +! Validation 1 174.389 0.005 0.99 4.08 23.9 0.883 1.19 1.94 2.41 0.00485 0.00603 +Wall time: 174.39011605409905 +! Best model 1 23.881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 10.6 0.47 1.24 0.616 0.818 1.14 1.33 0.00285 0.00332 + 2 118 9.24 0.401 1.21 0.57 0.756 0.895 1.31 0.00224 0.00328 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 11.1 0.392 3.25 0.556 0.747 2.11 2.15 0.00527 0.00538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 2 341.830 0.005 0.753 2.41 17.5 0.764 1.04 1.43 1.86 0.00358 0.00464 +! Validation 2 341.830 0.005 0.393 4.1 12 0.561 0.748 2.21 2.42 0.00551 0.00604 +Wall time: 341.83007658226416 +! Best model 2 11.957 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 5.96 0.25 0.955 0.448 0.597 0.857 1.17 0.00214 0.00292 + 3 118 6.12 0.246 1.2 0.441 0.592 1.12 1.31 0.00281 0.00326 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 4.75 0.234 0.0671 0.425 0.578 0.225 0.309 0.000562 0.000773 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 3 509.367 0.005 0.292 1.31 7.16 0.482 0.646 1.08 1.37 0.00269 0.00342 +! Validation 3 509.367 0.005 0.237 0.808 5.54 0.435 0.581 0.788 1.07 0.00197 0.00268 +Wall time: 509.3696379433386 +! Best model 3 5.541 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 5.7 0.194 1.83 0.393 0.525 1.4 1.61 0.0035 0.00403 + 4 118 4.16 0.186 0.444 0.387 0.514 0.592 0.796 0.00148 0.00199 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 3.5 0.173 0.038 0.367 0.497 0.187 0.233 0.000468 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 4 676.806 0.005 0.201 1.08 5.11 0.402 0.536 0.987 1.24 0.00247 0.00311 +! Validation 4 676.806 0.005 0.181 0.626 4.25 0.38 0.508 0.69 0.944 0.00173 0.00236 +Wall time: 676.8062033960596 +! Best model 4 4.251 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 3.43 0.15 0.425 0.345 0.462 0.587 0.778 0.00147 0.00195 + 5 118 3.49 0.148 0.541 0.341 0.459 0.765 0.878 0.00191 0.00219 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 2.81 0.137 0.0794 0.326 0.441 0.297 0.336 0.000743 0.000841 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 844.237 0.005 0.16 0.744 3.94 0.357 0.477 0.821 1.03 0.00205 0.00258 +! Validation 5 844.237 0.005 0.147 0.951 3.9 0.341 0.458 0.926 1.16 0.00231 0.00291 +Wall time: 844.2370626013726 +! Best model 5 3.896 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 3.69 0.127 1.14 0.318 0.426 1.1 1.28 0.00276 0.00319 + 6 118 3.58 0.131 0.96 0.323 0.432 1 1.17 0.0025 0.00292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 4.32 0.119 1.94 0.303 0.412 1.66 1.66 0.00414 0.00416 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 1012.072 0.005 0.136 0.806 3.52 0.328 0.44 0.858 1.07 0.00214 0.00268 +! Validation 6 1012.072 0.005 0.129 1.21 3.79 0.318 0.429 1.17 1.31 0.00292 0.00328 +Wall time: 1012.0729714543559 +! Best model 6 3.791 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 2.58 0.117 0.242 0.303 0.408 0.464 0.587 0.00116 0.00147 + 7 118 2.94 0.114 0.657 0.299 0.403 0.894 0.967 0.00224 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 2.52 0.103 0.464 0.283 0.383 0.8 0.813 0.002 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 1179.539 0.005 0.121 0.7 3.11 0.308 0.415 0.784 0.999 0.00196 0.0025 +! Validation 7 1179.539 0.005 0.115 0.362 2.66 0.3 0.405 0.582 0.718 0.00146 0.00179 +Wall time: 1179.5396996671334 +! Best model 7 2.664 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 2.75 0.106 0.637 0.288 0.388 0.813 0.953 0.00203 0.00238 + 8 118 2.71 0.0994 0.717 0.281 0.376 0.769 1.01 0.00192 0.00253 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 1.88 0.0933 0.0173 0.269 0.365 0.105 0.157 0.000262 0.000393 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 1347.121 0.005 0.108 0.637 2.79 0.291 0.392 0.765 0.952 0.00191 0.00238 +! Validation 8 1347.121 0.005 0.105 0.686 2.79 0.287 0.387 0.792 0.989 0.00198 0.00247 +Wall time: 1347.12112014601 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 2.12 0.0943 0.234 0.272 0.367 0.45 0.577 0.00112 0.00144 + 9 118 2.33 0.0942 0.451 0.272 0.366 0.678 0.802 0.0017 0.002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 2.36 0.0858 0.645 0.258 0.35 0.95 0.959 0.00238 0.0024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 1514.584 0.005 0.1 0.652 2.66 0.281 0.378 0.775 0.965 0.00194 0.00241 +! Validation 9 1514.584 0.005 0.0978 0.345 2.3 0.277 0.373 0.579 0.701 0.00145 0.00175 +Wall time: 1514.5844786730595 +! Best model 9 2.300 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 2.1 0.0913 0.273 0.268 0.361 0.484 0.623 0.00121 0.00156 + 10 118 2.51 0.0942 0.625 0.272 0.366 0.838 0.943 0.00209 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 1.89 0.0803 0.282 0.25 0.338 0.621 0.634 0.00155 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 10 1682.217 0.005 0.0928 0.532 2.39 0.27 0.363 0.697 0.87 0.00174 0.00218 +! Validation 10 1682.217 0.005 0.0923 0.258 2.1 0.269 0.363 0.481 0.606 0.0012 0.00151 +Wall time: 1682.2171152411029 +! Best model 10 2.105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 2.74 0.0851 1.04 0.261 0.348 1.11 1.22 0.00278 0.00304 + 11 118 2.36 0.0878 0.608 0.261 0.354 0.658 0.93 0.00165 0.00233 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 1.52 0.0753 0.0117 0.242 0.328 0.0961 0.129 0.00024 0.000322 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 11 1851.670 0.005 0.0875 0.617 2.37 0.263 0.353 0.768 0.938 0.00192 0.00234 +! Validation 11 1851.670 0.005 0.0868 0.533 2.27 0.261 0.352 0.69 0.871 0.00172 0.00218 +Wall time: 1851.6707933051512 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 2.09 0.0828 0.437 0.256 0.343 0.676 0.789 0.00169 0.00197 + 12 118 1.85 0.0837 0.176 0.259 0.345 0.377 0.5 0.000942 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 1.87 0.0703 0.464 0.235 0.316 0.802 0.813 0.002 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 12 2019.796 0.005 0.0836 0.549 2.22 0.257 0.345 0.696 0.887 0.00174 0.00222 +! Validation 12 2019.796 0.005 0.0818 0.285 1.92 0.254 0.341 0.528 0.638 0.00132 0.00159 +Wall time: 2019.79748495901 +! Best model 12 1.921 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 3.29 0.0767 1.75 0.246 0.331 1.49 1.58 0.00372 0.00395 + 13 118 2.35 0.0797 0.754 0.249 0.337 0.968 1.04 0.00242 0.00259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 1.4 0.0685 0.0314 0.231 0.312 0.168 0.211 0.000421 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 13 2187.118 0.005 0.0786 0.558 2.13 0.249 0.335 0.724 0.89 0.00181 0.00223 +! Validation 13 2187.118 0.005 0.0791 0.562 2.14 0.25 0.336 0.739 0.895 0.00185 0.00224 +Wall time: 2187.118289224338 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 1.59 0.0722 0.145 0.24 0.321 0.354 0.454 0.000886 0.00114 + 14 118 1.56 0.07 0.159 0.235 0.316 0.401 0.476 0.001 0.00119 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 1.55 0.0637 0.278 0.223 0.301 0.616 0.629 0.00154 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 14 2354.420 0.005 0.0751 0.508 2.01 0.244 0.327 0.69 0.853 0.00173 0.00213 +! Validation 14 2354.420 0.005 0.0749 1.25 2.75 0.243 0.327 1.22 1.34 0.00304 0.00334 +Wall time: 2354.420513842255 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 1.69 0.0702 0.288 0.236 0.316 0.504 0.641 0.00126 0.0016 + 15 118 1.87 0.068 0.511 0.232 0.311 0.737 0.853 0.00184 0.00213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 1.6 0.0594 0.411 0.217 0.291 0.754 0.765 0.00189 0.00191 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 15 2521.697 0.005 0.0715 0.42 1.85 0.238 0.319 0.618 0.773 0.00154 0.00193 +! Validation 15 2521.697 0.005 0.0705 0.246 1.66 0.236 0.317 0.481 0.592 0.0012 0.00148 +Wall time: 2521.6971200071275 +! Best model 15 1.656 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 1.64 0.0687 0.265 0.234 0.313 0.499 0.615 0.00125 0.00154 + 16 118 1.53 0.0656 0.221 0.227 0.306 0.474 0.561 0.00119 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 1.58 0.0578 0.427 0.214 0.287 0.769 0.78 0.00192 0.00195 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 16 2689.018 0.005 0.0686 0.512 1.88 0.233 0.313 0.691 0.856 0.00173 0.00214 +! Validation 16 2689.018 0.005 0.0679 0.252 1.61 0.232 0.311 0.49 0.599 0.00123 0.0015 +Wall time: 2689.0184356682003 +! Best model 16 1.609 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 2.03 0.0655 0.724 0.228 0.305 0.894 1.02 0.00224 0.00254 + 17 118 1.57 0.0665 0.242 0.231 0.308 0.484 0.587 0.00121 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 1.42 0.0553 0.311 0.209 0.281 0.654 0.666 0.00164 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 17 2856.605 0.005 0.0651 0.438 1.74 0.228 0.305 0.643 0.791 0.00161 0.00198 +! Validation 17 2856.605 0.005 0.0652 0.196 1.5 0.227 0.305 0.426 0.528 0.00107 0.00132 +Wall time: 2856.6054014242254 +! Best model 17 1.499 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 1.57 0.0626 0.313 0.224 0.299 0.555 0.668 0.00139 0.00167 + 18 118 1.47 0.06 0.274 0.219 0.292 0.532 0.625 0.00133 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 1.4 0.053 0.344 0.205 0.275 0.691 0.7 0.00173 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 18 3024.126 0.005 0.063 0.456 1.71 0.224 0.3 0.655 0.807 0.00164 0.00202 +! Validation 18 3024.126 0.005 0.0625 0.205 1.46 0.223 0.298 0.438 0.54 0.0011 0.00135 +Wall time: 3024.126394457184 +! Best model 18 1.456 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 1.91 0.0588 0.73 0.217 0.289 0.884 1.02 0.00221 0.00255 + 19 118 1.59 0.0632 0.323 0.224 0.3 0.518 0.678 0.0013 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 2.4 0.0514 1.37 0.202 0.271 1.39 1.4 0.00348 0.00349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 19 3191.427 0.005 0.0607 0.511 1.72 0.22 0.294 0.701 0.854 0.00175 0.00214 +! Validation 19 3191.427 0.005 0.0606 0.733 1.95 0.219 0.294 0.917 1.02 0.00229 0.00256 +Wall time: 3191.4276354052126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 2.03 0.0586 0.855 0.216 0.289 1.01 1.1 0.00252 0.00276 + 20 118 1.52 0.0593 0.336 0.217 0.291 0.556 0.692 0.00139 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 1.12 0.0483 0.153 0.197 0.262 0.451 0.467 0.00113 0.00117 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 20 3358.732 0.005 0.058 0.34 1.5 0.215 0.287 0.555 0.696 0.00139 0.00174 +! Validation 20 3358.732 0.005 0.0579 0.707 1.86 0.215 0.287 0.895 1 0.00224 0.00251 +Wall time: 3358.732003442012 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 1.8 0.0547 0.704 0.21 0.279 0.902 1 0.00226 0.0025 + 21 118 1.35 0.0556 0.237 0.21 0.281 0.47 0.581 0.00117 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 1 0.0469 0.0609 0.194 0.259 0.268 0.295 0.00067 0.000737 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 21 3526.019 0.005 0.0564 0.513 1.64 0.212 0.283 0.701 0.857 0.00175 0.00214 +! Validation 21 3526.019 0.005 0.0563 0.198 1.32 0.212 0.283 0.414 0.531 0.00104 0.00133 +Wall time: 3526.0196879622526 +! Best model 21 1.324 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 1.54 0.0549 0.437 0.21 0.28 0.68 0.789 0.0017 0.00197 + 22 118 1.18 0.0499 0.185 0.202 0.267 0.413 0.514 0.00103 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 1.4 0.0459 0.481 0.192 0.256 0.82 0.827 0.00205 0.00207 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 22 3693.414 0.005 0.0546 0.471 1.56 0.209 0.279 0.665 0.82 0.00166 0.00205 +! Validation 22 3693.414 0.005 0.0545 0.237 1.33 0.209 0.279 0.477 0.582 0.00119 0.00145 +Wall time: 3693.414395957254 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 1.39 0.0517 0.351 0.204 0.271 0.579 0.707 0.00145 0.00177 + 23 118 1.34 0.0507 0.327 0.201 0.269 0.605 0.683 0.00151 0.00171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 1.34 0.0444 0.452 0.188 0.252 0.795 0.802 0.00199 0.00201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 23 3860.712 0.005 0.0528 0.436 1.49 0.206 0.274 0.635 0.788 0.00159 0.00197 +! Validation 23 3860.712 0.005 0.0531 0.246 1.31 0.205 0.275 0.486 0.592 0.00122 0.00148 +Wall time: 3860.712141085416 +! Best model 23 1.307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 1.12 0.0494 0.137 0.2 0.265 0.354 0.441 0.000884 0.0011 + 24 118 2.04 0.0513 1.02 0.203 0.27 1.17 1.2 0.00293 0.00301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 2.9 0.0429 2.05 0.185 0.247 1.71 1.71 0.00426 0.00427 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 24 4029.069 0.005 0.0511 0.407 1.43 0.202 0.27 0.619 0.757 0.00155 0.00189 +! Validation 24 4029.069 0.005 0.0512 1.28 2.3 0.202 0.27 1.27 1.35 0.00318 0.00337 +Wall time: 4029.0699212430045 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 1.71 0.0518 0.672 0.203 0.272 0.9 0.979 0.00225 0.00245 + 25 118 1.32 0.05 0.317 0.202 0.267 0.482 0.672 0.00121 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 1.08 0.0415 0.25 0.182 0.243 0.585 0.597 0.00146 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 25 4207.325 0.005 0.0495 0.419 1.41 0.199 0.266 0.626 0.773 0.00156 0.00193 +! Validation 25 4207.325 0.005 0.0503 0.164 1.17 0.2 0.268 0.383 0.483 0.000958 0.00121 +Wall time: 4207.325034392066 +! Best model 25 1.169 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 1.14 0.0473 0.191 0.195 0.259 0.431 0.521 0.00108 0.0013 + 26 118 1.53 0.0436 0.657 0.187 0.249 0.92 0.968 0.0023 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 0.89 0.0396 0.0978 0.177 0.238 0.357 0.373 0.000892 0.000933 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 26 4374.650 0.005 0.0482 0.404 1.37 0.197 0.262 0.618 0.757 0.00155 0.00189 +! Validation 26 4374.650 0.005 0.0484 0.535 1.5 0.196 0.262 0.766 0.873 0.00192 0.00218 +Wall time: 4374.650422966108 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 1.05 0.0459 0.132 0.192 0.256 0.35 0.433 0.000875 0.00108 + 27 118 1.26 0.047 0.322 0.195 0.259 0.614 0.678 0.00153 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 0.984 0.0385 0.215 0.175 0.234 0.547 0.553 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 27 4542.068 0.005 0.0471 0.384 1.33 0.194 0.259 0.563 0.74 0.00141 0.00185 +! Validation 27 4542.068 0.005 0.0467 0.145 1.08 0.193 0.258 0.362 0.455 0.000905 0.00114 +Wall time: 4542.068860084284 +! Best model 27 1.079 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 1.09 0.0437 0.219 0.187 0.249 0.438 0.558 0.0011 0.0014 + 28 118 1.22 0.0441 0.335 0.189 0.251 0.595 0.69 0.00149 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 0.804 0.0369 0.0652 0.171 0.229 0.289 0.305 0.000722 0.000762 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 28 4709.400 0.005 0.0451 0.356 1.26 0.19 0.253 0.573 0.712 0.00143 0.00178 +! Validation 28 4709.400 0.005 0.0451 0.153 1.06 0.189 0.253 0.364 0.467 0.000911 0.00117 +Wall time: 4709.400411950424 +! Best model 28 1.055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 1.29 0.0448 0.397 0.19 0.253 0.661 0.752 0.00165 0.00188 + 29 118 1.4 0.0407 0.589 0.18 0.241 0.767 0.916 0.00192 0.00229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 0.827 0.0361 0.105 0.169 0.227 0.376 0.387 0.000941 0.000967 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 29 4876.716 0.005 0.0442 0.412 1.3 0.188 0.251 0.618 0.765 0.00155 0.00191 +! Validation 29 4876.716 0.005 0.0441 0.535 1.42 0.187 0.251 0.774 0.873 0.00194 0.00218 +Wall time: 4876.716017507017 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 0.94 0.0418 0.104 0.183 0.244 0.298 0.385 0.000745 0.000962 + 30 118 1.02 0.042 0.178 0.185 0.245 0.4 0.503 0.001 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 0.732 0.035 0.0317 0.167 0.223 0.189 0.213 0.000473 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 30 5044.004 0.005 0.0432 0.404 1.27 0.186 0.248 0.608 0.761 0.00152 0.0019 +! Validation 30 5044.004 0.005 0.043 0.355 1.22 0.185 0.248 0.603 0.711 0.00151 0.00178 +Wall time: 5044.004979152232 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 1.38 0.0422 0.539 0.184 0.245 0.809 0.876 0.00202 0.00219 + 31 118 1.22 0.0463 0.292 0.194 0.257 0.531 0.645 0.00133 0.00161 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 1.43 0.0342 0.748 0.165 0.221 1.03 1.03 0.00257 0.00258 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 31 5211.399 0.005 0.0414 0.338 1.17 0.182 0.243 0.567 0.694 0.00142 0.00173 +! Validation 31 5211.399 0.005 0.0418 0.413 1.25 0.182 0.244 0.673 0.767 0.00168 0.00192 +Wall time: 5211.399076862261 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 0.994 0.0406 0.182 0.18 0.24 0.406 0.51 0.00101 0.00127 + 32 118 0.9 0.0395 0.11 0.178 0.237 0.328 0.396 0.000821 0.000989 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 0.713 0.0334 0.0453 0.164 0.218 0.233 0.254 0.000583 0.000635 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 32 5378.698 0.005 0.0407 0.418 1.23 0.181 0.241 0.634 0.774 0.00159 0.00193 +! Validation 32 5378.698 0.005 0.0414 0.363 1.19 0.182 0.243 0.61 0.719 0.00153 0.0018 +Wall time: 5378.6982706203125 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 0.938 0.0404 0.131 0.18 0.24 0.351 0.431 0.000877 0.00108 + 33 118 0.98 0.0388 0.205 0.176 0.235 0.438 0.541 0.0011 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 0.676 0.0329 0.0172 0.162 0.217 0.13 0.157 0.000324 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 33 5545.987 0.005 0.0403 0.463 1.27 0.18 0.24 0.667 0.813 0.00167 0.00203 +! Validation 33 5545.987 0.005 0.0408 0.319 1.13 0.18 0.241 0.563 0.675 0.00141 0.00169 +Wall time: 5545.987347880378 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 1.17 0.0389 0.393 0.177 0.235 0.665 0.748 0.00166 0.00187 + 34 118 2 0.0373 1.25 0.173 0.231 1.24 1.34 0.00309 0.00334 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 0.863 0.032 0.224 0.16 0.213 0.556 0.564 0.00139 0.00141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 34 5713.788 0.005 0.0388 0.362 1.14 0.176 0.235 0.582 0.712 0.00146 0.00178 +! Validation 34 5713.788 0.005 0.0398 0.873 1.67 0.178 0.238 1.02 1.12 0.00255 0.00279 +Wall time: 5713.788826073054 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 0.934 0.0375 0.184 0.174 0.231 0.45 0.512 0.00113 0.00128 + 35 118 1.01 0.0402 0.206 0.178 0.239 0.454 0.542 0.00113 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 0.769 0.0307 0.155 0.157 0.209 0.465 0.47 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 35 5881.085 0.005 0.0379 0.308 1.07 0.175 0.232 0.532 0.663 0.00133 0.00166 +! Validation 35 5881.085 0.005 0.0382 0.569 1.33 0.174 0.233 0.81 0.9 0.00202 0.00225 +Wall time: 5881.085445408244 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 0.857 0.0368 0.122 0.172 0.229 0.328 0.416 0.000819 0.00104 + 36 118 0.94 0.0379 0.183 0.174 0.232 0.441 0.51 0.0011 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 1.46 0.0299 0.86 0.155 0.206 1.11 1.11 0.00276 0.00277 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 36 6048.490 0.005 0.037 0.369 1.11 0.172 0.23 0.578 0.726 0.00144 0.00181 +! Validation 36 6048.490 0.005 0.0371 0.562 1.31 0.172 0.23 0.812 0.895 0.00203 0.00224 +Wall time: 6048.490027527325 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 1.03 0.0359 0.312 0.169 0.226 0.589 0.666 0.00147 0.00167 + 37 118 0.803 0.0355 0.0928 0.169 0.225 0.29 0.364 0.000725 0.000909 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 0.579 0.0288 0.00413 0.152 0.202 0.0751 0.0767 0.000188 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 37 6215.764 0.005 0.0361 0.366 1.09 0.17 0.227 0.595 0.724 0.00149 0.00181 +! Validation 37 6215.764 0.005 0.0363 0.192 0.918 0.17 0.227 0.416 0.523 0.00104 0.00131 +Wall time: 6215.764725954272 +! Best model 37 0.918 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 0.906 0.0345 0.216 0.166 0.222 0.46 0.555 0.00115 0.00139 + 38 118 1.09 0.0367 0.359 0.173 0.229 0.623 0.716 0.00156 0.00179 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 1.1 0.0289 0.526 0.152 0.203 0.863 0.866 0.00216 0.00216 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 38 6383.056 0.005 0.0354 0.382 1.09 0.169 0.225 0.602 0.738 0.0015 0.00184 +! Validation 38 6383.056 0.005 0.0361 0.307 1.03 0.169 0.227 0.572 0.661 0.00143 0.00165 +Wall time: 6383.05659274105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.3 0.0344 0.614 0.167 0.221 0.868 0.935 0.00217 0.00234 + 39 118 0.812 0.0314 0.184 0.158 0.211 0.449 0.512 0.00112 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 0.576 0.0279 0.017 0.15 0.2 0.137 0.155 0.000343 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 39 6550.324 0.005 0.0345 0.388 1.08 0.167 0.222 0.617 0.744 0.00154 0.00186 +! Validation 39 6550.324 0.005 0.0354 0.248 0.956 0.168 0.225 0.49 0.594 0.00123 0.00149 +Wall time: 6550.324303986039 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 0.821 0.0331 0.158 0.164 0.217 0.392 0.474 0.000981 0.00119 + 40 118 1.04 0.0341 0.363 0.166 0.22 0.644 0.719 0.00161 0.0018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 1.1 0.0278 0.541 0.149 0.199 0.875 0.878 0.00219 0.00219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 40 6717.588 0.005 0.0343 0.409 1.09 0.166 0.221 0.62 0.763 0.00155 0.00191 +! Validation 40 6717.588 0.005 0.0346 0.313 1.01 0.166 0.222 0.579 0.667 0.00145 0.00167 +Wall time: 6717.588284178171 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 1.08 0.0339 0.403 0.165 0.22 0.657 0.758 0.00164 0.00189 + 41 118 0.773 0.0331 0.111 0.164 0.217 0.339 0.398 0.000848 0.000994 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 0.841 0.0265 0.31 0.147 0.194 0.662 0.664 0.00165 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 41 6884.975 0.005 0.0329 0.276 0.934 0.163 0.216 0.505 0.628 0.00126 0.00157 +! Validation 41 6884.975 0.005 0.0334 0.172 0.839 0.164 0.218 0.405 0.495 0.00101 0.00124 +Wall time: 6884.975482440088 +! Best model 41 0.839 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 1.21 0.0345 0.522 0.167 0.222 0.792 0.863 0.00198 0.00216 + 42 118 0.729 0.0308 0.114 0.158 0.209 0.32 0.403 0.000801 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 0.586 0.0258 0.0685 0.145 0.192 0.305 0.312 0.000762 0.000781 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 42 7052.265 0.005 0.0323 0.391 1.04 0.161 0.214 0.591 0.748 0.00148 0.00187 +! Validation 42 7052.265 0.005 0.0329 0.35 1.01 0.162 0.217 0.612 0.706 0.00153 0.00177 +Wall time: 7052.265214377083 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 0.903 0.0302 0.3 0.157 0.207 0.594 0.654 0.00148 0.00163 + 43 118 2.35 0.0382 1.59 0.176 0.233 1.45 1.5 0.00361 0.00376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 2.11 0.0355 1.4 0.169 0.225 1.41 1.41 0.00352 0.00353 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 43 7219.542 0.005 0.0309 0.293 0.912 0.158 0.21 0.48 0.636 0.0012 0.00159 +! Validation 43 7219.542 0.005 0.043 0.596 1.45 0.185 0.247 0.828 0.921 0.00207 0.0023 +Wall time: 7219.542159063276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 0.649 0.0285 0.0797 0.152 0.201 0.254 0.337 0.000635 0.000842 + 44 118 0.779 0.0316 0.147 0.158 0.212 0.375 0.458 0.000938 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 2.67 0.0255 2.16 0.143 0.191 1.75 1.76 0.00439 0.00439 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 44 7386.814 0.005 0.0312 0.335 0.959 0.159 0.211 0.539 0.692 0.00135 0.00173 +! Validation 44 7386.814 0.005 0.0326 1.27 1.92 0.161 0.216 1.28 1.34 0.00321 0.00336 +Wall time: 7386.8146933563985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 1.24 0.0308 0.627 0.158 0.21 0.895 0.945 0.00224 0.00236 + 45 118 0.73 0.03 0.13 0.156 0.207 0.374 0.431 0.000934 0.00108 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 0.688 0.0244 0.201 0.141 0.186 0.531 0.535 0.00133 0.00134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 45 7554.103 0.005 0.0305 0.416 1.03 0.157 0.208 0.619 0.772 0.00155 0.00193 +! Validation 45 7554.103 0.005 0.031 0.619 1.24 0.157 0.21 0.86 0.939 0.00215 0.00235 +Wall time: 7554.103037211113 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 0.653 0.0283 0.0871 0.152 0.201 0.291 0.352 0.000727 0.00088 + 46 118 0.704 0.029 0.124 0.154 0.203 0.335 0.421 0.000837 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 0.67 0.0225 0.22 0.136 0.179 0.557 0.559 0.00139 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 46 7723.808 0.005 0.0286 0.23 0.802 0.152 0.202 0.453 0.573 0.00113 0.00143 +! Validation 46 7723.808 0.005 0.029 0.177 0.757 0.152 0.203 0.406 0.502 0.00102 0.00125 +Wall time: 7723.808401583228 +! Best model 46 0.757 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 0.767 0.0278 0.211 0.15 0.199 0.467 0.548 0.00117 0.00137 + 47 118 0.662 0.0259 0.145 0.146 0.192 0.409 0.454 0.00102 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 0.434 0.0216 0.00272 0.134 0.175 0.0497 0.0622 0.000124 0.000156 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 47 7891.290 0.005 0.0279 0.294 0.851 0.15 0.199 0.531 0.648 0.00133 0.00162 +! Validation 47 7891.290 0.005 0.028 0.144 0.704 0.15 0.2 0.367 0.453 0.000918 0.00113 +Wall time: 7891.290293193422 +! Best model 47 0.704 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 0.819 0.027 0.278 0.148 0.196 0.56 0.63 0.0014 0.00157 + 48 118 0.996 0.026 0.476 0.147 0.192 0.765 0.823 0.00191 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 0.613 0.0213 0.187 0.133 0.174 0.514 0.516 0.00128 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 48 8058.767 0.005 0.0273 0.372 0.918 0.149 0.197 0.603 0.728 0.00151 0.00182 +! Validation 48 8058.767 0.005 0.0277 0.415 0.968 0.149 0.199 0.683 0.768 0.00171 0.00192 +Wall time: 8058.767576611135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 0.585 0.0257 0.0716 0.145 0.191 0.25 0.319 0.000625 0.000799 + 49 118 0.566 0.0242 0.0824 0.141 0.186 0.305 0.343 0.000762 0.000856 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 0.648 0.0207 0.234 0.132 0.172 0.574 0.578 0.00144 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 49 8226.225 0.005 0.0263 0.266 0.791 0.146 0.193 0.502 0.617 0.00125 0.00154 +! Validation 49 8226.225 0.005 0.0267 0.184 0.717 0.147 0.195 0.425 0.512 0.00106 0.00128 +Wall time: 8226.225609230343 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 0.888 0.0251 0.385 0.143 0.189 0.665 0.741 0.00166 0.00185 + 50 118 0.872 0.0262 0.347 0.146 0.193 0.654 0.703 0.00163 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 0.542 0.0194 0.155 0.127 0.166 0.465 0.47 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 50 8393.801 0.005 0.0252 0.244 0.749 0.143 0.19 0.471 0.589 0.00118 0.00147 +! Validation 50 8393.801 0.005 0.0255 0.112 0.622 0.143 0.191 0.318 0.4 0.000796 0.001 +Wall time: 8393.801434432156 +! Best model 50 0.622 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 1.36 0.0261 0.835 0.146 0.193 1.04 1.09 0.00259 0.00273 + 51 118 0.534 0.0226 0.0818 0.137 0.179 0.271 0.341 0.000676 0.000853 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 0.423 0.0196 0.0308 0.128 0.167 0.198 0.209 0.000495 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 51 8561.290 0.005 0.025 0.369 0.869 0.143 0.189 0.584 0.727 0.00146 0.00182 +! Validation 51 8561.290 0.005 0.0258 0.091 0.606 0.144 0.192 0.292 0.36 0.000729 0.0009 +Wall time: 8561.29105349537 +! Best model 51 0.606 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.683 0.0239 0.205 0.139 0.185 0.453 0.54 0.00113 0.00135 + 52 118 0.537 0.0245 0.0459 0.141 0.187 0.199 0.256 0.000497 0.000639 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.975 0.0187 0.602 0.125 0.163 0.922 0.926 0.00231 0.00232 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 52 8728.774 0.005 0.0238 0.198 0.675 0.139 0.184 0.428 0.533 0.00107 0.00133 +! Validation 52 8728.774 0.005 0.0246 0.361 0.854 0.141 0.187 0.644 0.717 0.00161 0.00179 +Wall time: 8728.774307037238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 0.54 0.0222 0.0962 0.135 0.178 0.307 0.37 0.000767 0.000925 + 53 118 0.457 0.0202 0.054 0.129 0.169 0.235 0.277 0.000586 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 0.398 0.0177 0.0443 0.122 0.159 0.235 0.251 0.000588 0.000628 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 53 8896.247 0.005 0.0231 0.234 0.696 0.137 0.181 0.462 0.579 0.00116 0.00145 +! Validation 53 8896.247 0.005 0.0234 0.0794 0.547 0.137 0.183 0.266 0.336 0.000665 0.000841 +Wall time: 8896.247454642318 +! Best model 53 0.547 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 0.652 0.0216 0.219 0.133 0.175 0.458 0.559 0.00115 0.0014 + 54 118 1.17 0.0199 0.77 0.128 0.168 1.02 1.05 0.00254 0.00262 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 0.918 0.0174 0.57 0.121 0.158 0.897 0.901 0.00224 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 54 9065.851 0.005 0.0226 0.248 0.699 0.136 0.179 0.454 0.59 0.00114 0.00147 +! Validation 54 9065.851 0.005 0.0231 0.8 1.26 0.136 0.181 1.01 1.07 0.00252 0.00267 +Wall time: 9065.851641494315 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.03 0.0241 0.544 0.14 0.185 0.815 0.88 0.00204 0.0022 + 55 118 0.495 0.0215 0.0645 0.133 0.175 0.27 0.303 0.000674 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 0.437 0.0177 0.0828 0.122 0.159 0.336 0.343 0.00084 0.000858 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 55 9234.576 0.005 0.0225 0.331 0.782 0.135 0.179 0.555 0.689 0.00139 0.00172 +! Validation 55 9234.576 0.005 0.0231 0.0885 0.551 0.137 0.181 0.281 0.355 0.000701 0.000887 +Wall time: 9234.576426569372 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 0.503 0.0215 0.0737 0.133 0.175 0.249 0.324 0.000622 0.00081 + 56 118 0.522 0.0226 0.0693 0.137 0.18 0.26 0.314 0.00065 0.000785 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 1.14 0.0178 0.782 0.122 0.159 1.05 1.06 0.00263 0.00264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 56 9402.461 0.005 0.0221 0.311 0.754 0.134 0.178 0.547 0.667 0.00137 0.00167 +! Validation 56 9402.461 0.005 0.0228 0.591 1.05 0.136 0.18 0.856 0.918 0.00214 0.00229 +Wall time: 9402.461244800128 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 0.902 0.0216 0.47 0.133 0.175 0.747 0.818 0.00187 0.00205 + 57 118 0.513 0.0198 0.116 0.128 0.168 0.347 0.406 0.000869 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 0.348 0.0167 0.014 0.119 0.154 0.113 0.141 0.000282 0.000353 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 57 9570.160 0.005 0.0214 0.229 0.657 0.132 0.175 0.459 0.572 0.00115 0.00143 +! Validation 57 9570.160 0.005 0.0218 0.0776 0.514 0.133 0.176 0.266 0.333 0.000664 0.000831 +Wall time: 9570.16061371006 +! Best model 57 0.514 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 0.49 0.021 0.0691 0.131 0.173 0.247 0.314 0.000616 0.000784 + 58 118 0.494 0.0198 0.0984 0.128 0.168 0.298 0.374 0.000746 0.000936 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 0.331 0.0162 0.00705 0.117 0.152 0.0874 0.1 0.000219 0.00025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 58 9737.695 0.005 0.021 0.243 0.664 0.131 0.173 0.47 0.59 0.00117 0.00148 +! Validation 58 9737.695 0.005 0.0214 0.12 0.548 0.131 0.175 0.329 0.414 0.000823 0.00103 +Wall time: 9737.69509622315 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.711 0.0202 0.307 0.128 0.17 0.611 0.662 0.00153 0.00165 + 59 118 0.694 0.0202 0.289 0.129 0.17 0.586 0.642 0.00146 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.341 0.0162 0.0179 0.117 0.152 0.126 0.16 0.000314 0.000399 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 59 9906.884 0.005 0.0204 0.222 0.629 0.129 0.17 0.452 0.561 0.00113 0.0014 +! Validation 59 9906.884 0.005 0.0212 0.127 0.551 0.13 0.174 0.355 0.426 0.000889 0.00106 +Wall time: 9906.88420266239 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.541 0.0196 0.15 0.127 0.167 0.386 0.462 0.000966 0.00116 + 60 118 0.478 0.019 0.0966 0.125 0.165 0.311 0.371 0.000777 0.000928 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.502 0.0161 0.181 0.116 0.151 0.494 0.507 0.00124 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 60 10074.509 0.005 0.0203 0.263 0.669 0.129 0.17 0.482 0.614 0.00121 0.00153 +! Validation 60 10074.509 0.005 0.0208 0.14 0.557 0.129 0.172 0.367 0.447 0.000916 0.00112 +Wall time: 10074.509431233164 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.498 0.0194 0.109 0.126 0.166 0.315 0.394 0.000787 0.000985 + 61 118 0.445 0.0198 0.049 0.126 0.168 0.225 0.264 0.000562 0.000661 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.319 0.0152 0.0147 0.113 0.147 0.11 0.145 0.000275 0.000361 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 61 10242.014 0.005 0.0196 0.191 0.583 0.126 0.167 0.428 0.523 0.00107 0.00131 +! Validation 61 10242.014 0.005 0.0202 0.138 0.541 0.127 0.169 0.364 0.443 0.00091 0.00111 +Wall time: 10242.01426461339 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 0.901 0.0191 0.519 0.125 0.165 0.795 0.86 0.00199 0.00215 + 62 118 0.572 0.0198 0.177 0.127 0.168 0.433 0.501 0.00108 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 0.389 0.0151 0.0865 0.113 0.147 0.337 0.351 0.000843 0.000878 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 62 10409.499 0.005 0.0193 0.225 0.611 0.125 0.166 0.457 0.566 0.00114 0.00142 +! Validation 62 10409.499 0.005 0.02 0.0894 0.489 0.127 0.169 0.286 0.357 0.000716 0.000892 +Wall time: 10409.499451737385 +! Best model 62 0.489 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 0.448 0.0185 0.0769 0.123 0.162 0.268 0.331 0.000671 0.000828 + 63 118 0.509 0.0188 0.134 0.124 0.163 0.388 0.437 0.00097 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 0.451 0.015 0.151 0.112 0.146 0.451 0.463 0.00113 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 63 10577.429 0.005 0.0191 0.225 0.607 0.125 0.165 0.461 0.566 0.00115 0.00142 +! Validation 63 10577.429 0.005 0.0196 0.323 0.714 0.125 0.167 0.606 0.678 0.00152 0.00169 +Wall time: 10577.429853369016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.496 0.0186 0.124 0.124 0.163 0.347 0.421 0.000867 0.00105 + 64 118 0.451 0.0177 0.0974 0.121 0.159 0.293 0.372 0.000733 0.000931 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.304 0.0148 0.00843 0.111 0.145 0.0926 0.11 0.000231 0.000274 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 64 10744.684 0.005 0.0188 0.26 0.637 0.124 0.164 0.498 0.61 0.00124 0.00152 +! Validation 64 10744.684 0.005 0.0194 0.092 0.48 0.125 0.166 0.289 0.362 0.000722 0.000905 +Wall time: 10744.68458234705 +! Best model 64 0.480 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 0.52 0.0179 0.162 0.121 0.16 0.405 0.48 0.00101 0.0012 + 65 118 0.413 0.0187 0.039 0.123 0.163 0.194 0.236 0.000485 0.000589 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 0.387 0.0146 0.0962 0.111 0.144 0.355 0.37 0.000887 0.000925 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 65 10914.305 0.005 0.0184 0.203 0.572 0.122 0.162 0.43 0.54 0.00107 0.00135 +! Validation 65 10914.305 0.005 0.019 0.105 0.485 0.123 0.165 0.312 0.386 0.000781 0.000965 +Wall time: 10914.305456178263 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.441 0.0178 0.0859 0.12 0.159 0.271 0.35 0.000678 0.000874 + 66 118 0.845 0.0177 0.491 0.12 0.159 0.8 0.836 0.002 0.00209 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.646 0.014 0.366 0.109 0.141 0.714 0.722 0.00178 0.00181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 66 11083.787 0.005 0.018 0.185 0.545 0.121 0.16 0.418 0.51 0.00104 0.00128 +! Validation 66 11083.787 0.005 0.0186 0.524 0.896 0.122 0.163 0.807 0.864 0.00202 0.00216 +Wall time: 11083.787373758387 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.498 0.0178 0.141 0.12 0.159 0.375 0.448 0.000937 0.00112 + 67 118 0.373 0.0173 0.0261 0.119 0.157 0.139 0.193 0.000348 0.000482 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.287 0.0138 0.0111 0.107 0.14 0.114 0.126 0.000286 0.000314 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 67 11252.676 0.005 0.0178 0.201 0.556 0.12 0.159 0.435 0.536 0.00109 0.00134 +! Validation 67 11252.676 0.005 0.0182 0.0749 0.439 0.121 0.161 0.264 0.327 0.000659 0.000817 +Wall time: 11252.676954683382 +! Best model 67 0.439 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.465 0.0179 0.107 0.121 0.16 0.326 0.39 0.000815 0.000975 + 68 118 0.431 0.0196 0.0388 0.125 0.167 0.193 0.235 0.000484 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.322 0.0135 0.0529 0.106 0.139 0.25 0.274 0.000625 0.000686 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 68 11420.006 0.005 0.0175 0.186 0.537 0.119 0.158 0.401 0.517 0.001 0.00129 +! Validation 68 11420.006 0.005 0.0178 0.078 0.434 0.119 0.159 0.265 0.333 0.000663 0.000833 +Wall time: 11420.00691516418 +! Best model 68 0.434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.838 0.0178 0.481 0.12 0.159 0.779 0.828 0.00195 0.00207 + 69 118 0.529 0.0165 0.2 0.116 0.153 0.499 0.534 0.00125 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.419 0.0139 0.141 0.108 0.141 0.437 0.447 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 69 11587.610 0.005 0.0176 0.291 0.643 0.12 0.158 0.523 0.645 0.00131 0.00161 +! Validation 69 11587.610 0.005 0.0183 0.117 0.483 0.121 0.162 0.33 0.408 0.000825 0.00102 +Wall time: 11587.610626030248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.405 0.0174 0.0562 0.119 0.158 0.224 0.283 0.000561 0.000707 + 70 118 0.365 0.0155 0.0547 0.113 0.149 0.249 0.279 0.000623 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.341 0.0131 0.0795 0.105 0.136 0.32 0.336 0.0008 0.000841 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 70 11754.952 0.005 0.017 0.156 0.496 0.118 0.156 0.381 0.473 0.000953 0.00118 +! Validation 70 11754.952 0.005 0.0174 0.0976 0.445 0.118 0.157 0.297 0.373 0.000744 0.000932 +Wall time: 11754.952778371982 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 0.658 0.0168 0.322 0.117 0.155 0.631 0.678 0.00158 0.00169 + 71 118 0.401 0.0162 0.0779 0.115 0.152 0.278 0.333 0.000695 0.000833 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 0.443 0.013 0.182 0.104 0.136 0.499 0.51 0.00125 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 71 11922.301 0.005 0.0168 0.234 0.57 0.117 0.155 0.485 0.579 0.00121 0.00145 +! Validation 71 11922.301 0.005 0.0174 0.219 0.567 0.118 0.157 0.469 0.558 0.00117 0.0014 +Wall time: 11922.301891655196 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 0.499 0.0173 0.153 0.118 0.157 0.403 0.467 0.00101 0.00117 + 72 118 0.494 0.0169 0.156 0.117 0.155 0.414 0.471 0.00103 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 0.623 0.0134 0.355 0.106 0.138 0.703 0.711 0.00176 0.00178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 72 12089.663 0.005 0.0168 0.22 0.555 0.117 0.154 0.454 0.561 0.00113 0.0014 +! Validation 72 12089.663 0.005 0.0177 0.529 0.884 0.119 0.159 0.807 0.868 0.00202 0.00217 +Wall time: 12089.663936155383 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 0.839 0.0163 0.514 0.115 0.152 0.798 0.855 0.002 0.00214 + 73 118 0.367 0.0159 0.049 0.113 0.15 0.206 0.264 0.000514 0.000661 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 0.361 0.0129 0.104 0.104 0.135 0.374 0.385 0.000936 0.000963 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 73 12257.023 0.005 0.0165 0.214 0.544 0.116 0.154 0.448 0.553 0.00112 0.00138 +! Validation 73 12257.023 0.005 0.0169 0.216 0.554 0.116 0.155 0.48 0.555 0.0012 0.00139 +Wall time: 12257.023547826335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 0.411 0.0165 0.0801 0.115 0.153 0.286 0.338 0.000715 0.000845 + 74 118 0.61 0.0147 0.315 0.11 0.145 0.599 0.67 0.0015 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 0.476 0.0125 0.226 0.103 0.133 0.56 0.567 0.0014 0.00142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 74 12424.516 0.005 0.0163 0.213 0.538 0.115 0.152 0.447 0.55 0.00112 0.00138 +! Validation 74 12424.516 0.005 0.0167 0.331 0.665 0.115 0.154 0.62 0.687 0.00155 0.00172 +Wall time: 12424.51602396043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 0.378 0.0157 0.0638 0.113 0.15 0.237 0.302 0.000592 0.000754 + 75 118 0.357 0.0151 0.054 0.111 0.147 0.232 0.277 0.000581 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 0.364 0.0122 0.12 0.101 0.132 0.402 0.414 0.001 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 75 12591.887 0.005 0.0157 0.146 0.461 0.113 0.15 0.372 0.458 0.000929 0.00114 +! Validation 75 12591.887 0.005 0.0163 0.264 0.59 0.114 0.152 0.544 0.613 0.00136 0.00153 +Wall time: 12591.887809145264 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 0.838 0.0151 0.537 0.11 0.146 0.831 0.874 0.00208 0.00219 + 76 118 0.438 0.0151 0.137 0.111 0.146 0.408 0.441 0.00102 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 0.304 0.0122 0.0607 0.101 0.132 0.278 0.294 0.000694 0.000735 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 76 12759.269 0.005 0.0154 0.147 0.455 0.112 0.148 0.372 0.458 0.000931 0.00115 +! Validation 76 12759.269 0.005 0.0162 0.0704 0.395 0.114 0.152 0.252 0.317 0.00063 0.000792 +Wall time: 12759.269485328346 +! Best model 76 0.395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 0.391 0.0158 0.0749 0.114 0.15 0.262 0.327 0.000656 0.000817 + 77 118 0.337 0.0136 0.0655 0.106 0.139 0.257 0.306 0.000643 0.000764 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 0.268 0.0121 0.0274 0.1 0.131 0.171 0.197 0.000426 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 77 12926.792 0.005 0.0153 0.212 0.518 0.112 0.148 0.456 0.551 0.00114 0.00138 +! Validation 77 12926.792 0.005 0.016 0.139 0.459 0.113 0.151 0.36 0.446 0.0009 0.00111 +Wall time: 12926.792797418311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 0.391 0.0158 0.0752 0.113 0.15 0.259 0.327 0.000647 0.000818 + 78 118 0.419 0.016 0.0991 0.113 0.151 0.341 0.376 0.000852 0.000939 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 0.244 0.0119 0.00566 0.0997 0.13 0.074 0.0898 0.000185 0.000224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 78 13094.228 0.005 0.0152 0.184 0.487 0.111 0.147 0.406 0.513 0.00101 0.00128 +! Validation 78 13094.228 0.005 0.0159 0.0721 0.39 0.113 0.15 0.256 0.32 0.00064 0.000801 +Wall time: 13094.228469375987 +! Best model 78 0.390 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 0.526 0.0158 0.21 0.113 0.15 0.494 0.548 0.00123 0.00137 + 79 118 0.323 0.0145 0.034 0.109 0.144 0.175 0.22 0.000439 0.00055 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 0.287 0.0117 0.0526 0.0991 0.129 0.262 0.274 0.000654 0.000685 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 79 13261.699 0.005 0.0149 0.182 0.481 0.11 0.146 0.422 0.511 0.00105 0.00128 +! Validation 79 13261.699 0.005 0.0157 0.0937 0.407 0.112 0.149 0.292 0.365 0.00073 0.000913 +Wall time: 13261.69981807936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 0.669 0.0143 0.383 0.108 0.143 0.673 0.739 0.00168 0.00185 + 80 118 0.365 0.0146 0.073 0.109 0.144 0.276 0.323 0.000689 0.000806 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 0.274 0.0118 0.038 0.099 0.13 0.218 0.233 0.000545 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 80 13429.044 0.005 0.0148 0.203 0.498 0.11 0.145 0.438 0.538 0.0011 0.00135 +! Validation 80 13429.044 0.005 0.0155 0.108 0.419 0.111 0.149 0.32 0.393 0.0008 0.000982 +Wall time: 13429.044799063355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 0.621 0.0143 0.335 0.109 0.143 0.643 0.69 0.00161 0.00173 + 81 118 0.345 0.0155 0.0339 0.113 0.149 0.176 0.22 0.00044 0.000549 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 0.487 0.0127 0.233 0.103 0.135 0.568 0.577 0.00142 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 81 13596.927 0.005 0.0147 0.212 0.505 0.109 0.144 0.45 0.551 0.00113 0.00138 +! Validation 81 13596.927 0.005 0.0165 0.332 0.663 0.115 0.154 0.61 0.688 0.00153 0.00172 +Wall time: 13596.92751547508 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.356 0.0154 0.048 0.111 0.148 0.217 0.261 0.000544 0.000653 + 82 118 0.388 0.0154 0.0801 0.112 0.148 0.281 0.338 0.000703 0.000844 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.23 0.0112 0.0069 0.0968 0.126 0.0834 0.0992 0.000208 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 82 13764.263 0.005 0.0144 0.136 0.424 0.108 0.143 0.347 0.44 0.000868 0.0011 +! Validation 82 13764.263 0.005 0.0149 0.0657 0.363 0.109 0.146 0.247 0.306 0.000617 0.000765 +Wall time: 13764.263124155346 +! Best model 82 0.363 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 0.381 0.0146 0.0897 0.109 0.144 0.294 0.358 0.000736 0.000894 + 83 118 0.341 0.0147 0.0468 0.11 0.145 0.224 0.258 0.000561 0.000646 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 0.309 0.0112 0.0859 0.0964 0.126 0.338 0.35 0.000844 0.000874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 83 13931.602 0.005 0.0141 0.176 0.458 0.107 0.142 0.408 0.503 0.00102 0.00126 +! Validation 83 13931.602 0.005 0.0147 0.132 0.427 0.109 0.145 0.357 0.434 0.000893 0.00108 +Wall time: 13931.602444554213 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 0.444 0.0146 0.152 0.109 0.144 0.4 0.465 0.001 0.00116 + 84 118 0.34 0.0139 0.0629 0.107 0.14 0.256 0.299 0.00064 0.000748 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 0.42 0.0111 0.197 0.0964 0.126 0.52 0.53 0.0013 0.00132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 84 14099.107 0.005 0.0141 0.197 0.48 0.107 0.142 0.433 0.531 0.00108 0.00133 +! Validation 84 14099.107 0.005 0.0148 0.187 0.483 0.109 0.145 0.437 0.516 0.00109 0.00129 +Wall time: 14099.107090077363 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 0.497 0.0139 0.218 0.106 0.141 0.502 0.558 0.00125 0.00139 + 85 118 0.319 0.0145 0.0281 0.109 0.144 0.175 0.2 0.000437 0.0005 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 0.343 0.0105 0.133 0.0936 0.122 0.423 0.435 0.00106 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 85 14267.651 0.005 0.0138 0.151 0.426 0.106 0.14 0.373 0.465 0.000932 0.00116 +! Validation 85 14267.651 0.005 0.0142 0.174 0.457 0.106 0.142 0.422 0.498 0.00105 0.00125 +Wall time: 14267.651786550414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.318 0.0133 0.0533 0.104 0.137 0.224 0.275 0.000559 0.000689 + 86 118 0.352 0.0143 0.0659 0.108 0.143 0.274 0.306 0.000685 0.000766 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.637 0.0106 0.424 0.0939 0.123 0.772 0.778 0.00193 0.00194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 86 14435.252 0.005 0.0136 0.171 0.443 0.105 0.139 0.404 0.495 0.00101 0.00124 +! Validation 86 14435.252 0.005 0.0141 0.358 0.641 0.106 0.142 0.66 0.714 0.00165 0.00178 +Wall time: 14435.252597165294 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.348 0.0133 0.0813 0.105 0.138 0.272 0.34 0.000681 0.000851 + 87 118 0.6 0.014 0.321 0.107 0.141 0.619 0.676 0.00155 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.499 0.0107 0.285 0.0946 0.123 0.632 0.637 0.00158 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 87 14604.741 0.005 0.0135 0.17 0.439 0.105 0.139 0.395 0.49 0.000987 0.00122 +! Validation 87 14604.741 0.005 0.0142 0.243 0.528 0.107 0.142 0.524 0.588 0.00131 0.00147 +Wall time: 14604.741336007137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.78 0.013 0.52 0.103 0.136 0.822 0.861 0.00205 0.00215 + 88 118 0.797 0.0128 0.541 0.103 0.135 0.849 0.878 0.00212 0.00219 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.369 0.0107 0.156 0.0941 0.123 0.464 0.471 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 88 14772.081 0.005 0.0133 0.17 0.436 0.104 0.138 0.392 0.489 0.000979 0.00122 +! Validation 88 14772.081 0.005 0.0143 0.28 0.566 0.107 0.143 0.567 0.632 0.00142 0.00158 +Wall time: 14772.08188039111 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.705 0.0134 0.437 0.104 0.138 0.752 0.789 0.00188 0.00197 + 89 118 0.38 0.0134 0.112 0.104 0.138 0.334 0.399 0.000834 0.000998 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.683 0.0111 0.461 0.0955 0.126 0.805 0.811 0.00201 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 89 14939.315 0.005 0.0133 0.196 0.462 0.104 0.138 0.441 0.53 0.0011 0.00132 +! Validation 89 14939.315 0.005 0.0146 0.501 0.792 0.108 0.144 0.774 0.844 0.00194 0.00211 +Wall time: 14939.315002339426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.317 0.0129 0.0584 0.102 0.136 0.229 0.288 0.000572 0.000721 + 90 118 0.492 0.0122 0.248 0.0996 0.132 0.564 0.594 0.00141 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.216 0.0102 0.0114 0.0919 0.121 0.115 0.127 0.000288 0.000318 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 90 15106.526 0.005 0.0132 0.167 0.431 0.104 0.137 0.383 0.487 0.000958 0.00122 +! Validation 90 15106.526 0.005 0.0136 0.0567 0.329 0.104 0.139 0.227 0.284 0.000568 0.00071 +Wall time: 15106.526050212327 +! Best model 90 0.329 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.47 0.0124 0.222 0.1 0.133 0.521 0.562 0.0013 0.00141 + 91 118 0.301 0.0119 0.0637 0.0987 0.13 0.267 0.301 0.000666 0.000753 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.227 0.00981 0.0313 0.0904 0.118 0.194 0.211 0.000484 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 91 15273.748 0.005 0.0127 0.132 0.386 0.102 0.135 0.353 0.434 0.000884 0.00109 +! Validation 91 15273.748 0.005 0.0132 0.095 0.359 0.103 0.137 0.29 0.368 0.000726 0.00092 +Wall time: 15273.748208103236 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.394 0.0121 0.152 0.0993 0.131 0.411 0.465 0.00103 0.00116 + 92 118 0.305 0.0127 0.0518 0.102 0.134 0.226 0.272 0.000566 0.000679 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.263 0.0103 0.0564 0.0923 0.121 0.267 0.283 0.000666 0.000709 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 92 15440.968 0.005 0.0126 0.165 0.418 0.101 0.134 0.399 0.486 0.000998 0.00122 +! Validation 92 15440.968 0.005 0.0136 0.066 0.337 0.104 0.139 0.243 0.307 0.000608 0.000767 +Wall time: 15440.968877958134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.284 0.012 0.043 0.0989 0.131 0.196 0.247 0.00049 0.000619 + 93 118 0.305 0.0117 0.0723 0.0985 0.129 0.269 0.321 0.000673 0.000802 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.267 0.0101 0.0649 0.0917 0.12 0.292 0.304 0.00073 0.00076 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 93 15608.289 0.005 0.0125 0.148 0.398 0.101 0.133 0.376 0.46 0.000939 0.00115 +! Validation 93 15608.289 0.005 0.0133 0.0901 0.357 0.103 0.138 0.288 0.358 0.000721 0.000896 +Wall time: 15608.289741460234 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.296 0.0115 0.0658 0.0967 0.128 0.25 0.306 0.000626 0.000765 + 94 118 0.337 0.0118 0.1 0.0983 0.13 0.313 0.378 0.000781 0.000945 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.212 0.00936 0.025 0.0881 0.115 0.163 0.189 0.000408 0.000472 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 94 15775.505 0.005 0.0123 0.128 0.374 0.1 0.132 0.347 0.428 0.000869 0.00107 +! Validation 94 15775.505 0.005 0.0128 0.0842 0.341 0.101 0.135 0.286 0.346 0.000714 0.000866 +Wall time: 15775.505967256147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.379 0.0121 0.137 0.099 0.131 0.394 0.441 0.000985 0.0011 + 95 118 0.293 0.0122 0.0479 0.1 0.132 0.193 0.261 0.000483 0.000653 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.322 0.00979 0.126 0.0901 0.118 0.415 0.424 0.00104 0.00106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 95 15942.749 0.005 0.0121 0.142 0.383 0.0991 0.131 0.362 0.45 0.000906 0.00113 +! Validation 95 15942.749 0.005 0.0129 0.215 0.474 0.102 0.136 0.483 0.553 0.00121 0.00138 +Wall time: 15942.749202632345 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.697 0.0125 0.447 0.101 0.133 0.747 0.798 0.00187 0.002 + 96 118 0.353 0.0119 0.114 0.099 0.13 0.335 0.404 0.000836 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.896 0.00971 0.702 0.0894 0.118 0.996 1 0.00249 0.0025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 96 16109.989 0.005 0.0122 0.185 0.429 0.0997 0.132 0.418 0.515 0.00105 0.00129 +! Validation 96 16109.989 0.005 0.0128 0.665 0.921 0.101 0.135 0.935 0.973 0.00234 0.00243 +Wall time: 16109.98919410538 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.591 0.0129 0.332 0.102 0.136 0.646 0.688 0.00161 0.00172 + 97 118 0.263 0.0111 0.0401 0.0965 0.126 0.199 0.239 0.000498 0.000598 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.237 0.00946 0.0482 0.0883 0.116 0.244 0.262 0.000609 0.000655 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 97 16277.213 0.005 0.0124 0.234 0.482 0.101 0.133 0.466 0.579 0.00117 0.00145 +! Validation 97 16277.213 0.005 0.0126 0.105 0.358 0.1 0.134 0.323 0.387 0.000809 0.000968 +Wall time: 16277.21388139436 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.558 0.0115 0.327 0.0974 0.128 0.633 0.683 0.00158 0.00171 + 98 118 0.276 0.0109 0.0591 0.0952 0.124 0.249 0.29 0.000623 0.000725 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.198 0.00907 0.0162 0.0867 0.114 0.136 0.152 0.00034 0.00038 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 98 16444.539 0.005 0.0117 0.126 0.36 0.0977 0.129 0.341 0.424 0.000853 0.00106 +! Validation 98 16444.539 0.005 0.0122 0.0619 0.306 0.0987 0.132 0.235 0.297 0.000588 0.000742 +Wall time: 16444.53935520025 +! Best model 98 0.306 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 0.365 0.0111 0.143 0.0959 0.126 0.4 0.451 0.000999 0.00113 + 99 118 0.272 0.00994 0.0733 0.0905 0.119 0.264 0.323 0.000659 0.000808 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 0.196 0.00915 0.0131 0.0867 0.114 0.103 0.136 0.000256 0.000341 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 99 16611.782 0.005 0.0117 0.147 0.381 0.0976 0.129 0.369 0.458 0.000922 0.00115 +! Validation 99 16611.782 0.005 0.0122 0.0632 0.306 0.0985 0.132 0.244 0.3 0.000611 0.00075 +Wall time: 16611.78269380424 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.275 0.0111 0.0541 0.0947 0.126 0.22 0.278 0.000549 0.000694 + 100 118 0.512 0.0112 0.289 0.0959 0.126 0.614 0.641 0.00154 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.739 0.00881 0.563 0.0854 0.112 0.891 0.896 0.00223 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 100 16778.998 0.005 0.0114 0.14 0.369 0.0964 0.128 0.364 0.446 0.00091 0.00111 +! Validation 100 16778.998 0.005 0.0119 0.702 0.94 0.0975 0.13 0.965 1 0.00241 0.0025 +Wall time: 16778.998718888033 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.295 0.0118 0.0578 0.0982 0.13 0.238 0.287 0.000596 0.000718 + 101 118 0.237 0.0102 0.0334 0.0917 0.12 0.147 0.218 0.000368 0.000545 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.246 0.00915 0.0627 0.0871 0.114 0.288 0.299 0.000719 0.000747 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 101 16946.221 0.005 0.0117 0.205 0.439 0.0977 0.129 0.438 0.542 0.0011 0.00136 +! Validation 101 16946.221 0.005 0.0122 0.112 0.356 0.0988 0.132 0.336 0.4 0.00084 0.001 +Wall time: 16946.221889146138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.312 0.0113 0.0872 0.0958 0.127 0.286 0.352 0.000716 0.000881 + 102 118 0.38 0.00994 0.181 0.09 0.119 0.468 0.507 0.00117 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.196 0.009 0.0156 0.0861 0.113 0.135 0.149 0.000338 0.000373 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 102 17113.473 0.005 0.0113 0.141 0.366 0.096 0.127 0.356 0.447 0.00089 0.00112 +! Validation 102 17113.473 0.005 0.0119 0.0486 0.288 0.0977 0.13 0.21 0.263 0.000526 0.000658 +Wall time: 17113.47351642931 +! Best model 102 0.288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.385 0.011 0.165 0.0945 0.125 0.431 0.486 0.00108 0.00121 + 103 118 0.24 0.0103 0.0333 0.0918 0.121 0.178 0.218 0.000444 0.000544 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.177 0.00855 0.00615 0.0841 0.11 0.0856 0.0936 0.000214 0.000234 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 103 17280.805 0.005 0.0109 0.104 0.323 0.0944 0.125 0.307 0.386 0.000767 0.000965 +! Validation 103 17280.805 0.005 0.0114 0.049 0.278 0.0956 0.128 0.21 0.264 0.000524 0.00066 +Wall time: 17280.8056041901 +! Best model 103 0.278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.254 0.0105 0.0433 0.0926 0.122 0.193 0.248 0.000483 0.000621 + 104 118 0.34 0.014 0.0603 0.104 0.141 0.226 0.293 0.000566 0.000733 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.533 0.00861 0.36 0.0846 0.111 0.712 0.717 0.00178 0.00179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 104 17448.043 0.005 0.011 0.169 0.389 0.0946 0.125 0.405 0.492 0.00101 0.00123 +! Validation 104 17448.043 0.005 0.0114 0.393 0.621 0.0956 0.128 0.699 0.748 0.00175 0.00187 +Wall time: 17448.043538108002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 0.699 0.0108 0.484 0.0934 0.124 0.798 0.83 0.002 0.00208 + 105 118 0.457 0.00965 0.264 0.0897 0.117 0.594 0.613 0.00148 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 1.2 0.00824 1.04 0.0827 0.108 1.21 1.21 0.00303 0.00304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 105 17615.264 0.005 0.0107 0.117 0.332 0.0936 0.124 0.321 0.407 0.000804 0.00102 +! Validation 105 17615.264 0.005 0.0113 0.961 1.19 0.0949 0.127 1.14 1.17 0.00286 0.00293 +Wall time: 17615.2645894601 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.301 0.0103 0.0957 0.0917 0.121 0.304 0.369 0.000761 0.000923 + 106 118 0.356 0.0105 0.145 0.0931 0.123 0.406 0.454 0.00101 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.198 0.00851 0.0277 0.0838 0.11 0.18 0.199 0.000449 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 106 17782.483 0.005 0.011 0.161 0.381 0.0946 0.125 0.375 0.48 0.000938 0.0012 +! Validation 106 17782.483 0.005 0.0113 0.0803 0.306 0.0949 0.127 0.279 0.338 0.000697 0.000845 +Wall time: 17782.483288375195 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.249 0.0108 0.0321 0.0937 0.124 0.168 0.214 0.000419 0.000535 + 107 118 0.223 0.0102 0.019 0.0915 0.12 0.148 0.164 0.00037 0.000411 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.19 0.00834 0.0228 0.0831 0.109 0.163 0.18 0.000406 0.000451 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 107 17949.810 0.005 0.0104 0.107 0.315 0.0921 0.122 0.316 0.391 0.000789 0.000977 +! Validation 107 17949.810 0.005 0.0112 0.0545 0.278 0.0947 0.126 0.222 0.279 0.000554 0.000696 +Wall time: 17949.810216679238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.278 0.0106 0.0664 0.0931 0.123 0.248 0.308 0.000619 0.000769 + 108 118 0.484 0.00987 0.286 0.0898 0.119 0.617 0.638 0.00154 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.694 0.00835 0.527 0.0833 0.109 0.862 0.866 0.00216 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 108 18117.072 0.005 0.0102 0.128 0.333 0.0915 0.121 0.346 0.425 0.000864 0.00106 +! Validation 108 18117.072 0.005 0.0111 0.685 0.907 0.0944 0.126 0.956 0.988 0.00239 0.00247 +Wall time: 18117.072539945133 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.302 0.0101 0.101 0.0904 0.12 0.336 0.379 0.000839 0.000947 + 109 118 0.26 0.009 0.0805 0.0868 0.113 0.282 0.339 0.000705 0.000847 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.185 0.00811 0.0231 0.0824 0.108 0.164 0.181 0.000409 0.000454 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 109 18284.343 0.005 0.0104 0.161 0.37 0.0923 0.122 0.393 0.48 0.000982 0.0012 +! Validation 109 18284.343 0.005 0.0109 0.0534 0.272 0.0935 0.125 0.219 0.276 0.000547 0.00069 +Wall time: 18284.343496088404 +! Best model 109 0.272 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.262 0.00987 0.0648 0.0896 0.119 0.248 0.304 0.000621 0.00076 + 110 118 0.274 0.01 0.0736 0.0904 0.119 0.301 0.324 0.000754 0.000809 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.321 0.00764 0.169 0.0796 0.104 0.482 0.49 0.0012 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 110 18451.584 0.005 0.00997 0.0883 0.288 0.0902 0.119 0.283 0.355 0.000708 0.000887 +! Validation 110 18451.584 0.005 0.0104 0.194 0.402 0.0911 0.122 0.463 0.526 0.00116 0.00131 +Wall time: 18451.58469338203 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.239 0.00936 0.052 0.0876 0.115 0.217 0.272 0.000543 0.00068 + 111 118 0.289 0.00977 0.0934 0.0899 0.118 0.303 0.365 0.000756 0.000912 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.169 0.00761 0.0163 0.0793 0.104 0.13 0.152 0.000326 0.000381 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 111 18618.806 0.005 0.0101 0.149 0.351 0.0908 0.12 0.368 0.461 0.000919 0.00115 +! Validation 111 18618.806 0.005 0.0105 0.0649 0.275 0.0916 0.122 0.25 0.304 0.000624 0.00076 +Wall time: 18618.806290874258 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.534 0.00952 0.343 0.0886 0.116 0.664 0.699 0.00166 0.00175 + 112 118 0.667 0.00932 0.481 0.0875 0.115 0.795 0.828 0.00199 0.00207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.466 0.0085 0.296 0.0836 0.11 0.644 0.65 0.00161 0.00162 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 112 18786.139 0.005 0.00999 0.172 0.372 0.0903 0.119 0.407 0.492 0.00102 0.00123 +! Validation 112 18786.139 0.005 0.0113 0.245 0.471 0.0948 0.127 0.537 0.591 0.00134 0.00148 +Wall time: 18786.138994014356 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.231 0.01 0.0302 0.0904 0.12 0.172 0.208 0.00043 0.000519 + 113 118 0.239 0.0102 0.0353 0.0913 0.12 0.196 0.224 0.00049 0.00056 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.21 0.00751 0.0603 0.0792 0.103 0.279 0.293 0.000698 0.000732 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 113 18953.368 0.005 0.00984 0.111 0.307 0.0896 0.118 0.312 0.398 0.000779 0.000994 +! Validation 113 18953.368 0.005 0.0103 0.107 0.313 0.0908 0.121 0.336 0.39 0.000839 0.000976 +Wall time: 18953.368637801148 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.221 0.00959 0.0298 0.0885 0.117 0.164 0.206 0.00041 0.000515 + 114 118 0.245 0.00859 0.0729 0.0841 0.111 0.28 0.322 0.000701 0.000806 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.162 0.00767 0.00818 0.0794 0.105 0.102 0.108 0.000256 0.00027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 114 19120.587 0.005 0.0096 0.125 0.317 0.0885 0.117 0.329 0.423 0.000823 0.00106 +! Validation 114 19120.587 0.005 0.0103 0.0552 0.26 0.0904 0.121 0.223 0.28 0.000557 0.000701 +Wall time: 19120.587096181232 +! Best model 114 0.260 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 0.215 0.00877 0.04 0.0848 0.112 0.189 0.239 0.000471 0.000597 + 115 118 0.289 0.00969 0.0949 0.0892 0.117 0.332 0.368 0.000831 0.000919 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 0.386 0.00749 0.237 0.079 0.103 0.576 0.58 0.00144 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 115 19287.816 0.005 0.00934 0.0768 0.264 0.0873 0.115 0.266 0.331 0.000665 0.000826 +! Validation 115 19287.816 0.005 0.0102 0.307 0.51 0.0902 0.12 0.608 0.661 0.00152 0.00165 +Wall time: 19287.816566231195 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 0.33 0.00872 0.156 0.0848 0.111 0.424 0.471 0.00106 0.00118 + 116 118 0.252 0.00957 0.0605 0.0886 0.117 0.243 0.294 0.000606 0.000734 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 0.219 0.00708 0.0772 0.0766 0.1 0.324 0.332 0.00081 0.000829 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 116 19455.029 0.005 0.00919 0.106 0.29 0.0865 0.114 0.316 0.39 0.00079 0.000974 +! Validation 116 19455.029 0.005 0.0097 0.158 0.352 0.088 0.118 0.397 0.474 0.000992 0.00119 +Wall time: 19455.029688897077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 0.348 0.00912 0.166 0.0862 0.114 0.451 0.486 0.00113 0.00122 + 117 118 0.429 0.00808 0.267 0.0826 0.107 0.573 0.617 0.00143 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 0.641 0.00753 0.49 0.079 0.104 0.833 0.835 0.00208 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 117 19622.358 0.005 0.0095 0.209 0.399 0.0881 0.116 0.453 0.545 0.00113 0.00136 +! Validation 117 19622.358 0.005 0.0101 0.49 0.691 0.0898 0.12 0.796 0.835 0.00199 0.00209 +Wall time: 19622.35853730142 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 0.22 0.00874 0.0449 0.0845 0.112 0.211 0.253 0.000526 0.000632 + 118 118 0.236 0.00829 0.0698 0.0827 0.109 0.262 0.315 0.000655 0.000789 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 0.144 0.00698 0.00436 0.0759 0.0997 0.0489 0.0788 0.000122 0.000197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 118 19789.587 0.005 0.00915 0.106 0.289 0.0864 0.114 0.316 0.39 0.00079 0.000975 +! Validation 118 19789.587 0.005 0.00952 0.0399 0.23 0.087 0.116 0.195 0.239 0.000487 0.000596 +Wall time: 19789.587790810037 +! Best model 118 0.230 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.406 0.00871 0.231 0.0847 0.111 0.527 0.574 0.00132 0.00144 + 119 118 0.273 0.00921 0.0887 0.087 0.115 0.333 0.355 0.000832 0.000889 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.166 0.0072 0.022 0.0769 0.101 0.157 0.177 0.000392 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 119 19956.819 0.005 0.00894 0.0996 0.278 0.0853 0.113 0.302 0.377 0.000756 0.000942 +! Validation 119 19956.819 0.005 0.00964 0.0669 0.26 0.0877 0.117 0.239 0.309 0.000596 0.000772 +Wall time: 19956.819589279126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.254 0.00865 0.0809 0.0839 0.111 0.279 0.34 0.000698 0.000849 + 120 118 0.863 0.00877 0.688 0.0851 0.112 0.954 0.99 0.00238 0.00247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.202 0.00811 0.04 0.0815 0.108 0.22 0.239 0.000551 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 120 20124.236 0.005 0.00883 0.112 0.289 0.0848 0.112 0.314 0.393 0.000785 0.000982 +! Validation 120 20124.236 0.005 0.0104 0.139 0.347 0.0911 0.122 0.381 0.445 0.000953 0.00111 +Wall time: 20124.236319039017 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.275 0.00851 0.105 0.0832 0.11 0.332 0.387 0.000831 0.000966 + 121 118 0.246 0.00915 0.0632 0.0873 0.114 0.288 0.3 0.000719 0.00075 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.208 0.00661 0.0756 0.0739 0.097 0.32 0.328 0.0008 0.00082 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 121 20291.466 0.005 0.00895 0.106 0.285 0.0854 0.113 0.308 0.389 0.00077 0.000973 +! Validation 121 20291.466 0.005 0.00907 0.113 0.294 0.0849 0.114 0.35 0.401 0.000874 0.001 +Wall time: 20291.46693757642 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.189 0.00793 0.0302 0.0806 0.106 0.163 0.208 0.000408 0.000519 + 122 118 0.35 0.00976 0.155 0.0895 0.118 0.421 0.469 0.00105 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.867 0.00921 0.683 0.0875 0.115 0.985 0.987 0.00246 0.00247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 122 20459.118 0.005 0.00855 0.104 0.275 0.0834 0.11 0.302 0.385 0.000755 0.000963 +! Validation 122 20459.118 0.005 0.0116 0.666 0.898 0.0975 0.129 0.942 0.974 0.00236 0.00244 +Wall time: 20459.118819710333 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.214 0.00838 0.0461 0.0822 0.109 0.209 0.256 0.000522 0.000641 + 123 118 0.394 0.0073 0.248 0.0781 0.102 0.578 0.595 0.00144 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.445 0.00635 0.318 0.0726 0.0951 0.67 0.673 0.00168 0.00168 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 123 20626.534 0.005 0.0088 0.116 0.292 0.0847 0.112 0.317 0.406 0.000792 0.00101 +! Validation 123 20626.534 0.005 0.00891 0.303 0.481 0.0841 0.113 0.617 0.657 0.00154 0.00164 +Wall time: 20626.5347535084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.19 0.00789 0.0321 0.0801 0.106 0.167 0.214 0.000417 0.000535 + 124 118 0.237 0.0088 0.0606 0.0845 0.112 0.249 0.294 0.000621 0.000735 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.136 0.00641 0.00779 0.0729 0.0956 0.101 0.105 0.000253 0.000263 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 124 20794.107 0.005 0.00863 0.156 0.328 0.0838 0.111 0.366 0.472 0.000915 0.00118 +! Validation 124 20794.107 0.005 0.00893 0.0419 0.221 0.0843 0.113 0.196 0.244 0.000489 0.000611 +Wall time: 20794.107730623335 +! Best model 124 0.221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.405 0.00856 0.234 0.0827 0.11 0.533 0.578 0.00133 0.00144 + 125 118 0.206 0.00885 0.0292 0.0841 0.112 0.162 0.204 0.000404 0.00051 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.453 0.00651 0.323 0.0736 0.0963 0.676 0.678 0.00169 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 125 20961.561 0.005 0.00821 0.0852 0.249 0.0817 0.108 0.277 0.349 0.000692 0.000873 +! Validation 125 20961.561 0.005 0.00891 0.32 0.498 0.0842 0.113 0.637 0.675 0.00159 0.00169 +Wall time: 20961.56106079137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.187 0.00756 0.036 0.0786 0.104 0.179 0.226 0.000448 0.000566 + 126 118 0.291 0.00891 0.113 0.0846 0.113 0.366 0.402 0.000915 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.22 0.0062 0.0956 0.0711 0.094 0.364 0.369 0.00091 0.000922 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 126 21129.087 0.005 0.0083 0.0899 0.256 0.0821 0.109 0.279 0.357 0.000697 0.000894 +! Validation 126 21129.087 0.005 0.00856 0.151 0.322 0.0823 0.11 0.415 0.464 0.00104 0.00116 +Wall time: 21129.087663974147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.222 0.00844 0.0529 0.0818 0.11 0.236 0.274 0.00059 0.000686 + 127 118 0.182 0.00796 0.0228 0.0801 0.106 0.15 0.18 0.000374 0.000451 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.141 0.00619 0.0167 0.0713 0.0939 0.139 0.154 0.000348 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 127 21296.497 0.005 0.00805 0.109 0.27 0.0808 0.107 0.315 0.395 0.000789 0.000988 +! Validation 127 21296.497 0.005 0.00843 0.0429 0.212 0.0817 0.11 0.193 0.247 0.000482 0.000618 +Wall time: 21296.497893372085 +! Best model 127 0.212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.191 0.00791 0.0328 0.0804 0.106 0.175 0.216 0.000437 0.00054 + 128 118 0.169 0.00733 0.0226 0.078 0.102 0.157 0.179 0.000392 0.000448 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.174 0.00606 0.0531 0.0707 0.0929 0.266 0.275 0.000666 0.000687 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 128 21463.930 0.005 0.00786 0.0876 0.245 0.0799 0.106 0.286 0.354 0.000714 0.000885 +! Validation 128 21463.930 0.005 0.00836 0.069 0.236 0.0815 0.109 0.255 0.314 0.000637 0.000784 +Wall time: 21463.930638383143 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.302 0.0081 0.14 0.0811 0.107 0.399 0.447 0.000998 0.00112 + 129 118 0.165 0.0076 0.0131 0.0784 0.104 0.114 0.136 0.000286 0.000341 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.134 0.00603 0.013 0.07 0.0927 0.113 0.136 0.000283 0.000341 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 129 21631.344 0.005 0.00782 0.101 0.257 0.0796 0.106 0.306 0.38 0.000764 0.00095 +! Validation 129 21631.344 0.005 0.0083 0.0497 0.216 0.081 0.109 0.219 0.266 0.000548 0.000665 +Wall time: 21631.34449160425 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.173 0.00772 0.0182 0.0795 0.105 0.121 0.161 0.000303 0.000403 + 130 118 0.215 0.00741 0.0667 0.0782 0.103 0.282 0.308 0.000704 0.000771 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.255 0.00618 0.131 0.0711 0.0938 0.426 0.432 0.00107 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 130 21798.759 0.005 0.00774 0.0971 0.252 0.0792 0.105 0.3 0.372 0.00075 0.000931 +! Validation 130 21798.759 0.005 0.00829 0.146 0.312 0.0811 0.109 0.406 0.456 0.00102 0.00114 +Wall time: 21798.759365015198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.232 0.00765 0.0787 0.0786 0.104 0.3 0.335 0.000751 0.000837 + 131 118 0.182 0.00736 0.035 0.076 0.102 0.17 0.223 0.000426 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.174 0.00585 0.0571 0.0687 0.0913 0.276 0.285 0.00069 0.000713 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 131 21966.280 0.005 0.00769 0.109 0.263 0.079 0.105 0.32 0.394 0.000801 0.000986 +! Validation 131 21966.280 0.005 0.00814 0.0934 0.256 0.0802 0.108 0.315 0.365 0.000788 0.000912 +Wall time: 21966.280871197116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.203 0.00729 0.0567 0.077 0.102 0.242 0.284 0.000606 0.00071 + 132 118 0.198 0.00675 0.0635 0.0739 0.098 0.266 0.301 0.000664 0.000752 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.125 0.00601 0.00535 0.0696 0.0925 0.0632 0.0873 0.000158 0.000218 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 132 22133.707 0.005 0.0075 0.0853 0.235 0.0779 0.103 0.285 0.349 0.000712 0.000872 +! Validation 132 22133.707 0.005 0.00821 0.0357 0.2 0.0806 0.108 0.182 0.226 0.000454 0.000564 +Wall time: 22133.707500743214 +! Best model 132 0.200 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.169 0.00705 0.0277 0.0756 0.1 0.158 0.199 0.000394 0.000496 + 133 118 0.314 0.00748 0.164 0.0776 0.103 0.467 0.484 0.00117 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.186 0.00569 0.0725 0.0679 0.09 0.316 0.321 0.00079 0.000803 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 133 22301.242 0.005 0.00753 0.0948 0.245 0.0781 0.104 0.287 0.367 0.000718 0.000917 +! Validation 133 22301.242 0.005 0.00794 0.0955 0.254 0.0791 0.106 0.318 0.369 0.000794 0.000922 +Wall time: 22301.24287525518 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.236 0.00783 0.0798 0.0792 0.106 0.281 0.337 0.000702 0.000843 + 134 118 0.18 0.00682 0.0438 0.075 0.0986 0.233 0.25 0.000582 0.000625 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.117 0.00559 0.00518 0.0671 0.0892 0.0596 0.0859 0.000149 0.000215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 134 22468.743 0.005 0.00738 0.0892 0.237 0.0773 0.103 0.292 0.357 0.000731 0.000893 +! Validation 134 22468.743 0.005 0.00775 0.0354 0.19 0.078 0.105 0.183 0.224 0.000458 0.000561 +Wall time: 22468.743268524297 +! Best model 134 0.190 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.183 0.00739 0.035 0.0772 0.103 0.165 0.223 0.000412 0.000558 + 135 118 0.2 0.00677 0.065 0.0745 0.0982 0.24 0.304 0.0006 0.000761 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.249 0.00595 0.13 0.069 0.0921 0.426 0.431 0.00107 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 135 22640.420 0.005 0.0073 0.0991 0.245 0.0768 0.102 0.307 0.376 0.000769 0.00094 +! Validation 135 22640.420 0.005 0.00795 0.126 0.285 0.0791 0.106 0.371 0.423 0.000929 0.00106 +Wall time: 22640.420629990287 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.203 0.00671 0.0685 0.0741 0.0978 0.274 0.312 0.000685 0.000781 + 136 118 0.183 0.00683 0.0461 0.0743 0.0986 0.225 0.256 0.000563 0.000641 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.134 0.00552 0.0233 0.0668 0.0887 0.17 0.182 0.000425 0.000455 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 136 22808.016 0.005 0.00715 0.0682 0.211 0.076 0.101 0.253 0.312 0.000633 0.00078 +! Validation 136 22808.016 0.005 0.00764 0.0588 0.212 0.0776 0.104 0.229 0.289 0.000572 0.000724 +Wall time: 22808.016607451253 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.323 0.00708 0.182 0.0761 0.1 0.472 0.509 0.00118 0.00127 + 137 118 0.322 0.00708 0.181 0.0757 0.1 0.452 0.507 0.00113 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.322 0.00561 0.21 0.0676 0.0894 0.544 0.547 0.00136 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 137 22975.513 0.005 0.00721 0.118 0.262 0.0763 0.101 0.334 0.409 0.000835 0.00102 +! Validation 137 22975.513 0.005 0.00774 0.236 0.391 0.078 0.105 0.531 0.58 0.00133 0.00145 +Wall time: 22975.513583051972 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.239 0.00691 0.101 0.0743 0.0992 0.324 0.379 0.000809 0.000948 + 138 118 0.162 0.00711 0.0197 0.076 0.101 0.142 0.168 0.000356 0.000419 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.114 0.00527 0.00875 0.0653 0.0866 0.0959 0.112 0.00024 0.000279 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 138 23143.009 0.005 0.00711 0.0801 0.222 0.0757 0.101 0.272 0.339 0.00068 0.000847 +! Validation 138 23143.009 0.005 0.00743 0.0375 0.186 0.0763 0.103 0.189 0.231 0.000472 0.000578 +Wall time: 23143.009526565205 +! Best model 138 0.186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.192 0.0072 0.0478 0.076 0.101 0.208 0.261 0.00052 0.000652 + 139 118 0.142 0.00657 0.01 0.0726 0.0968 0.0939 0.12 0.000235 0.000299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.158 0.00548 0.0488 0.0663 0.0883 0.257 0.264 0.000642 0.000659 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 139 23310.552 0.005 0.0069 0.0693 0.207 0.0745 0.0992 0.256 0.315 0.000639 0.000788 +! Validation 139 23310.552 0.005 0.00748 0.0862 0.236 0.0766 0.103 0.302 0.35 0.000755 0.000876 +Wall time: 23310.55246903142 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.208 0.00681 0.0714 0.0738 0.0985 0.269 0.319 0.000672 0.000797 + 140 118 0.169 0.00673 0.0344 0.0737 0.0979 0.202 0.221 0.000505 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.113 0.00549 0.00321 0.066 0.0884 0.0573 0.0677 0.000143 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 140 23478.063 0.005 0.00704 0.11 0.25 0.0754 0.1 0.318 0.396 0.000794 0.00099 +! Validation 140 23478.063 0.005 0.00746 0.0296 0.179 0.0765 0.103 0.167 0.205 0.000417 0.000513 +Wall time: 23478.063379858155 +! Best model 140 0.179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.15 0.00619 0.026 0.071 0.0939 0.153 0.193 0.000383 0.000481 + 141 118 0.182 0.00593 0.0632 0.0691 0.0919 0.257 0.3 0.000642 0.00075 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.11 0.00513 0.00739 0.0639 0.0855 0.0832 0.103 0.000208 0.000257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 141 23645.685 0.005 0.00678 0.067 0.203 0.0738 0.0983 0.241 0.309 0.000604 0.000772 +! Validation 141 23645.685 0.005 0.00713 0.0293 0.172 0.0746 0.101 0.166 0.204 0.000416 0.000511 +Wall time: 23645.685093481094 +! Best model 141 0.172 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.216 0.00667 0.0829 0.0731 0.0975 0.292 0.344 0.000729 0.000859 + 142 118 0.191 0.00677 0.0556 0.074 0.0982 0.234 0.281 0.000586 0.000703 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.141 0.00503 0.04 0.0635 0.0847 0.232 0.239 0.000581 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 142 23813.224 0.005 0.00667 0.0791 0.212 0.0731 0.0974 0.259 0.336 0.000647 0.00084 +! Validation 142 23813.224 0.005 0.00712 0.0652 0.207 0.0746 0.101 0.249 0.305 0.000622 0.000762 +Wall time: 23813.224482114427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.519 0.00778 0.364 0.0801 0.105 0.696 0.72 0.00174 0.0018 + 143 118 0.244 0.00925 0.0591 0.085 0.115 0.257 0.29 0.000643 0.000726 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.426 0.00518 0.322 0.0647 0.0859 0.676 0.678 0.00169 0.00169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 143 23985.182 0.005 0.00699 0.141 0.281 0.075 0.0997 0.369 0.449 0.000922 0.00112 +! Validation 143 23985.182 0.005 0.00738 0.298 0.446 0.0761 0.103 0.617 0.651 0.00154 0.00163 +Wall time: 23985.1826822944 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.158 0.00656 0.0267 0.0722 0.0967 0.157 0.195 0.000392 0.000488 + 144 118 0.137 0.00658 0.00531 0.0717 0.0968 0.0723 0.087 0.000181 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.123 0.00494 0.0237 0.063 0.0839 0.176 0.184 0.000441 0.00046 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 144 24152.659 0.005 0.0067 0.0705 0.204 0.0734 0.0977 0.245 0.318 0.000612 0.000795 +! Validation 144 24152.659 0.005 0.00694 0.0527 0.192 0.0735 0.0995 0.222 0.274 0.000554 0.000685 +Wall time: 24152.659754903987 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.269 0.00645 0.14 0.0721 0.0958 0.42 0.446 0.00105 0.00112 + 145 118 0.14 0.00618 0.0162 0.0712 0.0939 0.122 0.152 0.000304 0.00038 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.102 0.00494 0.00358 0.0627 0.0838 0.0642 0.0714 0.000161 0.000179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 145 24320.262 0.005 0.00647 0.0681 0.198 0.072 0.096 0.251 0.312 0.000628 0.000781 +! Validation 145 24320.262 0.005 0.00692 0.0292 0.168 0.0734 0.0993 0.162 0.204 0.000405 0.00051 +Wall time: 24320.262760837097 +! Best model 145 0.168 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.144 0.00613 0.0218 0.0705 0.0935 0.137 0.176 0.000342 0.000441 + 146 118 0.165 0.00659 0.0336 0.0726 0.0969 0.197 0.219 0.000491 0.000547 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.132 0.0048 0.0362 0.0621 0.0827 0.223 0.227 0.000558 0.000568 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 146 24487.757 0.005 0.00643 0.0766 0.205 0.0717 0.0957 0.268 0.331 0.00067 0.000827 +! Validation 146 24487.757 0.005 0.00684 0.0736 0.21 0.073 0.0987 0.278 0.324 0.000695 0.00081 +Wall time: 24487.757544776425 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.233 0.00633 0.106 0.0709 0.0949 0.351 0.389 0.000877 0.000973 + 147 118 0.173 0.0064 0.0448 0.0718 0.0954 0.219 0.253 0.000547 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.108 0.00496 0.00917 0.0627 0.084 0.0983 0.114 0.000246 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 147 24655.282 0.005 0.00641 0.0802 0.208 0.0716 0.0955 0.274 0.338 0.000685 0.000846 +! Validation 147 24655.282 0.005 0.00685 0.0357 0.173 0.0731 0.0988 0.178 0.225 0.000444 0.000564 +Wall time: 24655.28263354022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.2 0.00634 0.0737 0.0712 0.095 0.278 0.324 0.000694 0.00081 + 148 118 0.178 0.00671 0.0437 0.0732 0.0978 0.213 0.249 0.000534 0.000623 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.208 0.00476 0.113 0.0619 0.0824 0.398 0.401 0.000996 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 148 24822.807 0.005 0.00628 0.0697 0.195 0.0709 0.0946 0.255 0.316 0.000638 0.000789 +! Validation 148 24822.807 0.005 0.00679 0.145 0.28 0.0728 0.0984 0.41 0.454 0.00102 0.00113 +Wall time: 24822.807908348273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.205 0.00632 0.0782 0.0714 0.0949 0.298 0.334 0.000744 0.000835 + 149 118 0.165 0.00523 0.06 0.0655 0.0863 0.253 0.292 0.000632 0.000731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.109 0.00449 0.0193 0.0599 0.08 0.16 0.166 0.0004 0.000415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 149 24991.059 0.005 0.00622 0.0678 0.192 0.0706 0.0942 0.25 0.311 0.000626 0.000777 +! Validation 149 24991.059 0.005 0.00663 0.0393 0.172 0.0717 0.0972 0.188 0.237 0.00047 0.000592 +Wall time: 24991.059456220362 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.144 0.00613 0.0216 0.07 0.0934 0.147 0.176 0.000368 0.000439 + 150 118 0.229 0.00555 0.118 0.0672 0.0889 0.37 0.41 0.000924 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.0991 0.00483 0.0024 0.0622 0.083 0.0465 0.0584 0.000116 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 150 25158.734 0.005 0.0067 0.125 0.259 0.0733 0.0977 0.329 0.421 0.000822 0.00105 +! Validation 150 25158.734 0.005 0.00681 0.0287 0.165 0.0728 0.0985 0.161 0.202 0.000402 0.000506 +Wall time: 25158.734218377154 +! Best model 150 0.165 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.14 0.00587 0.023 0.0684 0.0914 0.147 0.181 0.000367 0.000453 + 151 118 0.133 0.00592 0.0149 0.0689 0.0918 0.118 0.145 0.000296 0.000364 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.0923 0.00453 0.00176 0.06 0.0803 0.043 0.05 0.000108 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 151 25326.299 0.005 0.0061 0.0585 0.18 0.0698 0.0932 0.234 0.289 0.000584 0.000723 +! Validation 151 25326.299 0.005 0.00647 0.0248 0.154 0.0708 0.096 0.15 0.188 0.000374 0.00047 +Wall time: 25326.299103798345 +! Best model 151 0.154 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.146 0.00629 0.0201 0.0713 0.0946 0.137 0.169 0.000343 0.000423 + 152 118 0.165 0.0058 0.049 0.068 0.0909 0.203 0.264 0.000508 0.00066 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.105 0.00475 0.01 0.0612 0.0822 0.106 0.12 0.000266 0.000299 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 152 25493.874 0.005 0.00612 0.098 0.22 0.0699 0.0934 0.305 0.374 0.000762 0.000936 +! Validation 152 25493.874 0.005 0.00664 0.0288 0.162 0.0718 0.0972 0.162 0.203 0.000404 0.000507 +Wall time: 25493.87423438439 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.158 0.00611 0.0356 0.0694 0.0933 0.175 0.225 0.000439 0.000563 + 153 118 0.118 0.00533 0.0109 0.0662 0.0872 0.0916 0.125 0.000229 0.000312 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.0945 0.00458 0.00287 0.0603 0.0808 0.0556 0.0639 0.000139 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 153 25661.482 0.005 0.00597 0.0528 0.172 0.069 0.0922 0.216 0.275 0.00054 0.000688 +! Validation 153 25661.482 0.005 0.0064 0.0271 0.155 0.0705 0.0955 0.157 0.196 0.000393 0.000491 +Wall time: 25661.482486329973 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.152 0.00638 0.0242 0.0714 0.0953 0.152 0.186 0.00038 0.000464 + 154 118 0.148 0.00586 0.0304 0.0689 0.0914 0.153 0.208 0.000382 0.00052 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.272 0.00456 0.181 0.0599 0.0806 0.505 0.507 0.00126 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 154 25829.048 0.005 0.00599 0.0875 0.207 0.0691 0.0924 0.28 0.354 0.000701 0.000885 +! Validation 154 25829.048 0.005 0.00646 0.193 0.323 0.0708 0.096 0.49 0.525 0.00123 0.00131 +Wall time: 25829.04864113126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.399 0.00587 0.282 0.0682 0.0915 0.615 0.633 0.00154 0.00158 + 155 118 0.374 0.00651 0.244 0.0724 0.0963 0.558 0.59 0.0014 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.32 0.00506 0.218 0.0632 0.0849 0.556 0.558 0.00139 0.00139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 155 25996.720 0.005 0.00591 0.0696 0.188 0.0686 0.0917 0.252 0.312 0.00063 0.000781 +! Validation 155 25996.720 0.005 0.0068 0.209 0.345 0.073 0.0984 0.506 0.545 0.00126 0.00136 +Wall time: 25996.72077461239 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.142 0.00591 0.0239 0.0687 0.0917 0.155 0.185 0.000386 0.000462 + 156 118 0.204 0.00547 0.095 0.0668 0.0883 0.337 0.368 0.000842 0.00092 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.107 0.00494 0.00793 0.0624 0.0839 0.09 0.106 0.000225 0.000266 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 156 26164.579 0.005 0.00597 0.0835 0.203 0.0691 0.0923 0.279 0.345 0.000697 0.000862 +! Validation 156 26164.579 0.005 0.00664 0.0311 0.164 0.0721 0.0972 0.17 0.21 0.000424 0.000526 +Wall time: 26164.579566477332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.185 0.00625 0.0604 0.0708 0.0944 0.248 0.293 0.000619 0.000733 + 157 118 0.206 0.00729 0.0599 0.0759 0.102 0.259 0.292 0.000648 0.00073 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.106 0.0044 0.0177 0.0595 0.0792 0.151 0.159 0.000378 0.000397 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 157 26332.051 0.005 0.00583 0.065 0.182 0.0681 0.091 0.239 0.304 0.000599 0.000761 +! Validation 157 26332.051 0.005 0.00635 0.0341 0.161 0.0703 0.0951 0.176 0.221 0.000441 0.000551 +Wall time: 26332.051970203407 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.162 0.00566 0.049 0.0674 0.0898 0.224 0.264 0.00056 0.000661 + 158 118 0.127 0.00582 0.0108 0.0687 0.091 0.109 0.124 0.000273 0.00031 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.0945 0.00435 0.00754 0.0588 0.0787 0.0916 0.104 0.000229 0.000259 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 158 26499.528 0.005 0.00578 0.065 0.181 0.0679 0.0908 0.249 0.305 0.000622 0.000763 +! Validation 158 26499.528 0.005 0.00615 0.0284 0.151 0.069 0.0936 0.16 0.201 0.0004 0.000502 +Wall time: 26499.528177629225 +! Best model 158 0.151 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.139 0.00612 0.0168 0.0696 0.0934 0.117 0.155 0.000292 0.000387 + 159 118 0.249 0.00566 0.136 0.0679 0.0898 0.424 0.44 0.00106 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.237 0.00425 0.152 0.0579 0.0778 0.464 0.466 0.00116 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 159 26667.053 0.005 0.00572 0.0593 0.174 0.0675 0.0902 0.236 0.289 0.00059 0.000723 +! Validation 159 26667.053 0.005 0.00603 0.15 0.27 0.0682 0.0927 0.426 0.462 0.00106 0.00115 +Wall time: 26667.0534506212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.125 0.00538 0.0172 0.0655 0.0875 0.123 0.157 0.000307 0.000392 + 160 118 0.348 0.00547 0.238 0.0654 0.0883 0.53 0.582 0.00133 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.11 0.00444 0.0207 0.0593 0.0796 0.169 0.172 0.000422 0.00043 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 160 26834.636 0.005 0.00565 0.0703 0.183 0.0671 0.0897 0.25 0.314 0.000625 0.000784 +! Validation 160 26834.636 0.005 0.00623 0.035 0.16 0.0696 0.0942 0.173 0.223 0.000433 0.000559 +Wall time: 26834.636298148427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.124 0.00539 0.016 0.0655 0.0876 0.12 0.151 0.000299 0.000377 + 161 118 0.16 0.0052 0.0562 0.0642 0.0861 0.223 0.283 0.000556 0.000707 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.126 0.00432 0.0398 0.0586 0.0785 0.232 0.238 0.000581 0.000595 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 161 27002.139 0.005 0.00583 0.0863 0.203 0.0682 0.0911 0.272 0.351 0.000679 0.000878 +! Validation 161 27002.139 0.005 0.00608 0.0521 0.174 0.0687 0.0931 0.229 0.272 0.000574 0.000681 +Wall time: 27002.139543771278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 0.123 0.0055 0.013 0.0658 0.0885 0.106 0.136 0.000265 0.00034 + 162 118 0.162 0.00545 0.0528 0.0658 0.0881 0.247 0.274 0.000618 0.000685 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 0.138 0.00412 0.0555 0.057 0.0766 0.277 0.281 0.000692 0.000703 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 162 27169.778 0.005 0.00557 0.0569 0.168 0.0666 0.0891 0.225 0.285 0.000562 0.000712 +! Validation 162 27169.778 0.005 0.00586 0.061 0.178 0.0672 0.0913 0.254 0.295 0.000635 0.000737 +Wall time: 27169.77902379725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 0.151 0.00555 0.0396 0.0665 0.0889 0.19 0.238 0.000476 0.000594 + 163 118 0.122 0.00509 0.0204 0.0633 0.0852 0.142 0.171 0.000354 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 0.0834 0.00405 0.00227 0.0565 0.076 0.046 0.0568 0.000115 0.000142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 163 27337.273 0.005 0.00546 0.0515 0.161 0.0659 0.0882 0.219 0.271 0.000548 0.000678 +! Validation 163 27337.273 0.005 0.00585 0.0223 0.139 0.0673 0.0913 0.144 0.178 0.00036 0.000445 +Wall time: 27337.27379459422 +! Best model 163 0.139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 0.124 0.00542 0.0158 0.0656 0.0879 0.121 0.15 0.000303 0.000375 + 164 118 0.179 0.00515 0.0762 0.0641 0.0857 0.285 0.329 0.000713 0.000824 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 0.114 0.00415 0.0309 0.0574 0.0769 0.204 0.21 0.000509 0.000525 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 164 27504.914 0.005 0.00551 0.0728 0.183 0.0662 0.0886 0.261 0.322 0.000652 0.000805 +! Validation 164 27504.914 0.005 0.00584 0.0442 0.161 0.0672 0.0912 0.209 0.251 0.000522 0.000628 +Wall time: 27504.914948088117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 0.136 0.00576 0.0209 0.0674 0.0906 0.138 0.173 0.000344 0.000431 + 165 118 0.182 0.00601 0.0624 0.0687 0.0925 0.271 0.298 0.000678 0.000745 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 0.0887 0.00435 0.00169 0.0587 0.0787 0.043 0.0491 0.000108 0.000123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 165 27672.451 0.005 0.00542 0.0714 0.18 0.0656 0.0878 0.257 0.319 0.000642 0.000798 +! Validation 165 27672.451 0.005 0.0061 0.0278 0.15 0.0687 0.0932 0.157 0.199 0.000392 0.000498 +Wall time: 27672.451660702005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 0.131 0.00527 0.0259 0.0643 0.0866 0.151 0.192 0.000377 0.00048 + 166 118 0.147 0.0058 0.0311 0.0667 0.0909 0.194 0.211 0.000486 0.000526 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 0.0821 0.00395 0.00314 0.0558 0.075 0.0504 0.0669 0.000126 0.000167 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 166 27839.959 0.005 0.00542 0.0575 0.166 0.0656 0.0878 0.227 0.287 0.000568 0.000717 +! Validation 166 27839.959 0.005 0.00569 0.0229 0.137 0.0662 0.0901 0.147 0.18 0.000367 0.000451 +Wall time: 27839.959808997344 +! Best model 166 0.137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 0.228 0.00518 0.125 0.0644 0.0859 0.389 0.422 0.000973 0.00105 + 167 118 0.137 0.00564 0.0243 0.0671 0.0897 0.159 0.186 0.000397 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 0.184 0.00415 0.101 0.057 0.0769 0.378 0.38 0.000945 0.00095 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 167 28007.465 0.005 0.00538 0.0852 0.193 0.0654 0.0875 0.287 0.349 0.000718 0.000873 +! Validation 167 28007.465 0.005 0.00581 0.143 0.26 0.0669 0.091 0.417 0.452 0.00104 0.00113 +Wall time: 28007.465776593424 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 0.127 0.00552 0.0171 0.0659 0.0886 0.123 0.156 0.000309 0.00039 + 168 118 0.116 0.00494 0.0175 0.0624 0.0839 0.144 0.158 0.00036 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 0.0831 0.00399 0.00322 0.0558 0.0754 0.0537 0.0677 0.000134 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 168 28174.959 0.005 0.00526 0.0527 0.158 0.0647 0.0866 0.219 0.275 0.000548 0.000687 +! Validation 168 28174.959 0.005 0.00559 0.0236 0.135 0.0656 0.0892 0.144 0.184 0.00036 0.000459 +Wall time: 28174.95953605231 +! Best model 168 0.135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 0.208 0.00483 0.111 0.0622 0.0829 0.37 0.398 0.000925 0.000995 + 169 118 0.147 0.00564 0.0344 0.0663 0.0896 0.182 0.221 0.000455 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 0.129 0.00421 0.0446 0.0571 0.0775 0.247 0.252 0.000617 0.00063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 169 28342.554 0.005 0.00534 0.0805 0.187 0.0652 0.0872 0.276 0.339 0.000689 0.000848 +! Validation 169 28342.554 0.005 0.00579 0.0666 0.182 0.0669 0.0908 0.266 0.308 0.000666 0.00077 +Wall time: 28342.554968371987 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 0.209 0.00506 0.108 0.0637 0.0849 0.355 0.392 0.000888 0.00098 + 170 118 0.223 0.00494 0.124 0.0631 0.0839 0.399 0.421 0.000997 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 0.0988 0.0047 0.0048 0.0609 0.0818 0.067 0.0827 0.000167 0.000207 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 170 28510.176 0.005 0.00522 0.0681 0.173 0.0644 0.0863 0.253 0.311 0.000632 0.000777 +! Validation 170 28510.176 0.005 0.00621 0.0249 0.149 0.0698 0.0941 0.154 0.188 0.000384 0.000471 +Wall time: 28510.17651259899 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 0.305 0.00524 0.201 0.0644 0.0864 0.52 0.535 0.0013 0.00134 + 171 118 0.154 0.00542 0.0457 0.0655 0.0879 0.226 0.255 0.000564 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 0.136 0.00412 0.0536 0.0571 0.0766 0.273 0.276 0.000682 0.000691 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 171 28677.715 0.005 0.00515 0.057 0.16 0.0639 0.0857 0.232 0.285 0.000579 0.000713 +! Validation 171 28677.715 0.005 0.00578 0.065 0.181 0.0668 0.0907 0.259 0.304 0.000648 0.000761 +Wall time: 28677.715894418303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 0.127 0.00497 0.0274 0.0627 0.0842 0.162 0.198 0.000405 0.000494 + 172 118 0.115 0.00454 0.0243 0.0615 0.0804 0.163 0.186 0.000408 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 0.188 0.0039 0.11 0.0555 0.0745 0.393 0.396 0.000983 0.000991 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 172 28845.260 0.005 0.00506 0.0461 0.147 0.0634 0.085 0.208 0.257 0.00052 0.000642 +! Validation 172 28845.260 0.005 0.00552 0.155 0.266 0.0654 0.0887 0.44 0.47 0.0011 0.00118 +Wall time: 28845.26090982929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.12 0.00466 0.0269 0.0615 0.0815 0.154 0.196 0.000386 0.000489 + 173 118 0.375 0.00612 0.253 0.0698 0.0934 0.578 0.6 0.00144 0.0015 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.345 0.00437 0.258 0.0588 0.0789 0.605 0.606 0.00151 0.00152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 173 29012.812 0.005 0.00508 0.063 0.165 0.0635 0.085 0.239 0.297 0.000598 0.000741 +! Validation 173 29012.812 0.005 0.0059 0.311 0.429 0.0679 0.0917 0.642 0.665 0.00161 0.00166 +Wall time: 29012.8123474163 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 0.12 0.00494 0.0213 0.0629 0.0839 0.148 0.174 0.00037 0.000436 + 174 118 0.118 0.00518 0.0145 0.0641 0.0859 0.109 0.144 0.000274 0.000359 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 0.0807 0.0038 0.00473 0.0544 0.0736 0.0691 0.0821 0.000173 0.000205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 174 29180.462 0.005 0.0052 0.0788 0.183 0.0643 0.0861 0.272 0.336 0.00068 0.00084 +! Validation 174 29180.462 0.005 0.00539 0.0282 0.136 0.0643 0.0876 0.156 0.2 0.000391 0.000501 +Wall time: 29180.462871633004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.158 0.00515 0.0546 0.0639 0.0856 0.232 0.279 0.00058 0.000697 + 175 118 0.127 0.00488 0.0296 0.0627 0.0834 0.17 0.205 0.000425 0.000513 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.0812 0.0039 0.00314 0.0555 0.0746 0.0579 0.0669 0.000145 0.000167 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 175 29348.009 0.005 0.00501 0.058 0.158 0.063 0.0845 0.232 0.288 0.00058 0.00072 +! Validation 175 29348.009 0.005 0.00552 0.0218 0.132 0.0654 0.0887 0.139 0.176 0.000348 0.000441 +Wall time: 29348.00976965204 +! Best model 175 0.132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 0.146 0.00476 0.051 0.0616 0.0824 0.226 0.27 0.000566 0.000674 + 176 118 0.154 0.00502 0.0539 0.0622 0.0845 0.229 0.277 0.000574 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 0.103 0.00452 0.0127 0.0602 0.0803 0.121 0.135 0.000303 0.000337 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 176 29515.706 0.005 0.00495 0.0496 0.149 0.0627 0.084 0.208 0.266 0.00052 0.000664 +! Validation 176 29515.706 0.005 0.00597 0.0556 0.175 0.0686 0.0922 0.231 0.281 0.000578 0.000703 +Wall time: 29515.706722578034 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.119 0.0049 0.0212 0.0616 0.0835 0.145 0.174 0.000363 0.000435 + 177 118 0.223 0.00414 0.14 0.0584 0.0768 0.429 0.447 0.00107 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.397 0.00379 0.321 0.0546 0.0734 0.675 0.676 0.00169 0.00169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 177 29683.220 0.005 0.00488 0.0513 0.149 0.0623 0.0834 0.218 0.269 0.000544 0.000672 +! Validation 177 29683.220 0.005 0.00539 0.33 0.438 0.0645 0.0876 0.665 0.686 0.00166 0.00171 +Wall time: 29683.220128229354 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 0.149 0.00491 0.0511 0.0627 0.0836 0.236 0.27 0.00059 0.000674 + 178 118 0.136 0.00507 0.035 0.063 0.085 0.204 0.223 0.000511 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 0.0763 0.0037 0.00234 0.0541 0.0726 0.0429 0.0578 0.000107 0.000144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 178 29850.717 0.005 0.00552 0.109 0.219 0.0664 0.0887 0.322 0.395 0.000804 0.000987 +! Validation 178 29850.717 0.005 0.00536 0.0197 0.127 0.0643 0.0874 0.138 0.167 0.000344 0.000419 +Wall time: 29850.717217535246 +! Best model 178 0.127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 0.185 0.0048 0.089 0.0621 0.0827 0.325 0.356 0.000813 0.00089 + 179 118 0.148 0.00587 0.0302 0.0683 0.0915 0.167 0.207 0.000418 0.000518 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 0.0897 0.00384 0.0128 0.0557 0.074 0.13 0.135 0.000325 0.000337 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 179 30018.315 0.005 0.00484 0.0453 0.142 0.0619 0.083 0.203 0.254 0.000508 0.000636 +! Validation 179 30018.315 0.005 0.00557 0.0264 0.138 0.0657 0.0891 0.156 0.194 0.00039 0.000485 +Wall time: 30018.315626908094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.113 0.00446 0.0238 0.0599 0.0797 0.147 0.184 0.000368 0.00046 + 180 118 0.114 0.00433 0.0278 0.0592 0.0786 0.186 0.199 0.000466 0.000497 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.101 0.00359 0.0291 0.053 0.0715 0.199 0.204 0.000498 0.000509 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 180 30185.837 0.005 0.00476 0.0407 0.136 0.0614 0.0824 0.195 0.241 0.000487 0.000603 +! Validation 180 30185.837 0.005 0.0051 0.051 0.153 0.0625 0.0852 0.223 0.269 0.000557 0.000674 +Wall time: 30185.837090429384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 0.141 0.00467 0.0473 0.0608 0.0815 0.227 0.259 0.000569 0.000649 + 181 118 0.237 0.00544 0.128 0.0646 0.088 0.385 0.428 0.000963 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 0.0829 0.00378 0.00719 0.0543 0.0734 0.086 0.101 0.000215 0.000253 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 181 30353.321 0.005 0.00488 0.062 0.16 0.0622 0.0833 0.236 0.296 0.000591 0.00074 +! Validation 181 30353.321 0.005 0.00522 0.0227 0.127 0.0634 0.0863 0.146 0.18 0.000364 0.00045 +Wall time: 30353.32179812342 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.12 0.00433 0.0335 0.0588 0.0785 0.178 0.218 0.000445 0.000546 + 182 118 0.111 0.00444 0.0221 0.0604 0.0795 0.134 0.177 0.000336 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.0863 0.00357 0.0148 0.053 0.0713 0.138 0.145 0.000345 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 182 30520.798 0.005 0.00474 0.0483 0.143 0.0613 0.0822 0.213 0.263 0.000532 0.000657 +! Validation 182 30520.798 0.005 0.00507 0.0366 0.138 0.0624 0.085 0.182 0.228 0.000454 0.000571 +Wall time: 30520.798558157403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 0.115 0.00476 0.0194 0.0619 0.0824 0.129 0.166 0.000323 0.000416 + 183 118 0.138 0.00501 0.0377 0.0621 0.0845 0.204 0.232 0.000511 0.000579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 0.118 0.00374 0.0432 0.0537 0.073 0.244 0.248 0.000611 0.00062 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 183 30688.388 0.005 0.00472 0.0516 0.146 0.0612 0.082 0.221 0.271 0.000552 0.000678 +! Validation 183 30688.388 0.005 0.00509 0.0682 0.17 0.0624 0.0852 0.273 0.312 0.000682 0.000779 +Wall time: 30688.388122553006 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.115 0.00507 0.0138 0.0622 0.085 0.104 0.14 0.00026 0.000351 + 184 118 0.107 0.00454 0.0164 0.0602 0.0805 0.127 0.153 0.000317 0.000382 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.0797 0.00372 0.00538 0.0542 0.0728 0.0646 0.0876 0.000162 0.000219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 184 30856.281 0.005 0.00488 0.0675 0.165 0.0624 0.0834 0.25 0.311 0.000624 0.000777 +! Validation 184 30856.281 0.005 0.00513 0.0202 0.123 0.0628 0.0855 0.136 0.169 0.00034 0.000424 +Wall time: 30856.281442234293 +! Best model 184 0.123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.142 0.00465 0.0485 0.0609 0.0814 0.237 0.263 0.000593 0.000657 + 185 118 0.0949 0.00426 0.00976 0.0577 0.0779 0.0931 0.118 0.000233 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.0729 0.00354 0.00214 0.0526 0.071 0.0479 0.0552 0.00012 0.000138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 185 31023.748 0.005 0.00461 0.0551 0.147 0.0605 0.0811 0.228 0.281 0.00057 0.000703 +! Validation 185 31023.748 0.005 0.00504 0.0211 0.122 0.0621 0.0848 0.137 0.173 0.000342 0.000433 +Wall time: 31023.7487268541 +! Best model 185 0.122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.21 0.00491 0.112 0.0623 0.0837 0.376 0.399 0.000941 0.000997 + 186 118 0.122 0.00462 0.0294 0.0599 0.0811 0.182 0.205 0.000455 0.000512 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.182 0.00364 0.109 0.0532 0.072 0.393 0.395 0.000982 0.000987 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 186 31191.188 0.005 0.00484 0.0902 0.187 0.062 0.083 0.287 0.359 0.000718 0.000898 +! Validation 186 31191.188 0.005 0.00508 0.129 0.231 0.0625 0.0851 0.398 0.429 0.000994 0.00107 +Wall time: 31191.18882505037 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.129 0.00464 0.0359 0.0611 0.0813 0.178 0.226 0.000446 0.000565 + 187 118 0.0986 0.00441 0.0104 0.0594 0.0792 0.109 0.122 0.000273 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.0719 0.00348 0.00218 0.052 0.0705 0.0485 0.0558 0.000121 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 187 31358.655 0.005 0.00462 0.046 0.138 0.0605 0.0811 0.208 0.257 0.000519 0.000642 +! Validation 187 31358.655 0.005 0.00491 0.0193 0.117 0.0613 0.0836 0.132 0.166 0.00033 0.000414 +Wall time: 31358.655960428994 +! Best model 187 0.117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.117 0.00414 0.0344 0.0575 0.0768 0.188 0.221 0.000469 0.000553 + 188 118 0.111 0.00451 0.0205 0.0595 0.0802 0.142 0.171 0.000355 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.0734 0.00351 0.00311 0.0527 0.0708 0.0603 0.0666 0.000151 0.000166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 188 31526.233 0.005 0.00449 0.0379 0.128 0.0596 0.0799 0.18 0.233 0.000451 0.000582 +! Validation 188 31526.233 0.005 0.00498 0.021 0.121 0.062 0.0843 0.136 0.173 0.00034 0.000433 +Wall time: 31526.23373403307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.105 0.00447 0.0159 0.0599 0.0798 0.116 0.15 0.000289 0.000376 + 189 118 0.135 0.00533 0.0286 0.0649 0.0871 0.166 0.202 0.000414 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.071 0.00349 0.00126 0.0524 0.0705 0.0376 0.0423 9.4e-05 0.000106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 189 31693.591 0.005 0.00461 0.0624 0.155 0.0605 0.081 0.234 0.299 0.000584 0.000747 +! Validation 189 31693.591 0.005 0.00488 0.0233 0.121 0.0612 0.0834 0.141 0.182 0.000354 0.000456 +Wall time: 31693.591722398996 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.128 0.00455 0.0373 0.0601 0.0805 0.195 0.231 0.000487 0.000576 + 190 118 0.246 0.006 0.126 0.0702 0.0925 0.405 0.424 0.00101 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.251 0.0037 0.177 0.0538 0.0726 0.499 0.502 0.00125 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 190 31860.912 0.005 0.00451 0.0485 0.139 0.0597 0.0801 0.213 0.262 0.000532 0.000654 +! Validation 190 31860.912 0.005 0.00504 0.145 0.246 0.0623 0.0848 0.427 0.455 0.00107 0.00114 +Wall time: 31860.912103345152 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.0945 0.00422 0.01 0.0581 0.0775 0.101 0.12 0.000253 0.000299 + 191 118 0.106 0.00494 0.00742 0.0619 0.0839 0.0772 0.103 0.000193 0.000257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.0699 0.00329 0.00412 0.0508 0.0685 0.0604 0.0767 0.000151 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 191 32028.242 0.005 0.00449 0.0471 0.137 0.0597 0.08 0.208 0.26 0.000519 0.00065 +! Validation 191 32028.242 0.005 0.00475 0.0195 0.115 0.0603 0.0823 0.131 0.167 0.000328 0.000417 +Wall time: 32028.24287068611 +! Best model 191 0.115 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.126 0.00486 0.0284 0.0617 0.0832 0.167 0.201 0.000417 0.000503 + 192 118 0.16 0.00426 0.0752 0.0574 0.0779 0.281 0.327 0.000704 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.105 0.00364 0.0323 0.0533 0.072 0.21 0.214 0.000526 0.000536 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 192 32195.584 0.005 0.00458 0.0621 0.154 0.0603 0.0808 0.242 0.297 0.000605 0.000743 +! Validation 192 32195.584 0.005 0.00493 0.0544 0.153 0.0616 0.0838 0.236 0.278 0.000589 0.000696 +Wall time: 32195.584687230177 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.285 0.00439 0.197 0.0593 0.0791 0.512 0.53 0.00128 0.00132 + 193 118 0.183 0.00506 0.0821 0.0629 0.0849 0.307 0.342 0.000768 0.000855 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.452 0.0042 0.368 0.057 0.0773 0.722 0.724 0.0018 0.00181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 193 32362.995 0.005 0.00441 0.0483 0.136 0.0591 0.0792 0.203 0.262 0.000509 0.000654 +! Validation 193 32362.995 0.005 0.00531 0.467 0.573 0.0643 0.087 0.79 0.816 0.00198 0.00204 +Wall time: 32362.99514681101 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.114 0.0045 0.0235 0.0599 0.0801 0.142 0.183 0.000356 0.000458 + 194 118 0.121 0.00481 0.0252 0.0619 0.0828 0.156 0.189 0.00039 0.000474 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.0896 0.00344 0.0207 0.0519 0.0701 0.167 0.172 0.000417 0.000429 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 194 32530.301 0.005 0.00785 0.262 0.42 0.076 0.106 0.386 0.613 0.000965 0.00153 +! Validation 194 32530.301 0.005 0.00491 0.0427 0.141 0.0613 0.0836 0.206 0.247 0.000516 0.000616 +Wall time: 32530.301234465092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.103 0.00451 0.0129 0.0596 0.0802 0.104 0.135 0.000261 0.000339 + 195 118 0.103 0.00453 0.0125 0.0602 0.0804 0.104 0.133 0.000259 0.000333 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.067 0.00329 0.00119 0.0509 0.0684 0.0401 0.0412 0.0001 0.000103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 195 32697.621 0.005 0.00439 0.0241 0.112 0.0589 0.0791 0.149 0.186 0.000372 0.000464 +! Validation 195 32697.621 0.005 0.00473 0.0218 0.116 0.0602 0.0821 0.137 0.176 0.000342 0.000441 +Wall time: 32697.621549515985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.0922 0.00412 0.00979 0.0573 0.0766 0.0962 0.118 0.000241 0.000295 + 196 118 0.128 0.00496 0.0285 0.06 0.084 0.172 0.201 0.000429 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.0701 0.00322 0.00577 0.0503 0.0677 0.0802 0.0907 0.0002 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 196 32864.940 0.005 0.00427 0.0229 0.108 0.0581 0.0779 0.144 0.18 0.00036 0.000451 +! Validation 196 32864.940 0.005 0.00464 0.0283 0.121 0.0595 0.0813 0.159 0.201 0.000397 0.000502 +Wall time: 32864.9400104573 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.0949 0.00403 0.0143 0.0568 0.0757 0.111 0.143 0.000277 0.000357 + 197 118 0.086 0.00396 0.0068 0.0555 0.0751 0.0661 0.0984 0.000165 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.0683 0.00324 0.00347 0.0503 0.0679 0.0594 0.0703 0.000148 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 197 33032.267 0.005 0.00423 0.0315 0.116 0.0579 0.0776 0.172 0.212 0.00043 0.000531 +! Validation 197 33032.267 0.005 0.00463 0.0168 0.11 0.0595 0.0812 0.123 0.155 0.000307 0.000387 +Wall time: 33032.267922905274 +! Best model 197 0.110 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.151 0.00425 0.0658 0.0584 0.0778 0.277 0.306 0.000692 0.000765 + 198 118 0.0898 0.00392 0.0115 0.0565 0.0747 0.0988 0.128 0.000247 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.072 0.00326 0.00675 0.0509 0.0682 0.0878 0.0981 0.000219 0.000245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 198 33199.706 0.005 0.0042 0.0397 0.124 0.0576 0.0773 0.189 0.238 0.000472 0.000596 +! Validation 198 33199.706 0.005 0.00471 0.0252 0.119 0.0602 0.0819 0.152 0.19 0.000381 0.000474 +Wall time: 33199.70621697605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.0934 0.00406 0.0123 0.0568 0.076 0.105 0.132 0.000263 0.00033 + 199 118 0.0969 0.00437 0.00956 0.0592 0.0789 0.0836 0.117 0.000209 0.000292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.0825 0.00313 0.0199 0.0495 0.0668 0.163 0.168 0.000406 0.000421 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 199 33367.032 0.005 0.00418 0.0377 0.121 0.0575 0.0771 0.189 0.232 0.000472 0.000581 +! Validation 199 33367.032 0.005 0.00451 0.038 0.128 0.0587 0.0802 0.195 0.233 0.000487 0.000582 +Wall time: 33367.032579347026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.0916 0.00382 0.0152 0.0553 0.0738 0.119 0.147 0.000297 0.000368 + 200 118 0.172 0.00422 0.0878 0.0571 0.0775 0.331 0.354 0.000828 0.000884 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.197 0.00313 0.134 0.0495 0.0668 0.435 0.437 0.00109 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 200 33534.369 0.005 0.00409 0.0264 0.108 0.0568 0.0763 0.154 0.192 0.000384 0.00048 +! Validation 200 33534.369 0.005 0.00446 0.154 0.243 0.0584 0.0797 0.443 0.468 0.00111 0.00117 +Wall time: 33534.36994172819 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.185 0.00452 0.0947 0.0582 0.0802 0.339 0.367 0.000848 0.000918 + 201 118 0.213 0.00362 0.14 0.0543 0.0718 0.438 0.447 0.00109 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.305 0.00345 0.236 0.0521 0.0701 0.578 0.58 0.00145 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 201 33701.686 0.005 0.00413 0.0567 0.139 0.0572 0.0767 0.233 0.283 0.000582 0.000707 +! Validation 201 33701.686 0.005 0.00468 0.2 0.293 0.06 0.0817 0.51 0.533 0.00127 0.00133 +Wall time: 33701.6866016211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 0.233 0.00411 0.15 0.0574 0.0765 0.447 0.463 0.00112 0.00116 + 202 118 0.1 0.0041 0.0183 0.0572 0.0764 0.133 0.161 0.000331 0.000403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 0.167 0.00317 0.104 0.05 0.0672 0.382 0.384 0.000955 0.00096 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 202 33869.113 0.005 0.00418 0.0507 0.134 0.0576 0.0772 0.22 0.269 0.00055 0.000673 +! Validation 202 33869.113 0.005 0.00454 0.123 0.214 0.059 0.0804 0.387 0.419 0.000968 0.00105 +Wall time: 33869.11388413515 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.0977 0.00423 0.0132 0.0576 0.0776 0.107 0.137 0.000267 0.000343 + 203 118 0.088 0.00372 0.0136 0.0546 0.0728 0.111 0.139 0.000278 0.000349 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.0758 0.00303 0.0151 0.0488 0.0657 0.14 0.147 0.00035 0.000367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 203 34036.434 0.005 0.00408 0.0369 0.119 0.0569 0.0763 0.177 0.23 0.000443 0.000574 +! Validation 203 34036.434 0.005 0.00435 0.0356 0.123 0.0576 0.0787 0.185 0.225 0.000462 0.000563 +Wall time: 34036.434507693164 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.0877 0.00384 0.0108 0.0552 0.074 0.101 0.124 0.000253 0.000311 + 204 118 0.081 0.00368 0.00751 0.0541 0.0724 0.0844 0.103 0.000211 0.000259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.0691 0.00306 0.00785 0.0489 0.0661 0.0954 0.106 0.000239 0.000264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 204 34203.748 0.005 0.00404 0.0341 0.115 0.0565 0.0758 0.177 0.221 0.000443 0.000552 +! Validation 204 34203.748 0.005 0.00432 0.0174 0.104 0.0574 0.0784 0.126 0.158 0.000315 0.000394 +Wall time: 34203.74829590134 +! Best model 204 0.104 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.114 0.00404 0.033 0.0567 0.0759 0.187 0.217 0.000468 0.000542 + 205 118 0.0929 0.00384 0.0161 0.0558 0.074 0.12 0.152 0.000301 0.000379 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.0749 0.00321 0.0107 0.0499 0.0676 0.117 0.123 0.000292 0.000309 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 205 34371.231 0.005 0.00411 0.0594 0.142 0.0571 0.0765 0.23 0.292 0.000575 0.000729 +! Validation 205 34371.231 0.005 0.00441 0.0277 0.116 0.058 0.0792 0.159 0.199 0.000398 0.000497 +Wall time: 34371.231698742136 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.0964 0.0039 0.0185 0.0556 0.0745 0.133 0.162 0.000332 0.000405 + 206 118 0.0889 0.00345 0.02 0.0533 0.0701 0.142 0.169 0.000356 0.000421 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.068 0.00304 0.00716 0.049 0.0658 0.0944 0.101 0.000236 0.000253 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 206 34538.520 0.005 0.00404 0.0463 0.127 0.0566 0.0759 0.208 0.257 0.00052 0.000643 +! Validation 206 34538.520 0.005 0.00437 0.0171 0.104 0.0578 0.0789 0.126 0.156 0.000314 0.00039 +Wall time: 34538.520364675205 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.0961 0.0042 0.012 0.0572 0.0774 0.104 0.131 0.00026 0.000327 + 207 118 0.106 0.00445 0.0174 0.0582 0.0796 0.127 0.157 0.000317 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.0708 0.00313 0.00823 0.0494 0.0667 0.101 0.108 0.000252 0.000271 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 207 34705.920 0.005 0.00406 0.0569 0.138 0.0568 0.0761 0.236 0.285 0.000589 0.000713 +! Validation 207 34705.920 0.005 0.00435 0.0189 0.106 0.0577 0.0787 0.133 0.164 0.000332 0.00041 +Wall time: 34705.92079286929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.0926 0.00408 0.0109 0.0572 0.0763 0.0967 0.125 0.000242 0.000312 + 208 118 0.139 0.00374 0.0641 0.0551 0.073 0.278 0.302 0.000694 0.000756 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.131 0.00303 0.0704 0.0486 0.0657 0.313 0.317 0.000783 0.000792 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 208 34873.226 0.005 0.00407 0.0523 0.134 0.0569 0.0762 0.208 0.273 0.000519 0.000682 +! Validation 208 34873.226 0.005 0.00434 0.115 0.202 0.0577 0.0786 0.372 0.405 0.000931 0.00101 +Wall time: 34873.22607461503 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.174 0.00391 0.0956 0.0554 0.0747 0.34 0.369 0.00085 0.000922 + 209 118 0.0973 0.00356 0.026 0.0528 0.0713 0.152 0.192 0.000379 0.000481 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.089 0.00303 0.0283 0.0489 0.0657 0.197 0.201 0.000493 0.000502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 209 35040.530 0.005 0.00393 0.0316 0.11 0.0558 0.0748 0.17 0.212 0.000426 0.000531 +! Validation 209 35040.530 0.005 0.00434 0.0392 0.126 0.0576 0.0787 0.202 0.236 0.000505 0.000591 +Wall time: 35040.53069094615 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.102 0.00398 0.022 0.0562 0.0753 0.151 0.177 0.000379 0.000443 + 210 118 0.11 0.00356 0.039 0.0539 0.0712 0.215 0.236 0.000537 0.000589 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.173 0.00302 0.112 0.0491 0.0656 0.397 0.4 0.000992 0.000999 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 210 35207.916 0.005 0.00402 0.0484 0.129 0.0565 0.0757 0.212 0.263 0.000529 0.000657 +! Validation 210 35207.916 0.005 0.00434 0.194 0.28 0.0578 0.0787 0.498 0.525 0.00124 0.00131 +Wall time: 35207.916587716434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.18 0.00387 0.102 0.0554 0.0742 0.362 0.382 0.000906 0.000955 + 211 118 0.0759 0.00352 0.00555 0.0534 0.0708 0.0682 0.0889 0.000171 0.000222 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.0601 0.00293 0.00143 0.0483 0.0646 0.0377 0.0452 9.43e-05 0.000113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 211 35375.360 0.005 0.00389 0.0317 0.11 0.0555 0.0745 0.167 0.213 0.000418 0.000533 +! Validation 211 35375.360 0.005 0.00427 0.0171 0.103 0.0572 0.078 0.122 0.156 0.000306 0.00039 +Wall time: 35375.36040374404 +! Best model 211 0.103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.185 0.00413 0.102 0.0578 0.0767 0.359 0.381 0.000897 0.000953 + 212 118 0.104 0.00432 0.0174 0.0588 0.0785 0.108 0.158 0.000269 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.0718 0.00332 0.00527 0.051 0.0688 0.0755 0.0867 0.000189 0.000217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 212 35542.898 0.005 0.00399 0.0629 0.143 0.0563 0.0753 0.248 0.3 0.00062 0.00075 +! Validation 212 35542.898 0.005 0.00445 0.0378 0.127 0.0586 0.0796 0.187 0.232 0.000467 0.00058 +Wall time: 35542.89838630613 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.148 0.00376 0.0728 0.0545 0.0732 0.29 0.322 0.000725 0.000805 + 213 118 0.136 0.0041 0.0541 0.0568 0.0764 0.254 0.278 0.000636 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.138 0.00299 0.0783 0.0486 0.0652 0.331 0.334 0.000827 0.000835 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 213 35710.280 0.005 0.00388 0.0328 0.11 0.0554 0.0743 0.171 0.216 0.000428 0.000539 +! Validation 213 35710.280 0.005 0.0042 0.0726 0.157 0.0567 0.0774 0.293 0.322 0.000733 0.000804 +Wall time: 35710.28053522436 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.171 0.00361 0.0984 0.0531 0.0717 0.359 0.374 0.000897 0.000936 + 214 118 0.0968 0.00454 0.00598 0.0597 0.0804 0.0733 0.0923 0.000183 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.0606 0.00295 0.00163 0.0487 0.0648 0.0397 0.0482 9.93e-05 0.00012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 214 35877.538 0.005 0.00387 0.0414 0.119 0.0554 0.0742 0.196 0.244 0.000489 0.000609 +! Validation 214 35877.538 0.005 0.00424 0.0175 0.102 0.0572 0.0777 0.124 0.158 0.000311 0.000395 +Wall time: 35877.53825648641 +! Best model 214 0.102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.0834 0.00363 0.0108 0.0537 0.0719 0.101 0.124 0.000251 0.00031 + 215 118 0.11 0.00388 0.0321 0.0552 0.0743 0.197 0.214 0.000493 0.000535 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.126 0.00299 0.0668 0.0485 0.0652 0.306 0.308 0.000765 0.000771 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 215 36045.389 0.005 0.00384 0.0422 0.119 0.0552 0.074 0.199 0.245 0.000499 0.000613 +! Validation 215 36045.389 0.005 0.00421 0.0682 0.152 0.0569 0.0775 0.282 0.312 0.000705 0.000779 +Wall time: 36045.38955364702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.0801 0.00348 0.0106 0.0528 0.0704 0.0988 0.123 0.000247 0.000307 + 216 118 0.0893 0.00373 0.0147 0.0547 0.0729 0.127 0.145 0.000317 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.0757 0.00293 0.017 0.0478 0.0646 0.15 0.156 0.000374 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 216 36212.810 0.005 0.0038 0.0382 0.114 0.0549 0.0736 0.187 0.234 0.000467 0.000584 +! Validation 216 36212.810 0.005 0.00406 0.0322 0.113 0.0557 0.076 0.177 0.214 0.000443 0.000536 +Wall time: 36212.810430257116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.0956 0.00396 0.0164 0.055 0.0751 0.123 0.153 0.000307 0.000383 + 217 118 0.204 0.00422 0.12 0.0574 0.0775 0.399 0.413 0.000997 0.00103 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.246 0.00327 0.181 0.0501 0.0682 0.504 0.507 0.00126 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 217 36381.711 0.005 0.00373 0.0367 0.111 0.0544 0.0729 0.184 0.227 0.000459 0.000567 +! Validation 217 36381.711 0.005 0.00429 0.219 0.305 0.0573 0.0782 0.534 0.559 0.00134 0.0014 +Wall time: 36381.711485709064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.0871 0.00375 0.0122 0.0547 0.0731 0.102 0.132 0.000256 0.00033 + 218 118 0.102 0.00451 0.0122 0.0602 0.0802 0.11 0.132 0.000274 0.000329 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.0591 0.00291 0.000912 0.0479 0.0644 0.0301 0.036 7.52e-05 9.01e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 218 36550.808 0.005 0.00385 0.055 0.132 0.0553 0.0741 0.223 0.281 0.000558 0.000702 +! Validation 218 36550.808 0.005 0.00409 0.0263 0.108 0.056 0.0763 0.15 0.194 0.000374 0.000484 +Wall time: 36550.808973482344 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.11 0.00414 0.0276 0.0576 0.0768 0.163 0.198 0.000408 0.000495 + 219 118 0.0895 0.00356 0.0184 0.0533 0.0712 0.126 0.162 0.000314 0.000405 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.0848 0.00338 0.0173 0.0512 0.0694 0.145 0.157 0.000363 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 219 36718.338 0.005 0.00396 0.0612 0.14 0.0561 0.0752 0.239 0.296 0.000597 0.00074 +! Validation 219 36718.338 0.005 0.00445 0.0454 0.134 0.0586 0.0796 0.214 0.254 0.000536 0.000636 +Wall time: 36718.33836356038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.12 0.00386 0.0422 0.0551 0.0742 0.219 0.245 0.000547 0.000613 + 220 118 0.081 0.00348 0.0114 0.052 0.0704 0.0983 0.128 0.000246 0.000319 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.0693 0.00277 0.0138 0.0467 0.0629 0.135 0.14 0.000337 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 220 36885.885 0.005 0.00374 0.0366 0.111 0.0545 0.073 0.182 0.229 0.000455 0.000572 +! Validation 220 36885.885 0.005 0.00396 0.0315 0.111 0.0551 0.0752 0.175 0.212 0.000436 0.00053 +Wall time: 36885.88514932431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.0906 0.0037 0.0166 0.0543 0.0726 0.129 0.154 0.000323 0.000385 + 221 118 0.103 0.00309 0.041 0.05 0.0664 0.224 0.242 0.000561 0.000604 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.0704 0.0029 0.0124 0.048 0.0643 0.128 0.133 0.00032 0.000333 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 221 37053.535 0.005 0.00374 0.0415 0.116 0.0546 0.0731 0.195 0.243 0.000488 0.000608 +! Validation 221 37053.535 0.005 0.00414 0.0267 0.109 0.0563 0.0768 0.157 0.195 0.000393 0.000488 +Wall time: 37053.53566084523 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.0931 0.00378 0.0176 0.0548 0.0734 0.126 0.158 0.000316 0.000395 + 222 118 0.13 0.00404 0.0495 0.0571 0.0759 0.244 0.265 0.000609 0.000664 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.0671 0.00304 0.0062 0.049 0.0659 0.0885 0.0939 0.000221 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 222 37221.079 0.005 0.00371 0.0393 0.113 0.0542 0.0727 0.193 0.236 0.000483 0.000591 +! Validation 222 37221.079 0.005 0.00415 0.02 0.103 0.0568 0.0769 0.134 0.169 0.000335 0.000422 +Wall time: 37221.07977998443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.0917 0.00363 0.019 0.0538 0.0719 0.133 0.165 0.000334 0.000411 + 223 118 0.151 0.00372 0.0761 0.0543 0.0728 0.3 0.329 0.00075 0.000823 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.0876 0.00275 0.0326 0.0463 0.0626 0.212 0.216 0.000531 0.000539 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 223 37388.638 0.005 0.00361 0.0289 0.101 0.0535 0.0717 0.163 0.202 0.000407 0.000505 +! Validation 223 37388.638 0.005 0.00396 0.0525 0.132 0.055 0.0751 0.242 0.273 0.000604 0.000684 +Wall time: 37388.6389631764 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.111 0.00366 0.0377 0.0542 0.0722 0.202 0.232 0.000505 0.000579 + 224 118 0.085 0.00331 0.0187 0.0507 0.0687 0.14 0.163 0.00035 0.000408 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.0574 0.00271 0.00324 0.0461 0.0621 0.0552 0.0679 0.000138 0.00017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 224 37556.180 0.005 0.00373 0.05 0.125 0.0545 0.0729 0.211 0.267 0.000526 0.000668 +! Validation 224 37556.180 0.005 0.0039 0.0145 0.0925 0.0545 0.0745 0.114 0.144 0.000285 0.000359 +Wall time: 37556.18073687516 +! Best model 224 0.092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.128 0.00428 0.0428 0.0584 0.0781 0.212 0.247 0.00053 0.000617 + 225 118 0.114 0.00415 0.0312 0.0574 0.0769 0.169 0.211 0.000422 0.000527 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.137 0.00293 0.0786 0.0481 0.0646 0.332 0.335 0.000829 0.000837 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 225 37724.983 0.005 0.00378 0.0518 0.127 0.0548 0.0733 0.221 0.272 0.000552 0.00068 +! Validation 225 37724.983 0.005 0.00413 0.0769 0.159 0.0563 0.0767 0.299 0.331 0.000749 0.000827 +Wall time: 37724.983554077335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.0889 0.0036 0.0169 0.0534 0.0716 0.127 0.155 0.000318 0.000388 + 226 118 0.0921 0.00317 0.0288 0.0499 0.0672 0.173 0.203 0.000432 0.000506 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.0752 0.0028 0.0193 0.0468 0.0631 0.16 0.166 0.0004 0.000414 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 226 37892.667 0.005 0.0036 0.0318 0.104 0.0535 0.0717 0.171 0.213 0.000427 0.000532 +! Validation 226 37892.667 0.005 0.00393 0.0247 0.103 0.055 0.0748 0.154 0.188 0.000384 0.000469 +Wall time: 37892.66783031821 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.201 0.00344 0.132 0.0527 0.07 0.419 0.433 0.00105 0.00108 + 227 118 0.0799 0.00368 0.00628 0.0539 0.0724 0.0727 0.0946 0.000182 0.000236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.0693 0.00283 0.0128 0.0471 0.0635 0.125 0.135 0.000313 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 227 38060.237 0.005 0.00357 0.0342 0.106 0.0533 0.0713 0.178 0.221 0.000444 0.000554 +! Validation 227 38060.237 0.005 0.00394 0.0366 0.115 0.055 0.0749 0.188 0.228 0.00047 0.000571 +Wall time: 38060.237746926025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.0857 0.00365 0.0127 0.0531 0.0721 0.107 0.134 0.000266 0.000336 + 228 118 0.125 0.00352 0.0548 0.0528 0.0708 0.239 0.279 0.000597 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.0848 0.00267 0.0313 0.0459 0.0617 0.207 0.211 0.000517 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 228 38227.821 0.005 0.00368 0.0521 0.126 0.054 0.0724 0.221 0.272 0.000553 0.000681 +! Validation 228 38227.821 0.005 0.00387 0.0524 0.13 0.0544 0.0742 0.24 0.273 0.0006 0.000683 +Wall time: 38227.82147413725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.1 0.00339 0.0323 0.0518 0.0695 0.19 0.214 0.000474 0.000536 + 229 118 0.0873 0.00356 0.0162 0.0522 0.0712 0.116 0.152 0.000291 0.00038 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.0608 0.00266 0.00762 0.0455 0.0615 0.0951 0.104 0.000238 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 229 38395.541 0.005 0.00347 0.0252 0.0947 0.0525 0.0703 0.148 0.19 0.00037 0.000475 +! Validation 229 38395.541 0.005 0.00375 0.0194 0.0945 0.0535 0.0731 0.132 0.166 0.000331 0.000416 +Wall time: 38395.541461289395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.0871 0.00328 0.0214 0.0512 0.0684 0.139 0.175 0.000347 0.000437 + 230 118 0.0908 0.00333 0.0243 0.051 0.0688 0.165 0.186 0.000412 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.0967 0.00268 0.0431 0.0459 0.0618 0.244 0.248 0.00061 0.000619 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 230 38562.855 0.005 0.00344 0.0299 0.0988 0.0522 0.07 0.164 0.207 0.000411 0.000517 +! Validation 230 38562.855 0.005 0.00379 0.0466 0.122 0.0539 0.0735 0.224 0.258 0.00056 0.000644 +Wall time: 38562.855570863 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.193 0.00369 0.12 0.054 0.0725 0.396 0.413 0.000989 0.00103 + 231 118 0.101 0.00316 0.0382 0.0504 0.0671 0.208 0.233 0.00052 0.000583 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.0558 0.00272 0.00133 0.0463 0.0623 0.0321 0.0436 8.03e-05 0.000109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 231 38730.244 0.005 0.00357 0.0479 0.119 0.0533 0.0713 0.213 0.262 0.000532 0.000654 +! Validation 231 38730.244 0.005 0.00384 0.0153 0.0922 0.0543 0.074 0.116 0.148 0.000291 0.000369 +Wall time: 38730.244427985046 +! Best model 231 0.092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.134 0.00364 0.0611 0.0536 0.0721 0.274 0.295 0.000686 0.000737 + 232 118 0.087 0.00332 0.0206 0.0514 0.0688 0.15 0.171 0.000375 0.000428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.0654 0.00259 0.0137 0.0448 0.0607 0.135 0.14 0.000336 0.000349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 232 38897.505 0.005 0.0035 0.0405 0.111 0.0527 0.0706 0.194 0.241 0.000484 0.000602 +! Validation 232 38897.505 0.005 0.00375 0.0302 0.105 0.0535 0.0731 0.173 0.207 0.000432 0.000519 +Wall time: 38897.505707249045 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.0794 0.00333 0.0129 0.0516 0.0688 0.107 0.135 0.000266 0.000339 + 233 118 0.152 0.00334 0.0854 0.0521 0.069 0.331 0.349 0.000827 0.000872 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.145 0.00281 0.0888 0.0474 0.0633 0.354 0.356 0.000885 0.000889 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 233 39064.785 0.005 0.00342 0.0338 0.102 0.052 0.0698 0.175 0.218 0.000439 0.000545 +! Validation 233 39064.785 0.005 0.00409 0.129 0.211 0.0562 0.0763 0.396 0.429 0.000989 0.00107 +Wall time: 39064.78552664304 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.0911 0.00363 0.0185 0.053 0.0719 0.14 0.162 0.00035 0.000405 + 234 118 0.111 0.00303 0.0499 0.0497 0.0658 0.25 0.267 0.000624 0.000667 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.211 0.00263 0.158 0.0454 0.0612 0.473 0.474 0.00118 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 234 39232.042 0.005 0.0035 0.042 0.112 0.0528 0.0707 0.198 0.244 0.000495 0.000611 +! Validation 234 39232.042 0.005 0.00373 0.147 0.222 0.0535 0.0729 0.436 0.458 0.00109 0.00115 +Wall time: 39232.04280584538 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0726 0.00326 0.00736 0.0506 0.0682 0.0826 0.102 0.000207 0.000256 + 235 118 0.0786 0.00331 0.0124 0.052 0.0687 0.108 0.133 0.00027 0.000332 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0598 0.00288 0.00215 0.0475 0.0641 0.0402 0.0553 0.0001 0.000138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 235 39399.307 0.005 0.00342 0.0264 0.0948 0.0521 0.0698 0.152 0.194 0.00038 0.000485 +! Validation 235 39399.307 0.005 0.00391 0.0133 0.0915 0.0549 0.0746 0.108 0.138 0.00027 0.000345 +Wall time: 39399.30728081521 +! Best model 235 0.091 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.758 0.0338 0.0829 0.155 0.219 0.28 0.344 0.000699 0.000859 + 236 118 0.56 0.0249 0.0622 0.134 0.188 0.253 0.298 0.000633 0.000744 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.557 0.0208 0.14 0.123 0.172 0.434 0.447 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 236 39566.680 0.005 0.0987 1.54 3.51 0.229 0.376 0.929 1.49 0.00232 0.00371 +! Validation 236 39566.680 0.005 0.0253 0.14 0.647 0.135 0.19 0.379 0.447 0.000947 0.00112 +Wall time: 39566.68046694435 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.416 0.0159 0.0979 0.108 0.15 0.328 0.373 0.000819 0.000934 + 237 118 0.439 0.0151 0.138 0.106 0.147 0.404 0.443 0.00101 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.247 0.0123 0.00122 0.0957 0.132 0.0406 0.0417 0.000101 0.000104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 237 39733.934 0.005 0.0182 0.193 0.556 0.115 0.161 0.412 0.524 0.00103 0.00131 +! Validation 237 39733.934 0.005 0.015 0.0534 0.354 0.105 0.146 0.219 0.276 0.000547 0.00069 +Wall time: 39733.93411203707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.438 0.0115 0.208 0.0928 0.128 0.505 0.545 0.00126 0.00136 + 238 118 0.35 0.00995 0.151 0.0871 0.119 0.442 0.464 0.0011 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.486 0.00894 0.307 0.0821 0.113 0.66 0.661 0.00165 0.00165 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 238 39901.216 0.005 0.0124 0.113 0.361 0.096 0.133 0.325 0.402 0.000813 0.001 +! Validation 238 39901.216 0.005 0.0112 0.249 0.472 0.0913 0.126 0.559 0.595 0.0014 0.00149 +Wall time: 39901.21611926798 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.192 0.00851 0.022 0.0811 0.11 0.135 0.177 0.000339 0.000442 + 239 118 0.198 0.00797 0.0384 0.0792 0.107 0.195 0.234 0.000488 0.000585 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.138 0.00662 0.00569 0.0712 0.0971 0.0808 0.09 0.000202 0.000225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 239 40068.488 0.005 0.00944 0.0991 0.288 0.0849 0.116 0.306 0.377 0.000765 0.000941 +! Validation 239 40068.488 0.005 0.00846 0.0312 0.201 0.0805 0.11 0.169 0.211 0.000422 0.000527 +Wall time: 40068.48827444203 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.18 0.00664 0.0467 0.0727 0.0973 0.217 0.258 0.000543 0.000645 + 240 118 0.152 0.00659 0.0201 0.0725 0.0969 0.149 0.169 0.000373 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.116 0.00523 0.0115 0.064 0.0863 0.121 0.128 0.000303 0.000319 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 240 40235.854 0.005 0.00723 0.091 0.236 0.0755 0.102 0.296 0.361 0.00074 0.000903 +! Validation 240 40235.854 0.005 0.00683 0.0309 0.167 0.073 0.0986 0.168 0.21 0.000419 0.000524 +Wall time: 40235.85414658021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.125 0.00543 0.0162 0.0659 0.088 0.13 0.152 0.000326 0.00038 + 241 118 0.176 0.00504 0.0752 0.0639 0.0847 0.28 0.327 0.000701 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.0897 0.00435 0.00283 0.0586 0.0787 0.0492 0.0635 0.000123 0.000159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 241 40403.125 0.005 0.00578 0.0656 0.181 0.0681 0.0908 0.248 0.306 0.000619 0.000764 +! Validation 241 40403.125 0.005 0.00579 0.0189 0.135 0.0675 0.0908 0.134 0.164 0.000336 0.00041 +Wall time: 40403.125900730025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 0.14 0.00504 0.0388 0.0639 0.0848 0.197 0.235 0.000494 0.000588 + 242 118 0.113 0.00459 0.0216 0.0616 0.0808 0.15 0.175 0.000374 0.000438 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 0.132 0.00382 0.0557 0.0549 0.0738 0.278 0.282 0.000696 0.000704 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 242 40570.406 0.005 0.00502 0.0544 0.155 0.0636 0.0846 0.223 0.279 0.000558 0.000697 +! Validation 242 40570.406 0.005 0.00518 0.0584 0.162 0.0637 0.0859 0.253 0.288 0.000633 0.000721 +Wall time: 40570.40694660414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 0.107 0.0045 0.0173 0.0601 0.08 0.124 0.157 0.000311 0.000393 + 243 118 0.132 0.00425 0.0469 0.0583 0.0778 0.228 0.259 0.00057 0.000646 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 0.0837 0.00341 0.0155 0.0519 0.0697 0.144 0.149 0.000359 0.000371 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 243 40737.672 0.005 0.00456 0.0376 0.129 0.0606 0.0806 0.187 0.231 0.000468 0.000578 +! Validation 243 40737.672 0.005 0.0048 0.024 0.12 0.0612 0.0827 0.151 0.185 0.000378 0.000462 +Wall time: 40737.672519540414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 0.16 0.00445 0.0711 0.0593 0.0797 0.295 0.318 0.000737 0.000796 + 244 118 0.169 0.00402 0.0891 0.0569 0.0756 0.322 0.356 0.000805 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 0.199 0.00329 0.134 0.0509 0.0684 0.435 0.436 0.00109 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 244 40904.933 0.005 0.00431 0.0428 0.129 0.0588 0.0783 0.2 0.246 0.000501 0.000615 +! Validation 244 40904.933 0.005 0.00459 0.138 0.23 0.0597 0.0808 0.42 0.444 0.00105 0.00111 +Wall time: 40904.93374403007 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.0916 0.00389 0.0139 0.0561 0.0744 0.107 0.14 0.000267 0.000351 + 245 118 0.1 0.00349 0.0304 0.0536 0.0706 0.178 0.208 0.000444 0.000521 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.0769 0.00312 0.0146 0.0494 0.0666 0.139 0.144 0.000348 0.00036 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 245 41072.295 0.005 0.00413 0.0353 0.118 0.0576 0.0768 0.181 0.224 0.000453 0.000561 +! Validation 245 41072.295 0.005 0.00441 0.0219 0.11 0.0584 0.0792 0.147 0.177 0.000367 0.000442 +Wall time: 41072.295085913036 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 0.103 0.00395 0.0238 0.056 0.075 0.155 0.184 0.000386 0.000461 + 246 118 0.0821 0.00379 0.00636 0.0551 0.0734 0.0859 0.0952 0.000215 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 0.0763 0.00301 0.016 0.0486 0.0655 0.147 0.151 0.000367 0.000378 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 246 41239.568 0.005 0.00399 0.0289 0.109 0.0565 0.0754 0.165 0.203 0.000412 0.000508 +! Validation 246 41239.568 0.005 0.00429 0.0247 0.11 0.0576 0.0782 0.157 0.188 0.000391 0.000469 +Wall time: 41239.56808214029 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.0909 0.00379 0.015 0.055 0.0735 0.12 0.146 0.000299 0.000366 + 247 118 0.109 0.00404 0.0278 0.0564 0.0759 0.179 0.199 0.000449 0.000497 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.0691 0.00294 0.0103 0.0479 0.0647 0.116 0.121 0.000289 0.000303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 247 41406.811 0.005 0.00388 0.032 0.11 0.0557 0.0744 0.171 0.214 0.000427 0.000534 +! Validation 247 41406.811 0.005 0.00419 0.0248 0.109 0.0569 0.0773 0.151 0.188 0.000377 0.00047 +Wall time: 41406.81176061928 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.0844 0.00361 0.0122 0.0536 0.0717 0.107 0.132 0.000267 0.000329 + 248 118 0.164 0.00361 0.0922 0.0541 0.0718 0.349 0.362 0.000872 0.000906 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.181 0.00286 0.123 0.0473 0.0639 0.418 0.419 0.00104 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 248 41574.064 0.005 0.0038 0.0315 0.107 0.055 0.0736 0.166 0.21 0.000414 0.000526 +! Validation 248 41574.064 0.005 0.00411 0.139 0.221 0.0563 0.0765 0.422 0.445 0.00106 0.00111 +Wall time: 41574.06407823833 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.104 0.00362 0.0314 0.0536 0.0718 0.185 0.211 0.000461 0.000529 + 249 118 0.109 0.00456 0.0184 0.0596 0.0806 0.123 0.162 0.000307 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.0849 0.00286 0.0277 0.0471 0.0638 0.195 0.199 0.000488 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 249 41741.324 0.005 0.00374 0.04 0.115 0.0545 0.0729 0.195 0.239 0.000487 0.000598 +! Validation 249 41741.324 0.005 0.00407 0.0486 0.13 0.0559 0.0761 0.228 0.263 0.00057 0.000658 +Wall time: 41741.32457659999 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.102 0.00383 0.0254 0.055 0.0739 0.165 0.19 0.000413 0.000475 + 250 118 0.0958 0.00355 0.0248 0.0532 0.0711 0.177 0.188 0.000443 0.00047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.0558 0.00275 0.000667 0.0464 0.0626 0.0295 0.0308 7.37e-05 7.71e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 250 41908.700 0.005 0.00366 0.0327 0.106 0.054 0.0722 0.176 0.216 0.00044 0.00054 +! Validation 250 41908.700 0.005 0.00397 0.0144 0.0937 0.0552 0.0752 0.109 0.143 0.000274 0.000358 +Wall time: 41908.70081173722 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.105 0.00331 0.0388 0.0517 0.0686 0.208 0.235 0.000521 0.000588 + 251 118 0.176 0.00395 0.0972 0.0557 0.075 0.327 0.372 0.000817 0.00093 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.129 0.00291 0.0707 0.0476 0.0644 0.316 0.317 0.000791 0.000793 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 251 42075.967 0.005 0.00361 0.0351 0.107 0.0535 0.0716 0.181 0.222 0.000452 0.000555 +! Validation 251 42075.967 0.005 0.00405 0.0782 0.159 0.0558 0.0759 0.306 0.334 0.000764 0.000835 +Wall time: 42075.96701184334 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.0771 0.00321 0.0129 0.0509 0.0676 0.111 0.135 0.000278 0.000339 + 252 118 0.104 0.00371 0.0293 0.0552 0.0727 0.19 0.204 0.000475 0.00051 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.0717 0.00271 0.0176 0.0459 0.0621 0.155 0.158 0.000387 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 252 42243.424 0.005 0.00355 0.0232 0.0942 0.0531 0.0711 0.146 0.181 0.000364 0.000454 +! Validation 252 42243.424 0.005 0.00385 0.0371 0.114 0.0543 0.0741 0.195 0.23 0.000487 0.000575 +Wall time: 42243.42439697916 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.147 0.00362 0.0748 0.0532 0.0718 0.304 0.327 0.00076 0.000816 + 253 118 0.0657 0.00303 0.00511 0.0495 0.0657 0.068 0.0853 0.00017 0.000213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.063 0.0027 0.00904 0.0458 0.062 0.109 0.113 0.000273 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 253 42412.441 0.005 0.00351 0.0401 0.11 0.0528 0.0708 0.196 0.24 0.00049 0.000599 +! Validation 253 42412.441 0.005 0.00382 0.0184 0.0949 0.0541 0.0738 0.131 0.162 0.000328 0.000405 +Wall time: 42412.44192346139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.0882 0.00335 0.0211 0.0518 0.0691 0.147 0.173 0.000368 0.000434 + 254 118 0.079 0.00356 0.00771 0.0522 0.0713 0.0822 0.105 0.000206 0.000262 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.0531 0.00263 0.000592 0.0452 0.0612 0.0238 0.029 5.95e-05 7.26e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 254 42579.919 0.005 0.00347 0.0268 0.0962 0.0525 0.0703 0.158 0.196 0.000395 0.00049 +! Validation 254 42579.919 0.005 0.00377 0.0122 0.0877 0.0537 0.0733 0.102 0.132 0.000256 0.00033 +Wall time: 42579.91981325019 +! Best model 254 0.088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.0842 0.0036 0.0122 0.0532 0.0716 0.109 0.132 0.000274 0.00033 + 255 118 0.0824 0.00304 0.0217 0.0493 0.0658 0.16 0.176 0.000399 0.000439 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.0888 0.00261 0.0366 0.0451 0.061 0.227 0.228 0.000567 0.000571 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 255 42747.477 0.005 0.00341 0.023 0.0912 0.052 0.0697 0.145 0.181 0.000363 0.000453 +! Validation 255 42747.477 0.005 0.00375 0.0357 0.111 0.0535 0.0731 0.196 0.225 0.00049 0.000564 +Wall time: 42747.477251499426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.0851 0.00346 0.016 0.0522 0.0702 0.123 0.151 0.000308 0.000377 + 256 118 0.137 0.00315 0.0745 0.0508 0.0669 0.312 0.326 0.00078 0.000814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.0773 0.00267 0.024 0.0456 0.0616 0.183 0.185 0.000458 0.000462 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 256 42914.896 0.005 0.00347 0.049 0.118 0.0525 0.0704 0.198 0.264 0.000494 0.000659 +! Validation 256 42914.896 0.005 0.00375 0.0426 0.117 0.0536 0.073 0.213 0.246 0.000533 0.000616 +Wall time: 42914.89691913221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.133 0.00308 0.0713 0.05 0.0663 0.296 0.319 0.000739 0.000797 + 257 118 0.0756 0.00287 0.0181 0.0483 0.064 0.131 0.161 0.000328 0.000402 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.0538 0.00263 0.00115 0.045 0.0612 0.0324 0.0405 8.1e-05 0.000101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 257 43082.339 0.005 0.00337 0.0234 0.0908 0.0517 0.0693 0.146 0.183 0.000364 0.000457 +! Validation 257 43082.339 0.005 0.00369 0.0124 0.0862 0.0531 0.0725 0.103 0.133 0.000258 0.000332 +Wall time: 43082.340028061066 +! Best model 257 0.086 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.0738 0.00313 0.0112 0.0499 0.0668 0.0976 0.126 0.000244 0.000316 + 258 118 0.0705 0.00319 0.00664 0.0495 0.0675 0.0741 0.0973 0.000185 0.000243 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.0561 0.00255 0.00502 0.0445 0.0603 0.0797 0.0846 0.000199 0.000211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 258 43249.753 0.005 0.00334 0.0296 0.0963 0.0515 0.069 0.165 0.206 0.000413 0.000515 +! Validation 258 43249.753 0.005 0.00363 0.0227 0.0954 0.0527 0.0719 0.145 0.18 0.000362 0.00045 +Wall time: 43249.753200488165 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.144 0.00333 0.0772 0.0513 0.0689 0.314 0.332 0.000784 0.000829 + 259 118 0.211 0.00377 0.135 0.0535 0.0733 0.421 0.439 0.00105 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.0897 0.00262 0.0373 0.0449 0.0611 0.229 0.231 0.000574 0.000577 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 259 43417.351 0.005 0.00332 0.045 0.111 0.0513 0.0688 0.203 0.251 0.000507 0.000629 +! Validation 259 43417.351 0.005 0.00366 0.0524 0.126 0.0529 0.0722 0.239 0.273 0.000598 0.000683 +Wall time: 43417.351199432276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.14 0.00358 0.0683 0.0532 0.0714 0.294 0.312 0.000735 0.00078 + 260 118 0.076 0.0033 0.0099 0.0514 0.0686 0.0843 0.119 0.000211 0.000297 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.0587 0.00253 0.00815 0.0443 0.06 0.104 0.108 0.000259 0.000269 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 260 43584.985 0.005 0.0033 0.0258 0.0918 0.0512 0.0686 0.155 0.192 0.000388 0.00048 +! Validation 260 43584.985 0.005 0.00359 0.0141 0.0859 0.0524 0.0715 0.113 0.142 0.000284 0.000354 +Wall time: 43584.98560302006 +! Best model 260 0.086 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.0805 0.00334 0.0138 0.0513 0.0689 0.112 0.14 0.000279 0.000351 + 261 118 0.0895 0.00318 0.0258 0.0508 0.0673 0.152 0.192 0.000381 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.0533 0.00256 0.00222 0.0445 0.0603 0.0495 0.0562 0.000124 0.00014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 261 43752.986 0.005 0.00325 0.0276 0.0926 0.0508 0.0681 0.161 0.198 0.000401 0.000496 +! Validation 261 43752.986 0.005 0.0036 0.0202 0.0922 0.0525 0.0716 0.133 0.169 0.000334 0.000424 +Wall time: 43752.98621332226 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.202 0.00328 0.137 0.0509 0.0684 0.421 