Train start time: 2024-12-08_10:52:54 Torch device: cuda Processing dataset... Loaded data: Batch(atomic_numbers=[2256000, 1], batch=[2256000], cell=[6000, 3, 3], edge_cell_shift=[78220482, 3], edge_index=[2, 78220482], forces=[2256000, 3], pbc=[6000, 3], pos=[2256000, 3], ptr=[6001], total_energy=[6000, 1]) processed data size: ~3122.09 MB Cached processed data to disk Done! Successfully loaded the data set of type ASEDataset(6000)... Replace string dataset_per_atom_total_energy_mean to -347.7250539118233 Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -347.725054]. Replace string dataset_forces_rms to 1.1824571890867512 Initially outputs are globally scaled by: 1.1824571890867512, 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 1.04e+03 1.14 1.01e+03 0.945 1.26 37.6 37.7 0.1 0.1 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 6.965 0.005 1.1 964 986 0.918 1.24 36.6 36.7 0.0973 0.0976 Wall time: 6.965346620883793 ! Best model 0 985.605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 25.8 0.969 6.43 0.869 1.16 2.59 3 0.00689 0.00798 1 172 25 0.986 5.29 0.877 1.17 2 2.72 0.00532 0.00723 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 38.6 1.05 17.6 0.912 1.21 4.25 4.96 0.0113 0.0132 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 1 111.610 0.005 0.996 6.51e+03 6.53e+03 0.877 1.18 36.3 95.4 0.0965 0.254 ! Validation 1 111.610 0.005 1.02 2.03 22.3 0.884 1.19 1.29 1.69 0.00343 0.00449 Wall time: 111.61104584904388 ! Best model 1 22.344 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 26.6 1 6.54 0.88 1.18 2.41 3.02 0.0064 0.00804 2 172 23.5 0.959 4.33 0.859 1.16 2.05 2.46 0.00545 0.00655 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 35.8 1.05 14.9 0.91 1.21 3.81 4.56 0.0101 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 2 213.521 0.005 0.986 5.57 25.3 0.873 1.17 2.14 2.79 0.0057 0.00742 ! Validation 2 213.521 0.005 1.01 2.49 22.7 0.882 1.19 1.5 1.87 0.00398 0.00496 Wall time: 213.5209203120321 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 33.2 0.971 13.8 0.871 1.17 3.51 4.4 0.00934 0.0117 3 172 23.6 0.964 4.34 0.865 1.16 2.06 2.46 0.00548 0.00655 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 34.8 1.04 14 0.908 1.21 3.7 4.43 0.00983 0.0118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 316.687 0.005 0.982 5.26 24.9 0.871 1.17 2.07 2.71 0.00551 0.00721 ! Validation 3 316.687 0.005 1.01 2.37 22.5 0.88 1.19 1.47 1.82 0.0039 0.00484 Wall time: 316.6880410439335 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 24.7 0.955 5.64 0.86 1.16 2.34 2.81 0.00623 0.00747 4 172 23.3 0.975 3.83 0.871 1.17 1.65 2.31 0.00439 0.00615 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 37.8 1.03 17.1 0.905 1.2 4.26 4.89 0.0113 0.013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 4 418.645 0.005 0.976 4.92 24.5 0.869 1.17 1.99 2.62 0.0053 0.00698 ! Validation 4 418.645 0.005 1 2.17 22.2 0.878 1.18 1.37 1.74 0.00364 0.00464 Wall time: 418.6456730850041 ! Best model 4 22.180 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 23.4 0.974 3.94 0.868 1.17 1.92 2.35 0.0051 0.00624 5 172 23.4 0.988 3.69 0.872 1.18 1.61 2.27 0.00429 0.00604 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 36.7 1.03 16.1 0.903 1.2 4.16 4.75 0.0111 0.0126 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 521.036 0.005 0.971 4.79 24.2 0.867 1.17 1.96 2.59 0.0052 0.00688 ! Validation 5 521.036 0.005 0.995 1.79 21.7 0.875 1.18 1.22 1.58 0.00325 0.00421 Wall time: 521.0363384140655 ! Best model 5 21.690 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 24.8 0.977 5.3 0.87 1.17 2.03 2.72 0.00539 0.00724 6 172 23.6 0.947 4.61 0.86 1.15 2.25 2.54 0.00599 0.00675 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 32.7 1.02 12.3 0.9 1.2 3.51 4.14 0.00932 0.011 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 6 623.877 0.005 0.966 4.36 23.7 0.864 1.16 1.87 2.47 0.00497 0.00657 ! Validation 6 623.877 0.005 0.989 2.84 22.6 0.873 1.18 1.68 1.99 0.00447 0.0053 Wall time: 623.8774322359823 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 22.7 0.964 3.45 0.865 1.16 1.76 2.2 0.00469 0.00584 7 172 22.6 0.947 3.71 0.86 1.15 1.77 2.28 0.0047 0.00605 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 33.5 1.02 13.2 0.897 1.19 3.74 4.3 0.00995 0.0114 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 7 729.109 0.005 0.96 4 23.2 0.862 1.16 1.78 2.37 0.00474 0.00629 ! Validation 7 729.109 0.005 0.983 2.21 21.9 0.87 1.17 1.42 1.76 0.00378 0.00467 Wall time: 729.109371017199 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 26.3 0.965 6.98 0.869 1.16 2.27 3.12 0.00604 0.00831 8 172 23.6 0.932 4.94 0.851 1.14 2.02 2.63 0.00538 0.00699 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 44.3 1.01 24.1 0.894 1.19 5.43 5.8 0.0144 0.0154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 8 831.000 0.005 0.954 3.74 22.8 0.859 1.15 1.72 2.29 0.00457 0.00608 ! Validation 8 831.000 0.005 0.976 1.59 21.1 0.866 1.17 1.09 1.49 0.00291 0.00397 Wall time: 831.0003256439231 ! Best model 8 21.114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 23.9 0.962 4.62 0.86 1.16 1.69 2.54 0.00449 0.00676 9 172 20.4 0.926 1.84 0.845 1.14 1.23 1.6 0.00328 0.00427 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 50.9 1 30.8 0.89 1.18 6.25 6.57 0.0166 0.0175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 9 932.918 0.005 0.947 3.42 22.4 0.855 1.15 1.64 2.19 0.00435 0.00582 ! Validation 9 932.918 0.005 0.969 2.55 21.9 0.863 1.16 1.47 1.89 0.0039 0.00502 Wall time: 932.9188520140015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 22.9 0.924 4.41 0.845 1.14 1.87 2.48 0.00497 0.0066 10 172 21.3 0.906 3.16 0.841 1.13 1.83 2.1 0.00488 0.00559 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 40.5 0.996 20.6 0.887 1.18 5.02 5.37 0.0133 0.0143 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 1034.833 0.005 0.94 3.59 22.4 0.852 1.15 1.7 2.24 0.00453 0.00595 ! Validation 10 1034.833 0.005 0.962 1.58 20.8 0.859 1.16 1.12 1.49 0.00297 0.00395 Wall time: 1034.8331626430154 ! Best model 10 20.814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 23.4 0.936 4.64 0.847 1.14 1.54 2.55 0.00409 0.00678 11 172 21.4 0.907 3.27 0.839 1.13 1.64 2.14 0.00436 0.00568 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 39.6 0.989 19.8 0.883 1.18 4.94 5.26 0.0131 0.014 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 1136.727 0.005 0.933 3.31 22 0.848 1.14 1.64 2.15 0.00435 0.00572 ! Validation 11 1136.727 0.005 0.954 1.5 20.6 0.855 1.15 1.1 1.45 0.00292 0.00385 Wall time: 1136.7276135869324 ! Best model 11 20.573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 22.4 0.935 3.68 0.848 1.14 2.02 2.27 0.00537 0.00604 12 172 20.3 0.933 1.66 0.842 1.14 1.12 1.52 0.00299 0.00405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 35.8 0.98 16.2 0.879 1.17 4.42 4.76 0.0118 0.0127 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 1238.627 0.005 0.924 3.04 21.5 0.844 1.14 1.56 2.06 0.00414 0.00548 ! Validation 12 1238.627 0.005 0.945 1.49 20.4 0.851 1.15 1.11 1.44 0.00295 0.00384 Wall time: 1238.6290965140797 ! Best model 12 20.385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 19.6 0.906 1.49 0.836 1.13 1.07 1.44 0.00285 0.00383 13 172 21.9 0.914 3.62 0.839 1.13 1.63 2.25 0.00433 0.00599 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 36 0.971 16.6 0.874 1.16 4.51 4.82 0.012 0.0128 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 1340.517 0.005 0.915 2.99 21.3 0.839 1.13 1.55 2.04 0.00411 0.00543 ! Validation 13 1340.517 0.005 0.935 1.51 20.2 0.846 1.14 1.12 1.45 0.00298 0.00386 Wall time: 1340.5175767028704 ! Best model 13 20.201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 20.2 0.907 2.1 0.834 1.13 1.4 1.71 0.00371 0.00455 14 172 21.7 0.892 3.84 0.83 1.12 2 2.32 0.00533 0.00616 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 48.8 0.96 29.6 0.869 1.16 6.21 6.44 0.0165 0.0171 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 1442.414 0.005 0.905 3.11 21.2 0.834 1.12 1.6 2.09 0.00426 0.00555 ! Validation 14 1442.414 0.005 0.923 4.56 23 0.84 1.14 2.14 2.52 0.00569 0.00671 Wall time: 1442.4139754842035 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 19.5 0.894 1.57 0.825 1.12 1.22 1.48 0.00323 0.00394 15 172 21.7 0.856 4.62 0.814 1.09 2.1 2.54 0.00559 0.00676 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 30.5 0.945 11.6 0.863 1.15 3.67 4.02 0.00976 0.0107 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 1544.309 0.005 0.893 2.91 20.8 0.828 1.12 1.55 2.02 0.00412 0.00536 ! Validation 15 1544.309 0.005 0.91 1.63 19.8 0.834 1.13 1.19 1.51 0.00317 0.00401 Wall time: 1544.3090179981664 ! Best model 15 19.826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 20.2 0.882 2.59 0.825 1.11 1.58 1.9 0.0042 0.00506 16 172 19.6 0.86 2.42 0.813 1.1 1.31 1.84 0.00349 0.00489 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 33.9 0.928 15.4 0.854 1.14 4.33 4.63 0.0115 0.0123 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 1646.227 0.005 0.878 3.16 20.7 0.821 1.11 1.63 2.1 0.00433 0.00559 ! Validation 16 1646.227 0.005 0.892 1.94 19.8 0.825 1.12 1.3 1.65 0.00347 0.00438 Wall time: 1646.227148745209 ! Best model 16 19.782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 22.1 0.877 4.59 0.821 1.11 1.72 2.53 0.00456 0.00674 17 172 25 0.825 8.47 0.799 1.07 2.12 3.44 0.00563 0.00915 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 23.6 0.901 5.54 0.841 1.12 2.24 2.78 0.00597 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 17 1748.121 0.005 0.857 3.24 20.4 0.811 1.09 1.66 2.13 0.00442 0.00566 ! Validation 17 1748.121 0.005 0.865 2.03 19.3 0.812 1.1 1.36 1.69 0.00361 0.00448 Wall time: 1748.1211566268466 ! Best model 17 19.335 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 23.9 0.818 7.55 0.791 1.07 2.66 3.25 0.00708 0.00864 18 172 18.9 0.81 2.68 0.788 1.06 1.58 1.93 0.00421 0.00515 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 24.7 0.852 7.64 0.816 1.09 2.75 3.27 0.00731 0.00869 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 1850.017 0.005 0.82 3.06 19.5 0.792 1.07 1.6 2.07 0.00426 0.0055 ! Validation 18 1850.017 0.005 0.815 1.5 17.8 0.786 1.07 1.17 1.45 0.0031 0.00386 Wall time: 1850.0173952579498 ! Best model 18 17.798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 16.5 0.73 1.85 0.748 1.01 1.16 1.61 0.00307 0.00428 19 172 18 0.704 3.91 0.733 0.992 1.9 2.34 0.00505 0.00622 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 26.6 0.753 11.5 0.766 1.03 3.55 4.01 0.00944 0.0107 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 1951.985 0.005 0.752 3.84 18.9 0.758 1.03 1.82 2.32 0.00483 0.00616 ! Validation 19 1951.985 0.005 0.715 2.44 16.8 0.737 1 1.48 1.85 0.00395 0.00491 Wall time: 1951.9854435110465 ! Best model 19 16.752 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 13.1 0.588 1.31 0.67 0.907 1.13 1.36 0.003 0.0036 20 172 15.4 0.519 4.98 0.625 0.852 1.95 2.64 0.00518 0.00702 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 28.3 0.571 16.9 0.664 0.894 4.51 4.86 0.012 0.0129 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 2053.901 0.005 0.607 5.01 17.2 0.68 0.921 2.05 2.65 0.00546 0.00704 ! Validation 20 2053.901 0.005 0.528 20.2 30.7 0.632 0.86 5.12 5.31 0.0136 0.0141 Wall time: 2053.9017414338887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 12.2 0.465 2.89 0.588 0.807 1.69 2.01 0.0045 0.00535 21 172 22 0.436 13.2 0.567 0.781 3.87 4.3 0.0103 0.0114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 81.5 0.494 71.6 0.61 0.831 9.79 10 0.026 0.0266 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 2155.783 0.005 0.466 5.73 15.1 0.59 0.807 2.19 2.83 0.00582 0.00753 ! Validation 21 2155.783 0.005 0.443 37.8 46.6 0.572 0.787 7.15 7.27 0.019 0.0193 Wall time: 2155.7831081119366 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 14.3 0.414 6.02 0.555 0.76 2.57 2.9 0.00684 0.00772 22 172 17.1 0.416 8.78 0.553 0.763 3.06 3.5 0.00814 0.00932 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 21.7 0.465 12.4 0.59 0.807 3.63 4.17 0.00965 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 22 2257.671 0.005 0.425 7.12 15.6 0.561 0.771 2.57 3.15 0.00684 0.00839 ! Validation 22 2257.671 0.005 0.415 2.76 11.1 0.552 0.761 1.67 1.97 0.00445 0.00523 Wall time: 2257.6717315819114 ! Best model 22 11.055 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 12.8 0.38 5.2 0.53 0.729 2.4 2.7 0.00638 0.00717 23 172 10.3 0.374 2.8 0.525 0.724 1.62 1.98 0.00432 0.00526 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 22.9 0.433 14.2 0.569 0.778 3.95 4.46 0.0105 0.0119 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 2359.940 0.005 0.394 4.43 12.3 0.539 0.743 1.97 2.49 0.00524 0.00662 ! Validation 23 2359.940 0.005 0.383 3.66 11.3 0.531 0.732 2 2.26 0.00531 0.00602 Wall time: 2359.94000616204 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 9 0.367 1.67 0.522 0.716 1.23 1.53 0.00327 0.00406 24 172 9.39 0.364 2.12 0.519 0.713 1.44 1.72 0.00384 0.00458 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 12.1 0.419 3.76 0.561 0.765 2.01 2.29 0.00536 0.0061 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 2461.816 0.005 0.371 6.96 14.4 0.524 0.721 2.49 3.12 0.00662 0.0083 ! Validation 24 2461.816 0.005 0.372 1.3 8.75 0.524 0.722 1.07 1.35 0.00285 0.00358 Wall time: 2461.8162572490983 ! Best model 24 8.746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 17.6 0.357 10.4 0.514 0.706 3.57 3.82 0.00951 0.0102 25 172 16.3 0.345 9.44 0.504 0.694 3.34 3.63 0.00889 0.00966 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 65.9 0.385 58.2 0.538 0.734 8.77 9.02 0.0233 0.024 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 2563.698 0.005 0.352 4.37 11.4 0.511 0.702 1.94 2.47 0.00517 0.00657 ! Validation 25 2563.698 0.005 0.341 27.1 33.9 0.502 0.69 6.03 6.15 0.016 0.0164 Wall time: 2563.6981531027704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 8.75 0.323 2.3 0.491 0.672 1.5 1.79 0.00398 0.00476 26 172 26.5 0.313 20.3 0.483 0.661 5.2 5.32 0.0138 0.0142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 55.1 0.36 47.9 0.52 0.709 7.92 8.19 0.0211 0.0218 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 2665.758 0.005 0.329 4.6 11.2 0.495 0.679 2.04 2.53 0.00543 0.00674 ! Validation 26 2665.758 0.005 0.318 23.9 30.3 0.486 0.667 5.68 5.78 0.0151 0.0154 Wall time: 2665.758598979097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 11.6 0.321 5.15 0.488 0.67 2.48 2.68 0.00661 0.00714 27 172 7.92 0.312 1.68 0.484 0.66 1.39 1.53 0.0037 0.00408 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 11.4 0.345 4.5 0.511 0.694 2.07 2.51 0.0055 0.00667 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 2767.782 0.005 0.313 6.18 12.4 0.483 0.662 2.37 2.94 0.0063 0.00782 ! Validation 27 2767.782 0.005 0.305 0.671 6.77 0.477 0.653 0.758 0.969 0.00202 0.00258 Wall time: 2767.7822998538613 ! Best model 27 6.768 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 13.5 0.29 7.68 0.467 0.637 3.04 3.28 0.00809 0.00872 28 172 12.9 0.276 7.34 0.457 0.622 2.96 3.2 0.00788 0.00852 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 36.8 0.318 30.4 0.491 0.666 6.23 6.52 0.0166 0.0173 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 2870.744 0.005 0.292 4.71 10.6 0.468 0.639 2.09 2.57 0.00555 0.00682 ! Validation 28 2870.744 0.005 0.282 17.8 23.4 0.461 0.628 4.9 4.99 0.013 0.0133 Wall time: 2870.744375757873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 14.6 0.27 9.18 0.449 0.614 3.39 3.58 0.00902 0.00953 29 172 6.66 0.275 1.15 0.457 0.62 0.982 1.27 0.00261 0.00338 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 10.3 0.305 4.17 0.484 0.653 1.88 2.41 0.00499 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 29 2972.632 0.005 0.274 5.82 11.3 0.455 0.619 2.25 2.85 0.00599 0.00759 ! Validation 29 2972.632 0.005 0.272 1.16 6.6 0.453 0.616 1.07 1.27 0.00285 0.00339 Wall time: 2972.6326219029725 ! Best model 29 6.596 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 5.91 0.25 0.911 0.436 0.591 0.864 1.13 0.0023 0.003 30 172 6.03 0.256 0.904 0.438 0.599 0.939 1.12 0.0025 0.00299 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 8.89 0.276 3.37 0.459 0.621 1.84 2.17 0.00489 0.00578 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 3074.511 0.005 0.257 4.44 9.58 0.442 0.599 2 2.49 0.00531 0.00663 ! Validation 30 3074.511 0.005 0.248 3.06 8.01 0.434 0.588 1.88 2.07 0.005 0.0055 Wall time: 3074.5115105141886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 7.04 0.25 2.04 0.438 0.591 1.5 1.69 0.00399 0.00449 31 172 10.7 0.239 5.93 0.427 0.578 2.71 2.88 0.00721 0.00766 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 7.75 0.264 2.47 0.45 0.608 1.58 1.86 0.00421 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 31 3176.435 0.005 0.243 5.78 10.6 0.431 0.583 2.24 2.84 0.00595 0.00756 ! Validation 31 3176.435 0.005 0.237 0.538 5.28 0.426 0.576 0.696 0.867 0.00185 0.00231 Wall time: 3176.4358355570585 ! Best model 31 5.285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 5.53 0.231 0.918 0.423 0.568 0.919 1.13 0.00244 0.00301 32 172 5.97 0.219 1.59 0.413 0.554 1.31 1.49 0.00349 0.00396 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 9.72 0.242 4.87 0.431 0.582 2.13 2.61 0.00568 0.00694 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 3278.226 0.005 0.227 3.64 8.19 0.418 0.564 1.78 2.26 0.00472 0.006 ! Validation 32 3278.226 0.005 0.216 2.94 7.26 0.408 0.55 1.88 2.03 0.00499 0.00539 Wall time: 3278.226034327876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 6.71 0.214 2.43 0.406 0.547 1.21 1.84 0.00322 0.0049 33 172 4.87 0.207 0.731 0.403 0.538 0.824 1.01 0.00219 0.00269 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 9.6 0.235 4.91 0.425 0.573 2.09 2.62 0.00557 0.00697 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 3379.997 0.005 0.214 4.86 9.14 0.407 0.547 2.11 2.61 0.00562 0.00693 ! Validation 33 3379.997 0.005 0.207 2.22 6.37 0.401 0.539 1.58 1.76 0.0042 0.00469 Wall time: 3379.996899122838 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 10.5 0.204 6.42 0.397 0.534 2.87 3 0.00764 0.00797 34 172 9.22 0.206 5.1 0.402 0.537 2.53 2.67 0.00674 0.0071 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 7.2 0.234 2.52 0.425 0.572 1.38 1.88 0.00368 0.00499 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 3481.768 0.005 0.203 4.86 8.92 0.398 0.533 2.1 2.61 0.00557 0.00693 ! Validation 34 3481.768 0.005 0.205 2.01 6.11 0.4 0.536 1.5 1.67 0.00399 0.00445 Wall time: 3481.768490140792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 4.38 0.193 0.523 0.388 0.519 0.686 0.855 0.00183 0.00228 35 172 11.4 0.197 7.5 0.391 0.525 3.09 3.24 0.00823 0.00861 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 7.71 0.221 3.3 0.412 0.555 1.79 2.15 0.00476 0.00571 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 3584.580 0.005 0.197 4.16 8.09 0.392 0.524 1.94 2.41 0.00516 0.00641 ! Validation 35 3584.580 0.005 0.191 3.13 6.94 0.387 0.516 1.95 2.09 0.00519 0.00556 Wall time: 3584.5805448009633 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 8.71 0.183 5.04 0.379 0.506 2.56 2.66 0.0068 0.00706 36 172 6.88 0.188 3.12 0.384 0.513 1.86 2.09 0.00495 0.00556 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 9.68 0.215 5.39 0.407 0.548 2.55 2.74 0.00677 0.0073 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 3686.447 0.005 0.186 4.09 7.82 0.383 0.511 1.92 2.39 0.0051 0.00636 ! Validation 36 3686.447 0.005 0.183 4.53 8.2 0.379 0.506 2.41 2.52 0.0064 0.00669 Wall time: 3686.447349361144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 5.49 0.176 1.97 0.372 0.496 1.36 1.66 0.00361 0.00441 37 172 6.93 0.165 3.64 0.362 0.48 2.18 2.26 0.0058 0.006 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 6.19 0.202 2.16 0.392 0.531 1.46 1.74 0.00389 0.00462 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 3788.325 0.005 0.174 2.39 5.88 0.37 0.493 1.48 1.83 0.00393 0.00486 ! Validation 37 3788.325 0.005 0.164 1.51 4.79 0.359 0.479 1.27 1.45 0.00339 0.00386 Wall time: 3788.324951136019 ! Best model 37 4.788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 5.88 0.169 2.5 0.365 0.486 1.68 1.87 0.00448 0.00497 38 172 15.7 0.161 12.5 0.357 0.474 4.09 4.18 0.0109 0.0111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 19.4 0.201 15.4 0.394 0.53 4.53 4.63 0.0121 0.0123 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 3890.189 0.005 0.172 5.1 8.54 0.368 0.491 2.18 2.67 0.00581 0.0071 ! Validation 38 3890.189 0.005 0.164 14.5 17.7 0.36 0.478 4.44 4.5 0.0118 0.012 Wall time: 3890.1893405108713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 5.49 0.161 2.27 0.36 0.475 1.62 1.78 0.00432 0.00474 39 172 3.82 0.155 0.725 0.35 0.465 0.842 1.01 0.00224 0.00268 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 4.24 0.195 0.342 0.387 0.522 0.663 0.692 0.00176 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 39 3992.051 0.005 0.162 3.32 6.57 0.358 0.476 1.66 2.16 0.00441 0.00573 ! Validation 39 3992.051 0.005 0.152 0.675 3.72 0.347 0.461 0.805 0.971 0.00214 0.00258 Wall time: 3992.0510934782214 ! Best model 39 3.719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 3.36 0.149 0.374 0.344 0.457 0.592 0.723 0.00157 0.00192 40 172 3.98 0.149 0.993 0.343 0.457 0.953 1.18 0.00253 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 4.58 0.188 0.821 0.379 0.513 0.92 1.07 0.00245 0.00285 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 4093.925 0.005 0.151 2.81 5.82 0.346 0.459 1.59 1.98 0.00423 0.00527 ! Validation 40 4093.925 0.005 0.142 0.522 3.37 0.336 0.446 0.661 0.854 0.00176 0.00227 Wall time: 4093.9249812229536 ! Best model 40 3.369 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 12.1 0.147 9.13 0.343 0.454 3.5 3.57 0.00932 0.0095 41 172 7.09 0.144 4.21 0.338 0.449 2.33 2.43 0.0062 0.00645 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 5.86 0.187 2.12 0.38 0.512 1.59 1.72 0.00422 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 41 4196.879 0.005 0.144 3.4 6.29 0.338 0.449 1.68 2.18 0.00446 0.0058 ! Validation 41 4196.879 0.005 0.143 1.3 4.17 0.338 0.448 1.15 1.35 0.00306 0.00358 Wall time: 4196.879220996983 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 4.15 0.14 1.35 0.333 0.443 1.17 1.37 0.0031 0.00365 42 172 3.9 0.133 1.23 0.328 0.432 1.16 1.31 0.00308 0.00349 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 3.82 0.175 0.318 0.366 0.495 0.653 0.667 0.00174 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 42 4300.476 0.005 0.135 2.41 5.11 0.327 0.435 1.49 1.84 0.00396 0.00489 ! Validation 42 4300.476 0.005 0.13 0.298 2.9 0.321 0.426 0.497 0.646 0.00132 0.00172 Wall time: 4300.476579805836 ! Best model 42 2.897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 3.36 0.132 0.722 0.324 0.429 0.819 1 0.00218 0.00267 43 172 2.89 0.125 0.387 0.313 0.418 0.579 0.736 0.00154 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 4.71 0.17 1.32 0.357 0.487 1.31 1.36 0.00347 0.00361 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 4402.360 0.005 0.128 2.37 4.93 0.319 0.424 1.42 1.82 0.00378 0.00484 ! Validation 43 4402.360 0.005 0.118 0.987 3.34 0.306 0.406 1.01 1.17 0.0027 0.00312 Wall time: 4402.360870587174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 4.43 0.114 2.15 0.302 0.399 1.61 1.73 0.00428 0.00461 44 172 5.32 0.116 3 0.303 0.403 1.92 2.05 0.0051 0.00545 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 5.88 0.163 2.63 0.351 0.477 1.81 1.92 0.00482 0.0051 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 4504.663 0.005 0.118 2.25 4.62 0.306 0.407 1.45 1.77 0.00387 0.00472 ! Validation 44 4504.663 0.005 0.114 1.43 3.72 0.301 0.4 1.29 1.41 0.00344 0.00376 Wall time: 4504.663440348115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 3.7 0.118 1.34 0.306 0.406 1.21 1.37 0.00322 0.00364 45 172 5.4 0.111 3.19 0.295 0.393 2.01 2.11 0.00535 0.00561 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 3.26 0.16 0.0642 0.346 0.473 0.237 0.3 0.000631 0.000797 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 4606.432 0.005 0.116 2.64 4.97 0.303 0.404 1.51 1.92 0.00401 0.00511 ! Validation 45 4606.432 0.005 0.111 0.266 2.48 0.296 0.393 0.484 0.61 0.00129 0.00162 Wall time: 4606.432581899222 ! Best model 45 2.480 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 4.07 0.11 1.86 0.295 0.393 1.45 1.61 0.00385 0.00429 46 172 3.22 0.114 0.94 0.297 0.399 1 1.15 0.00267 0.00305 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 4.53 0.153 1.47 0.339 0.463 1.41 1.43 0.00374 0.00381 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 4708.392 0.005 0.112 2.31 4.55 0.297 0.395 1.45 1.8 0.00384 0.00478 ! Validation 46 4708.392 0.005 0.104 2.17 4.26 0.287 0.382 1.64 1.74 0.00437 0.00464 Wall time: 4708.392494549975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 2.65 0.111 0.438 0.295 0.394 0.653 0.782 0.00174 0.00208 47 172 2.41 0.106 0.287 0.291 0.385 0.47 0.633 0.00125 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 5.33 0.149 2.35 0.336 0.456 1.8 1.81 0.00478 0.00482 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 4810.296 0.005 0.108 2.73 4.9 0.293 0.389 1.53 1.95 0.00406 0.0052 ! Validation 47 4810.296 0.005 0.104 1.47 3.56 0.287 0.382 1.28 1.43 0.00341 0.00381 Wall time: 4810.296467225067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.87 0.101 0.849 0.283 0.376 0.987 1.09 0.00262 0.0029 48 172 8.12 0.114 5.85 0.297 0.399 2.72 2.86 0.00724 0.0076 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 20.3 0.147 17.3 0.331 0.454 4.91 4.92 0.0131 0.0131 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 4912.331 0.005 0.105 2.11 4.21 0.288 0.383 1.37 1.72 0.00364 0.00457 ! Validation 48 4912.331 0.005 0.0978 12.4 14.4 0.278 0.37 4.11 4.17 0.0109 0.0111 Wall time: 4912.331772116013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.26 0.0977 0.312 0.277 0.37 0.534 0.66 0.00142 0.00176 49 172 8.14 0.0943 6.26 0.273 0.363 2.85 2.96 0.00757 0.00787 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 19.7 0.145 16.8 0.331 0.451 4.84 4.84 0.0129 0.0129 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 5014.199 0.005 0.0988 1.75 3.73 0.279 0.372 1.27 1.56 0.00336 0.00416 ! Validation 49 5014.199 0.005 0.0979 18.1 20.1 0.278 0.37 5 5.03 0.0133 0.0134 Wall time: 5014.199097794015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.45 0.1 0.446 0.28 0.374 0.66 0.79 0.00176 0.0021 50 172 2.64 0.1 0.636 0.279 0.375 0.825 0.943 0.00219 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 4.22 0.141 1.4 0.325 0.444 1.37 1.4 0.00364 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 50 5116.158 0.005 0.102 2.39 4.42 0.283 0.377 1.4 1.83 0.00372 0.00487 ! Validation 50 5116.158 0.005 0.0969 1.24 3.18 0.277 0.368 1.19 1.32 0.00315 0.00351 Wall time: 5116.1586098182015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 2.26 0.088 0.504 0.264 0.351 0.652 0.84 0.00173 0.00223 51 172 3.21 0.0926 1.36 0.269 0.36 1.16 1.38 0.00308 0.00367 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 2.97 0.135 0.269 0.317 0.435 0.559 0.613 0.00149 0.00163 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 5218.033 0.005 0.0939 1.43 3.31 0.272 0.362 1.12 1.41 0.00298 0.00376 ! Validation 51 5218.033 0.005 0.0887 0.282 2.06 0.265 0.352 0.485 0.628 0.00129 0.00167 Wall time: 5218.0335502647795 ! Best model 51 2.056 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 4.13 0.0988 2.15 0.274 0.372 1.53 1.73 0.00408 0.00461 52 172 4.73 0.0843 3.05 0.258 0.343 1.97 2.06 0.00525 0.00549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 5.32 0.132 2.68 0.315 0.429 1.92 1.94 0.00512 0.00515 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 5319.928 0.005 0.0906 1.64 3.45 0.267 0.356 1.19 1.51 0.00315 0.00402 ! Validation 52 5319.928 0.005 0.0896 2.3 4.09 0.266 0.354 1.71 1.79 0.00454 0.00477 Wall time: 5319.928394818213 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 2.11 0.0869 0.375 0.263 0.349 0.604 0.724 0.00161 0.00192 53 172 2.54 0.0999 0.544 0.282 0.374 0.769 0.872 0.00205 0.00232 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 11.6 0.138 8.79 0.328 0.439 3.49 3.51 0.00928 0.00933 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 5425.242 0.005 0.0886 1.9 3.67 0.264 0.352 1.25 1.63 0.00334 0.00434 ! Validation 53 5425.242 0.005 0.104 8.19 10.3 0.287 0.381 3.34 3.38 0.00887 0.009 Wall time: 5425.242331578862 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 3.5 0.0807 1.89 0.252 0.336 1.49 1.62 0.00395 0.00432 54 172 3.55 0.0846 1.85 0.259 0.344 1.47 1.61 0.00392 0.00428 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 3.21 0.126 0.679 0.308 0.42 0.943 0.974 0.00251 0.00259 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 5527.148 0.005 0.0896 1.53 3.32 0.266 0.354 1.17 1.46 0.00311 0.00389 ! Validation 54 5527.148 0.005 0.0856 0.491 2.2 0.26 0.346 0.697 0.829 0.00185 0.0022 Wall time: 5527.14866804285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 2.62 0.0807 1 0.252 0.336 1.08 1.19 0.00288 0.00315 55 172 4.38 0.0807 2.77 0.254 0.336 1.84 1.97 0.0049 0.00523 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 3.86 0.119 1.47 0.298 0.408 1.42 1.44 0.00376 0.00382 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 5629.101 0.005 0.0843 1.3 2.98 0.258 0.343 1.09 1.35 0.00289 0.00358 ! Validation 55 5629.101 0.005 0.0802 2.26 3.86 0.252 0.335 1.7 1.78 0.00451 0.00473 Wall time: 5629.1018693568185 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 2.4 0.0898 0.601 0.264 0.354 0.711 0.917 0.00189 0.00244 56 172 2.81 0.083 1.15 0.254 0.341 1.09 1.27 0.00289 0.00337 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 3.78 0.118 1.42 0.295 0.406 1.38 1.41 0.00367 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 56 5730.985 0.005 0.0842 1.69 3.38 0.258 0.343 1.23 1.54 0.00327 0.00409 ! Validation 56 5730.985 0.005 0.0789 1.08 2.66 0.25 0.332 1.06 1.23 0.00282 0.00327 Wall time: 5730.984991256148 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 2.15 0.0833 0.479 0.256 0.341 0.668 0.818 0.00178 0.00218 57 172 2.17 0.0757 0.66 0.243 0.325 0.872 0.961 0.00232 0.00256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 2.45 0.118 0.0921 0.296 0.406 0.335 0.359 0.000891 0.000954 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 5832.859 0.005 0.0822 1.51 3.16 0.255 0.339 1.16 1.46 0.00308 0.00387 ! Validation 57 5832.859 0.005 0.0774 0.777 2.32 0.248 0.329 0.919 1.04 0.00244 0.00277 Wall time: 5832.859175431076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 2.01 0.0741 0.531 0.243 0.322 0.748 0.862 0.00199 0.00229 58 172 4.86 0.0803 3.26 0.254 0.335 2.07 2.13 0.0055 0.00568 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 10.2 0.11 8 0.288 0.393 3.33 3.34 0.00887 0.00889 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 5934.736 0.005 0.0772 1.16 2.71 0.247 0.328 1.01 1.27 0.0027 0.00339 ! Validation 58 5934.736 0.005 0.0764 5.69 7.21 0.246 0.327 2.73 2.82 0.00726 0.0075 Wall time: 5934.73657172313 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.7 0.0733 0.237 0.243 0.32 0.471 0.575 0.00125 0.00153 59 172 1.84 0.0725 0.394 0.239 0.318 0.551 0.742 0.00147 0.00197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 2.9 0.109 0.713 0.284 0.391 0.936 0.998 0.00249 0.00265 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 6039.331 0.005 0.0765 1.14 2.67 0.246 0.327 1 1.26 0.00267 0.00336 ! Validation 59 6039.331 0.005 0.072 0.696 2.14 0.239 0.317 0.857 0.987 0.00228 0.00262 Wall time: 6039.331112711225 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 1.63 0.0701 0.223 0.237 0.313 0.472 0.558 0.00126 0.00148 60 172 1.64 0.071 0.223 0.238 0.315 0.452 0.559 0.0012 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 2.51 0.105 0.413 0.28 0.383 0.708 0.76 0.00188 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 60 6141.099 0.005 0.0724 1.04 2.49 0.239 0.318 0.979 1.21 0.0026 0.00321 ! Validation 60 6141.099 0.005 0.0703 0.209 1.62 0.236 0.314 0.415 0.541 0.0011 0.00144 Wall time: 6141.099777059164 ! Best model 60 1.615 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 1.74 0.0694 0.349 0.235 0.312 0.575 0.699 0.00153 0.00186 61 172 4.13 0.0662 2.81 0.23 0.304 1.91 1.98 0.00508 0.00527 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 3.68 0.102 1.65 0.274 0.377 1.49 1.52 0.00396 0.00403 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 6242.887 0.005 0.0711 1.16 2.59 0.238 0.315 1.01 1.27 0.00268 0.00339 ! Validation 61 6242.887 0.005 0.0671 1.82 3.16 0.231 0.306 1.53 1.6 0.00406 0.00424 Wall time: 6242.886957515962 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 2.32 0.0678 0.964 0.228 0.308 0.634 1.16 0.00169 0.00309 62 172 1.84 0.0659 0.52 0.23 0.304 0.743 0.853 0.00198 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 3 0.0958 1.08 0.267 0.366 1.18 1.23 0.00314 0.00327 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 6344.684 0.005 0.068 0.958 2.32 0.232 0.308 0.938 1.16 0.00249 0.00308 ! Validation 62 6344.684 0.005 0.064 0.699 1.98 0.226 0.299 0.881 0.989 0.00234 0.00263 Wall time: 6344.683906271122 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 1.57 0.067 0.232 0.231 0.306 0.481 0.569 0.00128 0.00151 63 172 4.9 0.0838 3.22 0.258 0.342 2.06 2.12 0.00547 0.00564 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 5.58 0.104 3.5 0.286 0.381 2.2 2.21 0.00584 0.00589 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 6446.709 0.005 0.0682 1.56 2.92 0.233 0.309 1.12 1.48 0.00297 0.00393 ! Validation 63 6446.709 0.005 0.0847 2.83 4.52 0.259 0.344 1.93 1.99 0.00514 0.00529 Wall time: 6446.709666993935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 1.99 0.0694 0.605 0.233 0.311 0.754 0.92 0.00201 0.00245 64 172 2.03 0.0608 0.813 0.22 0.292 0.924 1.07 0.00246 0.00284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 4.99 0.0913 3.16 0.261 0.357 2.09 2.1 0.00555 0.00559 