diff --git "a/ads_16_length_2000/ALLEGRO/results/adsorption/log" "b/ads_16_length_2000/ALLEGRO/results/adsorption/log" new file mode 100644--- /dev/null +++ "b/ads_16_length_2000/ALLEGRO/results/adsorption/log" @@ -0,0 +1,7103 @@ +Torch device: cuda +Number of weights: 1406856 +Number of trainable weights: 1406856 +! Starting training ... + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 0 100 472 1.08 450 0.918 1.24 25.4 25.4 0.0634 0.0634 + + + Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Initial Validation 0 7.196 0.005 1.07 495 516 0.914 1.23 25.9 26.6 0.0649 0.0665 +Wall time: 7.1963930069468915 +! Best model 0 516.104 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 47 279 0.989 259 0.881 1.19 18.7 19.2 0.0468 0.0481 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 1 100 79 1 59 0.884 1.2 9.16 9.18 0.0229 0.023 + + + 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 43.090 0.005 1.01 2.6e+04 2.6e+04 0.891 1.2 127 193 0.316 0.482 +! Validation 1 43.090 0.005 0.981 66.7 86.3 0.879 1.18 9.16 9.76 0.0229 0.0244 +Wall time: 43.090999900363386 +! Best model 1 86.291 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 47 36.3 1.01 16.1 0.89 1.2 3.99 4.79 0.00997 0.012 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 2 100 26 1 5.96 0.884 1.2 2.86 2.92 0.00715 0.00729 + + + 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 76.160 0.005 0.99 95.4 115 0.882 1.19 9.11 11.7 0.0228 0.0292 +! Validation 2 76.160 0.005 0.979 11.9 31.5 0.879 1.18 3.41 4.13 0.00852 0.0103 +Wall time: 76.16009983792901 +! Best model 2 31.513 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 47 26.1 0.985 6.42 0.882 1.19 2.61 3.03 0.00653 0.00757 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 3 100 23 1 2.98 0.884 1.19 1.98 2.06 0.00495 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 3 109.201 0.005 0.989 7.9 27.7 0.882 1.19 2.72 3.36 0.00681 0.0084 +! Validation 3 109.201 0.005 0.979 5.9 25.5 0.878 1.18 2.52 2.9 0.00629 0.00725 +Wall time: 109.20330212125555 +! Best model 3 25.466 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 47 27.3 1.01 7.1 0.887 1.2 2.69 3.18 0.00672 0.00796 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 4 100 21.8 0.999 1.82 0.883 1.19 1.51 1.61 0.00376 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 4 142.242 0.005 0.988 6.54 26.3 0.881 1.19 2.52 3.06 0.00629 0.00764 +! Validation 4 142.242 0.005 0.978 4.53 24.1 0.878 1.18 2.14 2.54 0.00535 0.00636 +Wall time: 142.24266247311607 +! Best model 4 24.077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 47 24.9 0.979 5.34 0.878 1.18 2.13 2.76 0.00534 0.0069 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 5 100 21.1 0.998 1.1 0.883 1.19 1.11 1.25 0.00278 0.00314 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 5 175.288 0.005 0.987 6.41 26.2 0.881 1.19 2.49 3.03 0.00624 0.00756 +! Validation 5 175.288 0.005 0.977 3.79 23.3 0.878 1.18 1.89 2.33 0.00472 0.00582 +Wall time: 175.28845357522368 +! Best model 5 23.324 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 47 25.4 1 5.36 0.887 1.2 2 2.77 0.005 0.00692 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 6 100 20.8 0.997 0.878 0.882 1.19 0.958 1.12 0.0024 0.0028 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 6 209.723 0.005 0.986 6.42 26.2 0.881 1.19 2.49 3.03 0.00623 0.00757 +! Validation 6 209.723 0.005 0.975 3.6 23.1 0.877 1.18 1.81 2.27 0.00452 0.00567 +Wall time: 209.72351844236255 +! Best model 6 23.110 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 47 26.7 0.977 7.13 0.878 1.18 2.65 3.19 0.00662 0.00798 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 7 100 21.4 0.996 1.48 0.882 1.19 1.33 1.45 0.00334 0.00363 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 7 242.766 0.005 0.985 6.34 26 0.88 1.19 2.48 3.01 0.0062 0.00752 +! Validation 7 242.766 0.005 0.974 3.96 23.4 0.877 1.18 1.97 2.38 0.00492 0.00594 +Wall time: 242.7661853712052 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 47 26.2 0.964 6.94 0.873 1.17 2.5 3.15 0.00625 0.00787 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 8 100 20.4 0.994 0.518 0.881 1.19 0.749 0.86 0.00187 0.00215 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 8 276.880 0.005 0.984 6.39 26.1 0.88 1.19 2.5 3.02 0.00624 0.00755 +! Validation 8 276.880 0.005 0.973 3.48 22.9 0.876 1.18 1.73 2.23 0.00433 0.00557 +Wall time: 276.880179583095 +! Best model 8 22.943 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 47 23.4 0.98 3.81 0.877 1.18 1.96 2.33 0.00491 0.00583 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 9 100 22.1 0.993 2.24 0.881 1.19 1.69 1.79 0.00423 0.00447 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 9 309.882 0.005 0.983 6.22 25.9 0.879 1.18 2.46 2.98 0.00616 0.00746 +! Validation 9 309.882 0.005 0.972 4.39 23.8 0.876 1.18 2.13 2.51 0.00532 0.00626 +Wall time: 309.8826294951141 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 47 27 0.977 7.42 0.875 1.18 2.85 3.26 0.00714 0.00814 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 10 100 22.1 0.992 2.26 0.88 1.19 1.7 1.8 0.00426 0.00449 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 10 342.871 0.005 0.982 6.28 25.9 0.878 1.18 2.46 2.99 0.00616 0.00748 +! Validation 10 342.871 0.005 0.971 4.46 23.9 0.875 1.18 2.15 2.52 0.00537 0.00631 +Wall time: 342.8711941181682 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 47 25.3 0.993 5.48 0.881 1.19 2.18 2.8 0.00546 0.007 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 11 100 21.5 0.991 1.66 0.879 1.19 1.43 1.54 0.00357 0.00385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 11 375.863 0.005 0.98 6.07 25.7 0.878 1.18 2.42 2.94 0.00604 0.00736 +! Validation 11 375.863 0.005 0.969 3.91 23.3 0.874 1.18 1.97 2.36 0.00492 0.00591 +Wall time: 375.863790362142 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 47 23.2 0.967 3.87 0.871 1.18 2.06 2.35 0.00515 0.00588 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 12 100 25 0.989 5.17 0.879 1.19 2.66 2.72 0.00664 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 12 408.841 0.005 0.979 6.3 25.9 0.877 1.18 2.45 3 0.00612 0.0075 +! Validation 12 408.841 0.005 0.968 6.71 26.1 0.874 1.18 2.73 3.1 0.00683 0.00774 +Wall time: 408.84290422918275 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 47 26.2 0.974 6.69 0.879 1.18 2.68 3.09 0.00671 0.00772 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 13 100 21.8 0.988 2.02 0.878 1.19 1.6 1.7 0.004 0.00424 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 13 441.835 0.005 0.978 6.28 25.8 0.877 1.18 2.47 3 0.00617 0.00749 +! Validation 13 441.835 0.005 0.967 4.02 23.4 0.873 1.17 2.02 2.4 0.00505 0.00599 +Wall time: 441.8354793502949 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 47 24.8 0.96 5.61 0.869 1.17 2.46 2.83 0.00615 0.00708 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 14 100 21.6 0.986 1.83 0.877 1.19 1.51 1.62 0.00378 0.00404 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 14 474.821 0.005 0.976 5.88 25.4 0.876 1.18 2.37 2.9 0.00593 0.00724 +! Validation 14 474.821 0.005 0.965 3.91 23.2 0.872 1.17 1.98 2.36 0.00495 0.00591 +Wall time: 474.82116180099547 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 47 27.2 0.981 7.57 0.88 1.18 2.83 3.29 0.00708 0.00822 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 15 100 21 0.985 1.27 0.877 1.19 1.22 1.35 0.00306 0.00337 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 15 507.830 0.005 0.975 5.69 25.2 0.875 1.18 2.35 2.85 0.00588 0.00712 +! Validation 15 507.830 0.005 0.964 3.54 22.8 0.872 1.17 1.84 2.25 0.00459 0.00562 +Wall time: 507.83072615228593 +! Best model 15 22.813 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 47 27.6 0.957 8.49 0.868 1.17 2.74 3.48 0.00684 0.0087 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 16 100 20.6 0.983 0.895 0.876 1.18 0.98 1.13 0.00245 0.00283 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 16 540.847 0.005 0.973 5.89 25.4 0.874 1.18 2.38 2.9 0.00594 0.00725 +! Validation 16 540.847 0.005 0.962 3.38 22.6 0.871 1.17 1.76 2.2 0.00439 0.0055 +Wall time: 540.8475920190103 +! Best model 16 22.633 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 47 23.9 0.984 4.22 0.878 1.19 1.82 2.46 0.00456 0.00614 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 17 100 20.8 0.982 1.21 0.875 1.18 1.19 1.31 0.00296 0.00328 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 17 573.855 0.005 0.972 5.77 25.2 0.874 1.18 2.36 2.87 0.0059 0.00718 +! Validation 17 573.855 0.005 0.961 3.53 22.8 0.87 1.17 1.83 2.25 0.00458 0.00561 +Wall time: 573.8558829203248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 47 23.6 0.96 4.41 0.869 1.17 2.21 2.51 0.00554 0.00627 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 18 100 21.8 0.98 2.24 0.874 1.18 1.7 1.79 0.00425 0.00447 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 18 606.854 0.005 0.97 5.83 25.2 0.873 1.18 2.37 2.89 0.00593 0.00722 +! Validation 18 606.854 0.005 0.96 4.13 23.3 0.87 1.17 2.07 2.43 0.00517 0.00607 +Wall time: 606.8542015459388 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 47 23.9 0.985 4.22 0.881 1.19 2.09 2.45 0.00523 0.00613 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 19 100 20.5 0.979 0.944 0.874 1.18 1.02 1.16 0.00254 0.0029 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 19 639.850 0.005 0.969 5.47 24.8 0.872 1.18 2.3 2.8 0.00574 0.00699 +! Validation 19 639.850 0.005 0.958 3.21 22.4 0.869 1.17 1.71 2.14 0.00428 0.00535 +Wall time: 639.8506926563568 +! Best model 19 22.370 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 47 25.9 0.982 6.27 0.88 1.18 2.65 2.99 0.00662 0.00748 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 20 100 20.9 0.977 1.37 0.873 1.18 1.28 1.4 0.0032 0.00349 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 20 672.865 0.005 0.967 5.54 24.9 0.872 1.18 2.3 2.81 0.00574 0.00703 +! Validation 20 672.865 0.005 0.956 3.47 22.6 0.868 1.17 1.83 2.23 0.00458 0.00556 +Wall time: 672.8653594702482 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 47 26.3 0.961 7.1 0.867 1.17 2.74 3.18 0.00686 0.00796 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 21 100 21.4 0.975 1.88 0.872 1.18 1.54 1.64 0.00386 0.0041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 21 706.040 0.005 0.966 5.76 25.1 0.871 1.17 2.35 2.87 0.00587 0.00717 +! Validation 21 706.040 0.005 0.955 3.64 22.7 0.867 1.17 1.92 2.28 0.00479 0.0057 +Wall time: 706.040500368923 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 47 22.5 0.939 3.76 0.86 1.16 1.88 2.32 0.00471 0.00579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 22 100 20.1 0.974 0.652 0.871 1.18 0.824 0.965 0.00206 0.00241 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 22 739.036 0.005 0.964 5.24 24.5 0.87 1.17 2.23 2.74 0.00558 0.00684 +! Validation 22 739.036 0.005 0.953 2.83 21.9 0.866 1.17 1.57 2.01 0.00392 0.00503 +Wall time: 739.0360666522756 +! Best model 22 21.897 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 47 23.5 0.957 4.32 0.868 1.17 1.91 2.48 0.00477 0.00621 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 23 100 20 0.972 0.531 0.87 1.18 0.752 0.871 0.00188 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 23 772.053 0.005 0.963 5.27 24.5 0.869 1.17 2.25 2.74 0.00562 0.00686 +! Validation 23 772.053 0.005 0.952 2.73 21.8 0.866 1.17 1.53 1.97 0.00382 0.00494 +Wall time: 772.053556912113 +! Best model 23 21.766 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 47 23.1 0.966 3.75 0.869 1.17 1.81 2.31 0.00452 0.00578 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 24 100 20.6 0.97 1.22 0.87 1.18 1.2 1.32 0.003 0.0033 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 24 805.069 0.005 0.961 5.06 24.3 0.868 1.17 2.2 2.69 0.00549 0.00672 +! Validation 24 805.069 0.005 0.95 3.13 22.1 0.865 1.16 1.72 2.11 0.0043 0.00528 +Wall time: 805.0689498409629 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 47 26.5 0.976 7.01 0.872 1.18 2.72 3.16 0.00681 0.00791 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 25 100 22.1 0.969 2.75 0.869 1.18 1.9 1.98 0.00476 0.00496 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 25 838.065 0.005 0.96 4.96 24.1 0.868 1.17 2.18 2.66 0.00545 0.00665 +! Validation 25 838.065 0.005 0.949 4.14 23.1 0.864 1.16 2.09 2.43 0.00523 0.00608 +Wall time: 838.0654606549069 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 47 23 0.945 4.08 0.861 1.16 1.75 2.41 0.00439 0.00604 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 26 100 21.5 0.967 2.14 0.868 1.18 1.66 1.75 0.00415 0.00437 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 26 871.618 0.005 0.958 5.09 24.2 0.867 1.17 2.2 2.7 0.00549 0.00674 +! Validation 26 871.618 0.005 0.947 3.62 22.6 0.863 1.16 1.93 2.28 0.00482 0.00569 +Wall time: 871.6183435502462 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 47 22.7 0.954 3.65 0.864 1.17 1.79 2.28 0.00447 0.00571 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 27 100 20.6 0.965 1.31 0.867 1.17 1.25 1.37 0.00313 0.00342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 27 905.084 0.005 0.956 4.57 23.7 0.866 1.17 2.1 2.56 0.00526 0.00639 +! Validation 27 905.084 0.005 0.945 3.16 22.1 0.862 1.16 1.75 2.13 0.00436 0.00531 +Wall time: 905.0841841851361 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 47 23.3 0.947 4.4 0.862 1.16 2 2.51 0.00501 0.00626 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 28 100 20.2 0.964 0.901 0.866 1.17 0.994 1.13 0.00248 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 28 938.121 0.005 0.955 4.61 23.7 0.865 1.17 2.1 2.57 0.00526 0.00642 +! Validation 28 938.121 0.005 0.944 2.86 21.7 0.861 1.16 1.61 2.02 0.00404 0.00506 +Wall time: 938.121144099161 +! Best model 28 21.740 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 47 23.5 0.941 4.69 0.86 1.16 2.1 2.59 0.00525 0.00647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 29 100 21.4 0.962 2.2 0.865 1.17 1.69 1.77 0.00422 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 29 971.178 0.005 0.953 4.53 23.6 0.864 1.17 2.07 2.54 0.00518 0.00636 +! Validation 29 971.178 0.005 0.942 3.49 22.3 0.861 1.16 1.9 2.23 0.00474 0.00558 +Wall time: 971.1786914933473 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 47 25.7 0.956 6.61 0.864 1.17 2.7 3.07 0.00675 0.00768 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 30 100 20.3 0.96 1.13 0.864 1.17 1.15 1.27 0.00287 0.00318 + + + 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 1004.215 0.005 0.951 4.39 23.4 0.863 1.17 2.05 2.5 0.00513 0.00625 +! Validation 30 1004.215 0.005 0.94 2.85 21.7 0.86 1.16 1.64 2.02 0.0041 0.00505 +Wall time: 1004.2154554510489 +! Best model 30 21.662 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 47 23 0.948 4.02 0.862 1.16 2.01 2.4 0.00502 0.00599 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 31 100 21.8 0.958 2.68 0.863 1.17 1.88 1.96 0.0047 0.00489 + + + 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 1037.269 0.005 0.95 4.26 23.3 0.862 1.16 2.01 2.47 0.00503 0.00617 +! Validation 31 1037.269 0.005 0.939 3.88 22.7 0.859 1.16 2.02 2.36 0.00506 0.00589 +Wall time: 1037.269645507913 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 47 22.1 0.948 3.18 0.86 1.16 1.79 2.13 0.00446 0.00532 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 32 100 19.9 0.956 0.823 0.862 1.17 0.937 1.08 0.00234 0.00271 + + + 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 1070.329 0.005 0.948 4.45 23.4 0.861 1.16 2.05 2.52 0.00511 0.0063 +! Validation 32 1070.329 0.005 0.937 2.48 21.2 0.858 1.16 1.49 1.88 0.00373 0.00471 +Wall time: 1070.3296975153498 +! Best model 32 21.220 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 47 24.3 0.954 5.24 0.864 1.17 1.95 2.74 0.00488 0.00684 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 33 100 19.8 0.954 0.675 0.861 1.17 0.829 0.981 0.00207 0.00245 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 33 1103.381 0.005 0.946 4.42 23.3 0.86 1.16 2.05 2.51 0.00512 0.00628 +! Validation 33 1103.381 0.005 0.935 2.47 21.2 0.857 1.16 1.47 1.88 0.00368 0.0047 +Wall time: 1103.38185192924 +! Best model 33 21.177 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 47 23.2 0.957 4.04 0.868 1.17 1.83 2.4 0.00458 0.006 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 34 100 20.8 0.953 1.72 0.86 1.17 1.47 1.57 0.00368 0.00392 + + + 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 1136.432 0.005 0.944 4 22.9 0.86 1.16 1.95 2.39 0.00487 0.00598 +! Validation 34 1136.432 0.005 0.933 3.17 21.8 0.856 1.15 1.79 2.13 0.00446 0.00532 +Wall time: 1136.4325814219192 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 47 21.3 0.952 2.26 0.862 1.17 1.44 1.8 0.00359 0.0045 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 35 100 19.5 0.951 0.533 0.859 1.17 0.747 0.872 0.00187 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 35 1169.465 0.005 0.943 4.16 23 0.859 1.16 1.98 2.44 0.00496 0.0061 +! Validation 35 1169.465 0.005 0.932 2.19 20.8 0.855 1.15 1.36 1.77 0.00341 0.00442 +Wall time: 1169.46572938608 +! Best model 35 20.823 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 47 22.4 0.907 4.25 0.84 1.14 2.11 2.46 0.00528 0.00616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 36 100 20.6 0.949 1.59 0.858 1.16 1.41 1.51 0.00351 0.00376 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 36 1202.626 0.005 0.941 3.79 22.6 0.858 1.16 1.89 2.33 0.00473 0.00582 +! Validation 36 1202.626 0.005 0.93 2.73 21.3 0.854 1.15 1.64 1.97 0.0041 0.00494 +Wall time: 1202.6259355982766 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 47 22.9 0.942 4 0.856 1.16 2.03 2.39 0.00507 0.00598 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 37 100 24 0.947 5.1 0.857 1.16 2.65 2.7 0.00661 0.00675 + + + 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 1235.659 0.005 0.939 4.16 22.9 0.856 1.16 1.98 2.44 0.00495 0.00609 +! Validation 37 1235.659 0.005 0.928 5.75 24.3 0.853 1.15 2.53 2.87 0.00633 0.00717 +Wall time: 1235.6594868940301 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 47 24.1 0.936 5.4 0.856 1.16 2.1 2.78 0.00526 0.00694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 38 100 19.3 0.944 0.381 0.856 1.16 0.637 0.737 0.00159 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 38 1268.689 0.005 0.937 3.69 22.4 0.855 1.16 1.86 2.29 0.00466 0.00573 +! Validation 38 1268.689 0.005 0.926 2.12 20.6 0.852 1.15 1.32 1.74 0.0033 0.00435 +Wall time: 1268.6897659981623 +! Best model 38 20.636 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 47 24.2 0.94 5.4 0.857 1.16 2.23 2.78 0.00559 0.00694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 39 100 19.6 0.942 0.757 0.854 1.16 0.893 1.04 0.00223 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 39 1301.738 0.005 0.935 3.72 22.4 0.854 1.16 1.86 2.3 0.00466 0.00576 +! Validation 39 1301.738 0.005 0.924 2.24 20.7 0.85 1.15 1.41 1.79 0.00353 0.00447 +Wall time: 1301.7388024730608 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 47 22.1 0.915 3.76 0.847 1.14 1.97 2.32 0.00492 0.00579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 40 100 20.4 0.94 1.63 0.853 1.16 1.43 1.53 0.00358 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 40 1334.770 0.005 0.933 3.87 22.5 0.853 1.15 1.9 2.35 0.00476 0.00588 +! Validation 40 1334.770 0.005 0.922 2.83 21.3 0.849 1.15 1.68 2.01 0.00419 0.00502 +Wall time: 1334.770808239933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 47 24.9 0.926 6.34 0.852 1.15 2.31 3.01 0.00578 0.00752 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 41 100 19 0.938 0.269 0.852 1.16 0.563 0.619 0.00141 0.00155 + + + 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 1367.799 0.005 0.931 4.83 23.4 0.852 1.15 2.13 2.62 0.00532 0.00656 +! Validation 41 1367.799 0.005 0.92 2.11 20.5 0.848 1.15 1.3 1.74 0.00325 0.00434 +Wall time: 1367.7993871071376 +! Best model 41 20.501 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 47 21.3 0.95 2.26 0.859 1.16 1.52 1.8 0.00379 0.00449 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 42 100 19.8 0.935 1.05 0.851 1.16 1.1 1.22 0.00276 0.00306 + + + 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 1400.993 0.005 0.929 4.14 22.7 0.851 1.15 1.96 2.43 0.0049 0.00608 +! Validation 42 1400.993 0.005 0.917 2.25 20.6 0.847 1.14 1.45 1.79 0.00362 0.00448 +Wall time: 1400.9935996970162 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 47 21.4 0.924 2.96 0.847 1.15 1.6 2.05 0.00399 0.00514 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 43 100 20.4 0.933 1.77 0.849 1.15 1.5 1.59 0.00376 0.00398 + + + 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 1433.982 0.005 0.926 3.4 21.9 0.85 1.15 1.77 2.2 0.00442 0.00551 +! Validation 43 1433.982 0.005 0.915 2.76 21.1 0.846 1.14 1.66 1.98 0.00415 0.00496 +Wall time: 1433.9828886482865 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 47 21.4 0.926 2.92 0.849 1.15 1.49 2.04 0.00373 0.0051 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 44 100 18.9 0.93 0.33 0.848 1.15 0.59 0.686 0.00147 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 44 1466.978 0.005 0.924 3.23 21.7 0.848 1.15 1.73 2.15 0.00433 0.00537 +! Validation 44 1466.978 0.005 0.912 1.83 20.1 0.844 1.14 1.22 1.62 0.00305 0.00404 +Wall time: 1466.978648596909 +! Best model 44 20.071 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 47 22.8 0.914 4.52 0.842 1.14 1.99 2.54 0.00497 0.00635 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 45 100 18.8 0.927 0.235 0.846 1.15 0.541 0.58 0.00135 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 45 1499.995 0.005 0.921 3.43 21.9 0.847 1.15 1.78 2.21 0.00444 0.00553 +! Validation 45 1499.995 0.005 0.909 1.81 20 0.843 1.14 1.2 1.61 0.00301 0.00402 +Wall time: 1499.9957942720503 +! Best model 45 19.998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 47 21.6 0.89 3.82 0.834 1.13 2.04 2.33 0.00511 0.00584 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 46 100 21.2 0.924 2.73 0.845 1.15 1.91 1.97 0.00477 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 46 1533.003 0.005 0.918 3.34 21.7 0.845 1.15 1.76 2.18 0.0044 0.00546 +! Validation 46 1533.003 0.005 0.906 3.25 21.4 0.841 1.14 1.84 2.16 0.00461 0.00539 +Wall time: 1533.0037557692267 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 47 20.7 0.918 2.38 0.845 1.14 1.53 1.84 0.00383 0.0046 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 47 100 19 0.92 0.598 0.843 1.15 0.777 0.924 0.00194 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 47 1566.005 0.005 0.914 2.72 21 0.844 1.14 1.6 1.97 0.00399 0.00493 +! Validation 47 1566.005 0.005 0.902 1.73 19.8 0.839 1.13 1.22 1.57 0.00306 0.00393 +Wall time: 1566.0053869569674 +! Best model 47 19.773 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 47 19.5 0.907 1.38 0.839 1.14 1.07 1.4 0.00268 0.00351 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 48 100 18.5 0.915 0.174 0.84 1.14 0.475 0.498 0.00119 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 48 1599.012 0.005 0.91 2.81 21 0.842 1.14 1.62 2 0.00404 0.00501 +! Validation 48 1599.012 0.005 0.897 1.77 19.7 0.837 1.13 1.19 1.59 0.00297 0.00398 +Wall time: 1599.0128403431736 +! Best model 48 19.716 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 47 19.7 0.891 1.9 0.835 1.13 1.29 1.65 0.00323 0.00412 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 49 100 20.2 0.909 2.06 0.837 1.14 1.64 1.71 0.00411 0.00428 + + + 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 1632.027 0.005 0.905 2.35 20.4 0.839 1.14 1.46 1.83 0.00366 0.00458 +! Validation 49 1632.027 0.005 0.891 2.55 20.4 0.834 1.13 1.6 1.91 0.00399 0.00477 +Wall time: 1632.0278269061819 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 47 19.7 0.922 1.29 0.849 1.15 1.1 1.35 0.00276 0.00339 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 50 100 23.2 0.901 5.14 0.833 1.13 2.67 2.71 0.00667 0.00677 + + + 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 1665.020 0.005 0.897 2.33 20.3 0.835 1.13 1.47 1.83 0.00367 0.00457 +! Validation 50 1665.020 0.005 0.882 5.47 23.1 0.829 1.12 2.47 2.79 0.00617 0.00699 +Wall time: 1665.0202398463152 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 47 20.3 0.898 2.34 0.833 1.13 1.56 1.83 0.00389 0.00457 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 51 100 18.5 0.89 0.693 0.828 1.13 0.888 0.995 0.00222 0.00249 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 51 1698.027 0.005 0.888 2.01 19.8 0.831 1.13 1.35 1.69 0.00338 0.00423 +! Validation 51 1698.027 0.005 0.872 1.5 18.9 0.824 1.12 1.16 1.46 0.0029 0.00366 +Wall time: 1698.027252000291 +! Best model 51 18.941 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 47 18.7 0.88 1.1 0.826 1.12 1.02 1.25 0.00254 0.00313 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 52 100 18.3 0.878 0.699 0.822 1.12 0.905 0.999 0.00226 0.0025 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 52 1731.053 0.005 0.877 1.64 19.2 0.825 1.12 1.22 1.53 0.00305 0.00383 +! Validation 52 1731.053 0.005 0.859 1.48 18.7 0.817 1.11 1.15 1.45 0.00288 0.00363 +Wall time: 1731.0538244321942 +! Best model 52 18.664 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 47 18.6 0.858 1.4 0.815 1.11 1.09 1.42 0.00273 0.00354 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 53 100 18.9 0.863 1.63 0.814 1.11 1.47 1.52 0.00368 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 53 1764.061 0.005 0.863 1.48 18.7 0.818 1.11 1.16 1.45 0.0029 0.00363 +! Validation 53 1764.061 0.005 0.845 2.38 19.3 0.81 1.1 1.51 1.84 0.00378 0.00461 +Wall time: 1764.0617477721535 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 47 18.5 0.856 1.35 0.813 1.11 1.06 1.39 0.00266 0.00347 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 54 100 17.9 0.846 0.971 0.806 1.1 1.11 1.18 0.00278 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 54 1797.072 0.005 0.848 1.56 18.5 0.811 1.1 1.17 1.49 0.00294 0.00373 +! Validation 54 1797.072 0.005 0.828 1.84 18.4 0.802 1.09 1.29 1.62 0.00323 0.00405 +Wall time: 1797.0722423093393 +! Best model 54 18.391 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 47 18.4 0.807 2.29 0.791 1.07 1.48 1.81 0.00369 0.00452 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 55 100 16.7 0.824 0.218 0.795 1.08 0.51 0.558 0.00128 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 55 1830.082 0.005 0.829 1.56 18.1 0.802 1.09 1.19 1.49 0.00297 0.00373 +! Validation 55 1830.082 0.005 0.807 1.22 17.4 0.792 1.07 1.04 1.32 0.0026 0.0033 +Wall time: 1830.0826889672317 +! Best