0.441 0.00105 0.0011 + 262 118 0.0844 0.00367 0.011 0.0533 0.0723 0.089 0.125 0.000223 0.000313 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.0506 0.0025 0.000598 0.0441 0.0597 0.0254 0.0292 6.35e-05 7.3e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 262 43920.461 0.005 0.00327 0.0429 0.108 0.0509 0.0682 0.202 0.248 0.000505 0.000619 +! Validation 262 43920.461 0.005 0.00359 0.0133 0.0852 0.0524 0.0716 0.107 0.138 0.000266 0.000344 +Wall time: 43920.46203893004 +! Best model 262 0.085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.0881 0.00314 0.0252 0.0501 0.0669 0.169 0.19 0.000423 0.000474 + 263 118 0.0748 0.00294 0.016 0.0492 0.0647 0.137 0.151 0.000343 0.000377 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.103 0.00249 0.0533 0.0441 0.0596 0.274 0.275 0.000685 0.000689 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 263 44087.986 0.005 0.00323 0.0274 0.0919 0.0506 0.0678 0.157 0.198 0.000392 0.000494 +! Validation 263 44087.986 0.005 0.00354 0.051 0.122 0.052 0.071 0.243 0.27 0.000608 0.000674 +Wall time: 44087.986927095335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.148 0.00319 0.0843 0.0498 0.0674 0.326 0.347 0.000816 0.000867 + 264 118 0.0918 0.00371 0.0175 0.0537 0.0727 0.144 0.158 0.000359 0.000395 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.052 0.00248 0.00244 0.0438 0.0594 0.052 0.059 0.00013 0.000147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 264 44255.490 0.005 0.00324 0.0436 0.109 0.0507 0.068 0.203 0.25 0.000507 0.000624 +! Validation 264 44255.490 0.005 0.00354 0.0166 0.0875 0.052 0.0711 0.12 0.154 0.0003 0.000384 +Wall time: 44255.49055916816 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.0741 0.00327 0.00866 0.0509 0.0683 0.0897 0.111 0.000224 0.000278 + 265 118 0.0722 0.00277 0.0168 0.0474 0.0628 0.146 0.155 0.000364 0.000387 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.051 0.00245 0.00208 0.0435 0.059 0.0457 0.0545 0.000114 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 265 44423.098 0.005 0.00318 0.0231 0.0867 0.0502 0.0673 0.146 0.182 0.000365 0.000454 +! Validation 265 44423.098 0.005 0.00349 0.012 0.0818 0.0516 0.0705 0.101 0.131 0.000253 0.000327 +Wall time: 44423.0981862084 +! Best model 265 0.082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.0874 0.00331 0.0212 0.0509 0.0686 0.148 0.174 0.00037 0.000435 + 266 118 0.0767 0.00355 0.00568 0.0533 0.0711 0.07 0.0899 0.000175 0.000225 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.05 0.00246 0.000752 0.0435 0.0592 0.025 0.0327 6.24e-05 8.18e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 266 44590.552 0.005 0.00317 0.0267 0.09 0.0501 0.0671 0.157 0.196 0.000392 0.000489 +! Validation 266 44590.552 0.005 0.00345 0.0135 0.0826 0.0513 0.0701 0.106 0.139 0.000266 0.000347 +Wall time: 44590.55233573541 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.138 0.00294 0.0797 0.0484 0.0647 0.323 0.337 0.000808 0.000842 + 267 118 0.0756 0.00324 0.0108 0.0511 0.0679 0.0865 0.124 0.000216 0.000311 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.0746 0.0025 0.0246 0.0438 0.0597 0.185 0.187 0.000463 0.000468 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 267 44757.943 0.005 0.00316 0.0326 0.0958 0.05 0.0671 0.174 0.216 0.000435 0.00054 +! Validation 267 44757.943 0.005 0.00349 0.0323 0.102 0.0516 0.0705 0.186 0.214 0.000465 0.000536 +Wall time: 44757.943611039314 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.0843 0.00329 0.0185 0.051 0.0685 0.13 0.162 0.000325 0.000406 + 268 118 0.173 0.00328 0.107 0.0513 0.0683 0.375 0.391 0.000938 0.000978 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.146 0.00249 0.0965 0.0438 0.0595 0.37 0.371 0.000925 0.000927 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 268 44925.191 0.005 0.00315 0.0339 0.0969 0.05 0.067 0.169 0.218 0.000422 0.000545 +! Validation 268 44925.191 0.005 0.00352 0.122 0.192 0.0518 0.0708 0.397 0.417 0.000994 0.00104 +Wall time: 44925.191887859255 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.096 0.0031 0.0341 0.0497 0.0664 0.199 0.22 0.000497 0.000551 + 269 118 0.0972 0.0029 0.0392 0.0482 0.0643 0.215 0.236 0.000537 0.000591 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.158 0.00242 0.11 0.0432 0.0588 0.395 0.396 0.000987 0.000989 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 269 45092.474 0.005 0.00314 0.036 0.0989 0.0499 0.0669 0.186 0.226 0.000466 0.000566 +! Validation 269 45092.474 0.005 0.00342 0.0969 0.165 0.0511 0.0698 0.35 0.372 0.000874 0.000929 +Wall time: 45092.47438403824 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.0929 0.00282 0.0364 0.0476 0.0634 0.2 0.228 0.000501 0.000569 + 270 118 0.0701 0.00314 0.0073 0.0499 0.0669 0.0791 0.102 0.000198 0.000255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.109 0.0024 0.061 0.043 0.0584 0.294 0.295 0.000735 0.000737 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 270 45259.608 0.005 0.0031 0.0274 0.0894 0.0496 0.0665 0.157 0.198 0.000392 0.000495 +! Validation 270 45259.608 0.005 0.0034 0.06 0.128 0.0509 0.0696 0.266 0.292 0.000665 0.000731 +Wall time: 45259.6083851303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.093 0.00326 0.0278 0.0505 0.0681 0.175 0.199 0.000438 0.000497 + 271 118 0.16 0.00355 0.0894 0.0529 0.0711 0.338 0.357 0.000845 0.000892 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.0561 0.0024 0.0081 0.0431 0.0584 0.104 0.107 0.00026 0.000269 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 271 45426.730 0.005 0.00312 0.04 0.102 0.0497 0.0666 0.186 0.238 0.000465 0.000594 +! Validation 271 45426.730 0.005 0.00344 0.0366 0.105 0.0512 0.07 0.191 0.228 0.000476 0.000571 +Wall time: 45426.73028074205 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.0932 0.00298 0.0336 0.049 0.0652 0.193 0.219 0.000483 0.000547 + 272 118 0.0551 0.00257 0.00362 0.0452 0.0605 0.065 0.0718 0.000162 0.00018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.0622 0.00237 0.0147 0.0429 0.0581 0.142 0.145 0.000356 0.000362 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 272 45594.268 0.005 0.00313 0.0414 0.104 0.0499 0.0668 0.2 0.244 0.000499 0.000609 +! Validation 272 45594.268 0.005 0.00338 0.0181 0.0856 0.0508 0.0694 0.132 0.16 0.000329 0.000401 +Wall time: 45594.268560878 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.0752 0.00296 0.016 0.0484 0.065 0.123 0.151 0.000308 0.000377 + 273 118 0.072 0.00316 0.00879 0.0495 0.0671 0.0975 0.112 0.000244 0.00028 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.0515 0.0023 0.00552 0.0423 0.0572 0.0827 0.0886 0.000207 0.000222 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 273 45761.366 0.005 0.00307 0.0294 0.0909 0.0494 0.0662 0.161 0.205 0.000404 0.000513 +! Validation 273 45761.366 0.005 0.00336 0.0232 0.0903 0.0506 0.0691 0.148 0.182 0.000371 0.000454 +Wall time: 45761.36656318698 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0691 0.00309 0.00737 0.0493 0.0663 0.0844 0.102 0.000211 0.000256 + 274 118 0.171 0.00396 0.0919 0.0548 0.0751 0.352 0.362 0.000881 0.000905 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0639 0.00271 0.00962 0.0458 0.0622 0.116 0.117 0.00029 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 274 45928.588 0.005 0.00302 0.0187 0.079 0.0488 0.0655 0.128 0.161 0.00032 0.000402 +! Validation 274 45928.588 0.005 0.00368 0.0346 0.108 0.0532 0.0724 0.184 0.222 0.00046 0.000555 +Wall time: 45928.58872046927 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0817 0.00283 0.025 0.0473 0.0635 0.17 0.189 0.000426 0.000472 + 275 118 0.0851 0.00293 0.0266 0.0481 0.0646 0.159 0.195 0.000397 0.000486 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0659 0.00236 0.0186 0.0425 0.058 0.16 0.163 0.0004 0.000407 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 275 46095.736 0.005 0.00307 0.0292 0.0905 0.0493 0.0661 0.167 0.204 0.000418 0.00051 +! Validation 275 46095.736 0.005 0.00331 0.0216 0.0879 0.0502 0.0687 0.147 0.175 0.000368 0.000438 +Wall time: 46095.73655166011 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.146 0.00328 0.0807 0.0511 0.0684 0.323 0.339 0.000807 0.000848 + 276 118 0.085 0.00313 0.0224 0.0489 0.0668 0.167 0.179 0.000417 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.0531 0.00231 0.007 0.0424 0.0573 0.0958 0.0998 0.000239 0.00025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 276 46262.855 0.005 0.00304 0.0364 0.0971 0.049 0.0658 0.188 0.228 0.00047 0.00057 +! Validation 276 46262.855 0.005 0.00333 0.0201 0.0867 0.0504 0.0689 0.137 0.169 0.000342 0.000423 +Wall time: 46262.85526186507 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.0778 0.003 0.0178 0.0484 0.0653 0.127 0.159 0.000317 0.000399 + 277 118 0.175 0.00292 0.117 0.0483 0.0645 0.405 0.408 0.00101 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.263 0.00231 0.217 0.0424 0.0574 0.556 0.556 0.00139 0.00139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 277 46430.269 0.005 0.00305 0.0341 0.0951 0.0492 0.0659 0.178 0.219 0.000444 0.000546 +! Validation 277 46430.269 0.005 0.00334 0.222 0.289 0.0505 0.069 0.547 0.562 0.00137 0.00141 +Wall time: 46430.2690150463 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0666 0.00289 0.0089 0.048 0.0641 0.0878 0.113 0.00022 0.000282 + 278 118 0.061 0.00256 0.00979 0.0459 0.0604 0.103 0.118 0.000258 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0465 0.00229 0.000602 0.0421 0.0572 0.0252 0.0293 6.29e-05 7.32e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 278 46597.517 0.005 0.00302 0.0353 0.0957 0.049 0.0656 0.179 0.225 0.000448 0.000562 +! Validation 278 46597.517 0.005 0.00328 0.0144 0.08 0.05 0.0684 0.111 0.143 0.000278 0.000358 +Wall time: 46597.517018159386 +! Best model 278 0.080 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.0802 0.00285 0.0231 0.0479 0.0638 0.16 0.181 0.0004 0.000454 + 279 118 0.0806 0.00343 0.0119 0.0526 0.0699 0.106 0.13 0.000265 0.000326 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.0498 0.00236 0.00261 0.0425 0.0579 0.0557 0.0609 0.000139 0.000152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 279 46764.648 0.005 0.00303 0.0337 0.0942 0.049 0.0656 0.173 0.22 0.000433 0.000549 +! Validation 279 46764.648 0.005 0.00331 0.0116 0.0777 0.0502 0.0686 0.101 0.129 0.000253 0.000322 +Wall time: 46764.64885848714 +! Best model 279 0.078 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.136 0.00289 0.078 0.048 0.0642 0.309 0.333 0.000774 0.000833 + 280 118 0.0796 0.00348 0.01 0.0519 0.0704 0.0956 0.119 0.000239 0.000299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.122 0.00225 0.0766 0.0417 0.0566 0.329 0.33 0.000823 0.000826 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 280 46931.749 0.005 0.00297 0.0291 0.0884 0.0485 0.065 0.167 0.204 0.000418 0.00051 +! Validation 280 46931.749 0.005 0.00325 0.0742 0.139 0.0498 0.068 0.301 0.325 0.000753 0.000813 +Wall time: 46931.74925164925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.0719 0.00288 0.0144 0.0479 0.064 0.122 0.143 0.000304 0.000358 + 281 118 0.107 0.00322 0.0426 0.0502 0.0677 0.229 0.246 0.000573 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.0885 0.00237 0.0411 0.0427 0.0581 0.241 0.242 0.000603 0.000605 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 281 47098.851 0.005 0.00326 0.0433 0.109 0.0507 0.0682 0.179 0.248 0.000448 0.000621 +! Validation 281 47098.851 0.005 0.00333 0.0476 0.114 0.0504 0.0688 0.229 0.26 0.000573 0.000651 +Wall time: 47098.85184104927 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 0.135 0.00289 0.0772 0.0478 0.0641 0.311 0.332 0.000778 0.000829 + 282 118 0.065 0.00303 0.00448 0.0493 0.0657 0.0696 0.0799 0.000174 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 0.0623 0.00225 0.0172 0.0417 0.0567 0.155 0.157 0.000386 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 282 47265.997 0.005 0.00291 0.0184 0.0766 0.048 0.0644 0.129 0.162 0.000322 0.000405 +! Validation 282 47265.997 0.005 0.00321 0.0245 0.0886 0.0495 0.0676 0.158 0.187 0.000394 0.000467 +Wall time: 47265.99794334127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.0673 0.0027 0.0133 0.0465 0.062 0.116 0.137 0.000289 0.000344 + 283 118 0.0775 0.00314 0.0147 0.05 0.0669 0.13 0.145 0.000325 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.11 0.0022 0.0662 0.0413 0.056 0.306 0.307 0.000766 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 283 47433.222 0.005 0.00294 0.0323 0.0911 0.0483 0.0647 0.175 0.215 0.000439 0.000537 +! Validation 283 47433.222 0.005 0.00321 0.0686 0.133 0.0495 0.0676 0.289 0.313 0.000721 0.000781 +Wall time: 47433.222526392434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.0764 0.00292 0.018 0.0483 0.0645 0.13 0.16 0.000326 0.0004 + 284 118 0.0691 0.00318 0.00548 0.0497 0.0673 0.0803 0.0883 0.000201 0.000221 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.0504 0.00223 0.00587 0.0416 0.0563 0.0887 0.0915 0.000222 0.000229 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 284 47600.340 0.005 0.003 0.0415 0.102 0.0488 0.0654 0.201 0.244 0.000502 0.000609 +! Validation 284 47600.340 0.005 0.0032 0.0254 0.0893 0.0494 0.0675 0.155 0.19 0.000388 0.000475 +Wall time: 47600.34095426602 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.0863 0.00288 0.0287 0.0481 0.064 0.177 0.202 0.000442 0.000506 + 285 118 0.076 0.0031 0.0139 0.0492 0.0665 0.122 0.141 0.000304 0.000351 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.054 0.00227 0.00851 0.042 0.0569 0.108 0.11 0.000269 0.000275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 285 47767.454 0.005 0.00291 0.0268 0.085 0.048 0.0643 0.158 0.196 0.000396 0.00049 +! Validation 285 47767.454 0.005 0.00329 0.0172 0.0831 0.0502 0.0685 0.124 0.157 0.000309 0.000392 +Wall time: 47767.45462907711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0688 0.00295 0.0098 0.048 0.0648 0.0903 0.118 0.000226 0.000295 + 286 118 0.0693 0.00307 0.00795 0.049 0.0661 0.0867 0.106 0.000217 0.000266 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0668 0.00224 0.0221 0.0415 0.0565 0.176 0.177 0.000439 0.000443 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 286 47934.780 0.005 0.00288 0.0205 0.0781 0.0478 0.064 0.138 0.171 0.000344 0.000428 +! Validation 286 47934.780 0.005 0.00318 0.0211 0.0847 0.0493 0.0673 0.146 0.173 0.000366 0.000434 +Wall time: 47934.7800701051 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.0658 0.00284 0.00904 0.0477 0.0636 0.0978 0.113 0.000244 0.000284 + 287 118 0.18 0.00323 0.115 0.051 0.0678 0.388 0.406 0.000969 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.186 0.00289 0.128 0.0473 0.0642 0.426 0.427 0.00106 0.00107 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 287 48102.010 0.005 0.0029 0.0368 0.0949 0.048 0.0643 0.184 0.227 0.000461 0.000568 +! Validation 287 48102.010 0.005 0.00377 0.161 0.237 0.0543 0.0733 0.459 0.479 0.00115 0.0012 +Wall time: 48102.01046952233 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.0615 0.00277 0.00617 0.0472 0.0628 0.074 0.0938 0.000185 0.000234 + 288 118 0.0683 0.00268 0.0147 0.0466 0.0617 0.128 0.145 0.00032 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.0456 0.00218 0.00199 0.041 0.0557 0.0472 0.0533 0.000118 0.000133 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 288 48269.358 0.005 0.00292 0.0247 0.0831 0.0481 0.0645 0.15 0.188 0.000374 0.000469 +! Validation 288 48269.358 0.005 0.00313 0.0125 0.0752 0.0489 0.0668 0.104 0.133 0.00026 0.000333 +Wall time: 48269.3582121823 +! Best model 288 0.075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.0649 0.00268 0.0114 0.0464 0.0618 0.106 0.127 0.000266 0.000318 + 289 118 0.11 0.00294 0.0509 0.0484 0.0647 0.253 0.269 0.000634 0.000673 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.0534 0.00224 0.00861 0.0414 0.0565 0.108 0.111 0.000269 0.000277 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 289 48436.665 0.005 0.00291 0.0322 0.0904 0.0481 0.0644 0.174 0.214 0.000436 0.000535 +! Validation 289 48436.665 0.005 0.00317 0.0372 0.101 0.0491 0.0672 0.189 0.23 0.000472 0.000576 +Wall time: 48436.6654984802 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0865 0.00256 0.0354 0.0451 0.0603 0.206 0.225 0.000514 0.000561 + 290 118 0.0747 0.00338 0.00707 0.051 0.0694 0.0946 0.1 0.000236 0.000251 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0515 0.00219 0.0076 0.0412 0.0559 0.0992 0.104 0.000248 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 290 48603.962 0.005 0.00287 0.0325 0.09 0.0477 0.0639 0.179 0.216 0.000448 0.00054 +! Validation 290 48603.962 0.005 0.00314 0.0192 0.0819 0.0489 0.0668 0.135 0.165 0.000337 0.000413 +Wall time: 48603.96220276132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0643 0.00268 0.0106 0.0464 0.0618 0.1 0.123 0.000251 0.000308 + 291 118 0.0709 0.00305 0.00996 0.0482 0.0659 0.085 0.119 0.000213 0.000298 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0482 0.00215 0.00524 0.0406 0.0553 0.0838 0.0864 0.00021 0.000216 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 291 48771.289 0.005 0.00281 0.0238 0.08 0.0472 0.0632 0.149 0.184 0.000373 0.000461 +! Validation 291 48771.289 0.005 0.00307 0.0143 0.0756 0.0483 0.0661 0.113 0.143 0.000283 0.000357 +Wall time: 48771.28921243502 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.0597 0.00267 0.00627 0.046 0.0617 0.0779 0.0945 0.000195 0.000236 + 292 118 0.0659 0.00318 0.00244 0.0503 0.0673 0.0505 0.059 0.000126 0.000147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.0433 0.00215 0.000327 0.0407 0.0553 0.0179 0.0216 4.49e-05 5.4e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 292 48938.625 0.005 0.0028 0.0252 0.0811 0.0471 0.0631 0.152 0.19 0.000379 0.000475 +! Validation 292 48938.625 0.005 0.00305 0.0123 0.0733 0.0482 0.0659 0.105 0.132 0.000262 0.000331 +Wall time: 48938.62595797004 +! Best model 292 0.073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0865 0.0028 0.0305 0.0472 0.0632 0.188 0.208 0.000469 0.000521 + 293 118 0.0726 0.00261 0.0203 0.0456 0.061 0.14 0.17 0.00035 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0518 0.00225 0.00689 0.0416 0.0566 0.0952 0.0991 0.000238 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 293 49106.033 0.005 0.00276 0.0252 0.0804 0.0468 0.0628 0.151 0.189 0.000379 0.000474 +! Validation 293 49106.033 0.005 0.00314 0.0109 0.0738 0.049 0.0669 0.0967 0.125 0.000242 0.000312 +Wall time: 49106.03352235211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.077 0.00277 0.0217 0.0475 0.0628 0.154 0.176 0.000386 0.000439 + 294 118 0.0637 0.00272 0.00941 0.0462 0.0622 0.0891 0.116 0.000223 0.000289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.0608 0.0021 0.0188 0.0401 0.0547 0.162 0.164 0.000404 0.000409 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 294 49273.289 0.005 0.0029 0.0379 0.0959 0.048 0.0643 0.187 0.233 0.000468 0.000583 +! Validation 294 49273.289 0.005 0.00305 0.0223 0.0832 0.0482 0.0659 0.151 0.178 0.000378 0.000446 +Wall time: 49273.28914472135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.0676 0.00269 0.0138 0.0461 0.0619 0.115 0.14 0.000287 0.000351 + 295 118 0.0704 0.00265 0.0174 0.0464 0.0615 0.145 0.157 0.000362 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.0544 0.00211 0.0121 0.0404 0.0549 0.13 0.132 0.000324 0.000329 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 295 49440.543 0.005 0.00276 0.0218 0.077 0.0468 0.0627 0.143 0.176 0.000358 0.000441 +! Validation 295 49440.543 0.005 0.00303 0.0177 0.0783 0.0482 0.0657 0.131 0.159 0.000326 0.000397 +Wall time: 49440.54395594122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.0759 0.0028 0.0198 0.0474 0.0632 0.13 0.168 0.000325 0.00042 + 296 118 0.0826 0.00292 0.0243 0.0483 0.0645 0.162 0.186 0.000404 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.0717 0.00219 0.0279 0.0412 0.0559 0.196 0.199 0.000491 0.000498 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 296 49607.909 0.005 0.00286 0.0377 0.0949 0.0477 0.0639 0.179 0.232 0.000448 0.00058 +! Validation 296 49607.909 0.005 0.00305 0.0487 0.11 0.0482 0.0659 0.236 0.263 0.000589 0.000658 +Wall time: 49607.909074551426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.0844 0.00312 0.0219 0.0502 0.0667 0.157 0.177 0.000393 0.000442 + 297 118 0.148 0.00283 0.0918 0.048 0.0634 0.35 0.362 0.000876 0.000904 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.124 0.00237 0.0762 0.0431 0.0581 0.329 0.329 0.000822 0.000824 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 297 49775.263 0.005 0.00276 0.0298 0.085 0.0468 0.0627 0.166 0.204 0.000416 0.000511 +! Validation 297 49775.263 0.005 0.00327 0.0904 0.156 0.0502 0.0683 0.334 0.359 0.000836 0.000897 +Wall time: 49775.26359473402 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0778 0.00291 0.0196 0.0483 0.0643 0.15 0.167 0.000374 0.000418 + 298 118 0.0652 0.00277 0.00974 0.0469 0.0628 0.106 0.118 0.000265 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0529 0.00219 0.00901 0.0413 0.0559 0.111 0.113 0.000278 0.000283 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 298 49942.530 0.005 0.00298 0.0414 0.101 0.0489 0.0652 0.194 0.243 0.000485 0.000609 +! Validation 298 49942.530 0.005 0.00309 0.0175 0.0793 0.0487 0.0664 0.129 0.158 0.000323 0.000394 +Wall time: 49942.530039455276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0646 0.0028 0.00859 0.0468 0.0632 0.0887 0.111 0.000222 0.000277 + 299 118 0.109 0.00327 0.0434 0.0513 0.0682 0.232 0.249 0.00058 0.000622 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0481 0.00237 0.00067 0.0434 0.0581 0.0283 0.0309 7.07e-05 7.72e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 299 50109.814 0.005 0.00272 0.0253 0.0797 0.0464 0.0622 0.147 0.189 0.000367 0.000474 +! Validation 299 50109.814 0.005 0.0033 0.0199 0.086 0.0505 0.0686 0.123 0.169 0.000308 0.000421 +Wall time: 50109.81484296918 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.0653 0.00267 0.0118 0.0462 0.0617 0.104 0.13 0.00026 0.000324 + 300 118 0.0573 0.00267 0.0038 0.0467 0.0617 0.0562 0.0735 0.000141 0.000184 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.06 0.00212 0.0176 0.0409 0.055 0.156 0.158 0.000391 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 300 50277.070 0.005 0.00279 0.0293 0.0852 0.0471 0.0631 0.167 0.205 0.000418 0.000512 +! Validation 300 50277.070 0.005 0.00305 0.0207 0.0818 0.0486 0.066 0.146 0.172 0.000364 0.000429 +Wall time: 50277.070365081076 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.0573 0.00259 0.00545 0.0455 0.0608 0.0695 0.0881 0.000174 0.00022 + 301 118 0.136 0.00254 0.0854 0.0452 0.0602 0.343 0.349 0.000858 0.000872 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.129 0.00212 0.0865 0.0408 0.0549 0.35 0.351 0.000876 0.000878 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 301 50444.309 0.005 0.00274 0.0229 0.0776 0.0466 0.0624 0.145 0.179 0.000362 0.000448 +! Validation 301 50444.309 0.005 0.00302 0.102 0.163 0.0483 0.0656 0.361 0.382 0.000903 0.000955 +Wall time: 50444.30924368324 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.063 0.00282 0.0065 0.0473 0.0634 0.0765 0.0962 0.000191 0.000241 + 302 118 0.0701 0.00286 0.0128 0.0476 0.0639 0.104 0.135 0.00026 0.000338 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.0639 0.00204 0.023 0.0398 0.0539 0.178 0.181 0.000446 0.000453 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 302 50612.085 0.005 0.00275 0.0324 0.0873 0.0467 0.0625 0.17 0.215 0.000425 0.000538 +! Validation 302 50612.085 0.005 0.00295 0.0385 0.0975 0.0474 0.0648 0.203 0.234 0.000507 0.000585 +Wall time: 50612.08601517929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0776 0.00261 0.0254 0.0457 0.0609 0.168 0.19 0.00042 0.000476 + 303 118 0.0636 0.00252 0.0132 0.0457 0.0599 0.119 0.137 0.000298 0.000342 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0486 0.00202 0.00821 0.0396 0.0537 0.106 0.108 0.000264 0.00027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 303 50779.534 0.005 0.00274 0.0295 0.0843 0.0467 0.0625 0.16 0.205 0.000399 0.000514 +! Validation 303 50779.534 0.005 0.00296 0.0155 0.0748 0.0476 0.065 0.121 0.149 0.000302 0.000372 +Wall time: 50779.53466604743 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.181 0.00369 0.107 0.054 0.0725 0.366 0.391 0.000915 0.000978 + 304 118 0.0741 0.00294 0.0153 0.0485 0.0647 0.117 0.148 0.000293 0.000369 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.0435 0.00209 0.00163 0.0405 0.0546 0.0404 0.0482 0.000101 0.00012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 304 50947.055 0.005 0.00277 0.0271 0.0825 0.0468 0.0628 0.156 0.197 0.000389 0.000492 +! Validation 304 50947.055 0.005 0.00305 0.0108 0.0718 0.0484 0.0659 0.0972 0.124 0.000243 0.000311 +Wall time: 50947.0558908754 +! Best model 304 0.072 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0728 0.00253 0.0222 0.0446 0.06 0.159 0.178 0.000398 0.000445 + 305 118 0.061 0.00284 0.00423 0.0475 0.0636 0.0618 0.0776 0.000154 0.000194 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0473 0.00209 0.00541 0.0403 0.0546 0.0846 0.0878 0.000212 0.00022 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 305 51115.376 0.005 0.0027 0.0289 0.0829 0.0463 0.062 0.166 0.203 0.000414 0.000509 +! Validation 305 51115.376 0.005 0.00299 0.0169 0.0767 0.0478 0.0653 0.124 0.155 0.00031 0.000388 +Wall time: 51115.37665219605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.0782 0.00262 0.0259 0.0456 0.061 0.172 0.192 0.000431 0.00048 + 306 118 0.105 0.00288 0.0476 0.0476 0.0641 0.231 0.261 0.000577 0.000651 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.0633 0.0022 0.0193 0.0417 0.056 0.163 0.166 0.000407 0.000414 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 306 51282.641 0.005 0.00262 0.0241 0.0765 0.0456 0.0611 0.141 0.185 0.000354 0.000462 +! Validation 306 51282.641 0.005 0.00312 0.0327 0.095 0.0491 0.0666 0.186 0.216 0.000466 0.00054 +Wall time: 51282.641400039196 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.0609 0.00261 0.0088 0.0455 0.0609 0.0865 0.112 0.000216 0.00028 + 307 118 0.0576 0.00254 0.00693 0.0451 0.0601 0.0795 0.0993 0.000199 0.000248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.0403 0.00199 0.000469 0.0392 0.0533 0.0238 0.0258 5.96e-05 6.46e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 307 51451.369 0.005 0.00266 0.0249 0.0781 0.046 0.0616 0.152 0.189 0.00038 0.000472 +! Validation 307 51451.369 0.005 0.00283 0.0115 0.0681 0.0464 0.0635 0.1 0.128 0.00025 0.00032 +Wall time: 51451.369304433 +! Best model 307 0.068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.0957 0.0031 0.0336 0.0502 0.0665 0.2 0.219 0.0005 0.000547 + 308 118 0.0773 0.00273 0.0226 0.0465 0.0624 0.171 0.179 0.000427 0.000448 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.133 0.002 0.0926 0.0397 0.0533 0.362 0.363 0.000905 0.000908 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 308 51618.763 0.005 0.00266 0.0256 0.0788 0.046 0.0616 0.152 0.191 0.000379 0.000477 +! Validation 308 51618.763 0.005 0.0029 0.0968 0.155 0.0472 0.0643 0.345 0.371 0.000862 0.000928 +Wall time: 51618.7634622762 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.0639 0.00268 0.0102 0.0462 0.0618 0.0934 0.121 0.000233 0.000302 + 309 118 0.0733 0.00285 0.0163 0.0474 0.0637 0.12 0.152 0.000301 0.000381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.124 0.00212 0.0811 0.0405 0.055 0.339 0.34 0.000848 0.00085 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 309 51786.110 0.005 0.00276 0.0326 0.0878 0.0468 0.0627 0.175 0.216 0.000438 0.00054 +! Validation 309 51786.110 0.005 0.00298 0.0751 0.135 0.0478 0.0652 0.301 0.327 0.000752 0.000818 +Wall time: 51786.11101319501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0603 0.00239 0.0124 0.0437 0.0584 0.114 0.133 0.000284 0.000332 + 310 118 0.0863 0.00234 0.0395 0.0437 0.0577 0.23 0.237 0.000575 0.000593 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0502 0.002 0.0103 0.0391 0.0533 0.119 0.121 0.000298 0.000303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 310 51953.437 0.005 0.00261 0.0231 0.0753 0.0455 0.061 0.146 0.181 0.000366 0.000452 +! Validation 310 51953.437 0.005 0.00283 0.0263 0.0829 0.0464 0.0635 0.163 0.194 0.000406 0.000484 +Wall time: 51953.4370996044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.0623 0.00249 0.0125 0.0445 0.0596 0.107 0.133 0.000269 0.000333 + 311 118 0.0698 0.00299 0.00999 0.049 0.0653 0.0992 0.119 0.000248 0.000298 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.117 0.00201 0.0766 0.0396 0.0535 0.33 0.33 0.000824 0.000826 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 311 52120.770 0.005 0.00255 0.0228 0.0737 0.0449 0.0602 0.145 0.181 0.000362 0.000451 +! Validation 311 52120.770 0.005 0.00288 0.0693 0.127 0.047 0.064 0.288 0.314 0.00072 0.000786 +Wall time: 52120.770508140326 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0823 0.00256 0.0311 0.0452 0.0604 0.199 0.21 0.000499 0.000526 + 312 118 0.0817 0.00282 0.0252 0.0468 0.0634 0.165 0.189 0.000412 0.000474 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0889 0.00193 0.0503 0.0387 0.0524 0.267 0.268 0.000667 0.000669 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 312 52288.201 0.005 0.00266 0.0297 0.0828 0.0459 0.0615 0.167 0.206 0.000418 0.000514 +! Validation 312 52288.201 0.005 0.00279 0.0695 0.125 0.046 0.063 0.292 0.315 0.000729 0.000786 +Wall time: 52288.20114276605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0543 0.00229 0.00842 0.0429 0.0572 0.0888 0.11 0.000222 0.000274 + 313 118 0.0851 0.00227 0.0398 0.0431 0.0568 0.228 0.238 0.00057 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0663 0.00218 0.0226 0.0415 0.0558 0.178 0.179 0.000444 0.000448 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 313 52455.523 0.005 0.00268 0.0281 0.0817 0.0461 0.0618 0.159 0.2 0.000397 0.0005 +! Validation 313 52455.523 0.005 0.00303 0.0207 0.0812 0.0484 0.0657 0.144 0.172 0.000359 0.000429 +Wall time: 52455.523159111384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.146 0.00261 0.0939 0.0455 0.061 0.353 0.366 0.000882 0.000914 + 314 118 0.114 0.00369 0.0404 0.0545 0.0725 0.19 0.24 0.000475 0.000599 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.0495 0.00233 0.00287 0.0431 0.0576 0.0597 0.0639 0.000149 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 314 52622.838 0.005 0.00258 0.0313 0.083 0.0452 0.0606 0.163 0.211 0.000408 0.000528 +! Validation 314 52622.838 0.005 0.00321 0.0137 0.078 0.0504 0.0677 0.111 0.14 0.000277 0.000349 +Wall time: 52622.838325973134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0536 0.00224 0.00884 0.0424 0.0565 0.0953 0.112 0.000238 0.000281 + 315 118 0.0798 0.0027 0.0259 0.0461 0.062 0.172 0.192 0.000429 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0407 0.00198 0.00114 0.0395 0.0531 0.0354 0.0402 8.85e-05 0.000101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 315 52790.184 0.005 0.00259 0.0182 0.0701 0.0453 0.0608 0.127 0.161 0.000318 0.000402 +! Validation 315 52790.184 0.005 0.00283 0.0119 0.0686 0.0467 0.0635 0.103 0.13 0.000257 0.000326 +Wall time: 52790.18431021832 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0535 0.00226 0.00827 0.0424 0.0568 0.0897 0.109 0.000224 0.000271 + 316 118 0.0677 0.00294 0.00895 0.0474 0.0647 0.0898 0.113 0.000224 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0961 0.00197 0.0567 0.039 0.0529 0.282 0.284 0.000706 0.000711 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 316 52957.655 0.005 0.00249 0.0208 0.0706 0.0444 0.0595 0.14 0.172 0.000351 0.000431 +! Validation 316 52957.655 0.005 0.00277 0.0804 0.136 0.0459 0.0628 0.317 0.338 0.000793 0.000846 +Wall time: 52957.65511579998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0539 0.00235 0.00689 0.0432 0.0578 0.0814 0.0991 0.000203 0.000248 + 317 118 0.0718 0.00247 0.0224 0.0448 0.0594 0.165 0.179 0.000413 0.000446 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0389 0.00192 0.000555 0.0385 0.0523 0.0215 0.0281 5.38e-05 7.03e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 317 53125.049 0.005 0.00276 0.0316 0.0868 0.0466 0.0627 0.16 0.213 0.000399 0.000531 +! Validation 317 53125.049 0.005 0.0028 0.0143 0.0704 0.0463 0.0632 0.112 0.143 0.000281 0.000357 +Wall time: 53125.04906721506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.077 0.00252 0.0265 0.0449 0.06 0.16 0.194 0.000401 0.000486 + 318 118 0.0576 0.00249 0.00767 0.0445 0.0596 0.0807 0.105 0.000202 0.000261 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0633 0.00185 0.0263 0.038 0.0514 0.192 0.193 0.000479 0.000484 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 318 53292.433 0.005 0.0025 0.0238 0.0738 0.0446 0.0597 0.149 0.184 0.000372 0.000461 +! Validation 318 53292.433 0.005 0.00273 0.0299 0.0844 0.0456 0.0623 0.18 0.206 0.00045 0.000516 +Wall time: 53292.43334579328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.0522 0.00232 0.00574 0.0428 0.0575 0.0705 0.0904 0.000176 0.000226 + 319 118 0.0523 0.00211 0.0101 0.0411 0.0549 0.106 0.12 0.000264 0.0003 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.0375 0.00181 0.00138 0.0374 0.0507 0.0392 0.0444 9.81e-05 0.000111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 319 53459.816 0.005 0.00262 0.0311 0.0834 0.0456 0.0611 0.163 0.211 0.000407 0.000528 +! Validation 319 53459.816 0.005 0.00268 0.0122 0.0658 0.0451 0.0618 0.103 0.132 0.000258 0.000329 +Wall time: 53459.81624623621 +! Best model 319 0.066 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0666 0.00256 0.0154 0.0449 0.0604 0.119 0.148 0.000297 0.00037 + 320 118 0.113 0.00281 0.0565 0.047 0.0633 0.265 0.284 0.000663 0.000709 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0876 0.00193 0.0491 0.0387 0.0524 0.264 0.265 0.00066 0.000661 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 320 53627.217 0.005 0.00244 0.0176 0.0664 0.0439 0.0589 0.127 0.157 0.000317 0.000393 +! Validation 320 53627.217 0.005 0.00276 0.0655 0.121 0.0459 0.0627 0.284 0.305 0.000709 0.000764 +Wall time: 53627.21735367132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0945 0.00296 0.0353 0.0483 0.065 0.192 0.224 0.00048 0.00056 + 321 118 0.0605 0.00269 0.00662 0.0463 0.0619 0.0821 0.0971 0.000205 0.000243 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.043 0.00199 0.00321 0.0394 0.0532 0.0615 0.0676 0.000154 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 321 53794.648 0.005 0.00258 0.031 0.0826 0.0453 0.0607 0.165 0.211 0.000412 0.000526 +! Validation 321 53794.648 0.005 0.00281 0.0132 0.0695 0.0465 0.0633 0.109 0.137 0.000272 0.000343 +Wall time: 53794.64884055313 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0745 0.00228 0.0289 0.0429 0.057 0.179 0.203 0.000446 0.000508 + 322 118 0.0789 0.0023 0.0329 0.0431 0.0573 0.189 0.216 0.000474 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0516 0.00207 0.0102 0.0403 0.0543 0.117 0.12 0.000293 0.000301 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 322 53961.728 0.005 0.00249 0.0258 0.0757 0.0446 0.0596 0.156 0.192 0.00039 0.000479 +! Validation 322 53961.728 0.005 0.00285 0.0184 0.0753 0.0469 0.0637 0.133 0.162 0.000333 0.000404 +Wall time: 53961.7280065543 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.065 0.00239 0.0172 0.0434 0.0584 0.131 0.157 0.000329 0.000391 + 323 118 0.051 0.00231 0.00475 0.0429 0.0574 0.0647 0.0822 0.000162 0.000206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.0372 0.00183 0.000662 0.0377 0.051 0.0265 0.0307 6.63e-05 7.68e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 323 54128.995 0.005 0.00243 0.0188 0.0674 0.044 0.0589 0.133 0.164 0.000333 0.00041 +! Validation 323 54128.995 0.005 0.00264 0.0105 0.0633 0.0448 0.0613 0.0966 0.122 0.000241 0.000306 +Wall time: 54128.99588276632 +! Best model 323 0.063 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0598 0.00242 0.0115 0.0437 0.0587 0.104 0.128 0.000261 0.00032 + 324 118 0.083 0.00279 0.0272 0.0462 0.0631 0.168 0.197 0.000421 0.000492 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0412 0.00202 0.000761 0.0396 0.0536 0.0285 0.0329 7.13e-05 8.23e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 324 54296.081 0.005 0.00238 0.0187 0.0663 0.0434 0.0582 0.13 0.163 0.000325 0.000408 +! Validation 324 54296.081 0.005 0.00284 0.0104 0.0671 0.0467 0.0636 0.0953 0.122 0.000238 0.000304 +Wall time: 54296.08136485331 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 0.0482 0.00212 0.00589 0.0413 0.0549 0.0707 0.0916 0.000177 0.000229 + 325 118 0.0526 0.00232 0.0062 0.0431 0.0575 0.071 0.094 0.000177 0.000235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 0.0367 0.00179 0.00101 0.0373 0.0505 0.0287 0.038 7.19e-05 9.49e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 325 54463.164 0.005 0.00252 0.0227 0.0731 0.0447 0.0599 0.14 0.18 0.00035 0.000451 +! Validation 325 54463.164 0.005 0.00259 0.00942 0.0612 0.0444 0.0607 0.0894 0.116 0.000224 0.00029 +Wall time: 54463.16430546623 +! Best model 325 0.061 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 0.172 0.00236 0.125 0.0436 0.058 0.413 0.422 0.00103 0.00106 + 326 118 0.0723 0.00315 0.00927 0.0509 0.067 0.084 0.115 0.00021 0.000287 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 0.0493 0.00235 0.00232 0.0421 0.0578 0.0481 0.0575 0.00012 0.000144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 326 54630.323 0.005 0.00246 0.0271 0.0764 0.0441 0.0592 0.153 0.197 0.000381 0.000493 +! Validation 326 54630.323 0.005 0.00311 0.0108 0.073 0.0489 0.0666 0.0964 0.124 0.000241 0.000309 +Wall time: 54630.32307244139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 0.0559 0.00224 0.0111 0.0422 0.0565 0.102 0.126 0.000254 0.000315 + 327 118 0.0669 0.00229 0.0212 0.0421 0.0571 0.151 0.174 0.000378 0.000434 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 0.0417 0.0019 0.00376 0.0389 0.052 0.0703 0.0731 0.000176 0.000183 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 327 54797.381 0.005 0.0024 0.0196 0.0675 0.0436 0.0584 0.133 0.167 0.000331 0.000418 +! Validation 327 54797.381 0.005 0.00277 0.0115 0.0669 0.046 0.0628 0.101 0.128 0.000253 0.000321 +Wall time: 54797.38111921307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.0798 0.00224 0.035 0.0424 0.0565 0.211 0.223 0.000526 0.000559 + 328 118 0.0659 0.00261 0.0137 0.0448 0.061 0.0949 0.139 0.000237 0.000349 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.0358 0.00177 0.000427 0.0371 0.0502 0.0232 0.0247 5.81e-05 6.17e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 328 54964.462 0.005 0.00243 0.0222 0.0707 0.0438 0.0588 0.14 0.178 0.00035 0.000445 +! Validation 328 54964.462 0.005 0.00255 0.0103 0.0612 0.044 0.0602 0.0956 0.121 0.000239 0.000303 +Wall time: 54964.462285497226 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.0622 0.00231 0.0161 0.0426 0.0573 0.126 0.151 0.000314 0.000378 + 329 118 0.0694 0.00237 0.0221 0.0434 0.0581 0.157 0.177 0.000393 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.0691 0.0019 0.031 0.0387 0.0521 0.208 0.21 0.00052 0.000525 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 329 55131.530 0.005 0.0023 0.0136 0.0597 0.0427 0.0573 0.111 0.139 0.000278 0.000348 +! Validation 329 55131.530 0.005 0.00268 0.0544 0.108 0.0454 0.0618 0.254 0.278 0.000636 0.000696 +Wall time: 55131.53076200513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.0852 0.00239 0.0375 0.0435 0.0583 0.211 0.231 0.000529 0.000578 + 330 118 0.0608 0.00217 0.0173 0.0415 0.0557 0.143 0.157 0.000356 0.000392 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.0555 0.00179 0.0196 0.037 0.0505 0.166 0.167 0.000414 0.000418 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 330 55298.613 0.005 0.00258 0.0303 0.082 0.0452 0.0607 0.157 0.208 0.000393 0.000521 +! Validation 330 55298.613 0.005 0.00255 0.0337 0.0847 0.0441 0.0603 0.192 0.219 0.00048 0.000548 +Wall time: 55298.613438447006 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.0706 0.00297 0.0113 0.0493 0.065 0.102 0.127 0.000256 0.000317 + 331 118 0.0553 0.00254 0.00458 0.0445 0.0601 0.0701 0.0808 0.000175 0.000202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.0485 0.00197 0.00916 0.0394 0.053 0.109 0.114 0.000273 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 331 55465.761 0.005 0.00244 0.028 0.0767 0.044 0.0589 0.16 0.2 0.0004 0.0005 +! Validation 331 55465.761 0.005 0.00271 0.0193 0.0734 0.0458 0.0621 0.139 0.166 0.000347 0.000414 +Wall time: 55465.76118066907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.0634 0.00232 0.017 0.0431 0.0575 0.136 0.155 0.000341 0.000389 + 332 118 0.0562 0.0024 0.00809 0.0439 0.0585 0.0958 0.107 0.00024 0.000268 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.0484 0.0017 0.0143 0.0366 0.0493 0.141 0.143 0.000354 0.000357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 332 55632.993 0.005 0.00239 0.0226 0.0703 0.0435 0.0583 0.136 0.18 0.00034 0.000449 +! Validation 332 55632.993 0.005 0.00254 0.0276 0.0785 0.044 0.0602 0.172 0.198 0.000429 0.000496 +Wall time: 55632.99316800106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.0786 0.00229 0.0327 0.0429 0.0571 0.202 0.216 0.000506 0.00054 + 333 118 0.0678 0.00269 0.014 0.0458 0.0619 0.103 0.141 0.000257 0.000353 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.0365 0.00174 0.00178 0.0369 0.0497 0.0449 0.0503 0.000112 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 333 55800.062 0.005 0.00242 0.0305 0.0789 0.0439 0.0587 0.17 0.209 0.000426 0.000522 +! Validation 333 55800.062 0.005 0.00251 0.013 0.0632 0.0438 0.0598 0.108 0.136 0.000271 0.00034 +Wall time: 55800.0619708281 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.0509 0.00221 0.00658 0.042 0.0562 0.0798 0.0968 0.000199 0.000242 + 334 118 0.0766 0.00225 0.0316 0.0425 0.0566 0.203 0.212 0.000506 0.00053 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.0414 0.0019 0.0034 0.0382 0.052 0.0668 0.0695 0.000167 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 334 55967.130 0.005 0.00229 0.0175 0.0634 0.0427 0.0572 0.128 0.158 0.000319 0.000394 +! Validation 334 55967.130 0.005 0.00264 0.0187 0.0716 0.045 0.0614 0.13 0.163 0.000324 0.000408 +Wall time: 55967.13035213342 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.0582 0.00248 0.00857 0.0438 0.0594 0.0927 0.11 0.000232 0.000276 + 335 118 0.0505 0.00214 0.00764 0.0414 0.0552 0.0811 0.104 0.000203 0.000261 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.0403 0.00168 0.00671 0.036 0.0489 0.0958 0.0978 0.00024 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 335 56135.193 0.005 0.00233 0.0206 0.0673 0.0431 0.0577 0.134 0.172 0.000334 0.000429 +! Validation 335 56135.193 0.005 0.00244 0.0162 0.065 0.043 0.059 0.126 0.152 0.000314 0.000379 +Wall time: 56135.19370278437 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.0955 0.0024 0.0476 0.0436 0.0584 0.247 0.26 0.000618 0.000651 + 336 118 0.149 0.00301 0.0891 0.0495 0.0655 0.341 0.356 0.000853 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.0473 0.00228 0.00179 0.0432 0.0569 0.0443 0.0505 0.000111 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 336 56302.273 0.005 0.00235 0.032 0.079 0.0432 0.0578 0.173 0.212 0.000433 0.00053 +! Validation 336 56302.273 0.005 0.00307 0.0163 0.0776 0.0496 0.0661 0.118 0.152 0.000296 0.000381 +Wall time: 56302.27332799137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.0478 0.00212 0.00542 0.0413 0.055 0.0698 0.0879 0.000175 0.00022 + 337 118 0.0808 0.00238 0.0332 0.0429 0.0582 0.191 0.217 0.000476 0.000544 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.0434 0.00197 0.00405 0.0388 0.053 0.0737 0.076 0.000184 0.00019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 337 56469.534 0.005 0.00236 0.0202 0.0673 0.0432 0.0579 0.136 0.169 0.000339 0.000423 +! Validation 337 56469.534 0.005 0.00275 0.0132 0.0683 0.0459 0.0626 0.111 0.137 0.000276 0.000342 +Wall time: 56469.53475977527 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.0487 0.00215 0.00575 0.0412 0.0553 0.0719 0.0905 0.00018 0.000226 + 338 118 0.0851 0.00227 0.0398 0.0429 0.0568 0.209 0.238 0.000523 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.16 0.00199 0.12 0.04 0.0533 0.411 0.413 0.00103 0.00103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 338 56636.826 0.005 0.00228 0.0205 0.0661 0.0425 0.057 0.136 0.171 0.000339 0.000426 +! Validation 338 56636.826 0.005 0.00273 0.132 0.187 0.046 0.0624 0.419 0.434 0.00105 0.00109 +Wall time: 56636.82605886506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.0735 0.00232 0.0271 0.0427 0.0575 0.178 0.196 0.000446 0.000491 + 339 118 0.0612 0.00254 0.0105 0.0445 0.0601 0.0906 0.122 0.000226 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.0444 0.00179 0.00852 0.0372 0.0505 0.107 0.11 0.000267 0.000275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 339 56804.095 0.005 0.0024 0.0214 0.0695 0.0436 0.0585 0.14 0.175 0.000349 0.000437 +! Validation 339 56804.095 0.005 0.00254 0.0278 0.0786 0.044 0.0601 0.16 0.199 0.0004 0.000497 +Wall time: 56804.095140899066 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.05 0.0022 0.00588 0.0421 0.056 0.0729 0.0915 0.000182 0.000229 + 340 118 0.0451 0.00192 0.0067 0.039 0.0523 0.0861 0.0977 0.000215 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.0355 0.00173 0.000927 0.0367 0.0496 0.0285 0.0363 7.13e-05 9.08e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 340 56971.440 0.005 0.0024 0.0301 0.0782 0.0438 0.0585 0.16 0.208 0.0004 0.000519 +! Validation 340 56971.440 0.005 0.00247 0.0122 0.0616 0.0433 0.0593 0.103 0.132 0.000258 0.00033 +Wall time: 56971.44086865522 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.069 0.00226 0.0238 0.0422 0.0567 0.15 0.184 0.000375 0.00046 + 341 118 0.0603 0.00224 0.0156 0.0423 0.0565 0.133 0.149 0.000332 0.000372 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.0544 0.00181 0.0181 0.0378 0.0508 0.158 0.161 0.000396 0.000402 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 341 57138.703 0.005 0.00224 0.016 0.0609 0.0422 0.0565 0.119 0.151 0.000297 0.000377 +! Validation 341 57138.703 0.005 0.00252 0.0195 0.07 0.044 0.0599 0.142 0.167 0.000356 0.000417 +Wall time: 57138.7035633754 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0454 0.00204 0.00449 0.0404 0.054 0.0658 0.08 0.000165 0.0002 + 342 118 0.058 0.00239 0.0102 0.0428 0.0584 0.0999 0.121 0.00025 0.000302 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0401 0.00166 0.00693 0.0359 0.0486 0.0971 0.0993 0.000243 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 342 57305.976 0.005 0.00221 0.017 0.0612 0.0419 0.0561 0.125 0.156 0.000312 0.00039 +! Validation 342 57305.976 0.005 0.00242 0.0202 0.0686 0.0429 0.0587 0.137 0.17 0.000342 0.000424 +Wall time: 57305.97629727097 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.0488 0.00219 0.00496 0.0418 0.0559 0.0652 0.0841 0.000163 0.00021 + 343 118 0.0478 0.00213 0.0053 0.0418 0.055 0.0635 0.0869 0.000159 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.0401 0.00169 0.00623 0.0361 0.0491 0.0915 0.0942 0.000229 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 343 57473.260 0.005 0.00226 0.0271 0.0722 0.0423 0.0567 0.158 0.197 0.000394 0.000493 +! Validation 343 57473.260 0.005 0.00245 0.0129 0.062 0.0432 0.0591 0.111 0.136 0.000277 0.000339 +Wall time: 57473.2604618622 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.0515 0.0023 0.00556 0.0429 0.0572 0.0724 0.089 0.000181 0.000222 + 344 118 0.0624 0.00252 0.012 0.0446 0.0599 0.113 0.131 0.000283 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.0686 0.00177 0.0332 0.0374 0.0502 0.216 0.217 0.00054 0.000543 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 344 57640.711 0.005 0.00235 0.0323 0.0793 0.0433 0.0579 0.172 0.215 0.000431 0.000537 +! Validation 344 57640.711 0.005 0.00251 0.044 0.0941 0.0438 0.0598 0.229 0.25 0.000571 0.000626 +Wall time: 57640.71107348427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.0574 0.00233 0.0108 0.0429 0.0576 0.105 0.124 0.000263 0.00031 + 345 118 0.0467 0.00213 0.00419 0.0409 0.0551 0.0611 0.0772 0.000153 0.000193 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.0333 0.00164 0.000579 0.0357 0.0483 0.0268 0.0287 6.69e-05 7.18e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 345 57808.062 0.005 0.00217 0.0145 0.058 0.0415 0.0556 0.116 0.144 0.000291 0.00036 +! Validation 345 57808.062 0.005 0.00238 0.00829 0.056 0.0425 0.0583 0.0848 0.109 0.000212 0.000272 +Wall time: 57808.062660656404 +! Best model 345 0.056 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.0448 0.00206 0.00366 0.0405 0.0541 0.0604 0.0722 0.000151 0.000181 + 346 118 0.0572 0.0023 0.0111 0.043 0.0573 0.0948 0.126 0.000237 0.000315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.0353 0.00158 0.00381 0.0351 0.0474 0.071 0.0736 0.000178 0.000184 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 346 57975.364 0.005 0.00231 0.0231 0.0694 0.0428 0.0574 0.135 0.182 0.000338 0.000454 +! Validation 346 57975.364 0.005 0.00234 0.0116 0.0585 0.0421 0.0578 0.104 0.129 0.000261 0.000322 +Wall time: 57975.36411345098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.051 0.00224 0.00618 0.0426 0.0565 0.0734 0.0938 0.000183 0.000235 + 347 118 0.0489 0.00214 0.00607 0.0415 0.0552 0.0776 0.093 0.000194 0.000233 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.0362 0.00175 0.00113 0.0368 0.05 0.0366 0.0401 9.15e-05 0.0001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 347 58142.625 0.005 0.00222 0.0196 0.0641 0.042 0.0563 0.136 0.168 0.00034 0.000419 +! Validation 347 58142.625 0.005 0.00242 0.00916 0.0575 0.043 0.0587 0.0885 0.114 0.000221 0.000286 +Wall time: 58142.6255869302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.0761 0.00228 0.0306 0.0425 0.0569 0.185 0.209 0.000463 0.000522 + 348 118 0.0837 0.0022 0.0398 0.0424 0.0559 0.223 0.238 0.000556 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.079 0.00175 0.044 0.0373 0.0499 0.249 0.25 0.000623 0.000626 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 348 58309.888 0.005 0.00221 0.0254 0.0695 0.0419 0.0561 0.155 0.19 0.000388 0.000475 +! Validation 348 58309.888 0.005 0.00248 0.0492 0.0988 0.0437 0.0594 0.242 0.265 0.000605 0.000662 +Wall time: 58309.88902788516 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0505 0.00214 0.00767 0.041 0.0552 0.0864 0.105 0.000216 0.000261 + 349 118 0.0674 0.00238 0.0198 0.0433 0.0582 0.149 0.168 0.000374 0.00042 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0903 0.00165 0.0572 0.0355 0.0485 0.285 0.286 0.000713 0.000714 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 349 58477.134 0.005 0.00217 0.0174 0.0608 0.0416 0.0556 0.124 0.157 0.000311 0.000393 +! Validation 349 58477.134 0.005 0.00239 0.0684 0.116 0.0425 0.0583 0.292 0.312 0.00073 0.00078 +Wall time: 58477.1341531612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.0605 0.00239 0.0127 0.0436 0.0584 0.106 0.134 0.000265 0.000336 + 350 118 0.11 0.0022 0.0657 0.0423 0.056 0.283 0.306 0.000708 0.000765 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.202 0.00171 0.168 0.0368 0.0494 0.488 0.489 0.00122 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 350 58645.021 0.005 0.00217 0.0209 0.0643 0.0415 0.0556 0.135 0.171 0.000338 0.000428 +! Validation 350 58645.021 0.005 0.00246 0.186 0.236 0.0436 0.0593 0.501 0.515 0.00125 0.00129 +Wall time: 58645.02194620529 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0814 0.00215 0.0383 0.0413 0.0554 0.22 0.234 0.00055 0.000584 + 351 118 0.0432 0.00173 0.0085 0.0375 0.0497 0.101 0.11 0.000251 0.000275 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0337 0.00166 0.000517 0.0358 0.0486 0.0202 0.0271 5.06e-05 6.79e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 351 58812.296 0.005 0.00236 0.023 0.0703 0.0432 0.0581 0.138 0.182 0.000345 0.000454 +! Validation 351 58812.296 0.005 0.00234 0.0146 0.0614 0.0422 0.0578 0.106 0.144 0.000266 0.000361 +Wall time: 58812.29697465338 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0566 0.00216 0.0133 0.0416 0.0555 0.116 0.137 0.000289 0.000344 + 352 118 0.0552 0.0023 0.00912 0.0421 0.0573 0.0963 0.114 0.000241 0.000285 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0677 0.0018 0.0317 0.0372 0.0506 0.21 0.213 0.000525 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 352 58979.571 0.005 0.00215 0.0211 0.064 0.0413 0.0553 0.145 0.174 0.000362 0.000434 +! Validation 352 58979.571 0.005 0.00246 0.0358 0.0851 0.0435 0.0592 0.199 0.226 0.000496 0.000565 +Wall time: 58979.57189744804 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.069 0.00211 0.0267 0.0407 0.0549 0.181 0.195 0.000452 0.000488 + 353 118 0.0452 0.00203 0.0046 0.0405 0.0538 0.0653 0.081 0.000163 0.000202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.0328 0.00158 0.00126 0.0349 0.0474 0.0379 0.0423 9.46e-05 0.000106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 353 59146.834 0.005 0.00216 0.0178 0.061 0.0414 0.0555 0.126 0.159 0.000315 0.000399 +! Validation 353 59146.834 0.005 0.00228 0.00858 0.0542 0.0416 0.057 0.0857 0.111 0.000214 0.000276 +Wall time: 59146.83410271024 +! Best model 353 0.054 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0599 0.00224 0.015 0.0424 0.0565 0.126 0.146 0.000316 0.000366 + 354 118 0.0591 0.00265 0.00622 0.046 0.0614 0.0808 0.0941 0.000202 0.000235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0386 0.00162 0.00617 0.0358 0.0481 0.0911 0.0938 0.000228 0.000234 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 354 59314.481 0.005 0.00213 0.017 0.0596 0.041 0.055 0.119 0.156 0.000297 0.00039 +! Validation 354 59314.481 0.005 0.00237 0.0135 0.061 0.0427 0.0582 0.113 0.138 0.000282 0.000346 +Wall time: 59314.481210277416 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0831 0.00209 0.0414 0.0409 0.0545 0.222 0.243 0.000554 0.000607 + 355 118 0.0596 0.00192 0.0212 0.0393 0.0523 0.162 0.174 0.000405 0.000434 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0378 0.00168 0.00416 0.036 0.049 0.0745 0.077 0.000186 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 355 59481.555 0.005 0.00217 0.0242 0.0676 0.0416 0.0556 0.147 0.186 0.000367 0.000464 +! Validation 355 59481.555 0.005 0.00233 0.0145 0.061 0.0422 0.0576 0.116 0.144 0.000289 0.000359 +Wall time: 59481.55552888429 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0454 0.00204 0.00459 0.0401 0.0539 0.0616 0.0808 0.000154 0.000202 + 356 118 0.0456 0.00182 0.0093 0.0383 0.0509 0.102 0.115 0.000256 0.000288 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0466 0.00164 0.0137 0.0361 0.0484 0.139 0.14 0.000346 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 356 59648.630 0.005 0.00255 0.0271 0.0781 0.0447 0.0603 0.146 0.197 0.000364 0.000493 +! Validation 356 59648.630 0.005 0.00238 0.0182 0.0657 0.0428 0.0582 0.138 0.161 0.000345 0.000403 +Wall time: 59648.63005307317 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0476 0.00206 0.00647 0.0405 0.0542 0.0758 0.096 0.00019 0.00024 + 357 118 0.0473 0.00187 0.00979 0.0385 0.0517 0.1 0.118 0.00025 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0401 0.00154 0.00923 0.0346 0.0469 0.113 0.115 0.000284 0.000287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 357 59815.698 0.005 0.00207 0.0128 0.0542 0.0405 0.0543 0.11 0.135 0.000276 0.000338 +! Validation 357 59815.698 0.005 0.00223 0.0156 0.0603 0.0412 0.0564 0.126 0.149 0.000316 0.000373 +Wall time: 59815.698825838044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.067 0.00224 0.0221 0.0421 0.0565 0.164 0.177 0.000411 0.000444 + 358 118 0.0669 0.00211 0.0248 0.0411 0.0548 0.155 0.188 0.000387 0.00047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.0343 0.00167 0.00101 0.0364 0.0487 0.0335 0.038 8.37e-05 9.5e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 358 59982.769 0.005 0.00214 0.0246 0.0674 0.0413 0.0552 0.152 0.187 0.000381 0.000468 +! Validation 358 59982.769 0.005 0.00244 0.0157 0.0646 0.0434 0.059 0.115 0.15 0.000289 0.000374 +Wall time: 59982.769377996214 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0502 0.00211 0.00804 0.0408 0.0548 0.0854 0.107 0.000213 0.000268 + 359 118 0.0638 0.00243 0.0151 0.0437 0.0589 0.135 0.147 0.000338 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0336 0.00157 0.00218 0.0348 0.0473 0.0527 0.0557 0.000132 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 359 60149.931 0.005 0.00216 0.0222 0.0654 0.0414 0.0554 0.138 0.178 0.000345 0.000445 +! Validation 359 60149.931 0.005 0.0023 0.0115 0.0576 0.042 0.0573 0.101 0.128 0.000252 0.00032 +Wall time: 60149.93165967008 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0906 0.002 0.0506 0.0398 0.0534 0.257 0.269 0.000643 0.000672 + 360 118 0.112 0.00235 0.0645 0.0437 0.0579 0.289 0.303 0.000723 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0895 0.00164 0.0567 0.0358 0.0483 0.283 0.284 0.000708 0.000711 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 360 60317.026 0.005 0.00211 0.0229 0.0651 0.041 0.0548 0.142 0.179 0.000354 0.000448 +! Validation 360 60317.026 0.005 0.00236 0.0575 0.105 0.0425 0.058 0.264 0.286 0.000661 0.000716 +Wall time: 60317.02606905624 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0709 0.00197 0.0316 0.0394 0.0529 0.199 0.212 0.000498 0.000531 + 361 118 0.0744 0.0021 0.0324 0.0409 0.0547 0.179 0.215 0.000447 0.000537 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0571 0.00155 0.0261 0.0349 0.0469 0.192 0.193 0.000479 0.000482 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 361 60485.056 0.005 0.00206 0.016 0.0572 0.0405 0.0542 0.12 0.15 0.0003 0.000376 +! Validation 361 60485.056 0.005 0.00224 0.0343 0.079 0.0413 0.0564 0.194 0.221 0.000484 0.000553 +Wall time: 60485.05681521911 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0497 0.00217 0.00634 0.0415 0.0556 0.0747 0.095 0.000187 0.000238 + 362 118 0.0613 0.00172 0.0269 0.0375 0.0495 0.176 0.196 0.000441 0.000489 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0422 0.00171 0.00804 0.0364 0.0493 0.103 0.107 0.000258 0.000267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 362 60652.172 0.005 0.00212 0.0182 0.0606 0.0411 0.055 0.128 0.161 0.000319 0.000402 +! Validation 362 60652.172 0.005 0.00237 0.0158 0.0633 0.0428 0.0581 0.127 0.15 0.000317 0.000375 +Wall time: 60652.17219665134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.0471 0.00206 0.00589 0.0403 0.0542 0.074 0.0916 0.000185 0.000229 + 363 118 0.0399 0.00182 0.00356 0.038 0.0509 0.0612 0.0712 0.000153 0.000178 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.0453 0.0015 0.0153 0.0341 0.0462 0.147 0.148 0.000367 0.000369 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 363 60819.309 0.005 0.00229 0.0255 0.0713 0.0426 0.0572 0.14 0.191 0.00035 0.000478 +! Validation 363 60819.309 0.005 0.00219 0.0214 0.0653 0.0408 0.0559 0.15 0.175 0.000376 0.000436 +Wall time: 60819.30958537711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.0649 0.00206 0.0236 0.0405 0.0542 0.158 0.183 0.000395 0.000459 + 364 118 0.0754 0.00226 0.0302 0.0418 0.0568 0.179 0.207 0.000447 0.000518 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.0764 0.00169 0.0427 0.0365 0.049 0.245 0.247 0.000612 0.000616 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 364 60986.537 0.005 0.00204 0.017 0.0578 0.0402 0.0539 0.128 0.155 0.000319 0.000388 +! Validation 364 60986.537 0.005 0.00241 0.0559 0.104 0.0429 0.0586 0.263 0.282 0.000658 0.000706 +Wall time: 60986.537861155346 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.0525 0.00203 0.0119 0.0402 0.0538 0.107 0.13 0.000269 0.000326 + 365 118 0.0626 0.00203 0.0221 0.0399 0.0538 0.165 0.177 0.000412 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.113 0.00167 0.0799 0.0366 0.0487 0.337 0.337 0.000842 0.000844 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 365 61153.687 0.005 0.00211 0.0248 0.0669 0.041 0.0548 0.151 0.188 0.000378 0.00047 +! Validation 365 61153.687 0.005 0.00237 0.0791 0.127 0.0428 0.0582 0.32 0.336 0.0008 0.000839 +Wall time: 61153.6875461 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.0449 0.002 0.00488 0.0396 0.0534 0.0665 0.0834 0.000166 0.000208 + 366 118 0.0425 0.00196 0.00329 0.0394 0.0528 0.0581 0.0685 0.000145 0.000171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.0422 0.00146 0.0129 0.0336 0.0457 0.134 0.136 0.000336 0.000339 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 366 61320.836 0.005 0.00204 0.0148 0.0556 0.0403 0.0539 0.109 0.146 0.000272 0.000364 +! Validation 366 61320.836 0.005 0.00214 0.019 0.0619 0.0402 0.0552 0.133 0.165 0.000332 0.000412 +Wall time: 61320.83603031328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.079 0.0019 0.0409 0.0392 0.0521 0.229 0.241 0.000572 0.000603 + 367 118 0.0516 0.00221 0.00735 0.0409 0.0561 0.0852 0.102 0.000213 0.000256 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.0321 0.00157 0.000718 0.0353 0.0473 0.0235 0.032 5.87e-05 7.99e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 367 61488.158 0.005 0.00207 0.0229 0.0643 0.0406 0.0543 0.152 0.181 0.000379 0.000452 +! Validation 367 61488.158 0.005 0.00223 0.00806 0.0526 0.0413 0.0563 0.082 0.107 0.000205 0.000268 +Wall time: 61488.1584149641 +! Best model 367 0.053 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0629 0.00201 0.0226 0.0401 0.0535 0.159 0.18 0.000398 0.000449 + 368 118 0.0742 0.00191 0.036 0.0391 0.0522 0.215 0.226 0.000537 0.000566 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0719 0.00174 0.0371 0.0371 0.0497 0.229 0.23 0.000573 0.000575 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 368 61655.455 0.005 0.00204 0.0182 0.059 0.0402 0.0539 0.129 0.161 0.000322 0.000402 +! Validation 368 61655.455 0.005 0.00239 0.0665 0.114 0.0431 0.0583 0.259 0.308 0.000647 0.00077 +Wall time: 61655.45523726707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0415 0.00186 0.00432 0.0385 0.0514 0.0631 0.0784 0.000158 0.000196 + 369 118 0.121 0.0018 0.0848 0.0381 0.0506 0.341 0.348 0.000852 0.000869 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0594 0.00158 0.0278 0.0357 0.0474 0.198 0.199 0.000494 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 369 61822.819 0.005 0.00213 0.0247 0.0674 0.0412 0.0552 0.147 0.186 0.000367 0.000465 +! Validation 369 61822.819 0.005 0.0023 0.0592 0.105 0.0422 0.0572 0.244 0.29 0.000611 0.000726 +Wall time: 61822.81970582204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0466 0.00204 0.00575 0.0405 0.054 0.0709 0.0905 0.000177 0.000226 + 370 118 0.0491 0.00201 0.00885 0.0392 0.0536 0.102 0.112 0.000254 0.000281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0328 0.00157 0.00147 0.0347 0.0473 0.0387 0.0457 9.68e-05 0.000114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 370 61990.108 0.005 0.00206 0.0208 0.0621 0.0406 0.0542 0.137 0.173 0.000341 0.000431 +! Validation 370 61990.108 0.005 0.00222 0.00998 0.0543 0.0411 0.0562 0.0951 0.119 0.000238 0.000298 +Wall time: 61990.1081391722 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0553 0.00213 0.0128 0.0411 0.0551 0.118 0.135 0.000294 0.000337 + 371 118 0.0443 0.00179 0.00841 0.0375 0.0506 0.082 0.109 0.000205 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0347 0.00156 0.00357 0.0346 0.0471 0.0698 0.0713 0.000174 0.000178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 371 62157.397 0.005 0.00195 0.0109 0.0498 0.0393 0.0527 0.0984 0.125 0.000246 0.000312 +! Validation 371 62157.397 0.005 0.00218 0.0102 0.0537 0.0408 0.0557 0.0937 0.12 0.000234 0.000301 +Wall time: 62157.39716729941 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.109 0.00208 0.0674 0.0409 0.0545 0.299 0.31 0.000747 0.000774 + 372 118 0.141 0.00197 0.102 0.0398 0.053 0.374 0.381 0.000935 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0847 0.00201 0.0445 0.04 0.0535 0.251 0.252 0.000627 0.000629 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 372 62325.947 0.005 0.002 0.0243 0.0642 0.0398 0.0533 0.136 0.184 0.00034 0.00046 +! Validation 372 62325.947 0.005 0.00261 0.0671 0.119 0.0453 0.061 0.267 0.309 0.000667 0.000773 +Wall time: 62325.94768316718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.451 0.0187 0.0766 0.123 0.163 0.253 0.33 0.000632 0.000826 + 373 118 0.429 0.0124 0.181 0.101 0.133 0.477 0.508 0.00119 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.444 0.0111 0.223 0.095 0.126 0.559 0.564 0.0014 0.00141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 373 62494.411 0.005 0.0971 1.73 3.67 0.228 0.373 0.939 1.58 0.00235 0.00394 +! Validation 373 62494.411 0.005 0.0133 0.136 0.402 0.104 0.138 0.369 0.441 0.000922 0.0011 +Wall time: 62494.411849517375 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.146 0.00581 0.0296 0.0697 0.0909 0.16 0.205 0.000399 0.000513 + 374 118 0.148 0.00582 0.0318 0.0692 0.0911 0.188 0.213 0.00047 0.000532 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.113 0.00475 0.0176 0.0622 0.0823 0.144 0.159 0.00036 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 374 62661.548 0.005 0.00812 0.0557 0.218 0.0811 0.108 0.223 0.282 0.000556 0.000705 +! Validation 374 62661.548 0.005 0.00586 0.0305 0.148 0.0692 0.0914 0.169 0.208 0.000423 0.000521 +Wall time: 62661.548694444355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.0961 0.00414 0.0133 0.0589 0.0768 0.106 0.138 0.000266 0.000344 + 375 118 0.125 0.00418 0.0418 0.0591 0.0771 0.21 0.244 0.000524 0.00061 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.113 0.00334 0.0458 0.0522 0.0689 0.249 0.256 0.000623 0.000639 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 375 62828.678 0.005 0.00472 0.0323 0.127 0.0627 0.082 0.171 0.214 0.000427 0.000535 +! Validation 375 62828.678 0.005 0.00429 0.0476 0.133 0.059 0.0782 0.224 0.26 0.00056 0.000651 +Wall time: 62828.67809789302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.144 0.00347 0.0741 0.0534 0.0703 0.299 0.325 0.000747 0.000812 + 376 118 0.0786 0.00334 0.0118 0.0524 0.069 0.104 0.13 0.000261 0.000324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.0714 0.00262 0.0189 0.0461 0.0611 0.159 0.164 0.000397 0.00041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 376 62995.798 0.005 0.00365 0.0252 0.0982 0.0549 0.0721 0.152 0.19 0.000379 0.000475 +! Validation 376 62995.798 0.005 0.00356 0.0243 0.0956 0.0533 0.0712 0.155 0.186 0.000387 0.000465 +Wall time: 62995.798377080355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0746 0.00305 0.0136 0.0498 0.0659 0.122 0.139 0.000304 0.000348 + 377 118 0.073 0.0032 0.00906 0.0513 0.0675 0.1 0.114 0.000251 0.000284 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0497 0.00229 0.00399 0.0428 0.0571 0.0682 0.0754 0.000171 0.000188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 377 63162.903 0.005 0.00311 0.02 0.0821 0.0504 0.0665 0.137 0.169 0.000343 0.000423 +! Validation 377 63162.903 0.005 0.00316 0.0126 0.0757 0.0499 0.0671 0.107 0.134 0.000267 0.000335 +Wall time: 63162.903345452156 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0669 0.00268 0.0133 0.0464 0.0618 0.114 0.138 0.000285 0.000345 + 378 118 0.0713 0.00279 0.0154 0.0465 0.0631 0.122 0.148 0.000305 0.00037 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0582 0.00207 0.0168 0.0405 0.0543 0.153 0.155 0.000382 0.000387 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 378 63330.067 0.005 0.0028 0.0151 0.0711 0.0476 0.0632 0.119 0.147 0.000296 0.000366 +! Validation 378 63330.067 0.005 0.00293 0.0276 0.0861 0.0477 0.0646 0.169 0.198 0.000422 0.000496 +Wall time: 63330.06749619637 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0734 0.00272 0.0191 0.0466 0.0622 0.138 0.165 0.000344 0.000412 + 379 118 0.0791 0.0029 0.021 0.0476 0.0643 0.153 0.173 0.000383 0.000433 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0416 0.00194 0.00271 0.0393 0.0526 0.0577 0.0621 0.000144 0.000155 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 379 63497.185 0.005 0.00261 0.0116 0.0638 0.0457 0.061 0.102 0.128 0.000256 0.000321 +! Validation 379 63497.185 0.005 0.00276 0.0105 0.0658 0.0463 0.0628 0.0966 0.122 0.000241 0.000306 +Wall time: 63497.184973093215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0598 0.00251 0.00963 0.0448 0.0598 0.0959 0.117 0.00024 0.000293 + 380 118 0.0478 0.00216 0.00449 0.0414 0.0555 0.0653 0.08 0.000163 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0373 0.00184 0.000515 0.0382 0.0512 0.0219 0.0271 5.48e-05 6.77e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 380 63664.282 0.005 0.00248 0.018 0.0677 0.0446 0.0595 0.128 0.161 0.000319 0.000402 +! Validation 380 63664.282 0.005 0.00266 0.00884 0.062 0.0452 0.0615 0.0873 0.112 0.000218 0.000281 +Wall time: 63664.28202277422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0573 0.00249 0.00749 0.0444 0.0596 0.0823 0.103 0.000206 0.000258 + 381 118 0.0583 0.00231 0.012 0.0431 0.0574 0.114 0.131 0.000286 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0359 0.00177 0.00052 0.0374 0.0502 0.0229 0.0272 5.72e-05 6.8e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 381 63831.399 0.005 0.00238 0.0105 0.0582 0.0436 0.0583 0.0979 0.122 0.000245 0.000306 +! Validation 381 63831.399 0.005 0.00257 0.00874 0.0602 0.0444 0.0605 0.0863 0.112 0.000216 0.000279 +Wall time: 63831.3992936532 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.0516 0.00232 0.00529 0.0427 0.0574 0.0692 0.0868 0.000173 0.000217 + 382 118 0.0482 0.00217 0.00479 0.0416 0.0556 0.0615 0.0826 0.000154 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.0368 0.00173 0.00208 0.037 0.0497 0.0519 0.0544 0.00013 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 382 63998.525 0.005 0.00231 0.0132 0.0594 0.0429 0.0574 0.111 0.137 0.000277 0.000343 +! Validation 382 63998.525 0.005 0.00251 0.0105 0.0607 0.0438 0.0598 0.0943 0.122 0.000236 0.000306 +Wall time: 63998.52542071603 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0572 0.00227 0.0117 0.0424 0.0569 0.109 0.129 0.000272 0.000323 + 383 118 0.0536 0.00226 0.00846 0.0427 0.0567 0.0877 0.11 0.000219 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0365 0.00168 0.00282 0.0365 0.049 0.062 0.0634 0.000155 0.000159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 383 64165.704 0.005 0.00226 0.0143 0.0594 0.0424 0.0567 0.115 0.143 0.000287 0.000357 +! Validation 383 64165.704 0.005 0.00245 0.0123 0.0614 0.0433 0.0591 0.107 0.133 0.000267 0.000331 +Wall time: 64165.70474754041 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0565 0.00228 0.011 0.0423 0.057 0.101 0.125 0.000253 0.000313 + 384 118 0.0522 0.00239 0.0044 0.0435 0.0584 0.0627 0.0792 0.000157 0.000198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0336 0.00165 0.000688 0.0363 0.0484 0.0294 0.0313 7.36e-05 7.82e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 384 64332.807 0.005 0.00221 0.0145 0.0587 0.0419 0.0561 0.118 0.144 0.000295 0.00036 +! Validation 384 64332.807 0.005 0.00242 0.00885 0.0572 0.043 0.0587 0.0876 0.112 0.000219 0.000281 +Wall time: 64332.80722406227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.116 0.002 0.0763 0.0401 0.0534 0.322 0.33 0.000806 0.000824 + 385 118 0.0552 0.00223 0.0106 0.0418 0.0563 0.107 0.123 0.000268 0.000307 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0347 0.00166 0.00144 0.0363 0.0486 0.0443 0.0453 0.000111 0.000113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 385 64499.907 0.005 0.00217 0.0162 0.0595 0.0415 0.0556 0.119 0.152 0.000297 0.00038 +! Validation 385 64499.907 0.005 0.00241 0.0104 0.0586 0.0429 0.0586 0.0957 0.122 0.000239 0.000305 +Wall time: 64499.90764717199 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.048 0.00214 0.00508 0.0412 0.0553 0.0683 0.085 0.000171 0.000213 + 386 118 0.0504 0.00211 0.00808 0.0404 0.0549 0.0785 0.107 0.000196 0.000268 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0325 0.0016 0.000548 0.0355 0.0477 0.0229 0.0279 5.73e-05 6.99e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 386 64667.002 0.005 0.00214 0.0137 0.0564 0.0412 0.0552 0.113 0.14 0.000283 0.00035 +! Validation 386 64667.002 0.005 0.00234 0.00768 0.0545 0.0422 0.0577 0.0808 0.105 0.000202 0.000262 +Wall time: 64667.00235976931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.0601 0.00218 0.0165 0.0415 0.0557 0.132 0.153 0.000331 0.000384 + 387 118 0.0511 0.00233 0.00447 0.043 0.0577 0.0685 0.0798 0.000171 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.041 0.0016 0.00913 0.0356 0.0477 0.113 0.114 0.000283 0.000285 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 387 64834.053 0.005 0.00212 0.0219 0.0642 0.041 0.0549 0.141 0.177 0.000352 0.000443 +! Validation 387 64834.053 0.005 0.00234 0.0185 0.0653 0.0423 0.0578 0.136 0.162 0.000341 0.000405 +Wall time: 64834.0534157902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 0.0515 0.00208 0.00992 0.0401 0.0544 0.104 0.119 0.000259 0.000297 + 388 118 0.0768 0.00185 0.0399 0.0383 0.0513 0.222 0.238 0.000555 0.000596 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 0.0556 0.0016 0.0237 0.0354 0.0477 0.183 0.184 0.000458 0.000459 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 388 65001.202 0.005 0.00208 0.0129 0.0545 0.0406 0.0545 0.107 0.134 