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 6548.600 0.005 0.0664 0.786 2.11 0.23 0.305 0.829 1.05 0.0022 0.00279 ! Validation 64 6548.600 0.005 0.0608 2.96 4.17 0.22 0.292 1.98 2.03 0.00528 0.00541 Wall time: 6548.600505835842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.52 0.0586 0.353 0.216 0.286 0.59 0.703 0.00157 0.00187 65 172 2.27 0.0626 1.02 0.221 0.296 1.09 1.2 0.0029 0.00318 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.97 0.0891 0.19 0.259 0.353 0.484 0.516 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 65 6652.468 0.005 0.0623 0.992 2.24 0.223 0.295 0.924 1.18 0.00246 0.00313 ! Validation 65 6652.468 0.005 0.0611 0.413 1.64 0.221 0.292 0.656 0.76 0.00175 0.00202 Wall time: 6652.468866250943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 2.56 0.0624 1.31 0.223 0.295 1.25 1.35 0.00331 0.0036 66 172 1.87 0.0598 0.676 0.219 0.289 0.881 0.972 0.00234 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 3.25 0.0841 1.57 0.253 0.343 1.46 1.48 0.00388 0.00394 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 6754.916 0.005 0.0623 1.16 2.41 0.223 0.295 1.04 1.27 0.00277 0.00339 ! Validation 66 6754.916 0.005 0.0607 2.11 3.32 0.22 0.291 1.66 1.72 0.00441 0.00456 Wall time: 6754.9168444331735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.86 0.0574 0.709 0.215 0.283 0.924 0.996 0.00246 0.00265 67 172 1.37 0.0546 0.277 0.209 0.276 0.468 0.623 0.00124 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 2.25 0.0843 0.562 0.25 0.343 0.787 0.886 0.00209 0.00236 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 6859.200 0.005 0.0587 0.772 1.95 0.216 0.287 0.829 1.04 0.00221 0.00276 ! Validation 67 6859.200 0.005 0.055 1.32 2.42 0.21 0.277 1.29 1.36 0.00342 0.00362 Wall time: 6859.200006274972 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 3.57 0.0589 2.39 0.217 0.287 1.76 1.83 0.00469 0.00486 68 172 3.22 0.0662 1.9 0.23 0.304 1.47 1.63 0.00392 0.00433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 7.79 0.0832 6.13 0.256 0.341 2.92 2.93 0.00778 0.00778 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 6961.012 0.005 0.0568 1.1 2.23 0.213 0.282 0.978 1.24 0.0026 0.00329 ! Validation 68 6961.012 0.005 0.0658 4.39 5.7 0.229 0.303 2.42 2.48 0.00643 0.00659 Wall time: 6961.012852646876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.36 0.0523 0.311 0.205 0.27 0.522 0.659 0.00139 0.00175 69 172 2.22 0.0559 1.1 0.211 0.28 1.14 1.24 0.00304 0.00331 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.88 0.076 0.364 0.24 0.326 0.643 0.714 0.00171 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 69 7062.786 0.005 0.0565 0.857 1.99 0.212 0.281 0.898 1.09 0.00239 0.00291 ! Validation 69 7062.786 0.005 0.0534 0.91 1.98 0.207 0.273 1.05 1.13 0.00279 0.003 Wall time: 7062.7866428038105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 2.62 0.0569 1.48 0.214 0.282 1.31 1.44 0.00349 0.00383 70 172 2.11 0.0517 1.08 0.204 0.269 1.11 1.23 0.00295 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 3.55 0.0743 2.06 0.237 0.322 1.63 1.7 0.00434 0.00452 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 7164.784 0.005 0.0549 0.95 2.05 0.209 0.277 0.924 1.15 0.00246 0.00306 ! Validation 70 7164.784 0.005 0.0499 0.483 1.48 0.2 0.264 0.688 0.822 0.00183 0.00219 Wall time: 7164.78474775888 ! Best model 70 1.481 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 3.01 0.0504 2 0.201 0.265 1.58 1.67 0.0042 0.00445 71 172 1.7 0.0513 0.671 0.204 0.268 0.82 0.968 0.00218 0.00258 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 1.81 0.0737 0.331 0.235 0.321 0.618 0.68 0.00164 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 71 7267.132 0.005 0.0511 0.707 1.73 0.202 0.267 0.791 0.994 0.0021 0.00264 ! Validation 71 7267.132 0.005 0.0503 0.333 1.34 0.2 0.265 0.58 0.683 0.00154 0.00182 Wall time: 7267.131999495905 ! Best model 71 1.339 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.18 0.0514 0.153 0.203 0.268 0.391 0.462 0.00104 0.00123 72 172 1.22 0.0497 0.222 0.201 0.264 0.442 0.557 0.00118 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 2.19 0.0711 0.77 0.234 0.315 0.999 1.04 0.00266 0.00276 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 7369.068 0.005 0.0493 0.699 1.69 0.198 0.262 0.773 0.989 0.00206 0.00263 ! Validation 72 7369.068 0.005 0.0499 0.726 1.72 0.201 0.264 0.918 1.01 0.00244 0.00268 Wall time: 7369.067949670833 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 1.27 0.0496 0.278 0.199 0.263 0.455 0.623 0.00121 0.00166 73 172 1.29 0.0451 0.389 0.19 0.251 0.626 0.737 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 73 100 2.04 0.0698 0.649 0.228 0.312 0.796 0.952 0.00212 0.00253 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 7470.991 0.005 0.05 0.963 1.96 0.2 0.264 0.903 1.16 0.0024 0.00309 ! Validation 73 7470.991 0.005 0.0453 0.162 1.07 0.19 0.252 0.377 0.476 0.001 0.00127 Wall time: 7470.991824958939 ! Best model 73 1.068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.18 0.0457 0.264 0.191 0.253 0.531 0.607 0.00141 0.00161 74 172 1.26 0.0429 0.401 0.185 0.245 0.633 0.749 0.00168 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.61 0.0654 0.299 0.222 0.302 0.596 0.646 0.00158 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 74 7573.053 0.005 0.0471 0.673 1.61 0.194 0.257 0.777 0.97 0.00207 0.00258 ! Validation 74 7573.053 0.005 0.044 0.556 1.44 0.188 0.248 0.788 0.882 0.0021 0.00235 Wall time: 7573.053095120937 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.5 0.0565 0.372 0.214 0.281 0.603 0.721 0.0016 0.00192 75 172 1.67 0.0444 0.777 0.188 0.249 0.928 1.04 0.00247 0.00277 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.58 0.0628 0.328 0.218 0.296 0.506 0.677 0.00134 0.0018 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 7674.967 0.005 0.048 0.998 1.96 0.196 0.259 0.934 1.18 0.00248 0.00314 ! Validation 75 7674.967 0.005 0.0433 0.143 1.01 0.186 0.246 0.359 0.447 0.000956 0.00119 Wall time: 7674.967015497852 ! Best model 75 1.008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.96 0.0483 0.991 0.197 0.26 0.985 1.18 0.00262 0.00313 76 172 0.992 0.0449 0.0931 0.191 0.251 0.3 0.361 0.000799 0.000959 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.77 0.063 0.506 0.22 0.297 0.746 0.841 0.00198 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 76 7777.715 0.005 0.0461 0.79 1.71 0.192 0.254 0.833 1.05 0.00222 0.0028 ! Validation 76 7777.715 0.005 0.0426 0.191 1.04 0.185 0.244 0.405 0.517 0.00108 0.00137 Wall time: 7777.714914639015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.05 0.0392 0.265 0.177 0.234 0.453 0.609 0.0012 0.00162 77 172 1.18 0.0445 0.294 0.189 0.249 0.543 0.641 0.00144 0.0017 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.62 0.057 0.479 0.207 0.282 0.763 0.818 0.00203 0.00218 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 7879.609 0.005 0.0422 0.457 1.3 0.183 0.243 0.63 0.799 0.00168 0.00213 ! Validation 77 7879.609 0.005 0.0395 0.43 1.22 0.177 0.235 0.654 0.775 0.00174 0.00206 Wall time: 7879.609082933981 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 2.05 0.0398 1.26 0.18 0.236 1.21 1.33 0.00321 0.00353 78 172 1.46 0.0411 0.637 0.182 0.24 0.826 0.944 0.0022 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 1.36 0.0591 0.176 0.212 0.288 0.438 0.496 0.00116 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 78 7981.771 0.005 0.0456 0.944 1.86 0.191 0.253 0.886 1.15 0.00236 0.00306 ! Validation 78 7981.771 0.005 0.0403 0.381 1.19 0.179 0.237 0.639 0.73 0.0017 0.00194 Wall time: 7981.770915484987 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 1.46 0.048 0.498 0.196 0.259 0.666 0.834 0.00177 0.00222 79 172 1.73 0.0431 0.867 0.185 0.245 0.989 1.1 0.00263 0.00293 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 3.03 0.0555 1.92 0.205 0.279 1.51 1.64 0.00401 0.00436 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 8084.299 0.005 0.0427 0.816 1.67 0.184 0.244 0.873 1.07 0.00232 0.00284 ! Validation 79 8084.299 0.005 0.0419 0.739 1.58 0.183 0.242 0.891 1.02 0.00237 0.0027 Wall time: 8084.29977134103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 0.963 0.0371 0.222 0.172 0.228 0.444 0.557 0.00118 0.00148 80 172 0.866 0.0388 0.0906 0.176 0.233 0.293 0.356 0.000778 0.000947 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 1.18 0.0515 0.151 0.197 0.268 0.403 0.459 0.00107 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 80 8186.981 0.005 0.0395 0.407 1.2 0.177 0.235 0.601 0.755 0.0016 0.00201 ! Validation 80 8186.981 0.005 0.0369 0.212 0.95 0.171 0.227 0.467 0.545 0.00124 0.00145 Wall time: 8186.980933101848 ! Best model 80 0.950 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 0.969 0.0387 0.195 0.174 0.233 0.414 0.522 0.0011 0.00139 81 172 1.08 0.0414 0.252 0.179 0.241 0.441 0.594 0.00117 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.17 0.0497 0.175 0.196 0.264 0.449 0.495 0.00119 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 81 8288.809 0.005 0.0382 0.531 1.29 0.174 0.231 0.697 0.862 0.00185 0.00229 ! Validation 81 8288.809 0.005 0.0362 0.255 0.979 0.17 0.225 0.513 0.597 0.00136 0.00159 Wall time: 8288.809663113207 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 0.964 0.0366 0.231 0.171 0.226 0.447 0.569 0.00119 0.00151 82 172 0.955 0.0396 0.163 0.177 0.235 0.322 0.477 0.000857 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 1.09 0.0487 0.121 0.193 0.261 0.324 0.411 0.000862 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 82 8390.696 0.005 0.0382 0.727 1.49 0.174 0.231 0.805 1.01 0.00214 0.00268 ! Validation 82 8390.696 0.005 0.0373 0.137 0.884 0.172 0.228 0.338 0.438 0.0009 0.00116 Wall time: 8390.696083562914 ! Best model 82 0.884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.01 0.0364 0.28 0.17 0.226 0.543 0.626 0.00144 0.00167 83 172 1.3 0.0361 0.576 0.169 0.225 0.73 0.897 0.00194 0.00239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.21 0.046 0.285 0.188 0.254 0.608 0.631 0.00162 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 83 8492.507 0.005 0.037 0.541 1.28 0.171 0.227 0.685 0.87 0.00182 0.00231 ! Validation 83 8492.507 0.005 0.0353 0.144 0.851 0.167 0.222 0.353 0.449 0.000939 0.00119 Wall time: 8492.506996157113 ! Best model 83 0.851 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.03 0.0357 0.318 0.168 0.223 0.56 0.667 0.00149 0.00177 84 172 0.922 0.0351 0.219 0.167 0.222 0.457 0.554 0.00122 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.87 0.045 0.974 0.185 0.251 1.01 1.17 0.0027 0.0031 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 8594.389 0.005 0.0355 0.478 1.19 0.168 0.223 0.661 0.818 0.00176 0.00217 ! Validation 84 8594.389 0.005 0.0333 0.816 1.48 0.162 0.216 0.996 1.07 0.00265 0.00284 Wall time: 8594.389042664785 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 1.38 0.035 0.676 0.166 0.221 0.895 0.973 0.00238 0.00259 85 172 2.63 0.039 1.85 0.172 0.233 1.36 1.61 0.00363 0.00427 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 0.925 0.0447 0.0298 0.186 0.25 0.188 0.204 0.000499 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 85 8696.252 0.005 0.0343 0.472 1.16 0.165 0.219 0.647 0.812 0.00172 0.00216 ! Validation 85 8696.252 0.005 0.0347 0.154 0.848 0.166 0.22 0.383 0.464 0.00102 0.00123 Wall time: 8696.252580186818 ! Best model 85 0.848 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.13 0.0365 0.402 0.169 0.226 0.605 0.749 0.00161 0.00199 86 172 2.12 0.0347 1.43 0.165 0.22 1.26 1.41 0.00335 0.00376 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.19 0.0457 0.274 0.188 0.253 0.528 0.619 0.0014 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 86 8798.131 0.005 0.034 0.486 1.17 0.164 0.218 0.641 0.824 0.00171 0.00219 ! Validation 86 8798.131 0.005 0.0346 0.253 0.944 0.166 0.22 0.507 0.594 0.00135 0.00158 Wall time: 8798.131173258182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.702 0.0309 0.0837 0.157 0.208 0.28 0.342 0.000745 0.00091 87 172 23.6 0.98 3.96 0.87 1.17 1.81 2.35 0.00482 0.00626 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 61.6 1.04 40.8 0.906 1.21 7.13 7.55 0.019 0.0201 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 8900.254 0.005 0.298 184 190 0.364 0.645 5.77 16.1 0.0153 0.0427 ! Validation 87 8900.254 0.005 1 6.73 26.8 0.879 1.19 2.42 3.07 0.00643 0.00816 Wall time: 8900.254123553168 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 21.8 0.912 3.58 0.836 1.13 1.69 2.24 0.00449 0.00595 88 172 20.5 0.868 3.12 0.817 1.1 1.79 2.09 0.00476 0.00555 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 23.8 0.95 4.81 0.864 1.15 1.88 2.59 0.00499 0.0069 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 9002.110 0.005 0.935 3.94 22.6 0.848 1.14 1.76 2.35 0.00468 0.00624 ! Validation 88 9002.110 0.005 0.915 2.02 20.3 0.835 1.13 1.37 1.68 0.00364 0.00446 Wall time: 9002.110376955941 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 19.3 0.82 2.87 0.79 1.07 1.46 2 0.00388 0.00533 89 172 18.4 0.781 2.77 0.772 1.05 1.19 1.97 0.00316 0.00524 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 18.4 0.802 2.38 0.794 1.06 1.51 1.82 0.00402 0.00485 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 9103.996 0.005 0.832 2.59 19.2 0.798 1.08 1.45 1.9 0.00385 0.00506 ! Validation 89 9103.996 0.005 0.777 3.27 18.8 0.768 1.04 1.81 2.14 0.00482 0.00568 Wall time: 9103.996537905186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 15.3 0.618 2.91 0.686 0.93 1.64 2.02 0.00436 0.00536 90 172 12 0.498 2.08 0.615 0.834 1.53 1.7 0.00406 0.00453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 26.7 0.535 16 0.643 0.865 4.51 4.73 0.012 0.0126 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 9205.859 0.005 0.636 2.3 15 0.694 0.943 1.4 1.79 0.00371 0.00477 ! Validation 90 9205.859 0.005 0.515 7.63 17.9 0.62 0.849 2.99 3.27 0.00795 0.00869 Wall time: 9205.859350083862 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 8.82 0.361 1.59 0.527 0.711 1.04 1.49 0.00275 0.00396 91 172 7.39 0.278 1.82 0.464 0.624 1.32 1.6 0.0035 0.00425 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 15.2 0.306 9.08 0.495 0.654 3.3 3.56 0.00877 0.00948 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 9307.731 0.005 0.378 2.54 10.1 0.535 0.727 1.48 1.88 0.00394 0.00501 ! Validation 91 9307.731 0.005 0.276 4.75 10.3 0.462 0.622 2.28 2.58 0.00606 0.00686 Wall time: 9307.730969983153 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 5.49 0.201 1.47 0.398 0.53 1.2 1.43 0.0032 0.00382 92 172 5.35 0.175 1.84 0.372 0.495 1.46 1.6 0.00388 0.00426 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 7.18 0.215 2.88 0.42 0.548 1.88 2.01 0.00499 0.00534 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 9410.131 0.005 0.213 1.9 6.16 0.408 0.546 1.29 1.63 0.00344 0.00434 ! Validation 92 9410.131 0.005 0.175 3.41 6.92 0.373 0.495 2 2.18 0.00533 0.00581 Wall time: 9410.131427842192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 3.66 0.147 0.719 0.342 0.453 0.781 1 0.00208 0.00267 93 172 3.26 0.137 0.509 0.33 0.438 0.703 0.843 0.00187 0.00224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 3.66 0.177 0.13 0.38 0.497 0.39 0.427 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 93 9512.025 0.005 0.156 1.39 4.5 0.351 0.466 1.13 1.4 0.003 0.00371 ! Validation 93 9512.025 0.005 0.136 0.886 3.61 0.33 0.436 0.932 1.11 0.00248 0.00296 Wall time: 9512.024903818965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 3.01 0.126 0.498 0.318 0.419 0.681 0.834 0.00181 0.00222 94 172 2.64 0.116 0.328 0.304 0.402 0.535 0.677 0.00142 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 3.11 0.154 0.0414 0.353 0.464 0.23 0.241 0.000612 0.00064 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 9613.938 0.005 0.127 1.15 3.69 0.319 0.422 0.997 1.27 0.00265 0.00337 ! Validation 94 9613.938 0.005 0.112 0.564 2.81 0.301 0.397 0.723 0.888 0.00192 0.00236 Wall time: 9613.938191485126 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 2.84 0.111 0.628 0.298 0.393 0.76 0.937 0.00202 0.00249 95 172 4.51 0.101 2.48 0.285 0.376 1.73 1.86 0.00461 0.00496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 3.13 0.135 0.432 0.329 0.434 0.754 0.777 0.00201 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 95 9716.709 0.005 0.106 0.59 2.72 0.292 0.386 0.72 0.907 0.00191 0.00241 ! Validation 95 9716.709 0.005 0.0959 1.84 3.76 0.278 0.366 1.47 1.6 0.00391 0.00427 Wall time: 9716.709594007116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 2.57 0.0911 0.75 0.27 0.357 0.912 1.02 0.00243 0.00272 96 172 3.39 0.0936 1.52 0.273 0.362 1.29 1.46 0.00344 0.00388 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 7.06 0.12 4.66 0.309 0.41 2.55 2.55 0.00678 0.00679 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 9818.674 0.005 0.0938 0.86 2.74 0.274 0.362 0.877 1.1 0.00233 0.00292 ! Validation 96 9818.674 0.005 0.0859 2.57 4.29 0.263 0.346 1.78 1.9 0.00474 0.00505 Wall time: 9818.674414655194 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 3.93 0.0907 2.12 0.27 0.356 1.63 1.72 0.00434 0.00458 97 172 2.47 0.0811 0.845 0.256 0.337 0.967 1.09 0.00257 0.00289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 2.52 0.112 0.283 0.299 0.396 0.618 0.63 0.00164 0.00167 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 9920.577 0.005 0.0861 1 2.72 0.263 0.347 0.946 1.18 0.00252 0.00315 ! Validation 97 9920.577 0.005 0.0791 0.298 1.88 0.253 0.332 0.512 0.645 0.00136 0.00172 Wall time: 9920.577437319793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 3.17 0.0796 1.58 0.254 0.334 1.41 1.49 0.00376 0.00395 98 172 2.86 0.0734 1.39 0.243 0.32 1.25 1.39 0.00331 0.00371 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 2.49 0.104 0.401 0.287 0.382 0.742 0.749 0.00197 0.00199 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 10022.470 0.005 0.0793 0.991 2.58 0.252 0.333 0.97 1.18 0.00258 0.00313 ! Validation 98 10022.470 0.005 0.0757 1.54 3.05 0.246 0.325 1.36 1.47 0.00361 0.0039 Wall time: 10022.470479671843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 1.65 0.071 0.226 0.238 0.315 0.428 0.562 0.00114 0.00149 99 172 1.75 0.0704 0.346 0.238 0.314 0.563 0.696 0.0015 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 2.05 0.0962 0.123 0.276 0.367 0.385 0.416 0.00102 0.00111 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 10126.629 0.005 0.0739 0.591 2.07 0.244 0.322 0.722 0.909 0.00192 0.00242 ! Validation 99 10126.629 0.005 0.0673 1.27 2.61 0.233 0.307 1.19 1.33 0.00318 0.00354 Wall time: 10126.629845960066 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 2.04 0.0711 0.614 0.24 0.315 0.773 0.927 0.00206 0.00246 100 172 2.37 0.0683 1.01 0.235 0.309 1.12 1.19 0.00297 0.00316 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 6.57 0.0876 4.82 0.263 0.35 2.59 2.59 0.00689 0.0069 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 10229.023 0.005 0.0684 0.839 2.21 0.234 0.309 0.858 1.08 0.00228 0.00288 ! Validation 100 10229.023 0.005 0.0641 2.82 4.1 0.227 0.299 1.91 1.99 0.00507 0.00528 Wall time: 10229.023823798168 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 2.03 0.0705 0.616 0.237 0.314 0.803 0.928 0.00214 0.00247 101 172 1.64 0.0638 0.365 0.227 0.299 0.563 0.715 0.0015 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 1.72 0.085 0.0177 0.259 0.345 0.133 0.157 0.000354 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 101 10330.900 0.005 0.068 1.12 2.48 0.233 0.308 0.996 1.25 0.00265 0.00332 ! Validation 101 10330.900 0.005 0.0621 0.671 1.91 0.223 0.295 0.831 0.969 0.00221 0.00258 Wall time: 10330.90054171998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 1.53 0.061 0.308 0.222 0.292 0.575 0.656 0.00153 0.00174 102 172 1.52 0.0591 0.343 0.217 0.287 0.599 0.693 0.00159 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 1.92 0.0783 0.358 0.248 0.331 0.701 0.708 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 102 10432.760 0.005 0.0619 0.569 1.81 0.223 0.294 0.712 0.892 0.00189 0.00237 ! Validation 102 10432.760 0.005 0.0569 0.288 1.43 0.214 0.282 0.513 0.634 0.00137 0.00169 Wall time: 10432.7599425558 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 1.51 0.0585 0.337 0.216 0.286 0.581 0.687 0.00155 0.00183 103 172 1.38 0.0568 0.242 0.214 0.282 0.476 0.582 0.00127 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 2.01 0.0769 0.471 0.247 0.328 0.805 0.812 0.00214 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 103 10535.266 0.005 0.0601 0.941 2.14 0.219 0.29 0.929 1.15 0.00247 0.00305 ! Validation 103 10535.266 0.005 0.0557 1.38 2.49 0.212 0.279 1.29 1.39 0.00344 0.00369 Wall time: 10535.266472041141 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 2.16 0.0547 1.06 0.209 0.277 1.17 1.22 0.00311 0.00324 104 172 1.22 0.0513 0.199 0.203 0.268 0.426 0.527 0.00113 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 1.56 0.0704 0.155 0.236 0.314 0.437 0.466 0.00116 0.00124 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 10637.124 0.005 0.0556 0.612 1.72 0.211 0.279 0.746 0.925 0.00198 0.00246 ! Validation 104 10637.124 0.005 0.0524 0.354 1.4 0.205 0.271 0.593 0.704 0.00158 0.00187 Wall time: 10637.124019836076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 1.56 0.0516 0.526 0.203 0.269 0.731 0.858 0.00194 0.00228 105 172 1.09 0.0453 0.188 0.191 0.252 0.428 0.513 0.00114 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 1.43 0.0671 0.0847 0.23 0.306 0.325 0.344 0.000864 0.000915 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 10740.107 0.005 0.0528 0.622 1.68 0.205 0.272 0.74 0.933 0.00197 0.00248 ! Validation 105 10740.107 0.005 0.0491 0.81 1.79 0.198 0.262 0.94 1.06 0.0025 0.00283 Wall time: 10740.107187982183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 1.92 0.0521 0.881 0.204 0.27 1.01 1.11 0.00269 0.00295 106 172 1.18 0.0495 0.191 0.199 0.263 0.389 0.517 0.00104 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 1.34 0.065 0.0364 0.226 0.301 0.206 0.226 0.000549 0.0006 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 10842.256 0.005 0.0517 0.871 1.9 0.203 0.269 0.903 1.1 0.0024 0.00294 ! Validation 106 10842.256 0.005 0.0487 0.17 1.14 0.197 0.261 0.394 0.488 0.00105 0.0013 Wall time: 10842.25663229404 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 1.15 0.048 0.19 0.197 0.259 0.449 0.515 0.00119 0.00137 107 172 1.39 0.0477 0.434 0.194 0.258 0.657 0.779 0.00175 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 1.27 0.0612 0.045 0.219 0.292 0.215 0.251 0.000571 0.000667 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 10944.126 0.005 0.0491 0.596 1.58 0.198 0.262 0.743 0.913 0.00198 0.00243 ! Validation 107 10944.126 0.005 0.0457 0.216 1.13 0.191 0.253 0.45 0.549 0.0012 0.00146 Wall time: 10944.126277553849 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 1.86 0.0436 0.986 0.188 0.247 1.13 1.17 0.003 0.00312 108 172 1.23 0.049 0.248 0.198 0.262 0.444 0.588 0.00118 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 2.02 0.0604 0.817 0.218 0.291 1.07 1.07 0.00284 0.00284 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 11046.014 0.005 0.0483 0.867 1.83 0.196 0.26 0.893 1.1 0.00238 0.00293 ! Validation 108 11046.014 0.005 0.048 0.624 1.58 0.196 0.259 0.825 0.934 0.00219 0.00248 Wall time: 11046.014810964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.74 0.0516 0.713 0.2 0.269 0.864 0.999 0.0023 0.00266 109 172 1.96 0.0443 1.08 0.188 0.249 1.11 1.23 0.00296 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.24 0.0574 0.0939 0.212 0.283 0.334 0.362 0.000888 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 109 11147.893 0.005 0.0464 0.569 1.5 0.192 0.255 0.718 0.892 0.00191 0.00237 ! Validation 109 11147.893 0.005 0.0426 0.3 1.15 0.184 0.244 0.541 0.647 0.00144 0.00172 Wall time: 11147.893839693163 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.67 0.0451 0.773 0.185 0.251 0.552 1.04 0.00147 0.00276 110 172 2.02 0.0422 1.17 0.179 0.243 0.706 1.28 0.00188 0.00341 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.41 0.0545 0.324 0.207 0.276 0.654 0.673 0.00174 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 110 11251.582 0.005 0.0447 0.616 1.51 0.189 0.25 0.706 0.928 0.00188 0.00247 ! Validation 110 11251.582 0.005 0.0403 0.293 1.1 0.18 0.237 0.53 0.64 0.00141 0.0017 Wall time: 11251.582295891829 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 2.17 0.0427 1.32 0.183 0.244 1.21 1.36 0.00323 0.00361 111 172 1.36 0.0402 0.556 0.18 0.237 0.811 0.882 0.00216 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.6 0.0523 0.553 0.204 0.27 0.867 0.88 0.0023 0.00234 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 11354.024 0.005 0.0423 0.652 1.5 0.183 0.243 0.771 0.955 0.00205 0.00254 ! Validation 111 11354.024 0.005 0.0398 0.266 1.06 0.179 0.236 0.493 0.61 0.00131 0.00162 Wall time: 11354.023911456112 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 5.65 0.042 4.81 0.183 0.242 2.57 2.59 0.00683 0.0069 112 172 1.2 0.0409 0.38 0.18 0.239 0.61 0.729 0.00162 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 1.54 0.051 0.518 0.201 0.267 0.841 0.851 0.00224 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 112 11455.886 0.005 0.0425 0.771 1.62 0.184 0.244 0.811 1.04 0.00216 0.00276 ! Validation 112 11455.886 0.005 0.0391 0.306 1.09 0.177 0.234 0.538 0.654 0.00143 0.00174 Wall time: 11455.886181388982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 0.947 0.0383 0.182 0.175 0.231 0.401 0.504 0.00107 0.00134 113 172 1.84 0.0408 1.02 0.18 0.239 1.15 1.2 0.00305 0.00318 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 2.2 0.0506 1.19 0.201 0.266 1.28 1.29 0.00341 0.00343 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 11557.933 0.005 0.0408 0.672 1.49 0.18 0.239 0.773 0.969 0.00206 0.00258 ! Validation 113 11557.933 0.005 0.0387 2 2.78 0.176 0.233 1.61 1.67 0.00429 0.00445 Wall time: 11557.933448663913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1.62 0.0369 0.884 0.172 0.227 1.04 1.11 0.00277 0.00296 114 172 0.928 0.0415 0.0992 0.181 0.241 0.27 0.372 0.000719 0.000991 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1.84 0.0481 0.876 0.195 0.259 1.08 1.11 0.00288 0.00294 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 11659.795 0.005 0.0392 0.636 1.42 0.177 0.234 0.746 0.943 0.00198 0.00251 ! Validation 114 11659.795 0.005 0.0383 0.595 1.36 0.175 0.232 0.81 0.912 0.00215 0.00243 Wall time: 11659.79495043587 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.971 0.0373 0.224 0.172 0.228 0.496 0.56 0.00132 0.00149 115 172 0.951 0.0378 0.195 0.174 0.23 0.434 0.522 0.00115 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 1.84 0.046 0.918 0.19 0.254 1.13 1.13 0.00301 0.00301 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 11761.647 0.005 0.0392 0.66 1.44 0.176 0.234 0.763 0.961 0.00203 0.00256 ! Validation 115 11761.647 0.005 0.036 0.528 1.25 0.169 0.224 0.768 0.859 0.00204 0.00229 Wall time: 11761.64726004703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 2.22 0.0385 1.45 0.175 0.232 1.38 1.42 0.00366 0.00378 116 172 0.861 0.0374 0.114 0.172 0.229 0.32 0.399 0.000852 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 2.57 0.0452 1.66 0.19 0.251 1.49 1.53 0.00397 0.00406 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 11863.731 0.005 0.0368 0.573 1.31 0.171 0.227 0.708 0.896 0.00188 0.00238 ! Validation 116 11863.731 0.005 0.0355 0.636 1.35 0.168 0.223 0.806 0.943 0.00214 0.00251 Wall time: 11863.730925572105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 0.801 0.0336 0.129 0.164 0.217 0.347 0.425 0.000922 0.00113 117 172 1.91 0.0338 1.24 0.164 0.217 1.27 1.31 0.00338 0.0035 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 3.56 0.0452 2.65 0.189 0.251 1.92 1.93 0.00511 0.00512 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 11968.528 0.005 0.0361 0.574 1.3 0.169 0.225 0.72 0.895 0.00191 0.00238 ! Validation 117 11968.528 0.005 0.0342 2.73 3.42 0.165 0.219 1.92 1.95 0.0051 0.0052 Wall time: 11968.528783351183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.97 0.0364 1.25 0.171 0.226 1.21 1.32 0.00323 0.00351 118 172 0.99 0.0358 0.275 0.168 0.224 0.514 0.62 0.00137 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.28 0.0432 0.417 0.185 0.246 0.751 0.764 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 118 12070.332 0.005 0.0365 0.727 1.46 0.17 0.226 0.829 1.01 0.00221 0.00268 ! Validation 118 12070.332 0.005 0.0337 0.458 1.13 0.164 0.217 0.717 0.8 0.00191 0.00213 Wall time: 12070.332564095035 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 0.797 0.0352 0.0928 0.166 0.222 0.291 0.36 0.000774 0.000958 119 172 0.941 0.0333 0.274 0.163 0.216 0.488 0.619 0.0013 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 2.54 0.0435 1.67 0.186 0.247 1.52 1.53 0.00405 0.00406 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 12172.555 0.005 0.0349 0.559 1.26 0.166 0.221 0.7 0.884 0.00186 0.00235 ! Validation 119 12172.555 0.005 0.0332 1.1 1.76 0.163 0.215 1.16 1.24 0.00307 0.0033 Wall time: 12172.5557817542 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.723 0.0329 0.066 0.161 0.214 0.252 0.304 0.000669 0.000808 120 172 1 0.0314 0.375 0.158 0.209 0.62 0.724 0.00165 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.999 0.0413 0.174 0.181 0.24 0.474 0.493 0.00126 0.00131 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 12274.364 0.005 0.0349 0.565 1.26 0.166 0.221 0.689 0.889 0.00183 0.00236 ! Validation 120 12274.364 0.005 0.0311 0.18 0.802 0.158 0.209 0.405 0.501 0.00108 0.00133 Wall time: 12274.364719970152 ! Best model 120 0.802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.753 0.0326 0.101 0.162 0.214 0.305 0.375 0.000812 0.000998 121 172 0.831 0.0311 0.209 0.157 0.208 0.472 0.541 0.00126 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 1.45 0.04 0.653 0.178 0.236 0.924 0.955 0.00246 0.00254 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 12376.169 0.005 0.0334 0.532 1.2 0.163 0.216 0.682 0.862 0.00182 0.00229 ! Validation 121 12376.169 0.005 0.0301 0.344 0.945 0.155 0.205 0.585 0.693 0.00156 0.00184 Wall time: 12376.169030595105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 1.52 0.0341 0.839 0.164 0.218 0.927 1.08 0.00247 0.00288 122 172 0.882 0.0322 0.239 0.161 0.212 0.47 0.578 0.00125 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 0.864 0.0405 0.0536 0.179 0.238 0.23 0.274 0.000611 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 122 12477.987 0.005 0.0336 0.728 1.4 0.163 0.217 0.829 1.01 0.00221 0.00268 ! Validation 122 12477.987 0.005 0.0312 0.158 0.783 0.158 0.209 0.389 0.471 0.00103 0.00125 Wall time: 12477.987760627177 ! Best model 122 0.783 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.682 0.0295 0.091 0.153 0.203 0.287 0.357 0.000763 0.000949 123 172 1.75 0.0327 1.09 0.161 0.214 1.14 1.24 0.00304 0.00328 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.929 0.038 0.17 0.174 0.23 0.477 0.487 0.00127 0.0013 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 12580.717 0.005 0.0313 0.403 1.03 0.158 0.209 0.609 0.75 0.00162 0.00199 ! Validation 123 12580.717 0.005 0.0302 0.2 0.804 0.155 0.205 0.443 0.529 0.00118 0.00141 Wall time: 12580.716944046784 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 3.25 0.0319 2.61 0.16 0.211 1.84 1.91 0.0049 0.00508 124 172 1.13 0.0306 0.513 0.156 0.207 0.721 0.847 0.00192 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 1.09 0.039 0.315 0.177 0.233 0.648 0.664 0.00172 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 124 12682.513 0.005 0.0309 0.502 1.12 0.157 0.208 0.67 0.838 0.00178 0.00223 ! Validation 124 12682.513 0.005 0.0291 0.35 0.931 0.152 0.202 0.608 0.699 0.00162 0.00186 Wall time: 12682.51337950211 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 1.56 0.0328 0.907 0.155 0.214 0.568 1.13 0.00151 0.003 125 172 3.58 0.044 2.7 0.187 0.248 1.69 1.94 0.0045 0.00517 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 5.87 0.0466 4.94 0.192 0.255 2.62 2.63 0.00697 0.00699 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 12784.336 0.005 0.0298 0.52 1.12 0.154 0.204 0.631 0.851 0.00168 0.00226 ! Validation 125 12784.336 0.005 0.0409 2.14 2.96 0.181 0.239 1.55 1.73 0.00413 0.0046 Wall time: 12784.336881250143 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.884 0.0289 0.306 0.152 0.201 0.569 0.654 0.00151 0.00174 126 172 0.93 0.0276 0.377 0.148 0.197 0.609 0.726 0.00162 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 1.2 0.036 0.478 0.169 0.224 0.752 0.817 0.002 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 126 12887.163 0.005 0.0308 0.449 1.07 0.156 0.207 0.615 0.793 0.00163 0.00211 ! Validation 126 12887.163 0.005 0.0273 0.18 0.725 0.147 0.195 0.402 0.501 0.00107 0.00133 Wall time: 12887.163102187216 ! Best model 126 0.725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.703 0.028 0.144 0.149 0.198 0.383 0.449 0.00102 0.00119 127 172 0.656 0.0273 0.11 0.147 0.195 0.299 0.392 0.000795 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 1.06 0.0354 0.349 0.169 0.223 0.683 0.699 0.00182 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 127 12989.064 0.005 0.0293 0.465 1.05 0.152 0.202 0.648 0.806 0.00172 0.00214 ! Validation 127 12989.064 0.005 0.0268 0.74 1.28 0.146 0.194 0.936 1.02 0.00249 0.00271 Wall time: 12989.064479040913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 1.55 0.0265 1.02 0.145 0.192 1.15 1.19 0.00305 0.00317 128 172 1 0.0283 0.438 0.149 0.199 0.637 0.783 0.00169 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.73 0.034 0.0499 0.165 0.218 0.214 0.264 0.000569 0.000702 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 13090.934 0.005 0.0275 0.343 0.892 0.147 0.196 0.553 0.692 0.00147 0.00184 ! Validation 128 13090.934 0.005 0.0254 0.197 0.705 0.142 0.189 0.432 0.524 0.00115 0.00139 Wall time: 13090.934810415842 ! Best model 128 0.705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.635 0.0272 0.0913 0.146 0.195 