model 55 17.354 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 47 19.7 0.791 3.92 0.783 1.06 2.08 2.37 0.00521 0.00591 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 56 100 16.9 0.792 1.09 0.78 1.06 1.19 1.25 0.00298 0.00312 + + + 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 1863.339 0.005 0.805 1.44 17.5 0.79 1.07 1.13 1.43 0.00284 0.00357 +! Validation 56 1863.339 0.005 0.777 2.27 17.8 0.777 1.05 1.52 1.8 0.00379 0.0045 +Wall time: 1863.3393722451292 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 47 16.4 0.742 1.58 0.756 1.03 1.24 1.5 0.00309 0.00375 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 57 100 18.6 0.747 3.68 0.758 1.03 2.26 2.29 0.00566 0.00573 + + + 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 1896.377 0.005 0.77 1.31 16.7 0.773 1.05 1.09 1.37 0.00273 0.00341 +! Validation 57 1896.377 0.005 0.735 4.53 19.2 0.756 1.02 2.23 2.54 0.00558 0.00636 +Wall time: 1896.3770591733046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 47 14.7 0.668 1.3 0.721 0.977 1.08 1.36 0.00271 0.00341 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 58 100 14 0.683 0.339 0.725 0.987 0.61 0.695 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 58 1929.417 0.005 0.718 1.83 16.2 0.747 1.01 1.31 1.62 0.00328 0.00405 +! Validation 58 1929.417 0.005 0.676 1.35 14.9 0.725 0.982 1.08 1.39 0.0027 0.00347 +Wall time: 1929.4177041333169 +! Best model 58 14.860 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 47 17.6 0.601 5.53 0.686 0.927 2.56 2.81 0.00641 0.00703 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 59 100 12.9 0.606 0.754 0.683 0.931 0.978 1.04 0.00245 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 59 1962.433 0.005 0.655 1.95 15.1 0.713 0.967 1.34 1.67 0.00334 0.00417 +! Validation 59 1962.433 0.005 0.606 1.86 14 0.686 0.93 1.36 1.63 0.00339 0.00408 +Wall time: 1962.433191396296 +! Best model 59 13.986 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 47 11.9 0.568 0.501 0.662 0.9 0.677 0.846 0.00169 0.00212 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 60 100 11.7 0.546 0.734 0.646 0.883 0.967 1.02 0.00242 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 60 1995.450 0.005 0.596 1.97 13.9 0.679 0.923 1.31 1.68 0.00327 0.0042 +! Validation 60 1995.450 0.005 0.551 1.73 12.7 0.652 0.887 1.27 1.57 0.00316 0.00393 +Wall time: 1995.4503638162278 +! Best model 60 12.744 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 47 11.8 0.526 1.31 0.635 0.867 1.05 1.37 0.00263 0.00342 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 61 100 10.2 0.494 0.308 0.612 0.84 0.596 0.664 0.00149 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 61 2028.465 0.005 0.542 1.74 12.6 0.647 0.88 1.26 1.57 0.00315 0.00394 +! Validation 61 2028.465 0.005 0.503 1.3 11.4 0.622 0.847 1.09 1.36 0.00273 0.00341 +Wall time: 2028.4654314960353 +! Best model 61 11.359 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 47 12.5 0.487 2.8 0.612 0.834 1.57 2 0.00393 0.005 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 62 100 9.37 0.464 0.0864 0.592 0.814 0.227 0.351 0.000567 0.000878 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 62 2061.476 0.005 0.507 2.47 12.6 0.624 0.851 1.51 1.88 0.00378 0.00469 +! Validation 62 2061.476 0.005 0.473 0.968 10.4 0.602 0.822 0.949 1.18 0.00237 0.00294 +Wall time: 2061.4763634889387 +! Best model 62 10.436 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 47 10.2 0.472 0.792 0.603 0.821 0.829 1.06 0.00207 0.00266 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 63 100 9.24 0.442 0.399 0.578 0.795 0.687 0.755 0.00172 0.00189 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 63 2094.492 0.005 0.482 2.74 12.4 0.608 0.829 1.61 1.98 0.00402 0.00495 +! Validation 63 2094.492 0.005 0.451 1.22 10.2 0.588 0.803 1.05 1.32 0.00263 0.0033 +Wall time: 2094.492627578322 +! Best model 63 10.248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 47 9.84 0.45 0.852 0.59 0.801 0.779 1.1 0.00195 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 64 100 8.47 0.419 0.0877 0.563 0.774 0.334 0.354 0.000836 0.000885 + + + 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 2127.532 0.005 0.462 1.5 10.7 0.595 0.812 1.18 1.46 0.00296 0.00366 +! Validation 64 2127.532 0.005 0.429 0.855 9.44 0.573 0.783 0.883 1.1 0.00221 0.00276 +Wall time: 2127.53221284505 +! Best model 64 9.443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 47 10 0.438 1.26 0.577 0.791 1.06 1.34 0.00265 0.00336 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 65 100 8.29 0.405 0.202 0.552 0.76 0.489 0.537 0.00122 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 65 2160.551 0.005 0.444 2.43 11.3 0.583 0.796 1.5 1.86 0.00374 0.00466 +! Validation 65 2160.551 0.005 0.415 0.945 9.24 0.563 0.769 0.92 1.16 0.0023 0.0029 +Wall time: 2160.551162543241 +! Best model 65 9.237 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 47 13 0.435 4.25 0.574 0.789 2.25 2.46 0.00562 0.00616 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 66 100 8.86 0.399 0.883 0.548 0.755 1.08 1.12 0.00269 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 66 2193.617 0.005 0.431 4.59 13.2 0.574 0.785 2.11 2.56 0.00528 0.0064 +! Validation 66 2193.617 0.005 0.409 1.58 9.76 0.558 0.764 1.22 1.5 0.00305 0.00376 +Wall time: 2193.617406143341 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 47 11 0.424 2.54 0.57 0.778 1.67 1.91 0.00418 0.00476 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 67 100 9.45 0.385 1.76 0.539 0.741 1.55 1.58 0.00389 0.00396 + + + 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 2226.657 0.005 0.424 1.94 10.4 0.569 0.778 1.33 1.66 0.00332 0.00416 +! Validation 67 2226.657 0.005 0.395 2.45 10.3 0.549 0.751 1.6 1.87 0.00401 0.00468 +Wall time: 2226.657826499082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 47 9.1 0.405 1 0.557 0.76 0.977 1.2 0.00244 0.00299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 68 100 7.75 0.376 0.235 0.532 0.732 0.491 0.58 0.00123 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 68 2259.696 0.005 0.411 2.83 11 0.56 0.766 1.66 2.01 0.00414 0.00503 +! Validation 68 2259.696 0.005 0.385 1.01 8.71 0.542 0.742 0.991 1.2 0.00248 0.003 +Wall time: 2259.696836997289 +! Best model 68 8.715 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 47 9.98 0.403 1.93 0.557 0.758 1.44 1.66 0.0036 0.00415 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 69 100 10.8 0.364 3.52 0.524 0.721 2.22 2.24 0.00555 0.0056 + + + 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 2292.739 0.005 0.4 2.15 10.1 0.552 0.756 1.4 1.75 0.00349 0.00438 +! Validation 69 2292.739 0.005 0.374 4.31 11.8 0.534 0.731 2.27 2.48 0.00569 0.0062 +Wall time: 2292.739357969258 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 47 12.5 0.381 4.85 0.542 0.738 2.44 2.63 0.00609 0.00658 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 70 100 10 0.359 2.85 0.52 0.716 1.99 2.02 0.00498 0.00504 + + + 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 2325.774 0.005 0.391 4.41 12.2 0.546 0.747 2.03 2.51 0.00506 0.00627 +! Validation 70 2325.774 0.005 0.369 3.62 11 0.53 0.726 2.07 2.27 0.00516 0.00569 +Wall time: 2325.7738986932673 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 47 21.6 0.388 13.8 0.54 0.745 4.38 4.44 0.011 0.0111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 71 100 18.5 0.351 11.5 0.515 0.708 4.04 4.05 0.0101 0.0101 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 71 2358.794 0.005 0.386 2.65 10.4 0.542 0.742 1.53 1.94 0.00382 0.00484 +! Validation 71 2358.794 0.005 0.361 11.9 19.1 0.525 0.718 4 4.11 0.00999 0.0103 +Wall time: 2358.79397165915 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 47 18.9 0.378 11.4 0.539 0.734 3.91 4.03 0.00977 0.0101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 72 100 14.6 0.344 7.71 0.51 0.701 3.3 3.32 0.00826 0.00829 + + + 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 2391.819 0.005 0.377 3.54 11.1 0.536 0.734 1.82 2.24 0.00456 0.0056 +! Validation 72 2391.819 0.005 0.354 8.19 15.3 0.52 0.711 3.28 3.42 0.0082 0.00855 +Wall time: 2391.8196987002157 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 47 9.44 0.369 2.05 0.531 0.726 1.44 1.71 0.00359 0.00428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 73 100 7.01 0.341 0.19 0.508 0.698 0.447 0.521 0.00112 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 73 2424.853 0.005 0.374 4.29 11.8 0.534 0.731 1.98 2.48 0.00495 0.00619 +! Validation 73 2424.853 0.005 0.351 0.729 7.75 0.518 0.708 0.792 1.02 0.00198 0.00255 +Wall time: 2424.8532119630836 +! Best model 73 7.747 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 47 8.67 0.359 1.49 0.525 0.716 1.28 1.46 0.00321 0.00365 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 74 100 6.83 0.334 0.144 0.503 0.691 0.388 0.454 0.000971 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 74 2457.901 0.005 0.366 3.59 10.9 0.528 0.723 1.89 2.27 0.00472 0.00566 +! Validation 74 2457.901 0.005 0.344 0.693 7.57 0.513 0.701 0.795 0.995 0.00199 0.00249 +Wall time: 2457.901886181906 +! Best model 74 7.573 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 47 9.76 0.348 2.8 0.516 0.705 1.85 2 0.00463 0.005 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 75 100 6.89 0.323 0.426 0.495 0.679 0.714 0.78 0.00178 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 75 2491.174 0.005 0.359 1.85 9.02 0.523 0.716 1.29 1.62 0.00323 0.00406 +! Validation 75 2491.174 0.005 0.333 1.04 7.7 0.505 0.69 1.02 1.22 0.00255 0.00304 +Wall time: 2491.173935429193 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 47 8.61 0.361 1.38 0.524 0.718 1.15 1.4 0.00289 0.00351 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 76 100 6.8 0.328 0.246 0.499 0.684 0.491 0.593 0.00123 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 76 2524.228 0.005 0.355 6.57 13.7 0.52 0.712 2.43 3.07 0.00608 0.00767 +! Validation 76 2524.228 0.005 0.338 0.878 7.63 0.508 0.694 0.925 1.12 0.00231 0.0028 +Wall time: 2524.228758998215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 47 11 0.345 4.08 0.513 0.702 2.16 2.41 0.00541 0.00603 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 77 100 10.7 0.315 4.36 0.489 0.67 2.48 2.5 0.00619 0.00624 + + + 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 2557.269 0.005 0.35 1.35 8.35 0.517 0.707 1.11 1.39 0.00278 0.00347 +! Validation 77 2557.269 0.005 0.324 4.6 11.1 0.499 0.681 2.39 2.56 0.00597 0.00641 +Wall time: 2557.269581614062 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 47 8.77 0.338 2 0.506 0.695 1.45 1.69 0.00363 0.00423 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 78 100 9.18 0.306 3.07 0.482 0.661 2.07 2.09 0.00518 0.00524 + + + 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 2590.314 0.005 0.339 2.42 9.21 0.509 0.696 1.51 1.86 0.00378 0.00465 +! Validation 78 2590.314 0.005 0.316 3.62 9.95 0.492 0.672 2.07 2.28 0.00518 0.00569 +Wall time: 2590.314611007925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 47 8.43 0.333 1.78 0.503 0.69 1.3 1.59 0.00325 0.00398 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 79 100 10.3 0.305 4.18 0.482 0.66 2.42 2.44 0.00606 0.00611 + + + 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 2623.350 0.005 0.333 6.2 12.9 0.504 0.69 2.38 2.98 0.00594 0.00745 +! Validation 79 2623.350 0.005 0.316 4.39 10.7 0.492 0.672 2.33 2.5 0.00582 0.00626 +Wall time: 2623.350693583023 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 47 7.8 0.331 1.17 0.504 0.688 1.06 1.29 0.00264 0.00323 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 80 100 9.59 0.307 3.46 0.483 0.662 2.2 2.22 0.0055 0.00555 + + + 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 2656.394 0.005 0.338 5.01 11.8 0.508 0.695 2.07 2.68 0.00517 0.0067 +! Validation 80 2656.394 0.005 0.317 4.42 10.8 0.493 0.673 2.34 2.51 0.00584 0.00628 +Wall time: 2656.3940098392777 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 47 7.3 0.327 0.755 0.501 0.684 0.823 1.04 0.00206 0.0026 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 81 100 6.9 0.295 1 0.474 0.649 1.15 1.2 0.00288 0.00299 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 81 2689.446 0.005 0.329 2.41 9 0.502 0.686 1.5 1.86 0.00374 0.00464 +! Validation 81 2689.446 0.005 0.305 1.82 7.93 0.484 0.66 1.4 1.61 0.00349 0.00403 +Wall time: 2689.4460729081184 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 47 11.9 0.317 5.57 0.489 0.673 2.73 2.82 0.00682 0.00705 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 82 100 11.6 0.29 5.76 0.47 0.644 2.85 2.87 0.00712 0.00717 + + + 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 2722.512 0.005 0.323 3.48 9.93 0.497 0.679 1.8 2.23 0.0045 0.00557 +! Validation 82 2722.512 0.005 0.301 5.93 11.9 0.481 0.655 2.76 2.91 0.00691 0.00727 +Wall time: 2722.5120943272486 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 47 10.3 0.336 3.55 0.507 0.692 2.01 2.25 0.00502 0.00563 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 83 100 7.23 0.295 1.32 0.475 0.65 1.34 1.37 0.00334 0.00344 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 83 2755.543 0.005 0.32 7.67 14.1 0.495 0.676 2.58 3.31 0.00644 0.00828 +! Validation 83 2755.543 0.005 0.306 2.13 8.25 0.486 0.661 1.55 1.75 0.00387 0.00436 +Wall time: 2755.5433784350753 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 47 16.1 0.319 9.7 0.492 0.675 3.63 3.72 0.00908 0.00931 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 84 100 15.2 0.286 9.48 0.468 0.639 3.67 3.68 0.00917 0.0092 + + + 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 2788.577 0.005 0.319 3.21 9.59 0.494 0.675 1.71 2.13 0.00428 0.00533 +! Validation 84 2788.577 0.005 0.296 9.46 15.4 0.478 0.651 3.56 3.68 0.0089 0.00919 +Wall time: 2788.5778434569947 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 47 7.33 0.315 1.03 0.49 0.671 0.964 1.21 0.00241 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 85 100 8.98 0.28 3.38 0.463 0.632 2.17 2.2 0.00544 0.00549 + + + 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 2821.619 0.005 0.311 3.67 9.89 0.488 0.667 1.92 2.29 0.00481 0.00573 +! Validation 85 2821.619 0.005 0.291 3.8 9.62 0.473 0.644 2.14 2.33 0.00535 0.00583 +Wall time: 2821.6197597701102 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 47 13.5 0.299 7.52 0.481 0.654 3.11 3.28 0.00777 0.00819 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 86 100 12.5 0.276 7.01 0.459 0.627 3.15 3.16 0.00787 0.00791 + + + 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 2854.666 0.005 0.306 3.64 9.76 0.484 0.661 1.79 2.28 0.00447 0.00569 +! Validation 86 2854.666 0.005 0.286 8.18 13.9 0.469 0.639 3.3 3.42 0.00825 0.00854 +Wall time: 2854.666372522246 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 47 7.57 0.296 1.65 0.477 0.65 1.36 1.54 0.0034 0.00384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 87 100 5.98 0.271 0.568 0.455 0.622 0.848 0.9 0.00212 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 87 2887.708 0.005 0.301 3.78 9.8 0.48 0.655 1.99 2.33 0.00496 0.00581 +! Validation 87 2887.708 0.005 0.281 1.28 6.9 0.465 0.633 1.16 1.35 0.0029 0.00338 +Wall time: 2887.7082540891133 +! Best model 87 6.898 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 47 10.8 0.29 5.03 0.473 0.644 2.57 2.68 0.00643 0.0067 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 88 100 5.53 0.269 0.152 0.454 0.62 0.403 0.465 0.00101 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 88 2920.712 0.005 0.296 5.76 11.7 0.477 0.651 2.39 2.87 0.00598 0.00717 +! Validation 88 2920.712 0.005 0.279 0.806 6.39 0.465 0.632 0.893 1.07 0.00223 0.00268 +Wall time: 2920.7125146533363 +! Best model 88 6.394 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 47 13 0.293 7.17 0.474 0.647 3.02 3.2 0.00755 0.008 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 89 100 13.9 0.262 8.63 0.448 0.612 3.5 3.51 0.00874 0.00878 + + + 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 2953.704 0.005 0.294 2.81 8.7 0.475 0.648 1.61 2 0.00403 0.005 +! Validation 89 2953.704 0.005 0.273 8.68 14.1 0.459 0.624 3.41 3.52 0.00852 0.0088 +Wall time: 2953.7046707002446 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 47 7.65 0.307 1.51 0.486 0.662 1.12 1.47 0.00281 0.00368 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 90 100 8.78 0.283 3.12 0.467 0.636 2.09 2.11 0.00522 0.00528 + + + 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 2986.691 0.005 0.296 13.3 19.2 0.477 0.65 3.57 4.36 0.00892 0.0109 +! Validation 90 2986.691 0.005 0.293 2.74 8.6 0.478 0.647 1.75 1.98 0.00439 0.00495 +Wall time: 2986.6912521370687 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 47 7.17 0.292 1.34 0.474 0.646 1.14 1.38 0.00285 0.00346 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 91 100 5.49 0.267 0.162 0.453 0.617 0.407 0.481 0.00102 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 91 3019.673 0.005 0.302 2.48 8.51 0.483 0.657 1.52 1.88 0.00379 0.0047 +! Validation 91 3019.673 0.005 0.277 0.787 6.32 0.464 0.629 0.881 1.06 0.0022 0.00265 +Wall time: 3019.6733220410533 +! Best model 91 6.325 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 47 32.2 0.296 26.3 0.476 0.65 6.04 6.13 0.0151 0.0153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 92 100 22.7 0.257 17.6 0.445 0.606 5 5.01 0.0125 0.0125 + + + 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 3052.670 0.005 0.287 4.5 10.2 0.47 0.64 1.84 2.52 0.00459 0.0063 +! Validation 92 3052.670 0.005 0.268 16.7 22.1 0.456 0.619 4.81 4.89 0.012 0.0122 +Wall time: 3052.67039085133 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 47 9.19 0.292 3.36 0.477 0.645 2.02 2.19 0.00505 0.00547 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 93 100 7.8 0.266 2.48 0.453 0.616 1.86 1.88 0.00465 0.0047 + + + 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 3085.659 0.005 0.294 7.87 13.7 0.476 0.648 2.65 3.36 0.00664 0.00839 +! Validation 93 3085.659 0.005 0.276 3.91 9.43 0.463 0.628 2.18 2.36 0.00546 0.00591 +Wall time: 3085.658895832021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 47 14.9 0.272 9.44 0.458 0.623 3.53 3.67 0.00882 0.00918 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 94 100 22.9 0.252 17.8 0.442 0.6 5.04 5.05 0.0126 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 94 3118.638 0.005 0.284 4.08 9.77 0.469 0.637 1.82 2.41 0.00456 0.00602 +! Validation 94 3118.638 0.005 0.263 19.3 24.5 0.453 0.613 5.12 5.25 0.0128 0.0131 +Wall time: 3118.6383001040667 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 47 17.7 0.296 11.8 0.478 0.65 4.02 4.11 0.01 0.0103 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 95 100 17.7 0.26 12.5 0.449 0.61 4.21 4.22 0.0105 0.0105 + + + 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 3151.629 0.005 0.283 10.1 15.7 0.468 0.636 3.29 3.79 0.00823 0.00947 +! Validation 95 3151.629 0.005 0.272 12.5 17.9 0.461 0.623 4.11 4.23 0.0103 0.0106 +Wall time: 3151.628935961984 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 47 6.41 0.29 0.603 0.475 0.644 0.771 0.928 0.00193 0.00232 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 96 100 6.71 0.25 1.71 0.441 0.598 1.53 1.56 0.00383 0.0039 + + + 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 3184.616 0.005 0.284 2.42 8.11 0.47 0.637 1.41 1.86 0.00352 0.00465 +! Validation 96 3184.616 0.005 0.261 1.68 6.9 0.451 0.61 1.31 1.55 0.00328 0.00388 +Wall time: 3184.616710657254 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 47 6.09 0.282 0.442 0.466 0.635 0.619 0.795 0.00155 0.00199 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 97 100 6.36 0.241 1.54 0.432 0.586 1.45 1.48 0.00363 0.00371 + + + 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 3217.599 0.005 0.271 2.89 8.31 0.458 0.622 1.62 2.03 0.00405 0.00508 +! Validation 97 3217.599 0.005 0.251 1.55 6.57 0.443 0.599 1.26 1.49 0.00314 0.00372 +Wall time: 3217.599745610263 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 47 17.1 0.279 11.5 0.464 0.631 3.98 4.06 0.00996 0.0101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 98 100 13.5 0.25 8.55 0.441 0.598 3.48 3.49 0.0087 0.00873 + + + 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 3250.591 0.005 0.268 11 16.4 0.455 0.618 3.28 3.96 0.0082 0.00991 +! Validation 98 3250.591 0.005 0.262 10.7 15.9 0.453 0.612 3.8 3.91 0.00951 0.00977 +Wall time: 3250.5910826739855 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 47 6.65 0.262 1.42 0.451 0.611 1.22 1.42 0.00305 0.00356 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 99 100 4.91 0.241 0.0866 0.433 0.587 0.297 0.352 0.000744 0.000879 + + + 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 3284.083 0.005 0.274 3.57 9.06 0.462 0.626 1.89 2.26 0.00473 0.00565 +! Validation 99 3284.083 0.005 0.252 0.661 5.7 0.444 0.6 0.802 0.971 0.002 0.00243 +Wall time: 3284.08311249502 +! Best model 99 5.703 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 47 7.43 0.262 2.19 0.451 0.612 1.57 1.77 0.00393 0.00442 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 100 100 7.69 0.23 3.08 0.423 0.573 2.08 2.1 0.00519 0.00525 + + + 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 3317.127 0.005 0.262 2.04 7.27 0.45 0.611 1.37 1.71 0.00341 0.00427 +! Validation 100 3317.127 0.005 0.241 4.09 8.91 0.434 0.586 2.27 2.42 0.00567 0.00604 +Wall time: 3317.12786944909 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 47 19.8 0.256 14.6 0.445 0.605 4.47 4.57 0.0112 0.0114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 101 100 10.9 0.228 6.31 0.421 0.571 2.99 3 0.00747 0.00751 + + + 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 3350.162 0.005 0.254 6.66 11.7 0.444 0.602 2.56 3.08 0.00641 0.0077 +! Validation 101 3350.162 0.005 0.24 5.57 10.4 0.433 0.585 2.68 2.82 0.0067 0.00705 +Wall time: 3350.1622201623395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 47 14.4 0.272 9 0.46 0.623 3.49 3.58 0.00872 0.00896 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 102 100 13 0.238 8.2 0.431 0.584 3.41 3.42 0.00853 0.00856 + + + 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 3383.190 0.005 0.263 9.46 14.7 0.452 0.613 3.14 3.68 0.00784 0.00919 +! Validation 102 3383.190 0.005 0.249 8.09 13.1 0.443 0.597 3.27 3.4 0.00817 0.0085 +Wall time: 3383.1901061120443 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 47 5.95 0.256 0.828 0.449 0.605 0.904 1.09 0.00226 0.00272 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 103 100 4.69 0.229 0.116 0.423 0.572 0.346 0.406 0.000864 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 103 3416.223 0.005 0.26 3.68 8.88 0.45 0.609 1.87 2.3 0.00468 0.00574 +! Validation 103 3416.223 0.005 0.239 0.923 5.71 0.433 0.584 0.95 1.15 0.00238 0.00287 +Wall time: 3416.223852366209 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 47 6.5 0.251 1.49 0.443 0.598 1.28 1.46 0.00321 0.00365 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 104 100 4.53 0.22 0.129 0.415 0.561 0.339 0.43 0.000848 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 104 3449.249 0.005 0.25 2.46 7.45 0.441 0.597 1.56 1.88 0.00391 0.00469 +! Validation 104 3449.249 0.005 0.23 0.549 5.15 0.425 0.573 0.716 0.885 0.00179 0.00221 +Wall time: 3449.24923142232 +! Best model 104 5.153 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 47 5.55 0.245 0.646 0.436 0.591 0.819 0.96 0.00205 0.0024 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 105 100 5 0.215 0.692 0.41 0.554 0.951 0.994 0.00238 0.00248 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 105 3482.393 0.005 0.243 3.9 8.76 0.435 0.589 1.91 2.36 0.00478 0.0059 +! Validation 105 3482.393 0.005 0.225 0.802 5.31 0.421 0.567 0.846 1.07 0.00212 0.00267 +Wall time: 3482.393008332234 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 47 14.3 0.226 9.75 0.417 0.568 3.66 3.73 0.00915 0.00933 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 106 100 19.9 0.209 15.8 0.404 0.546 4.74 4.75 0.0118 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 106 3515.410 0.005 0.237 3.63 8.36 0.429 0.582 1.9 2.27 0.00475 0.00568 +! Validation 106 3515.410 0.005 0.219 15 19.4 0.415 0.56 4.56 4.63 0.0114 0.0116 +Wall time: 3515.410842099227 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 47 6.85 0.241 2.03 0.431 0.586 1.53 1.7 0.00383 0.00426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 107 100 4.37 0.213 0.121 0.408 0.551 0.353 0.416 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 107 3548.425 0.005 0.236 6.66 11.4 0.429 0.581 2.63 3.09 0.00658 0.00772 +! Validation 107 3548.425 0.005 0.223 0.78 5.24 0.419 0.564 0.883 1.06 0.00221 0.00264 +Wall time: 3548.4250210099854 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 47 9.1 0.238 4.34 0.429 0.583 2.32 2.49 0.00579 0.00623 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 108 100 13.8 0.209 9.59 0.405 0.546 3.69 3.7 0.00922 0.00925 + + + 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 3581.439 0.005 0.234 5.93 10.6 0.427 0.578 2.3 2.91 0.00575 0.00728 +! Validation 108 3581.439 0.005 0.22 8.79 13.2 0.417 0.561 3.44 3.54 0.00861 0.00886 +Wall time: 3581.4398619369604 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 47 4.99 0.231 0.363 0.422 0.575 0.61 0.72 0.00152 0.0018 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 109 100 9.4 0.207 5.27 0.403 0.543 2.73 2.74 0.00682 0.00686 + + + 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 3614.524 0.005 0.236 4.9 9.61 0.429 0.58 2.16 2.65 0.00539 0.00662 +! Validation 109 3614.524 0.005 0.218 4.75 9.11 0.415 0.558 2.47 2.6 0.00618 0.00651 +Wall time: 3614.5240322602913 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 47 17.8 0.224 13.3 0.42 0.566 4.31 4.36 0.0108 0.0109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 110 100 17 0.201 12.9 0.398 0.536 4.29 4.3 0.0107 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 110 3647.553 0.005 0.23 3.58 8.19 0.424 0.574 1.88 2.25 0.00469 0.00563 +! Validation 110 3647.553 0.005 0.212 12.5 16.8 0.409 0.55 4.14 4.23 0.0104 0.0106 +Wall time: 3647.5531337801367 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 47 5.23 0.223 0.762 0.42 0.565 0.847 1.04 0.00212 0.00261 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 111 100 4.36 0.207 0.231 0.403 0.543 0.502 0.574 0.00126 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 111 3680.575 0.005 0.233 7.57 12.2 0.427 0.576 2.35 3.29 0.00587 0.00823 +! Validation 111 3680.575 0.005 0.217 0.523 4.86 0.414 0.556 0.662 0.864 0.00165 0.00216 +Wall time: 3680.5753088779747 +! Best model 111 4.857 