0.000267 0.000336 +! Validation 388 65001.202 0.005 0.00231 0.0257 0.0719 0.0419 0.0573 0.169 0.191 0.000422 0.000479 +Wall time: 65001.20240403339 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.0467 0.00211 0.00451 0.0408 0.0548 0.0656 0.0801 0.000164 0.0002 + 389 118 0.0477 0.0019 0.00961 0.0391 0.0521 0.0999 0.117 0.00025 0.000292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.0362 0.00159 0.00441 0.0356 0.0476 0.0783 0.0792 0.000196 0.000198 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 389 65168.268 0.005 0.00206 0.0162 0.0575 0.0404 0.0542 0.124 0.152 0.000311 0.000381 +! Validation 389 65168.268 0.005 0.00229 0.0113 0.0572 0.0418 0.0572 0.101 0.127 0.000252 0.000317 +Wall time: 65168.268060688395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.0974 0.00202 0.057 0.04 0.0537 0.27 0.285 0.000674 0.000712 + 390 118 0.0589 0.00235 0.0119 0.043 0.0579 0.108 0.13 0.000271 0.000325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.0557 0.00151 0.0254 0.0347 0.0465 0.19 0.19 0.000475 0.000476 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 390 65335.327 0.005 0.00204 0.0162 0.0571 0.0402 0.0539 0.125 0.152 0.000312 0.00038 +! Validation 390 65335.327 0.005 0.00226 0.0377 0.0828 0.0414 0.0567 0.21 0.232 0.000525 0.000579 +Wall time: 65335.32774687419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.0452 0.00207 0.00376 0.0405 0.0543 0.0578 0.0732 0.000144 0.000183 + 391 118 0.043 0.00201 0.00276 0.0399 0.0535 0.0523 0.0627 0.000131 0.000157 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.0367 0.00151 0.00649 0.0346 0.0464 0.0955 0.0961 0.000239 0.00024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 391 65502.381 0.005 0.00202 0.0136 0.054 0.04 0.0536 0.113 0.14 0.000281 0.000349 +! Validation 391 65502.381 0.005 0.00222 0.0141 0.0586 0.0411 0.0563 0.111 0.142 0.000277 0.000354 +Wall time: 65502.381958957296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.091 0.00204 0.0502 0.04 0.0539 0.258 0.267 0.000645 0.000669 + 392 118 0.0533 0.00191 0.0151 0.0392 0.0522 0.132 0.147 0.000331 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.0334 0.00156 0.00216 0.035 0.0472 0.0523 0.0554 0.000131 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 392 65669.529 0.005 0.002 0.0147 0.0546 0.0397 0.0533 0.115 0.144 0.000288 0.000361 +! Validation 392 65669.529 0.005 0.00225 0.0127 0.0577 0.0413 0.0566 0.108 0.135 0.00027 0.000337 +Wall time: 65669.52993379533 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.046 0.00204 0.00525 0.0396 0.0539 0.0703 0.0865 0.000176 0.000216 + 393 118 0.0457 0.00188 0.008 0.0387 0.0518 0.087 0.107 0.000217 0.000267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.0305 0.00151 0.000383 0.0344 0.0464 0.0199 0.0234 4.99e-05 5.84e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 393 65836.626 0.005 0.00198 0.0116 0.0512 0.0396 0.0532 0.104 0.129 0.00026 0.000322 +! Validation 393 65836.626 0.005 0.0022 0.00765 0.0516 0.0408 0.0559 0.0805 0.104 0.000201 0.000261 +Wall time: 65836.62640166515 +! Best model 393 0.052 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.0439 0.00193 0.00542 0.0389 0.0524 0.0699 0.0879 0.000175 0.00022 + 394 118 0.0521 0.00216 0.00895 0.0414 0.0554 0.0963 0.113 0.000241 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.043 0.00148 0.0134 0.0342 0.0459 0.137 0.138 0.000342 0.000345 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 394 66003.731 0.005 0.00196 0.0139 0.0531 0.0394 0.0529 0.114 0.141 0.000285 0.000352 +! Validation 394 66003.731 0.005 0.00217 0.0261 0.0695 0.0406 0.0556 0.17 0.193 0.000425 0.000482 +Wall time: 66003.73177754506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.0465 0.00194 0.00774 0.0391 0.0526 0.0887 0.105 0.000222 0.000262 + 395 118 0.0484 0.00197 0.009 0.0398 0.053 0.0954 0.113 0.000238 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.0311 0.0015 0.00112 0.0343 0.0462 0.0373 0.0399 9.32e-05 9.98e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 395 66170.816 0.005 0.00198 0.0244 0.0641 0.0396 0.0531 0.145 0.187 0.000364 0.000468 +! Validation 395 66170.816 0.005 0.00219 0.00821 0.0521 0.0408 0.0559 0.0845 0.108 0.000211 0.00027 +Wall time: 66170.81677797018 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0647 0.00193 0.0261 0.0388 0.0525 0.181 0.193 0.000451 0.000482 + 396 118 0.0722 0.00183 0.0356 0.0381 0.051 0.214 0.225 0.000535 0.000563 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0388 0.00147 0.00949 0.034 0.0457 0.116 0.116 0.000289 0.000291 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 396 66337.907 0.005 0.00194 0.0101 0.0489 0.0392 0.0526 0.0942 0.119 0.000235 0.000298 +! Validation 396 66337.907 0.005 0.00216 0.0158 0.059 0.0404 0.0555 0.128 0.15 0.00032 0.000375 +Wall time: 66337.90741622122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.0596 0.00189 0.0218 0.0389 0.0519 0.159 0.176 0.000396 0.00044 + 397 118 0.0438 0.00191 0.00563 0.039 0.0521 0.069 0.0895 0.000172 0.000224 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.0311 0.00147 0.00179 0.0342 0.0457 0.0482 0.0505 0.000121 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 397 66505.082 0.005 0.00194 0.0197 0.0586 0.0392 0.0526 0.135 0.168 0.000339 0.00042 +! Validation 397 66505.082 0.005 0.00216 0.00886 0.0521 0.0405 0.0555 0.0878 0.112 0.000219 0.000281 +Wall time: 66505.08240563422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0704 0.00175 0.0353 0.0374 0.05 0.213 0.224 0.000533 0.00056 + 398 118 0.0435 0.00195 0.00453 0.0387 0.0527 0.0587 0.0803 0.000147 0.000201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0341 0.00147 0.00474 0.0341 0.0457 0.0811 0.0822 0.000203 0.000206 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 398 66672.178 0.005 0.00193 0.0145 0.053 0.039 0.0524 0.118 0.144 0.000296 0.00036 +! Validation 398 66672.178 0.005 0.00216 0.00845 0.0517 0.0405 0.0555 0.0876 0.11 0.000219 0.000274 +Wall time: 66672.1786948964 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0434 0.00181 0.00731 0.0379 0.0507 0.0813 0.102 0.000203 0.000255 + 399 118 0.0604 0.00181 0.0242 0.0381 0.0508 0.181 0.186 0.000453 0.000464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0701 0.00149 0.0402 0.0345 0.0461 0.239 0.239 0.000597 0.000598 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 399 66839.266 0.005 0.00193 0.0207 0.0594 0.0391 0.0525 0.139 0.171 0.000346 0.000429 +! Validation 399 66839.266 0.005 0.00219 0.0417 0.0854 0.0408 0.0558 0.224 0.244 0.00056 0.000609 +Wall time: 66839.26620955206 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.0419 0.0019 0.00399 0.0386 0.052 0.0572 0.0754 0.000143 0.000189 + 400 118 0.0663 0.00226 0.0211 0.042 0.0567 0.149 0.173 0.000371 0.000433 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.0422 0.00145 0.0131 0.0341 0.0455 0.136 0.137 0.00034 0.000342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 400 67006.360 0.005 0.00191 0.0112 0.0495 0.0389 0.0522 0.102 0.126 0.000254 0.000315 +! Validation 400 67006.360 0.005 0.00213 0.026 0.0687 0.0402 0.0551 0.17 0.192 0.000425 0.000481 +Wall time: 67006.36062486703 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.0512 0.00198 0.0117 0.0397 0.053 0.108 0.129 0.000269 0.000322 + 401 118 0.0627 0.0021 0.0208 0.0402 0.0547 0.16 0.172 0.000399 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.0782 0.00143 0.0496 0.0335 0.0452 0.265 0.266 0.000664 0.000664 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 401 67173.455 0.005 0.00189 0.0122 0.05 0.0387 0.0519 0.105 0.131 0.000262 0.000328 +! Validation 401 67173.455 0.005 0.0021 0.054 0.096 0.0399 0.0547 0.259 0.277 0.000648 0.000693 +Wall time: 67173.45520490035 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0417 0.00195 0.00262 0.0391 0.0527 0.0473 0.0611 0.000118 0.000153 + 402 118 0.0584 0.00169 0.0246 0.037 0.0491 0.175 0.187 0.000439 0.000468 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0436 0.00143 0.0151 0.0334 0.0451 0.146 0.146 0.000364 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 402 67340.637 0.005 0.00189 0.0174 0.0553 0.0387 0.0519 0.125 0.157 0.000313 0.000393 +! Validation 402 67340.637 0.005 0.0021 0.0179 0.0599 0.0399 0.0547 0.134 0.16 0.000336 0.000399 +Wall time: 67340.63700787816 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0517 0.00191 0.0135 0.0387 0.0522 0.118 0.138 0.000295 0.000346 + 403 118 0.0391 0.00179 0.00321 0.0376 0.0506 0.0588 0.0677 0.000147 0.000169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0286 0.00142 0.000192 0.0335 0.045 0.0118 0.0166 2.95e-05 4.14e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 403 67507.725 0.005 0.00189 0.0175 0.0553 0.0387 0.0519 0.129 0.158 0.000322 0.000395 +! Validation 403 67507.725 0.005 0.00209 0.00723 0.049 0.0398 0.0546 0.079 0.101 0.000197 0.000254 +Wall time: 67507.72568219714 +! Best model 403 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.061 0.00193 0.0224 0.039 0.0524 0.157 0.179 0.000392 0.000446 + 404 118 0.0435 0.00188 0.0059 0.0387 0.0518 0.0754 0.0917 0.000188 0.000229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0299 0.00141 0.00173 0.0333 0.0448 0.0469 0.0496 0.000117 0.000124 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 404 67674.835 0.005 0.0019 0.0236 0.0617 0.0388 0.0521 0.141 0.184 0.000352 0.00046 +! Validation 404 67674.835 0.005 0.0021 0.00739 0.0494 0.0399 0.0547 0.0788 0.103 0.000197 0.000256 +Wall time: 67674.83577976702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0386 0.00175 0.00351 0.0374 0.05 0.0563 0.0707 0.000141 0.000177 + 405 118 0.0385 0.00176 0.00329 0.0376 0.0501 0.0566 0.0685 0.000142 0.000171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0547 0.00143 0.0262 0.0334 0.0451 0.193 0.193 0.000482 0.000483 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 405 67841.919 0.005 0.00188 0.0153 0.0529 0.0386 0.0518 0.124 0.148 0.000309 0.00037 +! Validation 405 67841.919 0.005 0.00209 0.0306 0.0724 0.0398 0.0545 0.186 0.209 0.000465 0.000522 +Wall time: 67841.91909417138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.0486 0.00166 0.0154 0.0365 0.0486 0.137 0.148 0.000343 0.000371 + 406 118 0.0457 0.00188 0.00803 0.0387 0.0518 0.098 0.107 0.000245 0.000267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.032 0.00146 0.00281 0.0338 0.0456 0.0621 0.0632 0.000155 0.000158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 406 68008.999 0.005 0.00186 0.0128 0.05 0.0383 0.0514 0.109 0.135 0.000273 0.000339 +! Validation 406 68008.999 0.005 0.00213 0.01 0.0525 0.0401 0.055 0.0968 0.119 0.000242 0.000298 +Wall time: 68008.99987710919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0705 0.00184 0.0336 0.0382 0.0513 0.207 0.219 0.000517 0.000547 + 407 118 0.0513 0.00233 0.0047 0.0425 0.0576 0.0713 0.0818 0.000178 0.000205 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0302 0.00143 0.00158 0.0334 0.0451 0.0444 0.0474 0.000111 0.000118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 407 68176.170 0.005 0.00188 0.0209 0.0586 0.0386 0.0517 0.142 0.173 0.000355 0.000433 +! Validation 407 68176.170 0.005 0.0021 0.0086 0.0507 0.0399 0.0547 0.0856 0.111 0.000214 0.000277 +Wall time: 68176.17067886237 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0433 0.00187 0.00589 0.0382 0.0516 0.0664 0.0916 0.000166 0.000229 + 408 118 0.0461 0.00197 0.00669 0.0385 0.053 0.0789 0.0976 0.000197 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0437 0.00142 0.0153 0.0335 0.045 0.147 0.147 0.000367 0.000369 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 408 68343.264 0.005 0.00185 0.0119 0.0489 0.0382 0.0513 0.106 0.13 0.000264 0.000326 +! Validation 408 68343.264 0.005 0.00206 0.022 0.0632 0.0396 0.0542 0.149 0.177 0.000373 0.000442 +Wall time: 68343.26489450503 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.043 0.00184 0.0062 0.038 0.0512 0.0823 0.094 0.000206 0.000235 + 409 118 0.0384 0.00167 0.00492 0.0366 0.0488 0.0765 0.0838 0.000191 0.000209 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0308 0.00142 0.00239 0.0335 0.045 0.0559 0.0583 0.00014 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 409 68510.360 0.005 0.00185 0.0134 0.0505 0.0383 0.0514 0.109 0.139 0.000272 0.000347 +! Validation 409 68510.360 0.005 0.00207 0.0163 0.0577 0.0396 0.0543 0.126 0.153 0.000316 0.000381 +Wall time: 68510.36005530227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0452 0.0018 0.00925 0.0378 0.0506 0.0971 0.115 0.000243 0.000287 + 410 118 0.0475 0.00179 0.0117 0.0377 0.0505 0.114 0.129 0.000285 0.000323 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0284 0.0014 0.000342 0.0332 0.0447 0.0204 0.0221 5.1e-05 5.52e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 410 68677.447 0.005 0.00189 0.0196 0.0574 0.0387 0.0519 0.126 0.167 0.000315 0.000418 +! Validation 410 68677.447 0.005 0.00205 0.00779 0.0488 0.0395 0.0541 0.0827 0.105 0.000207 0.000263 +Wall time: 68677.44754249137 +! Best model 410 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0648 0.0018 0.0287 0.0378 0.0507 0.177 0.202 0.000444 0.000506 + 411 118 0.051 0.00194 0.0121 0.0388 0.0526 0.117 0.131 0.000293 0.000329 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0614 0.0014 0.0333 0.0333 0.0447 0.216 0.218 0.000541 0.000544 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 411 68844.630 0.005 0.00186 0.0199 0.0572 0.0384 0.0515 0.135 0.168 0.000336 0.000421 +! Validation 411 68844.630 0.005 0.00208 0.0339 0.0755 0.0398 0.0544 0.198 0.22 0.000495 0.00055 +Wall time: 68844.6300143674 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0626 0.002 0.0225 0.0396 0.0534 0.168 0.179 0.000419 0.000448 + 412 118 0.0412 0.00184 0.00435 0.0384 0.0512 0.0578 0.0787 0.000144 0.000197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0289 0.0014 0.000832 0.0333 0.0447 0.0271 0.0344 6.79e-05 8.61e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 412 69011.710 0.005 0.00186 0.0139 0.0511 0.0384 0.0515 0.114 0.141 0.000286 0.000353 +! Validation 412 69011.710 0.005 0.00207 0.00731 0.0486 0.0396 0.0542 0.0788 0.102 0.000197 0.000255 +Wall time: 69011.71014996013 +! Best model 412 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0807 0.00174 0.0459 0.0373 0.0498 0.245 0.256 0.000611 0.000639 + 413 118 0.0657 0.0018 0.0296 0.0377 0.0507 0.177 0.205 0.000443 0.000514 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0773 0.00167 0.0439 0.0367 0.0487 0.249 0.25 0.000622 0.000625 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 413 69178.808 0.005 0.00183 0.0171 0.0538 0.0381 0.0511 0.124 0.156 0.00031 0.000389 +! Validation 413 69178.808 0.005 0.00236 0.0603 0.107 0.0428 0.058 0.275 0.293 0.000688 0.000733 +Wall time: 69178.80890340032 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.047 0.00175 0.012 0.0375 0.0499 0.116 0.131 0.00029 0.000327 + 414 118 0.0551 0.00157 0.0238 0.036 0.0472 0.168 0.184 0.000421 0.00046 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0413 0.00138 0.0137 0.033 0.0443 0.139 0.14 0.000347 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 414 69345.893 0.005 0.00187 0.0178 0.0553 0.0386 0.0517 0.129 0.159 0.000322 0.000398 +! Validation 414 69345.893 0.005 0.00202 0.0129 0.0533 0.0392 0.0536 0.113 0.136 0.000283 0.000339 +Wall time: 69345.89319208404 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0446 0.00192 0.00617 0.039 0.0523 0.0772 0.0937 0.000193 0.000234 + 415 118 0.0548 0.00212 0.0125 0.0405 0.0549 0.115 0.133 0.000287 0.000333 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0556 0.00136 0.0284 0.0328 0.044 0.2 0.201 0.0005 0.000503 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 415 69512.989 0.005 0.0018 0.0121 0.0481 0.0377 0.0506 0.105 0.131 0.000262 0.000329 +! Validation 415 69512.989 0.005 0.002 0.0391 0.0791 0.039 0.0534 0.208 0.236 0.00052 0.00059 +Wall time: 69512.98951757932 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0393 0.00176 0.00408 0.0374 0.0501 0.0624 0.0763 0.000156 0.000191 + 416 118 0.0601 0.00196 0.0208 0.0394 0.0529 0.156 0.172 0.000391 0.000431 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0365 0.00139 0.00877 0.0333 0.0445 0.111 0.112 0.000276 0.000279 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 416 69680.172 0.005 0.0018 0.0121 0.048 0.0377 0.0506 0.104 0.131 0.00026 0.000327 +! Validation 416 69680.172 0.005 0.00207 0.0181 0.0595 0.0398 0.0543 0.14 0.161 0.00035 0.000402 +Wall time: 69680.17257623328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0718 0.00194 0.0329 0.0395 0.0526 0.201 0.217 0.000503 0.000541 + 417 118 0.0494 0.00193 0.0108 0.0393 0.0524 0.11 0.124 0.000275 0.000311 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0344 0.0016 0.00245 0.0351 0.0477 0.0543 0.0591 0.000136 0.000148 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 417 69847.255 0.005 0.00183 0.0218 0.0585 0.0382 0.0511 0.131 0.176 0.000328 0.000441 +! Validation 417 69847.255 0.005 0.00221 0.0107 0.0549 0.0412 0.0561 0.0979 0.123 0.000245 0.000308 +Wall time: 69847.2556810393 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.0371 0.00164 0.00434 0.0364 0.0483 0.0639 0.0786 0.00016 0.000197 + 418 118 0.0412 0.00172 0.00678 0.0372 0.0495 0.0812 0.0982 0.000203 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.031 0.0014 0.00304 0.0328 0.0446 0.0635 0.0658 0.000159 0.000164 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 418 70014.350 0.005 0.00183 0.0107 0.0473 0.0381 0.0511 0.0983 0.124 0.000246 0.000309 +! Validation 418 70014.350 0.005 0.00202 0.0117 0.0521 0.0392 0.0537 0.101 0.129 0.000252 0.000322 +Wall time: 70014.35071223509 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0387 0.00173 0.00423 0.037 0.0496 0.0606 0.0777 0.000152 0.000194 + 419 118 0.0371 0.00175 0.00199 0.037 0.05 0.0379 0.0532 9.47e-05 0.000133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0294 0.00135 0.00235 0.0325 0.0439 0.0545 0.0579 0.000136 0.000145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 419 70181.439 0.005 0.00242 0.038 0.0865 0.0435 0.0588 0.162 0.234 0.000404 0.000584 +! Validation 419 70181.439 0.005 0.00197 0.0103 0.0497 0.0387 0.053 0.0984 0.121 0.000246 0.000302 +Wall time: 70181.43924827408 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0492 0.00172 0.0149 0.0369 0.0494 0.128 0.146 0.00032 0.000365 + 420 118 0.0466 0.00199 0.00687 0.0393 0.0532 0.0876 0.0989 0.000219 0.000247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0274 0.00136 0.000193 0.0326 0.0441 0.016 0.0166 3.99e-05 4.15e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 420 70348.538 0.005 0.00178 0.0119 0.0474 0.0375 0.0503 0.105 0.13 0.000263 0.000325 +! Validation 420 70348.538 0.005 0.00197 0.00793 0.0473 0.0387 0.053 0.0837 0.106 0.000209 0.000266 +Wall time: 70348.53874227032 +! Best model 420 0.047 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0412 0.00177 0.0059 0.0375 0.0502 0.0767 0.0916 0.000192 0.000229 + 421 118 0.0376 0.00158 0.00603 0.0358 0.0474 0.0863 0.0927 0.000216 0.000232 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0373 0.00135 0.0104 0.0324 0.0438 0.121 0.122 0.000303 0.000305 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 421 70515.718 0.005 0.00177 0.0109 0.0464 0.0375 0.0503 0.098 0.125 0.000245 0.000312 +! Validation 421 70515.718 0.005 0.00194 0.0172 0.0561 0.0384 0.0526 0.129 0.157 0.000322 0.000392 +Wall time: 70515.71858712332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0379 0.00177 0.00246 0.0374 0.0502 0.0474 0.0591 0.000119 0.000148 + 422 118 0.0434 0.002 0.00343 0.0394 0.0534 0.0573 0.0699 0.000143 0.000175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0274 0.00134 0.000608 0.0323 0.0436 0.0256 0.0294 6.4e-05 7.35e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 422 70682.809 0.005 0.00177 0.0147 0.0501 0.0374 0.0502 0.114 0.145 0.000284 0.000363 +! Validation 422 70682.809 0.005 0.00195 0.00724 0.0463 0.0384 0.0527 0.0799 0.102 0.0002 0.000254 +Wall time: 70682.80907406425 +! Best model 422 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0536 0.00168 0.02 0.0365 0.0489 0.159 0.169 0.000396 0.000422 + 423 118 0.0414 0.00194 0.00261 0.0394 0.0525 0.0516 0.061 0.000129 0.000152 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0288 0.00141 0.0007 0.0332 0.0448 0.0261 0.0316 6.52e-05 7.89e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 423 70849.918 0.005 0.00175 0.0124 0.0474 0.0372 0.0499 0.107 0.133 0.000268 0.000333 +! Validation 423 70849.918 0.005 0.00203 0.00697 0.0476 0.0394 0.0538 0.077 0.0996 0.000192 0.000249 +Wall time: 70849.91871393938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.062 0.0019 0.0241 0.0386 0.052 0.169 0.185 0.000421 0.000463 + 424 118 0.0397 0.00184 0.00295 0.0383 0.0511 0.05 0.0648 0.000125 0.000162 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.0297 0.00133 0.00321 0.0322 0.0435 0.066 0.0676 0.000165 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 424 71017.008 0.005 0.00179 0.0166 0.0523 0.0377 0.0504 0.124 0.154 0.000309 0.000386 +! Validation 424 71017.008 0.005 0.00193 0.00846 0.0471 0.0383 0.0525 0.0871 0.11 0.000218 0.000274 +Wall time: 71017.00884547736 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0457 0.0018 0.00967 0.0376 0.0507 0.101 0.117 0.000254 0.000293 + 425 118 0.0411 0.0017 0.007 0.0369 0.0493 0.087 0.0998 0.000218 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0306 0.00153 9.15e-05 0.0345 0.0466 0.00969 0.0114 2.42e-05 2.85e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 425 71184.110 0.005 0.00174 0.012 0.0469 0.0372 0.0498 0.101 0.131 0.000253 0.000328 +! Validation 425 71184.110 0.005 0.00206 0.00723 0.0484 0.0399 0.0542 0.0792 0.101 0.000198 0.000254 +Wall time: 71184.11009953031 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.0565 0.00189 0.0186 0.0389 0.0519 0.15 0.163 0.000375 0.000407 + 426 118 0.0567 0.00193 0.0182 0.0391 0.0524 0.14 0.161 0.000349 0.000403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.0316 0.00147 0.00222 0.0339 0.0458 0.0542 0.0563 0.000136 0.000141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 426 71351.289 0.005 0.00182 0.0241 0.0605 0.0381 0.0509 0.154 0.186 0.000386 0.000464 +! Validation 426 71351.289 0.005 0.00207 0.00943 0.0508 0.0399 0.0543 0.0932 0.116 0.000233 0.00029 +Wall time: 71351.28979588812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0387 0.00179 0.00282 0.0376 0.0505 0.0512 0.0634 0.000128 0.000159 + 427 118 0.0336 0.00143 0.00505 0.0341 0.0451 0.0689 0.0849 0.000172 0.000212 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0323 0.00132 0.006 0.032 0.0433 0.0902 0.0924 0.000226 0.000231 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 427 71518.377 0.005 0.00179 0.0177 0.0535 0.0377 0.0505 0.121 0.159 0.000301 0.000398 +! Validation 427 71518.377 0.005 0.00193 0.0136 0.0523 0.0383 0.0525 0.112 0.139 0.00028 0.000349 +Wall time: 71518.377276049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0498 0.00168 0.0162 0.0363 0.0489 0.13 0.152 0.000324 0.00038 + 428 118 0.0397 0.00177 0.00433 0.0377 0.0502 0.0593 0.0785 0.000148 0.000196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0274 0.00136 0.000166 0.0326 0.0441 0.0133 0.0154 3.33e-05 3.84e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 428 71685.463 0.005 0.0018 0.016 0.0519 0.0378 0.0506 0.122 0.151 0.000304 0.000379 +! Validation 428 71685.463 0.005 0.00194 0.0082 0.047 0.0384 0.0526 0.0845 0.108 0.000211 0.00027 +Wall time: 71685.46361045213 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0443 0.00171 0.0101 0.0368 0.0494 0.1 0.12 0.00025 0.0003 + 429 118 0.0441 0.00175 0.009 0.0372 0.05 0.0881 0.113 0.00022 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0286 0.00141 0.000442 0.0329 0.0448 0.0224 0.0251 5.6e-05 6.28e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 429 71852.708 0.005 0.0018 0.0147 0.0507 0.0378 0.0506 0.116 0.145 0.000291 0.000363 +! Validation 429 71852.708 0.005 0.00201 0.00668 0.0468 0.0392 0.0534 0.0761 0.0976 0.00019 0.000244 +Wall time: 71852.70810610242 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0525 0.00179 0.0167 0.0376 0.0504 0.143 0.154 0.000357 0.000386 + 430 118 0.117 0.00215 0.0744 0.0414 0.0553 0.309 0.326 0.000773 0.000814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0525 0.00191 0.0143 0.0389 0.0521 0.142 0.143 0.000356 0.000357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 430 72019.883 0.005 0.00176 0.0149 0.0501 0.0374 0.0501 0.115 0.143 0.000287 0.000359 +! Validation 430 72019.883 0.005 0.00248 0.0473 0.0969 0.0442 0.0594 0.225 0.26 0.000564 0.000649 +Wall time: 72019.88361944817 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0413 0.00167 0.00802 0.0364 0.0487 0.0934 0.107 0.000233 0.000267 + 431 118 0.059 0.00167 0.0257 0.036 0.0488 0.171 0.191 0.000428 0.000478 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.044 0.00135 0.017 0.0328 0.0439 0.154 0.155 0.000385 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 431 72191.339 0.005 0.00198 0.0201 0.0597 0.0397 0.0531 0.13 0.169 0.000326 0.000422 +! Validation 431 72191.339 0.005 0.00198 0.032 0.0715 0.0389 0.0531 0.19 0.213 0.000476 0.000534 +Wall time: 72191.3398076212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0386 0.00176 0.00329 0.0374 0.0501 0.0555 0.0685 0.000139 0.000171 + 432 118 0.0351 0.00154 0.00428 0.0354 0.0468 0.0578 0.078 0.000145 0.000195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.028 0.00131 0.00185 0.0318 0.0432 0.0493 0.0513 0.000123 0.000128 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 432 72358.408 0.005 0.00174 0.0156 0.0505 0.0372 0.0498 0.12 0.15 0.0003 0.000374 +! Validation 432 72358.408 0.005 0.00192 0.00732 0.0457 0.0382 0.0523 0.0792 0.102 0.000198 0.000255 +Wall time: 72358.4082747642 +! Best model 432 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0529 0.00179 0.017 0.0377 0.0506 0.134 0.156 0.000335 0.000389 + 433 118 0.0414 0.00148 0.0118 0.0347 0.0459 0.111 0.129 0.000277 0.000324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0413 0.00132 0.015 0.0319 0.0433 0.145 0.146 0.000364 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 433 72525.490 0.005 0.00178 0.0149 0.0506 0.0377 0.0504 0.113 0.146 0.000282 0.000365 +! Validation 433 72525.490 0.005 0.00192 0.0243 0.0627 0.0383 0.0523 0.162 0.186 0.000404 0.000465 +Wall time: 72525.49019691022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 0.0627 0.0017 0.0287 0.0368 0.0492 0.196 0.202 0.000489 0.000505 + 434 118 0.0522 0.00157 0.0208 0.0354 0.0473 0.153 0.172 0.000383 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 0.043 0.00134 0.0162 0.0323 0.0437 0.151 0.152 0.000377 0.00038 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 434 72692.557 0.005 0.00172 0.0177 0.0522 0.037 0.0496 0.126 0.159 0.000316 0.000397 +! Validation 434 72692.557 0.005 0.00199 0.0283 0.0682 0.039 0.0533 0.174 0.201 0.000434 0.000502 +Wall time: 72692.55776370037 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 0.0421 0.00171 0.00787 0.0369 0.0494 0.0864 0.106 0.000216 0.000265 + 435 118 0.0542 0.00192 0.0158 0.0388 0.0523 0.125 0.15 0.000313 0.000375 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 0.0274 0.00126 0.00209 0.0313 0.0424 0.0517 0.0545 0.000129 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 435 72859.717 0.005 0.00175 0.0166 0.0516 0.0373 0.0499 0.126 0.154 0.000315 0.000385 +! Validation 435 72859.717 0.005 0.00188 0.00796 0.0456 0.0378 0.0518 0.0818 0.106 0.000204 0.000266 +Wall time: 72859.71757974243 +! Best model 435 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.0428 0.00177 0.00749 0.0374 0.0502 0.0857 0.103 0.000214 0.000258 + 436 118 0.0642 0.00159 0.0324 0.0358 0.0476 0.2 0.215 0.000499 0.000537 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.0462 0.00147 0.0169 0.0334 0.0457 0.153 0.155 0.000384 0.000388 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 436 73026.802 0.005 0.00169 0.0145 0.0483 0.0366 0.0491 0.106 0.143 0.000266 0.000357 +! Validation 436 73026.802 0.005 0.00204 0.0178 0.0585 0.0395 0.0539 0.14 0.159 0.000349 0.000398 +Wall time: 73026.80273851939 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.0465 0.00163 0.014 0.0361 0.0482 0.125 0.141 0.000312 0.000353 + 437 118 0.0438 0.00201 0.00365 0.0397 0.0535 0.0601 0.0721 0.00015 0.00018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.032 0.00155 0.00103 0.0349 0.047 0.0325 0.0383 8.13e-05 9.59e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 437 73193.874 0.005 0.00178 0.0193 0.0549 0.0377 0.0503 0.136 0.166 0.00034 0.000416 +! Validation 437 73193.874 0.005 0.00213 0.0105 0.053 0.0406 0.055 0.0971 0.122 0.000243 0.000306 +Wall time: 73193.87460639141 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.0428 0.00165 0.0098 0.0364 0.0485 0.103 0.118 0.000258 0.000295 + 438 118 0.0639 0.00207 0.0225 0.0404 0.0543 0.175 0.179 0.000438 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.0269 0.00133 0.000244 0.032 0.0436 0.0154 0.0187 3.85e-05 4.66e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 438 73360.943 0.005 0.00171 0.0109 0.0451 0.0369 0.0493 0.099 0.124 0.000247 0.000311 +! Validation 438 73360.943 0.005 0.00192 0.0164 0.0548 0.0382 0.0523 0.115 0.153 0.000288 0.000382 +Wall time: 73360.94355804939 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.0443 0.00173 0.00965 0.0373 0.0497 0.0958 0.117 0.000239 0.000293 + 439 118 0.0498 0.00177 0.0143 0.0373 0.0503 0.134 0.143 0.000336 0.000357 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.0327 0.00149 0.00289 0.0341 0.0461 0.0623 0.0642 0.000156 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 439 73528.010 0.005 0.0019 0.0278 0.0657 0.0389 0.052 0.165 0.199 0.000413 0.000498 +! Validation 439 73528.010 0.005 0.00199 0.00941 0.0493 0.0392 0.0533 0.0933 0.116 0.000233 0.00029 +Wall time: 73528.01069072029 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.07 0.00172 0.0355 0.0372 0.0495 0.187 0.225 0.000468 0.000562 + 440 118 0.0368 0.00154 0.006 0.0353 0.0468 0.0814 0.0924 0.000204 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.0412 0.0016 0.00912 0.0354 0.0478 0.112 0.114 0.000279 0.000285 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 440 73695.174 0.005 0.00174 0.0167 0.0514 0.0371 0.0498 0.115 0.154 0.000288 0.000386 +! Validation 440 73695.174 0.005 0.00209 0.0201 0.0619 0.0402 0.0545 0.136 0.169 0.00034 0.000423 +Wall time: 73695.17483167443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.0439 0.00168 0.0104 0.0366 0.0489 0.107 0.122 0.000267 0.000304 + 441 118 0.0612 0.00171 0.027 0.0369 0.0494 0.178 0.196 0.000446 0.000491 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.0822 0.00188 0.0445 0.0384 0.0518 0.25 0.252 0.000625 0.000629 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 441 73862.268 0.005 0.00169 0.0113 0.045 0.0366 0.049 0.101 0.126 0.000253 0.000316 +! Validation 441 73862.268 0.005 0.00238 0.0407 0.0882 0.043 0.0582 0.218 0.241 0.000544 0.000602 +Wall time: 73862.26894260943 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.0683 0.00176 0.0331 0.0379 0.0501 0.207 0.217 0.000517 0.000543 + 442 118 0.0399 0.00185 0.00299 0.0382 0.0513 0.0493 0.0653 0.000123 0.000163 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.0278 0.00136 0.000599 0.0328 0.044 0.0259 0.0292 6.48e-05 7.3e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 442 74029.348 0.005 0.00171 0.0109 0.045 0.0369 0.0493 0.101 0.125 0.000252 0.000312 +! Validation 442 74029.348 0.005 0.00192 0.00757 0.046 0.0384 0.0523 0.08 0.104 0.0002 0.00026 +Wall time: 74029.3487562174 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.0769 0.00195 0.038 0.0394 0.0526 0.215 0.233 0.000537 0.000582 + 443 118 0.0366 0.00166 0.00334 0.0365 0.0487 0.0589 0.0689 0.000147 0.000172 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.03 0.00135 0.00305 0.0323 0.0438 0.0644 0.0659 0.000161 0.000165 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 443 74196.449 0.005 0.00174 0.0168 0.0517 0.0373 0.0498 0.124 0.155 0.000311 0.000388 +! Validation 443 74196.449 0.005 0.0019 0.0133 0.0513 0.0382 0.052 0.111 0.138 0.000277 0.000344 +Wall time: 74196.44904303737 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.044 0.00162 0.0115 0.0359 0.0481 0.117 0.128 0.000292 0.00032 + 444 118 0.0437 0.00161 0.0115 0.0361 0.0479 0.121 0.128 0.000303 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.0334 0.00134 0.00669 0.0321 0.0436 0.0969 0.0977 0.000242 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 444 74363.532 0.005 0.00166 0.011 0.0442 0.0363 0.0486 0.0993 0.125 0.000248 0.000314 +! Validation 444 74363.532 0.005 0.00187 0.0135 0.0509 0.0378 0.0516 0.109 0.139 0.000273 0.000346 +Wall time: 74363.5321343313 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.0405 0.00167 0.00716 0.0363 0.0487 0.0851 0.101 0.000213 0.000253 + 445 118 0.0395 0.00164 0.00668 0.0362 0.0484 0.0857 0.0976 0.000214 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.0248 0.00122 0.000327 0.031 0.0417 0.0204 0.0216 5.1e-05 5.4e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 445 74530.680 0.005 0.0017 0.0186 0.0526 0.0368 0.0492 0.126 0.163 0.000316 0.000407 +! Validation 445 74530.680 0.005 0.0018 0.00671 0.0427 0.037 0.0507 0.0767 0.0978 0.000192 0.000244 +Wall time: 74530.68032540614 +! Best model 445 0.043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.0409 0.00179 0.00523 0.0377 0.0504 0.0677 0.0864 0.000169 0.000216 + 446 118 0.283 0.002 0.243 0.0411 0.0534 0.584 0.588 0.00146 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.537 0.00512 0.434 0.0637 0.0854 0.783 0.787 0.00196 0.00197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 446 74697.765 0.005 0.00176 0.0217 0.0569 0.0373 0.0501 0.129 0.169 0.000323 0.000424 +! Validation 446 74697.765 0.005 0.00589 0.278 0.396 0.068 0.0916 0.579 0.63 0.00145 0.00157 +Wall time: 74697.76545484224 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.513 0.0222 0.0703 0.128 0.178 0.251 0.316 0.000627 0.000791 + 447 118 0.426 0.0178 0.07 0.115 0.159 0.266 0.316 0.000664 0.00079 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.349 0.0162 0.0256 0.11 0.152 0.178 0.191 0.000446 0.000477 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 447 74864.822 0.005 0.102 1.91 3.95 0.239 0.383 0.995 1.65 0.00249 0.00413 +! Validation 447 74864.822 0.005 0.0187 0.205 0.58 0.117 0.163 0.435 0.541 0.00109 0.00135 +Wall time: 74864.82235335419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.321 0.0111 0.0985 0.0902 0.126 0.31 0.375 0.000774 0.000937 + 448 118 0.296 0.0112 0.0719 0.0909 0.126 0.258 0.32 0.000644 0.0008 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.218 0.00904 0.0373 0.0819 0.113 0.216 0.23 0.00054 0.000576 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 448 75031.876 0.005 0.0138 0.128 0.403 0.1 0.14 0.349 0.427 0.000872 0.00107 +! Validation 448 75031.876 0.005 0.0106 0.0533 0.266 0.0886 0.123 0.224 0.276 0.00056 0.000689 +Wall time: 75031.87635863712 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.186 0.00743 0.0375 0.0759 0.103 0.188 0.231 0.000469 0.000578 + 449 118 0.157 0.00644 0.0278 0.0714 0.0958 0.172 0.199 0.000431 0.000498 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.12 0.00573 0.00511 0.0668 0.0904 0.0805 0.0853 0.000201 0.000213 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 449 75199.027 0.005 0.00833 0.0848 0.251 0.0795 0.109 0.278 0.348 0.000695 0.000871 +! Validation 449 75199.027 0.005 0.00679 0.026 0.162 0.0725 0.0984 0.152 0.192 0.000379 0.000481 +Wall time: 75199.02743237512 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.121 0.005 0.0212 0.0634 0.0844 0.139 0.174 0.000347 0.000434 + 450 118 0.17 0.00433 0.0838 0.0598 0.0786 0.332 0.346 0.00083 0.000864 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.245 0.00409 0.163 0.0575 0.0763 0.476 0.482 0.00119 0.00121 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 450 75366.096 0.005 0.00553 0.0747 0.185 0.0665 0.0889 0.265 0.326 0.000663 0.000815 +! Validation 450 75366.096 0.005 0.00496 0.185 0.285 0.063 0.0841 0.487 0.514 0.00122 0.00128 +Wall time: 75366.0965986452 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0936 0.00397 0.0143 0.0569 0.0752 0.111 0.143 0.000278 0.000356 + 451 118 0.0951 0.00358 0.0234 0.0546 0.0714 0.163 0.183 0.000407 0.000457 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0828 0.00321 0.0186 0.0513 0.0676 0.15 0.163 0.000376 0.000407 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 451 75533.158 0.005 0.00418 0.0432 0.127 0.0585 0.0772 0.201 0.248 0.000502 0.000621 +! Validation 451 75533.158 0.005 0.00409 0.0233 0.105 0.0572 0.0764 0.149 0.182 0.000373 0.000456 +Wall time: 75533.15805786522 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.0825 0.00331 0.0162 0.052 0.0687 0.122 0.152 0.000304 0.00038 + 452 118 0.0738 0.00345 0.0048 0.0533 0.0701 0.0672 0.0827 0.000168 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.0792 0.00272 0.0247 0.0473 0.0623 0.182 0.188 0.000454 0.000469 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 452 75700.218 0.005 0.00356 0.026 0.0971 0.054 0.0712 0.156 0.193 0.00039 0.000482 +! Validation 452 75700.218 0.005 0.00362 0.041 0.113 0.0537 0.0718 0.209 0.242 0.000524 0.000604 +Wall time: 75700.21805254323 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.0983 0.0029 0.0403 0.0487 0.0643 0.212 0.239 0.000529 0.000599 + 453 118 0.0807 0.0032 0.0168 0.0511 0.0675 0.119 0.155 0.000298 0.000387 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.112 0.00246 0.0624 0.045 0.0592 0.295 0.298 0.000738 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 453 75867.290 0.005 0.00319 0.024 0.0878 0.051 0.0674 0.147 0.185 0.000368 0.000462 +! Validation 453 75867.290 0.005 0.00335 0.077 0.144 0.0515 0.069 0.311 0.331 0.000776 0.000828 +Wall time: 75867.29089192906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.0792 0.00297 0.0198 0.049 0.0651 0.136 0.168 0.00034 0.000419 + 454 118 0.0631 0.00284 0.00629 0.0484 0.0636 0.0788 0.0947 0.000197 0.000237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.0477 0.00222 0.00327 0.0427 0.0562 0.0564 0.0683 0.000141 0.000171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 454 76034.443 0.005 0.00296 0.0284 0.0877 0.0491 0.065 0.161 0.202 0.000403 0.000504 +! Validation 454 76034.443 0.005 0.0031 0.0135 0.0755 0.0494 0.0665 0.11 0.139 0.000275 0.000347 +Wall time: 76034.44327511312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0783 0.00284 0.0216 0.0479 0.0636 0.149 0.175 0.000373 0.000438 + 455 118 0.0928 0.00278 0.0372 0.0478 0.063 0.214 0.23 0.000536 0.000575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.165 0.00209 0.123 0.0414 0.0546 0.417 0.418 0.00104 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 455 76201.524 0.005 0.00277 0.0207 0.0761 0.0474 0.0628 0.137 0.171 0.000344 0.000428 +! Validation 455 76201.524 0.005 0.00294 0.144 0.203 0.0481 0.0648 0.439 0.453 0.0011 0.00113 +Wall time: 76201.52430943307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.0638 0.00269 0.00994 0.0466 0.0619 0.0967 0.119 0.000242 0.000297 + 456 118 0.0884 0.00284 0.0317 0.0476 0.0636 0.202 0.212 0.000504 0.000531 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.0843 0.00198 0.0446 0.0402 0.0532 0.25 0.252 0.000624 0.00063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 456 76368.585 0.005 0.00266 0.0309 0.084 0.0464 0.0615 0.162 0.21 0.000406 0.000524 +! Validation 456 76368.585 0.005 0.00283 0.0469 0.103 0.047 0.0635 0.236 0.258 0.00059 0.000646 +Wall time: 76368.58503506798 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.0963 0.00256 0.0451 0.0454 0.0604 0.236 0.254 0.00059 0.000634 + 457 118 0.0528 0.0024 0.00481 0.0444 0.0585 0.0615 0.0828 0.000154 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.0388 0.00188 0.00106 0.0393 0.0518 0.0293 0.0388 7.31e-05 9.71e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 457 76535.646 0.005 0.00253 0.0205 0.0712 0.0452 0.0601 0.139 0.171 0.000349 0.000429 +! Validation 457 76535.646 0.005 0.00272 0.00894 0.0633 0.046 0.0622 0.0891 0.113 0.000223 0.000282 +Wall time: 76535.6459748433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.067 0.00253 0.0165 0.0449 0.06 0.131 0.153 0.000327 0.000383 + 458 118 0.0511 0.00206 0.00979 0.0411 0.0542 0.1 0.118 0.000251 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.0395 0.00181 0.00333 0.0385 0.0508 0.0625 0.0689 0.000156 0.000172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 458 76702.703 0.005 0.00243 0.0141 0.0626 0.0443 0.0588 0.113 0.142 0.000283 0.000355 +! Validation 458 76702.703 0.005 0.00262 0.0142 0.0666 0.0452 0.0611 0.117 0.142 0.000291 0.000355 +Wall time: 76702.70326701412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.0674 0.00223 0.0229 0.0425 0.0563 0.151 0.18 0.000378 0.000451 + 459 118 0.0774 0.00242 0.0291 0.044 0.0587 0.176 0.203 0.00044 0.000509 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.0497 0.00178 0.0142 0.0382 0.0503 0.141 0.142 0.000351 0.000355 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 459 76869.834 0.005 0.00234 0.0138 0.0607 0.0434 0.0578 0.113 0.14 0.000282 0.000349 +! Validation 459 76869.834 0.005 0.00257 0.0275 0.079 0.0448 0.0606 0.176 0.198 0.000439 0.000495 +Wall time: 76869.83413400734 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.055 0.00242 0.00662 0.0442 0.0587 0.0776 0.0971 0.000194 0.000243 + 460 118 0.0965 0.00215 0.0536 0.0421 0.0553 0.268 0.276 0.000671 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.203 0.00173 0.168 0.0374 0.0496 0.488 0.489 0.00122 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 460 77036.907 0.005 0.00228 0.016 0.0617 0.0429 0.057 0.121 0.15 0.000302 0.000374 +! Validation 460 77036.907 0.005 0.00249 0.154 0.203 0.044 0.0596 0.456 0.468 0.00114 0.00117 +Wall time: 77036.90741181513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.0528 0.00225 0.00791 0.0427 0.0566 0.0849 0.106 0.000212 0.000265 + 461 118 0.0573 0.00245 0.00841 0.0428 0.059 0.0858 0.109 0.000215 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.0401 0.00166 0.00684 0.0368 0.0487 0.0962 0.0987 0.000241 0.000247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 461 77203.966 0.005 0.00224 0.0205 0.0653 0.0424 0.0565 0.133 0.171 0.000332 0.000428 +! Validation 461 77203.966 0.005 0.00243 0.0108 0.0594 0.0434 0.0588 0.0976 0.124 0.000244 0.000309 +Wall time: 77203.96644927422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.0595 0.00233 0.0128 0.0434 0.0577 0.118 0.135 0.000294 0.000338 + 462 118 0.101 0.00247 0.0516 0.0443 0.0594 0.26 0.271 0.000651 0.000678 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.128 0.00162 0.0957 0.0364 0.048 0.369 0.369 0.000922 0.000923 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 462 77371.019 0.005 0.00218 0.0183 0.0619 0.0418 0.0557 0.132 0.161 0.000331 0.000402 +! Validation 462 77371.019 0.005 0.00239 0.0961 0.144 0.043 0.0583 0.356 0.37 0.00089 0.000925 +Wall time: 77371.01989729516 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0489 0.0021 0.00684 0.0409 0.0547 0.0793 0.0987 0.000198 0.000247 + 463 118 0.0566 0.00238 0.00909 0.044 0.0582 0.0955 0.114 0.000239 0.000284 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0468 0.00159 0.015 0.0359 0.0476 0.144 0.146 0.000361 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 463 77538.084 0.005 0.00215 0.0171 0.06 0.0415 0.0553 0.122 0.156 0.000305 0.000391 +! Validation 463 77538.084 0.005 0.00233 0.0167 0.0632 0.0424 0.0576 0.129 0.154 0.000322 0.000385 +Wall time: 77538.08408458019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.0524 0.002 0.0125 0.0401 0.0533 0.116 0.133 0.000291 0.000333 + 464 118 0.0841 0.00212 0.0417 0.0408 0.055 0.23 0.244 0.000574 0.000609 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.091 0.0016 0.059 0.036 0.0478 0.289 0.29 0.000721 0.000725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 464 77705.225 0.005 0.00209 0.0175 0.0594 0.041 0.0546 0.123 0.157 0.000307 0.000393 +! Validation 464 77705.225 0.005 0.00232 0.0558 0.102 0.0424 0.0575 0.265 0.282 0.000662 0.000705 +Wall time: 77705.22519544419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0464 0.00206 0.00515 0.0405 0.0542 0.0672 0.0857 0.000168 0.000214 + 465 118 0.0403 0.00178 0.00466 0.0381 0.0504 0.0687 0.0815 0.000172 0.000204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0311 0.00154 0.000227 0.0355 0.0469 0.0162 0.018 4.05e-05 4.5e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 465 77872.291 0.005 0.00206 0.0188 0.06 0.0407 0.0542 0.131 0.164 0.000328 0.00041 +! Validation 465 77872.291 0.005 0.00227 0.00719 0.0525 0.0419 0.0568 0.0809 0.101 0.000202 0.000253 +Wall time: 77872.29150789743 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0604 0.00229 0.0146 0.0426 0.0571 0.125 0.144 0.000314 0.000361 + 466 118 0.0488 0.00174 0.0139 0.0379 0.0498 0.136 0.141 0.000341 0.000352 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0695 0.00152 0.0391 0.035 0.0465 0.235 0.236 0.000588 0.00059 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 466 78039.347 0.005 0.00203 0.0215 0.0622 0.0404 0.0539 0.133 0.175 0.000333 0.000438 +! Validation 466 78039.347 0.005 0.00223 0.0487 0.0933 0.0415 0.0563 0.242 0.263 0.000606 0.000659 +Wall time: 78039.34796407307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0473 0.00208 0.0058 0.0408 0.0544 0.0765 0.0909 0.000191 0.000227 + 467 118 0.0695 0.00196 0.0304 0.0396 0.0528 0.193 0.208 0.000482 0.00052 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0627 0.0015 0.0327 0.0349 0.0462 0.215 0.216 0.000538 0.000539 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 467 78206.412 0.005 0.002 0.0145 0.0544 0.04 0.0533 0.116 0.143 0.000289 0.000358 +! Validation 467 78206.412 0.005 0.00221 0.054 0.0981 0.0412 0.0561 0.261 0.277 0.000653 0.000694 +Wall time: 78206.4123771023 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0494 0.00194 0.0106 0.0392 0.0526 0.103 0.123 0.000257 0.000307 + 468 118 0.0472 0.00215 0.00427 0.0419 0.0553 0.0563 0.078 0.000141 0.000195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0317 0.00149 0.00195 0.0346 0.046 0.0488 0.0527 0.000122 0.000132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 468 78373.551 0.005 0.00197 0.0213 0.0607 0.0397 0.053 0.142 0.175 0.000356 0.000436 +! Validation 468 78373.551 0.005 0.00218 0.00896 0.0526 0.041 0.0558 0.0922 0.113 0.00023 0.000282 +Wall time: 78373.5518672443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0463 0.00208 0.00472 0.0405 0.0544 0.0655 0.082 0.000164 0.000205 + 469 118 0.101 0.00152 0.0709 0.0356 0.0466 0.302 0.318 0.000756 0.000794 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0657 0.0015 0.0358 0.0346 0.0462 0.225 0.226 0.000562 0.000564 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 469 78540.609 0.005 0.00195 0.0202 0.0592 0.0396 0.0527 0.136 0.168 0.000339 0.000421 +! Validation 469 78540.609 0.005 0.00219 0.044 0.0877 0.0411 0.0558 0.232 0.25 0.000581 0.000626 +Wall time: 78540.60991637502 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.051 0.00196 0.0118 0.0395 0.0529 0.111 0.13 0.000278 0.000324 + 470 118 0.0505 0.00165 0.0174 0.0368 0.0485 0.144 0.158 0.00036 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.0465 0.00144 0.0176 0.0341 0.0453 0.157 0.158 0.000393 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 470 78707.663 0.005 0.00193 0.0187 0.0574 0.0394 0.0525 0.132 0.163 0.000331 0.000408 +! Validation 470 78707.663 0.005 0.00212 0.0199 0.0624 0.0404 0.055 0.145 0.169 0.000364 0.000421 +Wall time: 78707.66336160107 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0489 0.00199 0.00902 0.0399 0.0533 0.0937 0.113 0.000234 0.000283 + 471 118 0.0467 0.00147 0.0173 0.0348 0.0458 0.143 0.157 0.000357 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0394 0.00142 0.0109 0.0339 0.045 0.124 0.125 0.00031 0.000312 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 471 78874.723 0.005 0.00189 0.014 0.0519 0.0389 0.052 0.116 0.141 0.00029 0.000353 +! Validation 471 78874.723 0.005 0.00211 0.0217 0.0639 0.0403 0.0548 0.156 0.176 0.00039 0.00044 +Wall time: 78874.72352551902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0591 0.002 0.0192 0.04 0.0533 0.149 0.165 0.000371 0.000413 + 472 118 0.0459 0.00193 0.00731 0.039 0.0524 0.092 0.102 0.00023 0.000255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0372 0.0014 0.00918 0.0336 0.0447 0.112 0.114 0.000281 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 472 79041.784 0.005 0.00188 0.0177 0.0553 0.0388 0.0518 0.13 0.159 0.000325 0.000397 +! Validation 472 79041.784 0.005 0.00207 0.0114 0.0528 0.0399 0.0543 0.102 0.127 0.000256 0.000319 +Wall time: 79041.78493498033 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0625 0.00182 0.0261 0.0381 0.0509 0.178 0.193 0.000444 0.000482 + 473 118 0.044 0.00184 0.00713 0.0385 0.0512 0.0868 0.101 0.000217 0.000252 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.06 0.00137 0.0325 0.0332 0.0442 0.214 0.215 0.000536 0.000538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 473 79208.926 0.005 0.00187 0.0167 0.054 0.0386 0.0516 0.125 0.154 0.000313 0.000386 +! Validation 473 79208.926 0.005 0.00205 0.0309 0.0719 0.0396 0.054 0.19 0.21 0.000474 0.000524 +Wall time: 79208.92644595727 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0466 0.00183 0.0099 0.0383 0.0511 0.102 0.119 0.000254 0.000297 + 474 118 0.049 0.00177 0.0137 0.0377 0.0502 0.119 0.14 0.000298 0.00035 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0281 0.00137 0.00072 0.0331 0.0441 0.0279 0.032 6.99e-05 8.01e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 474 79375.999 0.005 0.00183 0.0125 0.0492 0.0383 0.0511 0.106 0.133 0.000265 0.000333 +! Validation 474 79375.999 0.005 0.00202 0.00791 0.0483 0.0393 0.0537 0.0855 0.106 0.000214 0.000265 +Wall time: 79375.99954807106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0664 0.00195 0.0274 0.0396 0.0527 0.19 0.198 0.000475 0.000494 + 475 118 0.047 0.0019 0.00898 0.0392 0.052 0.0974 0.113 0.000244 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0475 0.00142 0.019 0.0336 0.045 0.164 0.165 0.000411 0.000412 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 475 79543.062 0.005 0.00184 0.0264 0.0633 0.0383 0.0512 0.159 0.195 0.000398 0.000486 +! Validation 475 79543.062 0.005 0.00207 0.0236 0.065 0.0399 0.0543 0.164 0.183 0.000411 0.000459 +Wall time: 79543.06276267627 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0602 0.00183 0.0236 0.0382 0.0511 0.168 0.183 0.000421 0.000459 + 476 118 0.0687 0.00172 0.0344 0.0373 0.0494 0.2 0.221 0.000499 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0656 0.00135 0.0386 0.033 0.0438 0.234 0.235 0.000586 0.000587 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 476 79710.124 0.005 0.00182 0.0154 0.0518 0.0381 0.0509 0.12 0.148 0.0003 0.000369 +! Validation 476 79710.124 0.005 0.00202 0.048 0.0884 0.0393 0.0536 0.246 0.261 0.000614 0.000653 +Wall time: 79710.12492981227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0409 0.00178 0.00536 0.0377 0.0503 0.0701 0.0873 0.000175 0.000218 + 477 118 0.0406 0.00184 0.00386 0.0384 0.0511 0.0664 0.0741 0.000166 0.000185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0323 0.00134 0.00548 0.0327 0.0437 0.0866 0.0883 0.000216 0.000221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 477 79877.202 0.005 0.0018 0.0152 0.0512 0.0379 0.0506 0.121 0.148 0.000302 0.000369 +! Validation 477 79877.202 0.005 0.00199 0.00752 0.0473 0.039 0.0532 0.0821 0.104 0.000205 0.000259 +Wall time: 79877.20205353433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0445 0.00189 0.00657 0.0387 0.052 0.0759 0.0967 0.00019 0.000242 + 478 118 0.046 0.00151 0.0158 0.0347 0.0463 0.132 0.15 0.000331 0.000376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0415 0.00133 0.0149 0.0326 0.0435 0.145 0.146 0.000362 0.000365 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 478 80044.333 0.005 0.0018 0.0175 0.0535 0.0379 0.0506 0.13 0.158 0.000324 0.000395 +! Validation 478 80044.333 0.005 0.00197 0.0159 0.0554 0.0389 0.053 0.126 0.151 0.000316 0.000376 +Wall time: 80044.33380503021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0388 0.00174 0.00393 0.0372 0.0499 0.057 0.0749 0.000143 0.000187 + 479 118 0.0379 0.0015 0.00789 0.0354 0.0463 0.0838 0.106 0.000209 0.000265 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0304 0.00135 0.00337 0.0329 0.0439 0.0689 0.0693 0.000172 0.000173 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 479 80211.394 0.005 0.00176 0.0131 0.0484 0.0375 0.0501 0.111 0.137 0.000278 0.000342 +! Validation 479 80211.394 0.005 0.00199 0.0206 0.0604 0.0391 0.0532 0.146 0.171 0.000366 0.000428 +Wall time: 80211.39396943711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0408 0.00179 0.0051 0.0377 0.0504 0.0736 0.0852 0.000184 0.000213 + 480 118 0.0422 0.00166 0.00889 0.0361 0.0487 0.101 0.113 0.000253 0.000281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0376 0.0013 0.0116 0.0322 0.0431 0.128 0.129 0.000319 0.000321 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 480 80378.448 0.005 0.00177 0.0206 0.056 0.0376 0.0502 0.134 0.172 0.000334 0.000429 +! Validation 480 80378.448 0.005 0.00193 0.0214 0.0601 0.0384 0.0525 0.155 0.175 0.000386 0.000437 +Wall time: 80378.44857572718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.101 0.00182 0.0646 0.038 0.0509 0.296 0.303 0.00074 0.000758 + 481 118 0.0491 0.0021 0.00702 0.041 0.0547 0.0903 0.1 0.000226 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.0278 0.00132 0.0014 0.0323 0.0434 0.0404 0.0446 0.000101 0.000111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 481 80545.508 0.005 0.00175 0.0172 0.0521 0.0373 0.0498 0.129 0.157 0.000323 0.000392 +! Validation 481 80545.508 0.005 0.00195 0.00842 0.0474 0.0386 0.0527 0.0839 0.109 0.00021 0.000274 +Wall time: 80545.50832085218 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 0.0635 0.00166 0.0302 0.0366 0.0487 0.193 0.207 0.000483 0.000519 + 482 118 0.0717 0.00183 0.0351 0.0383 0.0511 0.208 0.224 0.00052 0.000559 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 0.0393 0.00134 0.0126 0.0325 0.0436 0.133 0.134 0.000332 0.000335 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 482 80712.579 0.005 0.00173 0.0122 0.0467 0.0371 0.0496 0.106 0.131 0.000264 0.000327 +! Validation 482 80712.579 0.005 0.00194 0.0158 0.0547 0.0386 0.0526 0.131 0.15 0.000327 0.000375 +Wall time: 80712.57933237916 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.0629 0.00166 0.0298 0.0365 0.0486 0.19 0.206 0.000476 0.000515 + 483 118 0.0588 0.00182 0.0224 0.0381 0.0509 0.164 0.179 0.000409 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.0576 0.00131 0.0315 0.0323 0.0431 0.211 0.212 0.000528 0.000529 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 483 80879.722 0.005 0.00174 0.0208 0.0555 0.0372 0.0497 0.143 0.172 0.000358 0.00043 +! Validation 483 80879.722 0.005 0.00193 0.0494 0.088 0.0385 0.0524 0.246 0.265 0.000616 0.000663 +Wall time: 80879.7227387433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.0395 0.00168 0.00588 0.0366 0.0489 0.0776 0.0915 0.000194 0.000229 + 484 118 0.0368 0.00173 0.00224 0.0371 0.0496 0.051 0.0565 0.000127 0.000141 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.0268 0.00127 0.0013 0.0318 0.0426 0.0418 0.043 0.000104 0.000108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 484 81046.779 0.005 0.00172 0.0152 0.0497 0.0371 0.0496 0.117 0.147 0.000292 0.000368 +! Validation 484 81046.779 0.005 0.00189 0.00624 0.044 0.038 0.0519 0.0721 0.0943 0.00018 0.000236 +Wall time: 81046.77983568702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.0366 0.00171 0.00244 0.0368 0.0494 0.0488 0.0589 0.000122 0.000147 + 485 118 0.0386 0.00169 0.00482 0.0369 0.049 0.0609 0.0829 0.000152 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.0315 0.00128 0.00594 0.0319 0.0427 0.0897 0.092 0.000224 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 485 81213.832 0.005 0.00172 0.013 0.0474 0.037 0.0495 0.109 0.137 0.000272 0.000341 +! Validation 485 81213.832 0.005 0.00188 0.00913 0.0468 0.0379 0.0518 0.0888 0.114 0.000222 0.000285 +Wall time: 81213.83205513936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.0508 0.00172 0.0163 0.0371 0.0496 0.135 0.152 0.000338 0.000381 + 486 118 0.0434 0.00171 0.00928 0.0368 0.0493 0.101 0.115 0.000251 0.000287 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.0435 0.00125 0.0185 0.0316 0.0423 0.161 0.162 0.000404 0.000405 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 486 81380.884 0.005 0.00168 0.0102 0.0437 0.0365 0.0489 0.0964 0.12 0.000241 0.000301 +! Validation 486 81380.884 0.005 0.00185 0.0195 0.0566 0.0376 0.0514 0.142 0.167 0.000355 0.000417 +Wall time: 81380.88423900306 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.0368 0.00165 0.00384 0.0364 0.0484 0.0565 0.0739 0.000141 0.000185 + 487 118 0.0478 0.00202 0.00742 0.0401 0.0536 0.07 0.103 0.000175 0.000257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.0427 0.0013 0.0166 0.0322 0.0431 0.153 0.154 0.000383 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 487 81548.024 0.005 0.00174 0.0283 0.063 0.0372 0.0497 0.155 0.201 0.000388 0.000503 +! Validation 487 81548.024 0.005 0.0019 0.0184 0.0564 0.0382 0.052 0.137 0.162 0.000341 0.000405 +Wall time: 81548.02489401912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.0383 0.00175 0.00333 0.0371 0.0499 0.0573 0.0689 0.000143 0.000172 + 488 118 0.0439 0.00156 0.0127 0.035 0.0472 0.117 0.134 0.000292 0.000336 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.0269 0.00126 0.00174 0.0317 0.0423 0.0475 0.0499 0.000119 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 488 81715.090 0.005 0.00169 0.0114 0.0453 0.0367 0.0491 0.1 0.128 0.00025 0.000319 +! Validation 488 81715.090 0.005 0.00186 0.00898 0.0462 0.0378 0.0515 0.0829 0.113 0.000207 0.000283 +Wall time: 81715.09081093501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.0364 0.00162 0.00404 0.036 0.048 0.0593 0.0758 0.000148 0.00019 + 489 118 0.0378 0.00152 0.00747 0.0347 0.0465 0.0983 0.103 0.000246 0.000258 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.0494 0.00123 0.0248 0.0313 0.0419 0.187 0.188 0.000468 0.00047 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 489 81882.147 0.005 0.00168 0.0141 0.0476 0.0366 0.0489 0.11 0.142 0.000275 0.000354 +! Validation 489 81882.147 0.005 0.00184 0.0236 0.0604 0.0375 0.0512 0.162 0.183 0.000405 0.000458 +Wall time: 81882.14757081727 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.0351 0.00153 0.00458 0.035 0.0466 0.0661 0.0808 0.000165 0.000202 + 490 118 0.054 0.00187 0.0165 0.0379 0.0517 0.134 0.154 0.000336 0.000384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.0315 0.00125 0.00653 0.0316 0.0422 0.0957 0.0965 0.000239 0.000241 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 490 82049.209 0.005 0.00169 0.0195 0.0534 0.0367 0.0491 0.129 0.167 0.000323 0.000417 +! Validation 490 82049.209 0.005 0.00184 0.0155 0.0523 0.0375 0.0512 0.122 0.148 0.000304 0.000371 +Wall time: 82049.20958673628 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.0337 0.00155 0.00256 0.0352 0.0471 0.0482 0.0604 0.000121 0.000151 + 491 118 0.0756 0.00171 0.0413 0.0364 0.0494 0.232 0.243 0.000579 0.000606 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.0412 0.00139 0.0134 0.0329 0.0445 0.137 0.138 0.000342 0.000346 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 491 82216.273 0.005 0.00166 0.0151 0.0482 0.0363 0.0486 0.118 0.146 0.000295 0.000364 +! Validation 491 82216.273 0.005 0.00196 0.0136 0.0528 0.0388 0.0528 0.114 0.139 0.000284 0.000348 +Wall time: 82216.27308622003 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0897 0.0017 0.0557 0.0367 0.0492 0.263 0.282 0.000657 0.000704 + 492 118 0.0421 0.00163 0.00944 0.0364 0.0482 0.0922 0.116 0.000231 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0569 0.00129 0.0312 0.032 0.0428 0.211 0.211 0.000526 0.000527 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 492 82383.420 0.005 0.00167 0.0153 0.0487 0.0365 0.0488 0.119 0.148 0.000297 0.00037 +! Validation 492 82383.420 0.005 0.00188 0.0397 0.0774 0.0381 0.0518 0.215 0.238 0.000538 0.000594 +Wall time: 82383.42081136536 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.0344 0.00154 0.00358 0.0353 0.0469 0.0584 0.0714 0.000146 0.000178 + 493 118 0.0486 0.00168 0.0151 0.037 0.0489 0.136 0.147 0.000339 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.0363 0.00124 0.0116 0.0314 0.042 0.128 0.129 0.000321 0.000321 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 493 82550.486 0.005 0.00166 0.0157 0.049 0.0364 0.0487 0.12 0.15 0.0003 0.000374 +! Validation 493 82550.486 0.005 0.00182 0.018 0.0544 0.0373 0.0509 0.133 0.16 0.000333 0.0004 +Wall time: 82550.48695991002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0379 0.00158 0.00631 0.0358 0.0474 0.0768 0.0948 0.000192 0.000237 + 494 118 0.0498 0.00154 0.019 0.0348 0.0468 0.146 0.165 0.000364 0.000412 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0847 0.00121 0.0604 0.031 0.0416 0.293 0.293 0.000733 0.000733 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 494 82717.550 0.005 0.00163 0.0115 0.0441 0.0361 0.0482 0.103 0.127 0.000257 0.000319 +! Validation 494 82717.550 0.005 0.0018 0.0554 0.0914 0.0371 0.0507 0.263 0.281 0.000659 0.000702 +Wall time: 82717.55040824739 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0473 0.00161 0.015 0.0358 0.0479 0.13 0.146 0.000324 0.000366 + 495 118 0.0361 0.00158 0.00453 0.0352 0.0474 0.0615 0.0803 0.000154 0.000201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0271 0.00117 0.00369 0.0305 0.0408 0.0719 0.0725 0.00018 0.000181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 495 82884.619 0.005 0.0017 0.0206 0.0546 0.0368 0.0492 0.135 0.172 0.000337 0.00043 +! Validation 495 82884.619 0.005 0.00178 0.0104 0.0461 0.0369 0.0504 0.0925 0.122 0.000231 0.000304 +Wall time: 82884.6194082452 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.0452 0.00165 0.0122 0.0363 0.0485 0.119 0.132 0.000297 0.00033 + 496 118 0.0405 0.00147 0.0111 0.0344 0.0457 0.117 0.126 0.000292 0.000315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.0343 0.00119 0.0105 0.031 0.0412 0.121 0.122 0.000302 0.000305 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 496 83051.679 0.005 0.00161 0.0106 0.0428 0.0358 0.0479 0.099 0.123 0.000247 0.000307 +! Validation 496 83051.679 0.005 0.00181 0.022 0.0582 0.0373 0.0508 0.158 0.177 0.000394 0.000442 +Wall time: 83051.67987200618 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0337 0.00155 0.00279 0.0351 0.0469 0.0504 0.0631 0.000126 0.000158 + 497 118 0.0346 0.00149 0.0048 0.0347 0.046 0.0699 0.0827 0.000175 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0314 0.00118 0.00789 0.0306 0.0409 0.106 0.106 0.000265 0.000265 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 497 83218.822 0.005 0.00161 0.013 0.0452 0.0358 0.0479 0.111 0.136 0.000278 0.000341 +! Validation 497 83218.822 0.005 0.00176 0.0127 0.048 0.0367 0.0501 0.107 0.135 0.000266 0.000336 +Wall time: 83218.82263633423 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0362 0.00162 0.00368 0.0361 0.0481 0.06 0.0725 0.00015 0.000181 + 498 118 0.0333 0.00153 0.00279 0.0352 0.0466 0.0481 0.063 0.00012 0.000158 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0252 0.00117 0.00176 0.0305 0.0409 0.0497 0.0501 0.000124 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 498 83385.884 0.005 0.00168 0.0215 0.0551 0.0366 0.0489 0.138 0.176 0.000344 0.000439 +! Validation 498 83385.884 0.005 0.00178 0.0104 0.0459 0.0368 0.0503 0.101 0.122 0.000253 0.000304 +Wall time: 83385.88402453018 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0394 0.00157 0.00806 0.0354 0.0473 0.0935 0.107 0.000234 0.000268 + 499 118 0.0384 0.00141 0.0102 0.0336 0.0448 0.0918 0.12 0.00023 0.000301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0314 0.0012 0.00743 0.0308 0.0413 0.102 0.103 0.000256 0.000257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 499 83552.939 0.005 0.0016 0.0162 0.0483 0.0358 0.0478 0.125 0.152 0.000312 0.00038 +! Validation 499 83552.939 0.005 0.00177 0.0169 0.0523 0.0368 0.0503 0.136 0.155 0.000341 0.000388 +Wall time: 83552.93995982641 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.037 0.00162 0.0045 0.036 0.0481 0.062 0.0801 0.000155 0.0002 + 500 118 0.039 0.00175 0.00396 0.0372 0.05 0.0652 0.0751 0.000163 0.000188 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.026 0.00124 0.00132 0.0316 0.042 0.0425 0.0433 0.000106 0.000108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 500 83719.997 0.005 0.00162 0.0137 0.0461 0.0359 0.048 0.112 0.14 0.000281 0.00035 +! Validation 500 83719.997 0.005 0.0018 0.00638 0.0424 0.0373 0.0506 0.0716 0.0953 0.000179 0.000238 +Wall time: 83719.99782425212 +! Best model 500 0.042 +! Stop training: max epochs +Wall time: 83720.02658369439 +Cumulative wall time: 83720.02658369439 diff --git a/ads_16_length_8000/NEQUIP/results/adsorption/metrics_batch_train.csv b/ads_16_length_8000/NEQUIP/results/adsorption/metrics_batch_train.csv new file mode 100644 index 0000000000000000000000000000000000000000..db0b38aef8435b00abdb6be1a2e57ee5a68cf6b0 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/results/adsorption/metrics_batch_train.csv @@ -0,0 +1,59001 @@ +epoch, batch, loss, loss_f, loss_e, f_mae, f_rmse, e_mae, e_rmse, e/N_mae, e/N_rmse + 1, 1, 24.489, 1.0008, 4.4725, 0.88635, 1.194, 2.1081, 2.5241, 0.0052702, 0.0063104 + 1, 2, 33.232, 0.99898, 13.252, 0.8877, 1.1929, 3.5747, 4.3449, 0.0089367, 0.010862 + 1, 3, 24.35, 1.0001, 4.3479, 0.88661, 1.1936, 1.759, 2.4887, 0.0043976, 0.0062218 + 1, 4, 27.125, 1.0011, 7.1032, 0.88635, 1.1942, 2.6992, 3.181, 0.0067479, 0.0079525 + 1, 5, 29.414, 0.99557, 9.5025, 0.88578, 1.1909, 3.1333, 3.6792, 0.0078334, 0.0091981 + 1, 6, 28.314, 0.99306, 8.4528, 0.88288, 1.1894, 2.8757, 3.4701, 0.0071893, 0.0086752 + 1, 7, 24.063, 0.97569, 4.5491, 0.8778, 1.1789, 2.1125, 2.5457, 0.0052812, 0.0063642 + 1, 8, 24.915, 1.0071, 4.7739, 0.88884, 1.1978, 2.1021, 2.6078, 0.0052553, 0.0065195 + 1, 9, 25.385, 0.9952, 5.4806, 0.88637, 1.1907, 2.1744, 2.7942, 0.0054361, 0.0069854 + 1, 10, 26.065, 1.0083, 5.8993, 0.89011, 1.1985, 2.1967, 2.8989, 0.0054917, 0.0072474 + 1, 11, 26.735, 0.99985, 6.7382, 0.89017, 1.1935, 2.3589, 3.0982, 0.0058973, 0.0077455 + 1, 12, 26.169, 1.0034, 6.1006, 0.88689, 1.1956, 2.2497, 2.948, 0.0056243, 0.0073699 + 1, 13, 22.885, 1.0024, 2.8369, 0.8896, 1.195, 1.6141, 2.0103, 0.0040353, 0.0050258 + 1, 14, 24.554, 0.99176, 4.7191, 0.88329, 1.1886, 2.1701, 2.5928, 0.0054252, 0.006482 + 1, 15, 25.21, 0.98705, 5.4691, 0.88254, 1.1858, 2.3724, 2.7912, 0.005931, 0.0069781 + 1, 16, 26.327, 0.99484, 6.4302, 0.88514, 1.1905, 2.6616, 3.0266, 0.006654, 0.0075664 + 1, 17, 25.436, 1.0239, 4.9576, 0.8957, 1.2077, 2.213, 2.6575, 0.0055325, 0.0066437 + 1, 18, 24.547, 0.98424, 4.8622, 0.87926, 1.1841, 2.2776, 2.6318, 0.0056939, 0.0065795 + 1, 19, 25.915, 0.98948, 6.1254, 0.88612, 1.1872, 2.2761, 2.954, 0.0056903, 0.0073849 + 1, 20, 24.449, 1.0062, 4.3243, 0.8888, 1.1973, 1.9627, 2.482, 0.0049067, 0.0062049 + 1, 21, 23.159, 0.98623, 3.4341, 0.88155, 1.1853, 1.7581, 2.2118, 0.0043952, 0.0055295 + 1, 22, 24.329, 0.98567, 4.6157, 0.88208, 1.185, 2.0245, 2.5642, 0.0050612, 0.0064105 + 1, 23, 25.183, 1.0094, 4.9946, 0.89127, 1.1991, 1.9776, 2.6674, 0.0049439, 0.0066685 + 1, 24, 24.622, 1.0019, 4.5832, 0.88853, 1.1947, 1.9409, 2.5552, 0.0048523, 0.006388 + 1, 25, 24.793, 1.006, 4.673, 0.89098, 1.1971, 1.9307, 2.5801, 0.0048268, 0.0064502 + 1, 26, 25.342, 1.0016, 5.3101, 0.88681, 1.1945, 2.1149, 2.7504, 0.0052872, 0.0068759 + 1, 27, 24.021, 0.99649, 4.0911, 0.88734, 1.1914, 1.9341, 2.4141, 0.0048353, 0.0060353 + 1, 28, 24.732, 1.0186, 4.3599, 0.89473, 1.2046, 2.0108, 2.4922, 0.005027, 0.0062304 + 1, 29, 25.257, 1.0042, 5.1741, 0.88914, 1.196, 2.2823, 2.7149, 0.0057056, 0.0067873 + 1, 30, 25.536, 0.99986, 5.5392, 0.88324, 1.1935, 2.3851, 2.8091, 0.0059627, 0.0070226 + 1, 31, 24.167, 0.99799, 4.2071, 0.88694, 1.1923, 2.0748, 2.4481, 0.005187, 0.0061203 + 1, 32, 25.958, 0.97829, 6.3926, 0.8809, 1.1805, 2.6043, 3.0177, 0.0065107, 0.0075443 + 1, 33, 24.547, 1.0214, 4.1189, 0.89398, 1.2062, 2.0462, 2.4223, 0.0051156, 0.0060558 + 1, 34, 24.786, 1.0085, 4.6156, 0.89409, 1.1986, 1.9424, 2.5642, 0.0048561, 0.0064105 + 1, 35, 23.844, 0.99195, 4.0047, 0.88357, 1.1887, 2.02, 2.3885, 0.00505, 0.0059712 + 1, 36, 27.155, 0.98626, 7.4296, 0.88101, 1.1853, 2.7377, 3.2533, 0.0068443, 0.0081332 + 1, 37, 24.472, 1.0084, 4.305, 0.88918, 1.1985, 1.9983, 2.4764, 0.0049958, 0.0061911 + 1, 38, 25.005, 0.98697, 5.2656, 0.88182, 1.1857, 2.0619, 2.7388, 0.0051548, 0.006847 + 1, 39, 24.279, 1.0083, 4.1138, 0.88902, 1.1985, 1.853, 2.4208, 0.0046325, 0.006052 + 1, 40, 25.858, 1.0023, 5.8106, 0.8882, 1.1949, 2.2899, 2.8771, 0.0057248, 0.0071927 + 1, 41, 25.638, 0.98774, 5.8832, 0.88227, 1.1862, 2.4812, 2.895, 0.006203, 0.0072375 + 1, 42, 23.965, 0.99727, 4.0191, 0.88655, 1.1919, 1.9267, 2.3928, 0.0048168, 0.0059819 + 1, 43, 24.031, 0.99845, 4.0616, 0.88485, 1.1926, 1.8823, 2.4054, 0.0047057, 0.0060135 + 1, 44, 25.155, 1.0141, 4.872, 0.89528, 1.2019, 2.2901, 2.6345, 0.0057253, 0.0065862 + 1, 45, 25.154, 1.0097, 4.9604, 0.89002, 1.1993, 2.1623, 2.6582, 0.0054057, 0.0066456 + 1, 46, 25.148, 0.99191, 5.31, 0.88454, 1.1887, 2.2169, 2.7503, 0.0055422, 0.0068758 + 1, 47, 24.276, 0.99053, 4.4658, 0.88277, 1.1879, 2.1932, 2.5222, 0.0054829, 0.0063056 + 1, 48, 24.408, 0.9864, 4.6796, 0.8839, 1.1854, 2.0512, 2.5819, 0.005128, 0.0064548 + 1, 49, 24.669, 1.0107, 4.4544, 0.88917, 1.1999, 2.0124, 2.519, 0.0050311, 0.0062976 + 1, 50, 23.939, 0.99756, 3.988, 0.88573, 1.1921, 1.9008, 2.3835, 0.0047521, 0.0059587 + 1, 51, 25.386, 0.995, 5.4863, 0.88587, 1.1906, 2.2218, 2.7956, 0.0055544, 0.0069891 + 1, 52, 24.928, 1.0247, 4.4345, 0.89626, 1.2082, 1.973, 2.5134, 0.0049326, 0.0062835 + 1, 53, 24.081, 0.99398, 4.2016, 0.88646, 1.1899, 2.0954, 2.4465, 0.0052384, 0.0061163 + 1, 54, 25.133, 1.0117, 4.8996, 0.89126, 1.2005, 2.1334, 2.6419, 0.0053335, 0.0066048 + 1, 55, 25.994, 1.0277, 5.4398, 0.89669, 1.21, 2.3265, 2.7838, 0.0058161, 0.0069594 + 1, 56, 24.864, 1.0132, 4.6003, 0.89277, 1.2014, 2.1146, 2.56, 0.0052865, 0.0063999 + 1, 57, 25.784, 1.0079, 5.6255, 0.89148, 1.1983, 2.3754, 2.8309, 0.0059385, 0.0070772 + 1, 58, 23.979, 0.99632, 4.0527, 0.88627, 1.1913, 1.9465, 2.4028, 0.0048663, 0.0060069 + 1, 59, 24.177, 0.98396, 4.4978, 0.87864, 1.1839, 2.1776, 2.5313, 0.0054439, 0.0063282 + 1, 60, 24.673, 0.98607, 4.9513, 0.88247, 1.1852, 2.2405, 2.6558, 0.0056013, 0.0066395 + 1, 61, 25.405, 1.014, 5.1252, 0.89488, 1.2019, 2.1746, 2.702, 0.0054365, 0.0067551 + 1, 62, 24.285, 1.0032, 4.2214, 0.88628, 1.1954, 2.0035, 2.4523, 0.0050087, 0.0061307 + 1, 63, 24.816, 1.001, 4.7959, 0.88711, 1.1942, 2.0989, 2.6138, 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LR,validation_loss_f,validation_loss_e,validation_loss,validation_f_mae,validation_f_rmse,validation_e_mae,validation_e_rmse,validation_e/N_mae,validation_e/N_rmse + 0, 6.358, 0.005, 0.99322, 4.4238, 24.288, 0.884, 1.19, 2.02, 2.51, 0.00506, 0.00628 diff --git a/ads_16_length_8000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml b/ads_16_length_8000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..906b5b5aa67bc70a044b209bbfe73545d7dce375 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/results/processed_dataset_755a510232d188b82abbf54786272f77fa69b204/params.yaml @@ -0,0 +1,13 @@ +AtomicData_options: + r_max: 5.0 +ase_args: + format: extxyz +dtype: torch.float64 +file_name: ../trainset.xyz +include_frames: null +include_keys: null +key_mapping: + force: forces +nequip_version: 0.6.1 +root: results +url: null diff --git a/ads_16_length_8000/NEQUIP/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml b/ads_16_length_8000/NEQUIP/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..40b87d4c1e16bd79aa0ed8dfc8a9327facf04bad --- /dev/null +++ b/ads_16_length_8000/NEQUIP/results/processed_dataset_d146e714f50ba36670243578e79f5a5a7b878205/params.yaml @@ -0,0 +1,13 @@ +AtomicData_options: + r_max: 5.0 +ase_args: + format: extxyz +dtype: torch.float64 +file_name: ../testset.xyz +include_frames: null +include_keys: null +key_mapping: + force: forces +nequip_version: 0.6.1 +root: results +url: null diff --git a/ads_16_length_8000/NEQUIP/slurm-254472.out b/ads_16_length_8000/NEQUIP/slurm-254472.out new file mode 100644 index 0000000000000000000000000000000000000000..045d1ed3a2c6d975cd02edb8229ea828294c6536 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/slurm-254472.out @@ -0,0 +1,32 @@ +Train start time: 2024-12-07_06:57:52 +Torch device: cuda +Processing dataset... +Loaded data: Batch(atomic_numbers=[2400000, 1], batch=[2400000], cell=[6000, 3, 3], edge_cell_shift=[82543766, 3], edge_index=[2, 82543766], forces=[2400000, 3], pbc=[6000, 3], pos=[2400000, 3], ptr=[6001], total_energy=[6000, 1]) + processed data size: ~3295.80 MB +Cached processed data to disk +Done! +Successfully loaded the data set of type ASEDataset(6000)... +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/bin/nequip-train", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 94, in main + trainer = fresh_start(config) + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/nequip/scripts/train.py", line 219, in fresh_start + trainer.set_dataset(dataset, validation_dataset) + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/nequip/train/trainer.py", line 1217, in set_dataset + raise ValueError( +ValueError: too little data for training and validation. please reduce n_train and n_val +Traceback (most recent call last): + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/bin/nequip-evaluate", line 8, in + sys.exit(main()) + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/nequip/scripts/evaluate.py", line 196, in main + trainer = torch.load( + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/torch/serialization.py", line 1319, in load + with _open_file_like(f, "rb") as opened_file: + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/torch/serialization.py", line 659, in _open_file_like + return _open_file(name_or_buffer, mode) + File "/users/PAS2490/marcusshen/miniconda3/envs/nequip_env/lib/python3.9/site-packages/torch/serialization.py", line 640, in __init__ + super().