0.322 0.357 0.000856 0.00095 129 172 1.39 0.0282 0.83 0.141 0.199 0.45 1.08 0.0012 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.797 0.0338 0.121 0.164 0.217 0.329 0.411 0.000876 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 129 13196.589 0.005 0.0269 0.422 0.961 0.146 0.194 0.611 0.768 0.00163 0.00204 ! Validation 129 13196.589 0.005 0.0252 0.0973 0.602 0.142 0.188 0.291 0.369 0.000773 0.000981 Wall time: 13196.589447799139 ! Best model 129 0.602 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 1.09 0.0252 0.584 0.141 0.188 0.797 0.904 0.00212 0.0024 130 172 1.21 0.0244 0.725 0.14 0.185 0.89 1.01 0.00237 0.00268 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 1.77 0.0327 1.12 0.162 0.214 1.24 1.25 0.0033 0.00333 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 13298.548 0.005 0.026 0.362 0.883 0.143 0.191 0.561 0.712 0.00149 0.00189 ! Validation 130 13298.548 0.005 0.0247 0.54 1.03 0.141 0.186 0.714 0.869 0.0019 0.00231 Wall time: 13298.548024853226 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 1.76 0.0281 1.2 0.149 0.198 1.24 1.3 0.0033 0.00345 131 172 0.84 0.0249 0.342 0.14 0.187 0.614 0.692 0.00163 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 0.933 0.0327 0.28 0.161 0.214 0.617 0.626 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 131 13400.419 0.005 0.027 0.491 1.03 0.146 0.194 0.657 0.828 0.00175 0.0022 ! Validation 131 13400.419 0.005 0.0243 0.224 0.711 0.139 0.184 0.453 0.56 0.00121 0.00149 Wall time: 13400.419739156961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.647 0.0241 0.166 0.139 0.183 0.375 0.481 0.000997 0.00128 132 172 2.78 0.0271 2.24 0.148 0.195 1.57 1.77 0.00418 0.0047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 3.21 0.0339 2.53 0.164 0.218 1.84 1.88 0.0049 0.005 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 13503.518 0.005 0.0249 0.315 0.813 0.14 0.187 0.519 0.662 0.00138 0.00176 ! Validation 132 13503.518 0.005 0.0263 2.88 3.41 0.145 0.192 1.97 2.01 0.00525 0.00534 Wall time: 13503.517908389214 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.662 0.0245 0.173 0.14 0.185 0.384 0.492 0.00102 0.00131 133 172 0.548 0.0229 0.0899 0.134 0.179 0.312 0.355 0.000829 0.000943 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.637 0.0308 0.0208 0.156 0.207 0.136 0.171 0.000362 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 133 13607.472 0.005 0.0261 0.476 0.997 0.144 0.191 0.643 0.816 0.00171 0.00217 ! Validation 133 13607.472 0.005 0.023 0.0758 0.536 0.135 0.179 0.26 0.325 0.000692 0.000866 Wall time: 13607.4727348811 ! Best model 133 0.536 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.598 0.0251 0.0967 0.14 0.187 0.274 0.368 0.00073 0.000978 134 172 0.871 0.0235 0.401 0.137 0.181 0.671 0.749 0.00179 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.759 0.0308 0.143 0.156 0.208 0.409 0.447 0.00109 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 134 13709.384 0.005 0.0244 0.355 0.844 0.139 0.185 0.557 0.705 0.00148 0.00187 ! Validation 134 13709.384 0.005 0.0224 0.184 0.632 0.133 0.177 0.424 0.508 0.00113 0.00135 Wall time: 13709.384753892198 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.957 0.0259 0.44 0.142 0.19 0.686 0.784 0.00182 0.00209 135 172 0.816 0.0232 0.353 0.135 0.18 0.639 0.702 0.0017 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 1.09 0.0304 0.477 0.155 0.206 0.789 0.817 0.0021 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 135 13811.279 0.005 0.0241 0.38 0.862 0.138 0.184 0.577 0.729 0.00153 0.00194 ! Validation 135 13811.279 0.005 0.0217 0.253 0.688 0.131 0.174 0.474 0.595 0.00126 0.00158 Wall time: 13811.27892597299 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.659 0.0266 0.127 0.145 0.193 0.366 0.421 0.000973 0.00112 136 172 0.885 0.0231 0.423 0.135 0.18 0.731 0.769 0.00194 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 1.85 0.0296 1.26 0.153 0.203 1.32 1.33 0.00351 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 136 13913.297 0.005 0.0232 0.338 0.802 0.135 0.18 0.549 0.687 0.00146 0.00183 ! Validation 136 13913.297 0.005 0.0216 1 1.44 0.131 0.174 1.12 1.18 0.00298 0.00315 Wall time: 13913.297663291916 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.846 0.0241 0.363 0.138 0.184 0.638 0.713 0.0017 0.0019 137 172 0.566 0.0237 0.0927 0.136 0.182 0.31 0.36 0.000825 0.000957 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.618 0.0294 0.029 0.153 0.203 0.185 0.201 0.000491 0.000535 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 14016.226 0.005 0.0227 0.356 0.81 0.134 0.178 0.555 0.706 0.00148 0.00188 ! Validation 137 14016.226 0.005 0.0209 0.108 0.525 0.129 0.171 0.309 0.389 0.000821 0.00103 Wall time: 14016.226388894953 ! Best model 137 0.525 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 1.02 0.0259 0.501 0.143 0.19 0.721 0.837 0.00192 0.00223 138 172 0.579 0.0227 0.125 0.134 0.178 0.347 0.418 0.000923 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.681 0.0297 0.0861 0.154 0.204 0.324 0.347 0.000862 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 138 14118.198 0.005 0.0251 0.568 1.07 0.141 0.187 0.672 0.892 0.00179 0.00237 ! Validation 138 14118.198 0.005 0.0216 0.252 0.684 0.131 0.174 0.507 0.594 0.00135 0.00158 Wall time: 14118.198393512052 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.5 0.0203 0.0953 0.127 0.168 0.299 0.365 0.000794 0.000971 139 172 0.739 0.0217 0.305 0.131 0.174 0.566 0.653 0.00151 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 1.05 0.0295 0.458 0.152 0.203 0.723 0.801 0.00192 0.00213 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 14220.225 0.005 0.0223 0.324 0.771 0.133 0.177 0.528 0.674 0.0014 0.00179 ! Validation 139 14220.225 0.005 0.0217 0.252 0.686 0.132 0.174 0.495 0.593 0.00132 0.00158 Wall time: 14220.225144439843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.552 0.0213 0.126 0.129 0.173 0.312 0.419 0.00083 0.00112 140 172 1.53 0.0225 1.08 0.133 0.177 1.15 1.23 0.00307 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.715 0.0305 0.104 0.156 0.207 0.311 0.382 0.000828 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 140 14322.090 0.005 0.0217 0.349 0.783 0.131 0.174 0.547 0.698 0.00145 0.00186 ! Validation 140 14322.090 0.005 0.0226 0.112 0.565 0.135 0.178 0.319 0.396 0.000849 0.00105 Wall time: 14322.090052471962 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 1.65 0.0194 1.26 0.124 0.165 1.29 1.33 0.00343 0.00353 141 172 0.488 0.0202 0.0842 0.126 0.168 0.283 0.343 0.000753 0.000912 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 0.919 0.0265 0.388 0.145 0.193 0.724 0.737 0.00193 0.00196 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 14425.065 0.005 0.0214 0.297 0.725 0.13 0.173 0.515 0.644 0.00137 0.00171 ! Validation 141 14425.065 0.005 0.0199 0.364 0.762 0.126 0.167 0.654 0.714 0.00174 0.0019 Wall time: 14425.06506492477 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.697 0.021 0.277 0.129 0.171 0.534 0.622 0.00142 0.00165 142 172 0.592 0.0171 0.249 0.117 0.155 0.539 0.59 0.00143 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.768 0.0257 0.253 0.143 0.19 0.57 0.595 0.00152 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 142 14526.968 0.005 0.0204 0.279 0.688 0.127 0.169 0.498 0.625 0.00132 0.00166 ! Validation 142 14526.968 0.005 0.0184 0.716 1.08 0.121 0.16 0.888 1 0.00236 0.00266 Wall time: 14526.968197129201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 1.07 0.0205 0.66 0.128 0.169 0.919 0.961 0.00244 0.00255 143 172 0.542 0.019 0.163 0.123 0.163 0.381 0.478 0.00101 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.62 0.0255 0.11 0.143 0.189 0.332 0.392 0.000882 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 143 14628.857 0.005 0.0203 0.325 0.73 0.127 0.168 0.536 0.674 0.00143 0.00179 ! Validation 143 14628.857 0.005 0.0179 0.152 0.51 0.12 0.158 0.39 0.462 0.00104 0.00123 Wall time: 14628.857604655903 ! Best model 143 0.510 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.765 0.0188 0.389 0.122 0.162 0.697 0.737 0.00185 0.00196 144 172 0.608 0.0189 0.23 0.122 0.163 0.479 0.567 0.00127 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 1.52 0.0253 1.01 0.142 0.188 1.18 1.19 0.00314 0.00316 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 14730.739 0.005 0.019 0.254 0.633 0.123 0.163 0.465 0.596 0.00124 0.00158 ! Validation 144 14730.739 0.005 0.0174 0.665 1.01 0.118 0.156 0.921 0.964 0.00245 0.00256 Wall time: 14730.73902833322 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.507 0.0204 0.0998 0.127 0.169 0.317 0.374 0.000843 0.000994 145 172 1.14 0.0247 0.646 0.143 0.186 0.873 0.951 0.00232 0.00253 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.576 0.0262 0.0509 0.145 0.192 0.223 0.267 0.000593 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 145 14832.575 0.005 0.0199 0.417 0.815 0.126 0.167 0.623 0.763 0.00166 0.00203 ! Validation 145 14832.575 0.005 0.02 0.0705 0.47 0.128 0.167 0.251 0.314 0.000669 0.000835 Wall time: 14832.57528716186 ! Best model 145 0.470 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 1.2 0.0188 0.822 0.123 0.162 1.04 1.07 0.00277 0.00285 146 172 0.8 0.0197 0.406 0.126 0.166 0.706 0.753 0.00188 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.63 0.026 0.11 0.143 0.191 0.355 0.392 0.000945 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 146 14934.469 0.005 0.0194 0.325 0.714 0.124 0.165 0.536 0.674 0.00143 0.00179 ! Validation 146 14934.469 0.005 0.0183 0.306 0.672 0.121 0.16 0.568 0.654 0.00151 0.00174 Wall time: 14934.46949602291 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.386 0.0167 0.0512 0.116 0.153 0.212 0.268 0.000563 0.000712 147 172 0.506 0.0186 0.134 0.12 0.161 0.345 0.432 0.000918 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.608 0.0227 0.153 0.134 0.178 0.437 0.463 0.00116 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 147 15036.274 0.005 0.0189 0.281 0.658 0.122 0.162 0.476 0.626 0.00127 0.00167 ! Validation 147 15036.274 0.005 0.0165 0.0586 0.388 0.115 0.152 0.225 0.286 0.000599 0.000761 Wall time: 15036.274426287971 ! Best model 147 0.388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.424 0.0157 0.111 0.112 0.148 0.341 0.394 0.000907 0.00105 148 172 0.56 0.0177 0.206 0.118 0.157 0.473 0.537 0.00126 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.479 0.0221 0.0381 0.132 0.176 0.213 0.231 0.000566 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 148 15138.067 0.005 0.0173 0.22 0.566 0.117 0.155 0.433 0.555 0.00115 0.00148 ! Validation 148 15138.067 0.005 0.0161 0.212 0.534 0.113 0.15 0.475 0.545 0.00126 0.00145 Wall time: 15138.067316382192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.505 0.018 0.146 0.12 0.159 0.399 0.451 0.00106 0.0012 149 172 0.498 0.0186 0.127 0.12 0.161 0.345 0.421 0.000919 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.507 0.0223 0.0618 0.133 0.176 0.247 0.294 0.000656 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 149 15239.862 0.005 0.0179 0.307 0.665 0.12 0.158 0.513 0.655 0.00136 0.00174 ! Validation 149 15239.862 0.005 0.0153 0.236 0.543 0.111 0.146 0.499 0.574 0.00133 0.00153 Wall time: 15239.862317871768 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.353 0.0153 0.0477 0.11 0.146 0.2 0.258 0.000533 0.000687 150 172 0.606 0.0224 0.158 0.135 0.177 0.368 0.469 0.000977 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 1.49 0.0247 0.993 0.141 0.186 1.16 1.18 0.00307 0.00313 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 15341.799 0.005 0.0165 0.264 0.594 0.115 0.152 0.456 0.608 0.00121 0.00162 ! Validation 150 15341.799 0.005 0.0194 1.45 1.84 0.126 0.165 1.4 1.43 0.00372 0.00379 Wall time: 15341.799307052046 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 1.02 0.0159 0.705 0.113 0.149 0.975 0.993 0.00259 0.00264 151 172 0.441 0.0172 0.0975 0.117 0.155 0.311 0.369 0.000828 0.000982 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.517 0.0211 0.0951 0.129 0.172 0.362 0.365 0.000963 0.00097 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 15443.583 0.005 0.017 0.295 0.635 0.116 0.154 0.508 0.642 0.00135 0.00171 ! Validation 151 15443.583 0.005 0.0157 0.213 0.526 0.113 0.148 0.478 0.545 0.00127 0.00145 Wall time: 15443.583737526089 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 1.14 0.0163 0.811 0.114 0.151 1.02 1.06 0.00272 0.00283 152 172 0.471 0.0172 0.126 0.118 0.155 0.34 0.42 0.000905 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.911 0.0197 0.518 0.125 0.166 0.845 0.851 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 152 15545.618 0.005 0.0157 0.235 0.549 0.112 0.148 0.454 0.574 0.00121 0.00153 ! Validation 152 15545.618 0.005 0.0147 0.505 0.798 0.109 0.143 0.797 0.84 0.00212 0.00223 Wall time: 15545.617998961825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.349 0.0142 0.0654 0.106 0.141 0.261 0.302 0.000695 0.000804 153 172 0.481 0.0177 0.128 0.12 0.157 0.353 0.422 0.000939 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.51 0.0207 0.0949 0.13 0.17 0.346 0.364 0.000921 0.000969 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 15647.514 0.005 0.0169 0.32 0.659 0.116 0.154 0.525 0.669 0.0014 0.00178 ! Validation 153 15647.514 0.005 0.0165 0.136 0.466 0.117 0.152 0.366 0.437 0.000972 0.00116 Wall time: 15647.514310833067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.403 0.013 0.143 0.102 0.135 0.401 0.448 0.00107 0.00119 154 172 0.308 0.0129 0.0491 0.102 0.134 0.21 0.262 0.000558 0.000697 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.456 0.0184 0.0883 0.121 0.16 0.33 0.351 0.000878 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 154 15749.412 0.005 0.015 0.2 0.499 0.109 0.145 0.421 0.529 0.00112 0.00141 ! Validation 154 15749.412 0.005 0.0129 0.161 0.418 0.102 0.134 0.389 0.475 0.00104 0.00126 Wall time: 15749.412065435201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.352 0.0134 0.0844 0.103 0.137 0.27 0.344 0.000719 0.000914 155 172 0.378 0.0135 0.107 0.103 0.138 0.294 0.387 0.000782 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 1.21 0.0189 0.829 0.122 0.163 1.07 1.08 0.00284 0.00286 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 15851.250 0.005 0.0147 0.27 0.565 0.108 0.143 0.481 0.615 0.00128 0.00164 ! Validation 155 15851.250 0.005 0.0129 0.218 0.475 0.102 0.134 0.498 0.552 0.00132 0.00147 Wall time: 15851.250560292043 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.32 0.0123 0.0734 0.0987 0.131 0.277 0.32 0.000737 0.000852 156 172 1.18 0.0136 0.904 0.106 0.138 1.09 1.12 0.00291 0.00299 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.533 0.0253 0.0269 0.142 0.188 0.169 0.194 0.00045 0.000516 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 15953.031 0.005 0.0136 0.195 0.467 0.104 0.138 0.398 0.522 0.00106 0.00139 ! Validation 156 15953.031 0.005 0.0205 0.123 0.532 0.129 0.169 0.342 0.414 0.000909 0.0011 Wall time: 15953.031807241961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.886 0.0158 0.57 0.114 0.149 0.831 0.893 0.00221 0.00237 157 172 5.86 0.0595 4.67 0.225 0.289 2.29 2.55 0.00609 0.00679 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 1.88e+03 0.504 1.87e+03 0.647 0.839 51.1 51.1 0.136 0.136 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 16054.822 0.005 0.0163 0.384 0.709 0.112 0.151 0.529 0.73 0.00141 0.00194 ! Validation 157 16054.822 0.005 0.498 1.59e+03 1.6e+03 0.655 0.834 47.2 47.2 0.125 0.126 Wall time: 16054.822102553211 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 25.1 0.917 6.77 0.84 1.13 2.04 3.08 0.00542 0.00818 158 172 19.4 0.862 2.2 0.818 1.1 1.34 1.75 0.00356 0.00466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 27.5 0.932 8.84 0.858 1.14 2.68 3.51 0.00712 0.00935 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 16156.615 0.005 0.983 182 202 0.865 1.17 6.32 16 0.0168 0.0425 ! Validation 158 16156.615 0.005 0.896 1.77 19.7 0.829 1.12 1.23 1.57 0.00327 0.00418 Wall time: 16156.615432937164 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 18.3 0.789 2.5 0.779 1.05 1.45 1.87 0.00386 0.00498 159 172 16.2 0.714 1.91 0.741 0.999 1.32 1.64 0.00351 0.00435 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 21 0.778 5.43 0.78 1.04 2 2.75 0.00531 0.00733 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 16258.394 0.005 0.802 2.96 19 0.786 1.06 1.52 2.03 0.00405 0.00541 ! Validation 159 16258.394 0.005 0.735 1.54 16.2 0.749 1.01 1.19 1.47 0.00317 0.0039 Wall time: 16258.394712952897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 14.6 0.589 2.86 0.671 0.907 1.16 2 0.00309 0.00531 160 172 12.9 0.474 3.41 0.606 0.815 1.61 2.18 0.00428 0.00581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 18 0.549 6.97 0.652 0.876 2.32 3.12 0.00618 0.0083 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 16361.461 0.005 0.604 1.85 13.9 0.68 0.919 1.21 1.61 0.00322 0.00428 ! Validation 160 16361.461 0.005 0.497 1.07 11 0.618 0.834 0.965 1.22 0.00257 0.00325 Wall time: 16361.461847372819 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 9.06 0.41 0.857 0.567 0.757 0.881 1.09 0.00234 0.00291 161 172 8.59 0.359 1.42 0.528 0.708 1.04 1.41 0.00277 0.00375 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 16.6 0.424 8.12 0.58 0.77 2.5 3.37 0.00666 0.00896 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 16464.676 0.005 0.419 1.77 10.2 0.571 0.766 1.21 1.57 0.00322 0.00419 ! Validation 161 16464.676 0.005 0.368 1.06 8.43 0.536 0.718 0.963 1.22 0.00256 0.00324 Wall time: 16464.6767006102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 8.28 0.337 1.55 0.513 0.686 1.15 1.47 0.00306 0.00392 162 172 8.32 0.297 2.38 0.484 0.645 1.67 1.82 0.00445 0.00485 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 12.5 0.353 5.46 0.526 0.703 2.21 2.76 0.00589 0.00735 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 16566.567 0.005 0.331 1.37 7.98 0.508 0.68 1.04 1.38 0.00276 0.00368 ! Validation 162 16566.567 0.005 0.299 0.837 6.81 0.482 0.646 0.857 1.08 0.00228 0.00288 Wall time: 16566.56785816094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 6.4 0.265 1.11 0.456 0.608 1.06 1.24 0.00281 0.00331 163 172 5.77 0.25 0.769 0.441 0.591 0.819 1.04 0.00218 0.00276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 10.2 0.294 4.31 0.477 0.641 1.99 2.46 0.00529 0.00653 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 16668.626 0.005 0.27 1.32 6.73 0.459 0.615 1.03 1.36 0.00274 0.00362 ! Validation 163 16668.626 0.005 0.243 0.755 5.62 0.436 0.583 0.821 1.03 0.00218 0.00273 Wall time: 16668.6262360001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 5.38 0.218 1.01 0.414 0.553 0.976 1.19 0.0026 0.00317 164 172 4.36 0.193 0.498 0.39 0.519 0.664 0.835 0.00177 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 7.73 0.243 2.87 0.435 0.583 1.64 2 0.00437 0.00532 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 16776.049 0.005 0.221 1.13 5.54 0.416 0.555 0.959 1.25 0.00255 0.00334 ! Validation 164 16776.049 0.005 0.199 0.675 4.66 0.395 0.528 0.781 0.972 0.00208 0.00258 Wall time: 16776.049147825222 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 6.46 0.177 2.91 0.375 0.498 1.9 2.02 0.00505 0.00537 165 172 4.92 0.175 1.42 0.374 0.495 1.15 1.41 0.00306 0.00374 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 5.46 0.21 1.26 0.406 0.542 1.16 1.33 0.00309 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 165 16877.982 0.005 0.186 1.38 5.09 0.383 0.51 1.08 1.39 0.00289 0.00369 ! Validation 165 16877.982 0.005 0.171 0.631 4.06 0.368 0.489 0.726 0.939 0.00193 0.0025 Wall time: 16877.982141851913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 4.25 0.162 0.999 0.36 0.477 0.937 1.18 0.00249 0.00314 166 172 5.42 0.166 2.1 0.362 0.482 1.55 1.71 0.00413 0.00456 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 5.01 0.194 1.13 0.391 0.521 1.1 1.26 0.00293 0.00334 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 16979.784 0.005 0.164 1.91 5.18 0.361 0.478 1.31 1.63 0.00349 0.00434 ! Validation 166 16979.784 0.005 0.161 1.45 4.68 0.356 0.475 1.23 1.43 0.00327 0.00379 Wall time: 16979.784734739922 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 3.33 0.147 0.393 0.344 0.453 0.615 0.741 0.00164 0.00197 167 172 4.15 0.139 1.37 0.336 0.441 1.24 1.39 0.00329 0.00368 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 4.04 0.169 0.656 0.367 0.486 0.819 0.958 0.00218 0.00255 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 17081.564 0.005 0.148 1.13 4.09 0.345 0.455 1.01 1.25 0.00268 0.00334 ! Validation 167 17081.564 0.005 0.137 0.456 3.19 0.331 0.437 0.626 0.799 0.00166 0.00212 Wall time: 17081.564475234132 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 3.78 0.13 1.19 0.325 0.426 1.03 1.29 0.00274 0.00343 168 172 3 0.115 0.695 0.306 0.401 0.823 0.986 0.00219 0.00262 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 4.66 0.147 1.71 0.343 0.454 1.42 1.55 0.00379 0.00411 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 17183.338 0.005 0.128 0.666 3.23 0.322 0.424 0.763 0.965 0.00203 0.00257 ! Validation 168 17183.338 0.005 0.118 1.18 3.54 0.308 0.405 1.14 1.29 0.00304 0.00342 Wall time: 17183.33866997715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 2.54 0.106 0.417 0.293 0.385 0.614 0.764 0.00163 0.00203 169 172 2.27 0.101 0.245 0.286 0.376 0.507 0.585 0.00135 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 3.11 0.129 0.525 0.319 0.425 0.811 0.857 0.00216 0.00228 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 17285.121 0.005 0.112 0.637 2.88 0.301 0.396 0.748 0.944 0.00199 0.00251 ! Validation 169 17285.121 0.005 0.103 0.495 2.55 0.287 0.379 0.644 0.832 0.00171 0.00221 Wall time: 17285.121791032143 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 3.64 0.0935 1.77 0.276 0.361 1.44 1.57 0.00384 0.00419 170 172 2.19 0.0933 0.328 0.274 0.361 0.576 0.677 0.00153 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 2.56 0.118 0.193 0.304 0.407 0.414 0.519 0.0011 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 170 17387.396 0.005 0.1 0.779 2.78 0.283 0.374 0.84 1.04 0.00223 0.00278 ! Validation 170 17387.396 0.005 0.0923 0.451 2.3 0.272 0.359 0.63 0.794 0.00168 0.00211 Wall time: 17387.396466255188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 4.2 0.0911 2.38 0.27 0.357 1.74 1.82 0.00462 0.00485 171 172 1.97 0.0855 0.263 0.261 0.346 0.502 0.607 0.00134 0.00161 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 2.27 0.11 0.064 0.292 0.392 0.268 0.299 0.000712 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 171 17489.301 0.005 0.0916 0.934 2.77 0.27 0.358 0.925 1.14 0.00246 0.00304 ! Validation 171 17489.301 0.005 0.0856 0.241 1.95 0.261 0.346 0.442 0.581 0.00118 0.00155 Wall time: 17489.30175555218 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 1.85 0.0799 0.25 0.253 0.334 0.506 0.592 0.00135 0.00157 172 172 1.78 0.0782 0.219 0.25 0.331 0.46 0.553 0.00122 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 3.17 0.102 1.13 0.28 0.378 1.23 1.25 0.00327 0.00334 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 17591.282 0.005 0.0843 0.774 2.46 0.258 0.343 0.844 1.04 0.00224 0.00277 ! Validation 172 17591.282 0.005 0.0781 1.21 2.77 0.249 0.331 1.21 1.3 0.00322 0.00346 Wall time: 17591.28234146582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 2.5 0.0732 1.03 0.241 0.32 1.03 1.2 0.00275 0.00319 173 172 2.6 0.0741 1.12 0.241 0.322 1.15 1.25 0.00307 0.00333 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 2.46 0.0957 0.546 0.269 0.366 0.85 0.874 0.00226 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 173 17693.182 0.005 0.0775 0.626 2.18 0.247 0.329 0.732 0.935 0.00195 0.00249 ! Validation 173 17693.182 0.005 0.0717 0.532 1.97 0.237 0.317 0.717 0.862 0.00191 0.00229 Wall time: 17693.182854850776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 1.88 0.0741 0.401 0.242 0.322 0.605 0.749 0.00161 0.00199 174 172 1.92 0.0743 0.432 0.24 0.322 0.646 0.777 0.00172 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 2.74 0.0925 0.891 0.266 0.36 1.1 1.12 0.00293 0.00297 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 17796.017 0.005 0.0744 1.01 2.5 0.241 0.323 0.921 1.19 0.00245 0.00316 ! Validation 174 17796.017 0.005 0.0714 1.11 2.54 0.236 0.316 1.15 1.25 0.00305 0.00332 Wall time: 17796.016988345888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 2.15 0.0718 0.71 0.234 0.317 0.518 0.996 0.00138 0.00265 175 172 1.74 0.0725 0.285 0.239 0.318 0.534 0.631 0.00142 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 2.93 0.0887 1.16 0.261 0.352 1.26 1.27 0.00334 0.00338 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 17897.978 0.005 0.071 0.888 2.31 0.235 0.315 0.899 1.11 0.00239 0.00296 ! Validation 175 17897.978 0.005 0.0696 1.05 2.44 0.233 0.312 1.14 1.21 0.00302 0.00322 Wall time: 17897.97877081018 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 1.66 0.0687 0.283 0.233 0.31 0.493 0.629 0.00131 0.00167 176 172 2.25 0.0654 0.945 0.225 0.302 1.05 1.15 0.00279 0.00306 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 2.22 0.0845 0.53 0.255 0.344 0.834 0.861 0.00222 0.00229 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 17999.838 0.005 0.0677 0.686 2.04 0.23 0.308 0.783 0.979 0.00208 0.0026 ! Validation 176 17999.838 0.005 0.0648 0.646 1.94 0.225 0.301 0.86 0.95 0.00229 0.00253 Wall time: 17999.838714056183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 1.85 0.0637 0.581 0.222 0.298 0.796 0.901 0.00212 0.0024 177 172 1.87 0.0649 0.57 0.225 0.301 0.793 0.893 0.00211 0.00238 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 1.88 0.0801 0.278 0.248 0.335 0.555 0.624 0.00148 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 177 18101.803 0.005 0.0642 0.713 2 0.224 0.3 0.802 0.999 0.00213 0.00266 ! Validation 177 18101.803 0.005 0.0614 0.137 1.37 0.219 0.293 0.339 0.438 0.0009 0.00117 Wall time: 18101.80311188288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 1.58 0.0639 0.305 0.221 0.299 0.541 0.653 0.00144 0.00174 178 172 1.37 0.0576 0.214 0.213 0.284 0.421 0.547 0.00112 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 1.73 0.0757 0.215 0.241 0.325 0.491 0.548 0.00131 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 178 18203.614 0.005 0.0609 0.605 1.82 0.218 0.292 0.721 0.92 0.00192 0.00245 ! Validation 178 18203.614 0.005 0.0575 0.129 1.28 0.212 0.283 0.331 0.424 0.00088 0.00113 Wall time: 18203.61484802887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 1.55 0.0557 0.438 0.21 0.279 0.636 0.782 0.00169 0.00208 179 172 1.84 0.0549 0.742 0.206 0.277 0.882 1.02 0.00234 0.00271 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 1.56 0.0717 0.129 0.235 0.317 0.353 0.425 0.000939 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 179 18305.420 0.005 0.0574 0.646 1.79 0.211 0.283 0.768 0.95 0.00204 0.00253 ! Validation 179 18305.420 0.005 0.0553 0.186 1.29 0.207 0.278 0.384 0.509 0.00102 0.00135 Wall time: 18305.420420654118 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 3.07 0.0545 1.98 0.207 0.276 1.61 1.67 0.00429 0.00443 180 172 1.36 0.052 0.321 0.203 0.27 0.57 0.669 0.00151 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 2.38 0.068 1.02 0.228 0.308 1.17 1.19 0.00312 0.00317 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 18407.215 0.005 0.0564 0.665 1.79 0.21 0.281 0.774 0.964 0.00206 0.00257 ! Validation 180 18407.215 0.005 0.0518 1.26 2.3 0.201 0.269 1.26 1.33 0.00336 0.00354 Wall time: 18407.215672505088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 5.01 0.0519 3.97 0.203 0.269 2.31 2.36 0.00614 0.00627 181 172 2.83 0.0486 1.86 0.196 0.261 1.56 1.61 0.00416 0.00429 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 3.33 0.065 2.03 0.223 0.301 1.67 1.69 0.00444 0.00448 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 18509.000 0.005 0.0534 0.637 1.71 0.204 0.273 0.733 0.943 0.00195 0.00251 ! Validation 181 18509.000 0.005 0.049 2.03 3.01 0.196 0.262 1.63 1.69 0.00434 0.00449 Wall time: 18509.000327400863 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.6 0.0641 0.315 0.225 0.299 0.503 0.664 0.00134 0.00177 182 172 1.17 0.0527 0.12 0.203 0.271 0.336 0.41 0.000892 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.35 0.0636 0.0804 0.222 0.298 0.291 0.335 0.000773 0.000892 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 18610.784 0.005 0.0542 0.886 1.97 0.206 0.275 0.839 1.11 0.00223 0.00296 ! Validation 182 18610.784 0.005 0.0492 0.148 1.13 0.196 0.262 0.364 0.455 0.000968 0.00121 Wall time: 18610.78422551183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 1.01 0.046 0.085 0.191 0.254 0.271 0.345 0.000721 0.000917 183 172 1.17 0.0461 0.249 0.192 0.254 0.512 0.591 0.00136 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 1.36 0.0588 0.182 0.213 0.287 0.433 0.504 0.00115 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 183 18712.582 0.005 0.0486 0.515 1.49 0.195 0.261 0.666 0.849 0.00177 0.00226 ! Validation 183 18712.582 0.005 0.0455 0.384 1.29 0.189 0.252 0.634 0.732 0.00169 0.00195 Wall time: 18712.582604419906 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 1.21 0.0498 0.211 0.197 0.264 0.422 0.543 0.00112 0.00144 184 172 1.15 0.0421 0.304 0.182 0.243 0.575 0.652 0.00153 0.00173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 1.84 0.0553 0.733 0.206 0.278 0.977 1.01 0.0026 0.00269 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 18814.375 0.005 0.0467 0.578 1.51 0.191 0.255 0.695 0.899 0.00185 0.00239 ! Validation 184 18814.375 0.005 0.042 0.607 1.45 0.182 0.242 0.845 0.921 0.00225 0.00245 Wall time: 18814.375096889213 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 0.956 0.0424 0.109 0.183 0.243 0.294 0.39 0.000782 0.00104 185 172 1.72 0.0418 0.889 0.181 0.242 1.04 1.11 0.00276 0.00296 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 1.71 0.0521 0.664 0.201 0.27 0.937 0.964 0.00249 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 185 18916.171 0.005 0.0436 0.613 1.48 0.185 0.247 0.733 0.926 0.00195 0.00246 ! Validation 185 18916.171 0.005 0.0402 1.04 1.85 0.178 0.237 1.13 1.21 0.00301 0.00321 Wall time: 18916.171598208137 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 1.47 0.0404 0.664 0.174 0.238 0.42 0.964 0.00112 0.00256 186 172 6.73 0.0374 5.98 0.174 0.229 2.87 2.89 0.00763 0.00769 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 2.96 0.0489 1.98 0.195 0.262 1.65 1.66 0.00438 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 186 19017.957 0.005 0.0411 0.619 1.44 0.18 0.24 0.717 0.928 0.00191 0.00247 ! Validation 186 19017.957 0.005 0.0376 1.92 2.67 0.172 0.229 1.58 1.64 0.00421 0.00436 Wall time: 19017.956957492977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 1.44 0.0405 0.628 0.179 0.238 0.819 0.937 0.00218 0.00249 187 172 0.834 0.036 0.115 0.169 0.224 0.322 0.4 0.000857 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 1.16 0.0467 0.223 0.191 0.256 0.522 0.558 0.00139 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 187 19119.753 0.005 0.041 0.625 1.44 0.18 0.239 0.723 0.935 0.00192 0.00249 ! Validation 187 19119.753 0.005 0.0366 0.189 0.92 0.17 0.226 0.433 0.514 0.00115 0.00137 Wall time: 19119.753003767226 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.994 0.0366 0.263 0.17 0.226 0.47 0.606 0.00125 0.00161 188 172 1.03 0.0353 0.327 0.167 0.222 0.602 0.676 0.0016 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.907 0.0434 0.0387 0.184 0.246 0.206 0.233 0.000549 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 188 19221.540 0.005 0.0368 0.519 1.25 0.17 0.227 0.682 0.852 0.00181 0.00227 ! Validation 188 19221.540 0.005 0.0343 0.103 0.788 0.164 0.219 0.301 0.38 0.000801 0.00101 Wall time: 19221.540490242187 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.989 0.0354 0.281 0.166 0.222 0.469 0.627 0.00125 0.00167 189 172 0.952 0.0385 0.181 0.175 0.232 0.408 0.503 0.00108 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 1.87 0.0428 1.01 0.184 0.245 1.18 1.19 0.00313 0.00317 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 19323.335 0.005 0.035 0.575 1.28 0.166 0.221 0.733 0.897 0.00195 0.00239 ! Validation 189 19323.335 0.005 0.0347 0.415 1.11 0.166 0.22 0.596 0.762 0.00158 0.00203 Wall time: 19323.335206237156 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.748 0.0325 0.0975 0.16 0.213 0.32 0.369 0.000851 0.000982 190 172 2.28 0.0327 1.63 0.161 0.214 1.46 1.51 0.00388 0.00401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 1.16 0.0408 0.348 0.179 0.239 0.659 0.698 0.00175 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 190 19425.138 0.005 0.0342 0.613 1.3 0.164 0.219 0.723 0.925 0.00192 0.00246 ! Validation 190 19425.138 0.005 0.0326 0.607 1.26 0.16 0.213 0.855 0.921 0.00227 0.00245 Wall time: 19425.138337743003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 1.02 0.0309 0.399 0.157 0.208 0.652 0.747 0.00174 0.00199 191 172 1.4 0.0322 0.756 0.158 0.212 0.942 1.03 0.00251 0.00273 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 1.55 0.0375 0.8 0.172 0.229 1.04 1.06 0.00276 0.00281 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 19526.933 0.005 0.0319 0.454 1.09 0.158 0.211 0.637 0.797 0.0017 0.00212 ! Validation 191 19526.933 0.005 0.0296 1.16 1.75 0.153 0.204 1.23 1.27 0.00327 0.00339 Wall time: 19526.93296668399 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.713 0.0307 0.0997 0.154 0.207 0.313 0.373 0.000832 0.000993 192 172 0.907 0.0287 0.333 0.151 0.2 0.596 0.683 0.00158 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.845 0.0364 0.117 0.17 0.225 0.364 0.405 0.000968 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 192 19628.734 0.005 0.0308 0.572 1.19 0.156 0.208 0.715 0.894 0.0019 0.00238 ! Validation 192 19628.734 0.005 0.0286 0.53 1.1 0.15 0.2 0.801 0.861 0.00213 0.00229 Wall time: 19628.734200295992 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 2.24 0.0293 1.66 0.152 0.202 1.45 1.52 0.00386 0.00405 193 172 