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 47 5.43 0.232 0.788 0.425 0.576 0.775 1.06 0.00194 0.00265 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 112 100 8.53 0.202 4.49 0.399 0.537 2.52 2.53 0.00629 0.00633 + + + 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 3713.616 0.005 0.227 4.87 9.42 0.422 0.57 2.16 2.64 0.00539 0.0066 +! Validation 112 3713.616 0.005 0.211 3.64 7.87 0.409 0.55 2.11 2.28 0.00528 0.0057 +Wall time: 3713.6166577283293 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 47 8.16 0.218 3.8 0.414 0.558 2.16 2.33 0.00539 0.00582 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 113 100 14.2 0.194 10.3 0.391 0.526 3.83 3.84 0.00958 0.00961 + + + 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 3746.658 0.005 0.221 2.85 7.28 0.416 0.562 1.6 2.02 0.00401 0.00504 +! Validation 113 3746.658 0.005 0.204 9.52 13.6 0.401 0.539 3.6 3.69 0.009 0.00922 +Wall time: 3746.6582028712146 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 47 4.97 0.216 0.648 0.412 0.555 0.784 0.962 0.00196 0.00241 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 114 100 4.03 0.198 0.0621 0.396 0.532 0.263 0.298 0.000657 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 114 3779.688 0.005 0.22 7.21 11.6 0.415 0.56 2.68 3.21 0.0067 0.00803 +! Validation 114 3779.688 0.005 0.209 0.574 4.75 0.407 0.546 0.745 0.905 0.00186 0.00226 +Wall time: 3779.68869359605 +! Best model 114 4.745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 47 8.04 0.205 3.94 0.402 0.541 2.24 2.37 0.0056 0.00593 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 115 100 13.2 0.191 9.41 0.389 0.522 3.65 3.67 0.00914 0.00916 + + + 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 3812.731 0.005 0.218 2.76 7.11 0.413 0.558 1.65 1.98 0.00411 0.00496 +! Validation 115 3812.731 0.005 0.2 8.48 12.5 0.399 0.535 3.39 3.48 0.00847 0.0087 +Wall time: 3812.7314274930395 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 47 5.11 0.213 0.853 0.408 0.551 0.93 1.1 0.00233 0.00276 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 116 100 5.1 0.186 1.38 0.384 0.516 1.37 1.4 0.00343 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 116 3845.768 0.005 0.213 2.91 7.17 0.409 0.551 1.66 2.04 0.00415 0.0051 +! Validation 116 3845.768 0.005 0.196 2.36 6.29 0.395 0.529 1.68 1.84 0.0042 0.00459 +Wall time: 3845.7681909990497 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 47 4.63 0.211 0.415 0.408 0.549 0.627 0.77 0.00157 0.00192 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 117 100 14.1 0.191 10.2 0.389 0.523 3.82 3.82 0.00954 0.00956 + + + 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 3878.911 0.005 0.213 7.68 11.9 0.409 0.551 2.77 3.32 0.00691 0.00829 +! Validation 117 3878.911 0.005 0.201 8.99 13 0.4 0.536 3.49 3.58 0.00873 0.00896 +Wall time: 3878.911758637987 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 47 5.83 0.21 1.63 0.409 0.548 1.36 1.53 0.0034 0.00381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 118 100 5.41 0.194 1.54 0.392 0.526 1.46 1.48 0.00365 0.00371 + + + 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 3911.942 0.005 0.216 7.53 11.8 0.412 0.555 2.9 3.28 0.00725 0.0082 +! Validation 118 3911.942 0.005 0.205 2.65 6.74 0.404 0.541 1.78 1.94 0.00445 0.00486 +Wall time: 3911.9420582540333 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 47 20.4 0.211 16.2 0.407 0.548 4.78 4.81 0.0119 0.012 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 119 100 10.2 0.187 6.47 0.386 0.517 3.03 3.04 0.00757 0.0076 + + + 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 3944.956 0.005 0.215 4.16 8.46 0.412 0.554 2 2.43 0.00501 0.00607 +! Validation 119 3944.956 0.005 0.197 6.01 9.95 0.396 0.53 2.81 2.93 0.00704 0.00733 +Wall time: 3944.956715563312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 47 4.67 0.214 0.398 0.41 0.552 0.624 0.753 0.00156 0.00188 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 120 100 3.81 0.185 0.12 0.383 0.514 0.34 0.414 0.000851 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 120 3977.974 0.005 0.211 3.69 7.91 0.408 0.549 1.77 2.3 0.00444 0.00574 +! Validation 120 3977.974 0.005 0.194 0.692 4.58 0.393 0.527 0.831 0.994 0.00208 0.00249 +Wall time: 3977.973948385101 +! Best model 120 4.577 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 47 10 0.199 6.02 0.395 0.533 2.85 2.93 0.00712 0.00733 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 121 100 6.72 0.178 3.17 0.376 0.504 2.11 2.13 0.00527 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 121 4011.014 0.005 0.204 2.28 6.36 0.401 0.54 1.46 1.8 0.00365 0.0045 +! Validation 121 4011.014 0.005 0.187 4.62 8.37 0.386 0.517 2.45 2.57 0.00612 0.00642 +Wall time: 4011.014854650013 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 47 4.53 0.206 0.414 0.404 0.542 0.634 0.769 0.00159 0.00192 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 122 100 4.7 0.181 1.09 0.38 0.508 1.22 1.25 0.00304 0.00312 + + + 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 4044.041 0.005 0.204 5.51 9.6 0.401 0.54 1.99 2.81 0.00498 0.00702 +! Validation 122 4044.041 0.005 0.19 0.994 4.78 0.389 0.52 0.981 1.19 0.00245 0.00298 +Wall time: 4044.0413874299265 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 47 5.92 0.221 1.51 0.421 0.561 1.29 1.47 0.00322 0.00367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 123 100 14.1 0.197 10.2 0.395 0.531 3.8 3.81 0.00951 0.00954 + + + 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 4077.069 0.005 0.205 11.1 15.2 0.402 0.54 3.21 3.99 0.00802 0.00997 +! Validation 123 4077.069 0.005 0.209 9.03 13.2 0.408 0.546 3.49 3.59 0.00872 0.00898 +Wall time: 4077.069638749119 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 47 10.8 0.203 6.77 0.401 0.538 3.03 3.11 0.00758 0.00778 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 124 100 8.64 0.185 4.94 0.385 0.514 2.64 2.66 0.0066 0.00664 + + + 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 4110.070 0.005 0.217 4.6 8.94 0.415 0.557 2.11 2.56 0.00526 0.00641 +! Validation 124 4110.070 0.005 0.195 6.56 10.5 0.395 0.528 2.96 3.06 0.00739 0.00765 +Wall time: 4110.070692135952 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 47 4.49 0.2 0.5 0.398 0.534 0.663 0.845 0.00166 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 125 100 3.62 0.175 0.113 0.375 0.5 0.311 0.401 0.000777 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 125 4143.189 0.005 0.202 1.84 5.89 0.4 0.537 1.32 1.62 0.0033 0.00406 +! Validation 125 4143.189 0.005 0.185 0.409 4.1 0.384 0.513 0.609 0.764 0.00152 0.00191 +Wall time: 4143.189045604318 +! Best model 125 4.100 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 47 5.08 0.199 1.11 0.396 0.532 1.05 1.26 0.00262 0.00315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 126 100 3.5 0.172 0.0535 0.371 0.496 0.25 0.276 0.000625 0.000691 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 126 4176.188 0.005 0.195 2.6 6.49 0.392 0.527 1.56 1.93 0.0039 0.00482 +! Validation 126 4176.188 0.005 0.181 0.451 4.07 0.38 0.508 0.662 0.803 0.00165 0.00201 +Wall time: 4176.188665420283 +! Best model 126 4.071 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 47 15.5 0.189 11.7 0.388 0.52 4.04 4.1 0.0101 0.0102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 127 100 13.3 0.168 9.94 0.367 0.49 3.76 3.77 0.0094 0.00942 + + + 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 4209.187 0.005 0.192 1.95 5.78 0.389 0.523 1.3 1.66 0.00326 0.00414 +! Validation 127 4209.187 0.005 0.176 11.9 15.4 0.375 0.502 4.06 4.13 0.0101 0.0103 +Wall time: 4209.18771394901 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 47 10.6 0.196 6.65 0.394 0.529 2.99 3.08 0.00749 0.00771 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 128 100 13 0.177 9.49 0.376 0.503 3.67 3.68 0.00918 0.0092 + + + 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 4242.177 0.005 0.194 7.99 11.9 0.392 0.526 2.91 3.38 0.00727 0.00845 +! Validation 128 4242.177 0.005 0.187 11.6 15.3 0.386 0.516 3.99 4.06 0.00997 0.0102 +Wall time: 4242.1774705382995 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 47 4.39 0.19 0.591 0.387 0.521 0.704 0.919 0.00176 0.0023 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 129 100 3.92 0.173 0.453 0.372 0.497 0.757 0.804 0.00189 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 129 4275.173 0.005 0.197 4.2 8.15 0.396 0.531 2.06 2.45 0.00514 0.00613 +! Validation 129 4275.173 0.005 0.182 1.26 4.9 0.381 0.509 1.16 1.34 0.00291 0.00336 +Wall time: 4275.173605657183 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 47 10.3 0.191 6.51 0.391 0.522 2.91 3.05 0.00727 0.00762 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 130 100 6.29 0.167 2.94 0.365 0.489 2.03 2.05 0.00508 0.00513 + + + 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 4308.159 0.005 0.19 3.33 7.13 0.388 0.521 1.85 2.18 0.00464 0.00544 +! Validation 130 4308.159 0.005 0.176 3.15 6.68 0.376 0.502 1.94 2.12 0.00486 0.0053 +Wall time: 4308.159754387103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 47 4.91 0.179 1.33 0.376 0.506 1.23 1.38 0.00307 0.00345 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 131 100 4.04 0.166 0.715 0.364 0.487 0.973 1.01 0.00243 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 131 4341.151 0.005 0.187 2.93 6.66 0.384 0.516 1.64 2.05 0.0041 0.00512 +! Validation 131 4341.151 0.005 0.175 1.43 4.93 0.374 0.5 1.26 1.43 0.00316 0.00357 +Wall time: 4341.150958314072 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 47 4.02 0.179 0.439 0.379 0.506 0.679 0.792 0.0017 0.00198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 132 100 4.5 0.161 1.28 0.359 0.479 1.32 1.35 0.00331 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 132 4374.133 0.005 0.184 2 5.68 0.382 0.513 1.42 1.69 0.00355 0.00423 +! Validation 132 4374.133 0.005 0.169 1.23 4.61 0.368 0.491 1.14 1.33 0.00285 0.00332 +Wall time: 4374.133314459119 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 47 5.94 0.176 2.42 0.375 0.502 1.76 1.86 0.0044 0.00465 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 133 100 5.86 0.158 2.7 0.355 0.475 1.94 1.97 0.00486 0.00491 + + + 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 4407.110 0.005 0.18 2.37 5.97 0.377 0.507 1.49 1.84 0.00374 0.0046 +! Validation 133 4407.110 0.005 0.166 2.65 5.97 0.364 0.487 1.78 1.94 0.00446 0.00486 +Wall time: 4407.1103847920895 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 47 3.87 0.176 0.343 0.375 0.502 0.584 0.7 0.00146 0.00175 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 134 100 3.33 0.161 0.103 0.359 0.48 0.306 0.383 0.000764 0.000957 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 134 4440.128 0.005 0.18 4.35 7.94 0.377 0.507 2 2.49 0.00499 0.00624 +! Validation 134 4440.128 0.005 0.17 0.685 4.08 0.368 0.492 0.825 0.989 0.00206 0.00247 +Wall time: 4440.128536018077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 47 3.84 0.178 0.287 0.376 0.503 0.553 0.64 0.00138 0.0016 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 135 100 3.88 0.155 0.784 0.352 0.47 1.02 1.06 0.00255 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 135 4473.162 0.005 0.177 1.54 5.09 0.375 0.503 1.16 1.48 0.0029 0.00371 +! Validation 135 4473.162 0.005 0.163 0.685 3.94 0.361 0.482 0.796 0.989 0.00199 0.00247 +Wall time: 4473.162622322328 +! Best model 135 3.936 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 47 5.16 0.203 1.11 0.4 0.538 1.06 1.26 0.00266 0.00315 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 136 100 7.75 0.181 4.13 0.378 0.509 2.41 2.43 0.00603 0.00607 + + + 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 4506.212 0.005 0.179 7.17 10.8 0.377 0.506 2.01 3.2 0.00502 0.00801 +! Validation 136 4506.212 0.005 0.19 3.98 7.78 0.389 0.521 2.2 2.38 0.00551 0.00596 +Wall time: 4506.212189943064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 47 6.17 0.175 2.67 0.374 0.5 1.85 1.95 0.00462 0.00488 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 137 100 6.7 0.158 3.53 0.356 0.475 2.23 2.25 0.00557 0.00562 + + + 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 4539.244 0.005 0.186 2.6 6.33 0.385 0.516 1.58 1.93 0.00396 0.00482 +! Validation 137 4539.244 0.005 0.167 5.08 8.42 0.366 0.488 2.59 2.69 0.00648 0.00673 +Wall time: 4539.243889442179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 47 5.67 0.179 2.1 0.373 0.505 1.53 1.73 0.00382 0.00433 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 138 100 5.49 0.152 2.46 0.348 0.466 1.85 1.87 0.00463 0.00468 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 138 4572.294 0.005 0.175 1.46 4.96 0.372 0.5 1.13 1.44 0.00283 0.00361 +! Validation 138 4572.294 0.005 0.159 2.03 5.2 0.357 0.476 1.55 1.7 0.00388 0.00425 +Wall time: 4572.294813196175 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 47 17.8 0.198 13.9 0.397 0.531 4.39 4.45 0.011 0.0111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 139 100 18.6 0.183 15 0.382 0.511 4.61 4.62 0.0115 0.0116 + + + 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 4605.345 0.005 0.176 10.8 14.3 0.373 0.501 2.84 3.93 0.0071 0.00983 +! Validation 139 4605.345 0.005 0.192 14.4 18.3 0.392 0.523 4.44 4.54 0.0111 0.0114 +Wall time: 4605.344931492116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 47 5.9 0.179 2.32 0.378 0.506 1.62 1.82 0.00405 0.00455 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 140 100 6.16 0.161 2.93 0.36 0.48 2.03 2.05 0.00507 0.00511 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 140 4638.385 0.005 0.195 4.18 8.09 0.395 0.528 1.94 2.45 0.00484 0.00611 +! Validation 140 4638.385 0.005 0.17 2.27 5.68 0.369 0.493 1.64 1.8 0.00409 0.00451 +Wall time: 4638.385287411977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 47 4.22 0.176 0.709 0.372 0.501 0.816 1.01 0.00204 0.00252 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 141 100 4.43 0.151 1.4 0.348 0.465 1.39 1.42 0.00347 0.00354 + + + 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 4671.418 0.005 0.175 1.54 5.05 0.373 0.5 1.22 1.48 0.00305 0.00371 +! Validation 141 4671.418 0.005 0.159 1.15 4.33 0.357 0.477 1.1 1.28 0.00275 0.0032 +Wall time: 4671.418030508328 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 47 3.66 0.171 0.25 0.368 0.494 0.494 0.597 0.00123 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 142 100 3.02 0.147 0.0745 0.344 0.459 0.293 0.326 0.000731 0.000815 + + + 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 4704.459 0.005 0.168 1.4 4.76 0.365 0.49 1.16 1.41 0.00289 0.00354 +! Validation 142 4704.459 0.005 0.154 0.366 3.45 0.352 0.469 0.593 0.723 0.00148 0.00181 +Wall time: 4704.459063344169 +! Best model 142 3.453 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 47 4.22 0.163 0.958 0.359 0.482 1.06 1.17 0.00265 0.00292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 143 100 3.2 0.145 0.313 0.34 0.454 0.605 0.669 0.00151 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 143 4737.507 0.005 0.164 1.52 4.79 0.36 0.483 1.1 1.47 0.00276 0.00368 +! Validation 143 4737.507 0.005 0.151 0.382 3.41 0.348 0.465 0.579 0.739 0.00145 0.00185 +Wall time: 4737.507670030929 +! Best model 143 3.410 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 47 3.96 0.166 0.64 0.36 0.487 0.762 0.956 0.00191 0.00239 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 144 100 3.12 0.141 0.293 0.336 0.449 0.584 0.647 0.00146 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 144 4770.581 0.005 0.161 1.86 5.09 0.358 0.48 1.27 1.63 0.00317 0.00407 +! Validation 144 4770.581 0.005 0.148 0.896 3.86 0.345 0.46 0.976 1.13 0.00244 0.00283 +Wall time: 4770.581098631956 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 47 4.71 0.165 1.42 0.362 0.485 1.3 1.42 0.00325 0.00356 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 145 100 2.94 0.144 0.0646 0.338 0.453 0.228 0.304 0.000571 0.000759 + + + 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 4803.614 0.005 0.161 3.61 6.84 0.358 0.48 1.82 2.27 0.00456 0.00568 +! Validation 145 4803.614 0.005 0.151 0.555 3.58 0.348 0.465 0.738 0.89 0.00185 0.00223 +Wall time: 4803.614646122325 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 47 5.45 0.168 2.09 0.366 0.49 1.53 1.73 0.00383 0.00432 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 146 100 4.19 0.148 1.24 0.343 0.459 1.3 1.33 0.00326 0.00332 + + + 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 4836.638 0.005 0.163 4.5 7.75 0.359 0.482 1.96 2.54 0.00489 0.00634 +! Validation 146 4836.638 0.005 0.155 2.19 5.29 0.352 0.471 1.63 1.77 0.00407 0.00442 +Wall time: 4836.637987372 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 47 7.26 0.158 4.1 0.355 0.476 2.3 2.42 0.00576 0.00605 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 147 100 3.39 0.14 0.587 0.333 0.447 0.874 0.916 0.00218 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 147 4869.669 0.005 0.16 2.49 5.7 0.357 0.479 1.56 1.89 0.00391 0.00471 +! Validation 147 4869.669 0.005 0.148 0.626 3.58 0.343 0.459 0.761 0.946 0.0019 0.00236 +Wall time: 4869.669683474116 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 47 4.21 0.157 1.07 0.353 0.474 1.15 1.23 0.00287 0.00309 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 148 100 6.9 0.136 4.18 0.33 0.441 2.43 2.44 0.00607 0.00611 + + + 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 4902.698 0.005 0.157 1.43 4.57 0.353 0.473 1.1 1.43 0.00276 0.00357 +! Validation 148 4902.698 0.005 0.143 3.59 6.45 0.338 0.452 2.17 2.26 0.00542 0.00566 +Wall time: 4902.698445428163 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 47 5.71 0.152 2.66 0.349 0.467 1.84 1.95 0.00459 0.00487 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 149 100 3.97 0.133 1.31 0.326 0.436 1.34 1.37 0.00334 0.00342 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 149 4935.737 0.005 0.153 2.25 5.32 0.349 0.468 1.47 1.79 0.00367 0.00448 +! Validation 149 4935.737 0.005 0.14 1.14 3.95 0.335 0.448 1.11 1.28 0.00278 0.00319 +Wall time: 4935.737424955238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 47 3.33 0.154 0.262 0.347 0.468 0.473 0.612 0.00118 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 150 100 3.35 0.137 0.606 0.331 0.442 0.891 0.93 0.00223 0.00233 + + + 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 4968.771 0.005 0.159 4.01 7.18 0.355 0.476 1.57 2.4 0.00394 0.00599 +! Validation 150 4968.771 0.005 0.144 0.521 3.4 0.34 0.454 0.685 0.862 0.00171 0.00216 +Wall time: 4968.771284154151 +! Best model 150 3.402 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 47 3.56 0.148 0.594 0.342 0.46 0.802 0.921 0.002 0.0023 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 151 100 2.69 0.131 0.064 0.323 0.433 0.282 0.302 0.000704 0.000756 + + + 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 5001.810 0.005 0.15 1.44 4.44 0.345 0.463 1.19 1.43 0.00298 0.00358 +! Validation 151 5001.810 0.005 0.138 0.386 3.14 0.332 0.444 0.608 0.742 0.00152 0.00186 +Wall time: 5001.8107945011 +! Best model 151 3.145 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 47 3.62 0.15 0.617 0.344 0.463 0.815 0.938 0.00204 0.00235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 152 100 2.79 0.133 0.135 0.326 0.435 0.366 0.439 0.000915 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 152 5034.849 0.005 0.152 3.39 6.43 0.347 0.466 1.78 2.2 0.00446 0.00551 +! Validation 152 5034.849 0.005 0.14 0.36 3.15 0.335 0.447 0.583 0.717 0.00146 0.00179 +Wall time: 5034.849105058238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 47 22.3 0.158 19.1 0.354 0.475 5.14 5.22 0.0129 0.0131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 153 100 10.2 0.137 7.43 0.33 0.442 3.25 3.26 0.00811 0.00814 + + + 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 5068.168 0.005 0.15 4.99 7.98 0.345 0.462 2.25 2.66 0.00564 0.00665 +! Validation 153 5068.168 0.005 0.144 6.27 9.16 0.34 0.454 2.91 2.99 0.00728 0.00748 +Wall time: 5068.16834933497 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 47 5.43 0.15 2.44 0.345 0.462 1.79 1.87 0.00447 0.00466 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 154 100 7 0.131 4.39 0.324 0.432 2.49 2.5 0.00622 0.00626 + + + 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 5101.210 0.005 0.154 2.31 5.4 0.35 0.47 1.5 1.82 0.00376 0.00454 +! Validation 154 5101.210 0.005 0.138 3.52 6.27 0.332 0.444 2.14 2.24 0.00536 0.0056 +Wall time: 5101.210028077941 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 47 5.29 0.141 2.47 0.335 0.449 1.78 1.88 0.00445 0.0047 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 155 100 6.68 0.126 4.16 0.318 0.425 2.42 2.44 0.00605 0.00609 + + + 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 5134.266 0.005 0.145 1.42 4.33 0.34 0.456 1.2 1.42 0.003 0.00356 +! Validation 155 5134.266 0.005 0.133 3.3 5.95 0.326 0.435 2.07 2.17 0.00519 0.00542 +Wall time: 5134.265893894248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 47 3.52 0.14 0.734 0.333 0.446 0.885 1.02 0.00221 0.00256 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 156 100 2.69 0.125 0.188 0.316 0.422 0.433 0.518 0.00108 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 156 5167.302 0.005 0.142 1.97 4.8 0.335 0.45 1.3 1.68 0.00326 0.00419 +! Validation 156 5167.302 0.005 0.131 0.755 3.38 0.325 0.433 0.883 1.04 0.00221 0.0026 +Wall time: 5167.302165327128 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 47 69.2 0.135 66.5 0.329 0.438 9.65 9.74 0.0241 0.0244 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 157 100 37.5 0.134 34.8 0.327 0.438 7.04 7.05 0.0176 0.0176 + + + 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 5200.341 0.005 0.138 6.19 8.96 0.331 0.445 1.63 2.93 0.00407 0.00733 +! Validation 157 5200.341 0.005 0.143 36.4 39.2 0.338 0.452 7.16 7.21 0.0179 0.018 +Wall time: 5200.341878155246 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 158 47 3.47 0.163 0.215 0.361 0.482 0.459 0.554 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 + 158 100 3.45 0.143 0.595 0.338 0.452 0.882 0.922 0.0022 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 158 5233.499 0.005 0.178 5.83 9.39 0.378 0.504 2.4 2.89 0.006 0.00722 +! Validation 158 5233.499 0.005 0.151 1.25 4.28 0.348 0.465 1.19 1.34 0.00297 0.00334 +Wall time: 5233.499778035097 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 47 3.21 0.148 0.255 0.34 0.459 0.495 0.604 0.00124 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 159 100 2.84 0.126 0.321 0.318 0.424 0.619 0.677 0.00155 0.00169 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 159 5266.536 0.005 0.149 1.49 4.46 0.344 0.461 1.18 1.46 0.00296 0.00365 +! Validation 159 5266.536 0.005 0.133 0.369 3.02 0.326 0.435 0.574 0.726 0.00144 0.00181 +Wall time: 5266.536807486322 +! Best model 159 3.024 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 47 3.04 0.137 0.296 0.329 0.443 0.489 0.65 0.00122 0.00162 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 160 100 2.5 0.121 0.0749 0.312 0.416 0.253 0.327 0.000634 0.000818 + + + 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 5299.602 0.005 0.139 1.11 3.89 0.332 0.445 1.03 1.26 0.00257 0.00315 +! Validation 160 5299.602 0.005 0.127 0.457 3 0.32 0.427 0.665 0.808 0.00166 0.00202 +Wall time: 5299.602234550286 +! Best model 160 3.004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 47 3.48 0.131 0.864 0.322 0.432 1 1.11 0.0025 0.00278 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 161 100 2.91 0.117 0.582 0.306 0.408 0.866 0.912 0.00217 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 161 5332.651 0.005 0.134 0.824 3.5 0.326 0.437 0.898 1.08 0.00225 0.00271 +! Validation 161 5332.651 0.005 0.122 0.478 2.92 0.313 0.418 0.669 0.826 0.00167 0.00206 +Wall time: 5332.651698524132 +! Best model 161 2.921 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 47 3.32 0.135 0.613 0.329 0.439 0.743 0.936 0.00186 0.00234 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 162 100 6.09 0.124 3.61 0.314 0.421 2.25 2.27 0.00563 0.00567 + + + 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 5365.706 0.005 0.132 3.87 6.5 0.324 0.434 1.8 2.35 0.0045 0.00588 +! Validation 162 5365.706 0.005 0.132 2.95 5.58 0.324 0.433 1.95 2.05 0.00486 0.00513 +Wall time: 5365.706019048113 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 47 2.99 0.127 0.446 0.319 0.426 0.649 0.798 0.00162 0.00199 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 163 100 2.38 0.115 0.0809 0.304 0.406 0.291 0.34 0.000727 0.00085 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 163 5398.747 0.005 0.138 1.39 4.16 0.331 0.444 1.1 1.41 0.00275 0.00353 +! Validation 163 5398.747 0.005 0.121 0.276 2.7 0.312 0.416 0.504 0.628 0.00126 0.00157 +Wall time: 5398.747649600264 +! Best model 163 2.704 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 47 3.14 0.137 0.408 0.328 0.442 0.624 0.763 0.00156 0.00191 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 164 100 3.45 0.118 1.09 0.308 0.41 1.22 1.25 0.00304 0.00312 + + + 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 5431.801 0.005 0.132 3.37 6.02 0.324 0.434 1.85 2.2 0.00463 0.00549 +! Validation 164 5431.801 0.005 0.125 0.78 3.28 0.317 0.422 0.878 1.06 0.0022 0.00264 +Wall time: 5431.801711558364 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 47 2.87 0.128 0.306 0.319 0.428 0.503 0.661 0.00126 0.00165 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 165 100 2.32 0.111 0.0945 0.299 0.399 0.325 0.367 0.000812 0.000919 + + + 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 5464.935 0.005 0.129 1.26 3.84 0.321 0.43 1.05 1.34 0.00261 0.00335 +! Validation 165 5464.935 0.005 0.118 0.272 2.62 0.307 0.41 0.501 0.623 0.00125 0.00156 +Wall time: 5464.935651578009 +! Best model 165 2.625 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 47 13.3 0.149 10.3 0.346 0.462 3.65 3.83 0.00913 0.00958 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 166 100 19.5 0.139 16.8 0.333 0.446 4.88 4.89 0.0122 0.0122 + + + 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 5497.992 0.005 0.127 5.81 8.35 0.317 0.426 1.77 2.88 0.00444 0.00719 +! Validation 166 5497.992 0.005 0.148 14.1 17.1 0.344 0.459 4.43 4.49 0.0111 0.0112 +Wall time: 5497.9927687332965 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 47 5.41 0.13 2.81 0.321 0.431 1.89 2 0.00472 0.00501 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 167 100 5.57 0.115 3.26 0.304 0.406 2.13 2.16 0.00534 0.0054 + + + 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 5531.039 0.005 0.149 2.12 5.11 0.346 0.462 1.4 1.74 