__init__(open(name, mode)) +FileNotFoundError: [Errno 2] No such file or directory: 'results/adsorption/trainer.pth' +Train end time: 2024-12-07_07:00:50 +Training duration: 0h 2m 58s diff --git a/ads_16_length_8000/NEQUIP/slurm-257529.out b/ads_16_length_8000/NEQUIP/slurm-257529.out new file mode 100644 index 0000000000000000000000000000000000000000..e5021b0f2acd5383e472f8018f07bfd9c25c38fe --- /dev/null +++ b/ads_16_length_8000/NEQUIP/slurm-257529.out @@ -0,0 +1,7650 @@ +Train start time: 2024-12-08_10:34:37 +Torch device: cuda +Processing dataset... +Loaded data: Batch(atomic_numbers=[3200000, 1], batch=[3200000], cell=[8000, 3, 3], edge_cell_shift=[110025848, 3], edge_index=[2, 110025848], forces=[3200000, 3], pbc=[8000, 3], pos=[3200000, 3], ptr=[8001], total_energy=[8000, 1]) + processed data size: ~4393.16 MB +Cached processed data to disk +Done! +Successfully loaded the data set of type ASEDataset(8000)... +Replace string dataset_per_atom_total_energy_mean to -347.4158314169961 +Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -347.415831]. +Replace string dataset_forces_rms to 1.1935424919787043 +Initially outputs are globally scaled by: 1.1935424919787043, total_energy are globally shifted by None. +Successfully built the network... +Number of weights: 363624 +Number of trainable weights: 363624 +! Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 28.7 1.02 8.37 0.896 1.2 3.39 3.45 0.00848 0.00863 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 6.358 0.005 0.993 4.42 24.3 0.884 1.19 2.02 2.51 0.00506 0.00628 +Wall time: 6.3580403393134475 +! Best model 0 24.288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 24.7 0.996 4.77 0.884 1.19 2.06 2.61 0.00514 0.00652 + 1 118 22.4 0.972 2.93 0.881 1.18 1.57 2.04 0.00391 0.00511 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 29 1.01 8.7 0.894 1.2 3.46 3.52 0.00866 0.0088 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 1 174.389 0.005 0.999 4.88 24.9 0.887 1.19 2.12 2.64 0.00531 0.0066 +! Validation 1 174.389 0.005 0.99 4.08 23.9 0.883 1.19 1.94 2.41 0.00485 0.00603 +Wall time: 174.39011605409905 +! Best model 1 23.881 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 10.6 0.47 1.24 0.616 0.818 1.14 1.33 0.00285 0.00332 + 2 118 9.24 0.401 1.21 0.57 0.756 0.895 1.31 0.00224 0.00328 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 11.1 0.392 3.25 0.556 0.747 2.11 2.15 0.00527 0.00538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 2 341.830 0.005 0.753 2.41 17.5 0.764 1.04 1.43 1.86 0.00358 0.00464 +! Validation 2 341.830 0.005 0.393 4.1 12 0.561 0.748 2.21 2.42 0.00551 0.00604 +Wall time: 341.83007658226416 +! Best model 2 11.957 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 5.96 0.25 0.955 0.448 0.597 0.857 1.17 0.00214 0.00292 + 3 118 6.12 0.246 1.2 0.441 0.592 1.12 1.31 0.00281 0.00326 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 4.75 0.234 0.0671 0.425 0.578 0.225 0.309 0.000562 0.000773 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 3 509.367 0.005 0.292 1.31 7.16 0.482 0.646 1.08 1.37 0.00269 0.00342 +! Validation 3 509.367 0.005 0.237 0.808 5.54 0.435 0.581 0.788 1.07 0.00197 0.00268 +Wall time: 509.3696379433386 +! Best model 3 5.541 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 5.7 0.194 1.83 0.393 0.525 1.4 1.61 0.0035 0.00403 + 4 118 4.16 0.186 0.444 0.387 0.514 0.592 0.796 0.00148 0.00199 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 3.5 0.173 0.038 0.367 0.497 0.187 0.233 0.000468 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 4 676.806 0.005 0.201 1.08 5.11 0.402 0.536 0.987 1.24 0.00247 0.00311 +! Validation 4 676.806 0.005 0.181 0.626 4.25 0.38 0.508 0.69 0.944 0.00173 0.00236 +Wall time: 676.8062033960596 +! Best model 4 4.251 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 3.43 0.15 0.425 0.345 0.462 0.587 0.778 0.00147 0.00195 + 5 118 3.49 0.148 0.541 0.341 0.459 0.765 0.878 0.00191 0.00219 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 2.81 0.137 0.0794 0.326 0.441 0.297 0.336 0.000743 0.000841 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 844.237 0.005 0.16 0.744 3.94 0.357 0.477 0.821 1.03 0.00205 0.00258 +! Validation 5 844.237 0.005 0.147 0.951 3.9 0.341 0.458 0.926 1.16 0.00231 0.00291 +Wall time: 844.2370626013726 +! Best model 5 3.896 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 3.69 0.127 1.14 0.318 0.426 1.1 1.28 0.00276 0.00319 + 6 118 3.58 0.131 0.96 0.323 0.432 1 1.17 0.0025 0.00292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 4.32 0.119 1.94 0.303 0.412 1.66 1.66 0.00414 0.00416 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 1012.072 0.005 0.136 0.806 3.52 0.328 0.44 0.858 1.07 0.00214 0.00268 +! Validation 6 1012.072 0.005 0.129 1.21 3.79 0.318 0.429 1.17 1.31 0.00292 0.00328 +Wall time: 1012.0729714543559 +! Best model 6 3.791 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 2.58 0.117 0.242 0.303 0.408 0.464 0.587 0.00116 0.00147 + 7 118 2.94 0.114 0.657 0.299 0.403 0.894 0.967 0.00224 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 2.52 0.103 0.464 0.283 0.383 0.8 0.813 0.002 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 1179.539 0.005 0.121 0.7 3.11 0.308 0.415 0.784 0.999 0.00196 0.0025 +! Validation 7 1179.539 0.005 0.115 0.362 2.66 0.3 0.405 0.582 0.718 0.00146 0.00179 +Wall time: 1179.5396996671334 +! Best model 7 2.664 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 2.75 0.106 0.637 0.288 0.388 0.813 0.953 0.00203 0.00238 + 8 118 2.71 0.0994 0.717 0.281 0.376 0.769 1.01 0.00192 0.00253 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 1.88 0.0933 0.0173 0.269 0.365 0.105 0.157 0.000262 0.000393 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 1347.121 0.005 0.108 0.637 2.79 0.291 0.392 0.765 0.952 0.00191 0.00238 +! Validation 8 1347.121 0.005 0.105 0.686 2.79 0.287 0.387 0.792 0.989 0.00198 0.00247 +Wall time: 1347.12112014601 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 2.12 0.0943 0.234 0.272 0.367 0.45 0.577 0.00112 0.00144 + 9 118 2.33 0.0942 0.451 0.272 0.366 0.678 0.802 0.0017 0.002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 2.36 0.0858 0.645 0.258 0.35 0.95 0.959 0.00238 0.0024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 1514.584 0.005 0.1 0.652 2.66 0.281 0.378 0.775 0.965 0.00194 0.00241 +! Validation 9 1514.584 0.005 0.0978 0.345 2.3 0.277 0.373 0.579 0.701 0.00145 0.00175 +Wall time: 1514.5844786730595 +! Best model 9 2.300 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 2.1 0.0913 0.273 0.268 0.361 0.484 0.623 0.00121 0.00156 + 10 118 2.51 0.0942 0.625 0.272 0.366 0.838 0.943 0.00209 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 1.89 0.0803 0.282 0.25 0.338 0.621 0.634 0.00155 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 10 1682.217 0.005 0.0928 0.532 2.39 0.27 0.363 0.697 0.87 0.00174 0.00218 +! Validation 10 1682.217 0.005 0.0923 0.258 2.1 0.269 0.363 0.481 0.606 0.0012 0.00151 +Wall time: 1682.2171152411029 +! Best model 10 2.105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 2.74 0.0851 1.04 0.261 0.348 1.11 1.22 0.00278 0.00304 + 11 118 2.36 0.0878 0.608 0.261 0.354 0.658 0.93 0.00165 0.00233 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 1.52 0.0753 0.0117 0.242 0.328 0.0961 0.129 0.00024 0.000322 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 11 1851.670 0.005 0.0875 0.617 2.37 0.263 0.353 0.768 0.938 0.00192 0.00234 +! Validation 11 1851.670 0.005 0.0868 0.533 2.27 0.261 0.352 0.69 0.871 0.00172 0.00218 +Wall time: 1851.6707933051512 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 2.09 0.0828 0.437 0.256 0.343 0.676 0.789 0.00169 0.00197 + 12 118 1.85 0.0837 0.176 0.259 0.345 0.377 0.5 0.000942 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 1.87 0.0703 0.464 0.235 0.316 0.802 0.813 0.002 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 12 2019.796 0.005 0.0836 0.549 2.22 0.257 0.345 0.696 0.887 0.00174 0.00222 +! Validation 12 2019.796 0.005 0.0818 0.285 1.92 0.254 0.341 0.528 0.638 0.00132 0.00159 +Wall time: 2019.79748495901 +! Best model 12 1.921 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 3.29 0.0767 1.75 0.246 0.331 1.49 1.58 0.00372 0.00395 + 13 118 2.35 0.0797 0.754 0.249 0.337 0.968 1.04 0.00242 0.00259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 1.4 0.0685 0.0314 0.231 0.312 0.168 0.211 0.000421 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 13 2187.118 0.005 0.0786 0.558 2.13 0.249 0.335 0.724 0.89 0.00181 0.00223 +! Validation 13 2187.118 0.005 0.0791 0.562 2.14 0.25 0.336 0.739 0.895 0.00185 0.00224 +Wall time: 2187.118289224338 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 1.59 0.0722 0.145 0.24 0.321 0.354 0.454 0.000886 0.00114 + 14 118 1.56 0.07 0.159 0.235 0.316 0.401 0.476 0.001 0.00119 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 1.55 0.0637 0.278 0.223 0.301 0.616 0.629 0.00154 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 14 2354.420 0.005 0.0751 0.508 2.01 0.244 0.327 0.69 0.853 0.00173 0.00213 +! Validation 14 2354.420 0.005 0.0749 1.25 2.75 0.243 0.327 1.22 1.34 0.00304 0.00334 +Wall time: 2354.420513842255 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 1.69 0.0702 0.288 0.236 0.316 0.504 0.641 0.00126 0.0016 + 15 118 1.87 0.068 0.511 0.232 0.311 0.737 0.853 0.00184 0.00213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 1.6 0.0594 0.411 0.217 0.291 0.754 0.765 0.00189 0.00191 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 15 2521.697 0.005 0.0715 0.42 1.85 0.238 0.319 0.618 0.773 0.00154 0.00193 +! Validation 15 2521.697 0.005 0.0705 0.246 1.66 0.236 0.317 0.481 0.592 0.0012 0.00148 +Wall time: 2521.6971200071275 +! Best model 15 1.656 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 1.64 0.0687 0.265 0.234 0.313 0.499 0.615 0.00125 0.00154 + 16 118 1.53 0.0656 0.221 0.227 0.306 0.474 0.561 0.00119 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 1.58 0.0578 0.427 0.214 0.287 0.769 0.78 0.00192 0.00195 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 16 2689.018 0.005 0.0686 0.512 1.88 0.233 0.313 0.691 0.856 0.00173 0.00214 +! Validation 16 2689.018 0.005 0.0679 0.252 1.61 0.232 0.311 0.49 0.599 0.00123 0.0015 +Wall time: 2689.0184356682003 +! Best model 16 1.609 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 2.03 0.0655 0.724 0.228 0.305 0.894 1.02 0.00224 0.00254 + 17 118 1.57 0.0665 0.242 0.231 0.308 0.484 0.587 0.00121 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 1.42 0.0553 0.311 0.209 0.281 0.654 0.666 0.00164 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 17 2856.605 0.005 0.0651 0.438 1.74 0.228 0.305 0.643 0.791 0.00161 0.00198 +! Validation 17 2856.605 0.005 0.0652 0.196 1.5 0.227 0.305 0.426 0.528 0.00107 0.00132 +Wall time: 2856.6054014242254 +! Best model 17 1.499 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 1.57 0.0626 0.313 0.224 0.299 0.555 0.668 0.00139 0.00167 + 18 118 1.47 0.06 0.274 0.219 0.292 0.532 0.625 0.00133 0.00156 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 1.4 0.053 0.344 0.205 0.275 0.691 0.7 0.00173 0.00175 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 18 3024.126 0.005 0.063 0.456 1.71 0.224 0.3 0.655 0.807 0.00164 0.00202 +! Validation 18 3024.126 0.005 0.0625 0.205 1.46 0.223 0.298 0.438 0.54 0.0011 0.00135 +Wall time: 3024.126394457184 +! Best model 18 1.456 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 1.91 0.0588 0.73 0.217 0.289 0.884 1.02 0.00221 0.00255 + 19 118 1.59 0.0632 0.323 0.224 0.3 0.518 0.678 0.0013 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 2.4 0.0514 1.37 0.202 0.271 1.39 1.4 0.00348 0.00349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 19 3191.427 0.005 0.0607 0.511 1.72 0.22 0.294 0.701 0.854 0.00175 0.00214 +! Validation 19 3191.427 0.005 0.0606 0.733 1.95 0.219 0.294 0.917 1.02 0.00229 0.00256 +Wall time: 3191.4276354052126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 2.03 0.0586 0.855 0.216 0.289 1.01 1.1 0.00252 0.00276 + 20 118 1.52 0.0593 0.336 0.217 0.291 0.556 0.692 0.00139 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 1.12 0.0483 0.153 0.197 0.262 0.451 0.467 0.00113 0.00117 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 20 3358.732 0.005 0.058 0.34 1.5 0.215 0.287 0.555 0.696 0.00139 0.00174 +! Validation 20 3358.732 0.005 0.0579 0.707 1.86 0.215 0.287 0.895 1 0.00224 0.00251 +Wall time: 3358.732003442012 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 1.8 0.0547 0.704 0.21 0.279 0.902 1 0.00226 0.0025 + 21 118 1.35 0.0556 0.237 0.21 0.281 0.47 0.581 0.00117 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 1 0.0469 0.0609 0.194 0.259 0.268 0.295 0.00067 0.000737 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 21 3526.019 0.005 0.0564 0.513 1.64 0.212 0.283 0.701 0.857 0.00175 0.00214 +! Validation 21 3526.019 0.005 0.0563 0.198 1.32 0.212 0.283 0.414 0.531 0.00104 0.00133 +Wall time: 3526.0196879622526 +! Best model 21 1.324 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 1.54 0.0549 0.437 0.21 0.28 0.68 0.789 0.0017 0.00197 + 22 118 1.18 0.0499 0.185 0.202 0.267 0.413 0.514 0.00103 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 1.4 0.0459 0.481 0.192 0.256 0.82 0.827 0.00205 0.00207 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 22 3693.414 0.005 0.0546 0.471 1.56 0.209 0.279 0.665 0.82 0.00166 0.00205 +! Validation 22 3693.414 0.005 0.0545 0.237 1.33 0.209 0.279 0.477 0.582 0.00119 0.00145 +Wall time: 3693.414395957254 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 1.39 0.0517 0.351 0.204 0.271 0.579 0.707 0.00145 0.00177 + 23 118 1.34 0.0507 0.327 0.201 0.269 0.605 0.683 0.00151 0.00171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 1.34 0.0444 0.452 0.188 0.252 0.795 0.802 0.00199 0.00201 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 23 3860.712 0.005 0.0528 0.436 1.49 0.206 0.274 0.635 0.788 0.00159 0.00197 +! Validation 23 3860.712 0.005 0.0531 0.246 1.31 0.205 0.275 0.486 0.592 0.00122 0.00148 +Wall time: 3860.712141085416 +! Best model 23 1.307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 1.12 0.0494 0.137 0.2 0.265 0.354 0.441 0.000884 0.0011 + 24 118 2.04 0.0513 1.02 0.203 0.27 1.17 1.2 0.00293 0.00301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 2.9 0.0429 2.05 0.185 0.247 1.71 1.71 0.00426 0.00427 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 24 4029.069 0.005 0.0511 0.407 1.43 0.202 0.27 0.619 0.757 0.00155 0.00189 +! Validation 24 4029.069 0.005 0.0512 1.28 2.3 0.202 0.27 1.27 1.35 0.00318 0.00337 +Wall time: 4029.0699212430045 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 1.71 0.0518 0.672 0.203 0.272 0.9 0.979 0.00225 0.00245 + 25 118 1.32 0.05 0.317 0.202 0.267 0.482 0.672 0.00121 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 1.08 0.0415 0.25 0.182 0.243 0.585 0.597 0.00146 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 25 4207.325 0.005 0.0495 0.419 1.41 0.199 0.266 0.626 0.773 0.00156 0.00193 +! Validation 25 4207.325 0.005 0.0503 0.164 1.17 0.2 0.268 0.383 0.483 0.000958 0.00121 +Wall time: 4207.325034392066 +! Best model 25 1.169 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 1.14 0.0473 0.191 0.195 0.259 0.431 0.521 0.00108 0.0013 + 26 118 1.53 0.0436 0.657 0.187 0.249 0.92 0.968 0.0023 0.00242 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 0.89 0.0396 0.0978 0.177 0.238 0.357 0.373 0.000892 0.000933 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 26 4374.650 0.005 0.0482 0.404 1.37 0.197 0.262 0.618 0.757 0.00155 0.00189 +! Validation 26 4374.650 0.005 0.0484 0.535 1.5 0.196 0.262 0.766 0.873 0.00192 0.00218 +Wall time: 4374.650422966108 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 1.05 0.0459 0.132 0.192 0.256 0.35 0.433 0.000875 0.00108 + 27 118 1.26 0.047 0.322 0.195 0.259 0.614 0.678 0.00153 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 0.984 0.0385 0.215 0.175 0.234 0.547 0.553 0.00137 0.00138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 27 4542.068 0.005 0.0471 0.384 1.33 0.194 0.259 0.563 0.74 0.00141 0.00185 +! Validation 27 4542.068 0.005 0.0467 0.145 1.08 0.193 0.258 0.362 0.455 0.000905 0.00114 +Wall time: 4542.068860084284 +! Best model 27 1.079 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 1.09 0.0437 0.219 0.187 0.249 0.438 0.558 0.0011 0.0014 + 28 118 1.22 0.0441 0.335 0.189 0.251 0.595 0.69 0.00149 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 0.804 0.0369 0.0652 0.171 0.229 0.289 0.305 0.000722 0.000762 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 28 4709.400 0.005 0.0451 0.356 1.26 0.19 0.253 0.573 0.712 0.00143 0.00178 +! Validation 28 4709.400 0.005 0.0451 0.153 1.06 0.189 0.253 0.364 0.467 0.000911 0.00117 +Wall time: 4709.400411950424 +! Best model 28 1.055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 1.29 0.0448 0.397 0.19 0.253 0.661 0.752 0.00165 0.00188 + 29 118 1.4 0.0407 0.589 0.18 0.241 0.767 0.916 0.00192 0.00229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 0.827 0.0361 0.105 0.169 0.227 0.376 0.387 0.000941 0.000967 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 29 4876.716 0.005 0.0442 0.412 1.3 0.188 0.251 0.618 0.765 0.00155 0.00191 +! Validation 29 4876.716 0.005 0.0441 0.535 1.42 0.187 0.251 0.774 0.873 0.00194 0.00218 +Wall time: 4876.716017507017 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 0.94 0.0418 0.104 0.183 0.244 0.298 0.385 0.000745 0.000962 + 30 118 1.02 0.042 0.178 0.185 0.245 0.4 0.503 0.001 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 0.732 0.035 0.0317 0.167 0.223 0.189 0.213 0.000473 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 30 5044.004 0.005 0.0432 0.404 1.27 0.186 0.248 0.608 0.761 0.00152 0.0019 +! Validation 30 5044.004 0.005 0.043 0.355 1.22 0.185 0.248 0.603 0.711 0.00151 0.00178 +Wall time: 5044.004979152232 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 1.38 0.0422 0.539 0.184 0.245 0.809 0.876 0.00202 0.00219 + 31 118 1.22 0.0463 0.292 0.194 0.257 0.531 0.645 0.00133 0.00161 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 1.43 0.0342 0.748 0.165 0.221 1.03 1.03 0.00257 0.00258 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 31 5211.399 0.005 0.0414 0.338 1.17 0.182 0.243 0.567 0.694 0.00142 0.00173 +! Validation 31 5211.399 0.005 0.0418 0.413 1.25 0.182 0.244 0.673 0.767 0.00168 0.00192 +Wall time: 5211.399076862261 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 0.994 0.0406 0.182 0.18 0.24 0.406 0.51 0.00101 0.00127 + 32 118 0.9 0.0395 0.11 0.178 0.237 0.328 0.396 0.000821 0.000989 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 0.713 0.0334 0.0453 0.164 0.218 0.233 0.254 0.000583 0.000635 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 32 5378.698 0.005 0.0407 0.418 1.23 0.181 0.241 0.634 0.774 0.00159 0.00193 +! Validation 32 5378.698 0.005 0.0414 0.363 1.19 0.182 0.243 0.61 0.719 0.00153 0.0018 +Wall time: 5378.6982706203125 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 0.938 0.0404 0.131 0.18 0.24 0.351 0.431 0.000877 0.00108 + 33 118 0.98 0.0388 0.205 0.176 0.235 0.438 0.541 0.0011 0.00135 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 0.676 0.0329 0.0172 0.162 0.217 0.13 0.157 0.000324 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 33 5545.987 0.005 0.0403 0.463 1.27 0.18 0.24 0.667 0.813 0.00167 0.00203 +! Validation 33 5545.987 0.005 0.0408 0.319 1.13 0.18 0.241 0.563 0.675 0.00141 0.00169 +Wall time: 5545.987347880378 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 1.17 0.0389 0.393 0.177 0.235 0.665 0.748 0.00166 0.00187 + 34 118 2 0.0373 1.25 0.173 0.231 1.24 1.34 0.00309 0.00334 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 0.863 0.032 0.224 0.16 0.213 0.556 0.564 0.00139 0.00141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 34 5713.788 0.005 0.0388 0.362 1.14 0.176 0.235 0.582 0.712 0.00146 0.00178 +! Validation 34 5713.788 0.005 0.0398 0.873 1.67 0.178 0.238 1.02 1.12 0.00255 0.00279 +Wall time: 5713.788826073054 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 0.934 0.0375 0.184 0.174 0.231 0.45 0.512 0.00113 0.00128 + 35 118 1.01 0.0402 0.206 0.178 0.239 0.454 0.542 0.00113 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 0.769 0.0307 0.155 0.157 0.209 0.465 0.47 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 35 5881.085 0.005 0.0379 0.308 1.07 0.175 0.232 0.532 0.663 0.00133 0.00166 +! Validation 35 5881.085 0.005 0.0382 0.569 1.33 0.174 0.233 0.81 0.9 0.00202 0.00225 +Wall time: 5881.085445408244 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 0.857 0.0368 0.122 0.172 0.229 0.328 0.416 0.000819 0.00104 + 36 118 0.94 0.0379 0.183 0.174 0.232 0.441 0.51 0.0011 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 1.46 0.0299 0.86 0.155 0.206 1.11 1.11 0.00276 0.00277 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 36 6048.490 0.005 0.037 0.369 1.11 0.172 0.23 0.578 0.726 0.00144 0.00181 +! Validation 36 6048.490 0.005 0.0371 0.562 1.31 0.172 0.23 0.812 0.895 0.00203 0.00224 +Wall time: 6048.490027527325 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 1.03 0.0359 0.312 0.169 0.226 0.589 0.666 0.00147 0.00167 + 37 118 0.803 0.0355 0.0928 0.169 0.225 0.29 0.364 0.000725 0.000909 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 0.579 0.0288 0.00413 0.152 0.202 0.0751 0.0767 0.000188 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 37 6215.764 0.005 0.0361 0.366 1.09 0.17 0.227 0.595 0.724 0.00149 0.00181 +! Validation 37 6215.764 0.005 0.0363 0.192 0.918 0.17 0.227 0.416 0.523 0.00104 0.00131 +Wall time: 6215.764725954272 +! Best model 37 0.918 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 0.906 0.0345 0.216 0.166 0.222 0.46 0.555 0.00115 0.00139 + 38 118 1.09 0.0367 0.359 0.173 0.229 0.623 0.716 0.00156 0.00179 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 1.1 0.0289 0.526 0.152 0.203 0.863 0.866 0.00216 0.00216 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 38 6383.056 0.005 0.0354 0.382 1.09 0.169 0.225 0.602 0.738 0.0015 0.00184 +! Validation 38 6383.056 0.005 0.0361 0.307 1.03 0.169 0.227 0.572 0.661 0.00143 0.00165 +Wall time: 6383.05659274105 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 1.3 0.0344 0.614 0.167 0.221 0.868 0.935 0.00217 0.00234 + 39 118 0.812 0.0314 0.184 0.158 0.211 0.449 0.512 0.00112 0.00128 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 0.576 0.0279 0.017 0.15 0.2 0.137 0.155 0.000343 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 39 6550.324 0.005 0.0345 0.388 1.08 0.167 0.222 0.617 0.744 0.00154 0.00186 +! Validation 39 6550.324 0.005 0.0354 0.248 0.956 0.168 0.225 0.49 0.594 0.00123 0.00149 +Wall time: 6550.324303986039 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 0.821 0.0331 0.158 0.164 0.217 0.392 0.474 0.000981 0.00119 + 40 118 1.04 0.0341 0.363 0.166 0.22 0.644 0.719 0.00161 0.0018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 1.1 0.0278 0.541 0.149 0.199 0.875 0.878 0.00219 0.00219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 40 6717.588 0.005 0.0343 0.409 1.09 0.166 0.221 0.62 0.763 0.00155 0.00191 +! Validation 40 6717.588 0.005 0.0346 0.313 1.01 0.166 0.222 0.579 0.667 0.00145 0.00167 +Wall time: 6717.588284178171 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 1.08 0.0339 0.403 0.165 0.22 0.657 0.758 0.00164 0.00189 + 41 118 0.773 0.0331 0.111 0.164 0.217 0.339 0.398 0.000848 0.000994 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 0.841 0.0265 0.31 0.147 0.194 0.662 0.664 0.00165 0.00166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 41 6884.975 0.005 0.0329 0.276 0.934 0.163 0.216 0.505 0.628 0.00126 0.00157 +! Validation 41 6884.975 0.005 0.0334 0.172 0.839 0.164 0.218 0.405 0.495 0.00101 0.00124 +Wall time: 6884.975482440088 +! Best model 41 0.839 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 1.21 0.0345 0.522 0.167 0.222 0.792 0.863 0.00198 0.00216 + 42 118 0.729 0.0308 0.114 0.158 0.209 0.32 0.403 0.000801 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 0.586 0.0258 0.0685 0.145 0.192 0.305 0.312 0.000762 0.000781 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 42 7052.265 0.005 0.0323 0.391 1.04 0.161 0.214 0.591 0.748 0.00148 0.00187 +! Validation 42 7052.265 0.005 0.0329 0.35 1.01 0.162 0.217 0.612 0.706 0.00153 0.00177 +Wall time: 7052.265214377083 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 0.903 0.0302 0.3 0.157 0.207 0.594 0.654 0.00148 0.00163 + 43 118 2.35 0.0382 1.59 0.176 0.233 1.45 1.5 0.00361 0.00376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 2.11 0.0355 1.4 0.169 0.225 1.41 1.41 0.00352 0.00353 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 43 7219.542 0.005 0.0309 0.293 0.912 0.158 0.21 0.48 0.636 0.0012 0.00159 +! Validation 43 7219.542 0.005 0.043 0.596 1.45 0.185 0.247 0.828 0.921 0.00207 0.0023 +Wall time: 7219.542159063276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 0.649 0.0285 0.0797 0.152 0.201 0.254 0.337 0.000635 0.000842 + 44 118 0.779 0.0316 0.147 0.158 0.212 0.375 0.458 0.000938 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 2.67 0.0255 2.16 0.143 0.191 1.75 1.76 0.00439 0.00439 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 44 7386.814 0.005 0.0312 0.335 0.959 0.159 0.211 0.539 0.692 0.00135 0.00173 +! Validation 44 7386.814 0.005 0.0326 1.27 1.92 0.161 0.216 1.28 1.34 0.00321 0.00336 +Wall time: 7386.8146933563985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 1.24 0.0308 0.627 0.158 0.21 0.895 0.945 0.00224 0.00236 + 45 118 0.73 0.03 0.13 0.156 0.207 0.374 0.431 0.000934 0.00108 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 0.688 0.0244 0.201 0.141 0.186 0.531 0.535 0.00133 0.00134 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 45 7554.103 0.005 0.0305 0.416 1.03 0.157 0.208 0.619 0.772 0.00155 0.00193 +! Validation 45 7554.103 0.005 0.031 0.619 1.24 0.157 0.21 0.86 0.939 0.00215 0.00235 +Wall time: 7554.103037211113 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 0.653 0.0283 0.0871 0.152 0.201 0.291 0.352 0.000727 0.00088 + 46 118 0.704 0.029 0.124 0.154 0.203 0.335 0.421 0.000837 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 0.67 0.0225 0.22 0.136 0.179 0.557 0.559 0.00139 0.0014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 46 7723.808 0.005 0.0286 0.23 0.802 0.152 0.202 0.453 0.573 0.00113 0.00143 +! Validation 46 7723.808 0.005 0.029 0.177 0.757 0.152 0.203 0.406 0.502 0.00102 0.00125 +Wall time: 7723.808401583228 +! Best model 46 0.757 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 0.767 0.0278 0.211 0.15 0.199 0.467 0.548 0.00117 0.00137 + 47 118 0.662 0.0259 0.145 0.146 0.192 0.409 0.454 0.00102 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 0.434 0.0216 0.00272 0.134 0.175 0.0497 0.0622 0.000124 0.000156 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 47 7891.290 0.005 0.0279 0.294 0.851 0.15 0.199 0.531 0.648 0.00133 0.00162 +! Validation 47 7891.290 0.005 0.028 0.144 0.704 0.15 0.2 0.367 0.453 0.000918 0.00113 +Wall time: 7891.290293193422 +! Best model 47 0.704 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 0.819 0.027 0.278 0.148 0.196 0.56 0.63 0.0014 0.00157 + 48 118 0.996 0.026 0.476 0.147 0.192 0.765 0.823 0.00191 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 0.613 0.0213 0.187 0.133 0.174 0.514 0.516 0.00128 0.00129 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 48 8058.767 0.005 0.0273 0.372 0.918 0.149 0.197 0.603 0.728 0.00151 0.00182 +! Validation 48 8058.767 0.005 0.0277 0.415 0.968 0.149 0.199 0.683 0.768 0.00171 0.00192 +Wall time: 8058.767576611135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 0.585 0.0257 0.0716 0.145 0.191 0.25 0.319 0.000625 0.000799 + 49 118 0.566 0.0242 0.0824 0.141 0.186 0.305 0.343 0.000762 0.000856 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 0.648 0.0207 0.234 0.132 0.172 0.574 0.578 0.00144 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 49 8226.225 0.005 0.0263 0.266 0.791 0.146 0.193 0.502 0.617 0.00125 0.00154 +! Validation 49 8226.225 0.005 0.0267 0.184 0.717 0.147 0.195 0.425 0.512 0.00106 0.00128 +Wall time: 8226.225609230343 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 0.888 0.0251 0.385 0.143 0.189 0.665 0.741 0.00166 0.00185 + 50 118 0.872 0.0262 0.347 0.146 0.193 0.654 0.703 0.00163 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 0.542 0.0194 0.155 0.127 0.166 0.465 0.47 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 50 8393.801 0.005 0.0252 0.244 0.749 0.143 0.19 0.471 0.589 0.00118 0.00147 +! Validation 50 8393.801 0.005 0.0255 0.112 0.622 0.143 0.191 0.318 0.4 0.000796 0.001 +Wall time: 8393.801434432156 +! Best model 50 0.622 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 1.36 0.0261 0.835 0.146 0.193 1.04 1.09 0.00259 0.00273 + 51 118 0.534 0.0226 0.0818 0.137 0.179 0.271 0.341 0.000676 0.000853 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 0.423 0.0196 0.0308 0.128 0.167 0.198 0.209 0.000495 0.000524 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 51 8561.290 0.005 0.025 0.369 0.869 0.143 0.189 0.584 0.727 0.00146 0.00182 +! Validation 51 8561.290 0.005 0.0258 0.091 0.606 0.144 0.192 0.292 0.36 0.000729 0.0009 +Wall time: 8561.29105349537 +! Best model 51 0.606 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.683 0.0239 0.205 0.139 0.185 0.453 0.54 0.00113 0.00135 + 52 118 0.537 0.0245 0.0459 0.141 0.187 0.199 0.256 0.000497 0.000639 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 0.975 0.0187 0.602 0.125 0.163 0.922 0.926 0.00231 0.00232 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 52 8728.774 0.005 0.0238 0.198 0.675 0.139 0.184 0.428 0.533 0.00107 0.00133 +! Validation 52 8728.774 0.005 0.0246 0.361 0.854 0.141 0.187 0.644 0.717 0.00161 0.00179 +Wall time: 8728.774307037238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 0.54 0.0222 0.0962 0.135 0.178 0.307 0.37 0.000767 0.000925 + 53 118 0.457 0.0202 0.054 0.129 0.169 0.235 0.277 0.000586 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 0.398 0.0177 0.0443 0.122 0.159 0.235 0.251 0.000588 0.000628 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 53 8896.247 0.005 0.0231 0.234 0.696 0.137 0.181 0.462 0.579 0.00116 0.00145 +! Validation 53 8896.247 0.005 0.0234 0.0794 0.547 0.137 0.183 0.266 0.336 0.000665 0.000841 +Wall time: 8896.247454642318 +! Best model 53 0.547 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 0.652 0.0216 0.219 0.133 0.175 0.458 0.559 0.00115 0.0014 + 54 118 1.17 0.0199 0.77 0.128 0.168 1.02 1.05 0.00254 0.00262 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 0.918 0.0174 0.57 0.121 0.158 0.897 0.901 0.00224 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 54 9065.851 0.005 0.0226 0.248 0.699 0.136 0.179 0.454 0.59 0.00114 0.00147 +! Validation 54 9065.851 0.005 0.0231 0.8 1.26 0.136 0.181 1.01 1.07 0.00252 0.00267 +Wall time: 9065.851641494315 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 1.03 0.0241 0.544 0.14 0.185 0.815 0.88 0.00204 0.0022 + 55 118 0.495 0.0215 0.0645 0.133 0.175 0.27 0.303 0.000674 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 0.437 0.0177 0.0828 0.122 0.159 0.336 0.343 0.00084 0.000858 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 55 9234.576 0.005 0.0225 0.331 0.782 0.135 0.179 0.555 0.689 0.00139 0.00172 +! Validation 55 9234.576 0.005 0.0231 0.0885 0.551 0.137 0.181 0.281 0.355 0.000701 0.000887 +Wall time: 9234.576426569372 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 0.503 0.0215 0.0737 0.133 0.175 0.249 0.324 0.000622 0.00081 + 56 118 0.522 0.0226 0.0693 0.137 0.18 0.26 0.314 0.00065 0.000785 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 1.14 0.0178 0.782 0.122 0.159 1.05 1.06 0.00263 0.00264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 56 9402.461 0.005 0.0221 0.311 0.754 0.134 0.178 0.547 0.667 0.00137 0.00167 +! Validation 56 9402.461 0.005 0.0228 0.591 1.05 0.136 0.18 0.856 0.918 0.00214 0.00229 +Wall time: 9402.461244800128 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 0.902 0.0216 0.47 0.133 0.175 0.747 0.818 0.00187 0.00205 + 57 118 0.513 0.0198 0.116 0.128 0.168 0.347 0.406 0.000869 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 0.348 0.0167 0.014 0.119 0.154 0.113 0.141 0.000282 0.000353 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 57 9570.160 0.005 0.0214 0.229 0.657 0.132 0.175 0.459 0.572 0.00115 0.00143 +! Validation 57 9570.160 0.005 0.0218 0.0776 0.514 0.133 0.176 0.266 0.333 0.000664 0.000831 +Wall time: 9570.16061371006 +! Best model 57 0.514 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 0.49 0.021 0.0691 0.131 0.173 0.247 0.314 0.000616 0.000784 + 58 118 0.494 0.0198 0.0984 0.128 0.168 0.298 0.374 0.000746 0.000936 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 0.331 0.0162 0.00705 0.117 0.152 0.0874 0.1 0.000219 0.00025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 58 9737.695 0.005 0.021 0.243 0.664 0.131 0.173 0.47 0.59 0.00117 0.00148 +! Validation 58 9737.695 0.005 0.0214 0.12 0.548 0.131 0.175 0.329 0.414 0.000823 0.00103 +Wall time: 9737.69509622315 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.711 0.0202 0.307 0.128 0.17 0.611 0.662 0.00153 0.00165 + 59 118 0.694 0.0202 0.289 0.129 0.17 0.586 0.642 0.00146 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 0.341 0.0162 0.0179 0.117 0.152 0.126 0.16 0.000314 0.000399 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 59 9906.884 0.005 0.0204 0.222 0.629 0.129 0.17 0.452 0.561 0.00113 0.0014 +! Validation 59 9906.884 0.005 0.0212 0.127 0.551 0.13 0.174 0.355 0.426 0.000889 0.00106 +Wall time: 9906.88420266239 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.541 0.0196 0.15 0.127 0.167 0.386 0.462 0.000966 0.00116 + 60 118 0.478 0.019 0.0966 0.125 0.165 0.311 0.371 0.000777 0.000928 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 0.502 0.0161 0.181 0.116 0.151 0.494 0.507 0.00124 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 60 10074.509 0.005 0.0203 0.263 0.669 0.129 0.17 0.482 0.614 0.00121 0.00153 +! Validation 60 10074.509 0.005 0.0208 0.14 0.557 0.129 0.172 0.367 0.447 0.000916 0.00112 +Wall time: 10074.509431233164 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.498 0.0194 0.109 0.126 0.166 0.315 0.394 0.000787 0.000985 + 61 118 0.445 0.0198 0.049 0.126 0.168 0.225 0.264 0.000562 0.000661 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 0.319 0.0152 0.0147 0.113 0.147 0.11 0.145 0.000275 0.000361 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 61 10242.014 0.005 0.0196 0.191 0.583 0.126 0.167 0.428 0.523 0.00107 0.00131 +! Validation 61 10242.014 0.005 0.0202 0.138 0.541 0.127 0.169 0.364 0.443 0.00091 0.00111 +Wall time: 10242.01426461339 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 0.901 0.0191 0.519 0.125 0.165 0.795 0.86 0.00199 0.00215 + 62 118 0.572 0.0198 0.177 0.127 0.168 0.433 0.501 0.00108 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 0.389 0.0151 0.0865 0.113 0.147 0.337 0.351 0.000843 0.000878 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 62 10409.499 0.005 0.0193 0.225 0.611 0.125 0.166 0.457 0.566 0.00114 0.00142 +! Validation 62 10409.499 0.005 0.02 0.0894 0.489 0.127 0.169 0.286 0.357 0.000716 0.000892 +Wall time: 10409.499451737385 +! Best model 62 0.489 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 0.448 0.0185 0.0769 0.123 0.162 0.268 0.331 0.000671 0.000828 + 63 118 0.509 0.0188 0.134 0.124 0.163 0.388 0.437 0.00097 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 0.451 0.015 0.151 0.112 0.146 0.451 0.463 0.00113 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 63 10577.429 0.005 0.0191 0.225 0.607 0.125 0.165 0.461 0.566 0.00115 0.00142 +! Validation 63 10577.429 0.005 0.0196 0.323 0.714 0.125 0.167 0.606 0.678 0.00152 0.00169 +Wall time: 10577.429853369016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.496 0.0186 0.124 0.124 0.163 0.347 0.421 0.000867 0.00105 + 64 118 0.451 0.0177 0.0974 0.121 0.159 0.293 0.372 0.000733 0.000931 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 0.304 0.0148 0.00843 0.111 0.145 0.0926 0.11 0.000231 0.000274 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 64 10744.684 0.005 0.0188 0.26 0.637 0.124 0.164 0.498 0.61 0.00124 0.00152 +! Validation 64 10744.684 0.005 0.0194 0.092 0.48 0.125 0.166 0.289 0.362 0.000722 0.000905 +Wall time: 10744.68458234705 +! Best model 64 0.480 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 0.52 0.0179 0.162 0.121 0.16 0.405 0.48 0.00101 0.0012 + 65 118 0.413 0.0187 0.039 0.123 0.163 0.194 0.236 0.000485 0.000589 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 0.387 0.0146 0.0962 0.111 0.144 0.355 0.37 0.000887 0.000925 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 65 10914.305 0.005 0.0184 0.203 0.572 0.122 0.162 0.43 0.54 0.00107 0.00135 +! Validation 65 10914.305 0.005 0.019 0.105 0.485 0.123 0.165 0.312 0.386 0.000781 0.000965 +Wall time: 10914.305456178263 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.441 0.0178 0.0859 0.12 0.159 0.271 0.35 0.000678 0.000874 + 66 118 0.845 0.0177 0.491 0.12 0.159 0.8 0.836 0.002 0.00209 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 0.646 0.014 0.366 0.109 0.141 0.714 0.722 0.00178 0.00181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 66 11083.787 0.005 0.018 0.185 0.545 0.121 0.16 0.418 0.51 0.00104 0.00128 +! Validation 66 11083.787 0.005 0.0186 0.524 0.896 0.122 0.163 0.807 0.864 0.00202 0.00216 +Wall time: 11083.787373758387 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.498 0.0178 0.141 0.12 0.159 0.375 0.448 0.000937 0.00112 + 67 118 0.373 0.0173 0.0261 0.119 0.157 0.139 0.193 0.000348 0.000482 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 0.287 0.0138 0.0111 0.107 0.14 0.114 0.126 0.000286 0.000314 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 67 11252.676 0.005 0.0178 0.201 0.556 0.12 0.159 0.435 0.536 0.00109 0.00134 +! Validation 67 11252.676 0.005 0.0182 0.0749 0.439 0.121 0.161 0.264 0.327 0.000659 0.000817 +Wall time: 11252.676954683382 +! Best model 67 0.439 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.465 0.0179 0.107 0.121 0.16 0.326 0.39 0.000815 0.000975 + 68 118 0.431 0.0196 0.0388 0.125 0.167 0.193 0.235 0.000484 0.000588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 0.322 0.0135 0.0529 0.106 0.139 0.25 0.274 0.000625 0.000686 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 68 11420.006 0.005 0.0175 0.186 0.537 0.119 0.158 0.401 0.517 0.001 0.00129 +! Validation 68 11420.006 0.005 0.0178 0.078 0.434 0.119 0.159 0.265 0.333 0.000663 0.000833 +Wall time: 11420.00691516418 +! Best model 68 0.434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.838 0.0178 0.481 0.12 0.159 0.779 0.828 0.00195 0.00207 + 69 118 0.529 0.0165 0.2 0.116 0.153 0.499 0.534 0.00125 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 0.419 0.0139 0.141 0.108 0.141 0.437 0.447 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 69 11587.610 0.005 0.0176 0.291 0.643 0.12 0.158 0.523 0.645 0.00131 0.00161 +! Validation 69 11587.610 0.005 0.0183 0.117 0.483 0.121 0.162 0.33 0.408 0.000825 0.00102 +Wall time: 11587.610626030248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.405 0.0174 0.0562 0.119 0.158 0.224 0.283 0.000561 0.000707 + 70 118 0.365 0.0155 0.0547 0.113 0.149 0.249 0.279 0.000623 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 0.341 0.0131 0.0795 0.105 0.136 0.32 0.336 0.0008 0.000841 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 70 11754.952 0.005 0.017 0.156 0.496 0.118 0.156 0.381 0.473 0.000953 0.00118 +! Validation 70 11754.952 0.005 0.0174 0.0976 0.445 0.118 0.157 0.297 0.373 0.000744 0.000932 +Wall time: 11754.952778371982 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 0.658 0.0168 0.322 0.117 0.155 0.631 0.678 0.00158 0.00169 + 71 118 0.401 0.0162 0.0779 0.115 0.152 0.278 0.333 0.000695 0.000833 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 0.443 0.013 0.182 0.104 0.136 0.499 0.51 0.00125 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 71 11922.301 0.005 0.0168 0.234 0.57 0.117 0.155 0.485 0.579 0.00121 0.00145 +! Validation 71 11922.301 0.005 0.0174 0.219 0.567 0.118 0.157 0.469 0.558 0.00117 0.0014 +Wall time: 11922.301891655196 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 0.499 0.0173 0.153 0.118 0.157 0.403 0.467 0.00101 0.00117 + 72 118 0.494 0.0169 0.156 0.117 0.155 0.414 0.471 0.00103 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 0.623 0.0134 0.355 0.106 0.138 0.703 0.711 0.00176 0.00178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 72 12089.663 0.005 0.0168 0.22 0.555 0.117 0.154 0.454 0.561 0.00113 0.0014 +! Validation 72 12089.663 0.005 0.0177 0.529 0.884 0.119 0.159 0.807 0.868 0.00202 0.00217 +Wall time: 12089.663936155383 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 0.839 0.0163 0.514 0.115 0.152 0.798 0.855 0.002 0.00214 + 73 118 0.367 0.0159 0.049 0.113 0.15 0.206 0.264 0.000514 0.000661 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 0.361 0.0129 0.104 0.104 0.135 0.374 0.385 0.000936 0.000963 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 73 12257.023 0.005 0.0165 0.214 0.544 0.116 0.154 0.448 0.553 0.00112 0.00138 +! Validation 73 12257.023 0.005 0.0169 0.216 0.554 0.116 0.155 0.48 0.555 0.0012 0.00139 +Wall time: 12257.023547826335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 0.411 0.0165 0.0801 0.115 0.153 0.286 0.338 0.000715 0.000845 + 74 118 0.61 0.0147 0.315 0.11 0.145 0.599 0.67 0.0015 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 0.476 0.0125 0.226 0.103 0.133 0.56 0.567 0.0014 0.00142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 74 12424.516 0.005 0.0163 0.213 0.538 0.115 0.152 0.447 0.55 0.00112 0.00138 +! Validation 74 12424.516 0.005 0.0167 0.331 0.665 0.115 0.154 0.62 0.687 0.00155 0.00172 +Wall time: 12424.51602396043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 0.378 0.0157 0.0638 0.113 0.15 0.237 0.302 0.000592 0.000754 + 75 118 0.357 0.0151 0.054 0.111 0.147 0.232 0.277 0.000581 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 0.364 0.0122 0.12 0.101 0.132 0.402 0.414 0.001 0.00104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 75 12591.887 0.005 0.0157 0.146 0.461 0.113 0.15 0.372 0.458 0.000929 0.00114 +! Validation 75 12591.887 0.005 0.0163 0.264 0.59 0.114 0.152 0.544 0.613 0.00136 0.00153 +Wall time: 12591.887809145264 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 0.838 0.0151 0.537 0.11 0.146 0.831 0.874 0.00208 0.00219 + 76 118 0.438 0.0151 0.137 0.111 0.146 0.408 0.441 0.00102 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 0.304 0.0122 0.0607 0.101 0.132 0.278 0.294 0.000694 0.000735 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 76 12759.269 0.005 0.0154 0.147 0.455 0.112 0.148 0.372 0.458 0.000931 0.00115 +! Validation 76 12759.269 0.005 0.0162 0.0704 0.395 0.114 0.152 0.252 0.317 0.00063 0.000792 +Wall time: 12759.269485328346 +! Best model 76 0.395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 0.391 0.0158 0.0749 0.114 0.15 0.262 0.327 0.000656 0.000817 + 77 118 0.337 0.0136 0.0655 0.106 0.139 0.257 0.306 0.000643 0.000764 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 0.268 0.0121 0.0274 0.1 0.131 0.171 0.197 0.000426 0.000494 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 77 12926.792 0.005 0.0153 0.212 0.518 0.112 0.148 0.456 0.551 0.00114 0.00138 +! Validation 77 12926.792 0.005 0.016 0.139 0.459 0.113 0.151 0.36 0.446 0.0009 0.00111 +Wall time: 12926.792797418311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 0.391 0.0158 0.0752 0.113 0.15 0.259 0.327 0.000647 0.000818 + 78 118 0.419 0.016 0.0991 0.113 0.151 0.341 0.376 0.000852 0.000939 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 0.244 0.0119 0.00566 0.0997 0.13 0.074 0.0898 0.000185 0.000224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 78 13094.228 0.005 0.0152 0.184 0.487 0.111 0.147 0.406 0.513 0.00101 0.00128 +! Validation 78 13094.228 0.005 0.0159 0.0721 0.39 0.113 0.15 0.256 0.32 0.00064 0.000801 +Wall time: 13094.228469375987 +! Best model 78 0.390 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 0.526 0.0158 0.21 0.113 0.15 0.494 0.548 0.00123 0.00137 + 79 118 0.323 0.0145 0.034 0.109 0.144 0.175 0.22 0.000439 0.00055 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 0.287 0.0117 0.0526 0.0991 0.129 0.262 0.274 0.000654 0.000685 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 79 13261.699 0.005 0.0149 0.182 0.481 0.11 0.146 0.422 0.511 0.00105 0.00128 +! Validation 79 13261.699 0.005 0.0157 0.0937 0.407 0.112 0.149 0.292 0.365 0.00073 0.000913 +Wall time: 13261.69981807936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 0.669 0.0143 0.383 0.108 0.143 0.673 0.739 0.00168 0.00185 + 80 118 0.365 0.0146 0.073 0.109 0.144 0.276 0.323 0.000689 0.000806 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 0.274 0.0118 0.038 0.099 0.13 0.218 0.233 0.000545 0.000582 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 80 13429.044 0.005 0.0148 0.203 0.498 0.11 0.145 0.438 0.538 0.0011 0.00135 +! Validation 80 13429.044 0.005 0.0155 0.108 0.419 0.111 0.149 0.32 0.393 0.0008 0.000982 +Wall time: 13429.044799063355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 0.621 0.0143 0.335 0.109 0.143 0.643 0.69 0.00161 0.00173 + 81 118 0.345 0.0155 0.0339 0.113 0.149 0.176 0.22 0.00044 0.000549 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 0.487 0.0127 0.233 0.103 0.135 0.568 0.577 0.00142 0.00144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 81 13596.927 0.005 0.0147 0.212 0.505 0.109 0.144 0.45 0.551 0.00113 0.00138 +! Validation 81 13596.927 0.005 0.0165 0.332 0.663 0.115 0.154 0.61 0.688 0.00153 0.00172 +Wall time: 13596.92751547508 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.356 0.0154 0.048 0.111 0.148 0.217 0.261 0.000544 0.000653 + 82 118 0.388 0.0154 0.0801 0.112 0.148 0.281 0.338 0.000703 0.000844 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 0.23 0.0112 0.0069 0.0968 0.126 0.0834 0.0992 0.000208 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 82 13764.263 0.005 0.0144 0.136 0.424 0.108 0.143 0.347 0.44 0.000868 0.0011 +! Validation 82 13764.263 0.005 0.0149 0.0657 0.363 0.109 0.146 0.247 0.306 0.000617 0.000765 +Wall time: 13764.263124155346 +! Best model 82 0.363 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 0.381 0.0146 0.0897 0.109 0.144 0.294 0.358 0.000736 0.000894 + 83 118 0.341 0.0147 0.0468 0.11 0.145 0.224 0.258 0.000561 0.000646 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 0.309 0.0112 0.0859 0.0964 0.126 0.338 0.35 0.000844 0.000874 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 83 13931.602 0.005 0.0141 0.176 0.458 0.107 0.142 0.408 0.503 0.00102 0.00126 +! Validation 83 13931.602 0.005 0.0147 0.132 0.427 0.109 0.145 0.357 0.434 0.000893 0.00108 +Wall time: 13931.602444554213 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 0.444 0.0146 0.152 0.109 0.144 0.4 0.465 0.001 0.00116 + 84 118 0.34 0.0139 0.0629 0.107 0.14 0.256 0.299 0.00064 0.000748 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 0.42 0.0111 0.197 0.0964 0.126 0.52 0.53 0.0013 0.00132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 84 14099.107 0.005 0.0141 0.197 0.48 0.107 0.142 0.433 0.531 0.00108 0.00133 +! Validation 84 14099.107 0.005 0.0148 0.187 0.483 0.109 0.145 0.437 0.516 0.00109 0.00129 +Wall time: 14099.107090077363 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 0.497 0.0139 0.218 0.106 0.141 0.502 0.558 0.00125 0.00139 + 85 118 0.319 0.0145 0.0281 0.109 0.144 0.175 0.2 0.000437 0.0005 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 0.343 0.0105 0.133 0.0936 0.122 0.423 0.435 0.00106 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 85 14267.651 0.005 0.0138 0.151 0.426 0.106 0.14 0.373 0.465 0.000932 0.00116 +! Validation 85 14267.651 0.005 0.0142 0.174 0.457 0.106 0.142 0.422 0.498 0.00105 0.00125 +Wall time: 14267.651786550414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.318 0.0133 0.0533 0.104 0.137 0.224 0.275 0.000559 0.000689 + 86 118 0.352 0.0143 0.0659 0.108 0.143 0.274 0.306 0.000685 0.000766 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 0.637 0.0106 0.424 0.0939 0.123 0.772 0.778 0.00193 0.00194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 86 14435.252 0.005 0.0136 0.171 0.443 0.105 0.139 0.404 0.495 0.00101 0.00124 +! Validation 86 14435.252 0.005 0.0141 0.358 0.641 0.106 0.142 0.66 0.714 0.00165 0.00178 +Wall time: 14435.252597165294 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.348 0.0133 0.0813 0.105 0.138 0.272 0.34 0.000681 0.000851 + 87 118 0.6 0.014 0.321 0.107 0.141 0.619 0.676 0.00155 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 0.499 0.0107 0.285 0.0946 0.123 0.632 0.637 0.00158 0.00159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 87 14604.741 0.005 0.0135 0.17 0.439 0.105 0.139 0.395 0.49 0.000987 0.00122 +! Validation 87 14604.741 0.005 0.0142 0.243 0.528 0.107 0.142 0.524 0.588 0.00131 0.00147 +Wall time: 14604.741336007137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.78 0.013 0.52 0.103 0.136 0.822 0.861 0.00205 0.00215 + 88 118 0.797 0.0128 0.541 0.103 0.135 0.849 0.878 0.00212 0.00219 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 0.369 0.0107 0.156 0.0941 0.123 0.464 0.471 0.00116 0.00118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 88 14772.081 0.005 0.0133 0.17 0.436 0.104 0.138 0.392 0.489 0.000979 0.00122 +! Validation 88 14772.081 0.005 0.0143 0.28 0.566 0.107 0.143 0.567 0.632 0.00142 0.00158 +Wall time: 14772.08188039111 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.705 0.0134 0.437 0.104 0.138 0.752 0.789 0.00188 0.00197 + 89 118 0.38 0.0134 0.112 0.104 0.138 0.334 0.399 0.000834 0.000998 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 0.683 0.0111 0.461 0.0955 0.126 0.805 0.811 0.00201 0.00203 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 89 14939.315 0.005 0.0133 0.196 0.462 0.104 0.138 0.441 0.53 0.0011 0.00132 +! Validation 89 14939.315 0.005 0.0146 0.501 0.792 0.108 0.144 0.774 0.844 0.00194 0.00211 +Wall time: 14939.315002339426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.317 0.0129 0.0584 0.102 0.136 0.229 0.288 0.000572 0.000721 + 90 118 0.492 0.0122 0.248 0.0996 0.132 0.564 0.594 0.00141 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 0.216 0.0102 0.0114 0.0919 0.121 0.115 0.127 0.000288 0.000318 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 90 15106.526 0.005 0.0132 0.167 0.431 0.104 0.137 0.383 0.487 0.000958 0.00122 +! Validation 90 15106.526 0.005 0.0136 0.0567 0.329 0.104 0.139 0.227 0.284 0.000568 0.00071 +Wall time: 15106.526050212327 +! Best model 90 0.329 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.47 0.0124 0.222 0.1 0.133 0.521 0.562 0.0013 0.00141 + 91 118 0.301 0.0119 0.0637 0.0987 0.13 0.267 0.301 0.000666 0.000753 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 0.227 0.00981 0.0313 0.0904 0.118 0.194 0.211 0.000484 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 91 15273.748 0.005 0.0127 0.132 0.386 0.102 0.135 0.353 0.434 0.000884 0.00109 +! Validation 91 15273.748 0.005 0.0132 0.095 0.359 0.103 0.137 0.29 0.368 0.000726 0.00092 +Wall time: 15273.748208103236 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.394 0.0121 0.152 0.0993 0.131 0.411 0.465 0.00103 0.00116 + 92 118 0.305 0.0127 0.0518 0.102 0.134 0.226 0.272 0.000566 0.000679 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 0.263 0.0103 0.0564 0.0923 0.121 0.267 0.283 0.000666 0.000709 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 92 15440.968 0.005 0.0126 0.165 0.418 0.101 0.134 0.399 0.486 0.000998 0.00122 +! Validation 92 15440.968 0.005 0.0136 0.066 0.337 0.104 0.139 0.243 0.307 0.000608 0.000767 +Wall time: 15440.968877958134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.284 0.012 0.043 0.0989 0.131 0.196 0.247 0.00049 0.000619 + 93 118 0.305 0.0117 0.0723 0.0985 0.129 0.269 0.321 0.000673 0.000802 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 0.267 0.0101 0.0649 0.0917 0.12 0.292 0.304 0.00073 0.00076 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 93 15608.289 0.005 0.0125 0.148 0.398 0.101 0.133 0.376 0.46 0.000939 0.00115 +! Validation 93 15608.289 0.005 0.0133 0.0901 0.357 0.103 0.138 0.288 0.358 0.000721 0.000896 +Wall time: 15608.289741460234 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.296 0.0115 0.0658 0.0967 0.128 0.25 0.306 0.000626 0.000765 + 94 118 0.337 0.0118 0.1 0.0983 0.13 0.313 0.378 0.000781 0.000945 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 0.212 0.00936 0.025 0.0881 0.115 0.163 0.189 0.000408 0.000472 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 94 15775.505 0.005 0.0123 0.128 0.374 0.1 0.132 0.347 0.428 0.000869 0.00107 +! Validation 94 15775.505 0.005 0.0128 0.0842 0.341 0.101 0.135 0.286 0.346 0.000714 0.000866 +Wall time: 15775.505967256147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.379 0.0121 0.137 0.099 0.131 0.394 0.441 0.000985 0.0011 + 95 118 0.293 0.0122 0.0479 0.1 0.132 0.193 0.261 0.000483 0.000653 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 0.322 0.00979 0.126 0.0901 0.118 0.415 0.424 0.00104 0.00106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 95 15942.749 0.005 0.0121 0.142 0.383 0.0991 0.131 0.362 0.45 0.000906 0.00113 +! Validation 95 15942.749 0.005 0.0129 0.215 0.474 0.102 0.136 0.483 0.553 0.00121 0.00138 +Wall time: 15942.749202632345 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.697 0.0125 0.447 0.101 0.133 0.747 0.798 0.00187 0.002 + 96 118 0.353 0.0119 0.114 0.099 0.13 0.335 0.404 0.000836 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 0.896 0.00971 0.702 0.0894 0.118 0.996 1 0.00249 0.0025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 96 16109.989 0.005 0.0122 0.185 0.429 0.0997 0.132 0.418 0.515 0.00105 0.00129 +! Validation 96 16109.989 0.005 0.0128 0.665 0.921 0.101 0.135 0.935 0.973 0.00234 0.00243 +Wall time: 16109.98919410538 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.591 0.0129 0.332 0.102 0.136 0.646 0.688 0.00161 0.00172 + 97 118 0.263 0.0111 0.0401 0.0965 0.126 0.199 0.239 0.000498 0.000598 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 0.237 0.00946 0.0482 0.0883 0.116 0.244 0.262 0.000609 0.000655 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 97 16277.213 0.005 0.0124 0.234 0.482 0.101 0.133 0.466 0.579 0.00117 0.00145 +! Validation 97 16277.213 0.005 0.0126 0.105 0.358 0.1 0.134 0.323 0.387 0.000809 0.000968 +Wall time: 16277.21388139436 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.558 0.0115 0.327 0.0974 0.128 0.633 0.683 0.00158 0.00171 + 98 118 0.276 0.0109 0.0591 0.0952 0.124 0.249 0.29 0.000623 0.000725 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 0.198 0.00907 0.0162 0.0867 0.114 0.136 0.152 0.00034 0.00038 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 98 16444.539 0.005 0.0117 0.126 0.36 0.0977 0.129 0.341 0.424 0.000853 0.00106 +! Validation 98 16444.539 0.005 0.0122 0.0619 0.306 0.0987 0.132 0.235 0.297 0.000588 0.000742 +Wall time: 16444.53935520025 +! Best model 98 0.306 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 0.365 0.0111 0.143 0.0959 0.126 0.4 0.451 0.000999 0.00113 + 99 118 0.272 0.00994 0.0733 0.0905 0.119 0.264 0.323 0.000659 0.000808 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 0.196 0.00915 0.0131 0.0867 0.114 0.103 0.136 0.000256 0.000341 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 99 16611.782 0.005 0.0117 0.147 0.381 0.0976 0.129 0.369 0.458 0.000922 0.00115 +! Validation 99 16611.782 0.005 0.0122 0.0632 0.306 0.0985 0.132 0.244 0.3 0.000611 0.00075 +Wall time: 16611.78269380424 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.275 0.0111 0.0541 0.0947 0.126 0.22 0.278 0.000549 0.000694 + 100 118 0.512 0.0112 0.289 0.0959 0.126 0.614 0.641 0.00154 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 0.739 0.00881 0.563 0.0854 0.112 0.891 0.896 0.00223 0.00224 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 100 16778.998 0.005 0.0114 0.14 0.369 0.0964 0.128 0.364 0.446 0.00091 0.00111 +! Validation 100 16778.998 0.005 0.0119 0.702 0.94 0.0975 0.13 0.965 1 0.00241 0.0025 +Wall time: 16778.998718888033 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.295 0.0118 0.0578 0.0982 0.13 0.238 0.287 0.000596 0.000718 + 101 118 0.237 0.0102 0.0334 0.0917 0.12 0.147 0.218 0.000368 0.000545 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 0.246 0.00915 0.0627 0.0871 0.114 0.288 0.299 0.000719 0.000747 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 101 16946.221 0.005 0.0117 0.205 0.439 0.0977 0.129 0.438 0.542 0.0011 0.00136 +! Validation 101 16946.221 0.005 0.0122 0.112 0.356 0.0988 0.132 0.336 0.4 0.00084 0.001 +Wall time: 16946.221889146138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.312 0.0113 0.0872 0.0958 0.127 0.286 0.352 0.000716 0.000881 + 102 118 0.38 0.00994 0.181 0.09 0.119 0.468 0.507 0.00117 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 0.196 0.009 0.0156 0.0861 0.113 0.135 0.149 0.000338 0.000373 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 102 17113.473 0.005 0.0113 0.141 0.366 0.096 0.127 0.356 0.447 0.00089 0.00112 +! Validation 102 17113.473 0.005 0.0119 0.0486 0.288 0.0977 0.13 0.21 0.263 0.000526 0.000658 +Wall time: 17113.47351642931 +! Best model 102 0.288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.385 0.011 0.165 0.0945 0.125 0.431 0.486 0.00108 0.00121 + 103 118 0.24 0.0103 0.0333 0.0918 0.121 0.178 0.218 0.000444 0.000544 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 0.177 0.00855 0.00615 0.0841 0.11 0.0856 0.0936 0.000214 0.000234 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 103 17280.805 0.005 0.0109 0.104 0.323 0.0944 0.125 0.307 0.386 0.000767 0.000965 +! Validation 103 17280.805 0.005 0.0114 0.049 0.278 0.0956 0.128 0.21 0.264 0.000524 0.00066 +Wall time: 17280.8056041901 +! Best model 103 0.278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.254 0.0105 0.0433 0.0926 0.122 0.193 0.248 0.000483 0.000621 + 104 118 0.34 0.014 0.0603 0.104 0.141 0.226 0.293 0.000566 0.000733 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 0.533 0.00861 0.36 0.0846 0.111 0.712 0.717 0.00178 0.00179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 104 17448.043 0.005 0.011 0.169 0.389 0.0946 0.125 0.405 0.492 0.00101 0.00123 +! Validation 104 17448.043 0.005 0.0114 0.393 0.621 0.0956 0.128 0.699 0.748 0.00175 0.00187 +Wall time: 17448.043538108002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 0.699 0.0108 0.484 0.0934 0.124 0.798 0.83 0.002 0.00208 + 105 118 0.457 0.00965 0.264 0.0897 0.117 0.594 0.613 0.00148 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 1.2 0.00824 1.04 0.0827 0.108 1.21 1.21 0.00303 0.00304 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 105 17615.264 0.005 0.0107 0.117 0.332 0.0936 0.124 0.321 0.407 0.000804 0.00102 +! Validation 105 17615.264 0.005 0.0113 0.961 1.19 0.0949 0.127 1.14 1.17 0.00286 0.00293 +Wall time: 17615.2645894601 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.301 0.0103 0.0957 0.0917 0.121 0.304 0.369 0.000761 0.000923 + 106 118 0.356 0.0105 0.145 0.0931 0.123 0.406 0.454 0.00101 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 0.198 0.00851 0.0277 0.0838 0.11 0.18 0.199 0.000449 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 106 17782.483 0.005 0.011 0.161 0.381 0.0946 0.125 0.375 0.48 0.000938 0.0012 +! Validation 106 17782.483 0.005 0.0113 0.0803 0.306 0.0949 0.127 0.279 0.338 0.000697 0.000845 +Wall time: 17782.483288375195 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.249 0.0108 0.0321 0.0937 0.124 0.168 0.214 0.000419 0.000535 + 107 118 0.223 0.0102 0.019 0.0915 0.12 0.148 0.164 0.00037 0.000411 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 0.19 0.00834 0.0228 0.0831 0.109 0.163 0.18 0.000406 0.000451 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 107 17949.810 0.005 0.0104 0.107 0.315 0.0921 0.122 0.316 0.391 0.000789 0.000977 +! Validation 107 17949.810 0.005 0.0112 0.0545 0.278 0.0947 0.126 0.222 0.279 0.000554 0.000696 +Wall time: 17949.810216679238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.278 0.0106 0.0664 0.0931 0.123 0.248 0.308 0.000619 0.000769 + 108 118 0.484 0.00987 0.286 0.0898 0.119 0.617 0.638 0.00154 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 0.694 0.00835 0.527 0.0833 0.109 0.862 0.866 0.00216 0.00217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 108 18117.072 0.005 0.0102 0.128 0.333 0.0915 0.121 0.346 0.425 0.000864 0.00106 +! Validation 108 18117.072 0.005 0.0111 0.685 0.907 0.0944 0.126 0.956 0.988 0.00239 0.00247 +Wall time: 18117.072539945133 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.302 0.0101 0.101 0.0904 0.12 0.336 0.379 0.000839 0.000947 + 109 118 0.26 0.009 0.0805 0.0868 0.113 0.282 0.339 0.000705 0.000847 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 0.185 0.00811 0.0231 0.0824 0.108 0.164 0.181 0.000409 0.000454 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 109 18284.343 0.005 0.0104 0.161 0.37 0.0923 0.122 0.393 0.48 0.000982 0.0012 +! Validation 109 18284.343 0.005 0.0109 0.0534 0.272 0.0935 0.125 0.219 0.276 0.000547 0.00069 +Wall time: 18284.343496088404 +! Best model 109 0.272 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.262 0.00987 0.0648 0.0896 0.119 0.248 0.304 0.000621 0.00076 + 110 118 0.274 0.01 0.0736 0.0904 0.119 0.301 0.324 0.000754 0.000809 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 0.321 0.00764 0.169 0.0796 0.104 0.482 0.49 0.0012 0.00123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 110 18451.584 0.005 0.00997 0.0883 0.288 0.0902 0.119 0.283 0.355 0.000708 0.000887 +! Validation 110 18451.584 0.005 0.0104 0.194 0.402 0.0911 0.122 0.463 0.526 0.00116 0.00131 +Wall time: 18451.58469338203 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.239 0.00936 0.052 0.0876 0.115 0.217 0.272 0.000543 0.00068 + 111 118 0.289 0.00977 0.0934 0.0899 0.118 0.303 0.365 0.000756 0.000912 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 0.169 0.00761 0.0163 0.0793 0.104 0.13 0.152 0.000326 0.000381 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 111 18618.806 0.005 0.0101 0.149 0.351 0.0908 0.12 0.368 0.461 0.000919 0.00115 +! Validation 111 18618.806 0.005 0.0105 0.0649 0.275 0.0916 0.122 0.25 0.304 0.000624 0.00076 +Wall time: 18618.806290874258 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.534 0.00952 0.343 0.0886 0.116 0.664 0.699 0.00166 0.00175 + 112 118 0.667 0.00932 0.481 0.0875 0.115 0.795 0.828 0.00199 0.00207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 0.466 0.0085 0.296 0.0836 0.11 0.644 0.65 0.00161 0.00162 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 112 18786.139 0.005 0.00999 0.172 0.372 0.0903 0.119 0.407 0.492 0.00102 0.00123 +! Validation 112 18786.139 0.005 0.0113 0.245 0.471 0.0948 0.127 0.537 0.591 0.00134 0.00148 +Wall time: 18786.138994014356 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.231 0.01 0.0302 0.0904 0.12 0.172 0.208 0.00043 0.000519 + 113 118 0.239 0.0102 0.0353 0.0913 0.12 0.196 0.224 0.00049 0.00056 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 0.21 0.00751 0.0603 0.0792 0.103 0.279 0.293 0.000698 0.000732 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 113 18953.368 0.005 0.00984 0.111 0.307 0.0896 0.118 0.312 0.398 0.000779 0.000994 +! Validation 113 18953.368 0.005 0.0103 0.107 0.313 0.0908 0.121 0.336 0.39 0.000839 0.000976 +Wall time: 18953.368637801148 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.221 0.00959 0.0298 0.0885 0.117 0.164 0.206 0.00041 0.000515 + 114 118 0.245 0.00859 0.0729 0.0841 0.111 0.28 0.322 0.000701 0.000806 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 0.162 0.00767 0.00818 0.0794 0.105 0.102 0.108 0.000256 0.00027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 114 19120.587 0.005 0.0096 0.125 0.317 0.0885 0.117 0.329 0.423 0.000823 0.00106 +! Validation 114 19120.587 0.005 0.0103 0.0552 0.26 0.0904 0.121 0.223 0.28 0.000557 0.000701 +Wall time: 19120.587096181232 +! Best model 114 0.260 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 0.215 0.00877 0.04 0.0848 0.112 0.189 0.239 0.000471 0.000597 + 115 118 0.289 0.00969 0.0949 0.0892 0.117 0.332 0.368 0.000831 0.000919 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 0.386 0.00749 0.237 0.079 0.103 0.576 0.58 0.00144 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 115 19287.816 0.005 0.00934 0.0768 0.264 0.0873 0.115 0.266 0.331 0.000665 0.000826 +! Validation 115 19287.816 0.005 0.0102 0.307 0.51 0.0902 0.12 0.608 0.661 0.00152 0.00165 +Wall time: 19287.816566231195 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 0.33 0.00872 0.156 0.0848 0.111 0.424 0.471 0.00106 0.00118 + 116 118 0.252 0.00957 0.0605 0.0886 0.117 0.243 0.294 0.000606 0.000734 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 0.219 0.00708 0.0772 0.0766 0.1 0.324 0.332 0.00081 0.000829 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 116 19455.029 0.005 0.00919 0.106 0.29 0.0865 0.114 0.316 0.39 0.00079 0.000974 +! Validation 116 19455.029 0.005 0.0097 0.158 0.352 0.088 0.118 0.397 0.474 0.000992 0.00119 +Wall time: 19455.029688897077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 0.348 0.00912 0.166 0.0862 0.114 0.451 0.486 0.00113 0.00122 + 117 118 0.429 0.00808 0.267 0.0826 0.107 0.573 0.617 0.00143 0.00154 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 0.641 0.00753 0.49 0.079 0.104 0.833 0.835 0.00208 0.00209 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 117 19622.358 0.005 0.0095 0.209 0.399 0.0881 0.116 0.453 0.545 0.00113 0.00136 +! Validation 117 19622.358 0.005 0.0101 0.49 0.691 0.0898 0.12 0.796 0.835 0.00199 0.00209 +Wall time: 19622.35853730142 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 0.22 0.00874 0.0449 0.0845 0.112 0.211 0.253 0.000526 0.000632 + 118 118 0.236 0.00829 0.0698 0.0827 0.109 0.262 0.315 0.000655 0.000789 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 0.144 0.00698 0.00436 0.0759 0.0997 0.0489 0.0788 0.000122 0.000197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 118 19789.587 0.005 0.00915 0.106 0.289 0.0864 0.114 0.316 0.39 0.00079 0.000975 +! Validation 118 19789.587 0.005 0.00952 0.0399 0.23 0.087 0.116 0.195 0.239 0.000487 0.000596 +Wall time: 19789.587790810037 +! Best model 118 0.230 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.406 0.00871 0.231 0.0847 0.111 0.527 0.574 0.00132 0.00144 + 119 118 0.273 0.00921 0.0887 0.087 0.115 0.333 0.355 0.000832 0.000889 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 0.166 0.0072 0.022 0.0769 0.101 0.157 0.177 0.000392 0.000442 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 119 19956.819 0.005 0.00894 0.0996 0.278 0.0853 0.113 0.302 0.377 0.000756 0.000942 +! Validation 119 19956.819 0.005 0.00964 0.0669 0.26 0.0877 0.117 0.239 0.309 0.000596 0.000772 +Wall time: 19956.819589279126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.254 0.00865 0.0809 0.0839 0.111 0.279 0.34 0.000698 0.000849 + 120 118 0.863 0.00877 0.688 0.0851 0.112 0.954 0.99 0.00238 0.00247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 0.202 0.00811 0.04 0.0815 0.108 0.22 0.239 0.000551 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 120 20124.236 0.005 0.00883 0.112 0.289 0.0848 0.112 0.314 0.393 0.000785 0.000982 +! Validation 120 20124.236 0.005 0.0104 0.139 0.347 0.0911 0.122 0.381 0.445 0.000953 0.00111 +Wall time: 20124.236319039017 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.275 0.00851 0.105 0.0832 0.11 0.332 0.387 0.000831 0.000966 + 121 118 0.246 0.00915 0.0632 0.0873 0.114 0.288 0.3 0.000719 0.00075 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 0.208 0.00661 0.0756 0.0739 0.097 0.32 0.328 0.0008 0.00082 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 121 20291.466 0.005 0.00895 0.106 0.285 0.0854 0.113 0.308 0.389 0.00077 0.000973 +! Validation 121 20291.466 0.005 0.00907 0.113 0.294 0.0849 0.114 0.35 0.401 0.000874 0.001 +Wall time: 20291.46693757642 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.189 0.00793 0.0302 0.0806 0.106 0.163 0.208 0.000408 0.000519 + 122 118 0.35 0.00976 0.155 0.0895 0.118 0.421 0.469 0.00105 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 0.867 0.00921 0.683 0.0875 0.115 0.985 0.987 0.00246 0.00247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 122 20459.118 0.005 0.00855 0.104 0.275 0.0834 0.11 0.302 0.385 0.000755 0.000963 +! Validation 122 20459.118 0.005 0.0116 0.666 0.898 0.0975 0.129 0.942 0.974 0.00236 0.00244 +Wall time: 20459.118819710333 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.214 0.00838 0.0461 0.0822 0.109 0.209 0.256 0.000522 0.000641 + 123 118 0.394 0.0073 0.248 0.0781 0.102 0.578 0.595 0.00144 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 0.445 0.00635 0.318 0.0726 0.0951 0.67 0.673 0.00168 0.00168 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 123 20626.534 0.005 0.0088 0.116 0.292 0.0847 0.112 0.317 0.406 0.000792 0.00101 +! Validation 123 20626.534 0.005 0.00891 0.303 0.481 0.0841 0.113 0.617 0.657 0.00154 0.00164 +Wall time: 20626.5347535084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.19 0.00789 0.0321 0.0801 0.106 0.167 0.214 0.000417 0.000535 + 124 118 0.237 0.0088 0.0606 0.0845 0.112 0.249 0.294 0.000621 0.000735 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 0.136 0.00641 0.00779 0.0729 0.0956 0.101 0.105 0.000253 0.000263 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 124 20794.107 0.005 0.00863 0.156 0.328 0.0838 0.111 0.366 0.472 0.000915 0.00118 +! Validation 124 20794.107 0.005 0.00893 0.0419 0.221 0.0843 0.113 0.196 0.244 0.000489 0.000611 +Wall time: 20794.107730623335 +! Best model 124 0.221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.405 0.00856 0.234 0.0827 0.11 0.533 0.578 0.00133 0.00144 + 125 118 0.206 0.00885 0.0292 0.0841 0.112 0.162 0.204 0.000404 0.00051 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 0.453 0.00651 0.323 0.0736 0.0963 0.676 0.678 0.00169 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 125 20961.561 0.005 0.00821 0.0852 0.249 0.0817 0.108 0.277 0.349 0.000692 0.000873 +! Validation 125 20961.561 0.005 0.00891 0.32 0.498 0.0842 0.113 0.637 0.675 0.00159 0.00169 +Wall time: 20961.56106079137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.187 0.00756 0.036 0.0786 0.104 0.179 0.226 0.000448 0.000566 + 126 118 0.291 0.00891 0.113 0.0846 0.113 0.366 0.402 0.000915 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 0.22 0.0062 0.0956 0.0711 0.094 0.364 0.369 0.00091 0.000922 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 126 21129.087 0.005 0.0083 0.0899 0.256 0.0821 0.109 0.279 0.357 0.000697 0.000894 +! Validation 126 21129.087 0.005 0.00856 0.151 0.322 0.0823 0.11 0.415 0.464 0.00104 0.00116 +Wall time: 21129.087663974147 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.222 0.00844 0.0529 0.0818 0.11 0.236 0.274 0.00059 0.000686 + 127 118 0.182 0.00796 0.0228 0.0801 0.106 0.15 0.18 0.000374 0.000451 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 0.141 0.00619 0.0167 0.0713 0.0939 0.139 0.154 0.000348 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 127 21296.497 0.005 0.00805 0.109 0.27 0.0808 0.107 0.315 0.395 0.000789 0.000988 +! Validation 127 21296.497 0.005 0.00843 0.0429 0.212 0.0817 0.11 0.193 0.247 0.000482 0.000618 +Wall time: 21296.497893372085 +! Best model 127 0.212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.191 0.00791 0.0328 0.0804 0.106 0.175 0.216 0.000437 0.00054 + 128 118 0.169 0.00733 0.0226 0.078 0.102 0.157 0.179 0.000392 0.000448 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 