0.744 0.0313 0.118 0.157 0.209 0.346 0.407 0.00092 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.732 0.035 0.0322 0.166 0.221 0.201 0.212 0.000534 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 193 19730.631 0.005 0.0293 0.464 1.05 0.152 0.202 0.641 0.805 0.00171 0.00214 ! Validation 193 19730.631 0.005 0.027 0.0938 0.633 0.146 0.194 0.293 0.362 0.00078 0.000963 Wall time: 19730.631604705937 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.608 0.0268 0.0718 0.145 0.194 0.236 0.317 0.000628 0.000843 194 172 1.2 0.027 0.656 0.147 0.194 0.9 0.958 0.00239 0.00255 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.835 0.0332 0.172 0.161 0.215 0.425 0.49 0.00113 0.0013 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 19832.472 0.005 0.0273 0.405 0.951 0.146 0.195 0.592 0.752 0.00158 0.002 ! Validation 194 19832.472 0.005 0.0258 0.26 0.775 0.143 0.19 0.508 0.603 0.00135 0.0016 Wall time: 19832.47271692287 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.747 0.0277 0.192 0.147 0.197 0.41 0.519 0.00109 0.00138 195 172 0.902 0.0252 0.397 0.141 0.188 0.683 0.745 0.00182 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.701 0.0319 0.063 0.158 0.211 0.236 0.297 0.000627 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 195 19934.409 0.005 0.0272 0.469 1.01 0.146 0.195 0.641 0.81 0.0017 0.00215 ! Validation 195 19934.409 0.005 0.0247 0.162 0.655 0.14 0.186 0.385 0.476 0.00103 0.00127 Wall time: 19934.40936639812 training # 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 0.0238 0.624 0.137 0.183 0.867 0.934 0.00231 0.00248 196 172 1.48 0.0258 0.964 0.136 0.19 0.771 1.16 0.00205 0.00309 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.69 0.0303 0.0836 0.156 0.206 0.293 0.342 0.000778 0.000909 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 20036.240 0.005 0.0253 0.364 0.87 0.141 0.188 0.568 0.713 0.00151 0.0019 ! Validation 196 20036.240 0.005 0.0236 0.11 0.582 0.137 0.182 0.317 0.393 0.000843 0.00104 Wall time: 20036.23994652694 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.883 0.0264 0.354 0.144 0.192 0.607 0.704 0.00162 0.00187 197 172 1.07 0.027 0.528 0.146 0.194 0.792 0.859 0.00211 0.00229 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.657 0.0304 0.0491 0.155 0.206 0.158 0.262 0.000421 0.000697 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 20138.063 0.005 0.0255 0.488 0.999 0.142 0.189 0.66 0.826 0.00176 0.0022 ! Validation 197 20138.063 0.005 0.0237 0.198 0.673 0.137 0.182 0.431 0.526 0.00115 0.0014 Wall time: 20138.063806250226 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 1.28 0.0267 0.743 0.139 0.193 0.628 1.02 0.00167 0.00271 198 172 0.73 0.0214 0.302 0.13 0.173 0.573 0.65 0.00152 0.00173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.958 0.0284 0.389 0.15 0.199 0.652 0.737 0.00173 0.00196 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 20239.877 0.005 0.0242 0.33 0.814 0.138 0.184 0.536 0.68 0.00142 0.00181 ! Validation 198 20239.877 0.005 0.0215 0.174 0.604 0.131 0.173 0.399 0.494 0.00106 0.00131 Wall time: 20239.877391763963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.774 0.0256 0.262 0.143 0.189 0.503 0.605 0.00134 0.00161 199 172 0.529 0.0233 0.0625 0.135 0.181 0.215 0.295 0.000571 0.000786 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.644 0.0285 0.0741 0.151 0.2 0.249 0.322 0.000664 0.000856 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 20341.672 0.005 0.0238 0.471 0.946 0.137 0.182 0.64 0.812 0.0017 0.00216 ! Validation 199 20341.672 0.005 0.022 0.233 0.673 0.132 0.175 0.477 0.571 0.00127 0.00152 Wall time: 20341.672807326075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.619 0.0217 0.185 0.13 0.174 0.423 0.509 0.00112 0.00135 200 172 0.503 0.0218 0.0669 0.13 0.175 0.255 0.306 0.000678 0.000814 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.729 0.0267 0.196 0.146 0.193 0.464 0.523 0.00123 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 200 20444.009 0.005 0.022 0.253 0.693 0.131 0.175 0.47 0.595 0.00125 0.00158 ! Validation 200 20444.009 0.005 0.0201 0.119 0.52 0.126 0.167 0.327 0.408 0.00087 0.00109 Wall time: 20444.009080559947 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.486 0.0204 0.0782 0.127 0.169 0.277 0.331 0.000737 0.000879 201 172 0.827 0.0204 0.42 0.127 0.169 0.723 0.766 0.00192 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.661 0.0257 0.147 0.143 0.19 0.38 0.453 0.00101 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 201 20545.933 0.005 0.0213 0.352 0.778 0.129 0.173 0.569 0.702 0.00151 0.00187 ! Validation 201 20545.933 0.005 0.0198 0.253 0.648 0.125 0.166 0.516 0.594 0.00137 0.00158 Wall time: 20545.93386678677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 1.55 0.0212 1.12 0.13 0.172 1.21 1.25 0.00322 0.00333 202 172 0.587 0.0213 0.162 0.129 0.172 0.393 0.476 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 202 100 0.788 0.0258 0.273 0.143 0.19 0.582 0.617 0.00155 0.00164 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 20647.850 0.005 0.0214 0.393 0.821 0.13 0.173 0.593 0.741 0.00158 0.00197 ! Validation 202 20647.850 0.005 0.0195 0.287 0.677 0.125 0.165 0.557 0.633 0.00148 0.00168 Wall time: 20647.850005040877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 1.17 0.0217 0.739 0.124 0.174 0.448 1.02 0.00119 0.0027 203 172 1.05 0.0198 0.657 0.126 0.166 0.828 0.959 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 203 100 1.41 0.0254 0.898 0.143 0.189 1.11 1.12 0.00294 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 203 20749.773 0.005 0.0205 0.335 0.744 0.127 0.169 0.551 0.684 0.00146 0.00182 ! Validation 203 20749.773 0.005 0.0186 0.449 0.821 0.121 0.161 0.741 0.792 0.00197 0.00211 Wall time: 20749.77298478689 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.878 0.0226 0.425 0.133 0.178 0.676 0.771 0.0018 0.00205 204 172 0.597 0.0196 0.206 0.123 0.165 0.457 0.537 0.00121 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.646 0.0243 0.16 0.139 0.184 0.424 0.474 0.00113 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 204 20851.694 0.005 0.0205 0.378 0.789 0.127 0.169 0.58 0.727 0.00154 0.00193 ! Validation 204 20851.694 0.005 0.0181 0.208 0.571 0.12 0.159 0.46 0.539 0.00122 0.00143 Wall time: 20851.694370088167 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.493 0.0185 0.124 0.121 0.161 0.32 0.416 0.000852 0.00111 205 172 0.693 0.0222 0.249 0.132 0.176 0.435 0.59 0.00116 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 1.14 0.0241 0.655 0.14 0.184 0.948 0.957 0.00252 0.00254 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 20954.000 0.005 0.0192 0.321 0.706 0.123 0.164 0.52 0.67 0.00138 0.00178 ! Validation 205 20954.000 0.005 0.0197 0.0777 0.473 0.126 0.166 0.264 0.33 0.000702 0.000877 Wall time: 20954.000350163784 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 1.65 0.0208 1.23 0.128 0.171 1.25 1.31 0.00333 0.00349 206 172 0.523 0.0193 0.138 0.123 0.164 0.343 0.439 0.000913 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.672 0.0225 0.221 0.134 0.178 0.472 0.556 0.00125 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 206 21055.875 0.005 0.019 0.314 0.693 0.122 0.163 0.527 0.662 0.0014 0.00176 ! Validation 206 21055.875 0.005 0.0171 0.37 0.712 0.117 0.155 0.633 0.719 0.00168 0.00191 Wall time: 21055.8753879969 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.527 0.0171 0.185 0.116 0.154 0.427 0.509 0.00114 0.00135 207 172 0.668 0.0182 0.304 0.12 0.159 0.58 0.652 0.00154 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.616 0.0218 0.181 0.132 0.175 0.423 0.503 0.00113 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 207 21158.089 0.005 0.018 0.261 0.621 0.119 0.158 0.479 0.605 0.00127 0.00161 ! Validation 207 21158.089 0.005 0.016 0.146 0.466 0.113 0.15 0.373 0.451 0.000992 0.0012 Wall time: 21158.089545655996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.628 0.0184 0.26 0.12 0.16 0.545 0.603 0.00145 0.0016 208 172 0.467 0.0173 0.121 0.116 0.156 0.341 0.411 0.000908 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.817 0.0215 0.386 0.132 0.174 0.693 0.735 0.00184 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 208 21259.884 0.005 0.017 0.242 0.581 0.116 0.154 0.455 0.582 0.00121 0.00155 ! Validation 208 21259.884 0.005 0.0163 0.207 0.533 0.114 0.151 0.458 0.537 0.00122 0.00143 Wall time: 21259.88390606921 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.64 0.0195 0.25 0.123 0.165 0.498 0.591 0.00132 0.00157 209 172 0.361 0.0146 0.0691 0.108 0.143 0.261 0.311 0.000694 0.000827 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.561 0.0205 0.152 0.128 0.169 0.379 0.461 0.00101 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 209 21361.672 0.005 0.0177 0.355 0.709 0.118 0.157 0.545 0.704 0.00145 0.00187 ! Validation 209 21361.672 0.005 0.0153 0.135 0.44 0.11 0.146 0.363 0.434 0.000965 0.00115 Wall time: 21361.671914436854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.551 0.018 0.191 0.119 0.159 0.434 0.516 0.00116 0.00137 210 172 0.679 0.0166 0.346 0.114 0.152 0.633 0.696 0.00168 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.6 0.0206 0.187 0.129 0.17 0.406 0.512 0.00108 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 210 21463.542 0.005 0.0175 0.355 0.706 0.117 0.157 0.542 0.705 0.00144 0.00187 ! Validation 210 21463.542 0.005 0.0154 0.146 0.455 0.111 0.147 0.376 0.452 0.001 0.0012 Wall time: 21463.54253642494 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.427 0.016 0.108 0.112 0.15 0.283 0.388 0.000752 0.00103 211 172 0.867 0.0155 0.556 0.112 0.147 0.821 0.882 0.00218 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.612 0.0196 0.22 0.126 0.166 0.496 0.554 0.00132 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 211 21565.680 0.005 0.0156 0.212 0.524 0.111 0.148 0.424 0.544 0.00113 0.00145 ! Validation 211 21565.680 0.005 0.0152 0.839 1.14 0.111 0.146 1.05 1.08 0.00278 0.00288 Wall time: 21565.679891060106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.34 0.0138 0.0647 0.105 0.139 0.227 0.301 0.000603 0.0008 212 172 1.84 0.0175 1.49 0.119 0.157 1.37 1.44 0.00363 0.00384 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.867 0.0219 0.43 0.134 0.175 0.765 0.775 0.00203 0.00206 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 21667.464 0.005 0.0153 0.282 0.587 0.11 0.146 0.473 0.627 0.00126 0.00167 ! Validation 212 21667.464 0.005 0.0176 0.494 0.847 0.119 0.157 0.762 0.831 0.00203 0.00221 Wall time: 21667.464180759154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.517 0.0152 0.213 0.11 0.146 0.477 0.546 0.00127 0.00145 213 172 0.506 0.0143 0.219 0.107 0.142 0.512 0.553 0.00136 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.543 0.0181 0.18 0.121 0.159 0.463 0.502 0.00123 0.00133 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 21769.284 0.005 0.0158 0.277 0.594 0.112 0.149 0.493 0.623 0.00131 0.00166 ! Validation 213 21769.284 0.005 0.0135 0.329 0.599 0.104 0.137 0.615 0.678 0.00164 0.0018 Wall time: 21769.284211928025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 1.01 0.016 0.687 0.112 0.15 0.925 0.98 0.00246 0.00261 214 172 0.458 0.0166 0.127 0.113 0.152 0.365 0.421 0.00097 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.425 0.0197 0.0298 0.126 0.166 0.176 0.204 0.000469 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 214 21875.202 0.005 0.0157 0.37 0.685 0.111 0.148 0.586 0.719 0.00156 0.00191 ! Validation 214 21875.202 0.005 0.0151 0.121 0.423 0.11 0.145 0.345 0.411 0.000917 0.00109 Wall time: 21875.202316370793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.754 0.0141 0.473 0.106 0.14 0.793 0.813 0.00211 0.00216 215 172 0.321 0.0132 0.0559 0.102 0.136 0.218 0.28 0.00058 0.000744 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.368 0.0169 0.031 0.117 0.154 0.189 0.208 0.000502 0.000554 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 21976.986 0.005 0.014 0.136 0.415 0.105 0.14 0.339 0.436 0.0009 0.00116 ! Validation 215 21976.986 0.005 0.012 0.0479 0.288 0.0981 0.13 0.217 0.259 0.000577 0.000688 Wall time: 21976.986612964887 ! Best model 215 0.288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.374 0.0135 0.104 0.103 0.137 0.336 0.381 0.000894 0.00101 216 172 0.377 0.0161 0.0557 0.113 0.15 0.23 0.279 0.000613 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.394 0.0188 0.018 0.124 0.162 0.13 0.159 0.000347 0.000422 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 22078.766 0.005 0.0135 0.252 0.523 0.103 0.138 0.472 0.594 0.00125 0.00158 ! Validation 216 22078.766 0.005 0.0144 0.129 0.417 0.108 0.142 0.358 0.425 0.000952 0.00113 Wall time: 22078.76651341701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.838 0.0131 0.577 0.102 0.135 0.876 0.898 0.00233 0.00239 217 172 0.817 0.0127 0.563 0.101 0.133 0.851 0.887 0.00226 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.459 0.0164 0.13 0.115 0.152 0.385 0.426 0.00102 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 217 22180.542 0.005 0.0141 0.258 0.54 0.105 0.14 0.464 0.601 0.00123 0.0016 ! Validation 217 22180.542 0.005 0.012 0.299 0.538 0.0977 0.129 0.607 0.646 0.00162 0.00172 Wall time: 22180.54243138479 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.619 0.0134 0.35 0.102 0.137 0.625 0.7 0.00166 0.00186 218 172 0.551 0.0115 0.32 0.0962 0.127 0.631 0.669 0.00168 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.441 0.0159 0.122 0.113 0.149 0.341 0.413 0.000908 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 218 22282.326 0.005 0.0129 0.206 0.464 0.101 0.134 0.424 0.536 0.00113 0.00143 ! Validation 218 22282.326 0.005 0.0114 0.103 0.331 0.0956 0.126 0.322 0.379 0.000856 0.00101 Wall time: 22282.326623608824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 1.34 0.0125 1.09 0.101 0.132 1.16 1.23 0.00309 0.00328 219 172 0.324 0.0112 0.0993 0.0943 0.125 0.312 0.373 0.00083 0.000991 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.5 0.0156 0.189 0.112 0.147 0.465 0.514 0.00124 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 219 22384.110 0.005 0.0127 0.229 0.482 0.0998 0.133 0.419 0.566 0.00111 0.0015 ! Validation 219 22384.110 0.005 0.011 0.151 0.371 0.0939 0.124 0.382 0.46 0.00102 0.00122 Wall time: 22384.110127457883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.284 0.0121 0.0416 0.0971 0.13 0.186 0.241 0.000496 0.000641 220 172 0.377 0.0135 0.108 0.102 0.137 0.295 0.388 0.000784 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.761 0.0158 0.444 0.113 0.149 0.689 0.788 0.00183 0.0021 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 22485.898 0.005 0.0126 0.209 0.461 0.0994 0.133 0.416 0.541 0.00111 0.00144 ! Validation 220 22485.898 0.005 0.0115 0.326 0.556 0.0961 0.127 0.627 0.675 0.00167 0.00179 Wall time: 22485.898164627142 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.271 0.0115 0.0403 0.0954 0.127 0.195 0.237 0.000518 0.000631 221 172 0.359 0.0124 0.11 0.099 0.132 0.334 0.393 0.000888 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.348 0.0166 0.0159 0.116 0.152 0.134 0.149 0.000357 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 221 22587.680 0.005 0.0118 0.191 0.426 0.0962 0.128 0.405 0.517 0.00108 0.00137 ! Validation 221 22587.680 0.005 0.0124 0.137 0.384 0.1 0.132 0.359 0.438 0.000956 0.00116 Wall time: 22587.68078188086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.497 0.0112 0.273 0.0948 0.125 0.558 0.618 0.00148 0.00164 222 172 0.464 0.0124 0.217 0.1 0.131 0.497 0.551 0.00132 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 1.94 0.0163 1.62 0.116 0.151 1.49 1.5 0.00395 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 222 22689.469 0.005 0.0119 0.234 0.472 0.097 0.129 0.455 0.572 0.00121 0.00152 ! Validation 222 22689.469 0.005 0.0114 0.791 1.02 0.0958 0.126 1.02 1.05 0.00272 0.0028 Wall time: 22689.469419200905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.316 0.0117 0.0823 0.0961 0.128 0.287 0.339 0.000763 0.000902 223 172 0.341 0.0117 0.107 0.0948 0.128 0.314 0.387 0.000836 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.474 0.0143 0.189 0.107 0.141 0.426 0.514 0.00113 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 223 22792.089 0.005 0.0116 0.169 0.401 0.0955 0.127 0.375 0.486 0.000998 0.00129 ! Validation 223 22792.089 0.005 0.0099 0.118 0.316 0.0891 0.118 0.349 0.407 0.000928 0.00108 Wall time: 22792.089881030843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.251 0.01 0.0507 0.0888 0.118 0.225 0.266 0.000599 0.000708 224 172 0.344 0.0133 0.0789 0.101 0.136 0.261 0.332 0.000693 0.000884 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.309 0.0145 0.0197 0.108 0.142 0.136 0.166 0.000361 0.000441 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 22893.873 0.005 0.0109 0.167 0.384 0.0924 0.123 0.39 0.483 0.00104 0.00128 ! Validation 224 22893.873 0.005 0.0104 0.0435 0.252 0.0917 0.121 0.201 0.247 0.000534 0.000656 Wall time: 22893.873114864808 ! Best model 224 0.252 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.455 0.0122 0.211 0.0989 0.131 0.463 0.543 0.00123 0.00144 225 172 0.358 0.0105 0.149 0.0911 0.121 0.402 0.456 0.00107 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.325 0.0143 0.04 0.107 0.141 0.189 0.236 0.000502 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 225 22995.684 0.005 0.0107 0.187 0.402 0.092 0.123 0.396 0.512 0.00105 0.00136 ! Validation 225 22995.684 0.005 0.01 0.0812 0.282 0.0897 0.119 0.278 0.337 0.00074 0.000896 Wall time: 22995.68441398302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.47 0.0103 0.263 0.09 0.12 0.55 0.606 0.00146 0.00161 226 172 0.366 0.00953 0.176 0.0866 0.115 0.459 0.495 0.00122 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.419 0.013 0.159 0.102 0.135 0.434 0.471 0.00115 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 226 23097.461 0.005 0.0104 0.155 0.363 0.0905 0.121 0.355 0.466 0.000945 0.00124 ! Validation 226 23097.461 0.005 0.00875 0.325 0.5 0.0838 0.111 0.638 0.674 0.0017 0.00179 Wall time: 23097.46102141589 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.274 0.00929 0.0885 0.0853 0.114 0.288 0.352 0.000766 0.000936 227 172 0.255 0.00873 0.08 0.0834 0.11 0.29 0.335 0.00077 0.00089 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.299 0.0127 0.0446 0.101 0.133 0.2 0.25 0.000531 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 227 23199.248 0.005 0.0098 0.143 0.339 0.0877 0.117 0.349 0.447 0.000927 0.00119 ! Validation 227 23199.248 0.005 0.00903 0.0655 0.246 0.0852 0.112 0.248 0.303 0.00066 0.000805 Wall time: 23199.2479854431 ! Best model 227 0.246 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.278 0.00927 0.0923 0.0849 0.114 0.309 0.359 0.000821 0.000956 228 172 0.308 0.0099 0.11 0.0882 0.118 0.307 0.392 0.000816 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.649 0.0129 0.39 0.102 0.135 0.733 0.738 0.00195 0.00196 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 23301.032 0.005 0.00975 0.147 0.342 0.0875 0.117 0.355 0.454 0.000944 0.00121 ! Validation 228 23301.032 0.005 0.0091 0.223 0.405 0.0855 0.113 0.496 0.558 0.00132 0.00148 Wall time: 23301.031958161853 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.236 0.0101 0.0329 0.0896 0.119 0.171 0.214 0.000456 0.00057 229 172 0.264 0.0104 0.0562 0.0911 0.121 0.21 0.28 0.000558 0.000745 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.583 0.013 0.323 0.102 0.135 0.637 0.672 0.00169 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 229 23402.816 0.005 0.01 0.213 0.414 0.0889 0.118 0.435 0.546 0.00116 0.00145 ! Validation 229 23402.816 0.005 0.0089 0.37 0.548 0.0844 0.112 0.684 0.72 0.00182 0.00191 Wall time: 23402.815901423804 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.222 0.00943 0.0333 0.0866 0.115 0.186 0.216 0.000494 0.000574 230 172 0.287 0.00851 0.117 0.0827 0.109 0.359 0.405 0.000955 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.255 0.0116 0.0225 0.0962 0.128 0.15 0.177 0.000399 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 230 23504.619 0.005 0.0104 0.187 0.396 0.0906 0.121 0.39 0.511 0.00104 0.00136 ! Validation 230 23504.619 0.005 0.00806 0.0402 0.201 0.0802 0.106 0.196 0.237 0.000522 0.00063 Wall time: 23504.61916315509 ! Best model 230 0.201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.89 0.0112 0.666 0.094 0.125 0.93 0.965 0.00247 0.00257 231 172 0.278 0.00923 0.0933 0.0854 0.114 0.273 0.361 0.000727 0.000961 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.261 0.0113 0.0348 0.0948 0.126 0.189 0.221 0.000502 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 231 23606.420 0.005 0.00941 0.118 0.307 0.0859 0.115 0.313 0.407 0.000833 0.00108 ! Validation 231 23606.420 0.005 0.00792 0.0265 0.185 0.0797 0.105 0.16 0.193 0.000426 0.000512 Wall time: 23606.42072942201 ! Best model 231 0.185 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.335 0.0082 0.171 0.0811 0.107 0.435 0.489 0.00116 0.0013 232 172 0.409 0.00821 0.245 0.0811 0.107 0.551 0.585 0.00147 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.427 0.0114 0.199 0.095 0.126 0.481 0.527 0.00128 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 232 23708.226 0.005 0.00869 0.119 0.292 0.0824 0.11 0.314 0.407 0.000836 0.00108 ! Validation 232 23708.226 0.005 0.00778 0.203 0.359 0.0789 0.104 0.495 0.533 0.00132 0.00142 Wall time: 23708.2265072898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.283 0.00912 0.1 0.0836 0.113 0.323 0.374 0.000858 0.000995 233 172 0.214 0.00792 0.0555 0.0782 0.105 0.251 0.279 0.000666 0.000741 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.414 0.0108 0.198 0.0929 0.123 0.492 0.526 0.00131 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 233 23810.218 0.005 0.00876 0.113 0.288 0.0828 0.111 0.3 0.397 0.000798 0.00106 ! Validation 233 23810.218 0.005 0.0072 0.0673 0.211 0.0759 0.1 0.256 0.307 0.000682 0.000816 Wall time: 23810.21831749892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.443 0.0124 0.196 0.0991 0.132 0.378 0.523 0.00101 0.00139 234 172 0.239 0.00954 0.0482 0.0853 0.115 0.212 0.26 0.000563 0.000691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.834 0.0109 0.615 0.0933 0.124 0.92 0.927 0.00245 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 234 23912.004 0.005 0.00929 0.188 0.374 0.0852 0.114 0.399 0.512 0.00106 0.00136 ! Validation 234 23912.004 0.005 0.00755 0.736 0.887 0.0776 0.103 0.959 1.01 0.00255 0.0027 Wall time: 23912.004498286173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.248 0.00774 0.0931 0.0782 0.104 0.303 0.361 0.000807 0.00096 235 172 0.361 0.0105 0.152 0.0917 0.121 0.391 0.461 0.00104 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 1.9 0.0139 1.62 0.105 0.139 1.5 1.51 0.00398 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 235 24013.795 0.005 0.00844 0.118 0.287 0.0811 0.109 0.296 0.407 0.000788 0.00108 ! Validation 235 24013.795 0.005 0.0106 0.61 0.823 0.0926 0.122 0.883 0.924 0.00235 0.00246 Wall time: 24013.79583438579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.18 0.00752 0.0296 0.0768 0.103 0.158 0.204 0.00042 0.000541 236 172 0.242 0.00759 0.0901 0.0769 0.103 0.298 0.355 0.000791 0.000944 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.271 0.0102 0.0669 0.0899 0.119 0.242 0.306 0.000645 0.000814 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 24115.580 0.005 0.00941 0.133 0.321 0.0851 0.115 0.318 0.431 0.000845 0.00115 ! Validation 236 24115.580 0.005 0.00677 0.0399 0.175 0.0734 0.0973 0.193 0.236 0.000512 0.000628 Wall time: 24115.579916669056 ! Best model 236 0.175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.22 0.00868 0.0464 0.0819 0.11 0.177 0.255 0.000472 0.000677 237 172 0.471 0.00953 0.281 0.0875 0.115 0.578 0.627 0.00154 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.771 0.0115 0.54 0.0964 0.127 0.835 0.869 0.00222 0.00231 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 24217.919 0.005 0.00825 0.119 0.284 0.0801 0.107 0.315 0.408 0.000838 0.00108 ! Validation 237 24217.919 0.005 0.00903 0.328 0.509 0.0864 0.112 0.646 0.677 0.00172 0.0018 Wall time: 24217.91892180685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.193 0.00761 0.0407 0.0775 0.103 0.208 0.239 0.000552 0.000635 238 172 0.351 0.0102 0.146 0.0885 0.12 0.409 0.452 0.00109 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.291 0.0101 0.089 0.0903 0.119 0.302 0.353 0.000804 0.000938 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 24319.705 0.005 0.00788 0.117 0.275 0.0784 0.105 0.309 0.405 0.000823 0.00108 ! Validation 238 24319.705 0.005 0.00728 0.0341 0.18 0.0766 0.101 0.181 0.218 0.000481 0.000581 Wall time: 24319.705447490793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.249 0.0111 0.0278 0.0934 0.124 0.156 0.197 0.000416 0.000524 239 172 0.144 0.00631 0.0176 0.0706 0.0939 0.122 0.157 0.000324 0.000418 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.224 0.00968 0.0303 0.0873 0.116 0.169 0.206 0.000451 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 239 24421.484 0.005 0.0092 0.181 0.365 0.0847 0.113 0.374 0.503 0.000995 0.00134 ! Validation 239 24421.484 0.005 0.00649 0.0263 0.156 0.0717 0.0952 0.156 0.192 0.000415 0.00051 Wall time: 24421.484452134 ! Best model 239 0.156 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.184 0.00728 0.038 0.0762 0.101 0.173 0.23 0.000461 0.000613 240 172 0.146 0.00612 0.0233 0.0699 0.0925 0.147 0.18 0.00039 0.00048 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.22 0.00951 0.0294 0.0872 0.115 0.163 0.203 0.000433 0.00054 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 24523.367 0.005 0.00777 0.104 0.26 0.0776 0.104 0.285 0.382 0.000759 0.00102 ! Validation 240 24523.367 0.005 0.00646 0.0499 0.179 0.0718 0.095 0.218 0.264 0.000581 0.000702 Wall time: 24523.367331936024 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.189 0.00732 0.0424 0.077 0.101 0.191 0.244 0.000508 0.000648 241 172 0.534 0.00668 0.401 0.0727 0.0966 0.69 0.749 0.00184 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.274 0.00994 0.0748 0.0895 0.118 0.314 0.323 0.000835 0.00086 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 24626.563 0.005 0.0074 0.0995 0.248 0.076 0.102 0.289 0.373 0.000768 0.000991 ! Validation 241 24626.563 0.005 0.00697 0.315 0.454 0.075 0.0987 0.624 0.664 0.00166 0.00176 Wall time: 24626.56380424695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.296 0.00702 0.156 0.0737 0.0991 0.425 0.467 0.00113 0.00124 242 172 0.256 0.00776 0.101 0.0775 0.104 0.342 0.376 0.00091 0.000999 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.247 0.00967 0.0537 0.088 0.116 0.228 0.274 0.000607 0.000729 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 24728.433 0.005 0.00747 0.123 0.272 0.0764 0.102 0.327 0.414 0.000869 0.0011 ! Validation 242 24728.433 0.005 0.00652 0.0933 0.224 0.0721 0.0955 0.307 0.361 0.000817 0.000961 Wall time: 24728.43317142781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.206 0.0085 0.0364 0.0815 0.109 0.143 0.226 0.000381 0.0006 243 172 0.378 0.00775 0.223 0.0798 0.104 0.518 0.559 0.00138 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.623 0.00925 0.438 0.0868 0.114 0.766 0.783 0.00204 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 243 24830.305 0.005 0.00722 0.0894 0.234 0.0749 0.1 0.265 0.353 0.000706 0.00094 ! Validation 243 24830.305 0.005 0.00661 0.132 0.264 0.073 0.0962 0.39 0.43 0.00104 0.00114 Wall time: 24830.305423644837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.2 0.00702 0.0595 0.0739 0.0991 0.238 0.288 0.000633 0.000767 244 172 0.158 0.00671 0.0235 0.073 0.0969 0.156 0.181 0.000414 0.000482 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.217 0.00851 0.0473 0.0821 0.109 0.213 0.257 0.000567 0.000684 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 24932.162 0.005 0.00715 0.0977 0.241 0.0747 0.1 0.284 0.37 0.000755 0.000983 ! Validation 244 24932.162 0.005 0.00616 0.0483 0.172 0.07 0.0928 0.215 0.26 0.000571 0.000691 Wall time: 24932.16237384919 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 21.9 0.832 5.25 0.803 1.08 1.83 2.71 0.00486 0.00721 245 172 9.25 0.355 2.16 0.522 0.704 1.48 1.74 0.00394 0.00462 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 12.6 0.398 4.6 0.547 0.746 1.96 2.54 0.0052 0.00674 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 25034.828 0.005 0.507 21.3 31.5 0.532 0.842 2.46 5.46 0.00653 0.0145 ! Validation 245 25034.828 0.005 0.352 0.865 7.9 0.518 0.701 0.881 1.1 0.00234 0.00293 Wall time: 25034.828162536956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 3.56 0.152 0.516 0.347 0.461 0.738 0.85 0.00196 0.00226 246 172 6.2 0.127 3.67 0.313 0.421 2.17 2.26 0.00578 0.00602 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 4.84 0.143 1.98 0.333 0.447 1.65 1.67 0.00438 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 246 25136.712 0.005 0.196 2.13 6.05 0.385 0.524 1.36 1.72 0.0036 0.00459 ! Validation 246 25136.712 0.005 0.122 1.14 3.58 0.311 0.413 1.14 1.26 0.00302 0.00335 Wall time: 25136.712211168837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 2.39 0.0984 0.426 0.28 0.371 0.603 0.772 0.0016 0.00205 247 172 2.02 0.0856 0.312 0.261 0.346 0.505 0.661 0.00134 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 2.21 0.102 0.18 0.279 0.377 0.378 0.501 0.00101 0.00133 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 25238.608 0.005 0.101 0.763 2.79 0.283 0.377 0.798 1.03 0.00212 0.00275 ! Validation 247 25238.608 0.005 0.0848 0.203 1.9 0.259 0.344 0.412 0.533 0.0011 0.00142 Wall time: 25238.60872763116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 3.12 0.0805 1.51 0.252 0.336 1.33 1.45 0.00353 0.00386 248 172 1.59 0.07 0.192 0.236 0.313 0.372 0.518 0.00099 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 1.8 0.0831 0.142 0.251 0.341 0.429 0.446 0.00114 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 248 25340.505 0.005 0.0808 0.831 2.45 0.252 0.336 0.857 1.08 0.00228 0.00287 ! Validation 248 25340.505 0.005 0.071 0.162 1.58 0.236 0.315 0.381 0.477 0.00101 0.00127 Wall time: 25340.505300926045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 1.69 0.0651 0.388 0.225 0.302 0.641 0.737 0.0017 0.00196 249 172 1.48 0.0607 0.269 0.217 0.291 0.495 0.614 0.00132 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 1.57 0.0676 0.22 0.227 0.307 0.492 0.555 0.00131 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 249 25442.390 0.005 0.0653 0.492 1.8 0.226 0.302 0.659 0.829 0.00175 0.00221 ! Validation 249 25442.390 0.005 0.0586 0.551 1.72 0.214 0.286 0.778 0.878 0.00207 0.00234 Wall time: 25442.39069148712 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 1.57 0.0584 0.402 0.214 0.286 0.609 0.75 0.00162 0.002 250 172 1.56 0.0496 0.572 0.198 0.263 0.757 0.894 0.00201 0.00238 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 1.32 0.0588 0.148 0.212 0.287 0.352 0.455 0.000935 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 250 25544.284 0.005 0.0561 0.592 1.71 0.209 0.28 0.728 0.909 0.00194 0.00242 ! Validation 250 25544.284 0.005 0.0513 0.145 1.17 0.2 0.268 0.359 0.45 0.000955 0.0012 Wall time: 25544.284043657128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 1.4 0.0491 0.415 0.196 0.262 0.659 0.762 0.00175 0.00203 251 172 0.947 0.0431 0.0845 0.184 0.246 0.279 0.344 0.000742 0.000914 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 1.35 0.0523 0.308 0.201 0.27 0.577 0.656 0.00154 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 251 25646.172 0.005 0.0499 0.543 1.54 0.197 0.264 0.692 0.872 0.00184 0.00232 ! Validation 251 25646.172 0.005 0.0453 0.273 1.18 0.188 0.252 0.533 0.618 0.00142 0.00164 Wall time: 25646.172743396834 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 1.13 0.044 0.248 0.186 0.248 0.468 0.589 0.00124 0.00157 252 172 1.71 0.0436 0.843 0.184 0.247 0.987 1.09 0.00263 0.00289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 1.66 0.0489 0.683 0.194 0.261 0.86 0.977 0.00229 0.0026 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 25748.061 0.005 0.0448 0.588 1.48 0.187 0.25 0.734 0.907 0.00195 0.00241 ! Validation 252 25748.061 0.005 0.0426 1.55 2.4 0.182 0.244 1.42 1.47 0.00377 0.00391 Wall time: 25748.06099807704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 1.01 0.0423 0.165 0.182 0.243 0.382 0.48 0.00102 0.00128 253 172 1.36 0.0437 0.486 0.186 0.247 0.697 0.824 0.00185 0.00219 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 1.95 0.0487 0.976 0.195 0.261 1.16 1.17 0.00307 0.00311 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 25849.941 0.005 0.0416 0.623 1.46 0.18 0.241 0.745 0.934 0.00198 0.00248 ! Validation 253 25849.941 0.005 0.0439 0.546 1.42 0.185 0.248 0.771 0.874 0.00205 0.00232 Wall time: 25849.94166273903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 1.48 0.0374 0.736 0.17 0.229 0.882 1.01 0.00235 0.0027 254 172 0.989 0.0393 0.204 0.175 0.234 0.435 0.534 0.00116 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 1.76 0.0424 0.914 0.181 0.244 1.1 1.13 0.00293 0.00301 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 25951.818 0.005 0.0387 0.475 1.25 0.174 0.233 0.656 0.815 0.00174 0.00217 ! Validation 254 25951.818 0.005 0.037 0.579 1.32 0.17 0.227 0.844 0.9 0.00224 0.00239 Wall time: 25951.81835880503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.826 0.0323 0.179 0.16 0.213 0.393 0.501 0.00104 0.00133 255 172 0.87 0.0328 0.214 0.161 0.214 0.45 0.547 0.0012 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.917 0.0386 0.144 0.174 0.232 0.369 0.449 0.00098 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 255 26053.708 0.005 0.0352 0.487 1.19 0.166 0.222 0.645 0.825 0.00172 0.00219 ! Validation 255 26053.708 0.005 0.0335 0.25 0.921 0.162 0.217 0.511 0.591 0.00136 0.00157 Wall time: 26053.70836012019 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 0.777 0.0311 0.155 0.157 0.209 0.368 0.465 0.000978 0.00124 256 172 1.29 0.0357 