0.00349 0.00435 +! Validation 167 5531.039 0.005 0.123 4.25 6.71 0.314 0.419 2.38 2.46 0.00596 0.00616 +Wall time: 5531.03888538992 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 47 4.98 0.127 2.44 0.317 0.426 1.7 1.87 0.00425 0.00467 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 168 100 4.06 0.11 1.85 0.298 0.397 1.6 1.63 0.00399 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 168 5564.075 0.005 0.128 2.06 4.63 0.32 0.428 1.41 1.72 0.00352 0.00429 +! Validation 168 5564.075 0.005 0.117 1.36 3.7 0.306 0.408 1.25 1.39 0.00312 0.00349 +Wall time: 5564.075846556108 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 47 7.45 0.129 4.87 0.321 0.429 2.59 2.64 0.00647 0.00659 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 169 100 6.53 0.11 4.33 0.297 0.396 2.47 2.49 0.00617 0.00622 + + + 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 5597.113 0.005 0.125 2.39 4.89 0.316 0.422 1.57 1.85 0.00392 0.00461 +! Validation 169 5597.113 0.005 0.117 3.56 5.89 0.306 0.408 2.17 2.25 0.00542 0.00563 +Wall time: 5597.113804250024 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 47 4.64 0.123 2.18 0.313 0.42 1.61 1.76 0.00404 0.00441 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 170 100 10.1 0.105 7.96 0.29 0.387 3.36 3.37 0.0084 0.00843 + + + 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 5630.142 0.005 0.122 1.05 3.48 0.311 0.417 0.953 1.22 0.00238 0.00305 +! Validation 170 5630.142 0.005 0.11 6.75 8.95 0.297 0.396 3.05 3.11 0.00762 0.00776 +Wall time: 5630.141995380167 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 47 15.2 0.129 12.6 0.321 0.429 4.21 4.24 0.0105 0.0106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 171 100 4.5 0.113 2.23 0.301 0.403 1.76 1.79 0.0044 0.00446 + + + 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 5663.177 0.005 0.124 4.58 7.05 0.314 0.42 2.23 2.55 0.00558 0.00638 +! Validation 171 5663.177 0.005 0.121 1.87 4.29 0.311 0.415 1.5 1.64 0.00375 0.00409 +Wall time: 5663.177801168989 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 47 5.78 0.122 3.34 0.314 0.417 2.08 2.18 0.00519 0.00546 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 172 100 5.39 0.105 3.28 0.291 0.387 2.14 2.17 0.00535 0.00541 + + + 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 5697.075 0.005 0.125 1.65 4.15 0.316 0.423 1.21 1.53 0.00303 0.00383 +! Validation 172 5697.075 0.005 0.111 5.05 7.27 0.299 0.398 2.59 2.68 0.00647 0.00671 +Wall time: 5697.075784496032 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 47 12.1 0.114 9.85 0.303 0.404 3.66 3.75 0.00916 0.00938 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 173 100 5.87 0.0996 3.88 0.283 0.377 2.33 2.35 0.00583 0.00588 + + + 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 5730.216 0.005 0.116 1.25 3.58 0.305 0.408 1 1.33 0.00251 0.00331 +! Validation 173 5730.216 0.005 0.106 5.22 7.33 0.292 0.388 2.66 2.73 0.00666 0.00682 +Wall time: 5730.216059003025 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 47 6.79 0.118 4.44 0.306 0.41 2.46 2.52 0.00614 0.00629 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 174 100 8.29 0.101 6.26 0.286 0.38 2.97 2.99 0.00744 0.00748 + + + 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 5763.381 0.005 0.12 2.5 4.9 0.309 0.413 1.34 1.89 0.00336 0.00472 +! Validation 174 5763.381 0.005 0.107 8 10.1 0.294 0.391 3.33 3.38 0.00832 0.00845 +Wall time: 5763.381782955956 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 47 2.48 0.11 0.283 0.297 0.396 0.556 0.636 0.00139 0.00159 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 175 100 2.18 0.0967 0.247 0.279 0.372 0.511 0.594 0.00128 0.00149 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 175 5796.388 0.005 0.113 0.946 3.2 0.3 0.401 0.863 1.16 0.00216 0.00291 +! Validation 175 5796.388 0.005 0.102 0.259 2.29 0.286 0.381 0.481 0.608 0.0012 0.00152 +Wall time: 5796.388843649067 +! Best model 175 2.289 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 47 3.3 0.108 1.13 0.295 0.393 1.15 1.27 0.00287 0.00317 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 176 100 2.67 0.094 0.79 0.275 0.366 1.01 1.06 0.00254 0.00266 + + + 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 5829.416 0.005 0.108 1.08 3.24 0.294 0.393 0.994 1.24 0.00248 0.00311 +! Validation 176 5829.416 0.005 0.0991 1.51 3.49 0.282 0.376 1.36 1.47 0.00339 0.00367 +Wall time: 5829.415909217205 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 47 10.6 0.107 8.51 0.292 0.39 3.44 3.49 0.00859 0.00871 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 177 100 9.53 0.0926 7.68 0.273 0.364 3.3 3.31 0.00825 0.00828 + + + 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 5862.434 0.005 0.106 1.82 3.93 0.29 0.388 1.31 1.61 0.00327 0.00401 +! Validation 177 5862.434 0.005 0.0976 6.53 8.48 0.28 0.373 3 3.05 0.0075 0.00763 +Wall time: 5862.434839105234 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 47 2.53 0.111 0.315 0.299 0.398 0.524 0.67 0.00131 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 178 100 2.21 0.102 0.179 0.286 0.381 0.416 0.506 0.00104 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 178 5895.460 0.005 0.111 3.24 5.45 0.297 0.398 1.81 2.15 0.00452 0.00538 +! Validation 178 5895.460 0.005 0.108 0.751 2.9 0.294 0.392 0.89 1.04 0.00223 0.00259 +Wall time: 5895.460313265212 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 47 4.03 0.112 1.8 0.299 0.4 1.49 1.6 0.00373 0.004 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 179 100 8.41 0.0953 6.5 0.277 0.369 3.03 3.05 0.00758 0.00762 + + + 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 5928.612 0.005 0.109 2.07 4.26 0.296 0.395 1.42 1.72 0.00354 0.0043 +! Validation 179 5928.612 0.005 0.101 5.29 7.3 0.285 0.38 2.68 2.75 0.0067 0.00687 +Wall time: 5928.612423405983 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 47 3.77 0.107 1.64 0.293 0.39 1.46 1.53 0.00366 0.00383 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 180 100 6.49 0.0926 4.64 0.274 0.364 2.56 2.57 0.00639 0.00643 + + + 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 5961.650 0.005 0.107 1.61 3.74 0.293 0.39 1.23 1.51 0.00307 0.00379 +! Validation 180 5961.650 0.005 0.0973 3.84 5.79 0.28 0.373 2.27 2.34 0.00568 0.00586 +Wall time: 5961.650806161109 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 47 6.76 0.104 4.68 0.288 0.385 2.48 2.59 0.0062 0.00647 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 181 100 2.52 0.0931 0.659 0.275 0.365 0.914 0.97 0.00229 0.00243 + + + 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 5994.671 0.005 0.107 2.9 5.04 0.293 0.391 1.68 2.04 0.00421 0.00509 +! Validation 181 5994.671 0.005 0.0988 1.24 3.21 0.282 0.376 1.21 1.33 0.00302 0.00332 +Wall time: 5994.671316347085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 47 3.69 0.098 1.73 0.28 0.374 1.48 1.57 0.00369 0.00393 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 182 100 1.85 0.0894 0.066 0.269 0.357 0.241 0.307 0.000604 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 182 6027.707 0.005 0.103 0.943 3 0.287 0.383 0.92 1.16 0.0023 0.0029 +! Validation 182 6027.707 0.005 0.0938 0.305 2.18 0.275 0.366 0.528 0.66 0.00132 0.00165 +Wall time: 6027.707609744277 +! Best model 182 2.181 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 47 2.2 0.1 0.194 0.282 0.378 0.429 0.526 0.00107 0.00132 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 183 100 1.84 0.0871 0.0932 0.266 0.353 0.321 0.365 0.000803 0.000912 + + + 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 6060.744 0.005 0.101 1.53 3.55 0.285 0.38 1.2 1.48 0.00301 0.0037 +! Validation 183 6060.744 0.005 0.092 0.242 2.08 0.273 0.363 0.466 0.588 0.00116 0.00147 +Wall time: 6060.744171518367 +! Best model 183 2.083 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 47 4.87 0.102 2.83 0.286 0.382 1.95 2.01 0.00488 0.00502 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 184 100 4.69 0.0887 2.92 0.268 0.356 2.02 2.04 0.00505 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 184 6093.787 0.005 0.0992 1.75 3.73 0.282 0.376 1.22 1.58 0.00306 0.00395 +! Validation 184 6093.787 0.005 0.094 2.37 4.25 0.275 0.366 1.74 1.84 0.00436 0.0046 +Wall time: 6093.786944505293 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 47 5.66 0.0951 3.76 0.278 0.369 2.24 2.32 0.00559 0.00579 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 185 100 7.72 0.0847 6.03 0.263 0.348 2.91 2.93 0.00729 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 185 6126.835 0.005 0.0975 1.46 3.41 0.28 0.373 1.22 1.44 0.00304 0.0036 +! Validation 185 6126.835 0.005 0.0895 4.57 6.36 0.269 0.357 2.48 2.55 0.00619 0.00639 +Wall time: 6126.835276317317 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 47 3.11 0.1 1.1 0.285 0.378 1.17 1.26 0.00292 0.00314 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 186 100 1.95 0.0888 0.175 0.269 0.356 0.405 0.5 0.00101 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 186 6159.868 0.005 0.1 2.39 4.39 0.283 0.378 1.52 1.85 0.0038 0.00463 +! Validation 186 6159.868 0.005 0.0939 0.642 2.52 0.276 0.366 0.82 0.958 0.00205 0.00239 +Wall time: 6159.868763227016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 47 3.86 0.0962 1.93 0.279 0.371 1.59 1.66 0.00397 0.00416 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 187 100 2.61 0.0833 0.942 0.261 0.345 1.12 1.16 0.00279 0.0029 + + + 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 6192.907 0.005 0.096 0.932 2.85 0.278 0.37 0.941 1.15 0.00235 0.00288 +! Validation 187 6192.907 0.005 0.0874 1.8 3.55 0.266 0.353 1.5 1.61 0.00375 0.00401 +Wall time: 6192.907090707216 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 47 2.86 0.095 0.956 0.277 0.368 1.04 1.17 0.00261 0.00292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 188 100 1.86 0.0869 0.119 0.265 0.352 0.296 0.412 0.000739 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 188 6225.931 0.005 0.0932 1.82 3.68 0.274 0.365 1.2 1.61 0.003 0.00403 +! Validation 188 6225.931 0.005 0.0917 0.851 2.68 0.272 0.362 0.907 1.1 0.00227 0.00276 +Wall time: 6225.93184928922 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 47 3.64 0.0926 1.79 0.274 0.364 1.49 1.6 0.00372 0.004 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 189 100 1.74 0.0835 0.0679 0.261 0.345 0.248 0.311 0.000619 0.000778 + + + 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 6258.968 0.005 0.0967 2.02 3.95 0.279 0.372 1.42 1.7 0.00354 0.00425 +! Validation 189 6258.968 0.005 0.0886 0.447 2.22 0.268 0.356 0.65 0.799 0.00162 0.002 +Wall time: 6258.96797566209 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 47 5.09 0.0963 3.16 0.278 0.371 2.06 2.13 0.00516 0.00531 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 190 100 5.8 0.0814 4.17 0.258 0.341 2.42 2.44 0.00605 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 190 6292.803 0.005 0.0928 1.45 3.31 0.273 0.364 1.22 1.44 0.00305 0.00359 +! Validation 190 6292.803 0.005 0.0858 3.34 5.06 0.264 0.35 2.11 2.18 0.00528 0.00546 +Wall time: 6292.803567299154 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 47 3.54 0.0953 1.64 0.278 0.369 1.39 1.53 0.00348 0.00382 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 191 100 2.51 0.0856 0.797 0.263 0.35 1.03 1.07 0.00257 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 191 6325.846 0.005 0.0917 1.94 3.78 0.272 0.362 1.36 1.67 0.00341 0.00417 +! Validation 191 6325.846 0.005 0.0898 1.54 3.34 0.269 0.358 1.37 1.49 0.00343 0.00371 +Wall time: 6325.8463269812055 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 47 1.94 0.089 0.156 0.269 0.356 0.366 0.472 0.000915 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 192 100 2.46 0.0789 0.88 0.254 0.336 1.08 1.12 0.0027 0.0028 + + + 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 6358.889 0.005 0.0913 0.986 2.81 0.271 0.361 0.972 1.19 0.00243 0.00297 +! Validation 192 6358.889 0.005 0.0827 0.564 2.22 0.259 0.344 0.748 0.898 0.00187 0.00224 +Wall time: 6358.889872164 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 47 2.23 0.0993 0.24 0.282 0.377 0.471 0.585 0.00118 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 193 100 2.51 0.0859 0.797 0.264 0.35 1.02 1.07 0.00256 0.00267 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 193 6391.930 0.005 0.103 3.93 5.99 0.288 0.383 1.73 2.37 0.00433 0.00593 +! Validation 193 6391.930 0.005 0.0908 0.559 2.38 0.271 0.36 0.742 0.894 0.00186 0.00223 +Wall time: 6391.929891202133 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 47 2.58 0.0856 0.872 0.263 0.35 0.943 1.12 0.00236 0.00279 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 194 100 1.72 0.0789 0.14 0.254 0.336 0.412 0.446 0.00103 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 194 6424.974 0.005 0.0912 0.957 2.78 0.272 0.361 0.938 1.17 0.00235 0.00292 +! Validation 194 6424.974 0.005 0.083 0.306 1.97 0.26 0.344 0.544 0.661 0.00136 0.00165 +Wall time: 6424.974449493922 +! Best model 194 1.967 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 47 7.15 0.0878 5.4 0.267 0.354 2.71 2.78 0.00678 0.00694 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 195 100 3.36 0.0791 1.78 0.254 0.336 1.56 1.59 0.0039 0.00398 + + + 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 6457.982 0.005 0.0879 1.33 3.09 0.267 0.354 1.1 1.38 0.00274 0.00344 +! Validation 195 6457.982 0.005 0.0831 2.66 4.32 0.26 0.344 1.86 1.95 0.00465 0.00487 +Wall time: 6457.982814061921 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 47 1.87 0.085 0.172 0.263 0.348 0.374 0.495 0.000935 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 196 100 2.08 0.077 0.539 0.251 0.332 0.816 0.878 0.00204 0.00219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 196 6490.963 0.005 0.0896 1.35 3.14 0.269 0.358 1.12 1.39 0.0028 0.00347 +! Validation 196 6490.963 0.005 0.0803 0.375 1.98 0.256 0.339 0.604 0.732 0.00151 0.00183 +Wall time: 6490.962999812327 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 47 7.26 0.084 5.58 0.261 0.346 2.69 2.82 0.00672 0.00706 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 197 100 3.12 0.0799 1.52 0.255 0.338 1.43 1.47 0.00357 0.00368 + + + 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 6523.966 0.005 0.0864 1.96 3.68 0.264 0.351 1.34 1.67 0.00335 0.00417 +! Validation 197 6523.966 0.005 0.0843 2.77 4.46 0.262 0.347 1.87 1.99 0.00468 0.00497 +Wall time: 6523.966127523221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 47 3.26 0.0881 1.5 0.268 0.355 1.36 1.46 0.0034 0.00366 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 198 100 5.91 0.0786 4.34 0.254 0.335 2.47 2.49 0.00618 0.00622 + + + 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 6556.956 0.005 0.0886 1.76 3.53 0.268 0.356 1.32 1.59 0.0033 0.00396 +! Validation 198 6556.956 0.005 0.0825 5.49 7.14 0.259 0.343 2.75 2.8 0.00686 0.007 +Wall time: 6556.9567566253245 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 47 3.47 0.0812 1.85 0.257 0.341 1.53 1.62 0.00383 0.00406 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 199 100 3.18 0.0753 1.67 0.249 0.328 1.51 1.54 0.00378 0.00386 + + + 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 6589.961 0.005 0.0863 0.95 2.68 0.264 0.351 0.934 1.16 0.00234 0.00291 +! Validation 199 6589.961 0.005 0.0783 2.58 4.15 0.253 0.334 1.84 1.92 0.0046 0.0048 +Wall time: 6589.961345455144 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 47 2.19 0.0823 0.547 0.259 0.343 0.746 0.883 0.00186 0.00221 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 200 100 1.55 0.0737 0.0742 0.246 0.324 0.288 0.325 0.00072 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 200 6622.965 0.005 0.0836 1.16 2.83 0.26 0.345 1.09 1.29 0.00273 0.00322 +! Validation 200 6622.965 0.005 0.0768 0.21 1.75 0.25 0.331 0.429 0.547 0.00107 0.00137 +Wall time: 6622.9654773040675 +! Best model 200 1.745 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 47 5.75 0.085 4.05 0.263 0.348 2.35 2.4 0.00588 0.00601 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 201 100 3.12 0.0751 1.61 0.248 0.327 1.48 1.52 0.00371 0.0038 + + + 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 6655.978 0.005 0.0822 1.43 3.07 0.258 0.343 1.16 1.43 0.0029 0.00356 +! Validation 201 6655.978 0.005 0.079 2.8 4.38 0.254 0.336 1.9 2 0.00475 0.005 +Wall time: 6655.9782128059305 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 47 2.21 0.0793 0.622 0.254 0.336 0.804 0.942 0.00201 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 202 100 1.68 0.0717 0.246 0.243 0.32 0.523 0.593 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 202 6688.976 0.005 0.0818 0.88 2.52 0.258 0.342 0.93 1.12 0.00233 0.0028 +! Validation 202 6688.976 0.005 0.0741 0.235 1.72 0.246 0.325 0.473 0.579 0.00118 0.00145 +Wall time: 6688.976147216279 +! Best model 202 1.717 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 47 1.96 0.0877 0.207 0.265 0.354 0.418 0.543 0.00104 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 203 100 1.61 0.076 0.0947 0.249 0.329 0.343 0.368 0.000858 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 203 6721.986 0.005 0.0908 3.04 4.85 0.271 0.36 1.65 2.09 0.00414 0.00521 +! Validation 203 6721.986 0.005 0.0796 0.284 1.88 0.254 0.337 0.517 0.637 0.00129 0.00159 +Wall time: 6721.9865026823245 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 47 4.2 0.0813 2.57 0.258 0.341 1.84 1.92 0.00459 0.00479 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 204 100 1.98 0.0738 0.502 0.246 0.325 0.783 0.847 0.00196 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 204 6754.973 0.005 0.0816 1.11 2.74 0.257 0.341 0.982 1.25 0.00245 0.00314 +! Validation 204 6754.973 0.005 0.0766 0.379 1.91 0.25 0.331 0.609 0.735 0.00152 0.00184 +Wall time: 6754.972978028934 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 47 4.16 0.0807 2.55 0.257 0.34 1.86 1.91 0.00465 0.00477 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 205 100 3.29 0.0726 1.83 0.244 0.322 1.59 1.62 0.00397 0.00405 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 205 6787.970 0.005 0.0808 1.28 2.9 0.256 0.34 1.09 1.35 0.00273 0.00338 +! Validation 205 6787.970 0.005 0.0757 1.52 3.03 0.249 0.329 1.37 1.47 0.00343 0.00368 +Wall time: 6787.969899732154 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 47 4.99 0.0857 3.28 0.264 0.35 2.04 2.16 0.00511 0.00541 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 206 100 12.4 0.0759 10.8 0.249 0.329 3.92 3.93 0.00981 0.00984 + + + 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 6820.959 0.005 0.0815 2.1 3.73 0.257 0.341 1.47 1.73 0.00368 0.00433 +! Validation 206 6820.959 0.005 0.079 9.07 10.6 0.253 0.336 3.55 3.6 0.00888 0.009 +Wall time: 6820.95936781913 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 47 3.44 0.0793 1.86 0.254 0.336 1.54 1.63 0.00384 0.00407 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 207 100 1.75 0.073 0.29 0.245 0.323 0.565 0.644 0.00141 0.00161 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 207 6853.956 0.005 0.0849 1.76 3.46 0.262 0.348 1.34 1.58 0.00335 0.00396 +! Validation 207 6853.956 0.005 0.0762 0.275 1.8 0.249 0.33 0.503 0.626 0.00126 0.00157 +Wall time: 6853.955910987221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 47 2.59 0.077 1.05 0.25 0.332 1.08 1.22 0.00271 0.00306 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 208 100 1.48 0.0686 0.107 0.238 0.313 0.285 0.391 0.000714 0.000978 + + + 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 6886.994 0.005 0.0786 0.549 2.12 0.253 0.335 0.709 0.884 0.00177 0.00221 +! Validation 208 6886.994 0.005 0.071 0.372 1.79 0.241 0.319 0.584 0.729 0.00146 0.00182 +Wall time: 6886.994689374231 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 47 2.21 0.0799 0.611 0.256 0.338 0.844 0.934 0.00211 0.00234 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 209 100 1.55 0.0709 0.134 0.242 0.318 0.308 0.437 0.000769 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 209 6919.979 0.005 0.0792 1.56 3.14 0.254 0.336 1.14 1.49 0.00286 0.00373 +! Validation 209 6919.979 0.005 0.0745 0.546 2.04 0.247 0.326 0.723 0.883 0.00181 0.00221 +Wall time: 6919.979034838267 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 47 3.61 0.0723 2.17 0.243 0.321 1.69 1.76 0.00422 0.0044 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 210 100 1.58 0.0695 0.185 0.239 0.315 0.471 0.514 0.00118 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 210 6952.973 0.005 0.0771 1.15 2.69 0.251 0.332 1.05 1.28 0.00262 0.0032 +! Validation 210 6952.973 0.005 0.0723 0.208 1.65 0.243 0.321 0.438 0.545 0.0011 0.00136 +Wall time: 6952.973862844054 +! Best model 210 1.654 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 47 1.7 0.0776 0.145 0.252 0.333 0.363 0.455 0.000907 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 211 100 1.45 0.0695 0.0599 0.239 0.315 0.224 0.292 0.00056 0.000731 + + + 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 6985.975 0.005 0.0771 1.18 2.73 0.25 0.332 1.04 1.3 0.00261 0.00326 +! Validation 211 6985.975 0.005 0.0723 0.222 1.67 0.243 0.321 0.44 0.563 0.0011 0.00141 +Wall time: 6985.975408439059 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 47 2.18 0.0871 0.437 0.266 0.353 0.624 0.79 0.00156 0.00198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 212 100 3.68 0.0774 2.13 0.251 0.332 1.72 1.75 0.0043 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 212 7018.973 0.005 0.0821 2.95 4.6 0.258 0.342 1.73 2.06 0.00432 0.00514 +! Validation 212 7018.973 0.005 0.0813 1.86 3.49 0.257 0.341 1.53 1.63 0.00383 0.00408 +Wall time: 7018.972873535007 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 47 2.99 0.0812 1.36 0.256 0.341 1.25 1.4 0.00311 0.00349 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 213 100 2.55 0.0701 1.15 0.24 0.316 1.25 1.28 0.00311 0.00321 + + + 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 7051.975 0.005 0.0789 1.04 2.62 0.253 0.336 1.02 1.22 0.00256 0.00305 +! Validation 213 7051.975 0.005 0.0733 1.59 3.05 0.245 0.323 1.41 1.5 0.00352 0.00376 +Wall time: 7051.9756982810795 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 47 1.68 0.0764 0.157 0.251 0.33 0.41 0.474 0.00102 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 214 100 1.9 0.0693 0.511 0.239 0.315 0.807 0.854 0.00202 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 214 7084.986 0.005 0.0777 1.44 2.99 0.251 0.333 1.19 1.43 0.00297 0.00358 +! Validation 214 7084.986 0.005 0.0717 0.412 1.85 0.242 0.32 0.637 0.767 0.00159 0.00192 +Wall time: 7084.986078786198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 47 2.58 0.0715 1.14 0.242 0.32 1.18 1.28 0.00295 0.0032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 215 100 1.98 0.0659 0.662 0.233 0.307 0.926 0.972 0.00231 0.00243 + + + 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 7117.979 0.005 0.0739 0.618 2.1 0.246 0.325 0.764 0.938 0.00191 0.00235 +! Validation 215 7117.979 0.005 0.068 0.569 1.93 0.236 0.312 0.786 0.901 0.00196 0.00225 +Wall time: 7117.979002092965 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 47 2.68 0.0841 0.996 0.262 0.347 1.06 1.19 0.00265 0.00298 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 216 100 1.62 0.072 0.179 0.244 0.321 0.404 0.505 0.00101 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 216 7150.965 0.005 0.0742 1.76 3.24 0.246 0.326 1.32 1.59 0.00329 0.00396 +! Validation 216 7150.965 0.005 0.0756 0.486 2 0.249 0.329 0.696 0.833 0.00174 0.00208 +Wall time: 7150.965205247048 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 47 2.33 0.0712 0.906 0.242 0.319 1 1.14 0.0025 0.00284 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 217 100 1.39 0.0646 0.0971 0.231 0.304 0.267 0.372 0.000666 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 217 7183.959 0.005 0.0738 0.563 2.04 0.245 0.325 0.731 0.896 0.00183 0.00224 +! Validation 217 7183.959 0.005 0.0668 0.448 1.78 0.234 0.309 0.64 0.8 0.0016 0.002 +Wall time: 7183.95894875098 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 47 11.1 0.0832 9.48 0.259 0.345 3.62 3.68 0.00904 0.0092 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 218 100 2.24 0.0764 0.714 0.249 0.33 0.963 1.01 0.00241 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 218 7216.949 0.005 0.0734 2.71 4.18 0.244 0.324 1.58 1.96 0.00395 0.0049 +! Validation 218 7216.949 0.005 0.0798 0.505 2.1 0.254 0.338 0.7 0.85 0.00175 0.00212 +Wall time: 7216.949545294046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 47 1.7 0.0764 0.172 0.25 0.33 0.427 0.496 0.00107 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 219 100 1.62 0.0665 0.287 0.234 0.308 0.577 0.64 0.00144 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 219 7249.940 0.005 0.082 0.955 2.59 0.258 0.342 0.846 1.17 0.00211 0.00292 +! Validation 219 7249.940 0.005 0.0682 0.598 1.96 0.237 0.312 0.787 0.924 0.00197 0.00231 +Wall time: 7249.940751796123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 47 1.56 0.0714 0.128 0.242 0.319 0.325 0.428 0.000811 0.00107 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 220 100 1.54 0.0644 0.251 0.23 0.303 0.531 0.598 0.00133 0.0015 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 220 7282.933 0.005 0.071 0.763 2.18 0.241 0.318 0.827 1.04 0.00207 0.00261 +! Validation 220 7282.933 0.005 0.0671 0.285 1.63 0.234 0.309 0.515 0.638 0.00129 0.0016 +Wall time: 7282.932960036211 +! Best model 220 1.627 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 47 1.78 0.0722 0.337 0.243 0.321 0.554 0.694 0.00139 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 221 100 1.67 0.0658 0.352 0.233 0.307 0.645 0.709 0.00161 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 221 7315.937 0.005 0.0698 0.892 2.29 0.239 0.316 0.888 1.13 0.00222 0.00282 +! Validation 221 7315.937 0.005 0.0683 0.728 2.09 0.237 0.312 0.886 1.02 0.00221 0.00255 +Wall time: 7315.937598610297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 47 4.1 0.0686 2.73 0.235 0.313 1.92 1.97 0.00479 0.00493 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 222 100 2.35 0.0656 1.04 0.233 0.306 1.19 1.22 0.00297 0.00305 + + + 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 7348.930 0.005 0.0704 1.28 2.68 0.24 0.317 1.15 1.35 0.00288 0.00337 +! Validation 222 7348.930 0.005 0.0674 1.57 2.92 0.235 0.31 1.4 1.5 0.0035 0.00374 +Wall time: 7348.929921766277 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 47 1.5 0.0673 0.155 0.235 0.31 0.369 0.47 0.000924 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 223 100 1.31 0.0628 0.0575 0.228 0.3 0.209 