0.174 0.00606 0.0531 0.0707 0.0929 0.266 0.275 0.000666 0.000687 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 128 21463.930 0.005 0.00786 0.0876 0.245 0.0799 0.106 0.286 0.354 0.000714 0.000885 +! Validation 128 21463.930 0.005 0.00836 0.069 0.236 0.0815 0.109 0.255 0.314 0.000637 0.000784 +Wall time: 21463.930638383143 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.302 0.0081 0.14 0.0811 0.107 0.399 0.447 0.000998 0.00112 + 129 118 0.165 0.0076 0.0131 0.0784 0.104 0.114 0.136 0.000286 0.000341 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 0.134 0.00603 0.013 0.07 0.0927 0.113 0.136 0.000283 0.000341 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 129 21631.344 0.005 0.00782 0.101 0.257 0.0796 0.106 0.306 0.38 0.000764 0.00095 +! Validation 129 21631.344 0.005 0.0083 0.0497 0.216 0.081 0.109 0.219 0.266 0.000548 0.000665 +Wall time: 21631.34449160425 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.173 0.00772 0.0182 0.0795 0.105 0.121 0.161 0.000303 0.000403 + 130 118 0.215 0.00741 0.0667 0.0782 0.103 0.282 0.308 0.000704 0.000771 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 0.255 0.00618 0.131 0.0711 0.0938 0.426 0.432 0.00107 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 130 21798.759 0.005 0.00774 0.0971 0.252 0.0792 0.105 0.3 0.372 0.00075 0.000931 +! Validation 130 21798.759 0.005 0.00829 0.146 0.312 0.0811 0.109 0.406 0.456 0.00102 0.00114 +Wall time: 21798.759365015198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.232 0.00765 0.0787 0.0786 0.104 0.3 0.335 0.000751 0.000837 + 131 118 0.182 0.00736 0.035 0.076 0.102 0.17 0.223 0.000426 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 0.174 0.00585 0.0571 0.0687 0.0913 0.276 0.285 0.00069 0.000713 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 131 21966.280 0.005 0.00769 0.109 0.263 0.079 0.105 0.32 0.394 0.000801 0.000986 +! Validation 131 21966.280 0.005 0.00814 0.0934 0.256 0.0802 0.108 0.315 0.365 0.000788 0.000912 +Wall time: 21966.280871197116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.203 0.00729 0.0567 0.077 0.102 0.242 0.284 0.000606 0.00071 + 132 118 0.198 0.00675 0.0635 0.0739 0.098 0.266 0.301 0.000664 0.000752 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 0.125 0.00601 0.00535 0.0696 0.0925 0.0632 0.0873 0.000158 0.000218 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 132 22133.707 0.005 0.0075 0.0853 0.235 0.0779 0.103 0.285 0.349 0.000712 0.000872 +! Validation 132 22133.707 0.005 0.00821 0.0357 0.2 0.0806 0.108 0.182 0.226 0.000454 0.000564 +Wall time: 22133.707500743214 +! Best model 132 0.200 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.169 0.00705 0.0277 0.0756 0.1 0.158 0.199 0.000394 0.000496 + 133 118 0.314 0.00748 0.164 0.0776 0.103 0.467 0.484 0.00117 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 0.186 0.00569 0.0725 0.0679 0.09 0.316 0.321 0.00079 0.000803 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 133 22301.242 0.005 0.00753 0.0948 0.245 0.0781 0.104 0.287 0.367 0.000718 0.000917 +! Validation 133 22301.242 0.005 0.00794 0.0955 0.254 0.0791 0.106 0.318 0.369 0.000794 0.000922 +Wall time: 22301.24287525518 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.236 0.00783 0.0798 0.0792 0.106 0.281 0.337 0.000702 0.000843 + 134 118 0.18 0.00682 0.0438 0.075 0.0986 0.233 0.25 0.000582 0.000625 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 0.117 0.00559 0.00518 0.0671 0.0892 0.0596 0.0859 0.000149 0.000215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 134 22468.743 0.005 0.00738 0.0892 0.237 0.0773 0.103 0.292 0.357 0.000731 0.000893 +! Validation 134 22468.743 0.005 0.00775 0.0354 0.19 0.078 0.105 0.183 0.224 0.000458 0.000561 +Wall time: 22468.743268524297 +! Best model 134 0.190 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.183 0.00739 0.035 0.0772 0.103 0.165 0.223 0.000412 0.000558 + 135 118 0.2 0.00677 0.065 0.0745 0.0982 0.24 0.304 0.0006 0.000761 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 0.249 0.00595 0.13 0.069 0.0921 0.426 0.431 0.00107 0.00108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 135 22640.420 0.005 0.0073 0.0991 0.245 0.0768 0.102 0.307 0.376 0.000769 0.00094 +! Validation 135 22640.420 0.005 0.00795 0.126 0.285 0.0791 0.106 0.371 0.423 0.000929 0.00106 +Wall time: 22640.420629990287 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.203 0.00671 0.0685 0.0741 0.0978 0.274 0.312 0.000685 0.000781 + 136 118 0.183 0.00683 0.0461 0.0743 0.0986 0.225 0.256 0.000563 0.000641 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 0.134 0.00552 0.0233 0.0668 0.0887 0.17 0.182 0.000425 0.000455 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 136 22808.016 0.005 0.00715 0.0682 0.211 0.076 0.101 0.253 0.312 0.000633 0.00078 +! Validation 136 22808.016 0.005 0.00764 0.0588 0.212 0.0776 0.104 0.229 0.289 0.000572 0.000724 +Wall time: 22808.016607451253 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.323 0.00708 0.182 0.0761 0.1 0.472 0.509 0.00118 0.00127 + 137 118 0.322 0.00708 0.181 0.0757 0.1 0.452 0.507 0.00113 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 0.322 0.00561 0.21 0.0676 0.0894 0.544 0.547 0.00136 0.00137 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 137 22975.513 0.005 0.00721 0.118 0.262 0.0763 0.101 0.334 0.409 0.000835 0.00102 +! Validation 137 22975.513 0.005 0.00774 0.236 0.391 0.078 0.105 0.531 0.58 0.00133 0.00145 +Wall time: 22975.513583051972 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.239 0.00691 0.101 0.0743 0.0992 0.324 0.379 0.000809 0.000948 + 138 118 0.162 0.00711 0.0197 0.076 0.101 0.142 0.168 0.000356 0.000419 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 0.114 0.00527 0.00875 0.0653 0.0866 0.0959 0.112 0.00024 0.000279 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 138 23143.009 0.005 0.00711 0.0801 0.222 0.0757 0.101 0.272 0.339 0.00068 0.000847 +! Validation 138 23143.009 0.005 0.00743 0.0375 0.186 0.0763 0.103 0.189 0.231 0.000472 0.000578 +Wall time: 23143.009526565205 +! Best model 138 0.186 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.192 0.0072 0.0478 0.076 0.101 0.208 0.261 0.00052 0.000652 + 139 118 0.142 0.00657 0.01 0.0726 0.0968 0.0939 0.12 0.000235 0.000299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 0.158 0.00548 0.0488 0.0663 0.0883 0.257 0.264 0.000642 0.000659 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 139 23310.552 0.005 0.0069 0.0693 0.207 0.0745 0.0992 0.256 0.315 0.000639 0.000788 +! Validation 139 23310.552 0.005 0.00748 0.0862 0.236 0.0766 0.103 0.302 0.35 0.000755 0.000876 +Wall time: 23310.55246903142 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.208 0.00681 0.0714 0.0738 0.0985 0.269 0.319 0.000672 0.000797 + 140 118 0.169 0.00673 0.0344 0.0737 0.0979 0.202 0.221 0.000505 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 0.113 0.00549 0.00321 0.066 0.0884 0.0573 0.0677 0.000143 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 140 23478.063 0.005 0.00704 0.11 0.25 0.0754 0.1 0.318 0.396 0.000794 0.00099 +! Validation 140 23478.063 0.005 0.00746 0.0296 0.179 0.0765 0.103 0.167 0.205 0.000417 0.000513 +Wall time: 23478.063379858155 +! Best model 140 0.179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.15 0.00619 0.026 0.071 0.0939 0.153 0.193 0.000383 0.000481 + 141 118 0.182 0.00593 0.0632 0.0691 0.0919 0.257 0.3 0.000642 0.00075 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 0.11 0.00513 0.00739 0.0639 0.0855 0.0832 0.103 0.000208 0.000257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 141 23645.685 0.005 0.00678 0.067 0.203 0.0738 0.0983 0.241 0.309 0.000604 0.000772 +! Validation 141 23645.685 0.005 0.00713 0.0293 0.172 0.0746 0.101 0.166 0.204 0.000416 0.000511 +Wall time: 23645.685093481094 +! Best model 141 0.172 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.216 0.00667 0.0829 0.0731 0.0975 0.292 0.344 0.000729 0.000859 + 142 118 0.191 0.00677 0.0556 0.074 0.0982 0.234 0.281 0.000586 0.000703 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 0.141 0.00503 0.04 0.0635 0.0847 0.232 0.239 0.000581 0.000597 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 142 23813.224 0.005 0.00667 0.0791 0.212 0.0731 0.0974 0.259 0.336 0.000647 0.00084 +! Validation 142 23813.224 0.005 0.00712 0.0652 0.207 0.0746 0.101 0.249 0.305 0.000622 0.000762 +Wall time: 23813.224482114427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.519 0.00778 0.364 0.0801 0.105 0.696 0.72 0.00174 0.0018 + 143 118 0.244 0.00925 0.0591 0.085 0.115 0.257 0.29 0.000643 0.000726 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 0.426 0.00518 0.322 0.0647 0.0859 0.676 0.678 0.00169 0.00169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 143 23985.182 0.005 0.00699 0.141 0.281 0.075 0.0997 0.369 0.449 0.000922 0.00112 +! Validation 143 23985.182 0.005 0.00738 0.298 0.446 0.0761 0.103 0.617 0.651 0.00154 0.00163 +Wall time: 23985.1826822944 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.158 0.00656 0.0267 0.0722 0.0967 0.157 0.195 0.000392 0.000488 + 144 118 0.137 0.00658 0.00531 0.0717 0.0968 0.0723 0.087 0.000181 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 0.123 0.00494 0.0237 0.063 0.0839 0.176 0.184 0.000441 0.00046 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 144 24152.659 0.005 0.0067 0.0705 0.204 0.0734 0.0977 0.245 0.318 0.000612 0.000795 +! Validation 144 24152.659 0.005 0.00694 0.0527 0.192 0.0735 0.0995 0.222 0.274 0.000554 0.000685 +Wall time: 24152.659754903987 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.269 0.00645 0.14 0.0721 0.0958 0.42 0.446 0.00105 0.00112 + 145 118 0.14 0.00618 0.0162 0.0712 0.0939 0.122 0.152 0.000304 0.00038 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 0.102 0.00494 0.00358 0.0627 0.0838 0.0642 0.0714 0.000161 0.000179 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 145 24320.262 0.005 0.00647 0.0681 0.198 0.072 0.096 0.251 0.312 0.000628 0.000781 +! Validation 145 24320.262 0.005 0.00692 0.0292 0.168 0.0734 0.0993 0.162 0.204 0.000405 0.00051 +Wall time: 24320.262760837097 +! Best model 145 0.168 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.144 0.00613 0.0218 0.0705 0.0935 0.137 0.176 0.000342 0.000441 + 146 118 0.165 0.00659 0.0336 0.0726 0.0969 0.197 0.219 0.000491 0.000547 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 0.132 0.0048 0.0362 0.0621 0.0827 0.223 0.227 0.000558 0.000568 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 146 24487.757 0.005 0.00643 0.0766 0.205 0.0717 0.0957 0.268 0.331 0.00067 0.000827 +! Validation 146 24487.757 0.005 0.00684 0.0736 0.21 0.073 0.0987 0.278 0.324 0.000695 0.00081 +Wall time: 24487.757544776425 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.233 0.00633 0.106 0.0709 0.0949 0.351 0.389 0.000877 0.000973 + 147 118 0.173 0.0064 0.0448 0.0718 0.0954 0.219 0.253 0.000547 0.000631 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 0.108 0.00496 0.00917 0.0627 0.084 0.0983 0.114 0.000246 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 147 24655.282 0.005 0.00641 0.0802 0.208 0.0716 0.0955 0.274 0.338 0.000685 0.000846 +! Validation 147 24655.282 0.005 0.00685 0.0357 0.173 0.0731 0.0988 0.178 0.225 0.000444 0.000564 +Wall time: 24655.28263354022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.2 0.00634 0.0737 0.0712 0.095 0.278 0.324 0.000694 0.00081 + 148 118 0.178 0.00671 0.0437 0.0732 0.0978 0.213 0.249 0.000534 0.000623 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 0.208 0.00476 0.113 0.0619 0.0824 0.398 0.401 0.000996 0.001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 148 24822.807 0.005 0.00628 0.0697 0.195 0.0709 0.0946 0.255 0.316 0.000638 0.000789 +! Validation 148 24822.807 0.005 0.00679 0.145 0.28 0.0728 0.0984 0.41 0.454 0.00102 0.00113 +Wall time: 24822.807908348273 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.205 0.00632 0.0782 0.0714 0.0949 0.298 0.334 0.000744 0.000835 + 149 118 0.165 0.00523 0.06 0.0655 0.0863 0.253 0.292 0.000632 0.000731 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 0.109 0.00449 0.0193 0.0599 0.08 0.16 0.166 0.0004 0.000415 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 149 24991.059 0.005 0.00622 0.0678 0.192 0.0706 0.0942 0.25 0.311 0.000626 0.000777 +! Validation 149 24991.059 0.005 0.00663 0.0393 0.172 0.0717 0.0972 0.188 0.237 0.00047 0.000592 +Wall time: 24991.059456220362 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.144 0.00613 0.0216 0.07 0.0934 0.147 0.176 0.000368 0.000439 + 150 118 0.229 0.00555 0.118 0.0672 0.0889 0.37 0.41 0.000924 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 0.0991 0.00483 0.0024 0.0622 0.083 0.0465 0.0584 0.000116 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 150 25158.734 0.005 0.0067 0.125 0.259 0.0733 0.0977 0.329 0.421 0.000822 0.00105 +! Validation 150 25158.734 0.005 0.00681 0.0287 0.165 0.0728 0.0985 0.161 0.202 0.000402 0.000506 +Wall time: 25158.734218377154 +! Best model 150 0.165 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.14 0.00587 0.023 0.0684 0.0914 0.147 0.181 0.000367 0.000453 + 151 118 0.133 0.00592 0.0149 0.0689 0.0918 0.118 0.145 0.000296 0.000364 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 0.0923 0.00453 0.00176 0.06 0.0803 0.043 0.05 0.000108 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 151 25326.299 0.005 0.0061 0.0585 0.18 0.0698 0.0932 0.234 0.289 0.000584 0.000723 +! Validation 151 25326.299 0.005 0.00647 0.0248 0.154 0.0708 0.096 0.15 0.188 0.000374 0.00047 +Wall time: 25326.299103798345 +! Best model 151 0.154 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.146 0.00629 0.0201 0.0713 0.0946 0.137 0.169 0.000343 0.000423 + 152 118 0.165 0.0058 0.049 0.068 0.0909 0.203 0.264 0.000508 0.00066 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 0.105 0.00475 0.01 0.0612 0.0822 0.106 0.12 0.000266 0.000299 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 152 25493.874 0.005 0.00612 0.098 0.22 0.0699 0.0934 0.305 0.374 0.000762 0.000936 +! Validation 152 25493.874 0.005 0.00664 0.0288 0.162 0.0718 0.0972 0.162 0.203 0.000404 0.000507 +Wall time: 25493.87423438439 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.158 0.00611 0.0356 0.0694 0.0933 0.175 0.225 0.000439 0.000563 + 153 118 0.118 0.00533 0.0109 0.0662 0.0872 0.0916 0.125 0.000229 0.000312 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 0.0945 0.00458 0.00287 0.0603 0.0808 0.0556 0.0639 0.000139 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 153 25661.482 0.005 0.00597 0.0528 0.172 0.069 0.0922 0.216 0.275 0.00054 0.000688 +! Validation 153 25661.482 0.005 0.0064 0.0271 0.155 0.0705 0.0955 0.157 0.196 0.000393 0.000491 +Wall time: 25661.482486329973 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.152 0.00638 0.0242 0.0714 0.0953 0.152 0.186 0.00038 0.000464 + 154 118 0.148 0.00586 0.0304 0.0689 0.0914 0.153 0.208 0.000382 0.00052 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 0.272 0.00456 0.181 0.0599 0.0806 0.505 0.507 0.00126 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 154 25829.048 0.005 0.00599 0.0875 0.207 0.0691 0.0924 0.28 0.354 0.000701 0.000885 +! Validation 154 25829.048 0.005 0.00646 0.193 0.323 0.0708 0.096 0.49 0.525 0.00123 0.00131 +Wall time: 25829.04864113126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.399 0.00587 0.282 0.0682 0.0915 0.615 0.633 0.00154 0.00158 + 155 118 0.374 0.00651 0.244 0.0724 0.0963 0.558 0.59 0.0014 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 0.32 0.00506 0.218 0.0632 0.0849 0.556 0.558 0.00139 0.00139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 155 25996.720 0.005 0.00591 0.0696 0.188 0.0686 0.0917 0.252 0.312 0.00063 0.000781 +! Validation 155 25996.720 0.005 0.0068 0.209 0.345 0.073 0.0984 0.506 0.545 0.00126 0.00136 +Wall time: 25996.72077461239 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.142 0.00591 0.0239 0.0687 0.0917 0.155 0.185 0.000386 0.000462 + 156 118 0.204 0.00547 0.095 0.0668 0.0883 0.337 0.368 0.000842 0.00092 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 0.107 0.00494 0.00793 0.0624 0.0839 0.09 0.106 0.000225 0.000266 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 156 26164.579 0.005 0.00597 0.0835 0.203 0.0691 0.0923 0.279 0.345 0.000697 0.000862 +! Validation 156 26164.579 0.005 0.00664 0.0311 0.164 0.0721 0.0972 0.17 0.21 0.000424 0.000526 +Wall time: 26164.579566477332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.185 0.00625 0.0604 0.0708 0.0944 0.248 0.293 0.000619 0.000733 + 157 118 0.206 0.00729 0.0599 0.0759 0.102 0.259 0.292 0.000648 0.00073 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 0.106 0.0044 0.0177 0.0595 0.0792 0.151 0.159 0.000378 0.000397 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 157 26332.051 0.005 0.00583 0.065 0.182 0.0681 0.091 0.239 0.304 0.000599 0.000761 +! Validation 157 26332.051 0.005 0.00635 0.0341 0.161 0.0703 0.0951 0.176 0.221 0.000441 0.000551 +Wall time: 26332.051970203407 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.162 0.00566 0.049 0.0674 0.0898 0.224 0.264 0.00056 0.000661 + 158 118 0.127 0.00582 0.0108 0.0687 0.091 0.109 0.124 0.000273 0.00031 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 100 0.0945 0.00435 0.00754 0.0588 0.0787 0.0916 0.104 0.000229 0.000259 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 158 26499.528 0.005 0.00578 0.065 0.181 0.0679 0.0908 0.249 0.305 0.000622 0.000763 +! Validation 158 26499.528 0.005 0.00615 0.0284 0.151 0.069 0.0936 0.16 0.201 0.0004 0.000502 +Wall time: 26499.528177629225 +! Best model 158 0.151 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.139 0.00612 0.0168 0.0696 0.0934 0.117 0.155 0.000292 0.000387 + 159 118 0.249 0.00566 0.136 0.0679 0.0898 0.424 0.44 0.00106 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 0.237 0.00425 0.152 0.0579 0.0778 0.464 0.466 0.00116 0.00116 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 159 26667.053 0.005 0.00572 0.0593 0.174 0.0675 0.0902 0.236 0.289 0.00059 0.000723 +! Validation 159 26667.053 0.005 0.00603 0.15 0.27 0.0682 0.0927 0.426 0.462 0.00106 0.00115 +Wall time: 26667.0534506212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.125 0.00538 0.0172 0.0655 0.0875 0.123 0.157 0.000307 0.000392 + 160 118 0.348 0.00547 0.238 0.0654 0.0883 0.53 0.582 0.00133 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 0.11 0.00444 0.0207 0.0593 0.0796 0.169 0.172 0.000422 0.00043 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 160 26834.636 0.005 0.00565 0.0703 0.183 0.0671 0.0897 0.25 0.314 0.000625 0.000784 +! Validation 160 26834.636 0.005 0.00623 0.035 0.16 0.0696 0.0942 0.173 0.223 0.000433 0.000559 +Wall time: 26834.636298148427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.124 0.00539 0.016 0.0655 0.0876 0.12 0.151 0.000299 0.000377 + 161 118 0.16 0.0052 0.0562 0.0642 0.0861 0.223 0.283 0.000556 0.000707 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 0.126 0.00432 0.0398 0.0586 0.0785 0.232 0.238 0.000581 0.000595 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 161 27002.139 0.005 0.00583 0.0863 0.203 0.0682 0.0911 0.272 0.351 0.000679 0.000878 +! Validation 161 27002.139 0.005 0.00608 0.0521 0.174 0.0687 0.0931 0.229 0.272 0.000574 0.000681 +Wall time: 27002.139543771278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 0.123 0.0055 0.013 0.0658 0.0885 0.106 0.136 0.000265 0.00034 + 162 118 0.162 0.00545 0.0528 0.0658 0.0881 0.247 0.274 0.000618 0.000685 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 0.138 0.00412 0.0555 0.057 0.0766 0.277 0.281 0.000692 0.000703 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 162 27169.778 0.005 0.00557 0.0569 0.168 0.0666 0.0891 0.225 0.285 0.000562 0.000712 +! Validation 162 27169.778 0.005 0.00586 0.061 0.178 0.0672 0.0913 0.254 0.295 0.000635 0.000737 +Wall time: 27169.77902379725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 0.151 0.00555 0.0396 0.0665 0.0889 0.19 0.238 0.000476 0.000594 + 163 118 0.122 0.00509 0.0204 0.0633 0.0852 0.142 0.171 0.000354 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 0.0834 0.00405 0.00227 0.0565 0.076 0.046 0.0568 0.000115 0.000142 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 163 27337.273 0.005 0.00546 0.0515 0.161 0.0659 0.0882 0.219 0.271 0.000548 0.000678 +! Validation 163 27337.273 0.005 0.00585 0.0223 0.139 0.0673 0.0913 0.144 0.178 0.00036 0.000445 +Wall time: 27337.27379459422 +! Best model 163 0.139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 0.124 0.00542 0.0158 0.0656 0.0879 0.121 0.15 0.000303 0.000375 + 164 118 0.179 0.00515 0.0762 0.0641 0.0857 0.285 0.329 0.000713 0.000824 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 0.114 0.00415 0.0309 0.0574 0.0769 0.204 0.21 0.000509 0.000525 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 164 27504.914 0.005 0.00551 0.0728 0.183 0.0662 0.0886 0.261 0.322 0.000652 0.000805 +! Validation 164 27504.914 0.005 0.00584 0.0442 0.161 0.0672 0.0912 0.209 0.251 0.000522 0.000628 +Wall time: 27504.914948088117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 0.136 0.00576 0.0209 0.0674 0.0906 0.138 0.173 0.000344 0.000431 + 165 118 0.182 0.00601 0.0624 0.0687 0.0925 0.271 0.298 0.000678 0.000745 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 0.0887 0.00435 0.00169 0.0587 0.0787 0.043 0.0491 0.000108 0.000123 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 165 27672.451 0.005 0.00542 0.0714 0.18 0.0656 0.0878 0.257 0.319 0.000642 0.000798 +! Validation 165 27672.451 0.005 0.0061 0.0278 0.15 0.0687 0.0932 0.157 0.199 0.000392 0.000498 +Wall time: 27672.451660702005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 0.131 0.00527 0.0259 0.0643 0.0866 0.151 0.192 0.000377 0.00048 + 166 118 0.147 0.0058 0.0311 0.0667 0.0909 0.194 0.211 0.000486 0.000526 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 0.0821 0.00395 0.00314 0.0558 0.075 0.0504 0.0669 0.000126 0.000167 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 166 27839.959 0.005 0.00542 0.0575 0.166 0.0656 0.0878 0.227 0.287 0.000568 0.000717 +! Validation 166 27839.959 0.005 0.00569 0.0229 0.137 0.0662 0.0901 0.147 0.18 0.000367 0.000451 +Wall time: 27839.959808997344 +! Best model 166 0.137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 0.228 0.00518 0.125 0.0644 0.0859 0.389 0.422 0.000973 0.00105 + 167 118 0.137 0.00564 0.0243 0.0671 0.0897 0.159 0.186 0.000397 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 0.184 0.00415 0.101 0.057 0.0769 0.378 0.38 0.000945 0.00095 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 167 28007.465 0.005 0.00538 0.0852 0.193 0.0654 0.0875 0.287 0.349 0.000718 0.000873 +! Validation 167 28007.465 0.005 0.00581 0.143 0.26 0.0669 0.091 0.417 0.452 0.00104 0.00113 +Wall time: 28007.465776593424 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 0.127 0.00552 0.0171 0.0659 0.0886 0.123 0.156 0.000309 0.00039 + 168 118 0.116 0.00494 0.0175 0.0624 0.0839 0.144 0.158 0.00036 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 0.0831 0.00399 0.00322 0.0558 0.0754 0.0537 0.0677 0.000134 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 168 28174.959 0.005 0.00526 0.0527 0.158 0.0647 0.0866 0.219 0.275 0.000548 0.000687 +! Validation 168 28174.959 0.005 0.00559 0.0236 0.135 0.0656 0.0892 0.144 0.184 0.00036 0.000459 +Wall time: 28174.95953605231 +! Best model 168 0.135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 0.208 0.00483 0.111 0.0622 0.0829 0.37 0.398 0.000925 0.000995 + 169 118 0.147 0.00564 0.0344 0.0663 0.0896 0.182 0.221 0.000455 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 0.129 0.00421 0.0446 0.0571 0.0775 0.247 0.252 0.000617 0.00063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 169 28342.554 0.005 0.00534 0.0805 0.187 0.0652 0.0872 0.276 0.339 0.000689 0.000848 +! Validation 169 28342.554 0.005 0.00579 0.0666 0.182 0.0669 0.0908 0.266 0.308 0.000666 0.00077 +Wall time: 28342.554968371987 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 0.209 0.00506 0.108 0.0637 0.0849 0.355 0.392 0.000888 0.00098 + 170 118 0.223 0.00494 0.124 0.0631 0.0839 0.399 0.421 0.000997 0.00105 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 0.0988 0.0047 0.0048 0.0609 0.0818 0.067 0.0827 0.000167 0.000207 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 170 28510.176 0.005 0.00522 0.0681 0.173 0.0644 0.0863 0.253 0.311 0.000632 0.000777 +! Validation 170 28510.176 0.005 0.00621 0.0249 0.149 0.0698 0.0941 0.154 0.188 0.000384 0.000471 +Wall time: 28510.17651259899 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 0.305 0.00524 0.201 0.0644 0.0864 0.52 0.535 0.0013 0.00134 + 171 118 0.154 0.00542 0.0457 0.0655 0.0879 0.226 0.255 0.000564 0.000638 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 0.136 0.00412 0.0536 0.0571 0.0766 0.273 0.276 0.000682 0.000691 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 171 28677.715 0.005 0.00515 0.057 0.16 0.0639 0.0857 0.232 0.285 0.000579 0.000713 +! Validation 171 28677.715 0.005 0.00578 0.065 0.181 0.0668 0.0907 0.259 0.304 0.000648 0.000761 +Wall time: 28677.715894418303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 0.127 0.00497 0.0274 0.0627 0.0842 0.162 0.198 0.000405 0.000494 + 172 118 0.115 0.00454 0.0243 0.0615 0.0804 0.163 0.186 0.000408 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 0.188 0.0039 0.11 0.0555 0.0745 0.393 0.396 0.000983 0.000991 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 172 28845.260 0.005 0.00506 0.0461 0.147 0.0634 0.085 0.208 0.257 0.00052 0.000642 +! Validation 172 28845.260 0.005 0.00552 0.155 0.266 0.0654 0.0887 0.44 0.47 0.0011 0.00118 +Wall time: 28845.26090982929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.12 0.00466 0.0269 0.0615 0.0815 0.154 0.196 0.000386 0.000489 + 173 118 0.375 0.00612 0.253 0.0698 0.0934 0.578 0.6 0.00144 0.0015 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 0.345 0.00437 0.258 0.0588 0.0789 0.605 0.606 0.00151 0.00152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 173 29012.812 0.005 0.00508 0.063 0.165 0.0635 0.085 0.239 0.297 0.000598 0.000741 +! Validation 173 29012.812 0.005 0.0059 0.311 0.429 0.0679 0.0917 0.642 0.665 0.00161 0.00166 +Wall time: 29012.8123474163 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 0.12 0.00494 0.0213 0.0629 0.0839 0.148 0.174 0.00037 0.000436 + 174 118 0.118 0.00518 0.0145 0.0641 0.0859 0.109 0.144 0.000274 0.000359 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 0.0807 0.0038 0.00473 0.0544 0.0736 0.0691 0.0821 0.000173 0.000205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 174 29180.462 0.005 0.0052 0.0788 0.183 0.0643 0.0861 0.272 0.336 0.00068 0.00084 +! Validation 174 29180.462 0.005 0.00539 0.0282 0.136 0.0643 0.0876 0.156 0.2 0.000391 0.000501 +Wall time: 29180.462871633004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.158 0.00515 0.0546 0.0639 0.0856 0.232 0.279 0.00058 0.000697 + 175 118 0.127 0.00488 0.0296 0.0627 0.0834 0.17 0.205 0.000425 0.000513 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 0.0812 0.0039 0.00314 0.0555 0.0746 0.0579 0.0669 0.000145 0.000167 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 175 29348.009 0.005 0.00501 0.058 0.158 0.063 0.0845 0.232 0.288 0.00058 0.00072 +! Validation 175 29348.009 0.005 0.00552 0.0218 0.132 0.0654 0.0887 0.139 0.176 0.000348 0.000441 +Wall time: 29348.00976965204 +! Best model 175 0.132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 0.146 0.00476 0.051 0.0616 0.0824 0.226 0.27 0.000566 0.000674 + 176 118 0.154 0.00502 0.0539 0.0622 0.0845 0.229 0.277 0.000574 0.000693 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 0.103 0.00452 0.0127 0.0602 0.0803 0.121 0.135 0.000303 0.000337 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 176 29515.706 0.005 0.00495 0.0496 0.149 0.0627 0.084 0.208 0.266 0.00052 0.000664 +! Validation 176 29515.706 0.005 0.00597 0.0556 0.175 0.0686 0.0922 0.231 0.281 0.000578 0.000703 +Wall time: 29515.706722578034 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.119 0.0049 0.0212 0.0616 0.0835 0.145 0.174 0.000363 0.000435 + 177 118 0.223 0.00414 0.14 0.0584 0.0768 0.429 0.447 0.00107 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 0.397 0.00379 0.321 0.0546 0.0734 0.675 0.676 0.00169 0.00169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 177 29683.220 0.005 0.00488 0.0513 0.149 0.0623 0.0834 0.218 0.269 0.000544 0.000672 +! Validation 177 29683.220 0.005 0.00539 0.33 0.438 0.0645 0.0876 0.665 0.686 0.00166 0.00171 +Wall time: 29683.220128229354 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 0.149 0.00491 0.0511 0.0627 0.0836 0.236 0.27 0.00059 0.000674 + 178 118 0.136 0.00507 0.035 0.063 0.085 0.204 0.223 0.000511 0.000558 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 0.0763 0.0037 0.00234 0.0541 0.0726 0.0429 0.0578 0.000107 0.000144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 178 29850.717 0.005 0.00552 0.109 0.219 0.0664 0.0887 0.322 0.395 0.000804 0.000987 +! Validation 178 29850.717 0.005 0.00536 0.0197 0.127 0.0643 0.0874 0.138 0.167 0.000344 0.000419 +Wall time: 29850.717217535246 +! Best model 178 0.127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 0.185 0.0048 0.089 0.0621 0.0827 0.325 0.356 0.000813 0.00089 + 179 118 0.148 0.00587 0.0302 0.0683 0.0915 0.167 0.207 0.000418 0.000518 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 0.0897 0.00384 0.0128 0.0557 0.074 0.13 0.135 0.000325 0.000337 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 179 30018.315 0.005 0.00484 0.0453 0.142 0.0619 0.083 0.203 0.254 0.000508 0.000636 +! Validation 179 30018.315 0.005 0.00557 0.0264 0.138 0.0657 0.0891 0.156 0.194 0.00039 0.000485 +Wall time: 30018.315626908094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.113 0.00446 0.0238 0.0599 0.0797 0.147 0.184 0.000368 0.00046 + 180 118 0.114 0.00433 0.0278 0.0592 0.0786 0.186 0.199 0.000466 0.000497 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 0.101 0.00359 0.0291 0.053 0.0715 0.199 0.204 0.000498 0.000509 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 180 30185.837 0.005 0.00476 0.0407 0.136 0.0614 0.0824 0.195 0.241 0.000487 0.000603 +! Validation 180 30185.837 0.005 0.0051 0.051 0.153 0.0625 0.0852 0.223 0.269 0.000557 0.000674 +Wall time: 30185.837090429384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 0.141 0.00467 0.0473 0.0608 0.0815 0.227 0.259 0.000569 0.000649 + 181 118 0.237 0.00544 0.128 0.0646 0.088 0.385 0.428 0.000963 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 0.0829 0.00378 0.00719 0.0543 0.0734 0.086 0.101 0.000215 0.000253 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 181 30353.321 0.005 0.00488 0.062 0.16 0.0622 0.0833 0.236 0.296 0.000591 0.00074 +! Validation 181 30353.321 0.005 0.00522 0.0227 0.127 0.0634 0.0863 0.146 0.18 0.000364 0.00045 +Wall time: 30353.32179812342 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.12 0.00433 0.0335 0.0588 0.0785 0.178 0.218 0.000445 0.000546 + 182 118 0.111 0.00444 0.0221 0.0604 0.0795 0.134 0.177 0.000336 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 0.0863 0.00357 0.0148 0.053 0.0713 0.138 0.145 0.000345 0.000364 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 182 30520.798 0.005 0.00474 0.0483 0.143 0.0613 0.0822 0.213 0.263 0.000532 0.000657 +! Validation 182 30520.798 0.005 0.00507 0.0366 0.138 0.0624 0.085 0.182 0.228 0.000454 0.000571 +Wall time: 30520.798558157403 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 0.115 0.00476 0.0194 0.0619 0.0824 0.129 0.166 0.000323 0.000416 + 183 118 0.138 0.00501 0.0377 0.0621 0.0845 0.204 0.232 0.000511 0.000579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 0.118 0.00374 0.0432 0.0537 0.073 0.244 0.248 0.000611 0.00062 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 183 30688.388 0.005 0.00472 0.0516 0.146 0.0612 0.082 0.221 0.271 0.000552 0.000678 +! Validation 183 30688.388 0.005 0.00509 0.0682 0.17 0.0624 0.0852 0.273 0.312 0.000682 0.000779 +Wall time: 30688.388122553006 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.115 0.00507 0.0138 0.0622 0.085 0.104 0.14 0.00026 0.000351 + 184 118 0.107 0.00454 0.0164 0.0602 0.0805 0.127 0.153 0.000317 0.000382 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 0.0797 0.00372 0.00538 0.0542 0.0728 0.0646 0.0876 0.000162 0.000219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 184 30856.281 0.005 0.00488 0.0675 0.165 0.0624 0.0834 0.25 0.311 0.000624 0.000777 +! Validation 184 30856.281 0.005 0.00513 0.0202 0.123 0.0628 0.0855 0.136 0.169 0.00034 0.000424 +Wall time: 30856.281442234293 +! Best model 184 0.123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.142 0.00465 0.0485 0.0609 0.0814 0.237 0.263 0.000593 0.000657 + 185 118 0.0949 0.00426 0.00976 0.0577 0.0779 0.0931 0.118 0.000233 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 0.0729 0.00354 0.00214 0.0526 0.071 0.0479 0.0552 0.00012 0.000138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 185 31023.748 0.005 0.00461 0.0551 0.147 0.0605 0.0811 0.228 0.281 0.00057 0.000703 +! Validation 185 31023.748 0.005 0.00504 0.0211 0.122 0.0621 0.0848 0.137 0.173 0.000342 0.000433 +Wall time: 31023.7487268541 +! Best model 185 0.122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.21 0.00491 0.112 0.0623 0.0837 0.376 0.399 0.000941 0.000997 + 186 118 0.122 0.00462 0.0294 0.0599 0.0811 0.182 0.205 0.000455 0.000512 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 0.182 0.00364 0.109 0.0532 0.072 0.393 0.395 0.000982 0.000987 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 186 31191.188 0.005 0.00484 0.0902 0.187 0.062 0.083 0.287 0.359 0.000718 0.000898 +! Validation 186 31191.188 0.005 0.00508 0.129 0.231 0.0625 0.0851 0.398 0.429 0.000994 0.00107 +Wall time: 31191.18882505037 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.129 0.00464 0.0359 0.0611 0.0813 0.178 0.226 0.000446 0.000565 + 187 118 0.0986 0.00441 0.0104 0.0594 0.0792 0.109 0.122 0.000273 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 0.0719 0.00348 0.00218 0.052 0.0705 0.0485 0.0558 0.000121 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 187 31358.655 0.005 0.00462 0.046 0.138 0.0605 0.0811 0.208 0.257 0.000519 0.000642 +! Validation 187 31358.655 0.005 0.00491 0.0193 0.117 0.0613 0.0836 0.132 0.166 0.00033 0.000414 +Wall time: 31358.655960428994 +! Best model 187 0.117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.117 0.00414 0.0344 0.0575 0.0768 0.188 0.221 0.000469 0.000553 + 188 118 0.111 0.00451 0.0205 0.0595 0.0802 0.142 0.171 0.000355 0.000427 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 0.0734 0.00351 0.00311 0.0527 0.0708 0.0603 0.0666 0.000151 0.000166 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 188 31526.233 0.005 0.00449 0.0379 0.128 0.0596 0.0799 0.18 0.233 0.000451 0.000582 +! Validation 188 31526.233 0.005 0.00498 0.021 0.121 0.062 0.0843 0.136 0.173 0.00034 0.000433 +Wall time: 31526.23373403307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.105 0.00447 0.0159 0.0599 0.0798 0.116 0.15 0.000289 0.000376 + 189 118 0.135 0.00533 0.0286 0.0649 0.0871 0.166 0.202 0.000414 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 0.071 0.00349 0.00126 0.0524 0.0705 0.0376 0.0423 9.4e-05 0.000106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 189 31693.591 0.005 0.00461 0.0624 0.155 0.0605 0.081 0.234 0.299 0.000584 0.000747 +! Validation 189 31693.591 0.005 0.00488 0.0233 0.121 0.0612 0.0834 0.141 0.182 0.000354 0.000456 +Wall time: 31693.591722398996 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.128 0.00455 0.0373 0.0601 0.0805 0.195 0.231 0.000487 0.000576 + 190 118 0.246 0.006 0.126 0.0702 0.0925 0.405 0.424 0.00101 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 0.251 0.0037 0.177 0.0538 0.0726 0.499 0.502 0.00125 0.00125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 190 31860.912 0.005 0.00451 0.0485 0.139 0.0597 0.0801 0.213 0.262 0.000532 0.000654 +! Validation 190 31860.912 0.005 0.00504 0.145 0.246 0.0623 0.0848 0.427 0.455 0.00107 0.00114 +Wall time: 31860.912103345152 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.0945 0.00422 0.01 0.0581 0.0775 0.101 0.12 0.000253 0.000299 + 191 118 0.106 0.00494 0.00742 0.0619 0.0839 0.0772 0.103 0.000193 0.000257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 0.0699 0.00329 0.00412 0.0508 0.0685 0.0604 0.0767 0.000151 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 191 32028.242 0.005 0.00449 0.0471 0.137 0.0597 0.08 0.208 0.26 0.000519 0.00065 +! Validation 191 32028.242 0.005 0.00475 0.0195 0.115 0.0603 0.0823 0.131 0.167 0.000328 0.000417 +Wall time: 32028.24287068611 +! Best model 191 0.115 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.126 0.00486 0.0284 0.0617 0.0832 0.167 0.201 0.000417 0.000503 + 192 118 0.16 0.00426 0.0752 0.0574 0.0779 0.281 0.327 0.000704 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 0.105 0.00364 0.0323 0.0533 0.072 0.21 0.214 0.000526 0.000536 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 192 32195.584 0.005 0.00458 0.0621 0.154 0.0603 0.0808 0.242 0.297 0.000605 0.000743 +! Validation 192 32195.584 0.005 0.00493 0.0544 0.153 0.0616 0.0838 0.236 0.278 0.000589 0.000696 +Wall time: 32195.584687230177 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.285 0.00439 0.197 0.0593 0.0791 0.512 0.53 0.00128 0.00132 + 193 118 0.183 0.00506 0.0821 0.0629 0.0849 0.307 0.342 0.000768 0.000855 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 0.452 0.0042 0.368 0.057 0.0773 0.722 0.724 0.0018 0.00181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 193 32362.995 0.005 0.00441 0.0483 0.136 0.0591 0.0792 0.203 0.262 0.000509 0.000654 +! Validation 193 32362.995 0.005 0.00531 0.467 0.573 0.0643 0.087 0.79 0.816 0.00198 0.00204 +Wall time: 32362.99514681101 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.114 0.0045 0.0235 0.0599 0.0801 0.142 0.183 0.000356 0.000458 + 194 118 0.121 0.00481 0.0252 0.0619 0.0828 0.156 0.189 0.00039 0.000474 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 0.0896 0.00344 0.0207 0.0519 0.0701 0.167 0.172 0.000417 0.000429 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 194 32530.301 0.005 0.00785 0.262 0.42 0.076 0.106 0.386 0.613 0.000965 0.00153 +! Validation 194 32530.301 0.005 0.00491 0.0427 0.141 0.0613 0.0836 0.206 0.247 0.000516 0.000616 +Wall time: 32530.301234465092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.103 0.00451 0.0129 0.0596 0.0802 0.104 0.135 0.000261 0.000339 + 195 118 0.103 0.00453 0.0125 0.0602 0.0804 0.104 0.133 0.000259 0.000333 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 0.067 0.00329 0.00119 0.0509 0.0684 0.0401 0.0412 0.0001 0.000103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 195 32697.621 0.005 0.00439 0.0241 0.112 0.0589 0.0791 0.149 0.186 0.000372 0.000464 +! Validation 195 32697.621 0.005 0.00473 0.0218 0.116 0.0602 0.0821 0.137 0.176 0.000342 0.000441 +Wall time: 32697.621549515985 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.0922 0.00412 0.00979 0.0573 0.0766 0.0962 0.118 0.000241 0.000295 + 196 118 0.128 0.00496 0.0285 0.06 0.084 0.172 0.201 0.000429 0.000504 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 0.0701 0.00322 0.00577 0.0503 0.0677 0.0802 0.0907 0.0002 0.000227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 196 32864.940 0.005 0.00427 0.0229 0.108 0.0581 0.0779 0.144 0.18 0.00036 0.000451 +! Validation 196 32864.940 0.005 0.00464 0.0283 0.121 0.0595 0.0813 0.159 0.201 0.000397 0.000502 +Wall time: 32864.9400104573 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.0949 0.00403 0.0143 0.0568 0.0757 0.111 0.143 0.000277 0.000357 + 197 118 0.086 0.00396 0.0068 0.0555 0.0751 0.0661 0.0984 0.000165 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 0.0683 0.00324 0.00347 0.0503 0.0679 0.0594 0.0703 0.000148 0.000176 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 197 33032.267 0.005 0.00423 0.0315 0.116 0.0579 0.0776 0.172 0.212 0.00043 0.000531 +! Validation 197 33032.267 0.005 0.00463 0.0168 0.11 0.0595 0.0812 0.123 0.155 0.000307 0.000387 +Wall time: 33032.267922905274 +! Best model 197 0.110 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.151 0.00425 0.0658 0.0584 0.0778 0.277 0.306 0.000692 0.000765 + 198 118 0.0898 0.00392 0.0115 0.0565 0.0747 0.0988 0.128 0.000247 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 0.072 0.00326 0.00675 0.0509 0.0682 0.0878 0.0981 0.000219 0.000245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 198 33199.706 0.005 0.0042 0.0397 0.124 0.0576 0.0773 0.189 0.238 0.000472 0.000596 +! Validation 198 33199.706 0.005 0.00471 0.0252 0.119 0.0602 0.0819 0.152 0.19 0.000381 0.000474 +Wall time: 33199.70621697605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.0934 0.00406 0.0123 0.0568 0.076 0.105 0.132 0.000263 0.00033 + 199 118 0.0969 0.00437 0.00956 0.0592 0.0789 0.0836 0.117 0.000209 0.000292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 0.0825 0.00313 0.0199 0.0495 0.0668 0.163 0.168 0.000406 0.000421 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 199 33367.032 0.005 0.00418 0.0377 0.121 0.0575 0.0771 0.189 0.232 0.000472 0.000581 +! Validation 199 33367.032 0.005 0.00451 0.038 0.128 0.0587 0.0802 0.195 0.233 0.000487 0.000582 +Wall time: 33367.032579347026 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.0916 0.00382 0.0152 0.0553 0.0738 0.119 0.147 0.000297 0.000368 + 200 118 0.172 0.00422 0.0878 0.0571 0.0775 0.331 0.354 0.000828 0.000884 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 0.197 0.00313 0.134 0.0495 0.0668 0.435 0.437 0.00109 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 200 33534.369 0.005 0.00409 0.0264 0.108 0.0568 0.0763 0.154 0.192 0.000384 0.00048 +! Validation 200 33534.369 0.005 0.00446 0.154 0.243 0.0584 0.0797 0.443 0.468 0.00111 0.00117 +Wall time: 33534.36994172819 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.185 0.00452 0.0947 0.0582 0.0802 0.339 0.367 0.000848 0.000918 + 201 118 0.213 0.00362 0.14 0.0543 0.0718 0.438 0.447 0.00109 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 0.305 0.00345 0.236 0.0521 0.0701 0.578 0.58 0.00145 0.00145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 201 33701.686 0.005 0.00413 0.0567 0.139 0.0572 0.0767 0.233 0.283 0.000582 0.000707 +! Validation 201 33701.686 0.005 0.00468 0.2 0.293 0.06 0.0817 0.51 0.533 0.00127 0.00133 +Wall time: 33701.6866016211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 0.233 0.00411 0.15 0.0574 0.0765 0.447 0.463 0.00112 0.00116 + 202 118 0.1 0.0041 0.0183 0.0572 0.0764 0.133 0.161 0.000331 0.000403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 0.167 0.00317 0.104 0.05 0.0672 0.382 0.384 0.000955 0.00096 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 202 33869.113 0.005 0.00418 0.0507 0.134 0.0576 0.0772 0.22 0.269 0.00055 0.000673 +! Validation 202 33869.113 0.005 0.00454 0.123 0.214 0.059 0.0804 0.387 0.419 0.000968 0.00105 +Wall time: 33869.11388413515 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.0977 0.00423 0.0132 0.0576 0.0776 0.107 0.137 0.000267 0.000343 + 203 118 0.088 0.00372 0.0136 0.0546 0.0728 0.111 0.139 0.000278 0.000349 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 0.0758 0.00303 0.0151 0.0488 0.0657 0.14 0.147 0.00035 0.000367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 203 34036.434 0.005 0.00408 0.0369 0.119 0.0569 0.0763 0.177 0.23 0.000443 0.000574 +! Validation 203 34036.434 0.005 0.00435 0.0356 0.123 0.0576 0.0787 0.185 0.225 0.000462 0.000563 +Wall time: 34036.434507693164 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.0877 0.00384 0.0108 0.0552 0.074 0.101 0.124 0.000253 0.000311 + 204 118 0.081 0.00368 0.00751 0.0541 0.0724 0.0844 0.103 0.000211 0.000259 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 0.0691 0.00306 0.00785 0.0489 0.0661 0.0954 0.106 0.000239 0.000264 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 204 34203.748 0.005 0.00404 0.0341 0.115 0.0565 0.0758 0.177 0.221 0.000443 0.000552 +! Validation 204 34203.748 0.005 0.00432 0.0174 0.104 0.0574 0.0784 0.126 0.158 0.000315 0.000394 +Wall time: 34203.74829590134 +! Best model 204 0.104 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.114 0.00404 0.033 0.0567 0.0759 0.187 0.217 0.000468 0.000542 + 205 118 0.0929 0.00384 0.0161 0.0558 0.074 0.12 0.152 0.000301 0.000379 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 0.0749 0.00321 0.0107 0.0499 0.0676 0.117 0.123 0.000292 0.000309 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 205 34371.231 0.005 0.00411 0.0594 0.142 0.0571 0.0765 0.23 0.292 0.000575 0.000729 +! Validation 205 34371.231 0.005 0.00441 0.0277 0.116 0.058 0.0792 0.159 0.199 0.000398 0.000497 +Wall time: 34371.231698742136 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.0964 0.0039 0.0185 0.0556 0.0745 0.133 0.162 0.000332 0.000405 + 206 118 0.0889 0.00345 0.02 0.0533 0.0701 0.142 0.169 0.000356 0.000421 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 0.068 0.00304 0.00716 0.049 0.0658 0.0944 0.101 0.000236 0.000253 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 206 34538.520 0.005 0.00404 0.0463 0.127 0.0566 0.0759 0.208 0.257 0.00052 0.000643 +! Validation 206 34538.520 0.005 0.00437 0.0171 0.104 0.0578 0.0789 0.126 0.156 0.000314 0.00039 +Wall time: 34538.520364675205 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.0961 0.0042 0.012 0.0572 0.0774 0.104 0.131 0.00026 0.000327 + 207 118 0.106 0.00445 0.0174 0.0582 0.0796 0.127 0.157 0.000317 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 0.0708 0.00313 0.00823 0.0494 0.0667 0.101 0.108 0.000252 0.000271 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 207 34705.920 0.005 0.00406 0.0569 0.138 0.0568 0.0761 0.236 0.285 0.000589 0.000713 +! Validation 207 34705.920 0.005 0.00435 0.0189 0.106 0.0577 0.0787 0.133 0.164 0.000332 0.00041 +Wall time: 34705.92079286929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.0926 0.00408 0.0109 0.0572 0.0763 0.0967 0.125 0.000242 0.000312 + 208 118 0.139 0.00374 0.0641 0.0551 0.073 0.278 0.302 0.000694 0.000756 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 0.131 0.00303 0.0704 0.0486 0.0657 0.313 0.317 0.000783 0.000792 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 208 34873.226 0.005 0.00407 0.0523 0.134 0.0569 0.0762 0.208 0.273 0.000519 0.000682 +! Validation 208 34873.226 0.005 0.00434 0.115 0.202 0.0577 0.0786 0.372 0.405 0.000931 0.00101 +Wall time: 34873.22607461503 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.174 0.00391 0.0956 0.0554 0.0747 0.34 0.369 0.00085 0.000922 + 209 118 0.0973 0.00356 0.026 0.0528 0.0713 0.152 0.192 0.000379 0.000481 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 0.089 0.00303 0.0283 0.0489 0.0657 0.197 0.201 0.000493 0.000502 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 209 35040.530 0.005 0.00393 0.0316 0.11 0.0558 0.0748 0.17 0.212 0.000426 0.000531 +! Validation 209 35040.530 0.005 0.00434 0.0392 0.126 0.0576 0.0787 0.202 0.236 0.000505 0.000591 +Wall time: 35040.53069094615 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.102 0.00398 0.022 0.0562 0.0753 0.151 0.177 0.000379 0.000443 + 210 118 0.11 0.00356 0.039 0.0539 0.0712 0.215 0.236 0.000537 0.000589 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 0.173 0.00302 0.112 0.0491 0.0656 0.397 0.4 0.000992 0.000999 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 210 35207.916 0.005 0.00402 0.0484 0.129 0.0565 0.0757 0.212 0.263 0.000529 0.000657 +! Validation 210 35207.916 0.005 0.00434 0.194 0.28 0.0578 0.0787 0.498 0.525 0.00124 0.00131 +Wall time: 35207.916587716434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.18 0.00387 0.102 0.0554 0.0742 0.362 0.382 0.000906 0.000955 + 211 118 0.0759 0.00352 0.00555 0.0534 0.0708 0.0682 0.0889 0.000171 0.000222 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 0.0601 0.00293 0.00143 0.0483 0.0646 0.0377 0.0452 9.43e-05 0.000113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 211 35375.360 0.005 0.00389 0.0317 0.11 0.0555 0.0745 0.167 0.213 0.000418 0.000533 +! Validation 211 35375.360 0.005 0.00427 0.0171 0.103 0.0572 0.078 0.122 0.156 0.000306 0.00039 +Wall time: 35375.36040374404 +! Best model 211 0.103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.185 0.00413 0.102 0.0578 0.0767 0.359 0.381 0.000897 0.000953 + 212 118 0.104 0.00432 0.0174 0.0588 0.0785 0.108 0.158 0.000269 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 0.0718 0.00332 0.00527 0.051 0.0688 0.0755 0.0867 0.000189 0.000217 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 212 35542.898 0.005 0.00399 0.0629 0.143 0.0563 0.0753 0.248 0.3 0.00062 0.00075 +! Validation 212 35542.898 0.005 0.00445 0.0378 0.127 0.0586 0.0796 0.187 0.232 0.000467 0.00058 +Wall time: 35542.89838630613 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.148 0.00376 0.0728 0.0545 0.0732 0.29 0.322 0.000725 0.000805 + 213 118 0.136 0.0041 0.0541 0.0568 0.0764 0.254 0.278 0.000636 0.000694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 0.138 0.00299 0.0783 0.0486 0.0652 0.331 0.334 0.000827 0.000835 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 213 35710.280 0.005 0.00388 0.0328 0.11 0.0554 0.0743 0.171 0.216 0.000428 0.000539 +! Validation 213 35710.280 0.005 0.0042 0.0726 0.157 0.0567 0.0774 0.293 0.322 0.000733 0.000804 +Wall time: 35710.28053522436 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.171 0.00361 0.0984 0.0531 0.0717 0.359 0.374 0.000897 0.000936 + 214 118 0.0968 0.00454 0.00598 0.0597 0.0804 0.0733 0.0923 0.000183 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 0.0606 0.00295 0.00163 0.0487 0.0648 0.0397 0.0482 9.93e-05 0.00012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 214 35877.538 0.005 0.00387 0.0414 0.119 0.0554 0.0742 0.196 0.244 0.000489 0.000609 +! Validation 214 35877.538 0.005 0.00424 0.0175 0.102 0.0572 0.0777 0.124 0.158 0.000311 0.000395 +Wall time: 35877.53825648641 +! Best model 214 0.102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.0834 0.00363 0.0108 0.0537 0.0719 0.101 0.124 0.000251 0.00031 + 215 118 0.11 0.00388 0.0321 0.0552 0.0743 0.197 0.214 0.000493 0.000535 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 0.126 0.00299 0.0668 0.0485 0.0652 0.306 0.308 0.000765 0.000771 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 215 36045.389 0.005 0.00384 0.0422 0.119 0.0552 0.074 0.199 0.245 0.000499 0.000613 +! Validation 215 36045.389 0.005 0.00421 0.0682 0.152 0.0569 0.0775 0.282 0.312 0.000705 0.000779 +Wall time: 36045.38955364702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.0801 0.00348 0.0106 0.0528 0.0704 0.0988 0.123 0.000247 0.000307 + 216 118 0.0893 0.00373 0.0147 0.0547 0.0729 0.127 0.145 0.000317 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 0.0757 0.00293 0.017 0.0478 0.0646 0.15 0.156 0.000374 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 216 36212.810 0.005 0.0038 0.0382 0.114 0.0549 0.0736 0.187 0.234 0.000467 0.000584 +! Validation 216 36212.810 0.005 0.00406 0.0322 0.113 0.0557 0.076 0.177 0.214 0.000443 0.000536 +Wall time: 36212.810430257116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.0956 0.00396 0.0164 0.055 0.0751 0.123 0.153 0.000307 0.000383 + 217 118 0.204 0.00422 0.12 0.0574 0.0775 0.399 0.413 0.000997 0.00103 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 0.246 0.00327 0.181 0.0501 0.0682 0.504 0.507 0.00126 0.00127 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 217 36381.711 0.005 0.00373 0.0367 0.111 0.0544 0.0729 0.184 0.227 0.000459 0.000567 +! Validation 217 36381.711 0.005 0.00429 0.219 0.305 0.0573 0.0782 0.534 0.559 0.00134 0.0014 +Wall time: 36381.711485709064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.0871 0.00375 0.0122 0.0547 0.0731 0.102 0.132 0.000256 0.00033 + 218 118 0.102 0.00451 0.0122 0.0602 0.0802 0.11 0.132 0.000274 0.000329 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 0.0591 0.00291 0.000912 0.0479 0.0644 0.0301 0.036 7.52e-05 9.01e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 218 36550.808 0.005 0.00385 0.055 0.132 0.0553 0.0741 0.223 0.281 0.000558 0.000702 +! Validation 218 36550.808 0.005 0.00409 0.0263 0.108 0.056 0.0763 0.15 0.194 0.000374 0.000484 +Wall time: 36550.808973482344 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.11 0.00414 0.0276 0.0576 0.0768 0.163 0.198 0.000408 0.000495 + 219 118 0.0895 0.00356 0.0184 0.0533 0.0712 0.126 0.162 0.000314 0.000405 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 0.0848 0.00338 0.0173 0.0512 0.0694 0.145 0.157 0.000363 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 219 36718.338 0.005 0.00396 0.0612 0.14 0.0561 0.0752 0.239 0.296 0.000597 0.00074 +! Validation 219 36718.338 0.005 0.00445 0.0454 0.134 0.0586 0.0796 0.214 0.254 0.000536 0.000636 +Wall time: 36718.33836356038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.12 0.00386 0.0422 0.0551 0.0742 0.219 0.245 0.000547 0.000613 + 220 118 0.081 0.00348 0.0114 0.052 0.0704 0.0983 0.128 0.000246 0.000319 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 0.0693 0.00277 0.0138 0.0467 0.0629 0.135 0.14 0.000337 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 220 36885.885 0.005 0.00374 0.0366 0.111 0.0545 0.073 0.182 0.229 0.000455 0.000572 +! Validation 220 36885.885 0.005 0.00396 0.0315 0.111 0.0551 0.0752 0.175 0.212 0.000436 0.00053 +Wall time: 36885.88514932431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.0906 0.0037 0.0166 0.0543 0.0726 0.129 0.154 0.000323 0.000385 + 221 118 0.103 0.00309 0.041 0.05 0.0664 0.224 0.242 0.000561 0.000604 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 0.0704 0.0029 0.0124 0.048 0.0643 0.128 0.133 0.00032 0.000333 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 221 37053.535 0.005 0.00374 0.0415 0.116 0.0546 0.0731 0.195 0.243 0.000488 0.000608 +! Validation 221 37053.535 0.005 0.00414 0.0267 0.109 0.0563 0.0768 0.157 0.195 0.000393 0.000488 +Wall time: 37053.53566084523 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.0931 0.00378 0.0176 0.0548 0.0734 0.126 0.158 0.000316 0.000395 + 222 118 0.13 0.00404 0.0495 0.0571 0.0759 0.244 0.265 0.000609 0.000664 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 0.0671 0.00304 0.0062 0.049 0.0659 0.0885 0.0939 0.000221 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 222 37221.079 0.005 0.00371 0.0393 0.113 0.0542 0.0727 0.193 0.236 0.000483 0.000591 +! Validation 222 37221.079 0.005 0.00415 0.02 0.103 0.0568 0.0769 0.134 0.169 0.000335 0.000422 +Wall time: 37221.07977998443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.0917 0.00363 0.019 0.0538 0.0719 0.133 0.165 0.000334 0.000411 + 223 118 0.151 0.00372 0.0761 0.0543 0.0728 0.3 0.329 0.00075 0.000823 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 0.0876 0.00275 0.0326 0.0463 0.0626 0.212 0.216 0.000531 0.000539 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 223 37388.638 0.005 0.00361 0.0289 0.101 0.0535 0.0717 0.163 0.202 0.000407 0.000505 +! Validation 223 37388.638 0.005 0.00396 0.0525 0.132 0.055 0.0751 0.242 0.273 0.000604 0.000684 +Wall time: 37388.6389631764 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.111 0.00366 0.0377 0.0542 0.0722 0.202 0.232 0.000505 0.000579 + 224 118 0.085 0.00331 0.0187 0.0507 0.0687 0.14 0.163 0.00035 0.000408 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 0.0574 0.00271 0.00324 0.0461 0.0621 0.0552 0.0679 0.000138 0.00017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 224 37556.180 0.005 0.00373 0.05 0.125 0.0545 0.0729 0.211 0.267 0.000526 0.000668 +! Validation 224 37556.180 0.005 0.0039 0.0145 0.0925 0.0545 0.0745 0.114 0.144 0.000285 0.000359 +Wall time: 37556.18073687516 +! Best model 224 0.092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.128 0.00428 0.0428 0.0584 0.0781 0.212 0.247 0.00053 0.000617 + 225 118 0.114 0.00415 0.0312 0.0574 0.0769 0.169 0.211 0.000422 0.000527 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 0.137 0.00293 0.0786 0.0481 0.0646 0.332 0.335 0.000829 0.000837 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 225 37724.983 0.005 0.00378 0.0518 0.127 0.0548 0.0733 0.221 0.272 0.000552 0.00068 +! Validation 225 37724.983 0.005 0.00413 0.0769 0.159 0.0563 0.0767 0.299 0.331 0.000749 0.000827 +Wall time: 37724.983554077335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.0889 0.0036 0.0169 0.0534 0.0716 0.127 0.155 0.000318 0.000388 + 226 118 0.0921 0.00317 0.0288 0.0499 0.0672 0.173 0.203 0.000432 0.000506 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 0.0752 0.0028 0.0193 0.0468 0.0631 0.16 0.166 0.0004 0.000414 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 226 37892.667 0.005 0.0036 0.0318 0.104 0.0535 0.0717 0.171 0.213 0.000427 0.000532 +! Validation 226 37892.667 0.005 0.00393 0.0247 0.103 0.055 0.0748 0.154 0.188 0.000384 0.000469 +Wall time: 37892.66783031821 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.201 0.00344 0.132 0.0527 0.07 0.419 0.433 0.00105 0.00108 + 227 118 0.0799 0.00368 0.00628 0.0539 0.0724 0.0727 0.0946 0.000182 0.000236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 100 0.0693 0.00283 0.0128 0.0471 0.0635 0.125 0.135 0.000313 0.000338 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 227 38060.237 0.005 0.00357 0.0342 0.106 0.0533 0.0713 0.178 0.221 0.000444 0.000554 +! Validation 227 38060.237 0.005 0.00394 0.0366 0.115 0.055 0.0749 0.188 0.228 0.00047 0.000571 +Wall time: 38060.237746926025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.0857 0.00365 0.0127 0.0531 0.0721 0.107 0.134 0.000266 0.000336 + 228 118 0.125 0.00352 0.0548 0.0528 0.0708 0.239 0.279 0.000597 0.000698 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 0.0848 0.00267 0.0313 0.0459 0.0617 0.207 0.211 0.000517 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 228 38227.821 0.005 0.00368 0.0521 0.126 0.054 0.0724 0.221 0.272 0.000553 0.000681 +! Validation 228 38227.821 0.005 0.00387 0.0524 0.13 0.0544 0.0742 0.24 0.273 0.0006 0.000683 +Wall time: 38227.82147413725 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.1 0.00339 0.0323 0.0518 0.0695 0.19 0.214 0.000474 0.000536 + 229 118 0.0873 0.00356 0.0162 0.0522 0.0712 0.116 0.152 0.000291 0.00038 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 0.0608 0.00266 0.00762 0.0455 0.0615 0.0951 0.104 0.000238 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 229 38395.541 0.005 0.00347 0.0252 0.0947 0.0525 0.0703 0.148 0.19 0.00037 0.000475 +! Validation 229 38395.541 0.005 0.00375 0.0194 0.0945 0.0535 0.0731 0.132 0.166 0.000331 0.000416 +Wall time: 38395.541461289395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.0871 0.00328 0.0214 0.0512 0.0684 0.139 0.175 0.000347 0.000437 + 230 118 0.0908 0.00333 0.0243 0.051 0.0688 0.165 0.186 0.000412 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 0.0967 0.00268 0.0431 0.0459 0.0618 0.244 0.248 0.00061 0.000619 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 230 38562.855 0.005 0.00344 0.0299 0.0988 0.0522 0.07 0.164 0.207 0.000411 0.000517 +! Validation 230 38562.855 0.005 0.00379 0.0466 0.122 0.0539 0.0735 0.224 0.258 0.00056 0.000644 +Wall time: 38562.855570863 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.193 0.00369 0.12 0.054 0.0725 0.396 0.413 0.000989 0.00103 + 231 118 0.101 0.00316 0.0382 0.0504 0.0671 0.208 0.233 0.00052 0.000583 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 0.0558 0.00272 0.00133 0.0463 0.0623 0.0321 0.0436 8.03e-05 0.000109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 231 38730.244 0.005 0.00357 0.0479 0.119 0.0533 0.0713 0.213 0.262 0.000532 0.000654 +! Validation 231 38730.244 0.005 0.00384 0.0153 0.0922 0.0543 0.074 0.116 0.148 0.000291 0.000369 +Wall time: 38730.244427985046 +! Best model 231 0.092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.134 0.00364 0.0611 0.0536 0.0721 0.274 0.295 0.000686 0.000737 + 232 118 0.087 0.00332 0.0206 0.0514 0.0688 0.15 0.171 0.000375 0.000428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 0.0654 0.00259 0.0137 0.0448 0.0607 0.135 0.14 0.000336 0.000349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 232 38897.505 0.005 0.0035 0.0405 0.111 0.0527 0.0706 0.194 0.241 0.000484 0.000602 +! Validation 232 38897.505 0.005 0.00375 0.0302 0.105 0.0535 0.0731 0.173 0.207 0.000432 0.000519 +Wall time: 38897.505707249045 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.0794 0.00333 0.0129 0.0516 0.0688 0.107 0.135 0.000266 0.000339 + 233 118 0.152 0.00334 0.0854 0.0521 0.069 0.331 0.349 0.000827 0.000872 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 0.145 0.00281 0.0888 0.0474 0.0633 0.354 0.356 0.000885 0.000889 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 233 39064.785 0.005 0.00342 0.0338 0.102 0.052 0.0698 0.175 0.218 0.000439 0.000545 +! Validation 233 39064.785 0.005 0.00409 0.129 0.211 0.0562 0.0763 0.396 0.429 0.000989 0.00107 +Wall time: 39064.78552664304 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.0911 0.00363 0.0185 0.053 0.0719 0.14 0.162 0.00035 0.000405 + 234 118 0.111 0.00303 0.0499 0.0497 0.0658 0.25 0.267 0.000624 0.000667 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 0.211 0.00263 0.158 0.0454 0.0612 0.473 0.474 0.00118 0.00119 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 234 39232.042 0.005 0.0035 0.042 0.112 0.0528 0.0707 0.198 0.244 0.000495 0.000611 +! Validation 234 39232.042 0.005 0.00373 0.147 0.222 0.0535 0.0729 0.436 0.458 0.00109 0.00115 +Wall time: 39232.04280584538 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0726 0.00326 0.00736 0.0506 0.0682 0.0826 0.102 0.000207 0.000256 + 235 118 0.0786 0.00331 0.0124 0.052 0.0687 0.108 0.133 0.00027 0.000332 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 0.0598 0.00288 0.00215 0.0475 0.0641 0.0402 0.0553 0.0001 0.000138 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 235 39399.307 0.005 0.00342 0.0264 0.0948 0.0521 0.0698 0.152 0.194 0.00038 0.000485 +! Validation 235 39399.307 0.005 0.00391 0.0133 0.0915 0.0549 0.0746 0.108 0.138 0.00027 0.000345 +Wall time: 39399.30728081521 +! Best model 235 0.091 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.758 0.0338 0.0829 0.155 0.219 0.28 0.344 0.000699 0.000859 + 236 118 0.56 0.0249 0.0622 0.134 0.188 0.253 0.298 0.000633 0.000744 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 0.557 0.0208 0.14 0.123 0.172 0.434 0.447 0.00109 0.00112 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 236 39566.680 0.005 0.0987 1.54 3.51 0.229 0.376 0.929 1.49 0.00232 0.00371 +! Validation 236 39566.680 0.005 0.0253 0.14 0.647 0.135 0.19 0.379 0.447 0.000947 0.00112 +Wall time: 39566.68046694435 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.416 0.0159 0.0979 0.108 0.15 0.328 0.373 0.000819 0.000934 + 237 118 0.439 0.0151 0.138 0.106 0.147 0.404 0.443 0.00101 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 0.247 0.0123 0.00122 0.0957 0.132 0.0406 0.0417 0.000101 0.000104 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 237 39733.934 0.005 0.0182 0.193 0.556 0.115 0.161 0.412 0.524 0.00103 0.00131 +! Validation 237 39733.934 0.005 0.015 0.0534 0.354 0.105 0.146 0.219 0.276 0.000547 0.00069 +Wall time: 39733.93411203707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.438 0.0115 0.208 0.0928 0.128 0.505 0.545 0.00126 0.00136 + 238 118 0.35 0.00995 0.151 0.0871 0.119 0.442 0.464 0.0011 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 0.486 0.00894 0.307 0.0821 0.113 0.66 0.661 0.00165 0.00165 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 238 39901.216 0.005 0.0124 0.113 0.361 0.096 0.133 0.325 0.402 0.000813 0.001 +! Validation 238 39901.216 0.005 0.0112 0.249 0.472 0.0913 0.126 0.559 0.595 0.0014 0.00149 +Wall time: 39901.21611926798 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.192 0.00851 0.022 0.0811 0.11 0.135 0.177 0.000339 0.000442 + 239 118 0.198 0.00797 0.0384 0.0792 0.107 0.195 0.234 0.000488 0.000585 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 0.138 0.00662 0.00569 0.0712 0.0971 0.0808 0.09 0.000202 0.000225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 239 40068.488 0.005 0.00944 0.0991 0.288 0.0849 0.116 0.306 0.377 0.000765 0.000941 +! Validation 239 40068.488 0.005 0.00846 0.0312 0.201 0.0805 0.11 0.169 0.211 0.000422 0.000527 +Wall time: 40068.48827444203 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.18 0.00664 0.0467 0.0727 0.0973 0.217 0.258 0.000543 0.000645 + 240 118 0.152 0.00659 0.0201 0.0725 0.0969 0.149 0.169 0.000373 0.000423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 0.116 0.00523 0.0115 0.064 0.0863 0.121 0.128 0.000303 0.000319 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 240 40235.854 0.005 0.00723 0.091 0.236 0.0755 0.102 0.296 0.361 0.00074 0.000903 +! Validation 240 40235.854 0.005 0.00683 0.0309 0.167 0.073 0.0986 0.168 0.21 0.000419 0.000524 +Wall time: 40235.85414658021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.125 0.00543 0.0162 0.0659 0.088 0.13 0.152 0.000326 0.00038 + 241 118 0.176 0.00504 0.0752 0.0639 0.0847 0.28 0.327 0.000701 0.000818 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 0.0897 0.00435 0.00283 0.0586 0.0787 0.0492 0.0635 0.000123 0.000159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 241 40403.125 0.005 0.00578 0.0656 0.181 0.0681 0.0908 0.248 0.306 0.000619 0.000764 +! Validation 241 40403.125 0.005 0.00579 0.0189 0.135 0.0675 0.0908 0.134 0.164 0.000336 0.00041 +Wall time: 40403.125900730025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 0.14 0.00504 0.0388 0.0639 0.0848 0.197 0.235 0.000494 0.000588 + 242 118 0.113 0.00459 0.0216 0.0616 0.0808 0.15 0.175 0.000374 0.000438 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 0.132 0.00382 0.0557 0.0549 0.0738 0.278 0.282 0.000696 0.000704 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 242 40570.406 0.005 0.00502 0.0544 0.155 0.0636 0.0846 0.223 0.279 0.000558 0.000697 +! Validation 242 40570.406 0.005 0.00518 0.0584 0.162 0.0637 0.0859 0.253 0.288 0.000633 0.000721 +Wall time: 40570.40694660414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 0.107 0.0045 0.0173 0.0601 0.08 0.124 0.157 0.000311 0.000393 + 243 118 0.132 0.00425 0.0469 0.0583 0.0778 0.228 0.259 0.00057 0.000646 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 0.0837 0.00341 0.0155 0.0519 0.0697 0.144 0.149 0.000359 0.000371 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 243 40737.672 0.005 0.00456 0.0376 0.129 0.0606 0.0806 0.187 0.231 0.000468 0.000578 +! Validation 243 40737.672 0.005 0.0048 0.024 0.12 0.0612 0.0827 0.151 0.185 0.000378 0.000462 +Wall time: 40737.672519540414 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 0.16 0.00445 0.0711 0.0593 0.0797 0.295 0.318 0.000737 0.000796 + 244 118 0.169 0.00402 0.0891 0.0569 0.0756 0.322 0.356 0.000805 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 0.199 0.00329 0.134 0.0509 0.0684 0.435 0.436 0.00109 0.00109 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 244 40904.933 0.005 0.00431 0.0428 0.129 0.0588 0.0783 0.2 0.246 0.000501 0.000615 +! Validation 244 40904.933 0.005 0.00459 0.138 0.23 0.0597 0.0808 0.42 0.444 0.00105 0.00111 +Wall time: 40904.93374403007 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.0916 0.00389 0.0139 0.0561 0.0744 0.107 0.14 0.000267 0.000351 + 245 118 0.1 0.00349 0.0304 0.0536 0.0706 0.178 0.208 0.000444 0.000521 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 0.0769 0.00312 0.0146 0.0494 0.0666 0.139 0.144 0.000348 0.00036 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 245 41072.295 0.005 0.00413 0.0353 0.118 0.0576 0.0768 0.181 0.224 0.000453 0.000561 +! Validation 245 41072.295 0.005 0.00441 0.0219 0.11 0.0584 0.0792 0.147 0.177 0.000367 0.000442 +Wall time: 41072.295085913036 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 0.103 0.00395 0.0238 0.056 0.075 0.155 0.184 0.000386 0.000461 + 246 118 0.0821 0.00379 0.00636 0.0551 0.0734 0.0859 0.0952 0.000215 0.000238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 0.0763 0.00301 0.016 0.0486 0.0655 0.147 0.151 0.000367 0.000378 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 246 41239.568 0.005 0.00399 0.0289 0.109 0.0565 0.0754 0.165 0.203 0.000412 0.000508 +! Validation 246 41239.568 0.005 0.00429 0.0247 0.11 0.0576 0.0782 0.157 0.188 0.000391 0.000469 +Wall time: 41239.56808214029 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.0909 0.00379 0.015 0.055 0.0735 0.12 0.146 0.000299 0.000366 + 247 118 0.109 0.00404 0.0278 0.0564 0.0759 0.179 0.199 0.000449 0.000497 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 0.0691 0.00294 0.0103 0.0479 0.0647 0.116 0.121 0.000289 0.000303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 247 41406.811 0.005 0.00388 0.032 0.11 0.0557 0.0744 0.171 0.214 0.000427 0.000534 +! Validation 247 41406.811 0.005 0.00419 0.0248 0.109 0.0569 0.0773 0.151 0.188 0.000377 0.00047 +Wall time: 41406.81176061928 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.0844 0.00361 0.0122 0.0536 0.0717 0.107 0.132 0.000267 0.000329 + 248 118 0.164 0.00361 0.0922 0.0541 0.0718 0.349 0.362 0.000872 0.000906 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 0.181 0.00286 0.123 0.0473 0.0639 0.418 0.419 0.00104 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 248 41574.064 0.005 0.0038 0.0315 0.107 0.055 0.0736 0.166 0.21 0.000414 0.000526 +! Validation 248 41574.064 0.005 0.00411 0.139 0.221 0.0563 0.0765 0.422 0.445 0.00106 0.00111 +Wall time: 41574.06407823833 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.104 0.00362 0.0314 0.0536 0.0718 0.185 0.211 0.000461 0.000529 + 249 118 0.109 0.00456 0.0184 0.0596 0.0806 0.123 0.162 0.000307 0.000404 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 0.0849 0.00286 0.0277 0.0471 0.0638 0.195 0.199 0.000488 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 249 41741.324 0.005 0.00374 0.04 0.115 0.0545 0.0729 0.195 0.239 0.000487 0.000598 +! Validation 249 41741.324 0.005 0.00407 0.0486 0.13 0.0559 0.0761 0.228 0.263 0.00057 0.000658 +Wall time: 41741.32457659999 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.102 0.00383 0.0254 0.055 0.0739 0.165 0.19 0.000413 0.000475 + 250 118 0.0958 0.00355 0.0248 0.0532 0.0711 0.177 0.188 0.000443 0.00047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 0.0558 0.00275 0.000667 0.0464 0.0626 0.0295 0.0308 7.37e-05 7.71e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 250 41908.700 0.005 0.00366 0.0327 0.106 0.054 0.0722 0.176 0.216 0.00044 0.00054 +! Validation 250 41908.700 0.005 0.00397 0.0144 0.0937 0.0552 0.0752 0.109 0.143 0.000274 0.000358 +Wall time: 41908.70081173722 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.105 0.00331 0.0388 0.0517 0.0686 0.208 0.235 0.000521 0.000588 + 251 118 0.176 0.00395 0.0972 0.0557 0.075 0.327 0.372 0.000817 0.00093 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 0.129 0.00291 0.0707 0.0476 0.0644 0.316 0.317 0.000791 0.000793 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 251 42075.967 0.005 0.00361 0.0351 0.107 0.0535 0.0716 0.181 0.222 0.000452 0.000555 +! Validation 251 42075.967 0.005 0.00405 0.0782 0.159 0.0558 0.0759 0.306 0.334 0.000764 0.000835 +Wall time: 42075.96701184334 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.0771 0.00321 0.0129 0.0509 0.0676 0.111 0.135 0.000278 0.000339 + 252 118 0.104 0.00371 0.0293 0.0552 0.0727 0.19 0.204 0.000475 0.00051 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 0.0717 0.00271 0.0176 0.0459 0.0621 0.155 0.158 0.000387 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 252 42243.424 0.005 0.00355 0.0232 0.0942 0.0531 0.0711 0.146 0.181 0.000364 0.000454 +! Validation 252 42243.424 0.005 0.00385 0.0371 0.114 0.0543 0.0741 0.195 0.23 0.000487 0.000575 +Wall time: 42243.42439697916 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.147 0.00362 0.0748 0.0532 0.0718 0.304 0.327 0.00076 0.000816 + 253 118 0.0657 0.00303 0.00511 0.0495 0.0657 0.068 0.0853 0.00017 0.000213 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 0.063 0.0027 0.00904 0.0458 0.062 0.109 0.113 0.000273 0.000284 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 253 42412.441 0.005 0.00351 0.0401 0.11 0.0528 0.0708 0.196 0.24 0.00049 0.000599 +! Validation 253 42412.441 0.005 0.00382 0.0184 0.0949 0.0541 0.0738 0.131 0.162 0.000328 0.000405 +Wall time: 42412.44192346139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.0882 0.00335 0.0211 0.0518 0.0691 0.147 0.173 0.000368 0.000434 + 254 118 0.079 0.00356 0.00771 0.0522 0.0713 0.0822 0.105 0.000206 0.000262 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 0.0531 0.00263 0.000592 0.0452 0.0612 0.0238 0.029 5.95e-05 7.26e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 254 42579.919 0.005 0.00347 0.0268 0.0962 0.0525 0.0703 0.158 0.196 0.000395 0.00049 +! Validation 254 42579.919 0.005 0.00377 0.0122 0.0877 0.0537 0.0733 0.102 0.132 0.000256 0.00033 +Wall time: 42579.91981325019 +! Best model 254 0.088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.0842 