0.58 0.167 0.223 0.78 0.9 0.00207 0.00239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 1.73 0.0377 0.978 0.172 0.23 1.15 1.17 0.00306 0.00311 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 26155.599 0.005 0.0336 0.6 1.27 0.163 0.217 0.729 0.916 0.00194 0.00244 ! Validation 256 26155.599 0.005 0.0334 0.385 1.05 0.162 0.216 0.647 0.734 0.00172 0.00195 Wall time: 26155.598910807166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 1.02 0.0295 0.43 0.153 0.203 0.694 0.776 0.00185 0.00206 257 172 2.51 0.0316 1.88 0.158 0.21 1.56 1.62 0.00414 0.00431 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 3.45 0.0343 2.77 0.165 0.219 1.95 1.97 0.00518 0.00523 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 26257.483 0.005 0.0307 0.395 1.01 0.156 0.207 0.573 0.742 0.00152 0.00197 ! Validation 257 26257.483 0.005 0.0296 2.02 2.61 0.153 0.203 1.63 1.68 0.00433 0.00447 Wall time: 26257.483256576117 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 1.45 0.0291 0.866 0.152 0.202 0.98 1.1 0.00261 0.00293 258 172 0.99 0.0298 0.394 0.154 0.204 0.657 0.742 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 258 100 0.766 0.0328 0.111 0.16 0.214 0.35 0.393 0.000932 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 258 26359.393 0.005 0.0288 0.535 1.11 0.151 0.201 0.701 0.865 0.00186 0.0023 ! Validation 258 26359.393 0.005 0.029 0.266 0.847 0.152 0.201 0.541 0.61 0.00144 0.00162 Wall time: 26359.393487628084 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.653 0.0258 0.138 0.144 0.19 0.365 0.439 0.000971 0.00117 259 172 0.665 0.0263 0.139 0.144 0.192 0.317 0.441 0.000844 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.614 0.0285 0.0432 0.151 0.2 0.22 0.246 0.000584 0.000654 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 26461.291 0.005 0.0262 0.352 0.876 0.145 0.192 0.562 0.701 0.00149 0.00186 ! Validation 259 26461.291 0.005 0.0241 0.0862 0.568 0.139 0.183 0.285 0.347 0.000759 0.000923 Wall time: 26461.29118477879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.737 0.0275 0.186 0.148 0.196 0.391 0.511 0.00104 0.00136 260 172 0.59 0.024 0.109 0.139 0.183 0.324 0.39 0.000862 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.61 0.0269 0.0727 0.147 0.194 0.297 0.319 0.000789 0.000848 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 26563.182 0.005 0.0259 0.503 1.02 0.144 0.19 0.644 0.839 0.00171 0.00223 ! Validation 260 26563.182 0.005 0.0225 0.085 0.534 0.135 0.177 0.283 0.345 0.000753 0.000917 Wall time: 26563.18241871521 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 1.31 0.0214 0.879 0.132 0.173 1.08 1.11 0.00287 0.00295 261 172 1.22 0.0204 0.818 0.128 0.169 1.01 1.07 0.0027 0.00284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.862 0.0249 0.364 0.141 0.187 0.643 0.714 0.00171 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 261 26665.066 0.005 0.0227 0.382 0.835 0.135 0.178 0.592 0.731 0.00158 0.00194 ! Validation 261 26665.066 0.005 0.0204 0.686 1.09 0.129 0.169 0.9 0.979 0.00239 0.0026 Wall time: 26665.065922417212 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 1.47 0.0225 1.02 0.134 0.178 1.15 1.19 0.00306 0.00317 262 172 2.52 0.0276 1.97 0.149 0.196 1.58 1.66 0.00421 0.00441 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 0.947 0.0309 0.329 0.157 0.208 0.565 0.678 0.0015 0.0018 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 26766.957 0.005 0.0221 0.564 1.01 0.133 0.176 0.698 0.887 0.00186 0.00236 ! Validation 262 26766.957 0.005 0.0272 0.191 0.735 0.148 0.195 0.41 0.516 0.00109 0.00137 Wall time: 26766.957565060817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.728 0.019 0.349 0.124 0.163 0.615 0.698 0.00164 0.00186 263 172 0.752 0.0206 0.339 0.13 0.17 0.605 0.689 0.00161 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.877 0.0243 0.391 0.139 0.184 0.736 0.739 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 263 26868.849 0.005 0.0215 0.351 0.782 0.131 0.174 0.554 0.7 0.00147 0.00186 ! Validation 263 26868.849 0.005 0.0206 0.0962 0.508 0.13 0.17 0.306 0.367 0.000815 0.000975 Wall time: 26868.84946087189 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.764 0.0189 0.386 0.125 0.163 0.682 0.734 0.00181 0.00195 264 172 0.395 0.0174 0.046 0.119 0.156 0.215 0.254 0.000572 0.000675 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.518 0.0213 0.0917 0.13 0.173 0.308 0.358 0.00082 0.000952 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 26971.506 0.005 0.0199 0.364 0.761 0.126 0.167 0.574 0.713 0.00153 0.0019 ! Validation 264 26971.506 0.005 0.0171 0.0599 0.402 0.118 0.155 0.227 0.289 0.000604 0.00077 Wall time: 26971.506764203776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.415 0.0179 0.0568 0.121 0.158 0.228 0.282 0.000607 0.00075 265 172 0.464 0.0167 0.129 0.116 0.153 0.364 0.425 0.000969 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.602 0.0208 0.185 0.128 0.171 0.46 0.509 0.00122 0.00135 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 27073.406 0.005 0.0182 0.368 0.732 0.121 0.16 0.583 0.717 0.00155 0.00191 ! Validation 265 27073.406 0.005 0.0163 0.193 0.519 0.115 0.151 0.428 0.519 0.00114 0.00138 Wall time: 27073.406388735864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.896 0.0166 0.565 0.116 0.152 0.856 0.889 0.00228 0.00236 266 172 0.791 0.0156 0.478 0.112 0.148 0.78 0.818 0.00208 0.00218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.793 0.0197 0.399 0.125 0.166 0.719 0.747 0.00191 0.00199 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 27175.325 0.005 0.0168 0.289 0.625 0.116 0.153 0.52 0.636 0.00138 0.00169 ! Validation 266 27175.325 0.005 0.0153 0.212 0.518 0.112 0.146 0.488 0.544 0.0013 0.00145 Wall time: 27175.325588476844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.564 0.0187 0.189 0.122 0.162 0.461 0.515 0.00123 0.00137 267 172 0.526 0.0178 0.171 0.118 0.158 0.384 0.488 0.00102 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 1.63 0.0193 1.24 0.123 0.164 1.31 1.32 0.0035 0.0035 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 27277.230 0.005 0.0169 0.358 0.697 0.117 0.154 0.557 0.708 0.00148 0.00188 ! Validation 267 27277.230 0.005 0.0149 0.395 0.693 0.11 0.145 0.697 0.743 0.00185 0.00198 Wall time: 27277.23025439121 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 1.27 0.0164 0.938 0.115 0.151 1.06 1.15 0.00282 0.00305 268 172 0.415 0.016 0.0949 0.112 0.15 0.306 0.364 0.000814 0.000969 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.528 0.0182 0.164 0.119 0.159 0.471 0.479 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 268 27379.130 0.005 0.0158 0.295 0.611 0.113 0.149 0.504 0.643 0.00134 0.00171 ! Validation 268 27379.130 0.005 0.0136 0.055 0.327 0.105 0.138 0.226 0.277 0.000601 0.000737 Wall time: 27379.13025908312 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.415 0.0147 0.121 0.109 0.143 0.349 0.412 0.000928 0.0011 269 172 0.822 0.0153 0.516 0.111 0.146 0.772 0.85 0.00205 0.00226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.418 0.0187 0.0438 0.122 0.162 0.173 0.248 0.00046 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 269 27481.032 0.005 0.0147 0.283 0.577 0.109 0.143 0.5 0.628 0.00133 0.00167 ! Validation 269 27481.032 0.005 0.0147 0.0729 0.366 0.11 0.143 0.254 0.319 0.000676 0.000849 Wall time: 27481.0320680961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.659 0.0158 0.344 0.112 0.148 0.627 0.694 0.00167 0.00184 270 172 0.545 0.015 0.244 0.111 0.145 0.524 0.585 0.00139 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.384 0.0182 0.02 0.12 0.16 0.152 0.167 0.000403 0.000445 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 27584.532 0.005 0.0152 0.369 0.673 0.111 0.146 0.586 0.718 0.00156 0.00191 ! Validation 270 27584.532 0.005 0.0143 0.0904 0.377 0.108 0.141 0.294 0.356 0.000782 0.000946 Wall time: 27584.53285266878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.41 0.0141 0.128 0.103 0.14 0.363 0.423 0.000967 0.00113 271 172 1.24 0.0154 0.927 0.111 0.147 1.1 1.14 0.00292 0.00303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.82 0.0189 0.442 0.122 0.163 0.786 0.786 0.00209 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 271 27686.447 0.005 0.0144 0.307 0.595 0.108 0.142 0.521 0.654 0.00139 0.00174 ! Validation 271 27686.447 0.005 0.015 0.13 0.431 0.111 0.145 0.366 0.426 0.000974 0.00113 Wall time: 27686.447851433884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 1.28 0.0143 0.999 0.107 0.141 1.14 1.18 0.00304 0.00314 272 172 0.312 0.0137 0.0378 0.104 0.138 0.18 0.23 0.000478 0.000612 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.775 0.0173 0.43 0.116 0.155 0.754 0.776 0.00201 0.00206 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 27788.359 0.005 0.0142 0.292 0.577 0.107 0.141 0.522 0.639 0.00139 0.0017 ! Validation 272 27788.359 0.005 0.0131 0.23 0.493 0.103 0.135 0.521 0.568 0.00139 0.00151 Wall time: 27788.359858036973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 2.33 0.0189 1.95 0.124 0.162 1.63 1.65 0.00433 0.0044 273 172 0.31 0.0124 0.0611 0.1 0.132 0.244 0.292 0.000648 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.402 0.0164 0.0747 0.113 0.151 0.272 0.323 0.000723 0.000859 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 27890.382 0.005 0.0141 0.269 0.55 0.106 0.14 0.441 0.613 0.00117 0.00163 ! Validation 273 27890.382 0.005 0.0121 0.111 0.353 0.0991 0.13 0.318 0.394 0.000846 0.00105 Wall time: 27890.38237166917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.32 0.0139 0.0432 0.106 0.139 0.201 0.246 0.000534 0.000653 274 172 0.921 0.0142 0.637 0.106 0.141 0.912 0.944 0.00242 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.368 0.0164 0.0389 0.114 0.152 0.177 0.233 0.00047 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 274 27992.560 0.005 0.0129 0.237 0.495 0.102 0.134 0.458 0.575 0.00122 0.00153 ! Validation 274 27992.560 0.005 0.0122 0.0488 0.292 0.0994 0.13 0.207 0.261 0.00055 0.000695 Wall time: 27992.560323923826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.265 0.0115 0.0349 0.0956 0.127 0.178 0.221 0.000474 0.000587 275 172 0.284 0.0116 0.0526 0.0964 0.127 0.232 0.271 0.000617 0.000721 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.343 0.0148 0.0464 0.108 0.144 0.215 0.255 0.000571 0.000677 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 28094.485 0.005 0.012 0.151 0.392 0.098 0.13 0.358 0.459 0.000953 0.00122 ! Validation 275 28094.485 0.005 0.0107 0.064 0.278 0.0933 0.122 0.25 0.299 0.000666 0.000796 Wall time: 28094.485291387886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.34 0.0117 0.106 0.0966 0.128 0.324 0.386 0.000862 0.00103 276 172 1.25 0.011 1.03 0.0942 0.124 1.17 1.2 0.00311 0.00319 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 1.46 0.0148 1.17 0.108 0.144 1.27 1.28 0.00337 0.00339 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 28196.397 0.005 0.0116 0.204 0.436 0.0962 0.127 0.422 0.534 0.00112 0.00142 ! Validation 276 28196.397 0.005 0.0106 0.884 1.1 0.093 0.122 1.08 1.11 0.00288 0.00296 Wall time: 28196.39740201505 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.655 0.0118 0.418 0.0971 0.129 0.714 0.764 0.0019 0.00203 277 172 0.274 0.0106 0.0613 0.0922 0.122 0.193 0.293 0.000515 0.000779 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.595 0.0144 0.306 0.107 0.142 0.573 0.654 0.00152 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 277 28298.990 0.005 0.0116 0.219 0.45 0.0961 0.127 0.433 0.554 0.00115 0.00147 ! Validation 277 28298.990 0.005 0.0106 0.215 0.427 0.093 0.122 0.5 0.548 0.00133 0.00146 Wall time: 28298.99082739791 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.339 0.011 0.118 0.0938 0.124 0.329 0.407 0.000874 0.00108 278 172 0.324 0.0137 0.0503 0.105 0.138 0.205 0.265 0.000544 0.000706 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.363 0.0157 0.0488 0.112 0.148 0.244 0.261 0.000648 0.000695 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 28400.906 0.005 0.012 0.323 0.563 0.0978 0.129 0.525 0.673 0.0014 0.00179 ! Validation 278 28400.906 0.005 0.0123 0.0634 0.309 0.1 0.131 0.241 0.298 0.000642 0.000792 Wall time: 28400.90607161494 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.6 0.00981 0.403 0.0894 0.117 0.733 0.751 0.00195 0.002 279 172 0.23 0.00955 0.039 0.0887 0.116 0.19 0.234 0.000506 0.000621 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.471 0.0131 0.209 0.101 0.135 0.526 0.541 0.0014 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 279 28502.825 0.005 0.0109 0.129 0.347 0.0933 0.124 0.336 0.425 0.000894 0.00113 ! Validation 279 28502.825 0.005 0.00971 0.126 0.32 0.0891 0.117 0.365 0.419 0.000972 0.00111 Wall time: 28502.82499972917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.57 0.0113 0.343 0.094 0.126 0.657 0.692 0.00175 0.00184 280 172 0.654 0.0111 0.432 0.0949 0.125 0.755 0.777 0.00201 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.47 0.0144 0.183 0.107 0.142 0.439 0.505 0.00117 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 280 28604.996 0.005 0.0106 0.191 0.403 0.0919 0.122 0.394 0.517 0.00105 0.00137 ! Validation 280 28604.996 0.005 0.0108 0.269 0.486 0.0943 0.123 0.551 0.613 0.00147 0.00163 Wall time: 28604.996085483115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.271 0.0101 0.0683 0.0906 0.119 0.238 0.309 0.000633 0.000822 281 172 0.365 0.0102 0.161 0.0904 0.119 0.445 0.475 0.00118 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.474 0.0128 0.219 0.1 0.134 0.517 0.553 0.00138 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 281 28706.900 0.005 0.0102 0.186 0.391 0.0904 0.12 0.403 0.511 0.00107 0.00136 ! Validation 281 28706.900 0.005 0.00936 0.103 0.29 0.0871 0.114 0.332 0.379 0.000884 0.00101 Wall time: 28706.89996692678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.415 0.0105 0.205 0.0919 0.121 0.495 0.536 0.00132 0.00142 282 172 0.298 0.0112 0.0731 0.093 0.125 0.27 0.32 0.000718 0.00085 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.341 0.0128 0.0856 0.101 0.134 0.318 0.346 0.000846 0.00092 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 28814.323 0.005 0.0108 0.268 0.483 0.0927 0.123 0.495 0.612 0.00132 0.00163 ! Validation 282 28814.323 0.005 0.00935 0.0497 0.237 0.0873 0.114 0.218 0.264 0.00058 0.000701 Wall time: 28814.32296202192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.234 0.0094 0.0464 0.0868 0.115 0.2 0.255 0.000532 0.000677 283 172 0.628 0.00878 0.452 0.0845 0.111 0.732 0.795 0.00195 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.338 0.0124 0.0899 0.0988 0.132 0.302 0.355 0.000803 0.000943 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 28916.207 0.005 0.00956 0.143 0.334 0.0872 0.116 0.354 0.447 0.000941 0.00119 ! Validation 283 28916.207 0.005 0.0088 0.0942 0.27 0.0845 0.111 0.313 0.363 0.000832 0.000965 Wall time: 28916.20731425006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.604 0.00985 0.407 0.0885 0.117 0.725 0.754 0.00193 0.00201 284 172 0.345 0.0106 0.132 0.093 0.122 0.334 0.43 0.000888 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.36 0.0131 0.0981 0.102 0.135 0.334 0.37 0.000888 0.000985 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 29018.093 0.005 0.00966 0.212 0.405 0.0877 0.116 0.435 0.545 0.00116 0.00145 ! Validation 284 29018.093 0.005 0.00976 0.183 0.379 0.0896 0.117 0.452 0.506 0.0012 0.00135 Wall time: 29018.09324288182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.213 0.00926 0.0281 0.0855 0.114 0.161 0.198 0.000428 0.000528 285 172 0.237 0.00972 0.0428 0.0887 0.117 0.18 0.245 0.000479 0.000651 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.356 0.0121 0.114 0.0983 0.13 0.298 0.4 0.000792 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 285 29119.994 0.005 0.0101 0.233 0.436 0.0898 0.119 0.44 0.571 0.00117 0.00152 ! Validation 285 29119.994 0.005 0.00924 0.0787 0.263 0.0868 0.114 0.271 0.332 0.000719 0.000882 Wall time: 29119.994864935055 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.208 0.0089 0.03 0.0827 0.112 0.161 0.205 0.000428 0.000545 286 172 0.184 0.00821 0.0197 0.0811 0.107 0.121 0.166 0.00032 0.000441 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.245 0.0112 0.0215 0.0933 0.125 0.149 0.174 0.000396 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 286 29221.886 0.005 0.009 0.12 0.3 0.0845 0.112 0.313 0.41 0.000833 0.00109 ! Validation 286 29221.886 0.005 0.00784 0.0507 0.207 0.0797 0.105 0.21 0.266 0.000559 0.000708 Wall time: 29221.8860742948 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.506 0.00821 0.342 0.081 0.107 0.668 0.691 0.00178 0.00184 287 172 0.347 0.00907 0.166 0.0851 0.113 0.433 0.481 0.00115 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.262 0.0118 0.026 0.0961 0.128 0.163 0.191 0.000433 0.000507 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 29323.855 0.005 0.00903 0.187 0.368 0.0846 0.112 0.392 0.512 0.00104 0.00136 ! Validation 287 29323.855 0.005 0.00874 0.0277 0.202 0.084 0.111 0.158 0.197 0.000421 0.000524 Wall time: 29323.85547429882 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.262 0.0106 0.0495 0.0922 0.122 0.191 0.263 0.000508 0.0007 288 172 0.196 0.00766 0.0426 0.0785 0.103 0.196 0.244 0.000522 0.000649 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.323 0.0106 0.111 0.0905 0.122 0.345 0.395 0.000918 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 288 29426.712 0.005 0.00877 0.151 0.326 0.0833 0.111 0.351 0.459 0.000933 0.00122 ! Validation 288 29426.712 0.005 0.00754 0.0575 0.208 0.078 0.103 0.241 0.284 0.000641 0.000754 Wall time: 29426.712571816053 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.364 0.00871 0.189 0.0829 0.11 0.454 0.515 0.00121 0.00137 289 172 0.503 0.0103 0.296 0.0921 0.12 0.598 0.644 0.00159 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.677 0.0128 0.42 0.102 0.134 0.743 0.767 0.00198 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 289 29528.586 0.005 0.00815 0.142 0.305 0.0802 0.107 0.334 0.445 0.000889 0.00118 ! Validation 289 29528.586 0.005 0.00986 0.178 0.375 0.0908 0.117 0.445 0.499 0.00118 0.00133 Wall time: 29528.5861773761 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.363 0.00822 0.199 0.0814 0.107 0.497 0.527 0.00132 0.0014 290 172 0.236 0.00705 0.0954 0.0757 0.0993 0.275 0.365 0.000731 0.000971 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.254 0.0104 0.0455 0.0901 0.121 0.193 0.252 0.000513 0.000671 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 29630.903 0.005 0.009 0.215 0.394 0.0846 0.112 0.434 0.548 0.00116 0.00146 ! Validation 290 29630.903 0.005 0.0075 0.0539 0.204 0.0777 0.102 0.221 0.275 0.000587 0.00073 Wall time: 29630.90291200392 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.167 0.00762 0.0151 0.0776 0.103 0.111 0.145 0.000294 0.000387 291 172 0.187 0.00853 0.0168 0.0822 0.109 0.125 0.153 0.000332 0.000408 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.35 0.0107 0.135 0.0917 0.123 0.42 0.434 0.00112 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 291 29732.782 0.005 0.00868 0.206 0.38 0.0827 0.11 0.384 0.537 0.00102 0.00143 ! Validation 291 29732.782 0.005 0.00772 0.0554 0.21 0.079 0.104 0.233 0.278 0.000619 0.00074 Wall time: 29732.782065966167 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.252 0.00717 0.109 0.0753 0.1 0.348 0.39 0.000927 0.00104 292 172 0.447 0.00727 0.301 0.0773 0.101 0.628 0.649 0.00167 0.00173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.37 0.0107 0.156 0.0919 0.123 0.391 0.466 0.00104 0.00124 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 29834.662 0.005 0.00773 0.0907 0.245 0.078 0.104 0.272 0.356 0.000723 0.000946 ! Validation 292 29834.662 0.005 0.00762 0.282 0.435 0.0787 0.103 0.603 0.628 0.0016 0.00167 Wall time: 29834.662348809186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.188 0.00769 0.0338 0.0782 0.104 0.163 0.218 0.000433 0.000579 293 172 0.207 0.00769 0.0528 0.0778 0.104 0.21 0.272 0.000558 0.000722 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.201 0.0095 0.0113 0.0861 0.115 0.116 0.126 0.000308 0.000334 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 29936.542 0.005 0.0078 0.139 0.295 0.0784 0.104 0.341 0.441 0.000908 0.00117 ! Validation 293 29936.542 0.005 0.00676 0.0221 0.157 0.0737 0.0972 0.141 0.176 0.000375 0.000467 Wall time: 29936.54270459013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.188 0.00777 0.0328 0.0787 0.104 0.179 0.214 0.000477 0.000569 294 172 0.159 0.00691 0.0211 0.0746 0.0983 0.133 0.172 0.000354 0.000457 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.211 0.00937 0.0232 0.0853 0.114 0.15 0.18 0.000399 0.000479 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 30040.883 0.005 0.00749 0.127 0.277 0.0767 0.102 0.331 0.421 0.000879 0.00112 ! Validation 294 30040.883 0.005 0.00642 0.0224 0.151 0.0716 0.0947 0.142 0.177 0.000377 0.00047 Wall time: 30040.882924419828 ! Best model 294 0.151 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.215 0.00769 0.0617 0.0773 0.104 0.24 0.294 0.00064 0.000781 295 172 0.191 0.00708 0.0496 0.0743 0.0995 0.229 0.263 0.00061 0.000701 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.384 0.00903 0.203 0.0837 0.112 0.517 0.533 0.00137 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 295 30142.805 0.005 0.00755 0.137 0.288 0.077 0.103 0.344 0.438 0.000914 0.00116 ! Validation 295 30142.805 0.005 0.00641 0.131 0.259 0.0715 0.0947 0.396 0.428 0.00105 0.00114 Wall time: 30142.805474596098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.156 0.00668 0.0224 0.072 0.0967 0.142 0.177 0.000378 0.00047 296 172 0.166 0.00673 0.0319 0.0732 0.097 0.165 0.211 0.000439 0.000561 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.285 0.00935 0.0982 0.0853 0.114 0.342 0.371 0.000908 0.000985 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 30244.699 0.005 0.00714 0.121 0.264 0.0747 0.0999 0.325 0.411 0.000865 0.00109 ! Validation 296 30244.699 0.005 0.00643 0.0564 0.185 0.0719 0.0948 0.243 0.281 0.000646 0.000747 Wall time: 30244.69987698598 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.206 0.00775 0.0509 0.0776 0.104 0.167 0.267 0.000443 0.000709 297 172 0.299 0.0126 0.0475 0.101 0.133 0.201 0.258 0.000535 0.000686 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 1.67 0.0142 1.38 0.107 0.141 1.35 1.39 0.00359 0.0037 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 30346.615 0.005 0.00727 0.155 0.301 0.0754 0.101 0.358 0.466 0.000953 0.00124 ! Validation 297 30346.615 0.005 0.0114 0.998 1.23 0.0975 0.127 1.16 1.18 0.00309 0.00314 Wall time: 30346.61581808515 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.618 0.0075 0.468 0.0786 0.102 0.784 0.809 0.00208 0.00215 298 172 0.326 0.0078 0.17 0.0786 0.104 0.458 0.487 0.00122 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.228 0.00913 0.0451 0.0845 0.113 0.213 0.251 0.000567 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 298 30448.993 0.005 0.00789 0.172 0.33 0.0789 0.105 0.371 0.491 0.000987 0.00131 ! Validation 298 30448.993 0.005 0.00637 0.0535 0.181 0.0716 0.0944 0.232 0.274 0.000616 0.000728 Wall time: 30448.99314366281 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.142 0.00608 0.0208 0.069 0.0922 0.144 0.17 0.000383 0.000453 299 172 0.267 0.0064 0.139 0.0711 0.0946 0.397 0.441 0.00106 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.238 0.0088 0.0624 0.0829 0.111 0.254 0.295 0.000674 0.000786 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 30551.051 0.005 0.00675 0.0914 0.226 0.0725 0.0971 0.269 0.357 0.000715 0.000951 ! Validation 299 30551.051 0.005 0.00621 0.145 0.269 0.0704 0.0932 0.398 0.451 0.00106 0.0012 Wall time: 30551.051477231085 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.252 0.00696 0.112 0.0736 0.0987 0.366 0.396 0.000974 0.00105 300 172 0.167 0.00713 0.024 0.0752 0.0998 0.138 0.183 0.000368 0.000487 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.217 0.00877 0.0416 0.0828 0.111 0.182 0.241 0.000483 0.000642 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 30652.923 0.005 0.0068 0.115 0.251 0.0729 0.0975 0.302 0.402 0.000803 0.00107 ! Validation 300 30652.923 0.005 0.00637 0.0274 0.155 0.0712 0.0944 0.159 0.196 0.000423 0.00052 Wall time: 30652.92367107887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.389 0.00623 0.265 0.0706 0.0934 0.582 0.608 0.00155 0.00162 301 172 0.165 0.00722 0.0207 0.0748 0.1 0.134 0.17 0.000355 0.000452 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.186 0.0083 0.0195 0.0806 0.108 0.161 0.165 0.000427 0.00044 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 30754.869 0.005 0.00676 0.112 0.247 0.0727 0.0972 0.303 0.396 0.000805 0.00105 ! Validation 301 30754.869 0.005 0.00611 0.0981 0.22 0.0697 0.0924 0.334 0.37 0.000889 0.000985 Wall time: 30754.868957587052 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.176 0.0056 0.0644 0.0668 0.0885 0.277 0.3 0.000738 0.000798 302 172 0.152 0.0065 0.0222 0.0723 0.0953 0.147 0.176 0.000392 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.213 0.00849 0.0435 0.0822 0.109 0.238 0.247 0.000634 0.000656 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 30856.748 0.005 0.00691 0.124 0.263 0.0735 0.0983 0.321 0.417 0.000854 0.00111 ! Validation 302 30856.748 0.005 0.00606 0.0896 0.211 0.0696 0.092 0.316 0.354 0.00084 0.000942 Wall time: 30856.74802974006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.435 0.00763 0.282 0.0789 0.103 0.595 0.628 0.00158 0.00167 303 172 0.163 0.00641 0.0344 0.0705 0.0947 0.171 0.219 0.000455 0.000583 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.177 0.00804 0.0166 0.0791 0.106 0.14 0.152 0.000371 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 303 30958.631 0.005 0.00651 0.114 0.244 0.0713 0.0954 0.309 0.399 0.000821 0.00106 ! Validation 303 30958.631 0.005 0.00583 0.021 0.138 0.068 0.0903 0.138 0.172 0.000367 0.000456 Wall time: 30958.631788216066 ! Best model 303 0.138 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.199 0.00648 0.0696 0.07 0.0952 0.281 0.312 0.000748 0.000829 304 172 0.391 0.00561 0.278 0.0664 0.0886 0.611 0.624 0.00163 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.195 0.00763 0.0425 0.0767 0.103 0.19 0.244 0.000505 0.000648 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 31060.527 0.005 0.00608 0.0858 0.207 0.0687 0.0922 0.265 0.346 0.000705 0.000921 ! Validation 304 31060.527 0.005 0.0054 0.0251 0.133 0.0654 0.0869 0.153 0.187 0.000406 0.000498 Wall time: 31060.527371621225 ! Best model 304 0.133 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.277 0.0085 0.107 0.0807 0.109 0.324 0.388 0.000861 0.00103 305 172 0.31 0.00679 0.174 0.0729 0.0974 0.477 0.494 0.00127 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.209 0.0085 0.0391 0.0813 0.109 0.202 0.234 0.000537 0.000622 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 31163.221 0.005 0.00712 0.162 0.304 0.0743 0.0998 0.37 0.475 0.000984 0.00126 ! Validation 305 31163.221 0.005 0.00572 0.0679 0.182 0.0676 0.0894 0.265 0.308 0.000704 0.00082 Wall time: 31163.221431178972 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.213 0.0065 0.0831 0.0721 0.0954 0.272 0.341 0.000722 0.000906 306 172 0.147 0.00606 0.0263 0.0683 0.092 0.141 0.192 0.000376 0.00051 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.232 0.00828 0.066 0.0803 0.108 0.249 0.304 0.000663 0.000808 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 31265.105 0.005 0.00628 0.0877 0.213 0.0699 0.0937 0.262 0.35 0.000696 0.000932 ! Validation 306 31265.105 0.005 0.00586 0.0252 0.142 0.0682 0.0905 0.15 0.188 0.0004 0.0005 Wall time: 31265.105663897004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.255 0.00964 0.0619 0.0891 0.116 0.256 0.294 0.00068 0.000782 307 172 0.372 0.00795 0.213 0.0804 0.105 0.507 0.546 0.00135 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.178 0.00832 0.0115 0.0816 0.108 0.102 0.127 0.00027 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 307 31367.057 0.005 0.00693 0.119 0.258 0.0727 0.0985 0.292 0.408 0.000777 0.00108 ! Validation 307 31367.057 0.005 0.0067 0.0657 0.2 0.0737 0.0968 0.26 0.303 0.000691 0.000806 Wall time: 31367.0573288179 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.163 0.00623 0.0388 0.0701 0.0934 0.2 0.233 0.000531 0.000619 308 172 0.443 0.00627 0.318 0.0701 0.0936 0.65 0.667 0.00173 0.00177 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.197 0.00766 0.044 0.0783 0.103 0.202 0.248 0.000537 0.00066 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 31468.967 0.005 0.00602 0.108 0.229 0.0684 0.0917 0.296 0.389 0.000786 0.00103 ! Validation 308 31468.967 0.005 0.00578 0.0355 0.151 0.0684 0.0899 0.181 0.223 0.000481 0.000593 Wall time: 31468.96712410217 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.195 0.00592 0.0767 0.0689 0.091 0.282 0.327 0.000749 0.000871 309 172 0.168 0.007 0.0277 0.075 0.0989 0.17 0.197 0.000452 0.000523 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.179 0.00808 0.0177 0.0798 0.106 0.144 0.157 0.000382 0.000419 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 31570.850 0.005 0.00668 0.123 0.257 0.0724 0.0966 0.323 0.415 0.000859 0.0011 ! Validation 309 31570.850 0.005 0.00579 0.0235 0.139 0.0681 0.09 0.149 0.181 0.000395 0.000482 Wall time: 31570.85085776681 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.139 0.00502 0.0387 0.0632 0.0838 0.197 0.233 0.000524 0.000619 310 172 0.149 0.00604 0.0285 0.0683 0.0919 0.165 0.2 0.00044 0.000531 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.363 0.00753 0.212 0.0765 0.103 0.439 0.544 0.00117 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 310 31672.747 0.005 0.0058 0.0908 0.207 0.0671 0.0901 0.263 0.356 0.000699 0.000948 ! Validation 310 31672.747 0.005 0.00522 0.0655 0.17 0.0645 0.0855 0.25 0.303 0.000664 0.000805 Wall time: 31672.74737370899 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.14 0.00502 0.0395 0.0624 0.0837 0.198 0.235 0.000526 0.000625 311 172 0.161 0.0054 0.0535 0.0652 0.0869 0.219 0.273 0.000582 0.000727 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.199 0.00668 0.0655 0.0722 0.0966 0.293 0.303 0.000779 0.000805 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 31775.688 0.005 0.00586 0.0842 0.201 0.0675 0.0905 0.258 0.343 0.000687 0.000912 ! Validation 311 31775.688 0.005 0.00488 0.0488 0.146 0.0621 0.0826 0.224 0.261 0.000594 0.000694 Wall time: 31775.68818473583 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.149 0.00574 0.0347 0.0664 0.0896 0.184 0.22 0.00049 0.000586 312 172 0.291 0.0105 0.0822 0.0912 0.121 0.277 0.339 0.000737 0.000901 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.246 0.0091 0.0644 0.0851 0.113 0.285 0.3 0.000757 0.000798 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 31877.527 0.005 0.00582 0.112 0.228 0.067 0.0902 0.301 0.396 0.000801 0.00105 ! Validation 312 31877.527 0.005 0.00749 0.333 0.483 0.0775 0.102 0.664 0.683 0.00177 0.00182 Wall time: 31877.527500989847 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.195 0.00634 0.0678 0.0685 0.0941 0.27 0.308 0.000718 0.000819 313 172 0.179 0.00724 0.0342 0.0775 0.101 0.167 0.219 0.000445 0.000581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.374 0.00901 0.194 0.086 0.112 0.49 0.521 0.0013 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 313 31979.316 0.005 0.00651 0.106 0.236 0.0708 0.0954 0.29 0.385 0.000772 0.00103 ! Validation 313 31979.316 0.005 0.00692 0.105 0.244 0.0757 0.0984 0.344 0.384 0.000916 0.00102 Wall time: 31979.31633267179 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.147 0.00499 0.0472 0.0623 0.0836 0.228 0.257 0.000606 0.000683 314 172 0.416 0.00538 0.309 0.0638 0.0867 0.635 0.657 0.00169 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.176 0.00645 0.0474 0.0711 0.095 0.247 0.257 0.000658 0.000684 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 32081.094 0.005 0.00563 0.0765 0.189 0.0661 0.0887 0.244 0.327 0.00065 0.000869 ! Validation 314 32081.094 0.005 0.00468 0.0631 0.157 0.0609 0.0809 0.265 0.297 0.000704 0.00079 Wall time: 32081.094400379807 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.127 0.00463 0.0339 0.0605 0.0805 0.197 0.218 0.000523 0.000579 315 172 0.165 0.00501 0.0645 0.0633 0.0837 0.275 0.3 0.000731 0.000799 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.166 0.00772 0.0111 0.0784 0.104 0.11 0.125 0.000293 0.000332 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 32182.890 0.005 0.00534 0.0844 0.191 0.0642 0.0864 0.26 0.344 0.000692 0.000914 ! Validation 315 32182.890 0.005 0.00568 0.0206 0.134 0.0672 0.0891 0.134 0.17 0.000356 0.000451 Wall time: 32182.889961055946 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.255 0.00599 0.135 0.0678 0.0915 0.402 0.435 0.00107 0.00116 316 172 80.1 0.143 77.3 0.329 0.446 10.4 10.4 0.0276 0.0276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 70.1 0.226 65.6 0.425 0.563 9.49 9.58 0.0252 0.0255 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 32284.689 0.005 0.00701 0.603 0.743 0.0696 0.0983 0.359 0.875 0.000954 0.00233 ! Validation 316 32284.689 0.005 0.226 41.4 45.9 0.421 0.562 7.55 7.61 0.0201 0.0202 Wall time: 32284.688997182064 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 7.05 0.216 2.72 0.409 0.55 1.79 1.95 0.00475 0.00519 317 172 3.35 0.0908 1.53 0.269 0.356 1.31 1.46 0.00347 0.00389 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 2.99 0.11 0.781 0.294 0.393 0.962 1.05 0.00256 0.00278 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 32386.477 0.005 0.477 10.5 20.1 0.56 0.817 2.12 3.84 0.00564 0.0102 ! Validation 317 32386.477 0.005 0.0894 0.536 2.32 0.267 0.354 0.739 0.866 0.00197 0.0023 Wall time: 32386.4771684031 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 2.3 0.0649 0.999 0.227 0.301 1.08 1.18 0.00287 0.00314 318 172 1.48 0.0521 0.434 0.202 0.27 0.661 0.779 0.00176 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 