0.287 0.000522 0.000716 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 223 7381.915 0.005 0.0709 1.11 2.53 0.241 0.318 1.04 1.26 0.0026 0.00315 +! Validation 223 7381.915 0.005 0.0648 0.257 1.55 0.231 0.304 0.482 0.606 0.00121 0.00151 +Wall time: 7381.915313381236 +! Best model 223 1.552 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 47 2.23 0.0696 0.835 0.238 0.315 0.933 1.09 0.00233 0.00273 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 224 100 2.56 0.0619 1.32 0.226 0.297 1.34 1.37 0.00335 0.00344 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 224 7414.909 0.005 0.07 1.01 2.42 0.239 0.316 0.941 1.2 0.00235 0.00301 +! Validation 224 7414.909 0.005 0.064 1.96 3.24 0.23 0.302 1.58 1.67 0.00396 0.00418 +Wall time: 7414.909492875915 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 47 2.21 0.0668 0.871 0.232 0.309 0.99 1.12 0.00247 0.00279 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 225 100 1.82 0.0611 0.596 0.225 0.295 0.872 0.923 0.00218 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 225 7447.945 0.005 0.0696 1.11 2.5 0.238 0.315 1.02 1.26 0.00256 0.00315 +! Validation 225 7447.945 0.005 0.0629 0.449 1.71 0.228 0.3 0.678 0.801 0.0017 0.002 +Wall time: 7447.944996762089 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 47 1.96 0.067 0.622 0.234 0.309 0.861 0.943 0.00215 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 226 100 1.9 0.0609 0.678 0.224 0.295 0.935 0.984 0.00234 0.00246 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 226 7480.968 0.005 0.0685 1.2 2.57 0.237 0.313 1.08 1.31 0.0027 0.00328 +! Validation 226 7480.968 0.005 0.063 1.22 2.48 0.228 0.3 1.21 1.32 0.00301 0.0033 +Wall time: 7480.968111412134 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 227 47 1.93 0.0642 0.641 0.23 0.303 0.829 0.957 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 + 227 100 1.24 0.0588 0.0673 0.22 0.29 0.255 0.31 0.000638 0.000775 + + + 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 7513.979 0.005 0.0673 0.787 2.13 0.235 0.31 0.854 1.06 0.00214 0.00265 +! Validation 227 7513.979 0.005 0.0604 0.262 1.47 0.223 0.294 0.485 0.611 0.00121 0.00153 +Wall time: 7513.978957032319 +! Best model 227 1.470 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 47 2.65 0.0742 1.16 0.245 0.326 1.21 1.29 0.00302 0.00322 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 228 100 1.8 0.0647 0.504 0.231 0.304 0.775 0.849 0.00194 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 228 7546.995 0.005 0.07 1.97 3.37 0.239 0.316 1.43 1.68 0.00356 0.00419 +! Validation 228 7546.995 0.005 0.068 0.416 1.78 0.237 0.312 0.633 0.771 0.00158 0.00193 +Wall time: 7546.995755158365 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 47 1.37 0.0615 0.144 0.225 0.296 0.353 0.453 0.000883 0.00113 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 229 100 1.38 0.059 0.202 0.221 0.29 0.444 0.538 0.00111 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 229 7579.993 0.005 0.068 0.693 2.05 0.236 0.312 0.798 0.996 0.00199 0.00249 +! Validation 229 7579.993 0.005 0.0607 0.505 1.72 0.224 0.294 0.706 0.849 0.00177 0.00212 +Wall time: 7579.9932891339995 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 47 1.55 0.0667 0.214 0.233 0.309 0.43 0.553 0.00108 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 230 100 1.31 0.06 0.108 0.223 0.293 0.374 0.393 0.000934 0.000984 + + + 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 7613.047 0.005 0.0656 1.03 2.34 0.231 0.306 0.971 1.22 0.00243 0.00304 +! Validation 230 7613.047 0.005 0.0625 0.18 1.43 0.227 0.299 0.403 0.507 0.00101 0.00127 +Wall time: 7613.047608945984 +! Best model 230 1.431 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 47 5.62 0.0672 4.28 0.235 0.31 2.43 2.47 0.00607 0.00618 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 231 100 4.03 0.0571 2.88 0.217 0.286 2 2.03 0.00501 0.00507 + + + 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 7646.095 0.005 0.0635 0.53 1.8 0.228 0.301 0.662 0.862 0.00166 0.00216 +! Validation 231 7646.095 0.005 0.0589 3.81 4.99 0.221 0.29 2.27 2.33 0.00568 0.00583 +Wall time: 7646.095694985241 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 47 2.01 0.0599 0.815 0.222 0.293 1 1.08 0.0025 0.0027 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 232 100 1.48 0.0562 0.353 0.216 0.283 0.654 0.71 0.00164 0.00178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 232 7679.124 0.005 0.0635 0.682 1.95 0.228 0.301 0.8 0.987 0.002 0.00247 +! Validation 232 7679.124 0.005 0.0574 0.314 1.46 0.218 0.286 0.556 0.67 0.00139 0.00167 +Wall time: 7679.124187724199 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 47 1.67 0.0641 0.386 0.229 0.303 0.663 0.742 0.00166 0.00186 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 233 100 1.6 0.0591 0.421 0.221 0.29 0.73 0.775 0.00182 0.00194 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 233 7712.161 0.005 0.0659 1.6 2.92 0.232 0.307 1.19 1.51 0.00299 0.00378 +! Validation 233 7712.161 0.005 0.0606 0.4 1.61 0.223 0.294 0.645 0.755 0.00161 0.00189 +Wall time: 7712.161670332309 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 47 1.46 0.0569 0.324 0.217 0.285 0.603 0.68 0.00151 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 234 100 1.15 0.0544 0.0586 0.212 0.279 0.214 0.289 0.000536 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 234 7745.213 0.005 0.0621 0.299 1.54 0.226 0.298 0.507 0.653 0.00127 0.00163 +! Validation 234 7745.213 0.005 0.0556 0.188 1.3 0.214 0.282 0.398 0.518 0.000995 0.0013 +Wall time: 7745.2130413712 +! Best model 234 1.301 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 47 1.39 0.0599 0.192 0.222 0.292 0.389 0.524 0.000972 0.00131 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 235 100 1.22 0.0571 0.0755 0.217 0.286 0.298 0.328 0.000746 0.000821 + + + 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 7778.263 0.005 0.0643 1.44 2.72 0.229 0.303 1.2 1.43 0.00299 0.00358 +! Validation 235 7778.263 0.005 0.0593 0.17 1.36 0.221 0.291 0.384 0.492 0.000961 0.00123 +Wall time: 7778.263699105009 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 47 2.07 0.0593 0.885 0.219 0.291 1.07 1.12 0.00267 0.00281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 236 100 2.67 0.0544 1.58 0.212 0.279 1.47 1.5 0.00369 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 236 7811.299 0.005 0.0612 0.596 1.82 0.224 0.296 0.742 0.922 0.00185 0.0023 +! Validation 236 7811.299 0.005 0.0559 2.25 3.37 0.215 0.282 1.72 1.79 0.00431 0.00449 +Wall time: 7811.2992521873675 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 47 1.81 0.0614 0.584 0.224 0.296 0.81 0.913 0.00202 0.00228 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 237 100 1.23 0.0555 0.124 0.214 0.282 0.395 0.42 0.000988 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 237 7844.319 0.005 0.0604 0.92 2.13 0.222 0.294 0.918 1.15 0.0023 0.00287 +! Validation 237 7844.319 0.005 0.0574 0.211 1.36 0.217 0.286 0.438 0.548 0.0011 0.00137 +Wall time: 7844.319078586064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 47 3.08 0.0605 1.87 0.223 0.294 1.59 1.64 0.00398 0.00409 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 238 100 2.45 0.054 1.37 0.211 0.278 1.37 1.4 0.00342 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 238 7877.347 0.005 0.0603 0.847 2.05 0.222 0.294 0.912 1.1 0.00228 0.00274 +! Validation 238 7877.347 0.005 0.0555 1.96 3.07 0.214 0.281 1.6 1.67 0.00399 0.00419 +Wall time: 7877.347768886946 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 47 2.96 0.06 1.76 0.222 0.293 1.53 1.58 0.00383 0.00396 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 239 100 1.44 0.0536 0.367 0.211 0.277 0.674 0.724 0.00168 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 239 7910.357 0.005 0.0603 0.976 2.18 0.222 0.293 0.948 1.18 0.00237 0.00295 +! Validation 239 7910.357 0.005 0.0549 0.616 1.71 0.213 0.28 0.821 0.938 0.00205 0.00234 +Wall time: 7910.357358726207 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 47 1.42 0.0585 0.253 0.219 0.289 0.453 0.601 0.00113 0.0015 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 240 100 1.77 0.053 0.713 0.209 0.275 0.974 1.01 0.00243 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 240 7943.448 0.005 0.0596 0.796 1.99 0.221 0.292 0.9 1.07 0.00225 0.00267 +! Validation 240 7943.448 0.005 0.0542 0.571 1.66 0.211 0.278 0.796 0.903 0.00199 0.00226 +Wall time: 7943.448183929082 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 47 1.53 0.0581 0.366 0.217 0.288 0.618 0.723 0.00154 0.00181 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 241 100 1.39 0.0517 0.355 0.207 0.272 0.655 0.712 0.00164 0.00178 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 241 7976.484 0.005 0.0584 0.766 1.93 0.218 0.289 0.835 1.05 0.00209 0.00262 +! Validation 241 7976.484 0.005 0.0533 0.673 1.74 0.21 0.276 0.858 0.98 0.00215 0.00245 +Wall time: 7976.484639462084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 47 2.01 0.0586 0.839 0.218 0.289 1.01 1.09 0.00252 0.00274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 242 100 1.61 0.0535 0.537 0.21 0.276 0.834 0.876 0.00208 0.00219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 242 8009.518 0.005 0.0587 1.13 2.3 0.219 0.289 1.04 1.27 0.00261 0.00318 +! Validation 242 8009.518 0.005 0.055 1.09 2.19 0.213 0.28 1.12 1.25 0.00281 0.00312 +Wall time: 8009.51821145229 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 47 2.17 0.0571 1.03 0.216 0.286 1.16 1.21 0.00289 0.00303 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 243 100 1.84 0.0508 0.82 0.205 0.269 1.05 1.08 0.00262 0.00271 + + + 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 8042.554 0.005 0.0567 0.448 1.58 0.215 0.285 0.651 0.798 0.00163 0.002 +! Validation 243 8042.554 0.005 0.0516 1.3 2.33 0.206 0.271 1.27 1.36 0.00317 0.00341 +Wall time: 8042.553921530023 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 47 2.06 0.0538 0.985 0.211 0.277 1.12 1.19 0.00279 0.00296 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 244 100 2.42 0.0506 1.41 0.204 0.269 1.39 1.42 0.00348 0.00355 + + + 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 8076.412 0.005 0.0557 0.651 1.77 0.213 0.282 0.787 0.963 0.00197 0.00241 +! Validation 244 8076.412 0.005 0.0519 1.92 2.96 0.207 0.272 1.58 1.66 0.00396 0.00414 +Wall time: 8076.41213932028 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 47 1.38 0.0562 0.257 0.214 0.283 0.496 0.605 0.00124 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 245 100 1.28 0.0532 0.212 0.209 0.276 0.483 0.55 0.00121 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 245 8109.472 0.005 0.0596 1.56 2.75 0.221 0.292 1.25 1.49 0.00312 0.00374 +! Validation 245 8109.472 0.005 0.0551 0.469 1.57 0.213 0.28 0.678 0.818 0.00169 0.00205 +Wall time: 8109.472531608306 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 47 1.87 0.0625 0.618 0.227 0.299 0.86 0.939 0.00215 0.00235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 246 100 1.31 0.0561 0.186 0.214 0.283 0.439 0.515 0.0011 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 246 8142.467 0.005 0.0594 1.48 2.66 0.22 0.291 1.18 1.45 0.00294 0.00363 +! Validation 246 8142.467 0.005 0.0581 0.409 1.57 0.219 0.288 0.635 0.765 0.00159 0.00191 +Wall time: 8142.467255635187 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 47 1.18 0.0532 0.114 0.21 0.276 0.328 0.404 0.00082 0.00101 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 247 100 1.06 0.0495 0.0695 0.202 0.266 0.228 0.315 0.00057 0.000788 + + + 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 8175.469 0.005 0.0567 0.36 1.49 0.216 0.285 0.575 0.717 0.00144 0.00179 +! Validation 247 8175.469 0.005 0.0508 0.218 1.24 0.205 0.269 0.427 0.559 0.00107 0.0014 +Wall time: 8175.469222581014 +! Best model 247 1.235 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 47 1.41 0.0597 0.219 0.219 0.292 0.439 0.559 0.0011 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 248 100 1.88 0.0525 0.826 0.207 0.274 1.05 1.09 0.00262 0.00271 + + + 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 8208.474 0.005 0.0553 0.953 2.06 0.213 0.281 0.93 1.17 0.00233 0.00292 +! Validation 248 8208.474 0.005 0.0542 1.3 2.39 0.211 0.278 1.27 1.36 0.00317 0.00341 +Wall time: 8208.474862167146 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 47 1.32 0.056 0.197 0.213 0.283 0.401 0.53 0.001 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 249 100 1.11 0.0497 0.115 0.203 0.266 0.317 0.405 0.000793 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 249 8241.469 0.005 0.0563 0.713 1.84 0.214 0.284 0.835 1.01 0.00209 0.00252 +! Validation 249 8241.469 0.005 0.0507 0.32 1.33 0.205 0.269 0.541 0.676 0.00135 0.00169 +Wall time: 8241.469381087925 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 47 1.24 0.0536 0.171 0.209 0.277 0.397 0.494 0.000991 0.00123 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 250 100 1.11 0.0474 0.159 0.198 0.26 0.385 0.476 0.000962 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 250 8274.475 0.005 0.0539 0.467 1.54 0.21 0.277 0.648 0.817 0.00162 0.00204 +! Validation 250 8274.475 0.005 0.0481 0.394 1.36 0.199 0.262 0.608 0.75 0.00152 0.00188 +Wall time: 8274.475094137248 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 47 1.59 0.0593 0.402 0.219 0.291 0.624 0.758 0.00156 0.00189 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 251 100 1.17 0.0517 0.132 0.206 0.272 0.397 0.435 0.000993 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 251 8307.470 0.005 0.0619 2.02 3.26 0.225 0.297 1.32 1.7 0.00329 0.00425 +! Validation 251 8307.470 0.005 0.0529 0.286 1.34 0.209 0.275 0.536 0.639 0.00134 0.0016 +Wall time: 8307.470647772308 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 47 3.62 0.055 2.51 0.213 0.28 1.85 1.89 0.00463 0.00474 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 252 100 2.81 0.0507 1.8 0.204 0.269 1.58 1.6 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 252 8340.470 0.005 0.0564 0.966 2.09 0.215 0.284 0.997 1.17 0.00249 0.00293 +! Validation 252 8340.470 0.005 0.0522 1.46 2.5 0.207 0.273 1.36 1.44 0.00341 0.00361 +Wall time: 8340.469896940049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 47 3.85 0.0537 2.77 0.21 0.277 1.92 1.99 0.00481 0.00497 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 253 100 3.47 0.0481 2.51 0.199 0.262 1.88 1.89 0.0047 0.00473 + + + 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 8373.470 0.005 0.055 0.619 1.72 0.212 0.28 0.735 0.935 0.00184 0.00234 +! Validation 253 8373.470 0.005 0.049 2.22 3.2 0.201 0.264 1.72 1.78 0.0043 0.00445 +Wall time: 8373.470257593319 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 47 1.18 0.0532 0.113 0.206 0.276 0.315 0.402 0.000789 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 254 100 2.04 0.0489 1.06 0.2 0.264 1.21 1.23 0.00301 0.00308 + + + 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 8406.491 0.005 0.053 0.713 1.77 0.208 0.275 0.823 1.01 0.00206 0.00252 +! Validation 254 8406.491 0.005 0.0498 1.42 2.42 0.203 0.267 1.34 1.43 0.00336 0.00356 +Wall time: 8406.491842079908 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 47 3.11 0.0537 2.04 0.209 0.277 1.58 1.71 0.00396 0.00426 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 255 100 1.37 0.0489 0.392 0.2 0.264 0.704 0.748 0.00176 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 255 8439.496 0.005 0.0531 0.902 1.96 0.208 0.275 0.942 1.13 0.00235 0.00283 +! Validation 255 8439.496 0.005 0.0506 0.739 1.75 0.204 0.269 0.914 1.03 0.00229 0.00257 +Wall time: 8439.49601554824 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 47 2.36 0.0512 1.33 0.204 0.27 1.19 1.38 0.00299 0.00345 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 256 100 3.4 0.0468 2.46 0.196 0.259 1.86 1.87 0.00464 0.00469 + + + 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 8472.502 0.005 0.0529 0.678 1.74 0.208 0.275 0.789 0.983 0.00197 0.00246 +! Validation 256 8472.502 0.005 0.048 2.79 3.75 0.199 0.262 1.93 1.99 0.00484 0.00499 +Wall time: 8472.502041957341 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 47 1.59 0.0528 0.536 0.208 0.275 0.764 0.875 0.00191 0.00219 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 257 100 1.11 0.0483 0.14 0.2 0.263 0.374 0.447 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 257 8505.508 0.005 0.0535 0.993 2.06 0.209 0.276 0.953 1.19 0.00238 0.00298 +! Validation 257 8505.508 0.005 0.0491 0.351 1.33 0.201 0.265 0.574 0.708 0.00144 0.00177 +Wall time: 8505.50858748518 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 258 47 1.56 0.0482 0.597 0.198 0.262 0.818 0.923 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 + 258 100 2.03 0.0451 1.13 0.192 0.254 1.24 1.27 0.0031 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 258 8538.618 0.005 0.0505 0.459 1.47 0.203 0.269 0.644 0.809 0.00161 0.00202 +! Validation 258 8538.618 0.005 0.0465 0.911 1.84 0.195 0.258 0.99 1.14 0.00248 0.00285 +Wall time: 8538.61838446837 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 259 47 1.36 0.0496 0.364 0.2 0.266 0.639 0.721 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 + 259 100 2.06 0.0443 1.17 0.19 0.252 1.26 1.29 0.00316 0.00323 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 259 8571.664 0.005 0.0493 0.388 1.37 0.201 0.265 0.595 0.744 0.00149 0.00186 +! Validation 259 8571.664 0.005 0.0454 1.02 1.92 0.193 0.255 1.12 1.2 0.0028 0.00301 +Wall time: 8571.664757085033 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 47 2.17 0.0509 1.15 0.205 0.27 1.17 1.28 0.00293 0.0032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 260 100 1.22 0.0469 0.277 0.197 0.259 0.587 0.629 0.00147 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 260 8604.707 0.005 0.0511 0.968 1.99 0.204 0.27 0.986 1.18 0.00246 0.00294 +! Validation 260 8604.707 0.005 0.0474 0.471 1.42 0.198 0.26 0.692 0.82 0.00173 0.00205 +Wall time: 8604.707352465019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 47 2.45 0.0552 1.34 0.212 0.281 1.23 1.38 0.00308 0.00346 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 261 100 1.1 0.0519 0.0611 0.205 0.272 0.284 0.295 0.00071 0.000738 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 261 8637.732 0.005 0.051 1.15 2.17 0.204 0.27 1.03 1.28 0.00258 0.0032 +! Validation 261 8637.732 0.005 0.0529 0.391 1.45 0.208 0.275 0.625 0.748 0.00156 0.00187 +Wall time: 8637.73217807617 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 47 1.3 0.0519 0.26 0.206 0.272 0.478 0.61 0.00119 0.00152 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 262 100 1.17 0.0443 0.286 0.19 0.252 0.586 0.639 0.00147 0.0016 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 262 8670.845 0.005 0.0519 0.696 1.73 0.206 0.272 0.803 0.998 0.00201 0.00249 +! Validation 262 8670.845 0.005 0.0455 0.529 1.44 0.193 0.255 0.752 0.869 0.00188 0.00217 +Wall time: 8670.845224190038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 47 1.42 0.0479 0.466 0.197 0.262 0.722 0.816 0.00181 0.00204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 263 100 0.917 0.0436 0.0444 0.189 0.25 0.199 0.252 0.000499 0.00063 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 263 8703.882 0.005 0.048 0.469 1.43 0.198 0.262 0.688 0.818 0.00172 0.00205 +! Validation 263 8703.882 0.005 0.0446 0.186 1.08 0.191 0.252 0.4 0.515 0.001 0.00129 +Wall time: 8703.882089599967 +! Best model 263 1.078 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 47 2.13 0.0516 1.1 0.206 0.271 1.16 1.25 0.0029 0.00313 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 264 100 1.97 0.0457 1.06 0.194 0.255 1.21 1.23 0.00302 0.00307 + + + 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 8736.939 0.005 0.0504 1.04 2.05 0.203 0.268 0.99 1.22 0.00248 0.00304 +! Validation 264 8736.939 0.005 0.0462 0.915 1.84 0.195 0.257 1.04 1.14 0.00261 0.00286 +Wall time: 8736.93896981515 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 47 1.15 0.048 0.189 0.197 0.262 0.412 0.519 0.00103 0.0013 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 265 100 1.2 0.0429 0.345 0.187 0.248 0.651 0.702 0.00163 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 265 8770.005 0.005 0.0481 0.517 1.48 0.198 0.262 0.706 0.86 0.00177 0.00215 +! Validation 265 8770.005 0.005 0.0441 0.671 1.55 0.19 0.251 0.859 0.979 0.00215 0.00245 +Wall time: 8770.005104570184 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 47 2.44 0.0447 1.55 0.191 0.253 1.42 1.49 0.00356 0.00372 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 266 100 1.36 0.0413 0.532 0.183 0.243 0.833 0.872 0.00208 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 266 8803.027 0.005 0.0467 0.413 1.35 0.195 0.258 0.623 0.765 0.00156 0.00191 +! Validation 266 8803.027 0.005 0.0423 0.911 1.76 0.186 0.246 1.03 1.14 0.00257 0.00285 +Wall time: 8803.027073869016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 47 1.56 0.046 0.64 0.193 0.256 0.884 0.956 0.00221 0.00239 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 267 100 1.61 0.0435 0.744 0.188 0.249 0.998 1.03 0.0025 0.00258 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 267 8836.117 0.005 0.0511 1.02 2.04 0.204 0.27 0.938 1.21 0.00234 0.00302 +! Validation 267 8836.117 0.005 0.0445 0.649 1.54 0.191 0.252 0.859 0.963 0.00215 0.00241 +Wall time: 8836.117653056048 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 47 1.05 0.0458 0.14 0.193 0.256 0.353 0.446 0.000882 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 268 100 0.965 0.0425 0.116 0.186 0.246 0.325 0.407 0.000812 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 268 8869.163 0.005 0.0468 0.583 1.52 0.195 0.259 0.752 0.913 0.00188 0.00228 +! Validation 268 8869.163 0.005 0.0436 0.334 1.21 0.189 0.249 0.561 0.691 0.0014 0.00173 +Wall time: 8869.16290545091 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 47 1.87 0.0481 0.909 0.198 0.262 1.03 1.14 0.00257 0.00285 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 269 100 1.07 0.0427 0.217 0.186 0.247 0.505 0.557 0.00126 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 269 8902.208 0.005 0.0468 0.749 1.69 0.195 0.259 0.858 1.03 0.00214 0.00259 +! Validation 269 8902.208 0.005 0.0435 0.392 1.26 0.189 0.249 0.624 0.748 0.00156 0.00187 +Wall time: 8902.208553641103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 47 1.53 0.0474 0.58 0.196 0.26 0.826 0.91 0.00207 0.00228 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 270 100 0.949 0.0413 0.122 0.184 0.243 0.383 0.417 0.000959 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 270 8935.250 0.005 0.0463 0.612 1.54 0.194 0.257 0.763 0.935 0.00191 0.00234 +! Validation 270 8935.250 0.005 0.0423 0.174 1.02 0.186 0.246 0.405 0.498 0.00101 0.00125 +Wall time: 8935.250689251348 +! Best model 270 1.019 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 47 1.05 0.0451 0.153 0.191 0.254 0.421 0.467 0.00105 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 271 100 0.865 0.0407 0.0524 0.182 0.241 0.215 0.273 0.000537 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 271 8968.293 0.005 0.0461 0.621 1.54 0.194 0.257 0.76 0.943 0.0019 0.00236 +! Validation 271 8968.293 0.005 0.0416 0.2 1.03 0.185 0.244 0.42 0.535 0.00105 0.00134 +Wall time: 8968.293100973126 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 47 1.15 0.0454 0.248 0.192 0.255 0.509 0.595 0.00127 0.00149 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 272 100 1.03 0.0423 0.185 0.185 0.246 0.451 0.514 0.00113 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 272 9001.330 0.005 0.0452 0.6 1.5 0.192 0.254 0.726 0.926 0.00182 0.00232 +! Validation 272 9001.330 0.005 0.0435 0.553 1.42 0.189 0.249 0.737 0.889 0.00184 0.00222 +Wall time: 9001.330702669919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 47 1.52 0.0465 0.589 0.195 0.258 0.783 0.917 0.00196 0.00229 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 273 100 1.56 0.0425 0.706 0.186 0.246 0.974 1 0.00243 0.00251 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 273 9034.382 0.005 0.0456 0.668 1.58 0.193 0.255 0.776 0.977 0.00194 0.00244 +! Validation 273 9034.382 0.005 0.0435 0.532 1.4 0.189 0.249 0.755 0.872 0.00189 0.00218 +Wall time: 9034.382737044245 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 47 1.13 0.0457 0.214 0.192 0.255 0.44 0.553 0.0011 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 274 100 0.955 0.039 0.175 0.178 0.236 0.44 0.499 0.0011 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 274 9067.751 0.005 0.0445 0.425 1.31 0.19 0.252 0.631 0.779 0.00158 0.00195 +! Validation 274 9067.751 0.005 0.0397 0.225 1.02 0.18 0.238 0.461 0.567 0.00115 0.00142 +Wall time: 9067.751118581276 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 47 1.03 0.0431 0.172 0.187 0.248 0.438 0.496 0.0011 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 275 100 0.871 0.0384 0.104 0.177 0.234 0.3 0.385 0.000749 0.000961 + + + 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 9100.786 0.005 0.0434 0.407 1.27 0.188 0.249 0.599 0.763 0.0015 0.00191 +! Validation 275 9100.786 0.005 0.0391 0.267 1.05 0.179 0.236 0.489 0.617 0.00122 0.00154 +Wall time: 9100.786294837017 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 47 1.13 0.0444 0.243 0.189 0.252 0.519 0.589 0.0013 0.00147 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 276 100 1.12 0.0424 0.277 0.186 0.246 0.588 0.628 0.00147 0.00157 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 276 9133.818 0.005 0.0516 1.47 2.5 0.204 0.271 1.11 1.45 0.00278 0.00362 +! Validation 276 9133.818 0.005 0.0428 0.501 1.36 0.187 0.247 0.727 0.846 0.00182 0.00211 +Wall time: 9133.81884396309 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 47 0.958 0.0433 0.092 0.188 0.249 0.284 0.363 0.00071 0.000906 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 277 100 0.975 0.0382 0.211 0.176 0.234 0.49 0.549 0.00123 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 277 9166.857 0.005 0.0434 0.28 1.15 0.188 0.249 0.517 0.633 0.00129 0.00158 +! Validation 277 9166.857 0.005 0.0389 0.221 0.998 0.178 0.236 0.47 0.561 0.00117 0.0014 +Wall time: 9166.857793821022 +! Best model 277 0.998 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 47 1.38 0.0468 0.441 0.195 0.258 0.654 0.793 0.00164 0.00198 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 278 100 2.68 0.0396 1.88 0.179 0.238 1.62 1.64 0.00406 0.0041 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 278 9199.907 0.005 0.0439 0.642 1.52 0.189 0.25 0.77 0.958 0.00192 