0.0036 0.0122 0.0532 0.0716 0.109 0.132 0.000274 0.00033 + 255 118 0.0824 0.00304 0.0217 0.0493 0.0658 0.16 0.176 0.000399 0.000439 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 0.0888 0.00261 0.0366 0.0451 0.061 0.227 0.228 0.000567 0.000571 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 255 42747.477 0.005 0.00341 0.023 0.0912 0.052 0.0697 0.145 0.181 0.000363 0.000453 +! Validation 255 42747.477 0.005 0.00375 0.0357 0.111 0.0535 0.0731 0.196 0.225 0.00049 0.000564 +Wall time: 42747.477251499426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.0851 0.00346 0.016 0.0522 0.0702 0.123 0.151 0.000308 0.000377 + 256 118 0.137 0.00315 0.0745 0.0508 0.0669 0.312 0.326 0.00078 0.000814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 0.0773 0.00267 0.024 0.0456 0.0616 0.183 0.185 0.000458 0.000462 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 256 42914.896 0.005 0.00347 0.049 0.118 0.0525 0.0704 0.198 0.264 0.000494 0.000659 +! Validation 256 42914.896 0.005 0.00375 0.0426 0.117 0.0536 0.073 0.213 0.246 0.000533 0.000616 +Wall time: 42914.89691913221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.133 0.00308 0.0713 0.05 0.0663 0.296 0.319 0.000739 0.000797 + 257 118 0.0756 0.00287 0.0181 0.0483 0.064 0.131 0.161 0.000328 0.000402 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 0.0538 0.00263 0.00115 0.045 0.0612 0.0324 0.0405 8.1e-05 0.000101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 257 43082.339 0.005 0.00337 0.0234 0.0908 0.0517 0.0693 0.146 0.183 0.000364 0.000457 +! Validation 257 43082.339 0.005 0.00369 0.0124 0.0862 0.0531 0.0725 0.103 0.133 0.000258 0.000332 +Wall time: 43082.340028061066 +! Best model 257 0.086 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.0738 0.00313 0.0112 0.0499 0.0668 0.0976 0.126 0.000244 0.000316 + 258 118 0.0705 0.00319 0.00664 0.0495 0.0675 0.0741 0.0973 0.000185 0.000243 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 100 0.0561 0.00255 0.00502 0.0445 0.0603 0.0797 0.0846 0.000199 0.000211 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 258 43249.753 0.005 0.00334 0.0296 0.0963 0.0515 0.069 0.165 0.206 0.000413 0.000515 +! Validation 258 43249.753 0.005 0.00363 0.0227 0.0954 0.0527 0.0719 0.145 0.18 0.000362 0.00045 +Wall time: 43249.753200488165 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.144 0.00333 0.0772 0.0513 0.0689 0.314 0.332 0.000784 0.000829 + 259 118 0.211 0.00377 0.135 0.0535 0.0733 0.421 0.439 0.00105 0.0011 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 100 0.0897 0.00262 0.0373 0.0449 0.0611 0.229 0.231 0.000574 0.000577 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 259 43417.351 0.005 0.00332 0.045 0.111 0.0513 0.0688 0.203 0.251 0.000507 0.000629 +! Validation 259 43417.351 0.005 0.00366 0.0524 0.126 0.0529 0.0722 0.239 0.273 0.000598 0.000683 +Wall time: 43417.351199432276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.14 0.00358 0.0683 0.0532 0.0714 0.294 0.312 0.000735 0.00078 + 260 118 0.076 0.0033 0.0099 0.0514 0.0686 0.0843 0.119 0.000211 0.000297 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 0.0587 0.00253 0.00815 0.0443 0.06 0.104 0.108 0.000259 0.000269 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 260 43584.985 0.005 0.0033 0.0258 0.0918 0.0512 0.0686 0.155 0.192 0.000388 0.00048 +! Validation 260 43584.985 0.005 0.00359 0.0141 0.0859 0.0524 0.0715 0.113 0.142 0.000284 0.000354 +Wall time: 43584.98560302006 +! Best model 260 0.086 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.0805 0.00334 0.0138 0.0513 0.0689 0.112 0.14 0.000279 0.000351 + 261 118 0.0895 0.00318 0.0258 0.0508 0.0673 0.152 0.192 0.000381 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 0.0533 0.00256 0.00222 0.0445 0.0603 0.0495 0.0562 0.000124 0.00014 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 261 43752.986 0.005 0.00325 0.0276 0.0926 0.0508 0.0681 0.161 0.198 0.000401 0.000496 +! Validation 261 43752.986 0.005 0.0036 0.0202 0.0922 0.0525 0.0716 0.133 0.169 0.000334 0.000424 +Wall time: 43752.98621332226 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.202 0.00328 0.137 0.0509 0.0684 0.421 0.441 0.00105 0.0011 + 262 118 0.0844 0.00367 0.011 0.0533 0.0723 0.089 0.125 0.000223 0.000313 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 0.0506 0.0025 0.000598 0.0441 0.0597 0.0254 0.0292 6.35e-05 7.3e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 262 43920.461 0.005 0.00327 0.0429 0.108 0.0509 0.0682 0.202 0.248 0.000505 0.000619 +! Validation 262 43920.461 0.005 0.00359 0.0133 0.0852 0.0524 0.0716 0.107 0.138 0.000266 0.000344 +Wall time: 43920.46203893004 +! Best model 262 0.085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.0881 0.00314 0.0252 0.0501 0.0669 0.169 0.19 0.000423 0.000474 + 263 118 0.0748 0.00294 0.016 0.0492 0.0647 0.137 0.151 0.000343 0.000377 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.103 0.00249 0.0533 0.0441 0.0596 0.274 0.275 0.000685 0.000689 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 263 44087.986 0.005 0.00323 0.0274 0.0919 0.0506 0.0678 0.157 0.198 0.000392 0.000494 +! Validation 263 44087.986 0.005 0.00354 0.051 0.122 0.052 0.071 0.243 0.27 0.000608 0.000674 +Wall time: 44087.986927095335 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.148 0.00319 0.0843 0.0498 0.0674 0.326 0.347 0.000816 0.000867 + 264 118 0.0918 0.00371 0.0175 0.0537 0.0727 0.144 0.158 0.000359 0.000395 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 0.052 0.00248 0.00244 0.0438 0.0594 0.052 0.059 0.00013 0.000147 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 264 44255.490 0.005 0.00324 0.0436 0.109 0.0507 0.068 0.203 0.25 0.000507 0.000624 +! Validation 264 44255.490 0.005 0.00354 0.0166 0.0875 0.052 0.0711 0.12 0.154 0.0003 0.000384 +Wall time: 44255.49055916816 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.0741 0.00327 0.00866 0.0509 0.0683 0.0897 0.111 0.000224 0.000278 + 265 118 0.0722 0.00277 0.0168 0.0474 0.0628 0.146 0.155 0.000364 0.000387 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 0.051 0.00245 0.00208 0.0435 0.059 0.0457 0.0545 0.000114 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 265 44423.098 0.005 0.00318 0.0231 0.0867 0.0502 0.0673 0.146 0.182 0.000365 0.000454 +! Validation 265 44423.098 0.005 0.00349 0.012 0.0818 0.0516 0.0705 0.101 0.131 0.000253 0.000327 +Wall time: 44423.0981862084 +! Best model 265 0.082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.0874 0.00331 0.0212 0.0509 0.0686 0.148 0.174 0.00037 0.000435 + 266 118 0.0767 0.00355 0.00568 0.0533 0.0711 0.07 0.0899 0.000175 0.000225 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 0.05 0.00246 0.000752 0.0435 0.0592 0.025 0.0327 6.24e-05 8.18e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 266 44590.552 0.005 0.00317 0.0267 0.09 0.0501 0.0671 0.157 0.196 0.000392 0.000489 +! Validation 266 44590.552 0.005 0.00345 0.0135 0.0826 0.0513 0.0701 0.106 0.139 0.000266 0.000347 +Wall time: 44590.55233573541 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.138 0.00294 0.0797 0.0484 0.0647 0.323 0.337 0.000808 0.000842 + 267 118 0.0756 0.00324 0.0108 0.0511 0.0679 0.0865 0.124 0.000216 0.000311 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 0.0746 0.0025 0.0246 0.0438 0.0597 0.185 0.187 0.000463 0.000468 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 267 44757.943 0.005 0.00316 0.0326 0.0958 0.05 0.0671 0.174 0.216 0.000435 0.00054 +! Validation 267 44757.943 0.005 0.00349 0.0323 0.102 0.0516 0.0705 0.186 0.214 0.000465 0.000536 +Wall time: 44757.943611039314 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.0843 0.00329 0.0185 0.051 0.0685 0.13 0.162 0.000325 0.000406 + 268 118 0.173 0.00328 0.107 0.0513 0.0683 0.375 0.391 0.000938 0.000978 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.146 0.00249 0.0965 0.0438 0.0595 0.37 0.371 0.000925 0.000927 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 268 44925.191 0.005 0.00315 0.0339 0.0969 0.05 0.067 0.169 0.218 0.000422 0.000545 +! Validation 268 44925.191 0.005 0.00352 0.122 0.192 0.0518 0.0708 0.397 0.417 0.000994 0.00104 +Wall time: 44925.191887859255 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.096 0.0031 0.0341 0.0497 0.0664 0.199 0.22 0.000497 0.000551 + 269 118 0.0972 0.0029 0.0392 0.0482 0.0643 0.215 0.236 0.000537 0.000591 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 0.158 0.00242 0.11 0.0432 0.0588 0.395 0.396 0.000987 0.000989 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 269 45092.474 0.005 0.00314 0.036 0.0989 0.0499 0.0669 0.186 0.226 0.000466 0.000566 +! Validation 269 45092.474 0.005 0.00342 0.0969 0.165 0.0511 0.0698 0.35 0.372 0.000874 0.000929 +Wall time: 45092.47438403824 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.0929 0.00282 0.0364 0.0476 0.0634 0.2 0.228 0.000501 0.000569 + 270 118 0.0701 0.00314 0.0073 0.0499 0.0669 0.0791 0.102 0.000198 0.000255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.109 0.0024 0.061 0.043 0.0584 0.294 0.295 0.000735 0.000737 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 270 45259.608 0.005 0.0031 0.0274 0.0894 0.0496 0.0665 0.157 0.198 0.000392 0.000495 +! Validation 270 45259.608 0.005 0.0034 0.06 0.128 0.0509 0.0696 0.266 0.292 0.000665 0.000731 +Wall time: 45259.6083851303 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.093 0.00326 0.0278 0.0505 0.0681 0.175 0.199 0.000438 0.000497 + 271 118 0.16 0.00355 0.0894 0.0529 0.0711 0.338 0.357 0.000845 0.000892 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.0561 0.0024 0.0081 0.0431 0.0584 0.104 0.107 0.00026 0.000269 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 271 45426.730 0.005 0.00312 0.04 0.102 0.0497 0.0666 0.186 0.238 0.000465 0.000594 +! Validation 271 45426.730 0.005 0.00344 0.0366 0.105 0.0512 0.07 0.191 0.228 0.000476 0.000571 +Wall time: 45426.73028074205 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.0932 0.00298 0.0336 0.049 0.0652 0.193 0.219 0.000483 0.000547 + 272 118 0.0551 0.00257 0.00362 0.0452 0.0605 0.065 0.0718 0.000162 0.00018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 0.0622 0.00237 0.0147 0.0429 0.0581 0.142 0.145 0.000356 0.000362 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 272 45594.268 0.005 0.00313 0.0414 0.104 0.0499 0.0668 0.2 0.244 0.000499 0.000609 +! Validation 272 45594.268 0.005 0.00338 0.0181 0.0856 0.0508 0.0694 0.132 0.16 0.000329 0.000401 +Wall time: 45594.268560878 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.0752 0.00296 0.016 0.0484 0.065 0.123 0.151 0.000308 0.000377 + 273 118 0.072 0.00316 0.00879 0.0495 0.0671 0.0975 0.112 0.000244 0.00028 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 0.0515 0.0023 0.00552 0.0423 0.0572 0.0827 0.0886 0.000207 0.000222 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 273 45761.366 0.005 0.00307 0.0294 0.0909 0.0494 0.0662 0.161 0.205 0.000404 0.000513 +! Validation 273 45761.366 0.005 0.00336 0.0232 0.0903 0.0506 0.0691 0.148 0.182 0.000371 0.000454 +Wall time: 45761.36656318698 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0691 0.00309 0.00737 0.0493 0.0663 0.0844 0.102 0.000211 0.000256 + 274 118 0.171 0.00396 0.0919 0.0548 0.0751 0.352 0.362 0.000881 0.000905 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.0639 0.00271 0.00962 0.0458 0.0622 0.116 0.117 0.00029 0.000293 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 274 45928.588 0.005 0.00302 0.0187 0.079 0.0488 0.0655 0.128 0.161 0.00032 0.000402 +! Validation 274 45928.588 0.005 0.00368 0.0346 0.108 0.0532 0.0724 0.184 0.222 0.00046 0.000555 +Wall time: 45928.58872046927 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0817 0.00283 0.025 0.0473 0.0635 0.17 0.189 0.000426 0.000472 + 275 118 0.0851 0.00293 0.0266 0.0481 0.0646 0.159 0.195 0.000397 0.000486 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.0659 0.00236 0.0186 0.0425 0.058 0.16 0.163 0.0004 0.000407 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 275 46095.736 0.005 0.00307 0.0292 0.0905 0.0493 0.0661 0.167 0.204 0.000418 0.00051 +! Validation 275 46095.736 0.005 0.00331 0.0216 0.0879 0.0502 0.0687 0.147 0.175 0.000368 0.000438 +Wall time: 46095.73655166011 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.146 0.00328 0.0807 0.0511 0.0684 0.323 0.339 0.000807 0.000848 + 276 118 0.085 0.00313 0.0224 0.0489 0.0668 0.167 0.179 0.000417 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 0.0531 0.00231 0.007 0.0424 0.0573 0.0958 0.0998 0.000239 0.00025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 276 46262.855 0.005 0.00304 0.0364 0.0971 0.049 0.0658 0.188 0.228 0.00047 0.00057 +! Validation 276 46262.855 0.005 0.00333 0.0201 0.0867 0.0504 0.0689 0.137 0.169 0.000342 0.000423 +Wall time: 46262.85526186507 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.0778 0.003 0.0178 0.0484 0.0653 0.127 0.159 0.000317 0.000399 + 277 118 0.175 0.00292 0.117 0.0483 0.0645 0.405 0.408 0.00101 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.263 0.00231 0.217 0.0424 0.0574 0.556 0.556 0.00139 0.00139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 277 46430.269 0.005 0.00305 0.0341 0.0951 0.0492 0.0659 0.178 0.219 0.000444 0.000546 +! Validation 277 46430.269 0.005 0.00334 0.222 0.289 0.0505 0.069 0.547 0.562 0.00137 0.00141 +Wall time: 46430.2690150463 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0666 0.00289 0.0089 0.048 0.0641 0.0878 0.113 0.00022 0.000282 + 278 118 0.061 0.00256 0.00979 0.0459 0.0604 0.103 0.118 0.000258 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 0.0465 0.00229 0.000602 0.0421 0.0572 0.0252 0.0293 6.29e-05 7.32e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 278 46597.517 0.005 0.00302 0.0353 0.0957 0.049 0.0656 0.179 0.225 0.000448 0.000562 +! Validation 278 46597.517 0.005 0.00328 0.0144 0.08 0.05 0.0684 0.111 0.143 0.000278 0.000358 +Wall time: 46597.517018159386 +! Best model 278 0.080 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.0802 0.00285 0.0231 0.0479 0.0638 0.16 0.181 0.0004 0.000454 + 279 118 0.0806 0.00343 0.0119 0.0526 0.0699 0.106 0.13 0.000265 0.000326 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 0.0498 0.00236 0.00261 0.0425 0.0579 0.0557 0.0609 0.000139 0.000152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 279 46764.648 0.005 0.00303 0.0337 0.0942 0.049 0.0656 0.173 0.22 0.000433 0.000549 +! Validation 279 46764.648 0.005 0.00331 0.0116 0.0777 0.0502 0.0686 0.101 0.129 0.000253 0.000322 +Wall time: 46764.64885848714 +! Best model 279 0.078 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.136 0.00289 0.078 0.048 0.0642 0.309 0.333 0.000774 0.000833 + 280 118 0.0796 0.00348 0.01 0.0519 0.0704 0.0956 0.119 0.000239 0.000299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.122 0.00225 0.0766 0.0417 0.0566 0.329 0.33 0.000823 0.000826 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 280 46931.749 0.005 0.00297 0.0291 0.0884 0.0485 0.065 0.167 0.204 0.000418 0.00051 +! Validation 280 46931.749 0.005 0.00325 0.0742 0.139 0.0498 0.068 0.301 0.325 0.000753 0.000813 +Wall time: 46931.74925164925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.0719 0.00288 0.0144 0.0479 0.064 0.122 0.143 0.000304 0.000358 + 281 118 0.107 0.00322 0.0426 0.0502 0.0677 0.229 0.246 0.000573 0.000616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 0.0885 0.00237 0.0411 0.0427 0.0581 0.241 0.242 0.000603 0.000605 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 281 47098.851 0.005 0.00326 0.0433 0.109 0.0507 0.0682 0.179 0.248 0.000448 0.000621 +! Validation 281 47098.851 0.005 0.00333 0.0476 0.114 0.0504 0.0688 0.229 0.26 0.000573 0.000651 +Wall time: 47098.85184104927 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 0.135 0.00289 0.0772 0.0478 0.0641 0.311 0.332 0.000778 0.000829 + 282 118 0.065 0.00303 0.00448 0.0493 0.0657 0.0696 0.0799 0.000174 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 0.0623 0.00225 0.0172 0.0417 0.0567 0.155 0.157 0.000386 0.000392 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 282 47265.997 0.005 0.00291 0.0184 0.0766 0.048 0.0644 0.129 0.162 0.000322 0.000405 +! Validation 282 47265.997 0.005 0.00321 0.0245 0.0886 0.0495 0.0676 0.158 0.187 0.000394 0.000467 +Wall time: 47265.99794334127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.0673 0.0027 0.0133 0.0465 0.062 0.116 0.137 0.000289 0.000344 + 283 118 0.0775 0.00314 0.0147 0.05 0.0669 0.13 0.145 0.000325 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 0.11 0.0022 0.0662 0.0413 0.056 0.306 0.307 0.000766 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 283 47433.222 0.005 0.00294 0.0323 0.0911 0.0483 0.0647 0.175 0.215 0.000439 0.000537 +! Validation 283 47433.222 0.005 0.00321 0.0686 0.133 0.0495 0.0676 0.289 0.313 0.000721 0.000781 +Wall time: 47433.222526392434 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.0764 0.00292 0.018 0.0483 0.0645 0.13 0.16 0.000326 0.0004 + 284 118 0.0691 0.00318 0.00548 0.0497 0.0673 0.0803 0.0883 0.000201 0.000221 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 0.0504 0.00223 0.00587 0.0416 0.0563 0.0887 0.0915 0.000222 0.000229 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 284 47600.340 0.005 0.003 0.0415 0.102 0.0488 0.0654 0.201 0.244 0.000502 0.000609 +! Validation 284 47600.340 0.005 0.0032 0.0254 0.0893 0.0494 0.0675 0.155 0.19 0.000388 0.000475 +Wall time: 47600.34095426602 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.0863 0.00288 0.0287 0.0481 0.064 0.177 0.202 0.000442 0.000506 + 285 118 0.076 0.0031 0.0139 0.0492 0.0665 0.122 0.141 0.000304 0.000351 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.054 0.00227 0.00851 0.042 0.0569 0.108 0.11 0.000269 0.000275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 285 47767.454 0.005 0.00291 0.0268 0.085 0.048 0.0643 0.158 0.196 0.000396 0.00049 +! Validation 285 47767.454 0.005 0.00329 0.0172 0.0831 0.0502 0.0685 0.124 0.157 0.000309 0.000392 +Wall time: 47767.45462907711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0688 0.00295 0.0098 0.048 0.0648 0.0903 0.118 0.000226 0.000295 + 286 118 0.0693 0.00307 0.00795 0.049 0.0661 0.0867 0.106 0.000217 0.000266 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 0.0668 0.00224 0.0221 0.0415 0.0565 0.176 0.177 0.000439 0.000443 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 286 47934.780 0.005 0.00288 0.0205 0.0781 0.0478 0.064 0.138 0.171 0.000344 0.000428 +! Validation 286 47934.780 0.005 0.00318 0.0211 0.0847 0.0493 0.0673 0.146 0.173 0.000366 0.000434 +Wall time: 47934.7800701051 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.0658 0.00284 0.00904 0.0477 0.0636 0.0978 0.113 0.000244 0.000284 + 287 118 0.18 0.00323 0.115 0.051 0.0678 0.388 0.406 0.000969 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.186 0.00289 0.128 0.0473 0.0642 0.426 0.427 0.00106 0.00107 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 287 48102.010 0.005 0.0029 0.0368 0.0949 0.048 0.0643 0.184 0.227 0.000461 0.000568 +! Validation 287 48102.010 0.005 0.00377 0.161 0.237 0.0543 0.0733 0.459 0.479 0.00115 0.0012 +Wall time: 48102.01046952233 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.0615 0.00277 0.00617 0.0472 0.0628 0.074 0.0938 0.000185 0.000234 + 288 118 0.0683 0.00268 0.0147 0.0466 0.0617 0.128 0.145 0.00032 0.000362 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 0.0456 0.00218 0.00199 0.041 0.0557 0.0472 0.0533 0.000118 0.000133 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 288 48269.358 0.005 0.00292 0.0247 0.0831 0.0481 0.0645 0.15 0.188 0.000374 0.000469 +! Validation 288 48269.358 0.005 0.00313 0.0125 0.0752 0.0489 0.0668 0.104 0.133 0.00026 0.000333 +Wall time: 48269.3582121823 +! Best model 288 0.075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.0649 0.00268 0.0114 0.0464 0.0618 0.106 0.127 0.000266 0.000318 + 289 118 0.11 0.00294 0.0509 0.0484 0.0647 0.253 0.269 0.000634 0.000673 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 0.0534 0.00224 0.00861 0.0414 0.0565 0.108 0.111 0.000269 0.000277 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 289 48436.665 0.005 0.00291 0.0322 0.0904 0.0481 0.0644 0.174 0.214 0.000436 0.000535 +! Validation 289 48436.665 0.005 0.00317 0.0372 0.101 0.0491 0.0672 0.189 0.23 0.000472 0.000576 +Wall time: 48436.6654984802 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0865 0.00256 0.0354 0.0451 0.0603 0.206 0.225 0.000514 0.000561 + 290 118 0.0747 0.00338 0.00707 0.051 0.0694 0.0946 0.1 0.000236 0.000251 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.0515 0.00219 0.0076 0.0412 0.0559 0.0992 0.104 0.000248 0.00026 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 290 48603.962 0.005 0.00287 0.0325 0.09 0.0477 0.0639 0.179 0.216 0.000448 0.00054 +! Validation 290 48603.962 0.005 0.00314 0.0192 0.0819 0.0489 0.0668 0.135 0.165 0.000337 0.000413 +Wall time: 48603.96220276132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0643 0.00268 0.0106 0.0464 0.0618 0.1 0.123 0.000251 0.000308 + 291 118 0.0709 0.00305 0.00996 0.0482 0.0659 0.085 0.119 0.000213 0.000298 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.0482 0.00215 0.00524 0.0406 0.0553 0.0838 0.0864 0.00021 0.000216 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 291 48771.289 0.005 0.00281 0.0238 0.08 0.0472 0.0632 0.149 0.184 0.000373 0.000461 +! Validation 291 48771.289 0.005 0.00307 0.0143 0.0756 0.0483 0.0661 0.113 0.143 0.000283 0.000357 +Wall time: 48771.28921243502 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.0597 0.00267 0.00627 0.046 0.0617 0.0779 0.0945 0.000195 0.000236 + 292 118 0.0659 0.00318 0.00244 0.0503 0.0673 0.0505 0.059 0.000126 0.000147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.0433 0.00215 0.000327 0.0407 0.0553 0.0179 0.0216 4.49e-05 5.4e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 292 48938.625 0.005 0.0028 0.0252 0.0811 0.0471 0.0631 0.152 0.19 0.000379 0.000475 +! Validation 292 48938.625 0.005 0.00305 0.0123 0.0733 0.0482 0.0659 0.105 0.132 0.000262 0.000331 +Wall time: 48938.62595797004 +! Best model 292 0.073 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0865 0.0028 0.0305 0.0472 0.0632 0.188 0.208 0.000469 0.000521 + 293 118 0.0726 0.00261 0.0203 0.0456 0.061 0.14 0.17 0.00035 0.000426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.0518 0.00225 0.00689 0.0416 0.0566 0.0952 0.0991 0.000238 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 293 49106.033 0.005 0.00276 0.0252 0.0804 0.0468 0.0628 0.151 0.189 0.000379 0.000474 +! Validation 293 49106.033 0.005 0.00314 0.0109 0.0738 0.049 0.0669 0.0967 0.125 0.000242 0.000312 +Wall time: 49106.03352235211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.077 0.00277 0.0217 0.0475 0.0628 0.154 0.176 0.000386 0.000439 + 294 118 0.0637 0.00272 0.00941 0.0462 0.0622 0.0891 0.116 0.000223 0.000289 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 0.0608 0.0021 0.0188 0.0401 0.0547 0.162 0.164 0.000404 0.000409 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 294 49273.289 0.005 0.0029 0.0379 0.0959 0.048 0.0643 0.187 0.233 0.000468 0.000583 +! Validation 294 49273.289 0.005 0.00305 0.0223 0.0832 0.0482 0.0659 0.151 0.178 0.000378 0.000446 +Wall time: 49273.28914472135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.0676 0.00269 0.0138 0.0461 0.0619 0.115 0.14 0.000287 0.000351 + 295 118 0.0704 0.00265 0.0174 0.0464 0.0615 0.145 0.157 0.000362 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.0544 0.00211 0.0121 0.0404 0.0549 0.13 0.132 0.000324 0.000329 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 295 49440.543 0.005 0.00276 0.0218 0.077 0.0468 0.0627 0.143 0.176 0.000358 0.000441 +! Validation 295 49440.543 0.005 0.00303 0.0177 0.0783 0.0482 0.0657 0.131 0.159 0.000326 0.000397 +Wall time: 49440.54395594122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.0759 0.0028 0.0198 0.0474 0.0632 0.13 0.168 0.000325 0.00042 + 296 118 0.0826 0.00292 0.0243 0.0483 0.0645 0.162 0.186 0.000404 0.000465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.0717 0.00219 0.0279 0.0412 0.0559 0.196 0.199 0.000491 0.000498 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 296 49607.909 0.005 0.00286 0.0377 0.0949 0.0477 0.0639 0.179 0.232 0.000448 0.00058 +! Validation 296 49607.909 0.005 0.00305 0.0487 0.11 0.0482 0.0659 0.236 0.263 0.000589 0.000658 +Wall time: 49607.909074551426 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.0844 0.00312 0.0219 0.0502 0.0667 0.157 0.177 0.000393 0.000442 + 297 118 0.148 0.00283 0.0918 0.048 0.0634 0.35 0.362 0.000876 0.000904 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.124 0.00237 0.0762 0.0431 0.0581 0.329 0.329 0.000822 0.000824 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 297 49775.263 0.005 0.00276 0.0298 0.085 0.0468 0.0627 0.166 0.204 0.000416 0.000511 +! Validation 297 49775.263 0.005 0.00327 0.0904 0.156 0.0502 0.0683 0.334 0.359 0.000836 0.000897 +Wall time: 49775.26359473402 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0778 0.00291 0.0196 0.0483 0.0643 0.15 0.167 0.000374 0.000418 + 298 118 0.0652 0.00277 0.00974 0.0469 0.0628 0.106 0.118 0.000265 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 0.0529 0.00219 0.00901 0.0413 0.0559 0.111 0.113 0.000278 0.000283 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 298 49942.530 0.005 0.00298 0.0414 0.101 0.0489 0.0652 0.194 0.243 0.000485 0.000609 +! Validation 298 49942.530 0.005 0.00309 0.0175 0.0793 0.0487 0.0664 0.129 0.158 0.000323 0.000394 +Wall time: 49942.530039455276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0646 0.0028 0.00859 0.0468 0.0632 0.0887 0.111 0.000222 0.000277 + 299 118 0.109 0.00327 0.0434 0.0513 0.0682 0.232 0.249 0.00058 0.000622 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 0.0481 0.00237 0.00067 0.0434 0.0581 0.0283 0.0309 7.07e-05 7.72e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 299 50109.814 0.005 0.00272 0.0253 0.0797 0.0464 0.0622 0.147 0.189 0.000367 0.000474 +! Validation 299 50109.814 0.005 0.0033 0.0199 0.086 0.0505 0.0686 0.123 0.169 0.000308 0.000421 +Wall time: 50109.81484296918 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.0653 0.00267 0.0118 0.0462 0.0617 0.104 0.13 0.00026 0.000324 + 300 118 0.0573 0.00267 0.0038 0.0467 0.0617 0.0562 0.0735 0.000141 0.000184 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.06 0.00212 0.0176 0.0409 0.055 0.156 0.158 0.000391 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 300 50277.070 0.005 0.00279 0.0293 0.0852 0.0471 0.0631 0.167 0.205 0.000418 0.000512 +! Validation 300 50277.070 0.005 0.00305 0.0207 0.0818 0.0486 0.066 0.146 0.172 0.000364 0.000429 +Wall time: 50277.070365081076 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.0573 0.00259 0.00545 0.0455 0.0608 0.0695 0.0881 0.000174 0.00022 + 301 118 0.136 0.00254 0.0854 0.0452 0.0602 0.343 0.349 0.000858 0.000872 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.129 0.00212 0.0865 0.0408 0.0549 0.35 0.351 0.000876 0.000878 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 301 50444.309 0.005 0.00274 0.0229 0.0776 0.0466 0.0624 0.145 0.179 0.000362 0.000448 +! Validation 301 50444.309 0.005 0.00302 0.102 0.163 0.0483 0.0656 0.361 0.382 0.000903 0.000955 +Wall time: 50444.30924368324 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.063 0.00282 0.0065 0.0473 0.0634 0.0765 0.0962 0.000191 0.000241 + 302 118 0.0701 0.00286 0.0128 0.0476 0.0639 0.104 0.135 0.00026 0.000338 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 0.0639 0.00204 0.023 0.0398 0.0539 0.178 0.181 0.000446 0.000453 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 302 50612.085 0.005 0.00275 0.0324 0.0873 0.0467 0.0625 0.17 0.215 0.000425 0.000538 +! Validation 302 50612.085 0.005 0.00295 0.0385 0.0975 0.0474 0.0648 0.203 0.234 0.000507 0.000585 +Wall time: 50612.08601517929 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0776 0.00261 0.0254 0.0457 0.0609 0.168 0.19 0.00042 0.000476 + 303 118 0.0636 0.00252 0.0132 0.0457 0.0599 0.119 0.137 0.000298 0.000342 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.0486 0.00202 0.00821 0.0396 0.0537 0.106 0.108 0.000264 0.00027 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 303 50779.534 0.005 0.00274 0.0295 0.0843 0.0467 0.0625 0.16 0.205 0.000399 0.000514 +! Validation 303 50779.534 0.005 0.00296 0.0155 0.0748 0.0476 0.065 0.121 0.149 0.000302 0.000372 +Wall time: 50779.53466604743 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.181 0.00369 0.107 0.054 0.0725 0.366 0.391 0.000915 0.000978 + 304 118 0.0741 0.00294 0.0153 0.0485 0.0647 0.117 0.148 0.000293 0.000369 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.0435 0.00209 0.00163 0.0405 0.0546 0.0404 0.0482 0.000101 0.00012 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 304 50947.055 0.005 0.00277 0.0271 0.0825 0.0468 0.0628 0.156 0.197 0.000389 0.000492 +! Validation 304 50947.055 0.005 0.00305 0.0108 0.0718 0.0484 0.0659 0.0972 0.124 0.000243 0.000311 +Wall time: 50947.0558908754 +! Best model 304 0.072 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0728 0.00253 0.0222 0.0446 0.06 0.159 0.178 0.000398 0.000445 + 305 118 0.061 0.00284 0.00423 0.0475 0.0636 0.0618 0.0776 0.000154 0.000194 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.0473 0.00209 0.00541 0.0403 0.0546 0.0846 0.0878 0.000212 0.00022 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 305 51115.376 0.005 0.0027 0.0289 0.0829 0.0463 0.062 0.166 0.203 0.000414 0.000509 +! Validation 305 51115.376 0.005 0.00299 0.0169 0.0767 0.0478 0.0653 0.124 0.155 0.00031 0.000388 +Wall time: 51115.37665219605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.0782 0.00262 0.0259 0.0456 0.061 0.172 0.192 0.000431 0.00048 + 306 118 0.105 0.00288 0.0476 0.0476 0.0641 0.231 0.261 0.000577 0.000651 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 100 0.0633 0.0022 0.0193 0.0417 0.056 0.163 0.166 0.000407 0.000414 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 306 51282.641 0.005 0.00262 0.0241 0.0765 0.0456 0.0611 0.141 0.185 0.000354 0.000462 +! Validation 306 51282.641 0.005 0.00312 0.0327 0.095 0.0491 0.0666 0.186 0.216 0.000466 0.00054 +Wall time: 51282.641400039196 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.0609 0.00261 0.0088 0.0455 0.0609 0.0865 0.112 0.000216 0.00028 + 307 118 0.0576 0.00254 0.00693 0.0451 0.0601 0.0795 0.0993 0.000199 0.000248 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 0.0403 0.00199 0.000469 0.0392 0.0533 0.0238 0.0258 5.96e-05 6.46e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 307 51451.369 0.005 0.00266 0.0249 0.0781 0.046 0.0616 0.152 0.189 0.00038 0.000472 +! Validation 307 51451.369 0.005 0.00283 0.0115 0.0681 0.0464 0.0635 0.1 0.128 0.00025 0.00032 +Wall time: 51451.369304433 +! Best model 307 0.068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.0957 0.0031 0.0336 0.0502 0.0665 0.2 0.219 0.0005 0.000547 + 308 118 0.0773 0.00273 0.0226 0.0465 0.0624 0.171 0.179 0.000427 0.000448 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.133 0.002 0.0926 0.0397 0.0533 0.362 0.363 0.000905 0.000908 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 308 51618.763 0.005 0.00266 0.0256 0.0788 0.046 0.0616 0.152 0.191 0.000379 0.000477 +! Validation 308 51618.763 0.005 0.0029 0.0968 0.155 0.0472 0.0643 0.345 0.371 0.000862 0.000928 +Wall time: 51618.7634622762 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.0639 0.00268 0.0102 0.0462 0.0618 0.0934 0.121 0.000233 0.000302 + 309 118 0.0733 0.00285 0.0163 0.0474 0.0637 0.12 0.152 0.000301 0.000381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 0.124 0.00212 0.0811 0.0405 0.055 0.339 0.34 0.000848 0.00085 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 309 51786.110 0.005 0.00276 0.0326 0.0878 0.0468 0.0627 0.175 0.216 0.000438 0.00054 +! Validation 309 51786.110 0.005 0.00298 0.0751 0.135 0.0478 0.0652 0.301 0.327 0.000752 0.000818 +Wall time: 51786.11101319501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0603 0.00239 0.0124 0.0437 0.0584 0.114 0.133 0.000284 0.000332 + 310 118 0.0863 0.00234 0.0395 0.0437 0.0577 0.23 0.237 0.000575 0.000593 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.0502 0.002 0.0103 0.0391 0.0533 0.119 0.121 0.000298 0.000303 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 310 51953.437 0.005 0.00261 0.0231 0.0753 0.0455 0.061 0.146 0.181 0.000366 0.000452 +! Validation 310 51953.437 0.005 0.00283 0.0263 0.0829 0.0464 0.0635 0.163 0.194 0.000406 0.000484 +Wall time: 51953.4370996044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.0623 0.00249 0.0125 0.0445 0.0596 0.107 0.133 0.000269 0.000333 + 311 118 0.0698 0.00299 0.00999 0.049 0.0653 0.0992 0.119 0.000248 0.000298 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.117 0.00201 0.0766 0.0396 0.0535 0.33 0.33 0.000824 0.000826 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 311 52120.770 0.005 0.00255 0.0228 0.0737 0.0449 0.0602 0.145 0.181 0.000362 0.000451 +! Validation 311 52120.770 0.005 0.00288 0.0693 0.127 0.047 0.064 0.288 0.314 0.00072 0.000786 +Wall time: 52120.770508140326 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0823 0.00256 0.0311 0.0452 0.0604 0.199 0.21 0.000499 0.000526 + 312 118 0.0817 0.00282 0.0252 0.0468 0.0634 0.165 0.189 0.000412 0.000474 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 0.0889 0.00193 0.0503 0.0387 0.0524 0.267 0.268 0.000667 0.000669 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 312 52288.201 0.005 0.00266 0.0297 0.0828 0.0459 0.0615 0.167 0.206 0.000418 0.000514 +! Validation 312 52288.201 0.005 0.00279 0.0695 0.125 0.046 0.063 0.292 0.315 0.000729 0.000786 +Wall time: 52288.20114276605 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0543 0.00229 0.00842 0.0429 0.0572 0.0888 0.11 0.000222 0.000274 + 313 118 0.0851 0.00227 0.0398 0.0431 0.0568 0.228 0.238 0.00057 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.0663 0.00218 0.0226 0.0415 0.0558 0.178 0.179 0.000444 0.000448 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 313 52455.523 0.005 0.00268 0.0281 0.0817 0.0461 0.0618 0.159 0.2 0.000397 0.0005 +! Validation 313 52455.523 0.005 0.00303 0.0207 0.0812 0.0484 0.0657 0.144 0.172 0.000359 0.000429 +Wall time: 52455.523159111384 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.146 0.00261 0.0939 0.0455 0.061 0.353 0.366 0.000882 0.000914 + 314 118 0.114 0.00369 0.0404 0.0545 0.0725 0.19 0.24 0.000475 0.000599 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.0495 0.00233 0.00287 0.0431 0.0576 0.0597 0.0639 0.000149 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 314 52622.838 0.005 0.00258 0.0313 0.083 0.0452 0.0606 0.163 0.211 0.000408 0.000528 +! Validation 314 52622.838 0.005 0.00321 0.0137 0.078 0.0504 0.0677 0.111 0.14 0.000277 0.000349 +Wall time: 52622.838325973134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0536 0.00224 0.00884 0.0424 0.0565 0.0953 0.112 0.000238 0.000281 + 315 118 0.0798 0.0027 0.0259 0.0461 0.062 0.172 0.192 0.000429 0.00048 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 0.0407 0.00198 0.00114 0.0395 0.0531 0.0354 0.0402 8.85e-05 0.000101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 315 52790.184 0.005 0.00259 0.0182 0.0701 0.0453 0.0608 0.127 0.161 0.000318 0.000402 +! Validation 315 52790.184 0.005 0.00283 0.0119 0.0686 0.0467 0.0635 0.103 0.13 0.000257 0.000326 +Wall time: 52790.18431021832 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0535 0.00226 0.00827 0.0424 0.0568 0.0897 0.109 0.000224 0.000271 + 316 118 0.0677 0.00294 0.00895 0.0474 0.0647 0.0898 0.113 0.000224 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 0.0961 0.00197 0.0567 0.039 0.0529 0.282 0.284 0.000706 0.000711 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 316 52957.655 0.005 0.00249 0.0208 0.0706 0.0444 0.0595 0.14 0.172 0.000351 0.000431 +! Validation 316 52957.655 0.005 0.00277 0.0804 0.136 0.0459 0.0628 0.317 0.338 0.000793 0.000846 +Wall time: 52957.65511579998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0539 0.00235 0.00689 0.0432 0.0578 0.0814 0.0991 0.000203 0.000248 + 317 118 0.0718 0.00247 0.0224 0.0448 0.0594 0.165 0.179 0.000413 0.000446 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 100 0.0389 0.00192 0.000555 0.0385 0.0523 0.0215 0.0281 5.38e-05 7.03e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 317 53125.049 0.005 0.00276 0.0316 0.0868 0.0466 0.0627 0.16 0.213 0.000399 0.000531 +! Validation 317 53125.049 0.005 0.0028 0.0143 0.0704 0.0463 0.0632 0.112 0.143 0.000281 0.000357 +Wall time: 53125.04906721506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.077 0.00252 0.0265 0.0449 0.06 0.16 0.194 0.000401 0.000486 + 318 118 0.0576 0.00249 0.00767 0.0445 0.0596 0.0807 0.105 0.000202 0.000261 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.0633 0.00185 0.0263 0.038 0.0514 0.192 0.193 0.000479 0.000484 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 318 53292.433 0.005 0.0025 0.0238 0.0738 0.0446 0.0597 0.149 0.184 0.000372 0.000461 +! Validation 318 53292.433 0.005 0.00273 0.0299 0.0844 0.0456 0.0623 0.18 0.206 0.00045 0.000516 +Wall time: 53292.43334579328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.0522 0.00232 0.00574 0.0428 0.0575 0.0705 0.0904 0.000176 0.000226 + 319 118 0.0523 0.00211 0.0101 0.0411 0.0549 0.106 0.12 0.000264 0.0003 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.0375 0.00181 0.00138 0.0374 0.0507 0.0392 0.0444 9.81e-05 0.000111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 319 53459.816 0.005 0.00262 0.0311 0.0834 0.0456 0.0611 0.163 0.211 0.000407 0.000528 +! Validation 319 53459.816 0.005 0.00268 0.0122 0.0658 0.0451 0.0618 0.103 0.132 0.000258 0.000329 +Wall time: 53459.81624623621 +! Best model 319 0.066 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0666 0.00256 0.0154 0.0449 0.0604 0.119 0.148 0.000297 0.00037 + 320 118 0.113 0.00281 0.0565 0.047 0.0633 0.265 0.284 0.000663 0.000709 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.0876 0.00193 0.0491 0.0387 0.0524 0.264 0.265 0.00066 0.000661 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 320 53627.217 0.005 0.00244 0.0176 0.0664 0.0439 0.0589 0.127 0.157 0.000317 0.000393 +! Validation 320 53627.217 0.005 0.00276 0.0655 0.121 0.0459 0.0627 0.284 0.305 0.000709 0.000764 +Wall time: 53627.21735367132 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.0945 0.00296 0.0353 0.0483 0.065 0.192 0.224 0.00048 0.00056 + 321 118 0.0605 0.00269 0.00662 0.0463 0.0619 0.0821 0.0971 0.000205 0.000243 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.043 0.00199 0.00321 0.0394 0.0532 0.0615 0.0676 0.000154 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 321 53794.648 0.005 0.00258 0.031 0.0826 0.0453 0.0607 0.165 0.211 0.000412 0.000526 +! Validation 321 53794.648 0.005 0.00281 0.0132 0.0695 0.0465 0.0633 0.109 0.137 0.000272 0.000343 +Wall time: 53794.64884055313 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0745 0.00228 0.0289 0.0429 0.057 0.179 0.203 0.000446 0.000508 + 322 118 0.0789 0.0023 0.0329 0.0431 0.0573 0.189 0.216 0.000474 0.000541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.0516 0.00207 0.0102 0.0403 0.0543 0.117 0.12 0.000293 0.000301 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 322 53961.728 0.005 0.00249 0.0258 0.0757 0.0446 0.0596 0.156 0.192 0.00039 0.000479 +! Validation 322 53961.728 0.005 0.00285 0.0184 0.0753 0.0469 0.0637 0.133 0.162 0.000333 0.000404 +Wall time: 53961.7280065543 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.065 0.00239 0.0172 0.0434 0.0584 0.131 0.157 0.000329 0.000391 + 323 118 0.051 0.00231 0.00475 0.0429 0.0574 0.0647 0.0822 0.000162 0.000206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.0372 0.00183 0.000662 0.0377 0.051 0.0265 0.0307 6.63e-05 7.68e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 323 54128.995 0.005 0.00243 0.0188 0.0674 0.044 0.0589 0.133 0.164 0.000333 0.00041 +! Validation 323 54128.995 0.005 0.00264 0.0105 0.0633 0.0448 0.0613 0.0966 0.122 0.000241 0.000306 +Wall time: 54128.99588276632 +! Best model 323 0.063 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0598 0.00242 0.0115 0.0437 0.0587 0.104 0.128 0.000261 0.00032 + 324 118 0.083 0.00279 0.0272 0.0462 0.0631 0.168 0.197 0.000421 0.000492 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.0412 0.00202 0.000761 0.0396 0.0536 0.0285 0.0329 7.13e-05 8.23e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 324 54296.081 0.005 0.00238 0.0187 0.0663 0.0434 0.0582 0.13 0.163 0.000325 0.000408 +! Validation 324 54296.081 0.005 0.00284 0.0104 0.0671 0.0467 0.0636 0.0953 0.122 0.000238 0.000304 +Wall time: 54296.08136485331 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 0.0482 0.00212 0.00589 0.0413 0.0549 0.0707 0.0916 0.000177 0.000229 + 325 118 0.0526 0.00232 0.0062 0.0431 0.0575 0.071 0.094 0.000177 0.000235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 0.0367 0.00179 0.00101 0.0373 0.0505 0.0287 0.038 7.19e-05 9.49e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 325 54463.164 0.005 0.00252 0.0227 0.0731 0.0447 0.0599 0.14 0.18 0.00035 0.000451 +! Validation 325 54463.164 0.005 0.00259 0.00942 0.0612 0.0444 0.0607 0.0894 0.116 0.000224 0.00029 +Wall time: 54463.16430546623 +! Best model 325 0.061 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 0.172 0.00236 0.125 0.0436 0.058 0.413 0.422 0.00103 0.00106 + 326 118 0.0723 0.00315 0.00927 0.0509 0.067 0.084 0.115 0.00021 0.000287 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 0.0493 0.00235 0.00232 0.0421 0.0578 0.0481 0.0575 0.00012 0.000144 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 326 54630.323 0.005 0.00246 0.0271 0.0764 0.0441 0.0592 0.153 0.197 0.000381 0.000493 +! Validation 326 54630.323 0.005 0.00311 0.0108 0.073 0.0489 0.0666 0.0964 0.124 0.000241 0.000309 +Wall time: 54630.32307244139 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 0.0559 0.00224 0.0111 0.0422 0.0565 0.102 0.126 0.000254 0.000315 + 327 118 0.0669 0.00229 0.0212 0.0421 0.0571 0.151 0.174 0.000378 0.000434 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 0.0417 0.0019 0.00376 0.0389 0.052 0.0703 0.0731 0.000176 0.000183 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 327 54797.381 0.005 0.0024 0.0196 0.0675 0.0436 0.0584 0.133 0.167 0.000331 0.000418 +! Validation 327 54797.381 0.005 0.00277 0.0115 0.0669 0.046 0.0628 0.101 0.128 0.000253 0.000321 +Wall time: 54797.38111921307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.0798 0.00224 0.035 0.0424 0.0565 0.211 0.223 0.000526 0.000559 + 328 118 0.0659 0.00261 0.0137 0.0448 0.061 0.0949 0.139 0.000237 0.000349 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.0358 0.00177 0.000427 0.0371 0.0502 0.0232 0.0247 5.81e-05 6.17e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 328 54964.462 0.005 0.00243 0.0222 0.0707 0.0438 0.0588 0.14 0.178 0.00035 0.000445 +! Validation 328 54964.462 0.005 0.00255 0.0103 0.0612 0.044 0.0602 0.0956 0.121 0.000239 0.000303 +Wall time: 54964.462285497226 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.0622 0.00231 0.0161 0.0426 0.0573 0.126 0.151 0.000314 0.000378 + 329 118 0.0694 0.00237 0.0221 0.0434 0.0581 0.157 0.177 0.000393 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 0.0691 0.0019 0.031 0.0387 0.0521 0.208 0.21 0.00052 0.000525 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 329 55131.530 0.005 0.0023 0.0136 0.0597 0.0427 0.0573 0.111 0.139 0.000278 0.000348 +! Validation 329 55131.530 0.005 0.00268 0.0544 0.108 0.0454 0.0618 0.254 0.278 0.000636 0.000696 +Wall time: 55131.53076200513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.0852 0.00239 0.0375 0.0435 0.0583 0.211 0.231 0.000529 0.000578 + 330 118 0.0608 0.00217 0.0173 0.0415 0.0557 0.143 0.157 0.000356 0.000392 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 100 0.0555 0.00179 0.0196 0.037 0.0505 0.166 0.167 0.000414 0.000418 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 330 55298.613 0.005 0.00258 0.0303 0.082 0.0452 0.0607 0.157 0.208 0.000393 0.000521 +! Validation 330 55298.613 0.005 0.00255 0.0337 0.0847 0.0441 0.0603 0.192 0.219 0.00048 0.000548 +Wall time: 55298.613438447006 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.0706 0.00297 0.0113 0.0493 0.065 0.102 0.127 0.000256 0.000317 + 331 118 0.0553 0.00254 0.00458 0.0445 0.0601 0.0701 0.0808 0.000175 0.000202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.0485 0.00197 0.00916 0.0394 0.053 0.109 0.114 0.000273 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 331 55465.761 0.005 0.00244 0.028 0.0767 0.044 0.0589 0.16 0.2 0.0004 0.0005 +! Validation 331 55465.761 0.005 0.00271 0.0193 0.0734 0.0458 0.0621 0.139 0.166 0.000347 0.000414 +Wall time: 55465.76118066907 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.0634 0.00232 0.017 0.0431 0.0575 0.136 0.155 0.000341 0.000389 + 332 118 0.0562 0.0024 0.00809 0.0439 0.0585 0.0958 0.107 0.00024 0.000268 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 100 0.0484 0.0017 0.0143 0.0366 0.0493 0.141 0.143 0.000354 0.000357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 332 55632.993 0.005 0.00239 0.0226 0.0703 0.0435 0.0583 0.136 0.18 0.00034 0.000449 +! Validation 332 55632.993 0.005 0.00254 0.0276 0.0785 0.044 0.0602 0.172 0.198 0.000429 0.000496 +Wall time: 55632.99316800106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.0786 0.00229 0.0327 0.0429 0.0571 0.202 0.216 0.000506 0.00054 + 333 118 0.0678 0.00269 0.014 0.0458 0.0619 0.103 0.141 0.000257 0.000353 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.0365 0.00174 0.00178 0.0369 0.0497 0.0449 0.0503 0.000112 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 333 55800.062 0.005 0.00242 0.0305 0.0789 0.0439 0.0587 0.17 0.209 0.000426 0.000522 +! Validation 333 55800.062 0.005 0.00251 0.013 0.0632 0.0438 0.0598 0.108 0.136 0.000271 0.00034 +Wall time: 55800.0619708281 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.0509 0.00221 0.00658 0.042 0.0562 0.0798 0.0968 0.000199 0.000242 + 334 118 0.0766 0.00225 0.0316 0.0425 0.0566 0.203 0.212 0.000506 0.00053 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 0.0414 0.0019 0.0034 0.0382 0.052 0.0668 0.0695 0.000167 0.000174 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 334 55967.130 0.005 0.00229 0.0175 0.0634 0.0427 0.0572 0.128 0.158 0.000319 0.000394 +! Validation 334 55967.130 0.005 0.00264 0.0187 0.0716 0.045 0.0614 0.13 0.163 0.000324 0.000408 +Wall time: 55967.13035213342 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.0582 0.00248 0.00857 0.0438 0.0594 0.0927 0.11 0.000232 0.000276 + 335 118 0.0505 0.00214 0.00764 0.0414 0.0552 0.0811 0.104 0.000203 0.000261 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 0.0403 0.00168 0.00671 0.036 0.0489 0.0958 0.0978 0.00024 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 335 56135.193 0.005 0.00233 0.0206 0.0673 0.0431 0.0577 0.134 0.172 0.000334 0.000429 +! Validation 335 56135.193 0.005 0.00244 0.0162 0.065 0.043 0.059 0.126 0.152 0.000314 0.000379 +Wall time: 56135.19370278437 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.0955 0.0024 0.0476 0.0436 0.0584 0.247 0.26 0.000618 0.000651 + 336 118 0.149 0.00301 0.0891 0.0495 0.0655 0.341 0.356 0.000853 0.000891 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 0.0473 0.00228 0.00179 0.0432 0.0569 0.0443 0.0505 0.000111 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 336 56302.273 0.005 0.00235 0.032 0.079 0.0432 0.0578 0.173 0.212 0.000433 0.00053 +! Validation 336 56302.273 0.005 0.00307 0.0163 0.0776 0.0496 0.0661 0.118 0.152 0.000296 0.000381 +Wall time: 56302.27332799137 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.0478 0.00212 0.00542 0.0413 0.055 0.0698 0.0879 0.000175 0.00022 + 337 118 0.0808 0.00238 0.0332 0.0429 0.0582 0.191 0.217 0.000476 0.000544 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 0.0434 0.00197 0.00405 0.0388 0.053 0.0737 0.076 0.000184 0.00019 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 337 56469.534 0.005 0.00236 0.0202 0.0673 0.0432 0.0579 0.136 0.169 0.000339 0.000423 +! Validation 337 56469.534 0.005 0.00275 0.0132 0.0683 0.0459 0.0626 0.111 0.137 0.000276 0.000342 +Wall time: 56469.53475977527 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.0487 0.00215 0.00575 0.0412 0.0553 0.0719 0.0905 0.00018 0.000226 + 338 118 0.0851 0.00227 0.0398 0.0429 0.0568 0.209 0.238 0.000523 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 0.16 0.00199 0.12 0.04 0.0533 0.411 0.413 0.00103 0.00103 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 338 56636.826 0.005 0.00228 0.0205 0.0661 0.0425 0.057 0.136 0.171 0.000339 0.000426 +! Validation 338 56636.826 0.005 0.00273 0.132 0.187 0.046 0.0624 0.419 0.434 0.00105 0.00109 +Wall time: 56636.82605886506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.0735 0.00232 0.0271 0.0427 0.0575 0.178 0.196 0.000446 0.000491 + 339 118 0.0612 0.00254 0.0105 0.0445 0.0601 0.0906 0.122 0.000226 0.000305 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 0.0444 0.00179 0.00852 0.0372 0.0505 0.107 0.11 0.000267 0.000275 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 339 56804.095 0.005 0.0024 0.0214 0.0695 0.0436 0.0585 0.14 0.175 0.000349 0.000437 +! Validation 339 56804.095 0.005 0.00254 0.0278 0.0786 0.044 0.0601 0.16 0.199 0.0004 0.000497 +Wall time: 56804.095140899066 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.05 0.0022 0.00588 0.0421 0.056 0.0729 0.0915 0.000182 0.000229 + 340 118 0.0451 0.00192 0.0067 0.039 0.0523 0.0861 0.0977 0.000215 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 0.0355 0.00173 0.000927 0.0367 0.0496 0.0285 0.0363 7.13e-05 9.08e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 340 56971.440 0.005 0.0024 0.0301 0.0782 0.0438 0.0585 0.16 0.208 0.0004 0.000519 +! Validation 340 56971.440 0.005 0.00247 0.0122 0.0616 0.0433 0.0593 0.103 0.132 0.000258 0.00033 +Wall time: 56971.44086865522 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.069 0.00226 0.0238 0.0422 0.0567 0.15 0.184 0.000375 0.00046 + 341 118 0.0603 0.00224 0.0156 0.0423 0.0565 0.133 0.149 0.000332 0.000372 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 0.0544 0.00181 0.0181 0.0378 0.0508 0.158 0.161 0.000396 0.000402 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 341 57138.703 0.005 0.00224 0.016 0.0609 0.0422 0.0565 0.119 0.151 0.000297 0.000377 +! Validation 341 57138.703 0.005 0.00252 0.0195 0.07 0.044 0.0599 0.142 0.167 0.000356 0.000417 +Wall time: 57138.7035633754 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0454 0.00204 0.00449 0.0404 0.054 0.0658 0.08 0.000165 0.0002 + 342 118 0.058 0.00239 0.0102 0.0428 0.0584 0.0999 0.121 0.00025 0.000302 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 0.0401 0.00166 0.00693 0.0359 0.0486 0.0971 0.0993 0.000243 0.000248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 342 57305.976 0.005 0.00221 0.017 0.0612 0.0419 0.0561 0.125 0.156 0.000312 0.00039 +! Validation 342 57305.976 0.005 0.00242 0.0202 0.0686 0.0429 0.0587 0.137 0.17 0.000342 0.000424 +Wall time: 57305.97629727097 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.0488 0.00219 0.00496 0.0418 0.0559 0.0652 0.0841 0.000163 0.00021 + 343 118 0.0478 0.00213 0.0053 0.0418 0.055 0.0635 0.0869 0.000159 0.000217 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 0.0401 0.00169 0.00623 0.0361 0.0491 0.0915 0.0942 0.000229 0.000235 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 343 57473.260 0.005 0.00226 0.0271 0.0722 0.0423 0.0567 0.158 0.197 0.000394 0.000493 +! Validation 343 57473.260 0.005 0.00245 0.0129 0.062 0.0432 0.0591 0.111 0.136 0.000277 0.000339 +Wall time: 57473.2604618622 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.0515 0.0023 0.00556 0.0429 0.0572 0.0724 0.089 0.000181 0.000222 + 344 118 0.0624 0.00252 0.012 0.0446 0.0599 0.113 0.131 0.000283 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 0.0686 0.00177 0.0332 0.0374 0.0502 0.216 0.217 0.00054 0.000543 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 344 57640.711 0.005 0.00235 0.0323 0.0793 0.0433 0.0579 0.172 0.215 0.000431 0.000537 +! Validation 344 57640.711 0.005 0.00251 0.044 0.0941 0.0438 0.0598 0.229 0.25 0.000571 0.000626 +Wall time: 57640.71107348427 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.0574 0.00233 0.0108 0.0429 0.0576 0.105 0.124 0.000263 0.00031 + 345 118 0.0467 0.00213 0.00419 0.0409 0.0551 0.0611 0.0772 0.000153 0.000193 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 0.0333 0.00164 0.000579 0.0357 0.0483 0.0268 0.0287 6.69e-05 7.18e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 345 57808.062 0.005 0.00217 0.0145 0.058 0.0415 0.0556 0.116 0.144 0.000291 0.00036 +! Validation 345 57808.062 0.005 0.00238 0.00829 0.056 0.0425 0.0583 0.0848 0.109 0.000212 0.000272 +Wall time: 57808.062660656404 +! Best model 345 0.056 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.0448 0.00206 0.00366 0.0405 0.0541 0.0604 0.0722 0.000151 0.000181 + 346 118 0.0572 0.0023 0.0111 0.043 0.0573 0.0948 0.126 0.000237 0.000315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 0.0353 0.00158 0.00381 0.0351 0.0474 0.071 0.0736 0.000178 0.000184 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 346 57975.364 0.005 0.00231 0.0231 0.0694 0.0428 0.0574 0.135 0.182 0.000338 0.000454 +! Validation 346 57975.364 0.005 0.00234 0.0116 0.0585 0.0421 0.0578 0.104 0.129 0.000261 0.000322 +Wall time: 57975.36411345098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.051 0.00224 0.00618 0.0426 0.0565 0.0734 0.0938 0.000183 0.000235 + 347 118 0.0489 0.00214 0.00607 0.0415 0.0552 0.0776 0.093 0.000194 0.000233 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 0.0362 0.00175 0.00113 0.0368 0.05 0.0366 0.0401 9.15e-05 0.0001 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 347 58142.625 0.005 0.00222 0.0196 0.0641 0.042 0.0563 0.136 0.168 0.00034 0.000419 +! Validation 347 58142.625 0.005 0.00242 0.00916 0.0575 0.043 0.0587 0.0885 0.114 0.000221 0.000286 +Wall time: 58142.6255869302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.0761 0.00228 0.0306 0.0425 0.0569 0.185 0.209 0.000463 0.000522 + 348 118 0.0837 0.0022 0.0398 0.0424 0.0559 0.223 0.238 0.000556 0.000595 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 0.079 0.00175 0.044 0.0373 0.0499 0.249 0.25 0.000623 0.000626 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 348 58309.888 0.005 0.00221 0.0254 0.0695 0.0419 0.0561 0.155 0.19 0.000388 0.000475 +! Validation 348 58309.888 0.005 0.00248 0.0492 0.0988 0.0437 0.0594 0.242 0.265 0.000605 0.000662 +Wall time: 58309.88902788516 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0505 0.00214 0.00767 0.041 0.0552 0.0864 0.105 0.000216 0.000261 + 349 118 0.0674 0.00238 0.0198 0.0433 0.0582 0.149 0.168 0.000374 0.00042 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 0.0903 0.00165 0.0572 0.0355 0.0485 0.285 0.286 0.000713 0.000714 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 349 58477.134 0.005 0.00217 0.0174 0.0608 0.0416 0.0556 0.124 0.157 0.000311 0.000393 +! Validation 349 58477.134 0.005 0.00239 0.0684 0.116 0.0425 0.0583 0.292 0.312 0.00073 0.00078 +Wall time: 58477.1341531612 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.0605 0.00239 0.0127 0.0436 0.0584 0.106 0.134 0.000265 0.000336 + 350 118 0.11 0.0022 0.0657 0.0423 0.056 0.283 0.306 0.000708 0.000765 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 0.202 0.00171 0.168 0.0368 0.0494 0.488 0.489 0.00122 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 350 58645.021 0.005 0.00217 0.0209 0.0643 0.0415 0.0556 0.135 0.171 0.000338 0.000428 +! Validation 350 58645.021 0.005 0.00246 0.186 0.236 0.0436 0.0593 0.501 0.515 0.00125 0.00129 +Wall time: 58645.02194620529 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0814 0.00215 0.0383 0.0413 0.0554 0.22 0.234 0.00055 0.000584 + 351 118 0.0432 0.00173 0.0085 0.0375 0.0497 0.101 0.11 0.000251 0.000275 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 0.0337 0.00166 0.000517 0.0358 0.0486 0.0202 0.0271 5.06e-05 6.79e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 351 58812.296 0.005 0.00236 0.023 0.0703 0.0432 0.0581 0.138 0.182 0.000345 0.000454 +! Validation 351 58812.296 0.005 0.00234 0.0146 0.0614 0.0422 0.0578 0.106 0.144 0.000266 0.000361 +Wall time: 58812.29697465338 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0566 0.00216 0.0133 0.0416 0.0555 0.116 0.137 0.000289 0.000344 + 352 118 0.0552 0.0023 0.00912 0.0421 0.0573 0.0963 0.114 0.000241 0.000285 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 0.0677 0.0018 0.0317 0.0372 0.0506 0.21 0.213 0.000525 0.000531 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 352 58979.571 0.005 0.00215 0.0211 0.064 0.0413 0.0553 0.145 0.174 0.000362 0.000434 +! Validation 352 58979.571 0.005 0.00246 0.0358 0.0851 0.0435 0.0592 0.199 0.226 0.000496 0.000565 +Wall time: 58979.57189744804 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.069 0.00211 0.0267 0.0407 0.0549 0.181 0.195 0.000452 0.000488 + 353 118 0.0452 0.00203 0.0046 0.0405 0.0538 0.0653 0.081 0.000163 0.000202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 0.0328 0.00158 0.00126 0.0349 0.0474 0.0379 0.0423 9.46e-05 0.000106 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 353 59146.834 0.005 0.00216 0.0178 0.061 0.0414 0.0555 0.126 0.159 0.000315 0.000399 +! Validation 353 59146.834 0.005 0.00228 0.00858 0.0542 0.0416 0.057 0.0857 0.111 0.000214 0.000276 +Wall time: 59146.83410271024 +! Best model 353 0.054 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0599 0.00224 0.015 0.0424 0.0565 0.126 0.146 0.000316 0.000366 + 354 118 0.0591 0.00265 0.00622 0.046 0.0614 0.0808 0.0941 0.000202 0.000235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 0.0386 0.00162 0.00617 0.0358 0.0481 0.0911 0.0938 0.000228 0.000234 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 354 59314.481 0.005 0.00213 0.017 0.0596 0.041 0.055 0.119 0.156 0.000297 0.00039 +! Validation 354 59314.481 0.005 0.00237 0.0135 0.061 0.0427 0.0582 0.113 0.138 0.000282 0.000346 +Wall time: 59314.481210277416 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0831 0.00209 0.0414 0.0409 0.0545 0.222 0.243 0.000554 0.000607 + 355 118 0.0596 0.00192 0.0212 0.0393 0.0523 0.162 0.174 0.000405 0.000434 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 0.0378 0.00168 0.00416 0.036 0.049 0.0745 0.077 0.000186 0.000192 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 355 59481.555 0.005 0.00217 0.0242 0.0676 0.0416 0.0556 0.147 0.186 0.000367 0.000464 +! Validation 355 59481.555 0.005 0.00233 0.0145 0.061 0.0422 0.0576 0.116 0.144 0.000289 0.000359 +Wall time: 59481.55552888429 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0454 0.00204 0.00459 0.0401 0.0539 0.0616 0.0808 0.000154 0.000202 + 356 118 0.0456 0.00182 0.0093 0.0383 0.0509 0.102 0.115 0.000256 0.000288 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 0.0466 0.00164 0.0137 0.0361 0.0484 0.139 0.14 0.000346 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 356 59648.630 0.005 0.00255 0.0271 0.0781 0.0447 0.0603 0.146 0.197 0.000364 0.000493 +! Validation 356 59648.630 0.005 0.00238 0.0182 0.0657 0.0428 0.0582 0.138 0.161 0.000345 0.000403 +Wall time: 59648.63005307317 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0476 0.00206 0.00647 0.0405 0.0542 0.0758 0.096 0.00019 0.00024 + 357 118 0.0473 0.00187 0.00979 0.0385 0.0517 0.1 0.118 0.00025 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 0.0401 0.00154 0.00923 0.0346 0.0469 0.113 0.115 0.000284 0.000287 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 357 59815.698 0.005 0.00207 0.0128 0.0542 0.0405 0.0543 0.11 0.135 0.000276 0.000338 +! Validation 357 59815.698 0.005 0.00223 0.0156 0.0603 0.0412 0.0564 0.126 0.149 0.000316 0.000373 +Wall time: 59815.698825838044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.067 0.00224 0.0221 0.0421 0.0565 0.164 0.177 0.000411 0.000444 + 358 118 0.0669 0.00211 0.0248 0.0411 0.0548 0.155 0.188 0.000387 0.00047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 0.0343 0.00167 0.00101 0.0364 0.0487 0.0335 0.038 8.37e-05 9.5e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 358 59982.769 0.005 0.00214 0.0246 0.0674 0.0413 0.0552 0.152 0.187 0.000381 0.000468 +! Validation 358 59982.769 0.005 0.00244 0.0157 0.0646 0.0434 0.059 0.115 0.15 0.000289 0.000374 +Wall time: 59982.769377996214 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0502 0.00211 0.00804 0.0408 0.0548 0.0854 0.107 0.000213 0.000268 + 359 118 0.0638 0.00243 0.0151 0.0437 0.0589 0.135 0.147 0.000338 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 0.0336 0.00157 0.00218 0.0348 0.0473 0.0527 0.0557 0.000132 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 359 60149.931 0.005 0.00216 0.0222 0.0654 0.0414 0.0554 0.138 0.178 0.000345 0.000445 +! Validation 359 60149.931 0.005 0.0023 0.0115 0.0576 0.042 0.0573 0.101 0.128 0.000252 0.00032 +Wall time: 60149.93165967008 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0906 0.002 0.0506 0.0398 0.0534 0.257 0.269 0.000643 0.000672 + 360 118 0.112 0.00235 0.0645 0.0437 0.0579 0.289 0.303 0.000723 0.000758 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 0.0895 0.00164 0.0567 0.0358 0.0483 0.283 0.284 0.000708 0.000711 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 360 60317.026 0.005 0.00211 0.0229 0.0651 0.041 0.0548 0.142 0.179 0.000354 0.000448 +! Validation 360 60317.026 0.005 0.00236 0.0575 0.105 0.0425 0.058 0.264 0.286 0.000661 0.000716 +Wall time: 60317.02606905624 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0709 0.00197 0.0316 0.0394 0.0529 0.199 0.212 0.000498 0.000531 + 361 118 0.0744 0.0021 0.0324 0.0409 0.0547 0.179 0.215 0.000447 0.000537 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 0.0571 0.00155 0.0261 0.0349 0.0469 0.192 0.193 0.000479 0.000482 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 361 60485.056 0.005 0.00206 0.016 0.0572 0.0405 0.0542 0.12 0.15 0.0003 0.000376 +! Validation 361 60485.056 0.005 0.00224 0.0343 0.079 0.0413 0.0564 0.194 0.221 0.000484 0.000553 +Wall time: 60485.05681521911 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0497 0.00217 0.00634 0.0415 0.0556 0.0747 0.095 0.000187 0.000238 + 362 118 0.0613 0.00172 0.0269 0.0375 0.0495 0.176 0.196 0.000441 0.000489 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 0.0422 0.00171 0.00804 0.0364 0.0493 0.103 0.107 0.000258 0.000267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 362 60652.172 0.005 0.00212 0.0182 0.0606 0.0411 0.055 0.128 0.161 0.000319 0.000402 +! Validation 362 60652.172 0.005 0.00237 0.0158 0.0633 0.0428 0.0581 0.127 0.15 0.000317 0.000375 +Wall time: 60652.17219665134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.0471 0.00206 0.00589 0.0403 0.0542 0.074 0.0916 0.000185 0.000229 + 363 118 0.0399 0.00182 0.00356 0.038 0.0509 0.0612 0.0712 0.000153 0.000178 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 0.0453 0.0015 0.0153 0.0341 0.0462 0.147 0.148 0.000367 0.000369 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 363 60819.309 0.005 0.00229 0.0255 0.0713 0.0426 0.0572 0.14 0.191 0.00035 0.000478 +! Validation 363 60819.309 0.005 0.00219 0.0214 0.0653 0.0408 0.0559 0.15 0.175 0.000376 0.000436 +Wall time: 60819.30958537711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.0649 0.00206 0.0236 0.0405 0.0542 0.158 0.183 0.000395 0.000459 + 364 118 0.0754 0.00226 0.0302 0.0418 0.0568 0.179 0.207 0.000447 0.000518 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 0.0764 0.00169 0.0427 0.0365 0.049 0.245 0.247 0.000612 0.000616 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 364 60986.537 0.005 0.00204 0.017 0.0578 0.0402 0.0539 0.128 0.155 0.000319 0.000388 +! Validation 364 60986.537 0.005 0.00241 0.0559 0.104 0.0429 0.0586 0.263 0.282 0.000658 0.000706 +Wall time: 60986.537861155346 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.0525 0.00203 0.0119 0.0402 0.0538 0.107 0.13 0.000269 0.000326 + 365 118 0.0626 0.00203 0.0221 0.0399 0.0538 0.165 0.177 0.000412 0.000443 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 0.113 0.00167 0.0799 0.0366 0.0487 0.337 0.337 0.000842 0.000844 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 365 61153.687 0.005 0.00211 0.0248 0.0669 0.041 0.0548 0.151 0.188 0.000378 0.00047 +! Validation 365 61153.687 0.005 0.00237 0.0791 0.127 0.0428 0.0582 0.32 0.336 0.0008 0.000839 +Wall time: 61153.6875461 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.0449 0.002 0.00488 0.0396 0.0534 0.0665 0.0834 0.000166 0.000208 + 366 118 0.0425 0.00196 0.00329 0.0394 0.0528 0.0581 0.0685 0.000145 0.000171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 0.0422 0.00146 0.0129 0.0336 0.0457 0.134 0.136 0.000336 0.000339 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 366 61320.836 0.005 0.00204 0.0148 0.0556 0.0403 0.0539 0.109 0.146 0.000272 0.000364 +! Validation 366 61320.836 0.005 0.00214 0.019 0.0619 0.0402 0.0552 0.133 0.165 0.000332 0.000412 +Wall time: 61320.83603031328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.079 0.0019 0.0409 0.0392 0.0521 0.229 0.241 0.000572 0.000603 + 367 118 0.0516 0.00221 0.00735 0.0409 0.0561 0.0852 0.102 0.000213 0.000256 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 0.0321 0.00157 0.000718 0.0353 0.0473 0.0235 0.032 5.87e-05 7.99e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 367 61488.158 0.005 0.00207 0.0229 0.0643 0.0406 0.0543 0.152 0.181 0.000379 0.000452 +! Validation 367 61488.158 0.005 0.00223 0.00806 0.0526 0.0413 0.0563 0.082 0.107 0.000205 0.000268 +Wall time: 61488.1584149641 +! Best model 367 0.053 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0629 0.00201 0.0226 0.0401 0.0535 0.159 0.18 0.000398 0.000449 + 368 118 0.0742 0.00191 0.036 0.0391 0.0522 0.215 0.226 0.000537 0.000566 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 0.0719 0.00174 0.0371 0.0371 0.0497 0.229 0.23 0.000573 0.000575 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 368 61655.455 0.005 0.00204 0.0182 0.059 0.0402 0.0539 0.129 0.161 0.000322 0.000402 +! Validation 368 61655.455 0.005 0.00239 0.0665 0.114 0.0431 0.0583 0.259 0.308 0.000647 0.00077 +Wall time: 61655.45523726707 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0415 0.00186 0.00432 0.0385 0.0514 0.0631 0.0784 0.000158 0.000196 + 369 118 0.121 0.0018 0.0848 0.0381 0.0506 0.341 0.348 0.000852 0.000869 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 0.0594 0.00158 0.0278 0.0357 0.0474 0.198 0.199 0.000494 0.000497 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 369 61822.819 0.005 0.00213 0.0247 0.0674 0.0412 0.0552 0.147 0.186 0.000367 0.000465 +! Validation 369 61822.819 0.005 0.0023 0.0592 0.105 0.0422 0.0572 0.244 0.29 0.000611 0.000726 +Wall time: 61822.81970582204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0466 0.00204 0.00575 0.0405 0.054 0.0709 0.0905 0.000177 0.000226 + 370 118 0.0491 0.00201 0.00885 0.0392 0.0536 0.102 0.112 0.000254 0.000281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 0.0328 0.00157 0.00147 0.0347 0.0473 0.0387 0.0457 9.68e-05 0.000114 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 370 61990.108 0.005 0.00206 0.0208 0.0621 0.0406 0.0542 0.137 0.173 0.000341 0.000431 +! Validation 370 61990.108 0.005 0.00222 0.00998 0.0543 0.0411 0.0562 0.0951 0.119 0.000238 0.000298 +Wall time: 61990.1081391722 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0553 0.00213 0.0128 0.0411 0.0551 0.118 0.135 0.000294 0.000337 + 371 118 0.0443 0.00179 0.00841 0.0375 0.0506 0.082 0.109 0.000205 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 0.0347 0.00156 0.00357 0.0346 0.0471 0.0698 0.0713 0.000174 0.000178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 371 62157.397 0.005 0.00195 0.0109 0.0498 0.0393 0.0527 0.0984 0.125 0.000246 0.000312 +! Validation 371 62157.397 0.005 0.00218 0.0102 0.0537 0.0408 0.0557 0.0937 0.12 0.000234 0.000301 +Wall time: 62157.39716729941 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.109 0.00208 0.0674 0.0409 0.0545 0.299 0.31 0.000747 0.000774 + 372 118 0.141 0.00197 0.102 0.0398 0.053 0.374 0.381 0.000935 0.000951 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 0.0847 0.00201 0.0445 0.04 0.0535 0.251 0.252 0.000627 0.000629 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 372 62325.947 0.005 0.002 0.0243 0.0642 0.0398 0.0533 0.136 0.184 0.00034 0.00046 +! Validation 372 62325.947 0.005 0.00261 0.0671 0.119 0.0453 0.061 0.267 0.309 0.000667 0.000773 +Wall time: 62325.94768316718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.451 0.0187 0.0766 0.123 0.163 0.253 0.33 0.000632 0.000826 + 373 118 0.429 0.0124 0.181 0.101 0.133 0.477 0.508 0.00119 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 0.444 0.0111 0.223 0.095 0.126 0.559 0.564 0.0014 0.00141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 373 62494.411 0.005 0.0971 1.73 3.67 0.228 0.373 0.939 1.58 0.00235 0.00394 +! Validation 373 62494.411 0.005 0.0133 0.136 0.402 0.104 0.138 0.369 0.441 0.000922 0.0011 +Wall time: 62494.411849517375 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.146 0.00581 0.0296 0.0697 0.0909 0.16 0.205 0.000399 0.000513 + 374 118 0.148 0.00582 0.0318 0.0692 0.0911 0.188 0.213 0.00047 0.000532 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 0.113 0.00475 0.0176 0.0622 0.0823 0.144 0.159 0.00036 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 374 62661.548 0.005 0.00812 0.0557 0.218 0.0811 0.108 0.223 0.282 0.000556 0.000705 +! Validation 374 62661.548 0.005 0.00586 0.0305 0.148 0.0692 0.0914 0.169 0.208 0.000423 0.000521 +Wall time: 62661.548694444355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.0961 0.00414 0.0133 0.0589 0.0768 0.106 0.138 0.000266 0.000344 + 375 118 0.125 0.00418 0.0418 0.0591 0.0771 0.21 0.244 0.000524 0.00061 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 0.113 0.00334 0.0458 0.0522 0.0689 0.249 0.256 0.000623 0.000639 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 375 62828.678 0.005 0.00472 0.0323 0.127 0.0627 0.082 0.171 0.214 0.000427 0.000535 +! Validation 375 62828.678 0.005 0.00429 0.0476 0.133 0.059 0.0782 0.224 0.26 0.00056 0.000651 +Wall time: 62828.67809789302 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.144 0.00347 0.0741 0.0534 0.0703 0.299 0.325 0.000747 0.000812 + 376 118 0.0786 0.00334 0.0118 0.0524 0.069 0.104 0.13 0.000261 0.000324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 0.0714 0.00262 0.0189 0.0461 0.0611 0.159 0.164 0.000397 0.00041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 376 62995.798 0.005 0.00365 0.0252 0.0982 0.0549 0.0721 0.152 0.19 0.000379 0.000475 +! Validation 376 62995.798 0.005 0.00356 0.0243 0.0956 0.0533 0.0712 0.155 0.186 0.000387 0.000465 +Wall time: 62995.798377080355 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0746 0.00305 0.0136 0.0498 0.0659 0.122 0.139 0.000304 0.000348 + 377 118 0.073 0.0032 0.00906 0.0513 0.0675 0.1 0.114 0.000251 0.000284 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 0.0497 0.00229 0.00399 0.0428 0.0571 0.0682 0.0754 0.000171 0.000188 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 377 63162.903 0.005 0.00311 0.02 0.0821 0.0504 0.0665 0.137 0.169 0.000343 0.000423 +! Validation 377 63162.903 0.005 0.00316 0.0126 0.0757 0.0499 0.0671 0.107 0.134 0.000267 0.000335 +Wall time: 63162.903345452156 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0669 0.00268 0.0133 0.0464 0.0618 0.114 0.138 0.000285 0.000345 + 378 118 0.0713 0.00279 0.0154 0.0465 0.0631 0.122 0.148 0.000305 0.00037 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 0.0582 0.00207 0.0168 0.0405 0.0543 0.153 0.155 0.000382 0.000387 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 378 63330.067 0.005 0.0028 0.0151 0.0711 0.0476 0.0632 0.119 0.147 0.000296 0.000366 +! Validation 378 63330.067 0.005 0.00293 0.0276 0.0861 0.0477 0.0646 0.169 0.198 0.000422 0.000496 +Wall time: 63330.06749619637 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0734 0.00272 0.0191 0.0466 0.0622 0.138 0.165 0.000344 0.000412 + 379 118 0.0791 0.0029 0.021 0.0476 0.0643 0.153 0.173 0.000383 0.000433 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 0.0416 0.00194 0.00271 0.0393 0.0526 0.0577 0.0621 0.000144 0.000155 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 379 63497.185 0.005 0.00261 0.0116 0.0638 0.0457 0.061 0.102 0.128 0.000256 0.000321 +! Validation 379 63497.185 0.005 0.00276 0.0105 0.0658 0.0463 0.0628 0.0966 0.122 0.000241 0.000306 +Wall time: 63497.184973093215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0598 0.00251 0.00963 0.0448 0.0598 0.0959 0.117 0.00024 0.000293 + 380 118 0.0478 0.00216 0.00449 0.0414 0.0555 0.0653 0.08 0.000163 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 0.0373 0.00184 0.000515 0.0382 0.0512 0.0219 0.0271 5.48e-05 6.77e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 380 63664.282 0.005 0.00248 0.018 0.0677 0.0446 0.0595 0.128 0.161 0.000319 0.000402 +! Validation 380 