1.5 0.0647 0.206 0.221 0.301 0.512 0.537 0.00136 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 318 32488.259 0.005 0.068 0.802 2.16 0.231 0.308 0.861 1.06 0.00229 0.00282 ! Validation 318 32488.259 0.005 0.0514 0.391 1.42 0.201 0.268 0.632 0.739 0.00168 0.00197 Wall time: 32488.259181630798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 1.33 0.0391 0.546 0.176 0.234 0.829 0.874 0.00221 0.00232 319 172 0.915 0.0376 0.163 0.172 0.229 0.384 0.477 0.00102 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 1.07 0.0457 0.16 0.187 0.253 0.382 0.473 0.00102 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 319 32590.043 0.005 0.0441 0.481 1.36 0.186 0.248 0.646 0.821 0.00172 0.00218 ! Validation 319 32590.043 0.005 0.0341 0.13 0.811 0.164 0.218 0.338 0.426 0.0009 0.00113 Wall time: 32590.043398499023 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.792 0.0337 0.118 0.162 0.217 0.318 0.406 0.000847 0.00108 320 172 0.777 0.0302 0.172 0.153 0.206 0.415 0.491 0.0011 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 1.39 0.0373 0.642 0.17 0.228 0.916 0.948 0.00244 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 320 32691.910 0.005 0.0388 0.617 1.39 0.173 0.233 0.678 0.929 0.0018 0.00247 ! Validation 320 32691.910 0.005 0.0281 0.279 0.841 0.148 0.198 0.533 0.624 0.00142 0.00166 Wall time: 32691.910347601864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 1.34 0.0283 0.768 0.142 0.199 0.734 1.04 0.00195 0.00276 321 172 0.496 0.0224 0.0476 0.132 0.177 0.22 0.258 0.000586 0.000686 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.677 0.0315 0.0467 0.156 0.21 0.169 0.255 0.000449 0.000679 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 32793.676 0.005 0.0277 0.351 0.905 0.146 0.197 0.56 0.701 0.00149 0.00186 ! Validation 321 32793.676 0.005 0.0232 0.176 0.639 0.134 0.18 0.415 0.496 0.0011 0.00132 Wall time: 32793.67682315502 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.592 0.0218 0.156 0.13 0.175 0.382 0.467 0.00101 0.00124 322 172 0.548 0.021 0.128 0.128 0.171 0.375 0.423 0.000997 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.707 0.0269 0.169 0.145 0.194 0.467 0.486 0.00124 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 322 32895.605 0.005 0.0227 0.282 0.736 0.132 0.178 0.505 0.629 0.00134 0.00167 ! Validation 322 32895.605 0.005 0.0198 0.328 0.723 0.124 0.166 0.611 0.677 0.00162 0.0018 Wall time: 32895.60494636884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 1.54 0.0223 1.09 0.123 0.177 0.857 1.24 0.00228 0.00329 323 172 0.635 0.0179 0.276 0.119 0.158 0.565 0.621 0.0015 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.479 0.0232 0.0145 0.135 0.18 0.104 0.142 0.000276 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 323 32997.374 0.005 0.0198 0.252 0.647 0.123 0.166 0.473 0.593 0.00126 0.00158 ! Validation 323 32997.374 0.005 0.0171 0.184 0.527 0.116 0.155 0.435 0.508 0.00116 0.00135 Wall time: 32997.37459325697 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.517 0.0184 0.148 0.119 0.161 0.388 0.455 0.00103 0.00121 324 172 0.506 0.0193 0.121 0.122 0.164 0.343 0.411 0.000913 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.538 0.0214 0.11 0.129 0.173 0.36 0.391 0.000956 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 324 33099.112 0.005 0.0175 0.254 0.605 0.116 0.157 0.468 0.597 0.00125 0.00159 ! Validation 324 33099.112 0.005 0.0158 0.0702 0.385 0.111 0.148 0.246 0.313 0.000653 0.000833 Wall time: 33099.112572480924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.466 0.0159 0.148 0.111 0.149 0.408 0.455 0.00108 0.00121 325 172 0.339 0.0145 0.0499 0.107 0.142 0.197 0.264 0.000523 0.000703 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.507 0.0196 0.116 0.123 0.165 0.391 0.403 0.00104 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 325 33200.846 0.005 0.0163 0.259 0.585 0.112 0.151 0.481 0.602 0.00128 0.0016 ! Validation 325 33200.846 0.005 0.0143 0.184 0.47 0.106 0.141 0.445 0.507 0.00118 0.00135 Wall time: 33200.846812470816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.416 0.0148 0.119 0.108 0.144 0.36 0.409 0.000958 0.00109 326 172 0.296 0.0131 0.0343 0.102 0.135 0.18 0.219 0.000479 0.000583 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.457 0.0173 0.111 0.116 0.156 0.391 0.394 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 326 33302.765 0.005 0.0146 0.198 0.491 0.106 0.143 0.421 0.527 0.00112 0.0014 ! Validation 326 33302.765 0.005 0.0128 0.0976 0.355 0.1 0.134 0.299 0.369 0.000794 0.000983 Wall time: 33302.765463325195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 1.24 0.0145 0.955 0.107 0.142 1.12 1.16 0.00299 0.00307 327 172 0.514 0.013 0.255 0.1 0.135 0.535 0.597 0.00142 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.374 0.0166 0.0414 0.113 0.152 0.199 0.241 0.000529 0.00064 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 33404.523 0.005 0.0139 0.264 0.543 0.104 0.139 0.465 0.608 0.00124 0.00162 ! Validation 327 33404.523 0.005 0.0122 0.152 0.395 0.0977 0.13 0.398 0.461 0.00106 0.00122 Wall time: 33404.52333461819 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.489 0.0122 0.245 0.0984 0.131 0.523 0.585 0.00139 0.00156 328 172 0.494 0.0113 0.268 0.0944 0.126 0.58 0.612 0.00154 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.322 0.0147 0.0279 0.107 0.143 0.166 0.198 0.000441 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 328 33506.279 0.005 0.0125 0.168 0.418 0.0983 0.132 0.389 0.485 0.00103 0.00129 ! Validation 328 33506.279 0.005 0.0107 0.12 0.334 0.092 0.122 0.352 0.41 0.000935 0.00109 Wall time: 33506.27918817289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.227 0.00979 0.0316 0.0889 0.117 0.175 0.21 0.000466 0.000559 329 172 1.43 0.0146 1.14 0.0946 0.143 0.822 1.26 0.00219 0.00336 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 1.32 0.0142 1.04 0.105 0.141 1.2 1.2 0.00319 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 329 33608.138 0.005 0.0112 0.174 0.398 0.0932 0.125 0.378 0.493 0.001 0.00131 ! Validation 329 33608.138 0.005 0.0106 0.938 1.15 0.0916 0.122 1.12 1.15 0.00299 0.00305 Wall time: 33608.13869366981 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.234 0.0102 0.0298 0.0895 0.119 0.16 0.204 0.000427 0.000543 330 172 0.517 0.00986 0.319 0.0877 0.117 0.639 0.668 0.0017 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.476 0.0134 0.209 0.102 0.137 0.436 0.54 0.00116 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 330 33709.822 0.005 0.0107 0.165 0.38 0.0914 0.123 0.371 0.48 0.000987 0.00128 ! Validation 330 33709.822 0.005 0.00953 0.51 0.701 0.087 0.115 0.807 0.844 0.00215 0.00225 Wall time: 33709.82243815996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.302 0.0118 0.0661 0.0957 0.128 0.262 0.304 0.000696 0.000809 331 172 0.434 0.00989 0.237 0.087 0.118 0.534 0.575 0.00142 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.335 0.0123 0.0894 0.0979 0.131 0.277 0.353 0.000737 0.00094 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 33811.521 0.005 0.0103 0.193 0.398 0.0893 0.12 0.403 0.52 0.00107 0.00138 ! Validation 331 33811.521 0.005 0.0089 0.132 0.31 0.0841 0.112 0.385 0.43 0.00103 0.00114 Wall time: 33811.521119968966 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.259 0.00983 0.0626 0.0878 0.117 0.241 0.296 0.000642 0.000787 332 172 0.343 0.00897 0.163 0.0839 0.112 0.43 0.478 0.00114 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.373 0.0116 0.141 0.0946 0.127 0.392 0.444 0.00104 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 332 33913.204 0.005 0.00955 0.165 0.356 0.0862 0.116 0.366 0.48 0.000973 0.00128 ! Validation 332 33913.204 0.005 0.0083 0.0477 0.214 0.081 0.108 0.207 0.258 0.000551 0.000687 Wall time: 33913.20438397722 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.211 0.00934 0.0241 0.0846 0.114 0.153 0.184 0.000406 0.000488 333 172 0.211 0.00794 0.0522 0.0792 0.105 0.226 0.27 0.000601 0.000719 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.301 0.0108 0.0851 0.0916 0.123 0.267 0.345 0.000711 0.000917 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 34014.894 0.005 0.00867 0.112 0.286 0.082 0.11 0.306 0.396 0.000813 0.00105 ! Validation 333 34014.894 0.005 0.00761 0.0273 0.18 0.0778 0.103 0.155 0.195 0.000412 0.00052 Wall time: 34014.89429031499 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.245 0.00892 0.0671 0.0842 0.112 0.266 0.306 0.000708 0.000815 334 172 0.213 0.00951 0.0224 0.0865 0.115 0.145 0.177 0.000385 0.000471 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.265 0.0108 0.048 0.0914 0.123 0.256 0.259 0.00068 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 334 34116.689 0.005 0.00841 0.16 0.329 0.0808 0.108 0.372 0.474 0.000988 0.00126 ! Validation 334 34116.689 0.005 0.00817 0.0858 0.249 0.0807 0.107 0.292 0.346 0.000775 0.000921 Wall time: 34116.68970775511 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.332 0.00789 0.174 0.0786 0.105 0.464 0.494 0.00123 0.00131 335 172 0.188 0.00811 0.0255 0.0792 0.106 0.142 0.189 0.000378 0.000502 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.476 0.0102 0.272 0.0898 0.12 0.527 0.616 0.0014 0.00164 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 34218.372 0.005 0.00801 0.115 0.275 0.0788 0.106 0.308 0.401 0.000818 0.00107 ! Validation 335 34218.372 0.005 0.007 0.0437 0.184 0.0746 0.0989 0.195 0.247 0.000517 0.000658 Wall time: 34218.372859575786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.279 0.00898 0.0991 0.0813 0.112 0.327 0.372 0.000871 0.00099 336 172 0.364 0.00957 0.173 0.0845 0.116 0.392 0.491 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 336 100 0.519 0.0103 0.312 0.0903 0.12 0.655 0.66 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 336 34320.064 0.005 0.00781 0.179 0.335 0.0778 0.104 0.385 0.5 0.00102 0.00133 ! Validation 336 34320.064 0.005 0.00779 0.53 0.686 0.079 0.104 0.837 0.861 0.00223 0.00229 Wall time: 34320.0647986508 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.165 0.00656 0.0333 0.0722 0.0958 0.188 0.216 0.0005 0.000574 337 172 0.284 0.0073 0.138 0.0733 0.101 0.404 0.44 0.00107 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.256 0.00927 0.0703 0.0852 0.114 0.245 0.313 0.000651 0.000834 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 34421.777 0.005 0.00754 0.109 0.259 0.0764 0.103 0.302 0.389 0.000803 0.00104 ! Validation 337 34421.777 0.005 0.00652 0.0673 0.198 0.0719 0.0954 0.256 0.307 0.00068 0.000816 Wall time: 34421.77751427796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.256 0.00646 0.127 0.0712 0.095 0.39 0.421 0.00104 0.00112 338 172 0.162 0.00704 0.0213 0.0739 0.0992 0.133 0.173 0.000354 0.000459 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.197 0.00881 0.0208 0.0831 0.111 0.129 0.17 0.000343 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 338 34523.473 0.005 0.00726 0.123 0.268 0.0749 0.101 0.315 0.414 0.000839 0.0011 ! Validation 338 34523.473 0.005 0.00634 0.0339 0.161 0.0709 0.0942 0.176 0.218 0.000468 0.000579 Wall time: 34523.4735026448 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.752 0.00739 0.604 0.076 0.102 0.889 0.919 0.00237 0.00244 339 172 0.516 0.00705 0.375 0.0748 0.0993 0.704 0.724 0.00187 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.322 0.00918 0.139 0.0856 0.113 0.36 0.441 0.000957 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 339 34625.161 0.005 0.00714 0.166 0.308 0.0743 0.0999 0.374 0.481 0.000995 0.00128 ! Validation 339 34625.161 0.005 0.00686 0.0927 0.23 0.074 0.0979 0.309 0.36 0.000823 0.000957 Wall time: 34625.16170722805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.137 0.00598 0.0176 0.0679 0.0914 0.119 0.157 0.000316 0.000417 340 172 0.164 0.00699 0.0241 0.0733 0.0989 0.152 0.183 0.000404 0.000488 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.212 0.0085 0.0419 0.0815 0.109 0.23 0.242 0.000612 0.000644 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 34726.847 0.005 0.00672 0.0797 0.214 0.0719 0.0969 0.246 0.334 0.000655 0.000888 ! Validation 340 34726.847 0.005 0.00622 0.0612 0.186 0.0703 0.0933 0.25 0.293 0.000666 0.000778 Wall time: 34726.8472996722 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.172 0.00649 0.0423 0.0717 0.0953 0.204 0.243 0.000543 0.000647 341 172 0.188 0.00587 0.0704 0.0678 0.0906 0.256 0.314 0.000681 0.000834 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.176 0.00788 0.0179 0.0788 0.105 0.151 0.158 0.000402 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 341 34830.078 0.005 0.00659 0.104 0.236 0.0713 0.096 0.276 0.381 0.000733 0.00101 ! Validation 341 34830.078 0.005 0.00561 0.0801 0.192 0.0665 0.0886 0.291 0.335 0.000775 0.00089 Wall time: 34830.07866281783 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.171 0.00678 0.0354 0.0703 0.0973 0.207 0.223 0.000551 0.000592 342 172 0.136 0.0055 0.0255 0.0659 0.0877 0.16 0.189 0.000425 0.000502 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.34 0.00765 0.187 0.0774 0.103 0.491 0.511 0.00131 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 342 34931.817 0.005 0.00621 0.0855 0.21 0.069 0.0932 0.268 0.346 0.000714 0.00092 ! Validation 342 34931.817 0.005 0.00551 0.121 0.232 0.0658 0.0878 0.379 0.412 0.00101 0.0011 Wall time: 34931.81691416679 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.185 0.00675 0.05 0.0705 0.0972 0.237 0.264 0.000631 0.000703 343 172 0.493 0.00538 0.385 0.0656 0.0867 0.715 0.734 0.0019 0.00195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.517 0.00822 0.352 0.0797 0.107 0.689 0.702 0.00183 0.00187 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 35033.554 0.005 0.00609 0.105 0.227 0.0684 0.0923 0.299 0.383 0.000796 0.00102 ! Validation 343 35033.554 0.005 0.0059 0.23 0.347 0.0681 0.0908 0.543 0.567 0.00145 0.00151 Wall time: 35033.554314667825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.475 0.00616 0.352 0.07 0.0928 0.677 0.701 0.0018 0.00187 344 172 0.238 0.0062 0.114 0.0691 0.0931 0.374 0.4 0.000994 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.27 0.00783 0.113 0.0786 0.105 0.385 0.398 0.00102 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 344 35135.286 0.005 0.00609 0.117 0.239 0.0684 0.0923 0.31 0.404 0.000825 0.00108 ! Validation 344 35135.286 0.005 0.0061 0.101 0.223 0.0694 0.0924 0.338 0.376 0.000898 0.001 Wall time: 35135.286569791846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.129 0.00557 0.018 0.0656 0.0883 0.12 0.158 0.000319 0.000421 345 172 0.14 0.00552 0.0295 0.0658 0.0879 0.159 0.203 0.000423 0.00054 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.168 0.00711 0.0261 0.0747 0.0997 0.169 0.191 0.000449 0.000508 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 35237.068 0.005 0.00602 0.102 0.222 0.068 0.0918 0.292 0.378 0.000777 0.001 ! Validation 345 35237.068 0.005 0.00523 0.0165 0.121 0.0641 0.0855 0.122 0.152 0.000325 0.000404 Wall time: 35237.06871667318 ! Best model 345 0.121 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.165 0.00579 0.0495 0.0657 0.0899 0.241 0.263 0.00064 0.0007 346 172 1.08 0.00934 0.892 0.0707 0.114 0.808 1.12 0.00215 0.00297 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.894 0.00712 0.752 0.0753 0.0997 0.984 1.03 0.00262 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 346 35338.820 0.005 0.00582 0.0982 0.215 0.0667 0.0902 0.28 0.369 0.000744 0.000983 ! Validation 346 35338.820 0.005 0.00543 0.172 0.281 0.0657 0.0871 0.399 0.491 0.00106 0.00131 Wall time: 35338.82058374677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.173 0.00524 0.0684 0.0639 0.0856 0.274 0.309 0.000728 0.000823 347 172 0.91 0.009 0.73 0.0677 0.112 0.287 1.01 0.000762 0.00269 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.545 0.00692 0.407 0.0738 0.0984 0.738 0.754 0.00196 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 347 35441.824 0.005 0.00605 0.11 0.231 0.0682 0.092 0.303 0.391 0.000806 0.00104 ! Validation 347 35441.824 0.005 0.0053 0.322 0.428 0.0644 0.0861 0.655 0.671 0.00174 0.00179 Wall time: 35441.824161225 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.13 0.00542 0.0218 0.0644 0.087 0.135 0.175 0.000359 0.000464 348 172 0.148 0.00607 0.0265 0.0683 0.0921 0.155 0.192 0.000413 0.000512 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.151 0.0068 0.0149 0.0732 0.0975 0.126 0.144 0.000335 0.000384 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 35543.562 0.005 0.00575 0.0951 0.21 0.0664 0.0897 0.278 0.365 0.000739 0.00097 ! Validation 348 35543.562 0.005 0.00508 0.0291 0.131 0.0632 0.0843 0.164 0.202 0.000435 0.000537 Wall time: 35543.56278769579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.153 0.00561 0.0406 0.0659 0.0886 0.213 0.238 0.000566 0.000633 349 172 0.121 0.00519 0.0175 0.0631 0.0852 0.133 0.156 0.000354 0.000416 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.275 0.00671 0.141 0.0729 0.0969 0.333 0.444 0.000887 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 349 35645.306 0.005 0.00574 0.111 0.225 0.0664 0.0896 0.295 0.393 0.000785 0.00105 ! Validation 349 35645.306 0.005 0.00484 0.0261 0.123 0.0616 0.0823 0.154 0.191 0.000409 0.000508 Wall time: 35645.3066816302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.145 0.00567 0.0317 0.066 0.089 0.162 0.211 0.000432 0.00056 350 172 0.257 0.00497 0.158 0.0616 0.0834 0.461 0.47 0.00123 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.163 0.00669 0.0296 0.0725 0.0967 0.159 0.203 0.000424 0.000541 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 35747.053 0.005 0.00554 0.0957 0.206 0.0651 0.088 0.284 0.366 0.000754 0.000973 ! Validation 350 35747.053 0.005 0.00492 0.0311 0.129 0.0621 0.0829 0.165 0.209 0.00044 0.000555 Wall time: 35747.05323681887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.172 0.00601 0.0515 0.0679 0.0917 0.235 0.268 0.000625 0.000714 351 172 0.857 0.00687 0.719 0.0734 0.098 0.916 1 0.00244 0.00267 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.212 0.00779 0.0556 0.0793 0.104 0.265 0.279 0.000705 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 351 35848.783 0.005 0.00544 0.0894 0.198 0.0645 0.0872 0.276 0.353 0.000735 0.000938 ! Validation 351 35848.783 0.005 0.00603 0.131 0.251 0.0699 0.0919 0.399 0.427 0.00106 0.00114 Wall time: 35848.78368965397 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.215 0.00482 0.119 0.0614 0.0821 0.366 0.408 0.000974 0.00108 352 172 0.128 0.00549 0.0187 0.0652 0.0876 0.139 0.162 0.00037 0.00043 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.209 0.00632 0.0823 0.0707 0.094 0.28 0.339 0.000745 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 352 35950.542 0.005 0.00555 0.082 0.193 0.0653 0.0881 0.254 0.339 0.000675 0.000901 ! Validation 352 35950.542 0.005 0.00472 0.0216 0.116 0.0609 0.0813 0.138 0.174 0.000368 0.000463 Wall time: 35950.542451403104 ! Best model 352 0.116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.255 0.0055 0.146 0.0653 0.0877 0.42 0.451 0.00112 0.0012 353 172 0.143 0.00522 0.0389 0.0637 0.0854 0.166 0.233 0.000442 0.00062 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.145 0.00652 0.0151 0.0719 0.0955 0.133 0.145 0.000354 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 353 36052.311 0.005 0.00592 0.149 0.268 0.0675 0.091 0.342 0.457 0.000909 0.00122 ! Validation 353 36052.311 0.005 0.00469 0.0722 0.166 0.0607 0.081 0.266 0.318 0.000708 0.000845 Wall time: 36052.31143448688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.142 0.00571 0.0278 0.0663 0.0893 0.135 0.197 0.000358 0.000524 354 172 0.13 0.00518 0.0264 0.0637 0.0851 0.135 0.192 0.000359 0.000511 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.136 0.00606 0.015 0.0691 0.0921 0.131 0.145 0.000349 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 354 36154.056 0.005 0.0052 0.0597 0.164 0.0631 0.0853 0.212 0.289 0.000563 0.000769 ! Validation 354 36154.056 0.005 0.00448 0.0505 0.14 0.0593 0.0791 0.222 0.266 0.000591 0.000707 Wall time: 36154.056860764045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.176 0.00506 0.0748 0.062 0.0841 0.302 0.323 0.000803 0.00086 355 172 0.17 0.00457 0.0784 0.0598 0.0799 0.308 0.331 0.00082 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.161 0.00627 0.0355 0.0708 0.0936 0.179 0.223 0.000476 0.000592 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 36255.913 0.005 0.00547 0.101 0.21 0.0649 0.0875 0.29 0.375 0.00077 0.000998 ! Validation 355 36255.913 0.005 0.00471 0.0229 0.117 0.0607 0.0812 0.147 0.179 0.00039 0.000476 Wall time: 36255.913867500145 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.362 0.00515 0.259 0.0624 0.0849 0.589 0.602 0.00157 0.0016 356 172 0.118 0.00497 0.0187 0.0617 0.0833 0.124 0.162 0.00033 0.00043 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.237 0.00615 0.114 0.0699 0.0927 0.329 0.399 0.000875 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 356 36357.666 0.005 0.00542 0.0866 0.195 0.0644 0.0871 0.262 0.348 0.000696 0.000926 ! Validation 356 36357.666 0.005 0.00451 0.035 0.125 0.0593 0.0794 0.186 0.221 0.000495 0.000588 Wall time: 36357.66658055782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.133 0.00529 0.0276 0.0642 0.086 0.172 0.196 0.000459 0.000522 357 172 0.112 0.00452 0.0217 0.0597 0.0795 0.157 0.174 0.000418 0.000463 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.158 0.00603 0.037 0.0691 0.0918 0.202 0.228 0.000537 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 357 36459.420 0.005 0.00512 0.0685 0.171 0.0626 0.0846 0.236 0.31 0.000629 0.000823 ! Validation 357 36459.420 0.005 0.00446 0.033 0.122 0.0591 0.079 0.178 0.215 0.000474 0.000571 Wall time: 36459.42013219418 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.182 0.00467 0.0887 0.0607 0.0808 0.315 0.352 0.000837 0.000937 358 172 0.186 0.00463 0.0936 0.0601 0.0805 0.328 0.362 0.000871 0.000962 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.161 0.00598 0.0416 0.0685 0.0914 0.228 0.241 0.000607 0.000641 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 36561.180 0.005 0.00485 0.0592 0.156 0.0608 0.0824 0.218 0.288 0.000579 0.000765 ! Validation 358 36561.180 0.005 0.00445 0.0297 0.119 0.059 0.0789 0.168 0.204 0.000446 0.000542 Wall time: 36561.18049177108 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.842 0.00792 0.684 0.0637 0.105 0.299 0.978 0.000794 0.0026 359 172 0.117 0.00481 0.0203 0.0612 0.082 0.129 0.168 0.000342 0.000448 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.318 0.00583 0.202 0.0681 0.0903 0.518 0.531 0.00138 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 359 36662.837 0.005 0.00506 0.0816 0.183 0.0623 0.0841 0.245 0.338 0.000651 0.000899 ! Validation 359 36662.837 0.005 0.00447 0.0615 0.151 0.0589 0.0791 0.252 0.293 0.000671 0.00078 Wall time: 36662.83744063694 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.125 0.00524 0.0206 0.0629 0.0856 0.135 0.17 0.000358 0.000452 360 172 0.334 0.00571 0.22 0.0676 0.0894 0.502 0.555 0.00133 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.156 0.0061 0.0343 0.0695 0.0923 0.2 0.219 0.000532 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 360 36764.485 0.005 0.00555 0.0952 0.206 0.0646 0.0881 0.267 0.365 0.000711 0.00097 ! Validation 360 36764.485 0.005 0.00449 0.0746 0.164 0.0593 0.0792 0.28 0.323 0.000743 0.000859 Wall time: 36764.48531921394 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.237 0.00513 0.134 0.0622 0.0847 0.405 0.433 0.00108 0.00115 361 172 0.157 0.00558 0.045 0.0646 0.0884 0.22 0.251 0.000586 0.000667 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.234 0.0062 0.11 0.0709 0.0931 0.353 0.393 0.000939 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 361 36866.154 0.005 0.0055 0.0782 0.188 0.0653 0.0877 0.251 0.331 0.000668 0.00088 ! Validation 361 36866.154 0.005 0.00493 0.0801 0.179 0.0629 0.083 0.302 0.335 0.000803 0.00089 Wall time: 36866.15480361413 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.108 0.00461 0.0162 0.0598 0.0803 0.121 0.151 0.000322 0.0004 362 172 0.127 0.00439 0.0391 0.0584 0.0784 0.206 0.234 0.000547 0.000622 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.205 0.00548 0.0952 0.0659 0.0875 0.351 0.365 0.000933 0.00097 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 36967.810 0.005 0.00503 0.0586 0.159 0.062 0.0839 0.206 0.286 0.000547 0.000762 ! Validation 362 36967.810 0.005 0.00409 0.0652 0.147 0.0563 0.0757 0.272 0.302 0.000725 0.000803 Wall time: 36967.81073794002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.123 0.0048 0.0269 0.0623 0.0819 0.156 0.194 0.000415 0.000516 363 172 0.574 0.00541 0.465 0.064 0.087 0.795 0.807 0.00211 0.00215 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 0.00582 0.0208 0.0684 0.0902 0.136 0.171 0.000363 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 363 37069.480 0.005 0.00521 0.096 0.2 0.063 0.0854 0.272 0.366 0.000723 0.000973 ! Validation 363 37069.480 0.005 0.00489 0.122 0.219 0.0619 0.0826 0.363 0.412 0.000965 0.0011 Wall time: 37069.48054672312 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.544 0.00391 0.465 0.0556 0.074 0.793 0.807 0.00211 0.00215 364 172 0.105 0.00394 0.0258 0.0551 0.0742 0.158 0.19 0.00042 0.000505 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.113 0.00544 0.00463 0.0653 0.0872 0.0718 0.0805 0.000191 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 364 37171.983 0.005 0.00514 0.0864 0.189 0.0627 0.0848 0.26 0.348 0.000691 0.000924 ! Validation 364 37171.983 0.005 0.0041 0.0191 0.101 0.0564 0.0757 0.135 0.163 0.000359 0.000434 Wall time: 37171.98312832089 ! Best model 364 0.101 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.154 0.00681 0.0182 0.0752 0.0976 0.138 0.16 0.000366 0.000425 365 172 1.16 0.00766 1.01 0.0604 0.104 0.475 1.19 0.00126 0.00316 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.18 0.00588 0.0623 0.0684 0.0907 0.279 0.295 0.000743 0.000785 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 37276.086 0.005 0.00503 0.065 0.166 0.062 0.0838 0.219 0.3 0.000583 0.000797 ! Validation 365 37276.086 0.005 0.00462 0.142 0.234 0.0603 0.0804 0.423 0.445 0.00112 0.00118 Wall time: 37276.08655176591 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.22 0.00433 0.133 0.0589 0.0778 0.415 0.432 0.0011 0.00115 366 172 0.266 0.00588 0.148 0.0687 0.0906 0.422 0.455 0.00112 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.24 0.00803 0.0794 0.0817 0.106 0.316 0.333 0.00084 0.000886 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 37377.826 0.005 0.00479 0.0679 0.164 0.0605 0.0818 0.227 0.308 0.000603 0.000819 ! Validation 366 37377.826 0.005 0.00698 0.0468 0.186 0.076 0.0988 0.208 0.256 0.000553 0.000681 Wall time: 37377.826858418994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.128 0.00417 0.0445 0.0577 0.0764 0.227 0.249 0.000603 0.000663 367 172 0.12 0.00476 0.0252 0.0623 0.0816 0.149 0.188 0.000395 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.113 0.00525 0.00793 0.0651 0.0857 0.0933 0.105 0.000248 0.00028 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 37479.561 0.005 0.00488 0.0523 0.15 0.0611 0.0826 0.195 0.27 0.000518 0.000719 ! Validation 367 37479.561 0.005 0.0042 0.0234 0.107 0.0575 0.0767 0.148 0.181 0.000394 0.000481 Wall time: 37479.56136367982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.167 0.00475 0.0719 0.0593 0.0815 0.27 0.317 0.000719 0.000843 368 172 0.135 0.00583 0.018 0.0667 0.0903 0.132 0.159 0.00035 0.000422 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.287 0.00611 0.165 0.07 0.0925 0.393 0.48 0.00105 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 368 37581.290 0.005 0.00634 0.0959 0.223 0.068 0.0941 0.268 0.366 0.000713 0.000974 ! Validation 368 37581.290 0.005 0.00444 0.0357 0.124 0.0591 0.0788 0.185 0.223 0.000493 0.000594 Wall time: 37581.29014934413 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.0985 0.00403 0.0179 0.0561 0.0751 0.127 0.158 0.000337 0.00042 369 172 0.0899 0.004 0.00984 0.0555 0.0748 0.087 0.117 0.000231 0.000312 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.169 0.00514 0.0658 0.0645 0.0848 0.247 0.303 0.000657 0.000807 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 37683.033 0.005 0.00445 0.051 0.14 0.0583 0.0789 0.191 0.267 0.000508 0.00071 ! Validation 369 37683.033 0.005 0.00385 0.0221 0.0991 0.0551 0.0734 0.142 0.176 0.000378 0.000468 Wall time: 37683.03355776379 ! Best model 369 0.099 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.0976 0.00431 0.0115 0.057 0.0776 0.101 0.127 0.000268 0.000338 370 172 0.164 0.0054 0.056 0.063 0.0869 0.229 0.28 0.000609 0.000744 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.414 0.00589 0.296 0.0696 0.0908 0.637 0.644 0.0017 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 370 37784.749 0.005 0.00465 0.064 0.157 0.0596 0.0806 0.219 0.299 0.000583 0.000796 ! Validation 370 37784.749 0.005 0.00442 0.0932 0.182 0.0598 0.0787 0.31 0.361 0.000824 0.00096 Wall time: 37784.74942877982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.17 0.00519 0.0661 0.0641 0.0852 0.283 0.304 0.000752 0.000809 371 172 0.0892 0.00391 0.0109 0.0546 0.074 0.104 0.123 0.000276 0.000328 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.194 0.00528 0.0885 0.0652 0.0859 0.336 0.352 0.000893 0.000935 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 37886.398 0.005 0.00474 0.0728 0.168 0.0602 0.0814 0.243 0.319 0.000645 0.000849 ! Validation 371 37886.398 0.005 0.00407 0.076 0.157 0.0564 0.0755 0.293 0.326 0.00078 0.000867 Wall time: 37886.39877447998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.127 0.00473 0.0328 0.0607 0.0813 0.178 0.214 0.000474 0.00057 372 172 0.159 0.00494 0.0597 0.0626 0.0832 0.237 0.289 0.000631 0.000768 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.561 0.00529 0.455 0.0652 0.086 0.78 0.798 0.00208 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 372 37988.065 0.005 0.00438 0.0613 0.149 0.0578 0.0783 0.212 0.293 0.000563 0.000778 ! Validation 372 37988.065 0.005 0.00422 0.134 0.218 0.0578 0.0768 0.401 0.433 0.00107 0.00115 Wall time: 37988.06526770815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.185 0.00788 0.0272 0.0796 0.105 0.167 0.195 0.000444 0.000519 373 172 0.0962 0.00415 0.0132 0.0559 0.0762 0.109 0.136 0.000289 0.000361 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.128 0.00627 0.00233 0.0711 0.0937 0.046 0.0571 0.000122 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 373 38089.726 0.005 0.00513 0.073 0.176 0.063 0.0847 0.248 0.32 0.000658 0.00085 ! Validation 373 38089.726 0.005 0.00467 0.0625 0.156 0.0611 0.0808 0.246 0.296 0.000654 0.000786 Wall time: 38089.72682516882 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.0926 0.00398 0.0131 0.0563 0.0746 0.108 0.135 0.000286 0.00036 374 172 0.165 0.00417 0.0813 0.0572 0.0764 0.3 0.337 0.000798 0.000896 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.101 0.005 0.00115 0.0632 0.0836 0.0349 0.0401 9.27e-05 0.000107 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 38191.382 0.005 0.00469 0.0677 0.161 0.06 0.0809 0.231 0.308 0.000615 0.000818 ! Validation 374 38191.382 0.005 0.00381 0.0306 0.107 0.0545 0.073 0.172 0.207 0.000457 0.00055 Wall time: 38191.38250862621 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.302 0.0041 0.22 0.0568 0.0757 0.538 0.554 0.00143 0.00147 375 172 0.104 0.0047 0.00952 0.06 0.0811 0.0926 0.115 0.000246 0.000307 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.269 0.00548 0.16 0.0659 0.0875 0.444 0.472 0.00118 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 375 38293.220 0.005 0.00437 0.0587 0.146 0.0579 0.0782 0.208 0.286 0.000552 0.000762 ! Validation 375 38293.220 0.005 0.0041 0.0962 0.178 0.0565 0.0757 0.323 0.367 0.000858 0.000975 Wall time: 38293.22047394095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.115 0.00423 0.0303 0.0575 0.0769 0.182 0.206 0.000484 0.000548 376 172 0.161 0.00456 0.0694 0.0596 0.0799 0.279 0.311 0.000742 0.000828 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.243 0.00473 0.149 0.0613 0.0813 0.393 0.456 0.00104 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 376 38396.156 0.005 0.00424 0.048 0.133 0.0569 0.0769 0.184 0.259 0.00049 0.000689 ! Validation 376 38396.156 0.005 0.00387 0.0645 0.142 0.0551 0.0735 0.264 0.3 0.000701 0.000798 Wall time: 38396.15632546088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.0959 0.00401 0.0157 0.0561 0.0749 0.122 0.148 0.000323 0.000394 377 172 0.102 0.00382 0.0259 0.0546 0.073 0.169 0.19 0.000449 0.000506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.171 0.00466 0.0779 0.0609 0.0807 0.311 0.33 0.000827 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 377 38497.924 0.005 0.0044 0.0531 0.141 0.0581 0.0785 0.197 0.272 0.000523 0.000725 ! Validation 377 38497.924 0.005 0.0035 0.0252 0.0951 0.0522 0.0699 0.153 0.188 0.000407 0.000499 Wall time: 38497.92463516211 ! Best model 377 0.095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.276 0.00365 0.202 0.0536 0.0715 0.523 0.532 0.00139 0.00141 378 172 0.0986 0.00415 0.0157 0.0551 0.0761 0.122 0.148 0.000325 0.000394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.113 0.00481 0.0166 0.0621 0.082 0.126 0.152 0.000335 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 378 38599.709 0.005 0.00425 0.0553 0.14 0.057 0.0771 0.205 0.278 0.000545 0.00074 ! Validation 378 38599.709 0.005 0.00383 0.024 0.101 0.0554 0.0732 0.146 0.183 0.000387 0.000487 Wall time: 38599.7092690859 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.127 0.00483 0.0302 0.0602 0.0822 0.16 0.206 0.000425 0.000547 379 172 0.13 0.0043 0.0437 0.0566 0.0776 0.221 0.247 0.000587 0.000658 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.148 0.00492 0.0496 0.0626 0.0829 0.242 0.263 0.000644 0.0007 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 38701.477 0.005 0.00438 0.0657 0.153 0.0579 0.0783 0.223 