0.00239 +! Validation 278 9199.907 0.005 0.0406 1.5 2.31 0.182 0.241 1.39 1.46 0.00347 0.00366 +Wall time: 9199.90715539828 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 47 1.05 0.0455 0.138 0.192 0.255 0.363 0.444 0.000907 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 279 100 1.09 0.0393 0.306 0.179 0.237 0.619 0.661 0.00155 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 279 9232.933 0.005 0.0441 0.781 1.66 0.189 0.251 0.876 1.06 0.00219 0.00264 +! Validation 279 9232.933 0.005 0.0398 0.339 1.13 0.18 0.238 0.601 0.696 0.0015 0.00174 +Wall time: 9232.93303282233 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 47 1.03 0.0435 0.164 0.189 0.249 0.381 0.484 0.000953 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 280 100 0.889 0.0403 0.0837 0.181 0.24 0.281 0.346 0.000702 0.000864 + + + 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 9265.960 0.005 0.0438 0.849 1.73 0.189 0.25 0.914 1.1 0.00228 0.00276 +! Validation 280 9265.960 0.005 0.0409 0.228 1.05 0.183 0.242 0.463 0.57 0.00116 0.00143 +Wall time: 9265.960863959044 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 47 1.76 0.0434 0.891 0.188 0.249 0.945 1.13 0.00236 0.00282 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 281 100 1.19 0.0415 0.365 0.183 0.243 0.682 0.722 0.0017 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 281 9299.245 0.005 0.0435 0.639 1.51 0.188 0.249 0.782 0.955 0.00196 0.00239 +! Validation 281 9299.245 0.005 0.0418 0.376 1.21 0.185 0.244 0.595 0.732 0.00149 0.00183 +Wall time: 9299.2454304751 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 47 1.56 0.0408 0.743 0.183 0.241 0.985 1.03 0.00246 0.00258 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 282 100 1.32 0.0371 0.575 0.174 0.23 0.872 0.906 0.00218 0.00227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 282 9332.267 0.005 0.0425 0.475 1.32 0.186 0.246 0.667 0.823 0.00167 0.00206 +! Validation 282 9332.267 0.005 0.038 1.03 1.79 0.176 0.233 1.09 1.22 0.00273 0.00304 +Wall time: 9332.267214575317 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 47 1.18 0.043 0.323 0.187 0.248 0.601 0.679 0.0015 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 283 100 1.39 0.0404 0.584 0.181 0.24 0.888 0.913 0.00222 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 283 9365.322 0.005 0.0414 0.631 1.46 0.183 0.243 0.773 0.95 0.00193 0.00237 +! Validation 283 9365.322 0.005 0.0413 0.553 1.38 0.184 0.243 0.79 0.889 0.00197 0.00222 +Wall time: 9365.322006264236 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 47 2.68 0.0488 1.7 0.199 0.264 1.49 1.56 0.00372 0.0039 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 284 100 1.07 0.0441 0.186 0.188 0.251 0.453 0.515 0.00113 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 284 9398.351 0.005 0.0423 0.875 1.72 0.185 0.246 0.882 1.12 0.0022 0.00279 +! Validation 284 9398.351 0.005 0.0454 0.204 1.11 0.192 0.255 0.437 0.539 0.00109 0.00135 +Wall time: 9398.351208101958 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 47 0.999 0.0421 0.157 0.185 0.245 0.374 0.474 0.000936 0.00118 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 285 100 0.838 0.037 0.0991 0.173 0.23 0.278 0.376 0.000696 0.000941 + + + 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 9431.569 0.005 0.0431 0.425 1.29 0.187 0.248 0.628 0.78 0.00157 0.00195 +! Validation 285 9431.569 0.005 0.0378 0.206 0.961 0.176 0.232 0.428 0.542 0.00107 0.00135 +Wall time: 9431.569464253262 +! Best model 285 0.961 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 47 0.961 0.0418 0.125 0.184 0.244 0.365 0.423 0.000913 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 286 100 1.12 0.0358 0.403 0.17 0.226 0.728 0.759 0.00182 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 286 9464.591 0.005 0.0401 0.365 1.17 0.18 0.239 0.589 0.723 0.00147 0.00181 +! Validation 286 9464.591 0.005 0.0367 0.598 1.33 0.173 0.229 0.823 0.924 0.00206 0.00231 +Wall time: 9464.591552377213 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 47 1.1 0.0399 0.301 0.181 0.239 0.532 0.656 0.00133 0.00164 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 287 100 0.916 0.0383 0.149 0.177 0.234 0.414 0.461 0.00103 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 287 9497.591 0.005 0.041 0.64 1.46 0.182 0.242 0.755 0.957 0.00189 0.00239 +! Validation 287 9497.591 0.005 0.0382 0.236 1 0.177 0.234 0.478 0.58 0.0012 0.00145 +Wall time: 9497.591026918031 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 47 1.08 0.0409 0.264 0.182 0.242 0.509 0.614 0.00127 0.00153 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 288 100 1.3 0.0367 0.561 0.173 0.229 0.866 0.895 0.00217 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 288 9530.589 0.005 0.0412 0.652 1.47 0.183 0.242 0.777 0.965 0.00194 0.00241 +! Validation 288 9530.589 0.005 0.0371 0.521 1.26 0.174 0.23 0.768 0.862 0.00192 0.00216 +Wall time: 9530.589085178915 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 47 1.15 0.047 0.206 0.195 0.259 0.406 0.543 0.00101 0.00136 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 289 100 2.77 0.0441 1.89 0.188 0.251 1.63 1.64 0.00407 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 289 9563.599 0.005 0.0401 0.677 1.48 0.18 0.239 0.755 0.984 0.00189 0.00246 +! Validation 289 9563.599 0.005 0.0448 2.2 3.09 0.191 0.253 1.7 1.77 0.00426 0.00443 +Wall time: 9563.598996005952 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 47 0.941 0.0391 0.158 0.178 0.236 0.38 0.476 0.000951 0.00119 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 290 100 0.978 0.0368 0.242 0.173 0.229 0.553 0.588 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 290 9596.602 0.005 0.0419 0.684 1.52 0.184 0.245 0.817 0.989 0.00204 0.00247 +! Validation 290 9596.602 0.005 0.0371 0.423 1.17 0.174 0.23 0.652 0.778 0.00163 0.00194 +Wall time: 9596.602669259068 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 47 1.91 0.0415 1.08 0.183 0.244 1.19 1.24 0.00296 0.00311 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 291 100 0.805 0.0381 0.0427 0.176 0.233 0.17 0.247 0.000425 0.000617 + + + 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 9629.608 0.005 0.0403 0.725 1.53 0.181 0.24 0.855 1.02 0.00214 0.00254 +! Validation 291 9629.608 0.005 0.0388 0.222 0.997 0.177 0.235 0.449 0.563 0.00112 0.00141 +Wall time: 9629.608657554258 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 47 0.971 0.0398 0.175 0.179 0.238 0.387 0.5 0.000967 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 292 100 0.794 0.0364 0.066 0.172 0.228 0.216 0.307 0.000539 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 292 9662.611 0.005 0.0408 0.643 1.46 0.182 0.241 0.792 0.959 0.00198 0.0024 +! Validation 292 9662.611 0.005 0.037 0.247 0.988 0.173 0.23 0.471 0.594 0.00118 0.00149 +Wall time: 9662.611849063076 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 47 2.08 0.0352 1.37 0.169 0.224 1.36 1.4 0.00341 0.0035 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 293 100 0.917 0.0348 0.221 0.169 0.223 0.511 0.561 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 293 9695.609 0.005 0.0388 0.386 1.16 0.177 0.235 0.585 0.74 0.00146 0.00185 +! Validation 293 9695.609 0.005 0.0352 0.49 1.19 0.169 0.224 0.695 0.837 0.00174 0.00209 +Wall time: 9695.609395275358 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 47 1.5 0.0364 0.772 0.172 0.228 0.979 1.05 0.00245 0.00263 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 294 100 1.25 0.0335 0.577 0.165 0.219 0.876 0.908 0.00219 0.00227 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 294 9728.630 0.005 0.0384 0.362 1.13 0.176 0.234 0.559 0.718 0.0014 0.0018 +! Validation 294 9728.630 0.005 0.0341 0.528 1.21 0.167 0.221 0.766 0.868 0.00191 0.00217 +Wall time: 9728.63083096221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 47 1.47 0.0415 0.642 0.183 0.244 0.865 0.958 0.00216 0.00239 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 295 100 0.765 0.0351 0.0636 0.168 0.224 0.283 0.301 0.000707 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 295 9761.637 0.005 0.0381 0.535 1.3 0.175 0.233 0.714 0.874 0.00179 0.00218 +! Validation 295 9761.637 0.005 0.0358 0.139 0.855 0.17 0.226 0.36 0.445 0.000901 0.00111 +Wall time: 9761.63721230207 +! Best model 295 0.855 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 47 0.87 0.0376 0.117 0.174 0.232 0.306 0.409 0.000766 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 296 100 0.721 0.0338 0.0453 0.166 0.22 0.225 0.254 0.000563 0.000636 + + + 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 9794.749 0.005 0.0379 0.467 1.22 0.175 0.233 0.667 0.817 0.00167 0.00204 +! Validation 296 9794.749 0.005 0.0342 0.135 0.819 0.167 0.221 0.35 0.438 0.000876 0.0011 +Wall time: 9794.749564802274 +! Best model 296 0.819 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 47 1.04 0.0359 0.325 0.171 0.226 0.585 0.681 0.00146 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 297 100 0.745 0.0334 0.0776 0.165 0.218 0.305 0.333 0.000763 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 297 9827.795 0.005 0.0379 0.496 1.25 0.175 0.233 0.682 0.842 0.0017 0.0021 +! Validation 297 9827.795 0.005 0.0337 0.147 0.821 0.166 0.219 0.372 0.458 0.000931 0.00115 +Wall time: 9827.795849345159 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 47 1.03 0.0357 0.318 0.17 0.226 0.579 0.673 0.00145 0.00168 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 298 100 1.12 0.0333 0.454 0.165 0.218 0.776 0.805 0.00194 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 298 9860.820 0.005 0.0365 0.396 1.13 0.172 0.228 0.618 0.753 0.00155 0.00188 +! Validation 298 9860.820 0.005 0.0339 0.706 1.38 0.166 0.22 0.901 1 0.00225 0.00251 +Wall time: 9860.82085099211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 47 0.867 0.0382 0.103 0.176 0.234 0.316 0.383 0.000791 0.000958 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 299 100 1.47 0.0374 0.718 0.174 0.231 0.985 1.01 0.00246 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 299 9894.257 0.005 0.0369 0.594 1.33 0.173 0.23 0.756 0.922 0.00189 0.00231 +! Validation 299 9894.257 0.005 0.0379 1.04 1.8 0.176 0.233 1.13 1.22 0.00284 0.00305 +Wall time: 9894.257064437028 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 47 1.01 0.0396 0.221 0.178 0.238 0.432 0.562 0.00108 0.00141 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 300 100 0.714 0.0335 0.0444 0.165 0.219 0.202 0.252 0.000506 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 300 9927.287 0.005 0.039 0.796 1.58 0.178 0.236 0.873 1.07 0.00218 0.00267 +! Validation 300 9927.287 0.005 0.0341 0.128 0.81 0.166 0.221 0.337 0.428 0.000843 0.00107 +Wall time: 9927.28695819294 +! Best model 300 0.810 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 47 0.814 0.0344 0.125 0.167 0.222 0.333 0.422 0.000832 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 301 100 0.751 0.0335 0.0813 0.165 0.219 0.248 0.341 0.000619 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 301 9960.334 0.005 0.0375 0.559 1.31 0.174 0.232 0.73 0.894 0.00183 0.00224 +! Validation 301 9960.334 0.005 0.0341 0.258 0.94 0.166 0.221 0.487 0.607 0.00122 0.00152 +Wall time: 9960.334162860177 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 47 1.21 0.0375 0.456 0.174 0.231 0.744 0.807 0.00186 0.00202 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 302 100 1.15 0.0347 0.46 0.167 0.223 0.781 0.81 0.00195 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 302 9993.370 0.005 0.0364 0.447 1.18 0.171 0.228 0.623 0.799 0.00156 0.002 +! Validation 302 9993.370 0.005 0.0355 0.662 1.37 0.17 0.225 0.867 0.973 0.00217 0.00243 +Wall time: 9993.370710635092 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 47 1.35 0.0355 0.636 0.17 0.225 0.839 0.953 0.0021 0.00238 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 303 100 0.871 0.0334 0.202 0.165 0.219 0.473 0.538 0.00118 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 303 10027.866 0.005 0.0363 0.387 1.11 0.171 0.228 0.602 0.742 0.00151 0.00186 +! Validation 303 10027.866 0.005 0.0342 0.27 0.955 0.167 0.221 0.504 0.621 0.00126 0.00155 +Wall time: 10027.866564522963 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 47 0.94 0.0356 0.228 0.169 0.226 0.468 0.57 0.00117 0.00143 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 304 100 0.685 0.032 0.0441 0.162 0.214 0.213 0.251 0.000533 0.000627 + + + 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 10060.904 0.005 0.0364 0.506 1.23 0.172 0.228 0.688 0.851 0.00172 0.00213 +! Validation 304 10060.904 0.005 0.0324 0.133 0.781 0.162 0.215 0.344 0.435 0.000861 0.00109 +Wall time: 10060.904010440223 +! Best model 304 0.781 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 47 0.772 0.0351 0.0697 0.169 0.224 0.247 0.316 0.000617 0.000789 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 305 100 0.669 0.0315 0.0387 0.161 0.212 0.191 0.235 0.000478 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 305 10093.950 0.005 0.0352 0.449 1.15 0.169 0.224 0.632 0.801 0.00158 0.002 +! Validation 305 10093.950 0.005 0.0323 0.141 0.786 0.162 0.215 0.353 0.448 0.000882 0.00112 +Wall time: 10093.950309761334 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 306 47 1.09 0.0375 0.338 0.173 0.231 0.616 0.694 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 + 306 100 1.29 0.0343 0.608 0.167 0.221 0.883 0.932 0.00221 0.00233 + + + 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 10126.991 0.005 0.0355 0.616 1.33 0.169 0.225 0.762 0.939 0.0019 0.00235 +! Validation 306 10126.991 0.005 0.0354 0.623 1.33 0.17 0.225 0.845 0.943 0.00211 0.00236 +Wall time: 10126.991302281152 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 47 1.87 0.0356 1.16 0.169 0.225 1.23 1.29 0.00308 0.00322 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 307 100 2 0.0308 1.38 0.158 0.21 1.39 1.4 0.00347 0.00351 + + + 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 10160.014 0.005 0.0356 0.426 1.14 0.169 0.226 0.617 0.778 0.00154 0.00194 +! Validation 307 10160.014 0.005 0.0313 1.24 1.87 0.159 0.211 1.22 1.33 0.00305 0.00333 +Wall time: 10160.013910238165 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 47 2.1 0.0348 1.4 0.167 0.223 1.38 1.41 0.00345 0.00354 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 308 100 0.711 0.0332 0.0467 0.163 0.218 0.205 0.258 0.000511 0.000646 + + + 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 10193.036 0.005 0.0355 0.655 1.36 0.169 0.225 0.813 0.965 0.00203 0.00241 +! Validation 308 10193.036 0.005 0.0339 0.172 0.849 0.166 0.22 0.397 0.495 0.000993 0.00124 +Wall time: 10193.036556025967 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 47 1.07 0.0381 0.305 0.176 0.233 0.533 0.66 0.00133 0.00165 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 309 100 1.24 0.0343 0.554 0.168 0.221 0.852 0.89 0.00213 0.00222 + + + 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 10226.060 0.005 0.0362 0.484 1.21 0.171 0.227 0.674 0.832 0.00168 0.00208 +! Validation 309 10226.060 0.005 0.0356 0.575 1.29 0.171 0.226 0.811 0.906 0.00203 0.00227 +Wall time: 10226.060841172934 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 47 0.873 0.0349 0.174 0.167 0.223 0.406 0.499 0.00101 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 310 100 0.657 0.0308 0.0412 0.159 0.21 0.221 0.243 0.000552 0.000606 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 310 10259.086 0.005 0.035 0.43 1.13 0.168 0.224 0.643 0.784 0.00161 0.00196 +! Validation 310 10259.086 0.005 0.0311 0.14 0.761 0.158 0.211 0.362 0.447 0.000905 0.00112 +Wall time: 10259.086449583992 +! Best model 310 0.761 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 47 0.834 0.0319 0.197 0.16 0.213 0.427 0.531 0.00107 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 311 100 0.643 0.0309 0.0244 0.159 0.21 0.163 0.187 0.000408 0.000467 + + + 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 10292.128 0.005 0.0334 0.252 0.92 0.164 0.218 0.479 0.6 0.0012 0.0015 +! Validation 311 10292.128 0.005 0.031 0.123 0.742 0.158 0.21 0.338 0.419 0.000845 0.00105 +Wall time: 10292.127883736975 +! Best model 311 0.742 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 47 2.89 0.0353 2.18 0.168 0.224 1.62 1.77 0.00406 0.00441 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 312 100 2.27 0.0316 1.63 0.16 0.213 1.51 1.53 0.00378 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 312 10325.164 0.005 0.033 0.383 1.04 0.163 0.217 0.577 0.735 0.00144 0.00184 +! Validation 312 10325.164 0.005 0.0321 1.54 2.19 0.161 0.214 1.42 1.48 0.00355 0.00371 +Wall time: 10325.16476660315 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 47 1.76 0.0384 0.988 0.177 0.234 1.02 1.19 0.00255 0.00297 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 313 100 0.776 0.0365 0.046 0.175 0.228 0.207 0.256 0.000517 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 313 10358.198 0.005 0.0494 1.38 2.37 0.199 0.266 1.15 1.41 0.00286 0.00352 +! Validation 313 10358.198 0.005 0.0365 0.144 0.874 0.175 0.228 0.366 0.453 0.000915 0.00113 +Wall time: 10358.198101663962 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 47 1.57 0.0362 0.843 0.171 0.227 1 1.1 0.0025 0.00274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 314 100 0.772 0.0315 0.141 0.16 0.212 0.397 0.449 0.000991 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 314 10391.229 0.005 0.0359 0.422 1.14 0.17 0.226 0.636 0.775 0.00159 0.00194 +! Validation 314 10391.229 0.005 0.0322 0.214 0.858 0.162 0.214 0.452 0.553 0.00113 0.00138 +Wall time: 10391.229105921928 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 47 1.51 0.034 0.825 0.165 0.22 1.05 1.09 0.00262 0.00271 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 315 100 1.52 0.0314 0.891 0.159 0.212 1.11 1.13 0.00278 0.00282 + + + 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 10424.253 0.005 0.0338 0.374 1.05 0.165 0.22 0.594 0.73 0.00149 0.00182 +! Validation 315 10424.253 0.005 0.0317 1 1.63 0.16 0.213 1.12 1.2 0.00281 0.00299 +Wall time: 10424.253593541216 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 47 1.11 0.0321 0.467 0.161 0.214 0.651 0.816 0.00163 0.00204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 316 100 2.75 0.0337 2.07 0.166 0.219 1.71 1.72 0.00428 0.0043 + + + 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 10457.369 0.005 0.0335 0.476 1.15 0.164 0.219 0.667 0.825 0.00167 0.00206 +! Validation 316 10457.369 0.005 0.0336 2.33 3.01 0.165 0.219 1.77 1.83 0.00442 0.00456 +Wall time: 10457.36954827793 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 317 47 0.904 0.0333 0.239 0.163 0.218 0.449 0.584 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 + 317 100 0.748 0.0301 0.146 0.156 0.207 0.41 0.456 0.00102 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 317 10490.742 0.005 0.0372 0.599 1.34 0.174 0.23 0.713 0.926 0.00178 0.00231 +! Validation 317 10490.742 0.005 0.0304 0.29 0.899 0.157 0.209 0.528 0.644 0.00132 0.00161 +Wall time: 10490.74225228792 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 47 1.55 0.0357 0.84 0.169 0.226 1.04 1.1 0.0026 0.00274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 318 100 0.664 0.0315 0.0339 0.159 0.212 0.202 0.22 0.000505 0.00055 + + + 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 10523.758 0.005 0.0343 0.585 1.27 0.166 0.221 0.767 0.913 0.00192 0.00228 +! Validation 318 10523.758 0.005 0.0321 0.122 0.765 0.161 0.214 0.334 0.418 0.000835 0.00104 +Wall time: 10523.758808201179 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 47 0.804 0.0328 0.148 0.162 0.217 0.379 0.459 0.000948 0.00115 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 319 100 0.644 0.0291 0.0622 0.153 0.204 0.226 0.298 0.000566 0.000745 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 319 10556.778 0.005 0.0333 0.326 0.993 0.163 0.218 0.552 0.683 0.00138 0.00171 +! Validation 319 10556.778 0.005 0.0295 0.237 0.828 0.154 0.205 0.469 0.582 0.00117 0.00146 +Wall time: 10556.77799122408 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 47 0.807 0.0322 0.164 0.161 0.214 0.413 0.484 0.00103 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 320 100 0.898 0.03 0.298 0.156 0.207 0.627 0.652 0.00157 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 320 10589.919 0.005 0.0321 0.328 0.97 0.16 0.214 0.559 0.684 0.0014 0.00171 +! Validation 320 10589.919 0.005 0.0303 0.603 1.21 0.156 0.208 0.79 0.928 0.00197 0.00232 +Wall time: 10589.919635951053 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 47 0.674 0.0306 0.0607 0.157 0.209 0.238 0.294 0.000596 0.000736 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 321 100 0.654 0.0278 0.0984 0.15 0.199 0.326 0.375 0.000814 0.000937 + + + 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 10622.931 0.005 0.0319 0.289 0.927 0.16 0.213 0.511 0.644 0.00128 0.00161 +! Validation 321 10622.931 0.005 0.0282 0.192 0.757 0.151 0.201 0.443 0.524 0.00111 0.00131 +Wall time: 10622.931498269085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 47 1.06 0.0333 0.39 0.163 0.218 0.583 0.746 0.00146 0.00187 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 322 100 0.811 0.0312 0.188 0.16 0.211 0.482 0.519 0.0012 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 322 10655.937 0.005 0.0313 0.423 1.05 0.159 0.212 0.651 0.777 0.00163 0.00194 +! Validation 322 10655.937 0.005 0.031 0.285 0.905 0.159 0.21 0.525 0.638 0.00131 0.0016 +Wall time: 10655.937721581198 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 47 0.698 0.0309 0.0804 0.157 0.21 0.279 0.339 0.000696 0.000847 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 323 100 0.584 0.0279 0.0258 0.151 0.2 0.176 0.192 0.000441 0.00048 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 323 10688.970 0.005 0.0318 0.371 1.01 0.16 0.213 0.587 0.729 0.00147 0.00182 +! Validation 323 10688.970 0.005 0.0283 0.11 0.675 0.151 0.201 0.317 0.395 0.000794 0.000989 +Wall time: 10688.970381269231 +! Best model 323 0.675 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 47 1.13 0.0309 0.517 0.157 0.21 0.73 0.86 0.00183 0.00215 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 324 100 0.703 0.0276 0.15 0.15 0.199 0.426 0.463 0.00106 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 324 10722.004 0.005 0.0311 0.318 0.94 0.158 0.211 0.548 0.673 0.00137 0.00168 +! Validation 324 10722.004 0.005 0.0278 0.21 0.767 0.15 0.199 0.456 0.547 0.00114 0.00137 +Wall time: 10722.004269771278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 47 1.07 0.0332 0.404 0.163 0.218 0.707 0.76 0.00177 0.0019 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 325 100 0.599 0.0282 0.0345 0.151 0.201 0.202 0.222 0.000505 0.000555 + + + 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 10755.025 0.005 0.0309 0.44 1.06 0.157 0.21 0.667 0.793 0.00167 0.00198 +! Validation 325 10755.025 0.005 0.0288 0.126 0.702 0.152 0.203 0.344 0.423 0.000859 0.00106 +Wall time: 10755.025566735305 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 47 0.692 0.0295 0.103 0.153 0.205 0.305 0.383 0.000762 0.000958 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 326 100 0.579 0.0269 0.0419 0.148 0.196 0.217 0.245 0.000543 0.000611 + + + 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 10788.040 0.005 0.0303 0.225 0.83 0.156 0.208 0.453 0.567 0.00113 0.00142 +! Validation 326 10788.040 0.005 0.0271 0.126 0.668 0.148 0.197 0.343 0.425 0.000859 0.00106 +Wall time: 10788.04071759805 +! Best model 326 0.668 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 47 0.846 0.0317 0.212 0.16 0.213 0.455 0.55 0.00114 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 327 100 1.41 0.0282 0.843 0.152 0.201 1.07 1.1 0.00267 0.00274 + + + 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 10821.169 0.005 0.0305 0.459 1.07 0.157 0.209 0.682 0.81 0.0017 0.00203 +! Validation 327 10821.169 0.005 0.0288 0.79 1.37 0.153 0.203 0.984 1.06 0.00246 0.00265 +Wall time: 10821.168956961948 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 47 0.739 0.0303 0.133 0.156 0.208 0.344 0.435 0.00086 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 328 100 0.644 0.027 0.103 0.148 0.197 0.327 0.384 0.000817 0.00096 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 328 10854.182 0.005 0.0303 0.358 0.965 0.156 0.208 0.593 0.716 0.00148 0.00179 +! Validation 328 10854.182 0.005 0.0274 0.173 0.721 0.149 0.198 0.411 0.497 0.00103 0.00124 +Wall time: 10854.181970156264 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 47 0.669 0.0289 0.0921 0.152 0.203 0.291 0.363 0.000726 0.000907 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 329 100 1.01 0.0273 0.463 0.149 0.197 0.794 0.813 0.00198 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 329 10887.169 0.005 0.0295 0.273 0.862 0.154 0.205 0.498 0.625 0.00124 0.00156 +! Validation 329 10887.169 0.005 0.0276 0.537 1.09 0.15 0.199 0.783 0.876 0.00196 0.00219 +Wall time: 10887.169009115081 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 330 47 1.01 0.0284 0.442 0.151 0.201 0.717 0.794 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 + 330 100 0.987 0.0268 0.452 0.147 0.196 0.785 0.803 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 330 10920.148 0.005 0.0294 0.348 0.936 0.154 0.205 0.578 0.705 0.00145 0.00176 +! Validation 330 10920.148 0.005 0.027 0.667 1.21 0.147 0.196 0.872 0.976 0.00218 0.00244 +Wall time: 10920.14811131917 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 47 0.657 0.0292 0.0722 0.153 0.204 0.251 0.321 0.000628 0.000803 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 331 100 0.666 0.0262 0.143 0.146 0.193 0.407 0.452 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 331 10953.137 0.005 0.0294 0.327 0.914 0.153 0.205 0.547 0.684 0.00137 0.00171 +! Validation 331 10953.137 0.005 0.0262 0.19 0.715 0.146 0.193 0.439 0.521 0.0011 0.0013 +Wall time: 10953.137042985298 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 332 47 0.739 0.0279 0.181 0.15 0.2 0.419 0.508 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 + 332 100 0.702 0.0258 0.186 0.145 0.192 0.482 0.515 0.0012 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 332 10986.140 0.005 0.0285 0.274 0.844 0.151 0.202 0.516 0.625 0.00129 0.00156 +! Validation 332 10986.140 0.005 0.026 0.306 0.826 0.145 0.193 0.554 0.661 0.00138 0.00165 +Wall time: 10986.140859038103 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 47 0.705 0.0296 0.113 0.154 0.206 0.339 0.402 0.000847 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 