63664.282 0.005 0.00266 0.00884 0.062 0.0452 0.0615 0.0873 0.112 0.000218 0.000281 +Wall time: 63664.28202277422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0573 0.00249 0.00749 0.0444 0.0596 0.0823 0.103 0.000206 0.000258 + 381 118 0.0583 0.00231 0.012 0.0431 0.0574 0.114 0.131 0.000286 0.000327 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 0.0359 0.00177 0.00052 0.0374 0.0502 0.0229 0.0272 5.72e-05 6.8e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 381 63831.399 0.005 0.00238 0.0105 0.0582 0.0436 0.0583 0.0979 0.122 0.000245 0.000306 +! Validation 381 63831.399 0.005 0.00257 0.00874 0.0602 0.0444 0.0605 0.0863 0.112 0.000216 0.000279 +Wall time: 63831.3992936532 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.0516 0.00232 0.00529 0.0427 0.0574 0.0692 0.0868 0.000173 0.000217 + 382 118 0.0482 0.00217 0.00479 0.0416 0.0556 0.0615 0.0826 0.000154 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 0.0368 0.00173 0.00208 0.037 0.0497 0.0519 0.0544 0.00013 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 382 63998.525 0.005 0.00231 0.0132 0.0594 0.0429 0.0574 0.111 0.137 0.000277 0.000343 +! Validation 382 63998.525 0.005 0.00251 0.0105 0.0607 0.0438 0.0598 0.0943 0.122 0.000236 0.000306 +Wall time: 63998.52542071603 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0572 0.00227 0.0117 0.0424 0.0569 0.109 0.129 0.000272 0.000323 + 383 118 0.0536 0.00226 0.00846 0.0427 0.0567 0.0877 0.11 0.000219 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 0.0365 0.00168 0.00282 0.0365 0.049 0.062 0.0634 0.000155 0.000159 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 383 64165.704 0.005 0.00226 0.0143 0.0594 0.0424 0.0567 0.115 0.143 0.000287 0.000357 +! Validation 383 64165.704 0.005 0.00245 0.0123 0.0614 0.0433 0.0591 0.107 0.133 0.000267 0.000331 +Wall time: 64165.70474754041 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0565 0.00228 0.011 0.0423 0.057 0.101 0.125 0.000253 0.000313 + 384 118 0.0522 0.00239 0.0044 0.0435 0.0584 0.0627 0.0792 0.000157 0.000198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 0.0336 0.00165 0.000688 0.0363 0.0484 0.0294 0.0313 7.36e-05 7.82e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 384 64332.807 0.005 0.00221 0.0145 0.0587 0.0419 0.0561 0.118 0.144 0.000295 0.00036 +! Validation 384 64332.807 0.005 0.00242 0.00885 0.0572 0.043 0.0587 0.0876 0.112 0.000219 0.000281 +Wall time: 64332.80722406227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.116 0.002 0.0763 0.0401 0.0534 0.322 0.33 0.000806 0.000824 + 385 118 0.0552 0.00223 0.0106 0.0418 0.0563 0.107 0.123 0.000268 0.000307 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 0.0347 0.00166 0.00144 0.0363 0.0486 0.0443 0.0453 0.000111 0.000113 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 385 64499.907 0.005 0.00217 0.0162 0.0595 0.0415 0.0556 0.119 0.152 0.000297 0.00038 +! Validation 385 64499.907 0.005 0.00241 0.0104 0.0586 0.0429 0.0586 0.0957 0.122 0.000239 0.000305 +Wall time: 64499.90764717199 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.048 0.00214 0.00508 0.0412 0.0553 0.0683 0.085 0.000171 0.000213 + 386 118 0.0504 0.00211 0.00808 0.0404 0.0549 0.0785 0.107 0.000196 0.000268 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 0.0325 0.0016 0.000548 0.0355 0.0477 0.0229 0.0279 5.73e-05 6.99e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 386 64667.002 0.005 0.00214 0.0137 0.0564 0.0412 0.0552 0.113 0.14 0.000283 0.00035 +! Validation 386 64667.002 0.005 0.00234 0.00768 0.0545 0.0422 0.0577 0.0808 0.105 0.000202 0.000262 +Wall time: 64667.00235976931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.0601 0.00218 0.0165 0.0415 0.0557 0.132 0.153 0.000331 0.000384 + 387 118 0.0511 0.00233 0.00447 0.043 0.0577 0.0685 0.0798 0.000171 0.0002 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 0.041 0.0016 0.00913 0.0356 0.0477 0.113 0.114 0.000283 0.000285 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 387 64834.053 0.005 0.00212 0.0219 0.0642 0.041 0.0549 0.141 0.177 0.000352 0.000443 +! Validation 387 64834.053 0.005 0.00234 0.0185 0.0653 0.0423 0.0578 0.136 0.162 0.000341 0.000405 +Wall time: 64834.0534157902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 0.0515 0.00208 0.00992 0.0401 0.0544 0.104 0.119 0.000259 0.000297 + 388 118 0.0768 0.00185 0.0399 0.0383 0.0513 0.222 0.238 0.000555 0.000596 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 0.0556 0.0016 0.0237 0.0354 0.0477 0.183 0.184 0.000458 0.000459 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 388 65001.202 0.005 0.00208 0.0129 0.0545 0.0406 0.0545 0.107 0.134 0.000267 0.000336 +! Validation 388 65001.202 0.005 0.00231 0.0257 0.0719 0.0419 0.0573 0.169 0.191 0.000422 0.000479 +Wall time: 65001.20240403339 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.0467 0.00211 0.00451 0.0408 0.0548 0.0656 0.0801 0.000164 0.0002 + 389 118 0.0477 0.0019 0.00961 0.0391 0.0521 0.0999 0.117 0.00025 0.000292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 0.0362 0.00159 0.00441 0.0356 0.0476 0.0783 0.0792 0.000196 0.000198 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 389 65168.268 0.005 0.00206 0.0162 0.0575 0.0404 0.0542 0.124 0.152 0.000311 0.000381 +! Validation 389 65168.268 0.005 0.00229 0.0113 0.0572 0.0418 0.0572 0.101 0.127 0.000252 0.000317 +Wall time: 65168.268060688395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.0974 0.00202 0.057 0.04 0.0537 0.27 0.285 0.000674 0.000712 + 390 118 0.0589 0.00235 0.0119 0.043 0.0579 0.108 0.13 0.000271 0.000325 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 0.0557 0.00151 0.0254 0.0347 0.0465 0.19 0.19 0.000475 0.000476 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 390 65335.327 0.005 0.00204 0.0162 0.0571 0.0402 0.0539 0.125 0.152 0.000312 0.00038 +! Validation 390 65335.327 0.005 0.00226 0.0377 0.0828 0.0414 0.0567 0.21 0.232 0.000525 0.000579 +Wall time: 65335.32774687419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.0452 0.00207 0.00376 0.0405 0.0543 0.0578 0.0732 0.000144 0.000183 + 391 118 0.043 0.00201 0.00276 0.0399 0.0535 0.0523 0.0627 0.000131 0.000157 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 0.0367 0.00151 0.00649 0.0346 0.0464 0.0955 0.0961 0.000239 0.00024 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 391 65502.381 0.005 0.00202 0.0136 0.054 0.04 0.0536 0.113 0.14 0.000281 0.000349 +! Validation 391 65502.381 0.005 0.00222 0.0141 0.0586 0.0411 0.0563 0.111 0.142 0.000277 0.000354 +Wall time: 65502.381958957296 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.091 0.00204 0.0502 0.04 0.0539 0.258 0.267 0.000645 0.000669 + 392 118 0.0533 0.00191 0.0151 0.0392 0.0522 0.132 0.147 0.000331 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 0.0334 0.00156 0.00216 0.035 0.0472 0.0523 0.0554 0.000131 0.000139 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 392 65669.529 0.005 0.002 0.0147 0.0546 0.0397 0.0533 0.115 0.144 0.000288 0.000361 +! Validation 392 65669.529 0.005 0.00225 0.0127 0.0577 0.0413 0.0566 0.108 0.135 0.00027 0.000337 +Wall time: 65669.52993379533 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.046 0.00204 0.00525 0.0396 0.0539 0.0703 0.0865 0.000176 0.000216 + 393 118 0.0457 0.00188 0.008 0.0387 0.0518 0.087 0.107 0.000217 0.000267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 0.0305 0.00151 0.000383 0.0344 0.0464 0.0199 0.0234 4.99e-05 5.84e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 393 65836.626 0.005 0.00198 0.0116 0.0512 0.0396 0.0532 0.104 0.129 0.00026 0.000322 +! Validation 393 65836.626 0.005 0.0022 0.00765 0.0516 0.0408 0.0559 0.0805 0.104 0.000201 0.000261 +Wall time: 65836.62640166515 +! Best model 393 0.052 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.0439 0.00193 0.00542 0.0389 0.0524 0.0699 0.0879 0.000175 0.00022 + 394 118 0.0521 0.00216 0.00895 0.0414 0.0554 0.0963 0.113 0.000241 0.000282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 0.043 0.00148 0.0134 0.0342 0.0459 0.137 0.138 0.000342 0.000345 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 394 66003.731 0.005 0.00196 0.0139 0.0531 0.0394 0.0529 0.114 0.141 0.000285 0.000352 +! Validation 394 66003.731 0.005 0.00217 0.0261 0.0695 0.0406 0.0556 0.17 0.193 0.000425 0.000482 +Wall time: 66003.73177754506 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.0465 0.00194 0.00774 0.0391 0.0526 0.0887 0.105 0.000222 0.000262 + 395 118 0.0484 0.00197 0.009 0.0398 0.053 0.0954 0.113 0.000238 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 0.0311 0.0015 0.00112 0.0343 0.0462 0.0373 0.0399 9.32e-05 9.98e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 395 66170.816 0.005 0.00198 0.0244 0.0641 0.0396 0.0531 0.145 0.187 0.000364 0.000468 +! Validation 395 66170.816 0.005 0.00219 0.00821 0.0521 0.0408 0.0559 0.0845 0.108 0.000211 0.00027 +Wall time: 66170.81677797018 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0647 0.00193 0.0261 0.0388 0.0525 0.181 0.193 0.000451 0.000482 + 396 118 0.0722 0.00183 0.0356 0.0381 0.051 0.214 0.225 0.000535 0.000563 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 0.0388 0.00147 0.00949 0.034 0.0457 0.116 0.116 0.000289 0.000291 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 396 66337.907 0.005 0.00194 0.0101 0.0489 0.0392 0.0526 0.0942 0.119 0.000235 0.000298 +! Validation 396 66337.907 0.005 0.00216 0.0158 0.059 0.0404 0.0555 0.128 0.15 0.00032 0.000375 +Wall time: 66337.90741622122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.0596 0.00189 0.0218 0.0389 0.0519 0.159 0.176 0.000396 0.00044 + 397 118 0.0438 0.00191 0.00563 0.039 0.0521 0.069 0.0895 0.000172 0.000224 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 0.0311 0.00147 0.00179 0.0342 0.0457 0.0482 0.0505 0.000121 0.000126 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 397 66505.082 0.005 0.00194 0.0197 0.0586 0.0392 0.0526 0.135 0.168 0.000339 0.00042 +! Validation 397 66505.082 0.005 0.00216 0.00886 0.0521 0.0405 0.0555 0.0878 0.112 0.000219 0.000281 +Wall time: 66505.08240563422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0704 0.00175 0.0353 0.0374 0.05 0.213 0.224 0.000533 0.00056 + 398 118 0.0435 0.00195 0.00453 0.0387 0.0527 0.0587 0.0803 0.000147 0.000201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 0.0341 0.00147 0.00474 0.0341 0.0457 0.0811 0.0822 0.000203 0.000206 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 398 66672.178 0.005 0.00193 0.0145 0.053 0.039 0.0524 0.118 0.144 0.000296 0.00036 +! Validation 398 66672.178 0.005 0.00216 0.00845 0.0517 0.0405 0.0555 0.0876 0.11 0.000219 0.000274 +Wall time: 66672.1786948964 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0434 0.00181 0.00731 0.0379 0.0507 0.0813 0.102 0.000203 0.000255 + 399 118 0.0604 0.00181 0.0242 0.0381 0.0508 0.181 0.186 0.000453 0.000464 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 0.0701 0.00149 0.0402 0.0345 0.0461 0.239 0.239 0.000597 0.000598 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 399 66839.266 0.005 0.00193 0.0207 0.0594 0.0391 0.0525 0.139 0.171 0.000346 0.000429 +! Validation 399 66839.266 0.005 0.00219 0.0417 0.0854 0.0408 0.0558 0.224 0.244 0.00056 0.000609 +Wall time: 66839.26620955206 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.0419 0.0019 0.00399 0.0386 0.052 0.0572 0.0754 0.000143 0.000189 + 400 118 0.0663 0.00226 0.0211 0.042 0.0567 0.149 0.173 0.000371 0.000433 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 0.0422 0.00145 0.0131 0.0341 0.0455 0.136 0.137 0.00034 0.000342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 400 67006.360 0.005 0.00191 0.0112 0.0495 0.0389 0.0522 0.102 0.126 0.000254 0.000315 +! Validation 400 67006.360 0.005 0.00213 0.026 0.0687 0.0402 0.0551 0.17 0.192 0.000425 0.000481 +Wall time: 67006.36062486703 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.0512 0.00198 0.0117 0.0397 0.053 0.108 0.129 0.000269 0.000322 + 401 118 0.0627 0.0021 0.0208 0.0402 0.0547 0.16 0.172 0.000399 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 0.0782 0.00143 0.0496 0.0335 0.0452 0.265 0.266 0.000664 0.000664 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 401 67173.455 0.005 0.00189 0.0122 0.05 0.0387 0.0519 0.105 0.131 0.000262 0.000328 +! Validation 401 67173.455 0.005 0.0021 0.054 0.096 0.0399 0.0547 0.259 0.277 0.000648 0.000693 +Wall time: 67173.45520490035 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0417 0.00195 0.00262 0.0391 0.0527 0.0473 0.0611 0.000118 0.000153 + 402 118 0.0584 0.00169 0.0246 0.037 0.0491 0.175 0.187 0.000439 0.000468 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 0.0436 0.00143 0.0151 0.0334 0.0451 0.146 0.146 0.000364 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 402 67340.637 0.005 0.00189 0.0174 0.0553 0.0387 0.0519 0.125 0.157 0.000313 0.000393 +! Validation 402 67340.637 0.005 0.0021 0.0179 0.0599 0.0399 0.0547 0.134 0.16 0.000336 0.000399 +Wall time: 67340.63700787816 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0517 0.00191 0.0135 0.0387 0.0522 0.118 0.138 0.000295 0.000346 + 403 118 0.0391 0.00179 0.00321 0.0376 0.0506 0.0588 0.0677 0.000147 0.000169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 0.0286 0.00142 0.000192 0.0335 0.045 0.0118 0.0166 2.95e-05 4.14e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 403 67507.725 0.005 0.00189 0.0175 0.0553 0.0387 0.0519 0.129 0.158 0.000322 0.000395 +! Validation 403 67507.725 0.005 0.00209 0.00723 0.049 0.0398 0.0546 0.079 0.101 0.000197 0.000254 +Wall time: 67507.72568219714 +! Best model 403 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.061 0.00193 0.0224 0.039 0.0524 0.157 0.179 0.000392 0.000446 + 404 118 0.0435 0.00188 0.0059 0.0387 0.0518 0.0754 0.0917 0.000188 0.000229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 0.0299 0.00141 0.00173 0.0333 0.0448 0.0469 0.0496 0.000117 0.000124 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 404 67674.835 0.005 0.0019 0.0236 0.0617 0.0388 0.0521 0.141 0.184 0.000352 0.00046 +! Validation 404 67674.835 0.005 0.0021 0.00739 0.0494 0.0399 0.0547 0.0788 0.103 0.000197 0.000256 +Wall time: 67674.83577976702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0386 0.00175 0.00351 0.0374 0.05 0.0563 0.0707 0.000141 0.000177 + 405 118 0.0385 0.00176 0.00329 0.0376 0.0501 0.0566 0.0685 0.000142 0.000171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 0.0547 0.00143 0.0262 0.0334 0.0451 0.193 0.193 0.000482 0.000483 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 405 67841.919 0.005 0.00188 0.0153 0.0529 0.0386 0.0518 0.124 0.148 0.000309 0.00037 +! Validation 405 67841.919 0.005 0.00209 0.0306 0.0724 0.0398 0.0545 0.186 0.209 0.000465 0.000522 +Wall time: 67841.91909417138 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.0486 0.00166 0.0154 0.0365 0.0486 0.137 0.148 0.000343 0.000371 + 406 118 0.0457 0.00188 0.00803 0.0387 0.0518 0.098 0.107 0.000245 0.000267 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 0.032 0.00146 0.00281 0.0338 0.0456 0.0621 0.0632 0.000155 0.000158 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 406 68008.999 0.005 0.00186 0.0128 0.05 0.0383 0.0514 0.109 0.135 0.000273 0.000339 +! Validation 406 68008.999 0.005 0.00213 0.01 0.0525 0.0401 0.055 0.0968 0.119 0.000242 0.000298 +Wall time: 68008.99987710919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0705 0.00184 0.0336 0.0382 0.0513 0.207 0.219 0.000517 0.000547 + 407 118 0.0513 0.00233 0.0047 0.0425 0.0576 0.0713 0.0818 0.000178 0.000205 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 0.0302 0.00143 0.00158 0.0334 0.0451 0.0444 0.0474 0.000111 0.000118 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 407 68176.170 0.005 0.00188 0.0209 0.0586 0.0386 0.0517 0.142 0.173 0.000355 0.000433 +! Validation 407 68176.170 0.005 0.0021 0.0086 0.0507 0.0399 0.0547 0.0856 0.111 0.000214 0.000277 +Wall time: 68176.17067886237 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0433 0.00187 0.00589 0.0382 0.0516 0.0664 0.0916 0.000166 0.000229 + 408 118 0.0461 0.00197 0.00669 0.0385 0.053 0.0789 0.0976 0.000197 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 0.0437 0.00142 0.0153 0.0335 0.045 0.147 0.147 0.000367 0.000369 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 408 68343.264 0.005 0.00185 0.0119 0.0489 0.0382 0.0513 0.106 0.13 0.000264 0.000326 +! Validation 408 68343.264 0.005 0.00206 0.022 0.0632 0.0396 0.0542 0.149 0.177 0.000373 0.000442 +Wall time: 68343.26489450503 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.043 0.00184 0.0062 0.038 0.0512 0.0823 0.094 0.000206 0.000235 + 409 118 0.0384 0.00167 0.00492 0.0366 0.0488 0.0765 0.0838 0.000191 0.000209 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 0.0308 0.00142 0.00239 0.0335 0.045 0.0559 0.0583 0.00014 0.000146 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 409 68510.360 0.005 0.00185 0.0134 0.0505 0.0383 0.0514 0.109 0.139 0.000272 0.000347 +! Validation 409 68510.360 0.005 0.00207 0.0163 0.0577 0.0396 0.0543 0.126 0.153 0.000316 0.000381 +Wall time: 68510.36005530227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0452 0.0018 0.00925 0.0378 0.0506 0.0971 0.115 0.000243 0.000287 + 410 118 0.0475 0.00179 0.0117 0.0377 0.0505 0.114 0.129 0.000285 0.000323 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 0.0284 0.0014 0.000342 0.0332 0.0447 0.0204 0.0221 5.1e-05 5.52e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 410 68677.447 0.005 0.00189 0.0196 0.0574 0.0387 0.0519 0.126 0.167 0.000315 0.000418 +! Validation 410 68677.447 0.005 0.00205 0.00779 0.0488 0.0395 0.0541 0.0827 0.105 0.000207 0.000263 +Wall time: 68677.44754249137 +! Best model 410 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0648 0.0018 0.0287 0.0378 0.0507 0.177 0.202 0.000444 0.000506 + 411 118 0.051 0.00194 0.0121 0.0388 0.0526 0.117 0.131 0.000293 0.000329 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 0.0614 0.0014 0.0333 0.0333 0.0447 0.216 0.218 0.000541 0.000544 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 411 68844.630 0.005 0.00186 0.0199 0.0572 0.0384 0.0515 0.135 0.168 0.000336 0.000421 +! Validation 411 68844.630 0.005 0.00208 0.0339 0.0755 0.0398 0.0544 0.198 0.22 0.000495 0.00055 +Wall time: 68844.6300143674 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0626 0.002 0.0225 0.0396 0.0534 0.168 0.179 0.000419 0.000448 + 412 118 0.0412 0.00184 0.00435 0.0384 0.0512 0.0578 0.0787 0.000144 0.000197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 0.0289 0.0014 0.000832 0.0333 0.0447 0.0271 0.0344 6.79e-05 8.61e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 412 69011.710 0.005 0.00186 0.0139 0.0511 0.0384 0.0515 0.114 0.141 0.000286 0.000353 +! Validation 412 69011.710 0.005 0.00207 0.00731 0.0486 0.0396 0.0542 0.0788 0.102 0.000197 0.000255 +Wall time: 69011.71014996013 +! Best model 412 0.049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0807 0.00174 0.0459 0.0373 0.0498 0.245 0.256 0.000611 0.000639 + 413 118 0.0657 0.0018 0.0296 0.0377 0.0507 0.177 0.205 0.000443 0.000514 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 0.0773 0.00167 0.0439 0.0367 0.0487 0.249 0.25 0.000622 0.000625 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 413 69178.808 0.005 0.00183 0.0171 0.0538 0.0381 0.0511 0.124 0.156 0.00031 0.000389 +! Validation 413 69178.808 0.005 0.00236 0.0603 0.107 0.0428 0.058 0.275 0.293 0.000688 0.000733 +Wall time: 69178.80890340032 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.047 0.00175 0.012 0.0375 0.0499 0.116 0.131 0.00029 0.000327 + 414 118 0.0551 0.00157 0.0238 0.036 0.0472 0.168 0.184 0.000421 0.00046 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 0.0413 0.00138 0.0137 0.033 0.0443 0.139 0.14 0.000347 0.00035 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 414 69345.893 0.005 0.00187 0.0178 0.0553 0.0386 0.0517 0.129 0.159 0.000322 0.000398 +! Validation 414 69345.893 0.005 0.00202 0.0129 0.0533 0.0392 0.0536 0.113 0.136 0.000283 0.000339 +Wall time: 69345.89319208404 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0446 0.00192 0.00617 0.039 0.0523 0.0772 0.0937 0.000193 0.000234 + 415 118 0.0548 0.00212 0.0125 0.0405 0.0549 0.115 0.133 0.000287 0.000333 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 0.0556 0.00136 0.0284 0.0328 0.044 0.2 0.201 0.0005 0.000503 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 415 69512.989 0.005 0.0018 0.0121 0.0481 0.0377 0.0506 0.105 0.131 0.000262 0.000329 +! Validation 415 69512.989 0.005 0.002 0.0391 0.0791 0.039 0.0534 0.208 0.236 0.00052 0.00059 +Wall time: 69512.98951757932 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0393 0.00176 0.00408 0.0374 0.0501 0.0624 0.0763 0.000156 0.000191 + 416 118 0.0601 0.00196 0.0208 0.0394 0.0529 0.156 0.172 0.000391 0.000431 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 0.0365 0.00139 0.00877 0.0333 0.0445 0.111 0.112 0.000276 0.000279 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 416 69680.172 0.005 0.0018 0.0121 0.048 0.0377 0.0506 0.104 0.131 0.00026 0.000327 +! Validation 416 69680.172 0.005 0.00207 0.0181 0.0595 0.0398 0.0543 0.14 0.161 0.00035 0.000402 +Wall time: 69680.17257623328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0718 0.00194 0.0329 0.0395 0.0526 0.201 0.217 0.000503 0.000541 + 417 118 0.0494 0.00193 0.0108 0.0393 0.0524 0.11 0.124 0.000275 0.000311 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 0.0344 0.0016 0.00245 0.0351 0.0477 0.0543 0.0591 0.000136 0.000148 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 417 69847.255 0.005 0.00183 0.0218 0.0585 0.0382 0.0511 0.131 0.176 0.000328 0.000441 +! Validation 417 69847.255 0.005 0.00221 0.0107 0.0549 0.0412 0.0561 0.0979 0.123 0.000245 0.000308 +Wall time: 69847.2556810393 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.0371 0.00164 0.00434 0.0364 0.0483 0.0639 0.0786 0.00016 0.000197 + 418 118 0.0412 0.00172 0.00678 0.0372 0.0495 0.0812 0.0982 0.000203 0.000246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 0.031 0.0014 0.00304 0.0328 0.0446 0.0635 0.0658 0.000159 0.000164 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 418 70014.350 0.005 0.00183 0.0107 0.0473 0.0381 0.0511 0.0983 0.124 0.000246 0.000309 +! Validation 418 70014.350 0.005 0.00202 0.0117 0.0521 0.0392 0.0537 0.101 0.129 0.000252 0.000322 +Wall time: 70014.35071223509 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0387 0.00173 0.00423 0.037 0.0496 0.0606 0.0777 0.000152 0.000194 + 419 118 0.0371 0.00175 0.00199 0.037 0.05 0.0379 0.0532 9.47e-05 0.000133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 0.0294 0.00135 0.00235 0.0325 0.0439 0.0545 0.0579 0.000136 0.000145 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 419 70181.439 0.005 0.00242 0.038 0.0865 0.0435 0.0588 0.162 0.234 0.000404 0.000584 +! Validation 419 70181.439 0.005 0.00197 0.0103 0.0497 0.0387 0.053 0.0984 0.121 0.000246 0.000302 +Wall time: 70181.43924827408 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0492 0.00172 0.0149 0.0369 0.0494 0.128 0.146 0.00032 0.000365 + 420 118 0.0466 0.00199 0.00687 0.0393 0.0532 0.0876 0.0989 0.000219 0.000247 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 0.0274 0.00136 0.000193 0.0326 0.0441 0.016 0.0166 3.99e-05 4.15e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 420 70348.538 0.005 0.00178 0.0119 0.0474 0.0375 0.0503 0.105 0.13 0.000263 0.000325 +! Validation 420 70348.538 0.005 0.00197 0.00793 0.0473 0.0387 0.053 0.0837 0.106 0.000209 0.000266 +Wall time: 70348.53874227032 +! Best model 420 0.047 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0412 0.00177 0.0059 0.0375 0.0502 0.0767 0.0916 0.000192 0.000229 + 421 118 0.0376 0.00158 0.00603 0.0358 0.0474 0.0863 0.0927 0.000216 0.000232 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 0.0373 0.00135 0.0104 0.0324 0.0438 0.121 0.122 0.000303 0.000305 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 421 70515.718 0.005 0.00177 0.0109 0.0464 0.0375 0.0503 0.098 0.125 0.000245 0.000312 +! Validation 421 70515.718 0.005 0.00194 0.0172 0.0561 0.0384 0.0526 0.129 0.157 0.000322 0.000392 +Wall time: 70515.71858712332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0379 0.00177 0.00246 0.0374 0.0502 0.0474 0.0591 0.000119 0.000148 + 422 118 0.0434 0.002 0.00343 0.0394 0.0534 0.0573 0.0699 0.000143 0.000175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 0.0274 0.00134 0.000608 0.0323 0.0436 0.0256 0.0294 6.4e-05 7.35e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 422 70682.809 0.005 0.00177 0.0147 0.0501 0.0374 0.0502 0.114 0.145 0.000284 0.000363 +! Validation 422 70682.809 0.005 0.00195 0.00724 0.0463 0.0384 0.0527 0.0799 0.102 0.0002 0.000254 +Wall time: 70682.80907406425 +! Best model 422 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0536 0.00168 0.02 0.0365 0.0489 0.159 0.169 0.000396 0.000422 + 423 118 0.0414 0.00194 0.00261 0.0394 0.0525 0.0516 0.061 0.000129 0.000152 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 0.0288 0.00141 0.0007 0.0332 0.0448 0.0261 0.0316 6.52e-05 7.89e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 423 70849.918 0.005 0.00175 0.0124 0.0474 0.0372 0.0499 0.107 0.133 0.000268 0.000333 +! Validation 423 70849.918 0.005 0.00203 0.00697 0.0476 0.0394 0.0538 0.077 0.0996 0.000192 0.000249 +Wall time: 70849.91871393938 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.062 0.0019 0.0241 0.0386 0.052 0.169 0.185 0.000421 0.000463 + 424 118 0.0397 0.00184 0.00295 0.0383 0.0511 0.05 0.0648 0.000125 0.000162 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 0.0297 0.00133 0.00321 0.0322 0.0435 0.066 0.0676 0.000165 0.000169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 424 71017.008 0.005 0.00179 0.0166 0.0523 0.0377 0.0504 0.124 0.154 0.000309 0.000386 +! Validation 424 71017.008 0.005 0.00193 0.00846 0.0471 0.0383 0.0525 0.0871 0.11 0.000218 0.000274 +Wall time: 71017.00884547736 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0457 0.0018 0.00967 0.0376 0.0507 0.101 0.117 0.000254 0.000293 + 425 118 0.0411 0.0017 0.007 0.0369 0.0493 0.087 0.0998 0.000218 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 0.0306 0.00153 9.15e-05 0.0345 0.0466 0.00969 0.0114 2.42e-05 2.85e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 425 71184.110 0.005 0.00174 0.012 0.0469 0.0372 0.0498 0.101 0.131 0.000253 0.000328 +! Validation 425 71184.110 0.005 0.00206 0.00723 0.0484 0.0399 0.0542 0.0792 0.101 0.000198 0.000254 +Wall time: 71184.11009953031 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.0565 0.00189 0.0186 0.0389 0.0519 0.15 0.163 0.000375 0.000407 + 426 118 0.0567 0.00193 0.0182 0.0391 0.0524 0.14 0.161 0.000349 0.000403 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 0.0316 0.00147 0.00222 0.0339 0.0458 0.0542 0.0563 0.000136 0.000141 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 426 71351.289 0.005 0.00182 0.0241 0.0605 0.0381 0.0509 0.154 0.186 0.000386 0.000464 +! Validation 426 71351.289 0.005 0.00207 0.00943 0.0508 0.0399 0.0543 0.0932 0.116 0.000233 0.00029 +Wall time: 71351.28979588812 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0387 0.00179 0.00282 0.0376 0.0505 0.0512 0.0634 0.000128 0.000159 + 427 118 0.0336 0.00143 0.00505 0.0341 0.0451 0.0689 0.0849 0.000172 0.000212 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 0.0323 0.00132 0.006 0.032 0.0433 0.0902 0.0924 0.000226 0.000231 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 427 71518.377 0.005 0.00179 0.0177 0.0535 0.0377 0.0505 0.121 0.159 0.000301 0.000398 +! Validation 427 71518.377 0.005 0.00193 0.0136 0.0523 0.0383 0.0525 0.112 0.139 0.00028 0.000349 +Wall time: 71518.377276049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0498 0.00168 0.0162 0.0363 0.0489 0.13 0.152 0.000324 0.00038 + 428 118 0.0397 0.00177 0.00433 0.0377 0.0502 0.0593 0.0785 0.000148 0.000196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 0.0274 0.00136 0.000166 0.0326 0.0441 0.0133 0.0154 3.33e-05 3.84e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 428 71685.463 0.005 0.0018 0.016 0.0519 0.0378 0.0506 0.122 0.151 0.000304 0.000379 +! Validation 428 71685.463 0.005 0.00194 0.0082 0.047 0.0384 0.0526 0.0845 0.108 0.000211 0.00027 +Wall time: 71685.46361045213 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0443 0.00171 0.0101 0.0368 0.0494 0.1 0.12 0.00025 0.0003 + 429 118 0.0441 0.00175 0.009 0.0372 0.05 0.0881 0.113 0.00022 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 0.0286 0.00141 0.000442 0.0329 0.0448 0.0224 0.0251 5.6e-05 6.28e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 429 71852.708 0.005 0.0018 0.0147 0.0507 0.0378 0.0506 0.116 0.145 0.000291 0.000363 +! Validation 429 71852.708 0.005 0.00201 0.00668 0.0468 0.0392 0.0534 0.0761 0.0976 0.00019 0.000244 +Wall time: 71852.70810610242 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0525 0.00179 0.0167 0.0376 0.0504 0.143 0.154 0.000357 0.000386 + 430 118 0.117 0.00215 0.0744 0.0414 0.0553 0.309 0.326 0.000773 0.000814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 0.0525 0.00191 0.0143 0.0389 0.0521 0.142 0.143 0.000356 0.000357 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 430 72019.883 0.005 0.00176 0.0149 0.0501 0.0374 0.0501 0.115 0.143 0.000287 0.000359 +! Validation 430 72019.883 0.005 0.00248 0.0473 0.0969 0.0442 0.0594 0.225 0.26 0.000564 0.000649 +Wall time: 72019.88361944817 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.0413 0.00167 0.00802 0.0364 0.0487 0.0934 0.107 0.000233 0.000267 + 431 118 0.059 0.00167 0.0257 0.036 0.0488 0.171 0.191 0.000428 0.000478 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 0.044 0.00135 0.017 0.0328 0.0439 0.154 0.155 0.000385 0.000389 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 431 72191.339 0.005 0.00198 0.0201 0.0597 0.0397 0.0531 0.13 0.169 0.000326 0.000422 +! Validation 431 72191.339 0.005 0.00198 0.032 0.0715 0.0389 0.0531 0.19 0.213 0.000476 0.000534 +Wall time: 72191.3398076212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.0386 0.00176 0.00329 0.0374 0.0501 0.0555 0.0685 0.000139 0.000171 + 432 118 0.0351 0.00154 0.00428 0.0354 0.0468 0.0578 0.078 0.000145 0.000195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.028 0.00131 0.00185 0.0318 0.0432 0.0493 0.0513 0.000123 0.000128 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 432 72358.408 0.005 0.00174 0.0156 0.0505 0.0372 0.0498 0.12 0.15 0.0003 0.000374 +! Validation 432 72358.408 0.005 0.00192 0.00732 0.0457 0.0382 0.0523 0.0792 0.102 0.000198 0.000255 +Wall time: 72358.4082747642 +! Best model 432 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0529 0.00179 0.017 0.0377 0.0506 0.134 0.156 0.000335 0.000389 + 433 118 0.0414 0.00148 0.0118 0.0347 0.0459 0.111 0.129 0.000277 0.000324 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 0.0413 0.00132 0.015 0.0319 0.0433 0.145 0.146 0.000364 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 433 72525.490 0.005 0.00178 0.0149 0.0506 0.0377 0.0504 0.113 0.146 0.000282 0.000365 +! Validation 433 72525.490 0.005 0.00192 0.0243 0.0627 0.0383 0.0523 0.162 0.186 0.000404 0.000465 +Wall time: 72525.49019691022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 0.0627 0.0017 0.0287 0.0368 0.0492 0.196 0.202 0.000489 0.000505 + 434 118 0.0522 0.00157 0.0208 0.0354 0.0473 0.153 0.172 0.000383 0.00043 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 0.043 0.00134 0.0162 0.0323 0.0437 0.151 0.152 0.000377 0.00038 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 434 72692.557 0.005 0.00172 0.0177 0.0522 0.037 0.0496 0.126 0.159 0.000316 0.000397 +! Validation 434 72692.557 0.005 0.00199 0.0283 0.0682 0.039 0.0533 0.174 0.201 0.000434 0.000502 +Wall time: 72692.55776370037 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 0.0421 0.00171 0.00787 0.0369 0.0494 0.0864 0.106 0.000216 0.000265 + 435 118 0.0542 0.00192 0.0158 0.0388 0.0523 0.125 0.15 0.000313 0.000375 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 0.0274 0.00126 0.00209 0.0313 0.0424 0.0517 0.0545 0.000129 0.000136 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 435 72859.717 0.005 0.00175 0.0166 0.0516 0.0373 0.0499 0.126 0.154 0.000315 0.000385 +! Validation 435 72859.717 0.005 0.00188 0.00796 0.0456 0.0378 0.0518 0.0818 0.106 0.000204 0.000266 +Wall time: 72859.71757974243 +! Best model 435 0.046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.0428 0.00177 0.00749 0.0374 0.0502 0.0857 0.103 0.000214 0.000258 + 436 118 0.0642 0.00159 0.0324 0.0358 0.0476 0.2 0.215 0.000499 0.000537 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.0462 0.00147 0.0169 0.0334 0.0457 0.153 0.155 0.000384 0.000388 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 436 73026.802 0.005 0.00169 0.0145 0.0483 0.0366 0.0491 0.106 0.143 0.000266 0.000357 +! Validation 436 73026.802 0.005 0.00204 0.0178 0.0585 0.0395 0.0539 0.14 0.159 0.000349 0.000398 +Wall time: 73026.80273851939 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.0465 0.00163 0.014 0.0361 0.0482 0.125 0.141 0.000312 0.000353 + 437 118 0.0438 0.00201 0.00365 0.0397 0.0535 0.0601 0.0721 0.00015 0.00018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.032 0.00155 0.00103 0.0349 0.047 0.0325 0.0383 8.13e-05 9.59e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 437 73193.874 0.005 0.00178 0.0193 0.0549 0.0377 0.0503 0.136 0.166 0.00034 0.000416 +! Validation 437 73193.874 0.005 0.00213 0.0105 0.053 0.0406 0.055 0.0971 0.122 0.000243 0.000306 +Wall time: 73193.87460639141 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.0428 0.00165 0.0098 0.0364 0.0485 0.103 0.118 0.000258 0.000295 + 438 118 0.0639 0.00207 0.0225 0.0404 0.0543 0.175 0.179 0.000438 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 0.0269 0.00133 0.000244 0.032 0.0436 0.0154 0.0187 3.85e-05 4.66e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 438 73360.943 0.005 0.00171 0.0109 0.0451 0.0369 0.0493 0.099 0.124 0.000247 0.000311 +! Validation 438 73360.943 0.005 0.00192 0.0164 0.0548 0.0382 0.0523 0.115 0.153 0.000288 0.000382 +Wall time: 73360.94355804939 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.0443 0.00173 0.00965 0.0373 0.0497 0.0958 0.117 0.000239 0.000293 + 439 118 0.0498 0.00177 0.0143 0.0373 0.0503 0.134 0.143 0.000336 0.000357 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 0.0327 0.00149 0.00289 0.0341 0.0461 0.0623 0.0642 0.000156 0.00016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 439 73528.010 0.005 0.0019 0.0278 0.0657 0.0389 0.052 0.165 0.199 0.000413 0.000498 +! Validation 439 73528.010 0.005 0.00199 0.00941 0.0493 0.0392 0.0533 0.0933 0.116 0.000233 0.00029 +Wall time: 73528.01069072029 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.07 0.00172 0.0355 0.0372 0.0495 0.187 0.225 0.000468 0.000562 + 440 118 0.0368 0.00154 0.006 0.0353 0.0468 0.0814 0.0924 0.000204 0.000231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 0.0412 0.0016 0.00912 0.0354 0.0478 0.112 0.114 0.000279 0.000285 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 440 73695.174 0.005 0.00174 0.0167 0.0514 0.0371 0.0498 0.115 0.154 0.000288 0.000386 +! Validation 440 73695.174 0.005 0.00209 0.0201 0.0619 0.0402 0.0545 0.136 0.169 0.00034 0.000423 +Wall time: 73695.17483167443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.0439 0.00168 0.0104 0.0366 0.0489 0.107 0.122 0.000267 0.000304 + 441 118 0.0612 0.00171 0.027 0.0369 0.0494 0.178 0.196 0.000446 0.000491 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.0822 0.00188 0.0445 0.0384 0.0518 0.25 0.252 0.000625 0.000629 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 441 73862.268 0.005 0.00169 0.0113 0.045 0.0366 0.049 0.101 0.126 0.000253 0.000316 +! Validation 441 73862.268 0.005 0.00238 0.0407 0.0882 0.043 0.0582 0.218 0.241 0.000544 0.000602 +Wall time: 73862.26894260943 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.0683 0.00176 0.0331 0.0379 0.0501 0.207 0.217 0.000517 0.000543 + 442 118 0.0399 0.00185 0.00299 0.0382 0.0513 0.0493 0.0653 0.000123 0.000163 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 0.0278 0.00136 0.000599 0.0328 0.044 0.0259 0.0292 6.48e-05 7.3e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 442 74029.348 0.005 0.00171 0.0109 0.045 0.0369 0.0493 0.101 0.125 0.000252 0.000312 +! Validation 442 74029.348 0.005 0.00192 0.00757 0.046 0.0384 0.0523 0.08 0.104 0.0002 0.00026 +Wall time: 74029.3487562174 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.0769 0.00195 0.038 0.0394 0.0526 0.215 0.233 0.000537 0.000582 + 443 118 0.0366 0.00166 0.00334 0.0365 0.0487 0.0589 0.0689 0.000147 0.000172 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 0.03 0.00135 0.00305 0.0323 0.0438 0.0644 0.0659 0.000161 0.000165 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 443 74196.449 0.005 0.00174 0.0168 0.0517 0.0373 0.0498 0.124 0.155 0.000311 0.000388 +! Validation 443 74196.449 0.005 0.0019 0.0133 0.0513 0.0382 0.052 0.111 0.138 0.000277 0.000344 +Wall time: 74196.44904303737 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.044 0.00162 0.0115 0.0359 0.0481 0.117 0.128 0.000292 0.00032 + 444 118 0.0437 0.00161 0.0115 0.0361 0.0479 0.121 0.128 0.000303 0.00032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 0.0334 0.00134 0.00669 0.0321 0.0436 0.0969 0.0977 0.000242 0.000244 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 444 74363.532 0.005 0.00166 0.011 0.0442 0.0363 0.0486 0.0993 0.125 0.000248 0.000314 +! Validation 444 74363.532 0.005 0.00187 0.0135 0.0509 0.0378 0.0516 0.109 0.139 0.000273 0.000346 +Wall time: 74363.5321343313 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.0405 0.00167 0.00716 0.0363 0.0487 0.0851 0.101 0.000213 0.000253 + 445 118 0.0395 0.00164 0.00668 0.0362 0.0484 0.0857 0.0976 0.000214 0.000244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 0.0248 0.00122 0.000327 0.031 0.0417 0.0204 0.0216 5.1e-05 5.4e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 445 74530.680 0.005 0.0017 0.0186 0.0526 0.0368 0.0492 0.126 0.163 0.000316 0.000407 +! Validation 445 74530.680 0.005 0.0018 0.00671 0.0427 0.037 0.0507 0.0767 0.0978 0.000192 0.000244 +Wall time: 74530.68032540614 +! Best model 445 0.043 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.0409 0.00179 0.00523 0.0377 0.0504 0.0677 0.0864 0.000169 0.000216 + 446 118 0.283 0.002 0.243 0.0411 0.0534 0.584 0.588 0.00146 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 0.537 0.00512 0.434 0.0637 0.0854 0.783 0.787 0.00196 0.00197 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 446 74697.765 0.005 0.00176 0.0217 0.0569 0.0373 0.0501 0.129 0.169 0.000323 0.000424 +! Validation 446 74697.765 0.005 0.00589 0.278 0.396 0.068 0.0916 0.579 0.63 0.00145 0.00157 +Wall time: 74697.76545484224 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.513 0.0222 0.0703 0.128 0.178 0.251 0.316 0.000627 0.000791 + 447 118 0.426 0.0178 0.07 0.115 0.159 0.266 0.316 0.000664 0.00079 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 0.349 0.0162 0.0256 0.11 0.152 0.178 0.191 0.000446 0.000477 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 447 74864.822 0.005 0.102 1.91 3.95 0.239 0.383 0.995 1.65 0.00249 0.00413 +! Validation 447 74864.822 0.005 0.0187 0.205 0.58 0.117 0.163 0.435 0.541 0.00109 0.00135 +Wall time: 74864.82235335419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.321 0.0111 0.0985 0.0902 0.126 0.31 0.375 0.000774 0.000937 + 448 118 0.296 0.0112 0.0719 0.0909 0.126 0.258 0.32 0.000644 0.0008 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 0.218 0.00904 0.0373 0.0819 0.113 0.216 0.23 0.00054 0.000576 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 448 75031.876 0.005 0.0138 0.128 0.403 0.1 0.14 0.349 0.427 0.000872 0.00107 +! Validation 448 75031.876 0.005 0.0106 0.0533 0.266 0.0886 0.123 0.224 0.276 0.00056 0.000689 +Wall time: 75031.87635863712 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.186 0.00743 0.0375 0.0759 0.103 0.188 0.231 0.000469 0.000578 + 449 118 0.157 0.00644 0.0278 0.0714 0.0958 0.172 0.199 0.000431 0.000498 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 0.12 0.00573 0.00511 0.0668 0.0904 0.0805 0.0853 0.000201 0.000213 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 449 75199.027 0.005 0.00833 0.0848 0.251 0.0795 0.109 0.278 0.348 0.000695 0.000871 +! Validation 449 75199.027 0.005 0.00679 0.026 0.162 0.0725 0.0984 0.152 0.192 0.000379 0.000481 +Wall time: 75199.02743237512 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.121 0.005 0.0212 0.0634 0.0844 0.139 0.174 0.000347 0.000434 + 450 118 0.17 0.00433 0.0838 0.0598 0.0786 0.332 0.346 0.00083 0.000864 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 0.245 0.00409 0.163 0.0575 0.0763 0.476 0.482 0.00119 0.00121 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 450 75366.096 0.005 0.00553 0.0747 0.185 0.0665 0.0889 0.265 0.326 0.000663 0.000815 +! Validation 450 75366.096 0.005 0.00496 0.185 0.285 0.063 0.0841 0.487 0.514 0.00122 0.00128 +Wall time: 75366.0965986452 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0936 0.00397 0.0143 0.0569 0.0752 0.111 0.143 0.000278 0.000356 + 451 118 0.0951 0.00358 0.0234 0.0546 0.0714 0.163 0.183 0.000407 0.000457 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.0828 0.00321 0.0186 0.0513 0.0676 0.15 0.163 0.000376 0.000407 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 451 75533.158 0.005 0.00418 0.0432 0.127 0.0585 0.0772 0.201 0.248 0.000502 0.000621 +! Validation 451 75533.158 0.005 0.00409 0.0233 0.105 0.0572 0.0764 0.149 0.182 0.000373 0.000456 +Wall time: 75533.15805786522 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.0825 0.00331 0.0162 0.052 0.0687 0.122 0.152 0.000304 0.00038 + 452 118 0.0738 0.00345 0.0048 0.0533 0.0701 0.0672 0.0827 0.000168 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.0792 0.00272 0.0247 0.0473 0.0623 0.182 0.188 0.000454 0.000469 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 452 75700.218 0.005 0.00356 0.026 0.0971 0.054 0.0712 0.156 0.193 0.00039 0.000482 +! Validation 452 75700.218 0.005 0.00362 0.041 0.113 0.0537 0.0718 0.209 0.242 0.000524 0.000604 +Wall time: 75700.21805254323 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.0983 0.0029 0.0403 0.0487 0.0643 0.212 0.239 0.000529 0.000599 + 453 118 0.0807 0.0032 0.0168 0.0511 0.0675 0.119 0.155 0.000298 0.000387 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 0.112 0.00246 0.0624 0.045 0.0592 0.295 0.298 0.000738 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 453 75867.290 0.005 0.00319 0.024 0.0878 0.051 0.0674 0.147 0.185 0.000368 0.000462 +! Validation 453 75867.290 0.005 0.00335 0.077 0.144 0.0515 0.069 0.311 0.331 0.000776 0.000828 +Wall time: 75867.29089192906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.0792 0.00297 0.0198 0.049 0.0651 0.136 0.168 0.00034 0.000419 + 454 118 0.0631 0.00284 0.00629 0.0484 0.0636 0.0788 0.0947 0.000197 0.000237 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.0477 0.00222 0.00327 0.0427 0.0562 0.0564 0.0683 0.000141 0.000171 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 454 76034.443 0.005 0.00296 0.0284 0.0877 0.0491 0.065 0.161 0.202 0.000403 0.000504 +! Validation 454 76034.443 0.005 0.0031 0.0135 0.0755 0.0494 0.0665 0.11 0.139 0.000275 0.000347 +Wall time: 76034.44327511312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.0783 0.00284 0.0216 0.0479 0.0636 0.149 0.175 0.000373 0.000438 + 455 118 0.0928 0.00278 0.0372 0.0478 0.063 0.214 0.23 0.000536 0.000575 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 0.165 0.00209 0.123 0.0414 0.0546 0.417 0.418 0.00104 0.00105 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 455 76201.524 0.005 0.00277 0.0207 0.0761 0.0474 0.0628 0.137 0.171 0.000344 0.000428 +! Validation 455 76201.524 0.005 0.00294 0.144 0.203 0.0481 0.0648 0.439 0.453 0.0011 0.00113 +Wall time: 76201.52430943307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.0638 0.00269 0.00994 0.0466 0.0619 0.0967 0.119 0.000242 0.000297 + 456 118 0.0884 0.00284 0.0317 0.0476 0.0636 0.202 0.212 0.000504 0.000531 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 0.0843 0.00198 0.0446 0.0402 0.0532 0.25 0.252 0.000624 0.00063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 456 76368.585 0.005 0.00266 0.0309 0.084 0.0464 0.0615 0.162 0.21 0.000406 0.000524 +! Validation 456 76368.585 0.005 0.00283 0.0469 0.103 0.047 0.0635 0.236 0.258 0.00059 0.000646 +Wall time: 76368.58503506798 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.0963 0.00256 0.0451 0.0454 0.0604 0.236 0.254 0.00059 0.000634 + 457 118 0.0528 0.0024 0.00481 0.0444 0.0585 0.0615 0.0828 0.000154 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.0388 0.00188 0.00106 0.0393 0.0518 0.0293 0.0388 7.31e-05 9.71e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 457 76535.646 0.005 0.00253 0.0205 0.0712 0.0452 0.0601 0.139 0.171 0.000349 0.000429 +! Validation 457 76535.646 0.005 0.00272 0.00894 0.0633 0.046 0.0622 0.0891 0.113 0.000223 0.000282 +Wall time: 76535.6459748433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.067 0.00253 0.0165 0.0449 0.06 0.131 0.153 0.000327 0.000383 + 458 118 0.0511 0.00206 0.00979 0.0411 0.0542 0.1 0.118 0.000251 0.000295 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.0395 0.00181 0.00333 0.0385 0.0508 0.0625 0.0689 0.000156 0.000172 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 458 76702.703 0.005 0.00243 0.0141 0.0626 0.0443 0.0588 0.113 0.142 0.000283 0.000355 +! Validation 458 76702.703 0.005 0.00262 0.0142 0.0666 0.0452 0.0611 0.117 0.142 0.000291 0.000355 +Wall time: 76702.70326701412 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.0674 0.00223 0.0229 0.0425 0.0563 0.151 0.18 0.000378 0.000451 + 459 118 0.0774 0.00242 0.0291 0.044 0.0587 0.176 0.203 0.00044 0.000509 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.0497 0.00178 0.0142 0.0382 0.0503 0.141 0.142 0.000351 0.000355 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 459 76869.834 0.005 0.00234 0.0138 0.0607 0.0434 0.0578 0.113 0.14 0.000282 0.000349 +! Validation 459 76869.834 0.005 0.00257 0.0275 0.079 0.0448 0.0606 0.176 0.198 0.000439 0.000495 +Wall time: 76869.83413400734 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.055 0.00242 0.00662 0.0442 0.0587 0.0776 0.0971 0.000194 0.000243 + 460 118 0.0965 0.00215 0.0536 0.0421 0.0553 0.268 0.276 0.000671 0.000691 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.203 0.00173 0.168 0.0374 0.0496 0.488 0.489 0.00122 0.00122 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 460 77036.907 0.005 0.00228 0.016 0.0617 0.0429 0.057 0.121 0.15 0.000302 0.000374 +! Validation 460 77036.907 0.005 0.00249 0.154 0.203 0.044 0.0596 0.456 0.468 0.00114 0.00117 +Wall time: 77036.90741181513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.0528 0.00225 0.00791 0.0427 0.0566 0.0849 0.106 0.000212 0.000265 + 461 118 0.0573 0.00245 0.00841 0.0428 0.059 0.0858 0.109 0.000215 0.000274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 0.0401 0.00166 0.00684 0.0368 0.0487 0.0962 0.0987 0.000241 0.000247 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 461 77203.966 0.005 0.00224 0.0205 0.0653 0.0424 0.0565 0.133 0.171 0.000332 0.000428 +! Validation 461 77203.966 0.005 0.00243 0.0108 0.0594 0.0434 0.0588 0.0976 0.124 0.000244 0.000309 +Wall time: 77203.96644927422 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.0595 0.00233 0.0128 0.0434 0.0577 0.118 0.135 0.000294 0.000338 + 462 118 0.101 0.00247 0.0516 0.0443 0.0594 0.26 0.271 0.000651 0.000678 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.128 0.00162 0.0957 0.0364 0.048 0.369 0.369 0.000922 0.000923 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 462 77371.019 0.005 0.00218 0.0183 0.0619 0.0418 0.0557 0.132 0.161 0.000331 0.000402 +! Validation 462 77371.019 0.005 0.00239 0.0961 0.144 0.043 0.0583 0.356 0.37 0.00089 0.000925 +Wall time: 77371.01989729516 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0489 0.0021 0.00684 0.0409 0.0547 0.0793 0.0987 0.000198 0.000247 + 463 118 0.0566 0.00238 0.00909 0.044 0.0582 0.0955 0.114 0.000239 0.000284 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.0468 0.00159 0.015 0.0359 0.0476 0.144 0.146 0.000361 0.000366 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 463 77538.084 0.005 0.00215 0.0171 0.06 0.0415 0.0553 0.122 0.156 0.000305 0.000391 +! Validation 463 77538.084 0.005 0.00233 0.0167 0.0632 0.0424 0.0576 0.129 0.154 0.000322 0.000385 +Wall time: 77538.08408458019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.0524 0.002 0.0125 0.0401 0.0533 0.116 0.133 0.000291 0.000333 + 464 118 0.0841 0.00212 0.0417 0.0408 0.055 0.23 0.244 0.000574 0.000609 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.091 0.0016 0.059 0.036 0.0478 0.289 0.29 0.000721 0.000725 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 464 77705.225 0.005 0.00209 0.0175 0.0594 0.041 0.0546 0.123 0.157 0.000307 0.000393 +! Validation 464 77705.225 0.005 0.00232 0.0558 0.102 0.0424 0.0575 0.265 0.282 0.000662 0.000705 +Wall time: 77705.22519544419 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0464 0.00206 0.00515 0.0405 0.0542 0.0672 0.0857 0.000168 0.000214 + 465 118 0.0403 0.00178 0.00466 0.0381 0.0504 0.0687 0.0815 0.000172 0.000204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 0.0311 0.00154 0.000227 0.0355 0.0469 0.0162 0.018 4.05e-05 4.5e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 465 77872.291 0.005 0.00206 0.0188 0.06 0.0407 0.0542 0.131 0.164 0.000328 0.00041 +! Validation 465 77872.291 0.005 0.00227 0.00719 0.0525 0.0419 0.0568 0.0809 0.101 0.000202 0.000253 +Wall time: 77872.29150789743 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0604 0.00229 0.0146 0.0426 0.0571 0.125 0.144 0.000314 0.000361 + 466 118 0.0488 0.00174 0.0139 0.0379 0.0498 0.136 0.141 0.000341 0.000352 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.0695 0.00152 0.0391 0.035 0.0465 0.235 0.236 0.000588 0.00059 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 466 78039.347 0.005 0.00203 0.0215 0.0622 0.0404 0.0539 0.133 0.175 0.000333 0.000438 +! Validation 466 78039.347 0.005 0.00223 0.0487 0.0933 0.0415 0.0563 0.242 0.263 0.000606 0.000659 +Wall time: 78039.34796407307 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0473 0.00208 0.0058 0.0408 0.0544 0.0765 0.0909 0.000191 0.000227 + 467 118 0.0695 0.00196 0.0304 0.0396 0.0528 0.193 0.208 0.000482 0.00052 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 0.0627 0.0015 0.0327 0.0349 0.0462 0.215 0.216 0.000538 0.000539 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 467 78206.412 0.005 0.002 0.0145 0.0544 0.04 0.0533 0.116 0.143 0.000289 0.000358 +! Validation 467 78206.412 0.005 0.00221 0.054 0.0981 0.0412 0.0561 0.261 0.277 0.000653 0.000694 +Wall time: 78206.4123771023 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0494 0.00194 0.0106 0.0392 0.0526 0.103 0.123 0.000257 0.000307 + 468 118 0.0472 0.00215 0.00427 0.0419 0.0553 0.0563 0.078 0.000141 0.000195 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 0.0317 0.00149 0.00195 0.0346 0.046 0.0488 0.0527 0.000122 0.000132 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 468 78373.551 0.005 0.00197 0.0213 0.0607 0.0397 0.053 0.142 0.175 0.000356 0.000436 +! Validation 468 78373.551 0.005 0.00218 0.00896 0.0526 0.041 0.0558 0.0922 0.113 0.00023 0.000282 +Wall time: 78373.5518672443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0463 0.00208 0.00472 0.0405 0.0544 0.0655 0.082 0.000164 0.000205 + 469 118 0.101 0.00152 0.0709 0.0356 0.0466 0.302 0.318 0.000756 0.000794 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.0657 0.0015 0.0358 0.0346 0.0462 0.225 0.226 0.000562 0.000564 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 469 78540.609 0.005 0.00195 0.0202 0.0592 0.0396 0.0527 0.136 0.168 0.000339 0.000421 +! Validation 469 78540.609 0.005 0.00219 0.044 0.0877 0.0411 0.0558 0.232 0.25 0.000581 0.000626 +Wall time: 78540.60991637502 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.051 0.00196 0.0118 0.0395 0.0529 0.111 0.13 0.000278 0.000324 + 470 118 0.0505 0.00165 0.0174 0.0368 0.0485 0.144 0.158 0.00036 0.000394 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 100 0.0465 0.00144 0.0176 0.0341 0.0453 0.157 0.158 0.000393 0.000396 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 470 78707.663 0.005 0.00193 0.0187 0.0574 0.0394 0.0525 0.132 0.163 0.000331 0.000408 +! Validation 470 78707.663 0.005 0.00212 0.0199 0.0624 0.0404 0.055 0.145 0.169 0.000364 0.000421 +Wall time: 78707.66336160107 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0489 0.00199 0.00902 0.0399 0.0533 0.0937 0.113 0.000234 0.000283 + 471 118 0.0467 0.00147 0.0173 0.0348 0.0458 0.143 0.157 0.000357 0.000393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.0394 0.00142 0.0109 0.0339 0.045 0.124 0.125 0.00031 0.000312 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 471 78874.723 0.005 0.00189 0.014 0.0519 0.0389 0.052 0.116 0.141 0.00029 0.000353 +! Validation 471 78874.723 0.005 0.00211 0.0217 0.0639 0.0403 0.0548 0.156 0.176 0.00039 0.00044 +Wall time: 78874.72352551902 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0591 0.002 0.0192 0.04 0.0533 0.149 0.165 0.000371 0.000413 + 472 118 0.0459 0.00193 0.00731 0.039 0.0524 0.092 0.102 0.00023 0.000255 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.0372 0.0014 0.00918 0.0336 0.0447 0.112 0.114 0.000281 0.000286 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 472 79041.784 0.005 0.00188 0.0177 0.0553 0.0388 0.0518 0.13 0.159 0.000325 0.000397 +! Validation 472 79041.784 0.005 0.00207 0.0114 0.0528 0.0399 0.0543 0.102 0.127 0.000256 0.000319 +Wall time: 79041.78493498033 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.0625 0.00182 0.0261 0.0381 0.0509 0.178 0.193 0.000444 0.000482 + 473 118 0.044 0.00184 0.00713 0.0385 0.0512 0.0868 0.101 0.000217 0.000252 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.06 0.00137 0.0325 0.0332 0.0442 0.214 0.215 0.000536 0.000538 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 473 79208.926 0.005 0.00187 0.0167 0.054 0.0386 0.0516 0.125 0.154 0.000313 0.000386 +! Validation 473 79208.926 0.005 0.00205 0.0309 0.0719 0.0396 0.054 0.19 0.21 0.000474 0.000524 +Wall time: 79208.92644595727 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0466 0.00183 0.0099 0.0383 0.0511 0.102 0.119 0.000254 0.000297 + 474 118 0.049 0.00177 0.0137 0.0377 0.0502 0.119 0.14 0.000298 0.00035 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 0.0281 0.00137 0.00072 0.0331 0.0441 0.0279 0.032 6.99e-05 8.01e-05 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 474 79375.999 0.005 0.00183 0.0125 0.0492 0.0383 0.0511 0.106 0.133 0.000265 0.000333 +! Validation 474 79375.999 0.005 0.00202 0.00791 0.0483 0.0393 0.0537 0.0855 0.106 0.000214 0.000265 +Wall time: 79375.99954807106 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0664 0.00195 0.0274 0.0396 0.0527 0.19 0.198 0.000475 0.000494 + 475 118 0.047 0.0019 0.00898 0.0392 0.052 0.0974 0.113 0.000244 0.000283 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.0475 0.00142 0.019 0.0336 0.045 0.164 0.165 0.000411 0.000412 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 475 79543.062 0.005 0.00184 0.0264 0.0633 0.0383 0.0512 0.159 0.195 0.000398 0.000486 +! Validation 475 79543.062 0.005 0.00207 0.0236 0.065 0.0399 0.0543 0.164 0.183 0.000411 0.000459 +Wall time: 79543.06276267627 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0602 0.00183 0.0236 0.0382 0.0511 0.168 0.183 0.000421 0.000459 + 476 118 0.0687 0.00172 0.0344 0.0373 0.0494 0.2 0.221 0.000499 0.000553 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 0.0656 0.00135 0.0386 0.033 0.0438 0.234 0.235 0.000586 0.000587 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 476 79710.124 0.005 0.00182 0.0154 0.0518 0.0381 0.0509 0.12 0.148 0.0003 0.000369 +! Validation 476 79710.124 0.005 0.00202 0.048 0.0884 0.0393 0.0536 0.246 0.261 0.000614 0.000653 +Wall time: 79710.12492981227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0409 0.00178 0.00536 0.0377 0.0503 0.0701 0.0873 0.000175 0.000218 + 477 118 0.0406 0.00184 0.00386 0.0384 0.0511 0.0664 0.0741 0.000166 0.000185 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 0.0323 0.00134 0.00548 0.0327 0.0437 0.0866 0.0883 0.000216 0.000221 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 477 79877.202 0.005 0.0018 0.0152 0.0512 0.0379 0.0506 0.121 0.148 0.000302 0.000369 +! Validation 477 79877.202 0.005 0.00199 0.00752 0.0473 0.039 0.0532 0.0821 0.104 0.000205 0.000259 +Wall time: 79877.20205353433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0445 0.00189 0.00657 0.0387 0.052 0.0759 0.0967 0.00019 0.000242 + 478 118 0.046 0.00151 0.0158 0.0347 0.0463 0.132 0.15 0.000331 0.000376 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.0415 0.00133 0.0149 0.0326 0.0435 0.145 0.146 0.000362 0.000365 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 478 80044.333 0.005 0.0018 0.0175 0.0535 0.0379 0.0506 0.13 0.158 0.000324 0.000395 +! Validation 478 80044.333 0.005 0.00197 0.0159 0.0554 0.0389 0.053 0.126 0.151 0.000316 0.000376 +Wall time: 80044.33380503021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0388 0.00174 0.00393 0.0372 0.0499 0.057 0.0749 0.000143 0.000187 + 479 118 0.0379 0.0015 0.00789 0.0354 0.0463 0.0838 0.106 0.000209 0.000265 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 0.0304 0.00135 0.00337 0.0329 0.0439 0.0689 0.0693 0.000172 0.000173 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 479 80211.394 0.005 0.00176 0.0131 0.0484 0.0375 0.0501 0.111 0.137 0.000278 0.000342 +! Validation 479 80211.394 0.005 0.00199 0.0206 0.0604 0.0391 0.0532 0.146 0.171 0.000366 0.000428 +Wall time: 80211.39396943711 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0408 0.00179 0.0051 0.0377 0.0504 0.0736 0.0852 0.000184 0.000213 + 480 118 0.0422 0.00166 0.00889 0.0361 0.0487 0.101 0.113 0.000253 0.000281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.0376 0.0013 0.0116 0.0322 0.0431 0.128 0.129 0.000319 0.000321 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 480 80378.448 0.005 0.00177 0.0206 0.056 0.0376 0.0502 0.134 0.172 0.000334 0.000429 +! Validation 480 80378.448 0.005 0.00193 0.0214 0.0601 0.0384 0.0525 0.155 0.175 0.000386 0.000437 +Wall time: 80378.44857572718 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.101 0.00182 0.0646 0.038 0.0509 0.296 0.303 0.00074 0.000758 + 481 118 0.0491 0.0021 0.00702 0.041 0.0547 0.0903 0.1 0.000226 0.00025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.0278 0.00132 0.0014 0.0323 0.0434 0.0404 0.0446 0.000101 0.000111 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 481 80545.508 0.005 0.00175 0.0172 0.0521 0.0373 0.0498 0.129 0.157 0.000323 0.000392 +! Validation 481 80545.508 0.005 0.00195 0.00842 0.0474 0.0386 0.0527 0.0839 0.109 0.00021 0.000274 +Wall time: 80545.50832085218 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 0.0635 0.00166 0.0302 0.0366 0.0487 0.193 0.207 0.000483 0.000519 + 482 118 0.0717 0.00183 0.0351 0.0383 0.0511 0.208 0.224 0.00052 0.000559 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 0.0393 0.00134 0.0126 0.0325 0.0436 0.133 0.134 0.000332 0.000335 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 482 80712.579 0.005 0.00173 0.0122 0.0467 0.0371 0.0496 0.106 0.131 0.000264 0.000327 +! Validation 482 80712.579 0.005 0.00194 0.0158 0.0547 0.0386 0.0526 0.131 0.15 0.000327 0.000375 +Wall time: 80712.57933237916 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.0629 0.00166 0.0298 0.0365 0.0486 0.19 0.206 0.000476 0.000515 + 483 118 0.0588 0.00182 0.0224 0.0381 0.0509 0.164 0.179 0.000409 0.000447 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.0576 0.00131 0.0315 0.0323 0.0431 0.211 0.212 0.000528 0.000529 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 483 80879.722 0.005 0.00174 0.0208 0.0555 0.0372 0.0497 0.143 0.172 0.000358 0.00043 +! Validation 483 80879.722 0.005 0.00193 0.0494 0.088 0.0385 0.0524 0.246 0.265 0.000616 0.000663 +Wall time: 80879.7227387433 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.0395 0.00168 0.00588 0.0366 0.0489 0.0776 0.0915 0.000194 0.000229 + 484 118 0.0368 0.00173 0.00224 0.0371 0.0496 0.051 0.0565 0.000127 0.000141 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.0268 0.00127 0.0013 0.0318 0.0426 0.0418 0.043 0.000104 0.000108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 484 81046.779 0.005 0.00172 0.0152 0.0497 0.0371 0.0496 0.117 0.147 0.000292 0.000368 +! Validation 484 81046.779 0.005 0.00189 0.00624 0.044 0.038 0.0519 0.0721 0.0943 0.00018 0.000236 +Wall time: 81046.77983568702 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.0366 0.00171 0.00244 0.0368 0.0494 0.0488 0.0589 0.000122 0.000147 + 485 118 0.0386 0.00169 0.00482 0.0369 0.049 0.0609 0.0829 0.000152 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.0315 0.00128 0.00594 0.0319 0.0427 0.0897 0.092 0.000224 0.00023 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 485 81213.832 0.005 0.00172 0.013 0.0474 0.037 0.0495 0.109 0.137 0.000272 0.000341 +! Validation 485 81213.832 0.005 0.00188 0.00913 0.0468 0.0379 0.0518 0.0888 0.114 0.000222 0.000285 +Wall time: 81213.83205513936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.0508 0.00172 0.0163 0.0371 0.0496 0.135 0.152 0.000338 0.000381 + 486 118 0.0434 0.00171 0.00928 0.0368 0.0493 0.101 0.115 0.000251 0.000287 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.0435 0.00125 0.0185 0.0316 0.0423 0.161 0.162 0.000404 0.000405 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 486 81380.884 0.005 0.00168 0.0102 0.0437 0.0365 0.0489 0.0964 0.12 0.000241 0.000301 +! Validation 486 81380.884 0.005 0.00185 0.0195 0.0566 0.0376 0.0514 0.142 0.167 0.000355 0.000417 +Wall time: 81380.88423900306 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.0368 0.00165 0.00384 0.0364 0.0484 0.0565 0.0739 0.000141 0.000185 + 487 118 0.0478 0.00202 0.00742 0.0401 0.0536 0.07 0.103 0.000175 0.000257 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.0427 0.0013 0.0166 0.0322 0.0431 0.153 0.154 0.000383 0.000385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 487 81548.024 0.005 0.00174 0.0283 0.063 0.0372 0.0497 0.155 0.201 0.000388 0.000503 +! Validation 487 81548.024 0.005 0.0019 0.0184 0.0564 0.0382 0.052 0.137 0.162 0.000341 0.000405 +Wall time: 81548.02489401912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.0383 0.00175 0.00333 0.0371 0.0499 0.0573 0.0689 0.000143 0.000172 + 488 118 0.0439 0.00156 0.0127 0.035 0.0472 0.117 0.134 0.000292 0.000336 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.0269 0.00126 0.00174 0.0317 0.0423 0.0475 0.0499 0.000119 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 488 81715.090 0.005 0.00169 0.0114 0.0453 0.0367 0.0491 0.1 0.128 0.00025 0.000319 +! Validation 488 81715.090 0.005 0.00186 0.00898 0.0462 0.0378 0.0515 0.0829 0.113 0.000207 0.000283 +Wall time: 81715.09081093501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.0364 0.00162 0.00404 0.036 0.048 0.0593 0.0758 0.000148 0.00019 + 489 118 0.0378 0.00152 0.00747 0.0347 0.0465 0.0983 0.103 0.000246 0.000258 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 0.0494 0.00123 0.0248 0.0313 0.0419 0.187 0.188 0.000468 0.00047 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 489 81882.147 0.005 0.00168 0.0141 0.0476 0.0366 0.0489 0.11 0.142 0.000275 0.000354 +! Validation 489 81882.147 0.005 0.00184 0.0236 0.0604 0.0375 0.0512 0.162 0.183 0.000405 0.000458 +Wall time: 81882.14757081727 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.0351 0.00153 0.00458 0.035 0.0466 0.0661 0.0808 0.000165 0.000202 + 490 118 0.054 0.00187 0.0165 0.0379 0.0517 0.134 0.154 0.000336 0.000384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.0315 0.00125 0.00653 0.0316 0.0422 0.0957 0.0965 0.000239 0.000241 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 490 82049.209 0.005 0.00169 0.0195 0.0534 0.0367 0.0491 0.129 0.167 0.000323 0.000417 +! Validation 490 82049.209 0.005 0.00184 0.0155 0.0523 0.0375 0.0512 0.122 0.148 0.000304 0.000371 +Wall time: 82049.20958673628 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.0337 0.00155 0.00256 0.0352 0.0471 0.0482 0.0604 0.000121 0.000151 + 491 118 0.0756 0.00171 0.0413 0.0364 0.0494 0.232 0.243 0.000579 0.000606 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.0412 0.00139 0.0134 0.0329 0.0445 0.137 0.138 0.000342 0.000346 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 491 82216.273 0.005 0.00166 0.0151 0.0482 0.0363 0.0486 0.118 0.146 0.000295 0.000364 +! Validation 491 82216.273 0.005 0.00196 0.0136 0.0528 0.0388 0.0528 0.114 0.139 0.000284 0.000348 +Wall time: 82216.27308622003 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0897 0.0017 0.0557 0.0367 0.0492 0.263 0.282 0.000657 0.000704 + 492 118 0.0421 0.00163 0.00944 0.0364 0.0482 0.0922 0.116 0.000231 0.00029 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.0569 0.00129 0.0312 0.032 0.0428 0.211 0.211 0.000526 0.000527 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 492 82383.420 0.005 0.00167 0.0153 0.0487 0.0365 0.0488 0.119 0.148 0.000297 0.00037 +! Validation 492 82383.420 0.005 0.00188 0.0397 0.0774 0.0381 0.0518 0.215 0.238 0.000538 0.000594 +Wall time: 82383.42081136536 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.0344 0.00154 0.00358 0.0353 0.0469 0.0584 0.0714 0.000146 0.000178 + 493 118 0.0486 0.00168 0.0151 0.037 0.0489 0.136 0.147 0.000339 0.000367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.0363 0.00124 0.0116 0.0314 0.042 0.128 0.129 0.000321 0.000321 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 493 82550.486 0.005 0.00166 0.0157 0.049 0.0364 0.0487 0.12 0.15 0.0003 0.000374 +! Validation 493 82550.486 0.005 0.00182 0.018 0.0544 0.0373 0.0509 0.133 0.16 0.000333 0.0004 +Wall time: 82550.48695991002 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0379 0.00158 0.00631 0.0358 0.0474 0.0768 0.0948 0.000192 0.000237 + 494 118 0.0498 0.00154 0.019 0.0348 0.0468 0.146 0.165 0.000364 0.000412 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.0847 0.00121 0.0604 0.031 0.0416 0.293 0.293 0.000733 0.000733 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 494 82717.550 0.005 0.00163 0.0115 0.0441 0.0361 0.0482 0.103 0.127 0.000257 0.000319 +! Validation 494 82717.550 0.005 0.0018 0.0554 0.0914 0.0371 0.0507 0.263 0.281 0.000659 0.000702 +Wall time: 82717.55040824739 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0473 0.00161 0.015 0.0358 0.0479 0.13 0.146 0.000324 0.000366 + 495 118 0.0361 0.00158 0.00453 0.0352 0.0474 0.0615 0.0803 0.000154 0.000201 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 0.0271 0.00117 0.00369 0.0305 0.0408 0.0719 0.0725 0.00018 0.000181 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 495 82884.619 0.005 0.0017 0.0206 0.0546 0.0368 0.0492 0.135 0.172 0.000337 0.00043 +! Validation 495 82884.619 0.005 0.00178 0.0104 0.0461 0.0369 0.0504 0.0925 0.122 0.000231 0.000304 +Wall time: 82884.6194082452 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.0452 0.00165 0.0122 0.0363 0.0485 0.119 0.132 0.000297 0.00033 + 496 118 0.0405 0.00147 0.0111 0.0344 0.0457 0.117 0.126 0.000292 0.000315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 0.0343 0.00119 0.0105 0.031 0.0412 0.121 0.122 0.000302 0.000305 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 496 83051.679 0.005 0.00161 0.0106 0.0428 0.0358 0.0479 0.099 0.123 0.000247 0.000307 +! Validation 496 83051.679 0.005 0.00181 0.022 0.0582 0.0373 0.0508 0.158 0.177 0.000394 0.000442 +Wall time: 83051.67987200618 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0337 0.00155 0.00279 0.0351 0.0469 0.0504 0.0631 0.000126 0.000158 + 497 118 0.0346 0.00149 0.0048 0.0347 0.046 0.0699 0.0827 0.000175 0.000207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.0314 0.00118 0.00789 0.0306 0.0409 0.106 0.106 0.000265 0.000265 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 497 83218.822 0.005 0.00161 0.013 0.0452 0.0358 0.0479 0.111 0.136 0.000278 0.000341 +! Validation 497 83218.822 0.005 0.00176 0.0127 0.048 0.0367 0.0501 0.107 0.135 0.000266 0.000336 +Wall time: 83218.82263633423 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0362 0.00162 0.00368 0.0361 0.0481 0.06 0.0725 0.00015 0.000181 + 498 118 0.0333 0.00153 0.00279 0.0352 0.0466 0.0481 0.063 0.00012 0.000158 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.0252 0.00117 0.00176 0.0305 0.0409 0.0497 0.0501 0.000124 0.000125 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 498 83385.884 0.005 0.00168 0.0215 0.0551 0.0366 0.0489 0.138 0.176 0.000344 0.000439 +! Validation 498 83385.884 0.005 0.00178 0.0104 0.0459 0.0368 0.0503 0.101 0.122 0.000253 0.000304 +Wall time: 83385.88402453018 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0394 0.00157 0.00806 0.0354 0.0473 0.0935 0.107 0.000234 0.000268 + 499 118 0.0384 0.00141 0.0102 0.0336 0.0448 0.0918 0.12 0.00023 0.000301 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.0314 0.0012 0.00743 0.0308 0.0413 0.102 0.103 0.000256 0.000257 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 499 83552.939 0.005 0.0016 0.0162 0.0483 0.0358 0.0478 0.125 0.152 0.000312 0.00038 +! Validation 499 83552.939 0.005 0.00177 0.0169 0.0523 0.0368 0.0503 0.136 0.155 0.000341 0.000388 +Wall time: 83552.93995982641 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.037 0.00162 0.0045 0.036 0.0481 0.062 0.0801 0.000155 0.0002 + 500 118 0.039 0.00175 0.00396 0.0372 0.05 0.0652 0.0751 0.000163 0.000188 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.026 0.00124 0.00132 0.0316 0.042 0.0425 0.0433 0.000106 0.000108 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 500 83719.997 0.005 0.00162 0.0137 0.0461 0.0359 0.048 0.112 0.14 0.000281 0.00035 +! Validation 500 83719.997 0.005 0.0018 0.00638 0.0424 0.0373 0.0506 0.0716 0.0953 0.000179 0.000238 +Wall time: 83719.99782425212 +! Best model 500 0.042 +! Stop training: max epochs +Wall time: 83720.02658369439 +Cumulative wall time: 83720.02658369439 +Using device: cuda +Please note that _all_ machine learning models running on CUDA hardware are generally somewhat nondeterministic and that this can manifest in small, generally unimportant variation in the final test errors. +Loading model... + loaded model +Loading dataset... +Processing dataset... +Done! +Loaded dataset specified in test_config.yaml. +Using all frames from the specified test dataset, yielding a test set size of 500 frames. +Starting... + +--- Final result: --- + f_mae = 0.038054 + f_rmse = 0.050578 + e_mae = 0.102744 + e_rmse = 0.129242 + e/N_mae = 0.000257 + e/N_rmse = 0.000323 + f_mae = 0.038054 + f_rmse = 0.050578 + e_mae = 0.102744 + e_rmse = 0.129242 + e/N_mae = 0.000257 + e/N_rmse = 0.000323 +Train end time: 2024-12-09_09:54:26 +Training duration: 23h 19m 49s diff --git a/ads_16_length_8000/NEQUIP/test_config.yaml b/ads_16_length_8000/NEQUIP/test_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..279b1cb5bcf20c526ba98c01f30065f42f7b07f5 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/test_config.yaml @@ -0,0 +1,53 @@ +default_dtype: float64 +dataset_file_name: ../testset.xyz +n_train: 0 +n_val: 500 + +root: ./results/ +run_name: adsorption +model_builders: + - SimpleIrrepsConfig + - EnergyModel + - PerSpeciesRescale + - StressForceOutput + - RescaleEnergyEtc +num_basis: 8 +r_max: 5.0 +l_max: 1 +num_layers: 4 +parity: false +num_features: 64 + +dataset: ase +dataset_key_mapping: + force: forces +ase_args: + format: extxyz +chemical_symbols: + - H + - C + - N + - Zn + - O + +metrics_components: + - - forces + - mae + - PerSpecies: False + report_per_component: False + - - total_energy + - mae + - PerAtom: False + - - forces + - rmse + - PerSpecies: False + report_per_component: False + - - total_energy + - rmse + - PerAtom: False + - - total_energy + - mae + - PerAtom: True + - - total_energy + - rmse + - PerAtom: True \ No newline at end of file diff --git a/ads_16_length_8000/NEQUIP/train_config.yaml b/ads_16_length_8000/NEQUIP/train_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a6a37d6f8c21540033920b3d405c5a6e9f857df2 --- /dev/null +++ b/ads_16_length_8000/NEQUIP/train_config.yaml @@ -0,0 +1,71 @@ +default_dtype: float64 +dataset_file_name: ../trainset.xyz +n_train: 7500 +n_val: 500 +batch_size: 64 +max_epochs: 500 +train_val_split: sequential + +root: ./results/ +run_name: adsorption +seed: 123 +dataset_seed: 456 +append: true +model_builders: + - SimpleIrrepsConfig + - EnergyModel + - PerSpeciesRescale + - StressForceOutput + - RescaleEnergyEtc +num_basis: 8 +r_max: 5.0 +l_max: 1 +num_layers: 4 +parity: false +num_features: 64 + +dataset: ase +dataset_key_mapping: + force: forces +ase_args: + format: extxyz +chemical_symbols: + - H + - C + - N + - Zn + - O + +wandb: false +learning_rate: 0.005 + +loss_coeffs: + forces: + - 20 + total_energy: + - 1 + 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