0.303 0.000592 0.000806 ! Validation 379 38701.477 0.005 0.00391 0.114 0.193 0.0554 0.074 0.374 0.4 0.000995 0.00106 Wall time: 38701.47731000092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.275 0.00629 0.149 0.0717 0.0938 0.422 0.457 0.00112 0.00121 380 172 0.0864 0.00344 0.0176 0.0512 0.0693 0.115 0.157 0.000307 0.000418 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.136 0.00466 0.0429 0.0611 0.0808 0.201 0.245 0.000533 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 380 38803.250 0.005 0.00511 0.0669 0.169 0.0622 0.0845 0.229 0.306 0.000609 0.000813 ! Validation 380 38803.250 0.005 0.00367 0.0206 0.094 0.0537 0.0716 0.14 0.17 0.000373 0.000451 Wall time: 38803.24989474984 ! Best model 380 0.094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.165 0.0044 0.0772 0.0583 0.0785 0.295 0.329 0.000785 0.000874 381 172 0.0719 0.00318 0.00836 0.0496 0.0667 0.0944 0.108 0.000251 0.000288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.135 0.00429 0.049 0.0581 0.0774 0.22 0.262 0.000586 0.000696 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 38905.112 0.005 0.00456 0.0613 0.152 0.0588 0.0798 0.21 0.293 0.00056 0.000779 ! Validation 381 38905.112 0.005 0.00329 0.0221 0.0878 0.0504 0.0678 0.144 0.176 0.000384 0.000467 Wall time: 38905.11270512082 ! Best model 381 0.088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.796 0.00739 0.648 0.0618 0.102 0.291 0.952 0.000773 0.00253 382 172 0.115 0.00413 0.0319 0.0565 0.076 0.187 0.211 0.000498 0.000562 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.123 0.00483 0.0263 0.062 0.0822 0.177 0.192 0.000471 0.00051 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 39006.907 0.005 0.00468 0.0726 0.166 0.0593 0.0809 0.226 0.319 0.0006 0.000848 ! Validation 382 39006.907 0.005 0.00358 0.017 0.0886 0.0527 0.0707 0.126 0.154 0.000335 0.00041 Wall time: 39006.9069827348 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.0928 0.00369 0.0189 0.0528 0.0719 0.129 0.163 0.000342 0.000432 383 172 0.112 0.0043 0.0259 0.0573 0.0775 0.147 0.19 0.000391 0.000506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.126 0.00559 0.0143 0.0669 0.0884 0.137 0.141 0.000364 0.000376 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 39108.681 0.005 0.00404 0.0592 0.14 0.0555 0.0752 0.204 0.288 0.000541 0.000765 ! Validation 383 39108.681 0.005 0.00466 0.0121 0.105 0.0603 0.0807 0.106 0.13 0.000281 0.000346 Wall time: 39108.68170204805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.104 0.00403 0.0234 0.0556 0.0751 0.158 0.181 0.000421 0.000481 384 172 0.1 0.0037 0.0263 0.0531 0.0719 0.149 0.192 0.000397 0.00051 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.181 0.00471 0.087 0.0609 0.0812 0.347 0.349 0.000922 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 384 39210.446 0.005 0.004 0.0482 0.128 0.0552 0.0748 0.185 0.26 0.000493 0.00069 ! Validation 384 39210.446 0.005 0.00368 0.109 0.183 0.0536 0.0717 0.356 0.391 0.000946 0.00104 Wall time: 39210.44654700579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.106 0.00366 0.0328 0.0536 0.0716 0.184 0.214 0.000489 0.000569 385 172 0.0997 0.00361 0.0275 0.0525 0.0711 0.178 0.196 0.000474 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.113 0.00444 0.0243 0.0591 0.0788 0.182 0.184 0.000483 0.00049 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 39312.223 0.005 0.00435 0.0594 0.146 0.0575 0.078 0.21 0.288 0.000559 0.000767 ! Validation 385 39312.223 0.005 0.00356 0.0594 0.131 0.0525 0.0706 0.257 0.288 0.000684 0.000766 Wall time: 39312.223026223015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.0879 0.00387 0.0106 0.0536 0.0735 0.103 0.122 0.000275 0.000324 386 172 80.4 0.541 69.6 0.66 0.87 9.69 9.86 0.0258 0.0262 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 61.7 0.75 46.7 0.773 1.02 7.77 8.08 0.0207 0.0215 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 39413.982 0.005 0.0147 1.92 2.21 0.0721 0.142 0.476 1.62 0.00127 0.0043 ! Validation 386 39413.982 0.005 0.724 9.42 23.9 0.75 1.01 2.99 3.63 0.00794 0.00965 Wall time: 39413.98254515184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 3.71 0.122 1.27 0.313 0.413 1.12 1.33 0.00298 0.00354 387 172 1.65 0.0725 0.204 0.239 0.318 0.43 0.534 0.00114 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 1.78 0.0844 0.0948 0.251 0.343 0.33 0.364 0.000878 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 387 39516.337 0.005 0.343 3.84 10.7 0.469 0.693 1.53 2.32 0.00406 0.00616 ! Validation 387 39516.337 0.005 0.0699 0.264 1.66 0.233 0.313 0.484 0.607 0.00129 0.00161 Wall time: 39516.33743853681 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 1.37 0.0543 0.288 0.206 0.276 0.547 0.634 0.00146 0.00169 388 172 1.08 0.047 0.138 0.19 0.256 0.343 0.44 0.000913 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 1.47 0.0599 0.275 0.207 0.289 0.556 0.62 0.00148 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 388 39618.299 0.005 0.0569 0.617 1.75 0.21 0.282 0.72 0.929 0.00191 0.00247 ! Validation 388 39618.299 0.005 0.0454 0.19 1.1 0.187 0.252 0.42 0.516 0.00112 0.00137 Wall time: 39618.29937631497 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 1.22 0.0358 0.502 0.165 0.224 0.755 0.837 0.00201 0.00223 389 172 0.656 0.028 0.0958 0.148 0.198 0.315 0.366 0.000837 0.000973 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.846 0.0372 0.102 0.164 0.228 0.363 0.377 0.000966 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 389 39720.071 0.005 0.0347 0.335 1.03 0.164 0.22 0.541 0.685 0.00144 0.00182 ! Validation 389 39720.071 0.005 0.0275 0.11 0.659 0.146 0.196 0.315 0.391 0.000838 0.00104 Wall time: 39720.07108711917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.678 0.0244 0.19 0.138 0.185 0.438 0.515 0.00116 0.00137 390 172 0.517 0.023 0.0577 0.133 0.179 0.235 0.284 0.000624 0.000755 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.729 0.0312 0.104 0.15 0.209 0.293 0.381 0.00078 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 390 39821.865 0.005 0.026 0.354 0.875 0.142 0.191 0.566 0.704 0.0015 0.00187 ! Validation 390 39821.865 0.005 0.0217 0.0767 0.511 0.13 0.174 0.255 0.327 0.000679 0.000871 Wall time: 39821.86524679791 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.466 0.0184 0.097 0.12 0.161 0.27 0.368 0.000718 0.00098 391 172 0.529 0.0202 0.124 0.126 0.168 0.358 0.416 0.000951 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.774 0.0263 0.247 0.138 0.192 0.539 0.587 0.00143 0.00156 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 39923.688 0.005 0.0205 0.262 0.673 0.126 0.169 0.49 0.606 0.0013 0.00161 ! Validation 391 39923.688 0.005 0.0199 0.641 1.04 0.125 0.167 0.9 0.947 0.00239 0.00252 Wall time: 39923.688618185 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 1.36 0.0163 1.03 0.113 0.151 1.16 1.2 0.00309 0.00319 392 172 0.374 0.0157 0.0591 0.11 0.148 0.219 0.288 0.000581 0.000765 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.543 0.0218 0.107 0.126 0.175 0.342 0.388 0.00091 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 392 40025.448 0.005 0.018 0.218 0.577 0.118 0.158 0.442 0.552 0.00118 0.00147 ! Validation 392 40025.448 0.005 0.0154 0.0988 0.407 0.11 0.147 0.305 0.372 0.00081 0.000989 Wall time: 40025.44847647706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.305 0.014 0.0258 0.104 0.14 0.163 0.19 0.000434 0.000505 393 172 0.377 0.0129 0.119 0.102 0.134 0.367 0.408 0.000976 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.549 0.0187 0.175 0.118 0.162 0.443 0.495 0.00118 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 393 40127.212 0.005 0.0151 0.167 0.47 0.108 0.145 0.367 0.483 0.000977 0.00129 ! Validation 393 40127.212 0.005 0.0132 0.165 0.429 0.102 0.136 0.418 0.481 0.00111 0.00128 Wall time: 40127.212765391916 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.357 0.0128 0.101 0.101 0.134 0.326 0.376 0.000866 0.001 394 172 0.694 0.0133 0.428 0.102 0.136 0.742 0.774 0.00197 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.693 0.0176 0.341 0.116 0.157 0.652 0.69 0.00173 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 394 40228.977 0.005 0.0133 0.205 0.472 0.102 0.137 0.434 0.535 0.00115 0.00142 ! Validation 394 40228.977 0.005 0.0127 0.339 0.593 0.101 0.133 0.643 0.689 0.00171 0.00183 Wall time: 40228.977660099976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.276 0.0119 0.0371 0.0964 0.129 0.183 0.228 0.000486 0.000606 395 172 0.276 0.00983 0.0798 0.089 0.117 0.273 0.334 0.000726 0.000888 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.406 0.014 0.127 0.103 0.14 0.351 0.421 0.000935 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 395 40333.241 0.005 0.0115 0.11 0.34 0.0948 0.127 0.3 0.393 0.000798 0.00104 ! Validation 395 40333.241 0.005 0.00981 0.0739 0.27 0.0885 0.117 0.269 0.321 0.000715 0.000855 Wall time: 40333.241652179975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.251 0.00955 0.0598 0.0873 0.116 0.224 0.289 0.000597 0.000769 396 172 0.263 0.0101 0.0613 0.0896 0.119 0.243 0.293 0.000645 0.000779 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.345 0.0136 0.0734 0.101 0.138 0.273 0.32 0.000726 0.000852 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 40435.011 0.005 0.0102 0.158 0.363 0.0898 0.119 0.363 0.471 0.000966 0.00125 ! Validation 396 40435.011 0.005 0.0093 0.0436 0.23 0.0861 0.114 0.198 0.247 0.000526 0.000657 Wall time: 40435.011274669785 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.252 0.00867 0.0784 0.0839 0.11 0.298 0.331 0.000793 0.000881 397 172 0.207 0.00794 0.0482 0.0798 0.105 0.217 0.26 0.000577 0.00069 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.481 0.0114 0.252 0.0927 0.126 0.574 0.594 0.00153 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 397 40536.781 0.005 0.00908 0.118 0.3 0.0847 0.113 0.321 0.407 0.000855 0.00108 ! Validation 397 40536.781 0.005 0.0081 0.268 0.43 0.0807 0.106 0.574 0.612 0.00153 0.00163 Wall time: 40536.78152486915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.178 0.00761 0.0256 0.0788 0.103 0.142 0.189 0.000378 0.000503 398 172 0.349 0.00811 0.187 0.0809 0.106 0.468 0.511 0.00124 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.663 0.011 0.444 0.0911 0.124 0.752 0.788 0.002 0.0021 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 40638.562 0.005 0.0082 0.124 0.288 0.0804 0.107 0.322 0.416 0.000856 0.00111 ! Validation 398 40638.562 0.005 0.00752 0.419 0.569 0.0779 0.103 0.744 0.765 0.00198 0.00203 Wall time: 40638.5621504318 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.312 0.00753 0.162 0.077 0.103 0.446 0.476 0.00119 0.00127 399 172 0.157 0.00642 0.0284 0.0722 0.0947 0.181 0.199 0.000481 0.00053 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.232 0.0101 0.0306 0.0872 0.119 0.146 0.207 0.00039 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 399 40740.357 0.005 0.00788 0.161 0.318 0.0789 0.105 0.377 0.474 0.001 0.00126 ! Validation 399 40740.357 0.005 0.00682 0.0275 0.164 0.074 0.0977 0.158 0.196 0.00042 0.000521 Wall time: 40740.35728607513 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.166 0.00678 0.0301 0.0734 0.0974 0.161 0.205 0.000428 0.000546 400 172 0.184 0.00716 0.0407 0.0756 0.1 0.2 0.239 0.000531 0.000635 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.665 0.0095 0.476 0.0846 0.115 0.754 0.815 0.002 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 400 40845.908 0.005 0.00707 0.104 0.246 0.0745 0.0994 0.286 0.382 0.000759 0.00102 ! Validation 400 40845.908 0.005 0.00635 0.211 0.338 0.0717 0.0942 0.49 0.543 0.0013 0.00144 Wall time: 40845.90816010488 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.154 0.00629 0.0284 0.0708 0.0938 0.165 0.199 0.000439 0.00053 401 172 0.135 0.00566 0.0217 0.067 0.089 0.141 0.174 0.000375 0.000463 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.208 0.00866 0.035 0.0804 0.11 0.202 0.221 0.000537 0.000588 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 40947.675 0.005 0.00652 0.0982 0.229 0.0714 0.0955 0.283 0.371 0.000753 0.000986 ! Validation 401 40947.675 0.005 0.00569 0.0379 0.152 0.0673 0.0892 0.194 0.23 0.000517 0.000612 Wall time: 40947.67520129122 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.138 0.00528 0.0326 0.0648 0.0859 0.171 0.214 0.000455 0.000568 402 172 0.27 0.00684 0.133 0.0734 0.0978 0.387 0.431 0.00103 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.297 0.00895 0.118 0.0819 0.112 0.356 0.405 0.000947 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 402 41049.754 0.005 0.0061 0.104 0.226 0.0688 0.0923 0.277 0.382 0.000736 0.00102 ! Validation 402 41049.754 0.005 0.00605 0.087 0.208 0.0698 0.092 0.314 0.349 0.000835 0.000927 Wall time: 41049.75485352287 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.141 0.00588 0.0232 0.0684 0.0907 0.146 0.18 0.000389 0.000479 403 172 0.164 0.00528 0.0587 0.0643 0.0859 0.251 0.286 0.000668 0.000762 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.186 0.00766 0.0329 0.0761 0.103 0.195 0.214 0.000519 0.00057 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 41154.179 0.005 0.00592 0.0803 0.199 0.0679 0.091 0.249 0.335 0.000662 0.000891 ! Validation 403 41154.179 0.005 0.00502 0.0443 0.145 0.0631 0.0838 0.21 0.249 0.000559 0.000662 Wall time: 41154.17911429005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.188 0.00584 0.071 0.0667 0.0904 0.278 0.315 0.00074 0.000838 404 172 0.27 0.00576 0.155 0.067 0.0897 0.447 0.465 0.00119 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.163 0.00755 0.0124 0.0764 0.103 0.11 0.132 0.000292 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 404 41255.936 0.005 0.00558 0.101 0.212 0.0657 0.0883 0.284 0.375 0.000756 0.000998 ! Validation 404 41255.936 0.005 0.00525 0.0546 0.159 0.0648 0.0856 0.231 0.276 0.000613 0.000735 Wall time: 41255.93607732514 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.199 0.00524 0.0941 0.0644 0.0856 0.344 0.363 0.000915 0.000965 405 172 0.113 0.00506 0.0119 0.0628 0.0841 0.11 0.129 0.000293 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.263 0.00706 0.122 0.0732 0.0993 0.387 0.412 0.00103 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 405 41357.955 0.005 0.00525 0.0606 0.166 0.0636 0.0857 0.217 0.291 0.000578 0.000775 ! Validation 405 41357.955 0.005 0.00467 0.117 0.211 0.0607 0.0808 0.379 0.405 0.00101 0.00108 Wall time: 41357.95534039382 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.142 0.0049 0.0437 0.0623 0.0827 0.226 0.247 0.0006 0.000658 406 172 0.181 0.00476 0.0861 0.0608 0.0815 0.33 0.347 0.000877 0.000923 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.163 0.00667 0.0301 0.0719 0.0965 0.146 0.205 0.000388 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 406 41459.738 0.005 0.00515 0.0762 0.179 0.0629 0.0848 0.253 0.326 0.000672 0.000868 ! Validation 406 41459.738 0.005 0.00453 0.0163 0.107 0.0597 0.0796 0.121 0.151 0.000322 0.000401 Wall time: 41459.73788820999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.111 0.00468 0.0179 0.0606 0.0809 0.128 0.158 0.00034 0.000421 407 172 0.119 0.00485 0.0219 0.0614 0.0824 0.149 0.175 0.000397 0.000466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.185 0.0066 0.0528 0.0712 0.096 0.259 0.272 0.000689 0.000723 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 41561.522 0.005 0.00505 0.0823 0.183 0.0623 0.084 0.258 0.339 0.000686 0.000903 ! Validation 407 41561.522 0.005 0.00456 0.0475 0.139 0.0599 0.0799 0.226 0.258 0.000601 0.000685 Wall time: 41561.52226518281 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.714 0.00469 0.621 0.0609 0.081 0.921 0.932 0.00245 0.00248 408 172 0.109 0.00445 0.0199 0.0591 0.0789 0.115 0.167 0.000305 0.000444 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.337 0.00623 0.212 0.0695 0.0933 0.513 0.545 0.00137 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 408 41664.289 0.005 0.00482 0.0687 0.165 0.0608 0.0821 0.228 0.31 0.000608 0.000825 ! Validation 408 41664.289 0.005 0.00425 0.0599 0.145 0.0579 0.0771 0.242 0.289 0.000645 0.00077 Wall time: 41664.28965293383 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.187 0.0062 0.0626 0.0639 0.0931 0.276 0.296 0.000733 0.000787 409 172 0.106 0.00442 0.0178 0.059 0.0786 0.101 0.158 0.000269 0.000419 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.14 0.00586 0.0226 0.0673 0.0905 0.151 0.178 0.000402 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 409 41769.110 0.005 0.0047 0.0681 0.162 0.0599 0.0811 0.241 0.309 0.000641 0.000821 ! Validation 409 41769.110 0.005 0.00403 0.0141 0.0946 0.056 0.075 0.111 0.14 0.000296 0.000373 Wall time: 41769.110478198156 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.155 0.00525 0.0505 0.0624 0.0857 0.217 0.266 0.000578 0.000707 410 172 0.285 0.00525 0.181 0.0639 0.0856 0.467 0.502 0.00124 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.135 0.00669 0.00119 0.0713 0.0967 0.0292 0.0408 7.75e-05 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 410 41870.861 0.005 0.0046 0.0757 0.168 0.0593 0.0802 0.255 0.325 0.000679 0.000865 ! Validation 410 41870.861 0.005 0.00497 0.0192 0.119 0.0624 0.0834 0.133 0.164 0.000353 0.000436 Wall time: 41870.86131028412 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.14 0.00443 0.0516 0.0587 0.0787 0.245 0.269 0.000652 0.000715 411 172 0.145 0.00532 0.0389 0.0623 0.0862 0.204 0.233 0.000541 0.00062 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.195 0.006 0.0752 0.0683 0.0916 0.246 0.324 0.000655 0.000862 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 41974.126 0.005 0.00477 0.0751 0.171 0.0605 0.0817 0.247 0.324 0.000656 0.000862 ! Validation 411 41974.126 0.005 0.00411 0.0242 0.106 0.0567 0.0758 0.144 0.184 0.000384 0.000489 Wall time: 41974.12602040777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.146 0.00429 0.0598 0.0572 0.0775 0.268 0.289 0.000712 0.000769 412 172 0.225 0.00442 0.137 0.0571 0.0786 0.411 0.437 0.00109 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.312 0.00595 0.193 0.0672 0.0912 0.511 0.519 0.00136 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 412 42075.874 0.005 0.00438 0.0544 0.142 0.0577 0.0782 0.199 0.276 0.000529 0.000733 ! Validation 412 42075.874 0.005 0.0043 0.211 0.297 0.0578 0.0775 0.528 0.543 0.0014 0.00145 Wall time: 42075.874228580855 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.129 0.00395 0.0503 0.0547 0.0743 0.217 0.265 0.000576 0.000705 413 172 0.187 0.00448 0.0977 0.0587 0.0791 0.341 0.37 0.000906 0.000983 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.161 0.0059 0.0433 0.0674 0.0908 0.238 0.246 0.000634 0.000654 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 42177.633 0.005 0.00448 0.0734 0.163 0.0584 0.0791 0.24 0.32 0.000637 0.000852 ! Validation 413 42177.633 0.005 0.00406 0.0397 0.121 0.0566 0.0754 0.192 0.236 0.00051 0.000627 Wall time: 42177.63322965382 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.0927 0.00385 0.0156 0.0551 0.0734 0.122 0.148 0.000325 0.000393 414 172 0.157 0.00418 0.0737 0.0569 0.0765 0.291 0.321 0.000773 0.000854 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.272 0.0058 0.156 0.0668 0.0901 0.457 0.467 0.00122 0.00124 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 42279.384 0.005 0.00432 0.0514 0.138 0.0575 0.0778 0.195 0.268 0.000518 0.000713 ! Validation 414 42279.384 0.005 0.00416 0.126 0.209 0.0572 0.0763 0.4 0.42 0.00106 0.00112 Wall time: 42279.38392122788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.108 0.00452 0.0178 0.0589 0.0795 0.133 0.158 0.000355 0.000419 415 172 0.123 0.00453 0.0319 0.0596 0.0796 0.16 0.211 0.000424 0.000562 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.224 0.00578 0.109 0.0667 0.0899 0.329 0.39 0.000874 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 415 42381.123 0.005 0.00441 0.0843 0.173 0.0581 0.0785 0.249 0.343 0.000662 0.000913 ! Validation 415 42381.123 0.005 0.00393 0.141 0.22 0.0554 0.0741 0.426 0.445 0.00113 0.00118 Wall time: 42381.123391855974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.0997 0.00384 0.0229 0.0547 0.0733 0.146 0.179 0.000389 0.000476 416 172 0.0999 0.00435 0.0129 0.0581 0.078 0.108 0.134 0.000288 0.000357 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.233 0.00543 0.124 0.0648 0.0871 0.382 0.417 0.00102 0.00111 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 42482.879 0.005 0.00424 0.0645 0.149 0.0569 0.077 0.228 0.3 0.000606 0.000799 ! Validation 416 42482.879 0.005 0.00378 0.117 0.193 0.0543 0.0727 0.386 0.404 0.00103 0.00108 Wall time: 42482.87918363977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.093 0.00368 0.0193 0.0539 0.0718 0.131 0.164 0.000348 0.000437 417 172 0.0933 0.00384 0.0165 0.0548 0.0733 0.127 0.152 0.000337 0.000404 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.118 0.00518 0.0139 0.0633 0.0851 0.126 0.14 0.000336 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 417 42584.638 0.005 0.00419 0.0605 0.144 0.0565 0.0765 0.213 0.291 0.000566 0.000774 ! Validation 417 42584.638 0.005 0.00376 0.0113 0.0865 0.0541 0.0725 0.101 0.125 0.000269 0.000334 Wall time: 42584.63833758794 ! Best model 417 0.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.0946 0.0041 0.0126 0.0551 0.0757 0.111 0.133 0.000297 0.000353 418 172 0.113 0.00411 0.0302 0.0565 0.0759 0.177 0.206 0.000471 0.000547 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.196 0.00518 0.0922 0.0634 0.0851 0.293 0.359 0.000778 0.000955 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 42686.423 0.005 0.0042 0.0643 0.148 0.0566 0.0766 0.214 0.3 0.00057 0.000797 ! Validation 418 42686.423 0.005 0.00362 0.0378 0.11 0.053 0.0711 0.194 0.23 0.000516 0.000611 Wall time: 42686.42357393587 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.193 0.00469 0.0997 0.0602 0.081 0.354 0.373 0.000941 0.000993 419 172 0.0931 0.00385 0.0161 0.0534 0.0734 0.12 0.15 0.00032 0.000399 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.106 0.00517 0.00281 0.0634 0.085 0.0539 0.0626 0.000143 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 419 42788.190 0.005 0.00419 0.0539 0.138 0.0566 0.0765 0.198 0.275 0.000527 0.000731 ! Validation 419 42788.190 0.005 0.00367 0.0166 0.09 0.0534 0.0717 0.123 0.152 0.000328 0.000405 Wall time: 42788.190481663216 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.0859 0.00367 0.0126 0.0525 0.0716 0.117 0.133 0.000312 0.000354 420 172 0.0958 0.00403 0.0152 0.0553 0.0751 0.123 0.146 0.000328 0.000387 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.138 0.0049 0.0399 0.062 0.0828 0.211 0.236 0.00056 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 420 42889.949 0.005 0.00428 0.0687 0.154 0.057 0.0773 0.221 0.31 0.000589 0.000825 ! Validation 420 42889.949 0.005 0.00347 0.0168 0.0862 0.0521 0.0697 0.122 0.153 0.000325 0.000407 Wall time: 42889.94974252302 ! Best model 420 0.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.0848 0.0038 0.00873 0.0544 0.0729 0.0784 0.11 0.000209 0.000294 421 172 0.0788 0.00348 0.00925 0.0519 0.0697 0.0965 0.114 0.000257 0.000303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.127 0.00501 0.027 0.0624 0.0837 0.153 0.194 0.000408 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 421 42991.718 0.005 0.00439 0.0711 0.159 0.0578 0.0783 0.218 0.315 0.000579 0.000839 ! Validation 421 42991.718 0.005 0.00354 0.015 0.0859 0.0524 0.0704 0.116 0.145 0.000308 0.000385 Wall time: 42991.71812213119 ! Best model 421 0.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.325 0.00577 0.21 0.0684 0.0898 0.522 0.542 0.00139 0.00144 422 172 0.287 0.00359 0.215 0.0535 0.0709 0.541 0.548 0.00144 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.541 0.00517 0.438 0.0634 0.085 0.776 0.782 0.00206 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 422 43093.491 0.005 0.00404 0.0559 0.137 0.0554 0.0751 0.198 0.279 0.000526 0.000743 ! Validation 422 43093.491 0.005 0.00368 0.558 0.631 0.0537 0.0717 0.874 0.883 0.00232 0.00235 Wall time: 43093.49177092593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.211 0.00455 0.12 0.0591 0.0798 0.377 0.409 0.001 0.00109 423 172 0.133 0.00336 0.0664 0.0512 0.0685 0.278 0.305 0.000739 0.00081 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.114 0.00471 0.0195 0.0611 0.0812 0.162 0.165 0.000431 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 423 43195.249 0.005 0.00506 0.0853 0.186 0.062 0.0841 0.26 0.345 0.000691 0.000919 ! Validation 423 43195.249 0.005 0.00341 0.0286 0.0968 0.0515 0.069 0.171 0.2 0.000455 0.000532 Wall time: 43195.24909362383 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.306 0.00532 0.2 0.0648 0.0863 0.495 0.529 0.00132 0.00141 424 172 0.119 0.00412 0.0371 0.0554 0.0759 0.183 0.228 0.000488 0.000606 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.23 0.00486 0.132 0.0612 0.0824 0.43 0.43 0.00114 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 424 43296.998 0.005 0.00415 0.0659 0.149 0.0564 0.0762 0.228 0.303 0.000607 0.000807 ! Validation 424 43296.998 0.005 0.00347 0.085 0.154 0.052 0.0697 0.326 0.345 0.000868 0.000917 Wall time: 43296.99837276619 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.251 0.00453 0.16 0.0592 0.0796 0.44 0.473 0.00117 0.00126 425 172 0.108 0.00505 0.00717 0.0616 0.084 0.0806 0.1 0.000214 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.145 0.00488 0.0471 0.062 0.0826 0.237 0.257 0.000629 0.000682 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 43398.753 0.005 0.00411 0.0606 0.143 0.056 0.0758 0.218 0.291 0.000579 0.000774 ! Validation 425 43398.753 0.005 0.00368 0.0149 0.0884 0.0536 0.0717 0.115 0.144 0.000306 0.000384 Wall time: 43398.752948157955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.0956 0.00402 0.0153 0.0563 0.0749 0.118 0.146 0.000314 0.000389 426 172 0.262 0.00518 0.158 0.0635 0.0851 0.443 0.47 0.00118 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.158 0.00544 0.0495 0.0647 0.0872 0.254 0.263 0.000676 0.0007 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 43500.512 0.005 0.00414 0.0605 0.143 0.0563 0.0761 0.211 0.291 0.000561 0.000773 ! Validation 426 43500.512 0.005 0.00406 0.0508 0.132 0.0564 0.0753 0.235 0.267 0.000625 0.000709 Wall time: 43500.51267176308 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.0862 0.00341 0.018 0.0517 0.069 0.118 0.159 0.000313 0.000422 427 172 0.113 0.00361 0.0409 0.0528 0.071 0.175 0.239 0.000466 0.000636 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.109 0.00521 0.00508 0.0638 0.0854 0.0716 0.0843 0.000191 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 427 43602.267 0.005 0.0041 0.0586 0.141 0.0557 0.0757 0.203 0.286 0.00054 0.000761 ! Validation 427 43602.267 0.005 0.00393 0.0376 0.116 0.0552 0.0742 0.198 0.229 0.000527 0.00061 Wall time: 43602.26712315297 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.195 0.00375 0.12 0.054 0.0724 0.384 0.409 0.00102 0.00109 428 172 0.155 0.00331 0.0889 0.0501 0.068 0.334 0.353 0.000887 0.000938 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.18 0.0046 0.0877 0.0603 0.0802 0.299 0.35 0.000796 0.000931 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 43704.128 0.005 0.00376 0.0434 0.118 0.0534 0.0725 0.179 0.246 0.000477 0.000655 ! Validation 428 43704.128 0.005 0.00325 0.0324 0.0974 0.0504 0.0674 0.182 0.213 0.000483 0.000566 Wall time: 43704.128380654845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.0805 0.00348 0.0109 0.053 0.0698 0.0979 0.123 0.00026 0.000328 429 172 0.117 0.00432 0.0305 0.0586 0.0778 0.179 0.207 0.000475 0.00055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.292 0.00539 0.184 0.0652 0.0868 0.485 0.507 0.00129 0.00135 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 43805.887 0.005 0.00394 0.0631 0.142 0.0548 0.0742 0.221 0.297 0.000587 0.00079 ! Validation 429 43805.887 0.005 0.00411 0.0721 0.154 0.0574 0.0758 0.289 0.318 0.000768 0.000845 Wall time: 43805.88716304721 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.115 0.00468 0.021 0.0609 0.0809 0.144 0.171 0.000384 0.000456 430 172 0.132 0.00328 0.0663 0.0508 0.0678 0.288 0.305 0.000765 0.00081 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.113 0.00487 0.0156 0.0617 0.0825 0.128 0.148 0.000341 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 430 43907.651 0.005 0.00425 0.0659 0.151 0.0572 0.0771 0.225 0.304 0.000599 0.000807 ! Validation 430 43907.651 0.005 0.0034 0.0229 0.0909 0.0515 0.0689 0.151 0.179 0.000401 0.000476 Wall time: 43907.651258808095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.0834 0.00339 0.0155 0.0516 0.0689 0.12 0.147 0.000318 0.000392 431 172 0.323 0.00383 0.246 0.0551 0.0732 0.554 0.587 0.00147 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.506 0.00523 0.401 0.0637 0.0855 0.734 0.749 0.00195 0.00199 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 44009.415 0.005 0.00392 0.0547 0.133 0.0546 0.074 0.2 0.276 0.000533 0.000734 ! Validation 431 44009.415 0.005 0.00397 0.177 0.257 0.0561 0.0745 0.48 0.498 0.00128 0.00132 Wall time: 44009.41560937418 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.118 0.00353 0.0476 0.052 0.0702 0.233 0.258 0.00062 0.000686 432 172 0.0804 0.0034 0.0123 0.0513 0.069 0.11 0.131 0.000294 0.000349 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.0964 0.00457 0.00506 0.0597 0.0799 0.067 0.0841 0.000178 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 432 44111.180 0.005 0.00413 0.0554 0.138 0.0561 0.076 0.203 0.278 0.00054 0.00074 ! Validation 432 44111.180 0.005 0.00342 0.0108 0.0793 0.0513 0.0692 0.0988 0.123 0.000263 0.000327 Wall time: 44111.18021130795 ! Best model 432 0.079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.184 0.00322 0.12 0.05 0.0671 0.39 0.409 0.00104 0.00109 433 172 0.0973 0.00332 0.0308 0.0511 0.0682 0.181 0.208 0.00048 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.0986 0.00464 0.00586 0.0602 0.0805 0.0774 0.0905 0.000206 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 433 44212.956 0.005 0.0038 0.0574 0.133 0.0538 0.0729 0.213 0.283 0.000567 0.000754 ! Validation 433 44212.956 0.005 0.00347 0.0167 0.0861 0.0519 0.0696 0.121 0.153 0.000323 0.000406 Wall time: 44212.95594564406 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.13 0.00372 0.0556 0.0539 0.0721 0.25 0.279 0.000664 0.000741 434 172 0.0768 0.00319 0.0131 0.0496 0.0667 0.109 0.135 0.000291 0.000359 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.0955 0.00433 0.00896 0.0578 0.0778 0.0968 0.112 0.000257 0.000298 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 44314.888 0.005 0.00394 0.0594 0.138 0.0548 0.0742 0.205 0.288 0.000546 0.000767 ! Validation 434 44314.888 0.005 0.00316 0.015 0.0782 0.0494 0.0665 0.116 0.145 0.000307 0.000385 Wall time: 44314.88837675983 ! Best model 434 0.078 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.0934 0.00393 0.0148 0.054 0.0741 0.116 0.144 0.000308 0.000383 435 172 0.183 0.00524 0.0782 0.0634 0.0856 0.258 0.331 0.000686 0.000879 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.252 0.0052 0.148 0.064 0.0853 0.425 0.455 0.00113 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 435 44416.652 0.005 0.00429 0.0547 0.141 0.0562 0.0775 0.192 0.277 0.000512 0.000736 ! Validation 435 44416.652 0.005 0.00364 0.0708 0.144 0.0532 0.0714 0.255 0.315 0.000677 0.000837 Wall time: 44416.65263276314 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.0832 0.0033 0.0173 0.0513 0.0679 0.13 0.156 0.000347 0.000414 436 172 0.0711 0.0033 0.00522 0.0508 0.0679 0.0645 0.0854 0.000172 0.000227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.124 0.00443 0.0353 0.0589 0.0787 0.171 0.222 0.000455 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 436 44518.422 0.005 0.00385 0.0504 0.127 0.0541 0.0733 0.194 0.265 0.000517 0.000706 ! Validation 436 44518.422 0.005 0.00325 0.0117 0.0766 0.0503 0.0674 0.102 0.128 0.00027 0.00034 Wall time: 44518.42214450287 ! Best model 436 0.077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.0839 0.00325 0.0189 0.0503 0.0674 0.134 0.162 0.000358 0.000432 437 172 0.128 0.00317 0.0642 0.0494 0.0666 0.272 0.3 0.000723 0.000797 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.152 0.00412 0.07 0.0567 0.0759 0.25 0.313 0.000664 0.000832 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 44620.190 0.005 0.00342 0.0354 0.104 0.0509 0.0692 0.156 0.222 0.000415 0.000592 ! Validation 437 44620.190 0.005 0.00306 0.0334 0.0945 0.0487 0.0654 0.177 0.216 0.00047 0.000574 Wall time: 44620.18990987213 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.153 0.00578 0.0371 0.0668 0.0899 0.192 0.228 0.00051 0.000606 438 172 0.0868 0.00307 0.0254 0.0485 0.0655 0.157 0.188 0.000418 0.000501 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.128 0.00429 0.0426 0.0583 0.0774 0.241 0.244 0.000642 0.000649 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 44721.950 0.005 0.00471 0.0831 0.177 0.0598 0.0811 0.254 0.341 0.000675 0.000907 ! Validation 438 44721.950 0.005 0.00305 0.0193 0.0803 0.0487 0.0653 0.135 0.164 0.000358 0.000437 Wall time: 44721.950791548006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.0729 0.00326 0.00782 0.0498 0.0675 0.0879 0.105 0.000234 0.000278 439 172 0.0721 0.00297 0.0127 0.0484 0.0645 0.108 0.133 0.000286 0.000354 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.115 0.0044 0.0269 0.059 0.0785 0.191 0.194 0.000509 0.000516 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 44823.719 0.005 0.0035 0.046 0.116 0.0515 0.07 0.178 0.254 0.000474 0.000675 ! Validation 439 44823.719 0.005 0.00318 0.0287 0.0922 0.0496 0.0667 0.167 0.2 0.000445 0.000533 Wall time: 44823.718932021875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.0877 0.00328 0.0221 0.0509 0.0677 0.137 0.176 0.000364 0.000468 440 172 0.116 0.00395 0.0374 0.0552 0.0743 0.192 0.229 0.000512 0.000608 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.194 0.00497 0.0947 0.0619 0.0834 0.364 