333 100 0.603 0.0268 0.0662 0.147 0.196 0.234 0.307 0.000584 0.000768 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 333 11019.140 0.005 0.029 0.436 1.02 0.153 0.203 0.654 0.79 0.00163 0.00197 +! Validation 333 11019.140 0.005 0.027 0.171 0.71 0.147 0.196 0.392 0.494 0.000981 0.00123 +Wall time: 11019.140834304038 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 47 133 0.96 113 0.866 1.17 12.2 12.7 0.0305 0.0318 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 334 100 20.2 0.968 0.852 0.87 1.18 0.97 1.1 0.00243 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 334 11052.126 0.005 0.536 745 756 0.545 0.874 17.2 32.7 0.0429 0.0816 +! Validation 334 11052.126 0.005 0.945 3.47 22.4 0.864 1.16 1.77 2.23 0.00444 0.00556 +Wall time: 11052.126842370257 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 47 25.2 0.934 6.53 0.857 1.15 2.49 3.05 0.00622 0.00764 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 335 100 19.2 0.95 0.239 0.861 1.16 0.532 0.584 0.00133 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 335 11085.208 0.005 0.947 15.9 34.8 0.863 1.16 3.81 4.76 0.00954 0.0119 +! Validation 335 11085.208 0.005 0.928 2.82 21.4 0.854 1.15 1.53 2.01 0.00382 0.00502 +Wall time: 11085.207906769123 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 47 22.4 0.902 4.36 0.834 1.13 2.12 2.5 0.00529 0.00624 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 336 100 18.7 0.911 0.433 0.84 1.14 0.744 0.786 0.00186 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 336 11118.228 0.005 0.918 4.3 22.7 0.848 1.15 2.03 2.48 0.00508 0.00619 +! Validation 336 11118.228 0.005 0.889 2.31 20.1 0.835 1.13 1.41 1.81 0.00353 0.00454 +Wall time: 11118.227930214256 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 47 20.7 0.826 4.13 0.807 1.09 1.87 2.43 0.00467 0.00607 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 337 100 17.7 0.858 0.554 0.814 1.11 0.824 0.89 0.00206 0.00222 + + + 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 11151.243 0.005 0.873 3.34 20.8 0.826 1.12 1.76 2.18 0.00439 0.00546 +! Validation 337 11151.243 0.005 0.837 2.05 18.8 0.809 1.09 1.35 1.71 0.00339 0.00428 +Wall time: 11151.243427239358 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 47 17.4 0.765 2.06 0.776 1.05 1.47 1.72 0.00367 0.00429 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 338 100 17 0.785 1.27 0.778 1.06 1.26 1.35 0.00315 0.00337 + + + 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 11184.264 0.005 0.811 2.47 18.7 0.795 1.08 1.51 1.88 0.00378 0.0047 +! Validation 338 11184.264 0.005 0.763 2.26 17.5 0.772 1.04 1.51 1.8 0.00378 0.00449 +Wall time: 11184.264576896094 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 47 14.4 0.672 0.991 0.726 0.979 0.968 1.19 0.00242 0.00297 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 339 100 14.3 0.687 0.537 0.729 0.991 0.794 0.876 0.00199 0.00219 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 339 11217.297 0.005 0.726 1.72 16.2 0.753 1.02 1.26 1.57 0.00315 0.00392 +! Validation 339 11217.297 0.005 0.668 1.18 14.5 0.723 0.976 1.03 1.3 0.00258 0.00324 +Wall time: 11217.297782350332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 47 13.3 0.584 1.64 0.674 0.913 1.2 1.53 0.003 0.00382 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 340 100 12 0.578 0.475 0.672 0.908 0.743 0.824 0.00186 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 340 11250.321 0.005 0.625 1.11 13.6 0.701 0.945 1.01 1.26 0.00252 0.00314 +! Validation 340 11250.321 0.005 0.562 0.817 12.1 0.666 0.896 0.858 1.08 0.00214 0.0027 +Wall time: 11250.32094268594 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 47 10.1 0.474 0.601 0.615 0.823 0.781 0.927 0.00195 0.00232 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 341 100 9.81 0.481 0.193 0.615 0.829 0.383 0.525 0.000958 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 341 11283.332 0.005 0.528 1.15 11.7 0.646 0.868 1.02 1.28 0.00255 0.00321 +! Validation 341 11283.332 0.005 0.469 0.944 10.3 0.609 0.818 0.941 1.16 0.00235 0.0029 +Wall time: 11283.332806254271 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 47 10.3 0.4 2.32 0.566 0.756 1.6 1.82 0.004 0.00455 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 342 100 8.37 0.402 0.337 0.564 0.757 0.56 0.693 0.0014 0.00173 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 342 11316.361 0.005 0.445 1.46 10.4 0.594 0.797 1.16 1.44 0.0029 0.0036 +! Validation 342 11316.361 0.005 0.393 1.13 8.99 0.559 0.749 1.07 1.27 0.00267 0.00317 +Wall time: 11316.361755037215 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 47 7.63 0.358 0.481 0.533 0.715 0.725 0.829 0.00181 0.00207 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 343 100 7.56 0.343 0.709 0.521 0.699 0.93 1.01 0.00232 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 343 11349.375 0.005 0.382 1.59 9.22 0.551 0.738 1.21 1.51 0.00302 0.00377 +! Validation 343 11349.375 0.005 0.337 1.57 8.32 0.518 0.694 1.29 1.5 0.00323 0.00374 +Wall time: 11349.375011109281 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 47 6.85 0.311 0.624 0.493 0.667 0.721 0.944 0.0018 0.00236 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 344 100 6.21 0.294 0.323 0.482 0.648 0.566 0.679 0.00142 0.0017 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 344 11382.397 0.005 0.332 0.951 7.59 0.513 0.688 0.925 1.17 0.00231 0.00292 +! Validation 344 11382.397 0.005 0.293 1.09 6.94 0.481 0.646 1.07 1.25 0.00267 0.00312 +Wall time: 11382.397737456951 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 47 6.7 0.287 0.953 0.478 0.641 0.984 1.17 0.00246 0.00292 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 345 100 5.42 0.266 0.103 0.457 0.616 0.353 0.384 0.000882 0.000959 + + + 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 11415.422 0.005 0.296 2.24 8.16 0.483 0.65 1.46 1.79 0.00365 0.00447 +! Validation 345 11415.422 0.005 0.266 0.5 5.83 0.459 0.617 0.678 0.845 0.0017 0.00211 +Wall time: 11415.422406094149 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 47 8.86 0.25 3.86 0.445 0.597 2.26 2.35 0.00565 0.00587 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 346 100 5.23 0.237 0.487 0.432 0.582 0.752 0.834 0.00188 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 346 11448.442 0.005 0.27 1.53 6.93 0.461 0.621 1.21 1.47 0.00303 0.00368 +! Validation 346 11448.442 0.005 0.241 0.529 5.35 0.436 0.587 0.69 0.869 0.00172 0.00217 +Wall time: 11448.442255390342 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 47 10.8 0.248 5.81 0.443 0.595 2.71 2.88 0.00677 0.0072 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 347 100 9.06 0.22 4.66 0.416 0.56 2.55 2.58 0.00638 0.00645 + + + 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 11481.454 0.005 0.249 1.79 6.78 0.443 0.596 1.3 1.6 0.00326 0.00399 +! Validation 347 11481.454 0.005 0.225 3.4 7.89 0.421 0.566 2.06 2.2 0.00515 0.00551 +Wall time: 11481.454140118323 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 47 6.43 0.23 1.84 0.428 0.573 1.42 1.62 0.00355 0.00405 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 348 100 8.09 0.212 3.85 0.408 0.55 2.32 2.34 0.00579 0.00586 + + + 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 11514.461 0.005 0.233 2.9 7.57 0.428 0.577 1.69 2.04 0.00423 0.00509 +! Validation 348 11514.461 0.005 0.217 2.83 7.17 0.414 0.557 1.87 2.01 0.00466 0.00502 +Wall time: 11514.461657436099 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 47 7.27 0.213 3.02 0.406 0.551 1.92 2.08 0.00479 0.00519 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 349 100 6.67 0.19 2.88 0.387 0.521 1.99 2.03 0.00499 0.00507 + + + 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 11547.483 0.005 0.221 1.55 5.97 0.417 0.562 1.21 1.49 0.00302 0.00372 +! Validation 349 11547.483 0.005 0.198 2.33 6.29 0.395 0.532 1.68 1.82 0.0042 0.00456 +Wall time: 11547.483489457984 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 47 5.63 0.211 1.41 0.409 0.549 1.21 1.42 0.00304 0.00355 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 350 100 3.76 0.175 0.255 0.372 0.5 0.476 0.603 0.00119 0.00151 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 350 11580.493 0.005 0.207 1.43 5.58 0.404 0.544 1.12 1.43 0.0028 0.00357 +! Validation 350 11580.493 0.005 0.184 0.938 4.63 0.382 0.513 0.976 1.16 0.00244 0.00289 +Wall time: 11580.493455130141 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 47 4.22 0.188 0.467 0.384 0.518 0.645 0.817 0.00161 0.00204 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 351 100 4.38 0.166 1.06 0.363 0.487 1.18 1.23 0.00294 0.00307 + + + 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 11613.511 0.005 0.193 1.22 5.08 0.39 0.525 1.07 1.32 0.00268 0.0033 +! Validation 351 11613.511 0.005 0.175 0.666 4.17 0.373 0.5 0.792 0.976 0.00198 0.00244 +Wall time: 11613.511628027074 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 47 3.91 0.173 0.443 0.371 0.497 0.676 0.795 0.00169 0.00199 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 352 100 3.45 0.158 0.279 0.355 0.476 0.519 0.631 0.0013 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 352 11646.518 0.005 0.182 1.4 5.05 0.38 0.51 1.16 1.42 0.0029 0.00354 +! Validation 352 11646.518 0.005 0.167 1.13 4.47 0.365 0.489 1.1 1.27 0.00275 0.00317 +Wall time: 11646.518682413269 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 47 3.53 0.17 0.126 0.369 0.493 0.358 0.423 0.000895 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 353 100 3.11 0.146 0.203 0.342 0.456 0.471 0.538 0.00118 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 353 11680.244 0.005 0.173 0.83 4.29 0.371 0.497 0.853 1.09 0.00213 0.00272 +! Validation 353 11680.244 0.005 0.155 0.35 3.46 0.352 0.471 0.565 0.707 0.00141 0.00177 +Wall time: 11680.244501958136 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 47 3.69 0.16 0.497 0.358 0.478 0.696 0.842 0.00174 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 354 100 3.38 0.137 0.642 0.333 0.442 0.889 0.957 0.00222 0.00239 + + + 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 11713.265 0.005 0.163 0.697 3.95 0.36 0.482 0.79 0.998 0.00197 0.00249 +! Validation 354 11713.265 0.005 0.147 0.438 3.37 0.343 0.458 0.641 0.791 0.0016 0.00198 +Wall time: 11713.265187860932 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 47 4.77 0.153 1.7 0.35 0.468 1.43 1.56 0.00357 0.00389 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 355 100 2.81 0.132 0.17 0.328 0.435 0.356 0.493 0.000891 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 355 11746.281 0.005 0.155 1.18 4.28 0.352 0.47 1.06 1.3 0.00265 0.00324 +! Validation 355 11746.281 0.005 0.141 0.772 3.6 0.337 0.449 0.888 1.05 0.00222 0.00263 +Wall time: 11746.28186324332 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 47 3.56 0.145 0.672 0.342 0.454 0.845 0.979 0.00211 0.00245 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 356 100 3.61 0.133 0.961 0.328 0.435 1.11 1.17 0.00279 0.00293 + + + 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 11779.311 0.005 0.155 3.22 6.32 0.353 0.471 1.68 2.15 0.00421 0.00536 +! Validation 356 11779.311 0.005 0.143 0.655 3.51 0.338 0.452 0.826 0.967 0.00206 0.00242 +Wall time: 11779.31149071129 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 47 3.02 0.136 0.309 0.332 0.44 0.536 0.665 0.00134 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 357 100 3.35 0.124 0.863 0.318 0.421 1.05 1.11 0.00262 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 357 11812.320 0.005 0.148 0.842 3.79 0.344 0.459 0.874 1.1 0.00218 0.00274 +! Validation 357 11812.320 0.005 0.134 0.617 3.29 0.328 0.437 0.794 0.939 0.00199 0.00235 +Wall time: 11812.32026697835 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 47 3.8 0.137 1.06 0.33 0.442 1.05 1.23 0.00261 0.00308 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 358 100 2.51 0.12 0.122 0.313 0.413 0.374 0.417 0.000936 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 358 11845.337 0.005 0.14 0.885 3.69 0.336 0.448 0.912 1.12 0.00228 0.00281 +! Validation 358 11845.337 0.005 0.128 0.297 2.87 0.322 0.428 0.515 0.651 0.00129 0.00163 +Wall time: 11845.337694718037 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 47 4.34 0.135 1.65 0.329 0.438 1.44 1.54 0.0036 0.00384 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 359 100 3.46 0.117 1.12 0.31 0.409 1.21 1.26 0.00303 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 359 11878.354 0.005 0.135 1.4 4.1 0.331 0.44 1.19 1.41 0.00297 0.00353 +! Validation 359 11878.354 0.005 0.126 2.27 4.79 0.319 0.424 1.69 1.8 0.00422 0.0045 +Wall time: 11878.354809958022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 47 2.94 0.133 0.282 0.329 0.436 0.477 0.635 0.00119 0.00159 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 360 100 2.65 0.112 0.412 0.303 0.4 0.681 0.768 0.0017 0.00192 + + + 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 11911.377 0.005 0.131 1.04 3.66 0.326 0.433 0.98 1.22 0.00245 0.00304 +! Validation 360 11911.377 0.005 0.12 1.2 3.61 0.312 0.415 1.17 1.31 0.00293 0.00328 +Wall time: 11911.37703776313 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 47 3.18 0.124 0.704 0.317 0.42 0.843 1 0.00211 0.00251 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 361 100 2.54 0.107 0.39 0.297 0.392 0.656 0.746 0.00164 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 361 11944.391 0.005 0.126 0.772 3.29 0.319 0.424 0.83 1.05 0.00208 0.00263 +! Validation 361 11944.391 0.005 0.115 0.287 2.59 0.306 0.406 0.507 0.64 0.00127 0.0016 +Wall time: 11944.39137950819 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 47 2.8 0.12 0.401 0.313 0.414 0.61 0.757 0.00152 0.00189 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 362 100 2.18 0.105 0.0845 0.294 0.387 0.298 0.347 0.000746 0.000869 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 362 11977.414 0.005 0.121 1.02 3.44 0.313 0.416 0.988 1.21 0.00247 0.00302 +! Validation 362 11977.414 0.005 0.112 0.351 2.59 0.302 0.4 0.57 0.708 0.00142 0.00177 +Wall time: 11977.414431316312 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 47 2.56 0.117 0.223 0.308 0.408 0.45 0.564 0.00113 0.00141 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 363 100 2.13 0.101 0.107 0.289 0.38 0.3 0.392 0.000749 0.000979 + + + 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 12010.691 0.005 0.118 1 3.36 0.31 0.41 0.937 1.2 0.00234 0.00299 +! Validation 363 12010.691 0.005 0.109 0.475 2.65 0.297 0.394 0.674 0.823 0.00168 0.00206 +Wall time: 12010.691342971288 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 47 3.08 0.116 0.769 0.308 0.406 0.941 1.05 0.00235 0.00262 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 364 100 3.8 0.102 1.75 0.29 0.382 1.54 1.58 0.00385 0.00395 + + + 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 12043.731 0.005 0.114 1.35 3.63 0.304 0.404 1.09 1.39 0.00274 0.00347 +! Validation 364 12043.731 0.005 0.109 1.16 3.33 0.297 0.394 1.16 1.29 0.0029 0.00321 +Wall time: 12043.731771524064 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 47 2.5 0.108 0.335 0.297 0.393 0.596 0.691 0.00149 0.00173 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 365 100 2.42 0.0975 0.471 0.284 0.373 0.743 0.82 0.00186 0.00205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 365 12076.761 0.005 0.112 1.01 3.26 0.302 0.401 0.986 1.2 0.00247 0.003 +! Validation 365 12076.761 0.005 0.104 0.321 2.4 0.291 0.385 0.54 0.678 0.00135 0.00169 +Wall time: 12076.761795711238 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 47 3.89 0.108 1.72 0.299 0.394 1.51 1.57 0.00377 0.00392 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 366 100 4.18 0.0945 2.29 0.279 0.367 1.77 1.81 0.00444 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 366 12109.788 0.005 0.108 0.853 3.01 0.297 0.393 0.897 1.1 0.00224 0.00276 +! Validation 366 12109.788 0.005 0.1 1.67 3.67 0.286 0.379 1.43 1.54 0.00358 0.00386 +Wall time: 12109.788117531221 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 47 2.66 0.106 0.542 0.294 0.389 0.73 0.88 0.00182 0.0022 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 367 100 3.4 0.0946 1.51 0.279 0.368 1.43 1.47 0.00356 0.00367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 367 12142.826 0.005 0.106 1.33 3.45 0.294 0.389 1.15 1.38 0.00288 0.00345 +! Validation 367 12142.826 0.005 0.1 0.963 2.97 0.286 0.378 1.03 1.17 0.00258 0.00293 +Wall time: 12142.826860978268 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 47 2.76 0.104 0.677 0.29 0.386 0.875 0.983 0.00219 0.00246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 368 100 3.44 0.094 1.56 0.278 0.366 1.45 1.49 0.00362 0.00373 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 368 12175.864 0.005 0.104 1.25 3.32 0.29 0.385 1.06 1.34 0.00265 0.00334 +! Validation 368 12175.864 0.005 0.0991 1 2.98 0.284 0.376 1.07 1.2 0.00267 0.00299 +Wall time: 12175.863958534319 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 47 2.37 0.103 0.321 0.29 0.383 0.529 0.677 0.00132 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 369 100 3.16 0.0927 1.3 0.277 0.364 1.32 1.36 0.0033 0.00341 + + + 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 12209.003 0.005 0.102 1.22 3.26 0.288 0.382 1.05 1.32 0.00263 0.0033 +! Validation 369 12209.003 0.005 0.0975 2.23 4.18 0.282 0.373 1.69 1.79 0.00423 0.00447 +Wall time: 12209.00349113997 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 47 3.57 0.1 1.57 0.286 0.379 1.36 1.5 0.0034 0.00374 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 370 100 3.15 0.0877 1.4 0.269 0.354 1.37 1.41 0.00343 0.00354 + + + 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 12241.999 0.005 0.101 0.839 2.85 0.286 0.379 0.886 1.09 0.00222 0.00273 +! Validation 370 12241.999 0.005 0.0925 0.937 2.79 0.275 0.363 1.03 1.16 0.00257 0.00289 +Wall time: 12241.999539873097 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 47 2.36 0.0941 0.475 0.278 0.367 0.705 0.823 0.00176 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 371 100 2.83 0.0869 1.1 0.268 0.352 1.2 1.25 0.00301 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 371 12275.636 0.005 0.0986 1.2 3.17 0.283 0.375 1.05 1.31 0.00262 0.00328 +! Validation 371 12275.636 0.005 0.0916 0.696 2.53 0.273 0.362 0.867 0.997 0.00217 0.00249 +Wall time: 12275.63650081912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 47 2.01 0.0915 0.178 0.275 0.361 0.445 0.505 0.00111 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 372 100 1.82 0.0844 0.127 0.264 0.347 0.392 0.427 0.00098 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 372 12308.656 0.005 0.0949 0.521 2.42 0.278 0.368 0.689 0.863 0.00172 0.00216 +! Validation 372 12308.656 0.005 0.088 0.24 2 0.268 0.355 0.464 0.585 0.00116 0.00146 +Wall time: 12308.656274680048 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 47 2.03 0.0915 0.202 0.273 0.361 0.406 0.537 0.00102 0.00134 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 373 100 2.15 0.0838 0.473 0.263 0.346 0.736 0.822 0.00184 0.00205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 373 12341.685 0.005 0.0924 0.936 2.78 0.275 0.363 0.945 1.16 0.00236 0.00289 +! Validation 373 12341.685 0.005 0.0873 0.32 2.07 0.267 0.353 0.544 0.676 0.00136 0.00169 +Wall time: 12341.685084929224 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 47 3.44 0.0885 1.67 0.269 0.355 1.49 1.55 0.00373 0.00386 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 374 100 2.15 0.0802 0.543 0.257 0.339 0.806 0.881 0.00201 0.0022 + + + 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 12374.711 0.005 0.0901 0.789 2.59 0.271 0.359 0.839 1.06 0.0021 0.00265 +! Validation 374 12374.711 0.005 0.0837 0.411 2.08 0.262 0.346 0.636 0.766 0.00159 0.00191 +Wall time: 12374.710944257211 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 47 5.84 0.0918 4.01 0.275 0.362 2.35 2.39 0.00588 0.00598 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 375 100 4.6 0.082 2.96 0.26 0.342 2.02 2.06 0.00506 0.00514 + + + 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 12407.737 0.005 0.0898 1.63 3.43 0.271 0.358 1.28 1.52 0.0032 0.00381 +! Validation 375 12407.737 0.005 0.0848 2.15 3.85 0.264 0.348 1.65 1.75 0.00413 0.00438 +Wall time: 12407.736979387235 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 47 2.03 0.0889 0.257 0.271 0.356 0.498 0.606 0.00124 0.00151 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 376 100 1.69 0.078 0.135 0.254 0.334 0.408 0.438 0.00102 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 376 12440.753 0.005 0.0883 0.501 2.27 0.269 0.355 0.658 0.846 0.00165 0.00212 +! Validation 376 12440.753 0.005 0.081 0.217 1.84 0.257 0.34 0.438 0.557 0.0011 0.00139 +Wall time: 12440.753208598122 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 47 2.68 0.0921 0.842 0.274 0.363 0.969 1.1 0.00242 0.00274 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 377 100 2.88 0.0792 1.3 0.256 0.336 1.32 1.36 0.00329 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 377 12473.776 0.005 0.0875 1.68 3.43 0.267 0.353 1.3 1.55 0.00326 0.00387 +! Validation 377 12473.776 0.005 0.0826 0.917 2.57 0.26 0.343 1.02 1.14 0.00254 0.00286 +Wall time: 12473.77653709706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 47 5.31 0.0864 3.58 0.266 0.351 2.18 2.26 0.00546 0.00565 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 378 100 4.91 0.0806 3.3 0.258 0.339 2.14 2.17 0.00536 0.00543 + + + 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 12506.796 0.005 0.0861 1.24 2.96 0.265 0.351 1.07 1.33 0.00268 0.00332 +! Validation 378 12506.796 0.005 0.0833 2.47 4.13 0.261 0.345 1.79 1.88 0.00447 0.00469 +Wall time: 12506.796834444162 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 47 1.93 0.0871 0.185 0.265 0.353 0.462 0.514 0.00116 0.00129 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 379 100 1.94 0.0788 0.364 0.255 0.335 0.632 0.721 0.00158 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 379 12539.809 0.005 0.0866 1.31 3.04 0.266 0.352 1.16 1.37 0.00289 0.00342 +! Validation 379 12539.809 0.005 0.0816 0.274 1.91 0.258 0.341 0.506 0.626 0.00126 0.00156 +Wall time: 12539.809362361208 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 47 3.51 0.08 1.91 0.255 0.338 1.55 1.65 0.00389 0.00413 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 380 100 2.72 0.0754 1.21 0.25 0.328 1.27 1.32 0.00317 0.00329 + + + 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 12572.827 0.005 0.0836 0.572 2.24 0.261 0.346 0.72 0.901 0.0018 0.00225 +! Validation 380 12572.827 0.005 0.0779 1.96 3.52 0.253 0.333 1.58 1.67 0.00395 0.00418 +Wall time: 12572.827863703948 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 47 1.9 0.0862 0.175 0.265 0.351 0.404 0.499 0.00101 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 381 100 2.13 0.0788 0.558 0.255 0.336 0.821 0.892 0.00205 0.00223 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 381 12605.855 0.005 0.0843 1.85 3.54 0.262 0.347 1.38 1.63 0.00346 0.00407 +! Validation 381 12605.855 0.005 0.0818 1.07 2.71 0.258 0.342 1.11 1.24 0.00278 0.00309 +Wall time: 12605.855517234188 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 47 2.02 0.0792 0.433 0.255 0.336 0.684 0.786 0.00171 0.00197 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 382 100 2 0.0738 0.528 0.247 0.325 0.794 0.868 0.00198 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 382 12638.855 0.005 0.083 0.582 2.24 0.26 0.344 0.746 0.912 0.00186 0.00228 +! Validation 382 12638.855 0.005 0.0762 0.376 1.9 0.25 0.33 0.604 0.732 0.00151 0.00183 +Wall time: 12638.855036056135 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 47 1.79 0.0797 0.198 0.254 0.337 0.43 0.531 0.00108 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 383 100 1.83 0.0711 0.406 0.242 0.319 0.671 0.761 0.00168 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 383 12671.830 0.005 0.0792 0.41 1.99 0.254 0.336 0.625 0.766 0.00156 0.00191 +! Validation 383 12671.830 0.005 0.073 0.311 1.77 0.245 0.323 0.543 0.667 0.00136 0.00167 +Wall time: 12671.830291965976 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 47 3.04 0.0766 1.51 0.251 0.331 1.42 1.47 0.00355 0.00367 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 384 100 4 0.0695 2.61 0.24 0.315 1.9 1.93 0.00475 0.00483 + + + 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 12704.816 0.005 0.0768 0.64 2.18 0.251 0.331 0.779 0.954 0.00195 0.00239 +! Validation 384 12704.816 0.005 0.0714 2.18 3.61 0.242 0.319 1.69 1.76 0.00421 0.00441 +Wall time: 12704.816566060297 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 47 1.87 0.0778 0.312 0.252 0.333 0.54 0.667 0.00135 0.00167 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 385 100 1.87 0.0696 0.478 0.24 0.315 0.746 0.826 0.00187 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 385 12737.793 0.005 0.0767 1.14 2.68 0.25 0.331 1.04 1.28 0.00261 0.00319 +! Validation 385 12737.793 0.005 0.0717 0.39 1.82 0.242 0.32 0.624 0.746 0.00156 0.00187 +Wall time: 12737.793597586919 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 47 1.92 0.0716 0.485 0.242 0.32 0.705 0.832 0.00176 0.00208 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 386 100 1.97 0.0683 0.607 0.238 0.312 0.863 0.931 0.00216 0.00233 + + + 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 12770.771 0.005 0.0757 0.793 2.31 0.249 0.329 0.839 1.06 0.0021 0.00266 +! Validation 386 12770.771 0.005 0.0701 1.1 2.5 0.24 0.317 1.14 1.25 0.00284 0.00313 +Wall time: 12770.771380553953 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 47 1.69 0.0724 0.239 0.244 0.321 0.5 0.584 0.00125 0.00146 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 387 100 1.7 0.0661 0.374 0.234 0.307 0.642 0.73 0.00161 0.00183 + + + 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 12803.754 0.005 0.0731 0.405 1.87 0.245 0.323 0.609 0.761 0.00152 0.0019 +! Validation 387 12803.754 0.005 0.0676 0.349 1.7 0.235 0.311 0.587 0.706 0.00147 0.00176 +Wall time: 12803.754545113072 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 47 2.04 0.0688 0.661 0.237 0.314 0.894 0.971 0.00224 0.00243 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 388 100 1.4 0.0656 0.0919 0.233 0.306 0.257 0.362 0.000642 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 388 12836.775 0.005 0.0718 0.827 2.26 0.242 0.32 0.877 1.09 0.00219 0.00272 +! Validation 388 12836.775 0.005 0.0671 0.33 1.67 0.235 0.31 0.547 0.686 0.00137 0.00172 +Wall time: 12836.775759954005 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 47 1.91 