0.364 0.000967 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 440 44925.482 0.005 0.00365 0.0506 0.124 0.0527 0.0714 0.191 0.266 0.000509 0.000708 ! Validation 440 44925.482 0.005 0.00399 0.0753 0.155 0.0557 0.0747 0.303 0.324 0.000805 0.000863 Wall time: 44925.48244383512 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.093 0.00322 0.0285 0.0495 0.0671 0.17 0.2 0.000451 0.000531 441 172 0.0948 0.00384 0.018 0.0545 0.0733 0.13 0.158 0.000345 0.000421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.162 0.00469 0.0688 0.0612 0.0809 0.239 0.31 0.000636 0.000825 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 45027.240 0.005 0.00349 0.0461 0.116 0.0515 0.0699 0.186 0.254 0.000494 0.000675 ! Validation 441 45027.240 0.005 0.00352 0.0278 0.0981 0.0528 0.0701 0.164 0.197 0.000436 0.000524 Wall time: 45027.23991430411 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0892 0.00348 0.0197 0.0515 0.0697 0.135 0.166 0.00036 0.000442 442 172 0.0959 0.00309 0.0341 0.049 0.0657 0.192 0.218 0.00051 0.000581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0894 0.00432 0.00311 0.0582 0.0777 0.0638 0.0659 0.00017 0.000175 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 45129.010 0.005 0.00353 0.047 0.118 0.0519 0.0703 0.188 0.256 0.000499 0.000682 ! Validation 442 45129.010 0.005 0.00342 0.0128 0.0812 0.0517 0.0692 0.108 0.134 0.000288 0.000356 Wall time: 45129.010196227115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.292 0.00483 0.195 0.063 0.0822 0.479 0.522 0.00127 0.00139 443 172 0.935 0.0066 0.803 0.0527 0.0961 0.29 1.06 0.000771 0.00282 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.222 0.00502 0.121 0.0631 0.0838 0.405 0.412 0.00108 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 443 45230.806 0.005 0.00397 0.062 0.141 0.0548 0.0745 0.215 0.293 0.000572 0.00078 ! Validation 443 45230.806 0.005 0.00395 0.11 0.189 0.0558 0.0743 0.37 0.392 0.000985 0.00104 Wall time: 45230.80654578516 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.212 0.00806 0.0503 0.0782 0.106 0.196 0.265 0.00052 0.000705 444 172 0.0895 0.00287 0.0322 0.0469 0.0633 0.192 0.212 0.000511 0.000564 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.0857 0.00419 0.00187 0.0576 0.0766 0.0442 0.0511 0.000118 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 444 45335.651 0.005 0.00396 0.0498 0.129 0.0548 0.0744 0.195 0.264 0.000518 0.000702 ! Validation 444 45335.651 0.005 0.00314 0.0121 0.0749 0.0496 0.0663 0.104 0.13 0.000278 0.000346 Wall time: 45335.65163317509 ! Best model 444 0.075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.11 0.00375 0.0355 0.054 0.0724 0.193 0.223 0.000515 0.000593 445 172 0.331 0.00507 0.23 0.0633 0.0842 0.513 0.567 0.00136 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.265 0.00609 0.143 0.0703 0.0923 0.442 0.447 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 445 45437.434 0.005 0.0033 0.0398 0.106 0.0499 0.0679 0.168 0.236 0.000446 0.000626 ! Validation 445 45437.434 0.005 0.00517 0.156 0.259 0.0644 0.085 0.44 0.467 0.00117 0.00124 Wall time: 45437.43418106111 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.0788 0.00304 0.018 0.0483 0.0652 0.135 0.159 0.00036 0.000422 446 172 22.8 0.857 5.67 0.808 1.09 2.21 2.82 0.00587 0.00749 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 28 0.897 10.1 0.839 1.12 3.21 3.76 0.00853 0.00999 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 45539.201 0.005 0.203 9.2 13.3 0.231 0.532 1.33 3.59 0.00355 0.00954 ! Validation 446 45539.201 0.005 0.862 1.25 18.5 0.812 1.1 1 1.32 0.00266 0.00352 Wall time: 45539.20140417991 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 2.24 0.0937 0.366 0.275 0.362 0.547 0.716 0.00145 0.0019 447 172 1.95 0.0664 0.619 0.231 0.305 0.821 0.93 0.00218 0.00247 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 2.21 0.0761 0.689 0.242 0.326 0.969 0.981 0.00258 0.00261 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 45641.003 0.005 0.225 1.61 6.1 0.382 0.561 1.14 1.5 0.00304 0.00399 ! Validation 447 45641.003 0.005 0.0649 1.56 2.85 0.226 0.301 1.38 1.48 0.00367 0.00392 Wall time: 45641.00375773013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 1.99 0.0453 1.08 0.187 0.252 1.16 1.23 0.00309 0.00327 448 172 1.26 0.0364 0.537 0.167 0.225 0.768 0.867 0.00204 0.00231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 1.07 0.048 0.115 0.188 0.259 0.284 0.401 0.000754 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 448 45742.749 0.005 0.0489 0.64 1.62 0.194 0.262 0.771 0.946 0.00205 0.00252 ! Validation 448 45742.749 0.005 0.0353 0.196 0.901 0.164 0.222 0.423 0.523 0.00113 0.00139 Wall time: 45742.7490783548 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.658 0.027 0.117 0.144 0.194 0.322 0.404 0.000857 0.00108 449 172 0.653 0.0261 0.13 0.141 0.191 0.323 0.426 0.00086 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 2.03 0.0324 1.38 0.155 0.213 1.31 1.39 0.00348 0.0037 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 45844.504 0.005 0.0294 0.305 0.892 0.149 0.203 0.518 0.653 0.00138 0.00174 ! Validation 449 45844.504 0.005 0.0238 1.26 1.74 0.135 0.183 1.27 1.33 0.00339 0.00353 Wall time: 45844.504384352826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.53 0.0208 0.114 0.126 0.171 0.343 0.399 0.000912 0.00106 450 172 0.469 0.0184 0.1 0.119 0.16 0.305 0.375 0.00081 0.000997 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.632 0.0254 0.124 0.137 0.188 0.383 0.417 0.00102 0.00111 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 45946.261 0.005 0.0228 0.235 0.69 0.131 0.178 0.457 0.573 0.00122 0.00152 ! Validation 450 45946.261 0.005 0.0188 0.0829 0.459 0.12 0.162 0.272 0.341 0.000724 0.000906 Wall time: 45946.260889916215 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.412 0.0177 0.0572 0.117 0.158 0.224 0.283 0.000595 0.000752 451 172 0.867 0.018 0.506 0.117 0.159 0.776 0.841 0.00206 0.00224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.499 0.0225 0.0488 0.129 0.177 0.237 0.261 0.00063 0.000694 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 46048.013 0.005 0.0185 0.255 0.624 0.118 0.161 0.486 0.597 0.00129 0.00159 ! Validation 451 46048.013 0.005 0.0166 0.0726 0.405 0.113 0.152 0.258 0.319 0.000687 0.000847 Wall time: 46048.01315635396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.55 0.0173 0.203 0.114 0.156 0.482 0.533 0.00128 0.00142 452 172 0.455 0.0149 0.156 0.107 0.145 0.393 0.467 0.00104 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.502 0.0182 0.138 0.117 0.16 0.38 0.439 0.00101 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 452 46152.844 0.005 0.0161 0.207 0.529 0.11 0.15 0.419 0.539 0.00111 0.00143 ! Validation 452 46152.844 0.005 0.014 0.0817 0.361 0.104 0.14 0.276 0.338 0.000733 0.000899 Wall time: 46152.84418107988 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.429 0.013 0.169 0.0996 0.135 0.436 0.487 0.00116 0.00129 453 172 0.314 0.0133 0.0492 0.0998 0.136 0.208 0.262 0.000554 0.000697 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.386 0.0159 0.0673 0.11 0.149 0.21 0.307 0.000559 0.000816 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 46254.587 0.005 0.0137 0.17 0.444 0.102 0.138 0.389 0.488 0.00103 0.0013 ! Validation 453 46254.587 0.005 0.012 0.17 0.41 0.0965 0.13 0.426 0.488 0.00113 0.0013 Wall time: 46254.58733549621 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.362 0.0116 0.13 0.0951 0.127 0.36 0.427 0.000957 0.00113 454 172 0.282 0.011 0.0631 0.093 0.124 0.25 0.297 0.000665 0.00079 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.338 0.0143 0.053 0.105 0.141 0.24 0.272 0.000638 0.000724 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 46356.422 0.005 0.0119 0.141 0.38 0.0959 0.129 0.346 0.445 0.000919 0.00118 ! Validation 454 46356.422 0.005 0.0105 0.0529 0.263 0.0909 0.121 0.215 0.272 0.000571 0.000723 Wall time: 46356.42221696023 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.334 0.0106 0.123 0.0917 0.122 0.329 0.414 0.000876 0.0011 455 172 0.231 0.00925 0.0464 0.0854 0.114 0.222 0.255 0.00059 0.000677 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.475 0.0126 0.222 0.099 0.133 0.496 0.557 0.00132 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 455 46458.089 0.005 0.0106 0.125 0.336 0.0906 0.122 0.323 0.418 0.000858 0.00111 ! Validation 455 46458.089 0.005 0.00922 0.189 0.374 0.0855 0.114 0.461 0.514 0.00123 0.00137 Wall time: 46458.08924232982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.289 0.0109 0.0712 0.0911 0.123 0.273 0.315 0.000726 0.000839 456 172 0.325 0.00999 0.125 0.088 0.118 0.377 0.419 0.001 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.275 0.0117 0.0415 0.0959 0.128 0.227 0.241 0.000603 0.000641 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 46559.753 0.005 0.0107 0.213 0.426 0.091 0.122 0.392 0.546 0.00104 0.00145 ! Validation 456 46559.753 0.005 0.00865 0.0366 0.21 0.083 0.11 0.182 0.226 0.000483 0.000601 Wall time: 46559.75372986682 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.233 0.00933 0.0461 0.0859 0.114 0.218 0.254 0.00058 0.000676 457 172 0.205 0.00803 0.0443 0.0802 0.106 0.212 0.249 0.000565 0.000662 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.324 0.0108 0.108 0.0921 0.123 0.334 0.389 0.000888 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 457 46661.404 0.005 0.00896 0.116 0.296 0.0837 0.112 0.313 0.404 0.000831 0.00107 ! Validation 457 46661.404 0.005 0.0078 0.0615 0.218 0.0791 0.104 0.241 0.293 0.00064 0.00078 Wall time: 46661.40481484309 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.384 0.00795 0.225 0.0795 0.105 0.524 0.561 0.00139 0.00149 458 172 0.276 0.00818 0.112 0.0793 0.107 0.336 0.396 0.000895 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.293 0.0105 0.0829 0.0909 0.121 0.291 0.341 0.000773 0.000906 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 46764.350 0.005 0.00813 0.136 0.298 0.08 0.107 0.341 0.435 0.000906 0.00116 ! Validation 458 46764.350 0.005 0.00789 0.0351 0.193 0.0794 0.105 0.177 0.221 0.000472 0.000589 Wall time: 46764.35040795291 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.383 0.00738 0.236 0.0763 0.102 0.549 0.574 0.00146 0.00153 459 172 0.398 0.00831 0.232 0.0795 0.108 0.506 0.569 0.00134 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.336 0.00934 0.149 0.0861 0.114 0.423 0.457 0.00113 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 459 46866.024 0.005 0.00762 0.0972 0.25 0.0774 0.103 0.279 0.369 0.000743 0.00098 ! Validation 459 46866.024 0.005 0.00686 0.16 0.298 0.0744 0.098 0.434 0.474 0.00115 0.00126 Wall time: 46866.02454478899 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.206 0.00661 0.0735 0.0726 0.0961 0.284 0.321 0.000755 0.000852 460 172 0.317 0.0069 0.179 0.0725 0.0982 0.469 0.5 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 460 100 0.301 0.00861 0.129 0.0823 0.11 0.395 0.424 0.00105 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 460 46967.702 0.005 0.00703 0.101 0.242 0.0743 0.0991 0.301 0.376 0.0008 0.001 ! Validation 460 46967.702 0.005 0.00626 0.107 0.232 0.071 0.0935 0.34 0.387 0.000905 0.00103 Wall time: 46967.7022350342 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.25 0.00738 0.103 0.0753 0.102 0.351 0.379 0.000934 0.00101 461 172 0.661 0.00601 0.54 0.07 0.0917 0.851 0.869 0.00226 0.00231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.375 0.00836 0.208 0.0816 0.108 0.518 0.539 0.00138 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 461 47069.382 0.005 0.00656 0.105 0.236 0.0718 0.0958 0.297 0.383 0.00079 0.00102 ! Validation 461 47069.382 0.005 0.0061 0.232 0.354 0.0704 0.0923 0.535 0.57 0.00142 0.00152 Wall time: 47069.38233671617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.137 0.00588 0.0191 0.0684 0.0907 0.131 0.164 0.00035 0.000435 462 172 1.04 0.00902 0.856 0.0682 0.112 0.641 1.09 0.0017 0.00291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.213 0.00755 0.0624 0.0772 0.103 0.282 0.295 0.000749 0.000785 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 47171.067 0.005 0.00638 0.106 0.233 0.0707 0.0945 0.291 0.384 0.000773 0.00102 ! Validation 462 47171.067 0.005 0.00556 0.107 0.218 0.0667 0.0881 0.351 0.387 0.000935 0.00103 Wall time: 47171.0675872541 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.153 0.00576 0.038 0.0677 0.0897 0.204 0.23 0.000544 0.000613 463 172 0.379 0.00793 0.22 0.0784 0.105 0.511 0.555 0.00136 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.201 0.00764 0.0485 0.0779 0.103 0.216 0.261 0.000574 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 463 47272.745 0.005 0.00599 0.111 0.23 0.0684 0.0915 0.297 0.393 0.00079 0.00105 ! Validation 463 47272.745 0.005 0.00592 0.0238 0.142 0.0691 0.091 0.148 0.183 0.000393 0.000486 Wall time: 47272.7448794432 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.138 0.006 0.0177 0.068 0.0916 0.124 0.157 0.000329 0.000418 464 172 0.211 0.00536 0.104 0.0646 0.0865 0.361 0.381 0.000959 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.157 0.00704 0.0166 0.0747 0.0992 0.136 0.153 0.000362 0.000406 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 47375.794 0.005 0.00565 0.0749 0.188 0.0664 0.0889 0.25 0.324 0.000664 0.000861 ! Validation 464 47375.794 0.005 0.00505 0.0194 0.12 0.0636 0.084 0.129 0.165 0.000344 0.000438 Wall time: 47375.79393900884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.144 0.00557 0.0322 0.0647 0.0883 0.17 0.212 0.000451 0.000565 465 172 0.116 0.00476 0.0206 0.061 0.0816 0.147 0.17 0.000391 0.000452 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.138 0.00634 0.011 0.0704 0.0942 0.116 0.124 0.000309 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 465 47480.555 0.005 0.00529 0.0584 0.164 0.0641 0.086 0.215 0.286 0.000572 0.00076 ! Validation 465 47480.555 0.005 0.00466 0.0178 0.111 0.0608 0.0807 0.124 0.158 0.000331 0.000419 Wall time: 47480.55508967815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.119 0.00489 0.0217 0.0624 0.0827 0.126 0.174 0.000335 0.000463 466 172 0.153 0.00558 0.0418 0.065 0.0883 0.205 0.242 0.000546 0.000643 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.136 0.00624 0.0114 0.0703 0.0934 0.12 0.126 0.000319 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 466 47582.223 0.005 0.0054 0.105 0.213 0.0648 0.0869 0.279 0.383 0.000742 0.00102 ! Validation 466 47582.223 0.005 0.00462 0.0167 0.109 0.0605 0.0804 0.124 0.153 0.00033 0.000406 Wall time: 47582.223078310955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.153 0.00514 0.0505 0.0633 0.0848 0.193 0.266 0.000513 0.000707 467 172 0.179 0.00495 0.08 0.0618 0.0832 0.301 0.334 0.000801 0.000889 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.132 0.00599 0.0124 0.0686 0.0915 0.108 0.132 0.000286 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 467 47683.893 0.005 0.00504 0.0844 0.185 0.0625 0.0839 0.258 0.344 0.000686 0.000914 ! Validation 467 47683.893 0.005 0.00439 0.0306 0.118 0.0589 0.0784 0.173 0.207 0.00046 0.00055 Wall time: 47683.892941725906 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.188 0.00518 0.0848 0.0629 0.0851 0.312 0.344 0.000831 0.000916 468 172 0.239 0.00571 0.125 0.0646 0.0894 0.393 0.417 0.00105 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.123 0.00588 0.00597 0.0675 0.0906 0.081 0.0914 0.000215 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 468 47785.572 0.005 0.00486 0.081 0.178 0.0613 0.0824 0.257 0.336 0.000685 0.000895 ! Validation 468 47785.572 0.005 0.00447 0.0261 0.116 0.0593 0.0791 0.152 0.191 0.000405 0.000508 Wall time: 47785.57206960395 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.0966 0.00422 0.0121 0.0578 0.0768 0.108 0.13 0.000286 0.000346 469 172 0.111 0.00436 0.0237 0.0584 0.078 0.149 0.182 0.000396 0.000484 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.242 0.00554 0.131 0.0658 0.088 0.419 0.428 0.00111 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 469 47887.248 0.005 0.00458 0.0582 0.15 0.0594 0.0801 0.216 0.285 0.000575 0.000759 ! Validation 469 47887.248 0.005 0.00423 0.0561 0.141 0.0576 0.0769 0.245 0.28 0.000653 0.000745 Wall time: 47887.24867589213 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.109 0.00458 0.017 0.0594 0.0801 0.127 0.154 0.000337 0.00041 470 172 0.0979 0.00404 0.0171 0.0566 0.0752 0.124 0.155 0.00033 0.000411 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.155 0.00555 0.0443 0.0661 0.0881 0.18 0.249 0.000477 0.000662 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 47988.977 0.005 0.00461 0.0788 0.171 0.0596 0.0803 0.26 0.332 0.000692 0.000883 ! Validation 470 47988.977 0.005 0.00422 0.0506 0.135 0.0576 0.0768 0.233 0.266 0.000619 0.000707 Wall time: 47988.97759001888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.299 0.00441 0.211 0.0592 0.0785 0.532 0.544 0.00142 0.00145 471 172 0.122 0.00463 0.0297 0.0603 0.0805 0.157 0.204 0.000418 0.000542 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.175 0.00555 0.0643 0.0658 0.0881 0.288 0.3 0.000767 0.000797 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 48090.640 0.005 0.0044 0.0589 0.147 0.0581 0.0784 0.208 0.287 0.000555 0.000763 ! Validation 471 48090.640 0.005 0.00427 0.0567 0.142 0.0579 0.0772 0.237 0.281 0.00063 0.000749 Wall time: 48090.64057083521 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.126 0.00468 0.0321 0.0603 0.0809 0.184 0.212 0.000489 0.000563 472 172 0.123 0.0045 0.0324 0.0589 0.0794 0.184 0.213 0.000489 0.000566 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.162 0.00538 0.0545 0.0652 0.0867 0.262 0.276 0.000697 0.000734 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 48192.314 0.005 0.00446 0.0773 0.166 0.0586 0.0789 0.249 0.329 0.000663 0.000874 ! Validation 472 48192.314 0.005 0.00407 0.0276 0.109 0.0567 0.0755 0.163 0.196 0.000434 0.000522 Wall time: 48192.31437533721 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.123 0.0045 0.0334 0.059 0.0793 0.192 0.216 0.000512 0.000574 473 172 0.118 0.00366 0.0451 0.0534 0.0715 0.217 0.251 0.000578 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.206 0.0052 0.102 0.0639 0.0853 0.37 0.377 0.000984 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 473 48294.039 0.005 0.00424 0.0575 0.142 0.0571 0.077 0.211 0.284 0.000561 0.000754 ! Validation 473 48294.039 0.005 0.00392 0.114 0.193 0.0553 0.074 0.374 0.4 0.000995 0.00106 Wall time: 48294.039287807886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.11 0.00419 0.0266 0.0566 0.0766 0.15 0.193 0.000399 0.000513 474 172 0.145 0.00474 0.0505 0.0602 0.0814 0.227 0.266 0.000605 0.000707 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.283 0.00514 0.18 0.0638 0.0848 0.488 0.502 0.0013 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 474 48395.725 0.005 0.00423 0.0719 0.156 0.057 0.0769 0.241 0.317 0.000642 0.000843 ! Validation 474 48395.725 0.005 0.00378 0.177 0.253 0.0544 0.0727 0.481 0.498 0.00128 0.00132 Wall time: 48395.725350792054 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.0825 0.00361 0.0103 0.0533 0.071 0.0976 0.12 0.00026 0.000319 475 172 0.11 0.00382 0.034 0.0541 0.0731 0.175 0.218 0.000465 0.00058 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.107 0.00516 0.00341 0.0645 0.0849 0.055 0.069 0.000146 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 475 48500.814 0.005 0.00399 0.0474 0.127 0.0552 0.0747 0.181 0.257 0.000482 0.000685 ! Validation 475 48500.814 0.005 0.00384 0.0407 0.118 0.055 0.0733 0.208 0.239 0.000554 0.000635 Wall time: 48500.8144354322 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.0935 0.00369 0.0198 0.0537 0.0718 0.142 0.166 0.000378 0.000442 476 172 0.0843 0.00388 0.00671 0.0543 0.0736 0.0734 0.0969 0.000195 0.000258 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.113 0.00487 0.0155 0.0618 0.0825 0.127 0.147 0.000337 0.000391 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 48602.820 0.005 0.00406 0.062 0.143 0.0558 0.0754 0.206 0.295 0.000547 0.000783 ! Validation 476 48602.820 0.005 0.00353 0.0201 0.0907 0.0524 0.0703 0.133 0.168 0.000353 0.000446 Wall time: 48602.820764061995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.226 0.00353 0.155 0.052 0.0703 0.454 0.466 0.00121 0.00124 477 172 0.111 0.00375 0.0356 0.0535 0.0724 0.202 0.223 0.000538 0.000594 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.117 0.00471 0.023 0.0608 0.0811 0.136 0.179 0.000362 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 477 48705.365 0.005 0.00385 0.0408 0.118 0.0541 0.0733 0.169 0.239 0.000449 0.000636 ! Validation 477 48705.365 0.005 0.00344 0.0297 0.0986 0.0517 0.0694 0.175 0.204 0.000466 0.000542 Wall time: 48705.365633287 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.107 0.00367 0.0335 0.0529 0.0717 0.153 0.217 0.000406 0.000576 478 172 0.103 0.00361 0.0304 0.0528 0.0711 0.161 0.206 0.000428 0.000549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.148 0.00451 0.0575 0.0591 0.0794 0.265 0.283 0.000704 0.000754 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 48807.163 0.005 0.00381 0.0648 0.141 0.0539 0.073 0.224 0.301 0.000596 0.000801 ! Validation 478 48807.163 0.005 0.00343 0.0408 0.109 0.0516 0.0693 0.203 0.239 0.00054 0.000635 Wall time: 48807.16298557678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.0808 0.00371 0.0066 0.0533 0.072 0.0763 0.0961 0.000203 0.000256 479 172 0.0951 0.00348 0.0256 0.0518 0.0697 0.172 0.189 0.000456 0.000503 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.0994 0.00443 0.0108 0.0587 0.0787 0.117 0.123 0.00031 0.000327 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 48909.657 0.005 0.00384 0.0474 0.124 0.0541 0.0733 0.173 0.257 0.000461 0.000685 ! Validation 479 48909.657 0.005 0.00331 0.0207 0.0869 0.0506 0.068 0.141 0.17 0.000376 0.000453 Wall time: 48909.65746416012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.0895 0.00354 0.0187 0.0529 0.0704 0.136 0.162 0.000361 0.00043 480 172 0.0897 0.00381 0.0135 0.0538 0.073 0.115 0.137 0.000306 0.000365 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.098 0.00461 0.00571 0.0598 0.0803 0.0819 0.0894 0.000218 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 480 49011.474 0.005 0.00375 0.0528 0.128 0.0534 0.0724 0.203 0.272 0.000541 0.000723 ! Validation 480 49011.474 0.005 0.00353 0.0155 0.086 0.0523 0.0702 0.116 0.147 0.000307 0.000391 Wall time: 49011.47475825017 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.083 0.00355 0.012 0.0526 0.0705 0.108 0.129 0.000287 0.000344 481 172 0.125 0.0037 0.0508 0.0535 0.0719 0.246 0.267 0.000655 0.000709 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.245 0.0046 0.153 0.0603 0.0802 0.437 0.462 0.00116 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 481 49113.296 0.005 0.00377 0.063 0.138 0.0536 0.0726 0.22 0.297 0.000584 0.000789 ! Validation 481 49113.296 0.005 0.00339 0.147 0.215 0.0515 0.0688 0.435 0.453 0.00116 0.0012 Wall time: 49113.29638194712 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.0723 0.00319 0.00853 0.0499 0.0668 0.0911 0.109 0.000242 0.000291 482 172 0.0965 0.0033 0.0305 0.0494 0.0679 0.189 0.206 0.000503 0.000549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.0927 0.00435 0.0057 0.0584 0.078 0.0736 0.0892 0.000196 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 482 49215.113 0.005 0.00358 0.0421 0.114 0.0521 0.0707 0.169 0.243 0.00045 0.000645 ! Validation 482 49215.113 0.005 0.00319 0.0124 0.0762 0.0497 0.0668 0.101 0.132 0.000268 0.00035 Wall time: 49215.11324852798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.075 0.00328 0.00949 0.0502 0.0677 0.0909 0.115 0.000242 0.000306 483 172 0.085 0.00351 0.0149 0.0518 0.07 0.106 0.144 0.000281 0.000384 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.0993 0.00454 0.00841 0.0599 0.0797 0.103 0.108 0.000273 0.000288 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 49316.975 0.005 0.00365 0.0565 0.13 0.0527 0.0715 0.2 0.281 0.000531 0.000747 ! Validation 483 49316.975 0.005 0.0033 0.0111 0.0771 0.0506 0.0679 0.0963 0.124 0.000256 0.000331 Wall time: 49316.975419797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.0869 0.00367 0.0135 0.0526 0.0716 0.103 0.137 0.000275 0.000365 484 172 0.774 0.00449 0.684 0.0598 0.0793 0.963 0.978 0.00256 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.829 0.00499 0.729 0.0625 0.0836 1.01 1.01 0.00268 0.00268 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 49418.799 0.005 0.00355 0.053 0.124 0.0519 0.0704 0.188 0.271 0.0005 0.000721 ! Validation 484 49418.799 0.005 0.00406 0.429 0.51 0.0565 0.0754 0.765 0.775 0.00204 0.00206 Wall time: 49418.7998663201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.0954 0.00382 0.019 0.0549 0.0731 0.132 0.163 0.000351 0.000434 485 172 0.0742 0.00328 0.00856 0.0497 0.0677 0.0849 0.109 0.000226 0.000291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.0859 0.00419 0.00212 0.0572 0.0765 0.0536 0.0545 0.000143 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 485 49521.915 0.005 0.00443 0.0657 0.154 0.0579 0.0787 0.219 0.303 0.000581 0.000807 ! Validation 485 49521.915 0.005 0.00311 0.014 0.0763 0.049 0.066 0.107 0.14 0.000285 0.000373 Wall time: 49521.91515357606 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.164 0.00532 0.0578 0.065 0.0862 0.235 0.284 0.000626 0.000756 486 172 0.0848 0.00347 0.0154 0.0517 0.0697 0.124 0.147 0.000329 0.00039 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.0852 0.0042 0.0012 0.0569 0.0766 0.0357 0.0409 9.49e-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 486 49623.720 0.005 0.00374 0.0544 0.129 0.0533 0.0723 0.194 0.276 0.000516 0.000734 ! Validation 486 49623.720 0.005 0.00324 0.013 0.0778 0.05 0.0673 0.104 0.135 0.000278 0.000358 Wall time: 49623.720798547845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.101 0.00349 0.0309 0.0527 0.0699 0.165 0.208 0.000438 0.000553 487 172 0.125 0.00375 0.0497 0.0532 0.0724 0.233 0.264 0.00062 0.000701 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.206 0.00434 0.119 0.0581 0.0779 0.386 0.408 0.00103 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 487 49725.619 0.005 0.00333 0.0331 0.0997 0.0502 0.0682 0.147 0.215 0.00039 0.000572 ! Validation 487 49725.619 0.005 0.00323 0.0351 0.0997 0.0501 0.0672 0.192 0.221 0.00051 0.000589 Wall time: 49725.61964056501 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.134 0.00394 0.0551 0.0548 0.0743 0.255 0.278 0.000679 0.000738 488 172 0.108 0.00335 0.0405 0.0509 0.0685 0.218 0.238 0.00058 0.000633 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.0888 0.00414 0.00595 0.0572 0.0761 0.0756 0.0912 0.000201 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 488 49827.403 0.005 0.00385 0.0522 0.129 0.0539 0.0733 0.186 0.27 0.000495 0.000719 ! Validation 488 49827.403 0.005 0.00301 0.0125 0.0727 0.0482 0.0649 0.103 0.132 0.000275 0.000352 Wall time: 49827.40381886205 ! Best model 488 0.073 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0819 0.00328 0.0164 0.0502 0.0677 0.121 0.151 0.000321 0.000403 489 172 0.0818 0.00337 0.0145 0.0504 0.0686 0.119 0.142 0.000317 0.000379 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0912 0.00408 0.00965 0.0562 0.0755 0.104 0.116 0.000277 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 489 49929.220 0.005 0.00359 0.0432 0.115 0.0523 0.0709 0.177 0.246 0.000471 0.000654 ! Validation 489 49929.220 0.005 0.00308 0.0307 0.0923 0.0487 0.0656 0.179 0.207 0.000476 0.000551 Wall time: 49929.21989719616 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.114 0.00334 0.0473 0.0509 0.0684 0.235 0.257 0.000624 0.000684 490 172 0.0862 0.00308 0.0246 0.0494 0.0656 0.157 0.185 0.000418 0.000493 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.104 0.0042 0.0198 0.0572 0.0766 0.148 0.166 0.000393 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 490 50035.619 0.005 0.00345 0.0638 0.133 0.0512 0.0694 0.224 0.299 0.000596 0.000794 ! Validation 490 50035.619 0.005 0.00312 0.0183 0.0807 0.0493 0.0661 0.131 0.16 0.000348 0.000425 Wall time: 50035.61890010908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.0729 0.00298 0.0132 0.0469 0.0646 0.113 0.136 0.000302 0.000362 491 172 0.0966 0.0034 0.0286 0.052 0.0689 0.169 0.2 0.000449 0.000532 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.273 0.00404 0.192 0.0564 0.0751 0.495 0.519 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 491 50137.452 0.005 0.0033 0.0469 0.113 0.05 0.0679 0.185 0.256 0.000491 0.000681 ! Validation 491 50137.452 0.005 0.00316 0.0476 0.111 0.0497 0.0665 0.218 0.258 0.00058 0.000686 Wall time: 50137.452742238995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.0861 0.00337 0.0188 0.0506 0.0686 0.127 0.162 0.000337 0.000431 492 172 0.0905 0.00339 0.0228 0.05 0.0688 0.143 0.178 0.00038 0.000475 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.109 0.00461 0.017 0.0602 0.0803 0.132 0.154 0.00035 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 492 50240.455 0.005 0.00418 0.0513 0.135 0.0561 0.0764 0.186 0.268 0.000495 0.000712 ! Validation 492 50240.455 0.005 0.00345 0.0271 0.0962 0.0517 0.0695 0.164 0.195 0.000437 0.000518 Wall time: 50240.45573180681 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.794 0.00618 0.67 0.0516 0.093 0.322 0.968 0.000858 0.00257 493 172 0.0628 0.00259 0.0109 0.045 0.0602 0.103 0.123 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 493 100 0.137 0.00382 0.0605 0.0547 0.0731 0.29 0.291 0.000771 0.000774 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 50342.496 0.005 0.00343 0.0409 0.11 0.051 0.0693 0.167 0.239 0.000443 0.000636 ! Validation 493 50342.496 0.005 0.00281 0.0323 0.0886 0.0465 0.0627 0.187 0.213 0.000498 0.000565 Wall time: 50342.49683520617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.0885 0.00329 0.0227 0.0493 0.0678 0.158 0.178 0.000419 0.000474 494 172 0.28 0.00974 0.0855 0.0899 0.117 0.221 0.346 0.000588 0.000919 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.261 0.00771 0.106 0.0813 0.104 0.371 0.386 0.000987 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 494 50444.171 0.005 0.00405 0.0698 0.151 0.0544 0.0752 0.223 0.312 0.000592 0.000831 ! Validation 494 50444.171 0.005 0.00728 0.148 0.293 0.0794 0.101 0.422 0.455 0.00112 0.00121 Wall time: 50444.17101972923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 3.95 0.104 1.87 0.289 0.382 1.5 1.62 0.004 0.0043 495 172 3.84 0.0688 2.46 0.231 0.31 1.81 1.85 0.00482 0.00493 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 2.77 0.08 1.17 0.249 0.334 1.23 1.28 0.00327 0.0034 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 50545.845 0.005 0.282 3.9 9.54 0.415 0.628 1.59 2.34 0.00422 0.00621 ! Validation 495 50545.845 0.005 0.0718 0.97 2.41 0.234 0.317 1.06 1.16 0.00281 0.0031 Wall time: 50545.84514523111 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 1.86 0.0489 0.883 0.192 0.261 1.05 1.11 0.00279 0.00296 496 172 0.977 0.0337 0.303 0.16 0.217 0.543 0.65 0.00144 0.00173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.943 0.0389 0.165 0.172 0.233 0.409 0.48 0.00109 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 496 50647.521 0.005 0.0538 0.501 1.58 0.201 0.274 0.667 0.837 0.00177 0.00223 ! Validation 496 50647.521 0.005 0.0343 0.182 0.869 0.161 0.219 0.407 0.505 0.00108 0.00134 Wall time: 50647.521224616095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.629 0.0238 0.153 0.134 0.183 0.349 0.462 0.000929 0.00123 497 172 0.52 0.0183 0.153 0.118 0.16 0.404 0.462 0.00108 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.696 0.0216 0.265 0.13 0.174 0.511 0.609 0.00136 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 497 50749.189 0.005 0.026 0.319 0.839 0.139 0.191 0.519 0.668 0.00138 0.00178 ! Validation 497 50749.189 0.005 0.0172 0.228 0.573 0.115 0.155 0.502 0.564 0.00134 0.0015 Wall time: 50749.18943983503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.475 0.0163 0.149 0.112 0.151 0.322 0.456 0.000858 0.00121 498 172 0.367 0.0128 0.112 0.0998 0.134 0.32 0.396 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 498 100 0.51 0.0152 0.206 0.109 0.146 0.5 0.537 0.00133 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 498 50850.856 0.005 0.0159 0.259 0.576 0.11 0.149 0.471 0.601 0.00125 0.0016 ! Validation 498 50850.856 0.005 0.0118 0.243 0.479 0.0957 0.128 0.528 0.583 0.00141 0.00155 Wall time: 50850.85614491394 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.335 0.0107 0.121 0.0912 0.122 0.364 0.411 0.000968 0.00109 499 172 0.229 0.00883 0.0524 0.0838 0.111 0.224 0.271 0.000596 0.00072 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.286 0.0116 0.0545 0.0957 0.127 0.254 0.276 0.000676 0.000734 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 50952.783 0.005 0.0109 0.16 0.377 0.0918 0.123 0.367 0.472 0.000975 0.00126 ! Validation 499 50952.783 0.005 0.00853 0.0563 0.227 0.0821 0.109 0.228 0.281 0.000606 0.000746 Wall time: 50952.78372855298 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.305 0.0086 0.133 0.083 0.11 0.378 0.431 0.001 0.00115 500 172 0.197 0.00821 0.033 0.0804 0.107 0.175 0.215 0.000464 0.000571 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.265 0.0102 0.0623 0.09 0.119 0.235 0.295 0.000626 0.000785 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 51054.408 0.005 0.00867 0.17 0.343 0.0826 0.11 0.383 0.488 0.00102 0.0013 ! Validation 500 51054.408 0.005 0.00725 0.135 0.28 0.0763 0.101 0.39 0.434 0.00104 0.00116 Wall time: 51054.408064487856 ! Stop training: max epochs Wall time: 51054.449038355146 Cumulative wall time: 51054.449038355146 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 = 0.052171 f_rmse = 0.072209 e_mae = 0.112095 e_rmse = 0.149503 e/N_mae = 0.000298 e/N_rmse = 0.000398 f_mae = 0.052171 f_rmse = 0.072209 e_mae = 0.112095 e_rmse = 0.149503 e/N_mae = 0.000298 e/N_rmse = 0.000398 Train end time: 2024-12-09_01:07:31 Training duration: 14h 14m 37s