0.0718 0.471 0.242 0.32 0.695 0.82 0.00174 0.00205 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 389 100 2.15 0.0656 0.835 0.233 0.306 1.03 1.09 0.00259 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 389 12869.847 0.005 0.0729 1.08 2.54 0.244 0.323 0.946 1.24 0.00237 0.00311 +! Validation 389 12869.847 0.005 0.0673 1.25 2.6 0.235 0.31 1.24 1.34 0.00309 0.00334 +Wall time: 12869.847689901013 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 47 1.91 0.0659 0.592 0.232 0.307 0.819 0.919 0.00205 0.0023 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 390 100 2.49 0.0636 1.22 0.23 0.301 1.27 1.32 0.00318 0.0033 + + + 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 12902.891 0.005 0.0702 0.453 1.86 0.24 0.317 0.645 0.804 0.00161 0.00201 +! Validation 390 12902.891 0.005 0.0649 1.84 3.13 0.231 0.304 1.53 1.62 0.00382 0.00405 +Wall time: 12902.890981703065 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 47 2.66 0.0759 1.14 0.249 0.329 1.19 1.28 0.00296 0.0032 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 391 100 2.18 0.0687 0.803 0.239 0.313 1.01 1.07 0.00254 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 391 12936.075 0.005 0.074 2.12 3.6 0.246 0.325 1.46 1.74 0.00365 0.00436 +! Validation 391 12936.075 0.005 0.0705 1.4 2.81 0.241 0.317 1.28 1.41 0.00321 0.00354 +Wall time: 12936.07551671099 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 47 1.64 0.0714 0.21 0.241 0.319 0.417 0.548 0.00104 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 392 100 1.8 0.0669 0.464 0.235 0.309 0.737 0.814 0.00184 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 392 12969.096 0.005 0.0726 1.13 2.59 0.244 0.322 1.08 1.27 0.00269 0.00318 +! Validation 392 12969.096 0.005 0.0685 0.895 2.26 0.237 0.313 1.01 1.13 0.00251 0.00283 +Wall time: 12969.096338090021 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 47 1.83 0.0666 0.498 0.233 0.308 0.718 0.844 0.00179 0.00211 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 393 100 2.55 0.0626 1.29 0.228 0.299 1.32 1.36 0.00329 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 393 13002.124 0.005 0.07 0.382 1.78 0.239 0.316 0.59 0.738 0.00147 0.00185 +! Validation 393 13002.124 0.005 0.0642 1.1 2.39 0.23 0.303 1.15 1.26 0.00289 0.00314 +Wall time: 13002.12422929192 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 47 1.52 0.068 0.161 0.236 0.312 0.349 0.48 0.000874 0.0012 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 394 100 1.53 0.0638 0.258 0.23 0.302 0.555 0.607 0.00139 0.00152 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 394 13035.152 0.005 0.0688 1.08 2.46 0.237 0.314 1.05 1.24 0.00262 0.00311 +! Validation 394 13035.152 0.005 0.065 0.254 1.55 0.231 0.305 0.488 0.602 0.00122 0.00151 +Wall time: 13035.15196088329 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 47 3.22 0.0635 1.95 0.227 0.301 1.64 1.67 0.00409 0.00417 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 395 100 2.23 0.0616 0.995 0.226 0.297 1.14 1.19 0.00285 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 395 13068.187 0.005 0.0673 0.483 1.83 0.235 0.31 0.67 0.827 0.00167 0.00207 +! Validation 395 13068.187 0.005 0.0626 1.39 2.64 0.227 0.299 1.32 1.41 0.00329 0.00352 +Wall time: 13068.187236960046 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 47 1.41 0.0625 0.164 0.227 0.299 0.343 0.484 0.000857 0.00121 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 396 100 1.31 0.0603 0.102 0.224 0.293 0.266 0.382 0.000664 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 396 13101.211 0.005 0.0661 0.663 1.99 0.233 0.307 0.795 0.974 0.00199 0.00243 +! Validation 396 13101.211 0.005 0.0613 0.272 1.5 0.224 0.296 0.497 0.623 0.00124 0.00156 +Wall time: 13101.211594002321 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 47 1.84 0.0669 0.496 0.235 0.309 0.728 0.842 0.00182 0.0021 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 397 100 1.42 0.0592 0.239 0.222 0.291 0.539 0.584 0.00135 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 397 13134.234 0.005 0.0647 0.672 1.97 0.23 0.304 0.815 0.98 0.00204 0.00245 +! Validation 397 13134.234 0.005 0.0602 0.242 1.44 0.222 0.293 0.48 0.588 0.0012 0.00147 +Wall time: 13134.23409488611 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 47 1.59 0.0633 0.322 0.227 0.301 0.57 0.678 0.00142 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 398 100 2.18 0.0604 0.976 0.224 0.294 1.13 1.18 0.00283 0.00295 + + + 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 13167.378 0.005 0.0639 0.928 2.21 0.229 0.302 0.958 1.15 0.0024 0.00288 +! Validation 398 13167.378 0.005 0.0614 1.37 2.6 0.225 0.296 1.31 1.4 0.00327 0.0035 +Wall time: 13167.37854008004 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 47 1.73 0.0654 0.424 0.231 0.306 0.68 0.778 0.0017 0.00194 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 399 100 1.26 0.0591 0.0801 0.222 0.29 0.278 0.338 0.000695 0.000845 + + + 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 13200.426 0.005 0.0658 0.942 2.26 0.232 0.307 0.925 1.16 0.00231 0.0029 +! Validation 399 13200.426 0.005 0.0601 0.18 1.38 0.222 0.293 0.401 0.508 0.001 0.00127 +Wall time: 13200.42655860912 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 47 1.56 0.0673 0.215 0.235 0.31 0.461 0.554 0.00115 0.00138 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 400 100 1.29 0.0589 0.109 0.222 0.29 0.378 0.395 0.000946 0.000988 + + + 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 13233.473 0.005 0.0649 1.05 2.34 0.231 0.304 0.995 1.22 0.00249 0.00306 +! Validation 400 13233.473 0.005 0.0603 0.172 1.38 0.223 0.294 0.39 0.495 0.000975 0.00124 +Wall time: 13233.47312100511 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 47 1.39 0.062 0.153 0.226 0.298 0.401 0.467 0.001 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 401 100 2.84 0.0596 1.64 0.223 0.292 1.49 1.53 0.00373 0.00383 + + + 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 13266.507 0.005 0.063 0.843 2.1 0.227 0.3 0.864 1.1 0.00216 0.00275 +! Validation 401 13266.507 0.005 0.0605 1.37 2.58 0.223 0.294 1.31 1.4 0.00327 0.0035 +Wall time: 13266.50731057534 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 47 2.22 0.061 1 0.224 0.295 1.11 1.19 0.00279 0.00299 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 402 100 1.57 0.0582 0.407 0.22 0.288 0.678 0.763 0.00169 0.00191 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 402 13299.562 0.005 0.0633 0.798 2.06 0.228 0.301 0.886 1.07 0.00222 0.00267 +! Validation 402 13299.562 0.005 0.0592 0.34 1.52 0.22 0.291 0.575 0.697 0.00144 0.00174 +Wall time: 13299.562800252344 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 47 2.19 0.0609 0.969 0.223 0.295 1.07 1.18 0.00267 0.00294 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 403 100 2.38 0.0552 1.28 0.215 0.281 1.31 1.35 0.00327 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 403 13332.681 0.005 0.0616 0.394 1.63 0.224 0.296 0.584 0.749 0.00146 0.00187 +! Validation 403 13332.681 0.005 0.056 1.04 2.16 0.215 0.283 1.12 1.22 0.00279 0.00305 +Wall time: 13332.681525920983 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 47 6.42 0.0608 5.21 0.224 0.295 2.69 2.73 0.00673 0.00682 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 404 100 5.46 0.0549 4.36 0.214 0.28 2.47 2.49 0.00618 0.00624 + + + 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 13365.723 0.005 0.0596 0.773 1.96 0.221 0.292 0.835 1.04 0.00209 0.00261 +! Validation 404 13365.723 0.005 0.0558 4.09 5.21 0.214 0.282 2.37 2.42 0.00592 0.00604 +Wall time: 13365.723264376167 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 47 1.38 0.0613 0.152 0.225 0.296 0.357 0.466 0.000894 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 405 100 1.37 0.0557 0.261 0.216 0.282 0.556 0.61 0.00139 0.00153 + + + 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 13398.763 0.005 0.0637 1.14 2.41 0.229 0.302 0.971 1.28 0.00243 0.00319 +! Validation 405 13398.763 0.005 0.0567 0.243 1.38 0.216 0.284 0.478 0.589 0.00119 0.00147 +Wall time: 13398.76296805637 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 47 3.26 0.0606 2.05 0.223 0.294 1.65 1.71 0.00414 0.00428 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 406 100 2.06 0.0565 0.928 0.217 0.284 1.11 1.15 0.00276 0.00288 + + + 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 13431.811 0.005 0.0607 1.07 2.28 0.223 0.294 1.01 1.23 0.00253 0.00308 +! Validation 406 13431.811 0.005 0.0576 0.853 2.01 0.217 0.287 0.999 1.1 0.0025 0.00276 +Wall time: 13431.811225425918 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 47 1.55 0.0571 0.405 0.216 0.285 0.668 0.761 0.00167 0.0019 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 407 100 1.19 0.0548 0.095 0.214 0.28 0.348 0.368 0.00087 0.000921 + + + 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 13464.843 0.005 0.06 0.552 1.75 0.222 0.293 0.711 0.888 0.00178 0.00222 +! Validation 407 13464.843 0.005 0.0554 0.186 1.29 0.213 0.281 0.406 0.515 0.00101 0.00129 +Wall time: 13464.84364933893 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 47 1.26 0.0554 0.146 0.214 0.281 0.367 0.457 0.000918 0.00114 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 408 100 1.16 0.0525 0.105 0.209 0.274 0.369 0.388 0.000923 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 408 13498.088 0.005 0.0578 0.333 1.49 0.218 0.287 0.559 0.69 0.0014 0.00172 +! Validation 408 13498.088 0.005 0.053 0.16 1.22 0.209 0.275 0.377 0.478 0.000943 0.0012 +Wall time: 13498.088388875127 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 47 3.05 0.0564 1.92 0.215 0.284 1.59 1.66 0.00399 0.00414 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 409 100 2.27 0.0524 1.22 0.209 0.274 1.28 1.32 0.0032 0.0033 + + + 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 13531.126 0.005 0.0575 0.841 1.99 0.217 0.287 0.885 1.09 0.00221 0.00273 +! Validation 409 13531.126 0.005 0.0532 1.07 2.14 0.209 0.276 1.14 1.24 0.00286 0.00309 +Wall time: 13531.126151658129 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 47 1.26 0.0566 0.132 0.216 0.284 0.339 0.435 0.000848 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 410 100 1.17 0.0527 0.12 0.21 0.274 0.398 0.413 0.000996 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 410 13564.146 0.005 0.0581 0.846 2.01 0.218 0.288 0.881 1.1 0.0022 0.00275 +! Validation 410 13564.146 0.005 0.0534 0.176 1.24 0.21 0.276 0.399 0.501 0.000997 0.00125 +Wall time: 13564.14612952806 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 47 1.39 0.0591 0.211 0.221 0.291 0.434 0.549 0.00108 0.00137 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 411 100 1.61 0.056 0.486 0.217 0.283 0.766 0.833 0.00192 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 411 13597.175 0.005 0.0568 0.971 2.11 0.216 0.285 0.915 1.18 0.00229 0.00295 +! Validation 411 13597.175 0.005 0.0566 0.871 2 0.216 0.284 1 1.12 0.0025 0.00279 +Wall time: 13597.175611477345 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 47 1.29 0.0585 0.118 0.218 0.289 0.293 0.41 0.000732 0.00102 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 412 100 1.1 0.0514 0.0766 0.207 0.271 0.282 0.331 0.000705 0.000827 + + + 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 13630.224 0.005 0.0575 0.6 1.75 0.217 0.287 0.757 0.927 0.00189 0.00232 +! Validation 412 13630.224 0.005 0.0521 0.168 1.21 0.207 0.273 0.386 0.49 0.000964 0.00122 +Wall time: 13630.224017617293 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 47 1.6 0.0523 0.555 0.207 0.273 0.807 0.89 0.00202 0.00223 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 413 100 1.21 0.0499 0.21 0.204 0.267 0.438 0.547 0.0011 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 413 13663.241 0.005 0.0549 0.443 1.54 0.212 0.28 0.632 0.795 0.00158 0.00199 +! Validation 413 13663.241 0.005 0.0504 0.42 1.43 0.204 0.268 0.646 0.774 0.00161 0.00194 +Wall time: 13663.241601065267 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 47 1.22 0.0536 0.152 0.21 0.277 0.343 0.466 0.000856 0.00116 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 414 100 1.56 0.0498 0.565 0.204 0.267 0.835 0.898 0.00209 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 414 13696.269 0.005 0.0539 0.588 1.67 0.21 0.277 0.76 0.917 0.0019 0.00229 +! Validation 414 13696.269 0.005 0.0502 0.892 1.9 0.203 0.268 1.03 1.13 0.00257 0.00282 +Wall time: 13696.26926142117 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 47 1.33 0.0519 0.295 0.207 0.272 0.562 0.65 0.0014 0.00162 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 415 100 1.29 0.0495 0.3 0.204 0.266 0.586 0.654 0.00147 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 415 13729.294 0.005 0.0561 0.912 2.03 0.215 0.283 0.868 1.14 0.00217 0.00286 +! Validation 415 13729.294 0.005 0.0501 0.253 1.26 0.203 0.268 0.495 0.601 0.00124 0.0015 +Wall time: 13729.294036972336 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 47 1.81 0.0556 0.698 0.214 0.282 0.871 0.999 0.00218 0.0025 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 416 100 1.18 0.0524 0.135 0.209 0.274 0.31 0.438 0.000775 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 416 13762.306 0.005 0.054 0.92 2 0.21 0.278 0.918 1.15 0.00229 0.00287 +! Validation 416 13762.306 0.005 0.0531 0.469 1.53 0.209 0.275 0.668 0.818 0.00167 0.00205 +Wall time: 13762.306532392278 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 47 1.43 0.0551 0.329 0.212 0.281 0.603 0.685 0.00151 0.00171 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 417 100 1.95 0.0507 0.939 0.206 0.269 1.11 1.16 0.00278 0.0029 + + + 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 13795.344 0.005 0.0544 0.832 1.92 0.211 0.279 0.909 1.09 0.00227 0.00273 +! Validation 417 13795.344 0.005 0.0511 0.762 1.78 0.205 0.27 0.943 1.04 0.00236 0.00261 +Wall time: 13795.344072847161 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 47 1.95 0.0548 0.849 0.211 0.28 1.04 1.1 0.0026 0.00275 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 418 100 1.06 0.0496 0.0645 0.204 0.266 0.206 0.303 0.000514 0.000759 + + + 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 13828.520 0.005 0.0558 1.03 2.15 0.214 0.282 0.971 1.21 0.00243 0.00303 +! Validation 418 13828.520 0.005 0.0505 0.164 1.17 0.204 0.269 0.383 0.484 0.000958 0.00121 +Wall time: 13828.520429587923 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 47 1.26 0.052 0.215 0.206 0.273 0.495 0.555 0.00124 0.00139 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 419 100 1.03 0.0478 0.0735 0.2 0.261 0.292 0.324 0.00073 0.00081 + + + 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 13861.542 0.005 0.0531 0.403 1.46 0.209 0.275 0.602 0.759 0.0015 0.0019 +! Validation 419 13861.542 0.005 0.0487 0.147 1.12 0.2 0.264 0.359 0.458 0.000897 0.00115 +Wall time: 13861.542006345931 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 47 1.26 0.0531 0.197 0.209 0.275 0.39 0.53 0.000975 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 420 100 1.54 0.0474 0.595 0.199 0.26 0.864 0.921 0.00216 0.0023 + + + 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 13894.565 0.005 0.052 0.606 1.65 0.206 0.272 0.775 0.931 0.00194 0.00233 +! Validation 420 13894.565 0.005 0.0479 0.468 1.43 0.199 0.262 0.718 0.818 0.00179 0.00204 +Wall time: 13894.565264562145 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 47 2.46 0.0578 1.31 0.217 0.287 1.28 1.37 0.00321 0.00341 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 421 100 2.22 0.053 1.16 0.211 0.275 1.24 1.29 0.00311 0.00321 + + + 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 13927.592 0.005 0.0521 0.982 2.02 0.207 0.273 0.959 1.18 0.0024 0.00296 +! Validation 421 13927.592 0.005 0.0531 0.881 1.94 0.21 0.275 1.02 1.12 0.00255 0.0028 +Wall time: 13927.592831738293 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 47 1.29 0.0508 0.27 0.203 0.269 0.527 0.621 0.00132 0.00155 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 422 100 1.69 0.0471 0.749 0.199 0.259 0.984 1.03 0.00246 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 422 13960.635 0.005 0.0524 0.574 1.62 0.207 0.274 0.745 0.906 0.00186 0.00226 +! Validation 422 13960.635 0.005 0.0478 0.571 1.53 0.198 0.261 0.797 0.903 0.00199 0.00226 +Wall time: 13960.635850360151 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 47 2.63 0.0532 1.57 0.209 0.276 1.42 1.5 0.00354 0.00374 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 423 100 1.16 0.0519 0.123 0.208 0.272 0.304 0.418 0.000761 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 423 13993.695 0.005 0.0504 0.909 1.92 0.203 0.268 0.866 1.14 0.00217 0.00285 +! Validation 423 13993.695 0.005 0.0524 0.268 1.32 0.208 0.274 0.5 0.619 0.00125 0.00155 +Wall time: 13993.695926724933 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 47 1.72 0.0555 0.606 0.214 0.282 0.865 0.93 0.00216 0.00233 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 424 100 1.44 0.0482 0.472 0.202 0.262 0.759 0.821 0.0019 0.00205 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 424 14026.731 0.005 0.0549 1.01 2.11 0.212 0.28 1.01 1.2 0.00253 0.003 +! Validation 424 14026.731 0.005 0.0486 0.413 1.39 0.201 0.264 0.654 0.768 0.00164 0.00192 +Wall time: 14026.73131202301 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 47 1.42 0.051 0.399 0.205 0.27 0.608 0.755 0.00152 0.00189 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 425 100 1.09 0.0466 0.16 0.198 0.258 0.455 0.479 0.00114 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 425 14059.857 0.005 0.0518 0.652 1.69 0.206 0.272 0.795 0.966 0.00199 0.00241 +! Validation 425 14059.857 0.005 0.0472 0.192 1.14 0.197 0.26 0.419 0.523 0.00105 0.00131 +Wall time: 14059.857665169053 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 47 1.17 0.0516 0.138 0.206 0.271 0.377 0.444 0.000941 0.00111 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 426 100 1.33 0.046 0.407 0.196 0.256 0.702 0.762 0.00175 0.00191 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 426 14093.337 0.005 0.0507 0.692 1.71 0.204 0.269 0.791 0.995 0.00198 0.00249 +! Validation 426 14093.337 0.005 0.0467 0.344 1.28 0.196 0.258 0.595 0.701 0.00149 0.00175 +Wall time: 14093.337569239084 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 47 2.91 0.0531 1.85 0.209 0.275 1.55 1.63 0.00387 0.00407 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 427 100 2.34 0.0495 1.35 0.204 0.266 1.35 1.39 0.00338 0.00347 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 427 14126.524 0.005 0.0504 0.968 1.98 0.203 0.268 0.945 1.17 0.00236 0.00294 +! Validation 427 14126.524 0.005 0.0503 1.75 2.76 0.204 0.268 1.51 1.58 0.00377 0.00395 +Wall time: 14126.52475846326 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 47 1.29 0.0484 0.325 0.199 0.263 0.596 0.682 0.00149 0.0017 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 428 100 1.18 0.0452 0.279 0.195 0.254 0.554 0.631 0.00139 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 428 14159.957 0.005 0.0515 0.642 1.67 0.205 0.271 0.758 0.958 0.0019 0.00239 +! Validation 428 14159.957 0.005 0.0458 0.516 1.43 0.194 0.256 0.743 0.858 0.00186 0.00215 +Wall time: 14159.957511763088 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 47 1.5 0.0515 0.475 0.205 0.271 0.668 0.824 0.00167 0.00206 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 429 100 3.21 0.045 2.31 0.194 0.254 1.79 1.82 0.00447 0.00454 + + + 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 14192.975 0.005 0.0493 0.737 1.72 0.201 0.265 0.853 1.03 0.00213 0.00257 +! Validation 429 14192.975 0.005 0.0457 1.84 2.75 0.194 0.255 1.54 1.62 0.00384 0.00405 +Wall time: 14192.975207256153 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 47 0.992 0.0447 0.0987 0.191 0.253 0.306 0.376 0.000765 0.000939 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 430 100 1.13 0.0431 0.271 0.19 0.248 0.543 0.622 0.00136 0.00155 + + + 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 14225.993 0.005 0.0481 0.353 1.32 0.199 0.262 0.553 0.711 0.00138 0.00178 +! Validation 430 14225.993 0.005 0.0434 0.467 1.34 0.189 0.249 0.705 0.817 0.00176 0.00204 +Wall time: 14225.993821430951 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 47 1.16 0.0524 0.113 0.207 0.273 0.327 0.402 0.000817 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 431 100 1.01 0.0456 0.102 0.196 0.255 0.249 0.383 0.000623 0.000956 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 431 14259.491 0.005 0.0521 1.39 2.43 0.207 0.273 1.12 1.41 0.00281 0.00353 +! Validation 431 14259.491 0.005 0.0464 0.26 1.19 0.196 0.257 0.492 0.61 0.00123 0.00152 +Wall time: 14259.491743051913 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 47 1.15 0.0485 0.178 0.2 0.263 0.442 0.504 0.00111 0.00126 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 432 100 0.939 0.0438 0.0641 0.192 0.25 0.282 0.303 0.000704 0.000756 + + + 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 14292.537 0.005 0.0486 0.595 1.57 0.2 0.263 0.737 0.923 0.00184 0.00231 +! Validation 432 14292.537 0.005 0.0444 0.151 1.04 0.191 0.252 0.366 0.464 0.000914 0.00116 +Wall time: 14292.537169161253 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 47 1.09 0.0469 0.153 0.196 0.259 0.387 0.467 0.000967 0.00117 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 433 100 1.04 0.0437 0.165 0.192 0.25 0.457 0.486 0.00114 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 433 14325.566 0.005 0.0484 0.807 1.77 0.199 0.263 0.877 1.07 0.00219 0.00269 +! Validation 433 14325.566 0.005 0.0444 0.209 1.1 0.191 0.252 0.439 0.546 0.0011 0.00136 +Wall time: 14325.566237736028 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 47 0.982 0.0438 0.105 0.189 0.25 0.312 0.388 0.00078 0.000969 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 434 100 0.998 0.0417 0.164 0.187 0.244 0.455 0.484 0.00114 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 434 14358.594 0.005 0.0465 0.334 1.26 0.195 0.258 0.564 0.691 0.00141 0.00173 +! Validation 434 14358.594 0.005 0.0424 0.164 1.01 0.187 0.246 0.39 0.483 0.000974 0.00121 +Wall time: 14358.594817620236 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 47 1.04 0.0464 0.107 0.196 0.257 0.318 0.391 0.000794 0.000977 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 435 100 1.21 0.0422 0.363 0.188 0.245 0.661 0.72 0.00165 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 435 14391.611 0.005 0.0454 0.574 1.48 0.193 0.255 0.691 0.906 0.00173 0.00227 +! Validation 435 14391.611 0.005 0.0428 0.287 1.14 0.188 0.247 0.538 0.64 0.00134 0.0016 +Wall time: 14391.61114851199 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 47 1.74 0.044 0.857 0.19 0.251 1.05 1.11 0.00263 0.00277 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 436 100 0.924 0.0418 0.0873 0.188 0.244 0.238 0.353 0.000594 0.000883 + + + 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 14424.645 0.005 0.046 0.731 1.65 0.194 0.256 0.854 1.02 0.00214 0.00255 +! Validation 436 14424.645 0.005 0.0423 0.257 1.1 0.187 0.246 0.492 0.605 0.00123 0.00151 +Wall time: 14424.64523722697 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 47 0.995 0.0434 0.126 0.189 0.249 0.314 0.425 0.000785 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 437 100 0.895 0.041 0.0745 0.185 0.242 0.204 0.326 0.00051 0.000815 + + + 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 14457.663 0.005 0.0447 0.463 1.36 0.192 0.253 0.655 0.814 0.00164 0.00204 +! Validation 437 14457.663 0.005 0.0417 0.255 1.09 0.185 0.244 0.486 0.603 0.00121 0.00151 +Wall time: 14457.66380868107 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 47 3.46 0.0448 2.56 0.192 0.253 1.87 1.91 0.00468 0.00478 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 438 100 2.85 0.0405 2.04 0.185 0.24 1.68 1.71 0.00421 0.00427 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 438 14490.800 0.005 0.0438 0.515 1.39 0.19 0.25 0.678 0.853 0.0017 0.00213 +! Validation 438 14490.800 0.005 0.0404 1.66 2.47 0.183 0.24 1.48 1.54 0.0037 0.00385 +Wall time: 14490.800207407214 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 47 1.21 0.0486 0.235 0.201 0.263 0.46 0.579 0.00115 0.00145 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 439 100 1.06 0.0448 0.163 0.194 0.253 0.384 0.483 0.00096 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 439 14523.813 0.005 0.0461 1.05 1.97 0.195 0.256 0.973 1.23 0.00243 0.00307 +! Validation 439 14523.813 0.005 0.0452 0.296 1.2 0.193 0.254 0.536 0.65 0.00134 0.00162 +Wall time: 14523.813558734022 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 47 1.06 0.0438 0.187 0.189 0.25 0.43 0.517 0.00108 0.00129 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 440 100 1.23 0.0398 0.431 0.183 0.238 0.736 0.784 0.00184 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 440 14556.833 0.005 0.0447 0.413 1.31 0.192 0.253 0.6 0.768 0.0015 0.00192 +! Validation 440 14556.833 0.005 0.0404 0.347 1.16 0.183 0.24 0.61 0.704 0.00152 0.00176 +Wall time: 14556.833060571924 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 47 1.22 0.0413 0.397 0.184 0.243 0.648 0.753 0.00162 0.00188 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 441 100 0.969 0.0394 0.181 0.182 0.237 0.428 0.508 0.00107 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 441 14589.857 0.005 0.0435 0.527 1.4 0.189 0.249 0.732 0.867 0.00183 0.00217 +! Validation 441 14589.857 0.005 0.04 0.39 1.19 0.182 0.239 0.633 0.747 0.00158 0.00187 +Wall time: 14589.856946451124 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 47 1.29 0.0485 0.321 0.2 0.263 0.585 0.677 0.00146 0.00169 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 442 100 2.09 0.045 1.19 0.194 0.253 1.27 1.3 0.00318 0.00326 + + + 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 14622.876 0.005 0.0441 1.05 1.93 0.19 0.251 0.975 1.22 0.00244 0.00306 +! Validation 442 14622.876 0.005 0.0457 0.892 1.81 0.194 0.255 1.04 1.13 0.00259 0.00282 +Wall time: 14622.87676980393 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 47 1.38 0.0408 0.562 0.184 0.241 0.814 0.896 0.00204 0.00224 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 443 100 1.01 0.0391 0.224 0.181 0.236 0.507 0.566 0.00127 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 443 14655.908 0.005 0.0447 0.507 1.4 0.192 0.253 0.694 0.851 0.00174 0.00213 +! Validation 443 14655.908 0.005 0.0396 0.193 0.986 0.181 0.238 0.429 0.525 0.00107 0.00131 +Wall time: 14655.908206120133 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 47 1.04 0.0402 0.232 0.183 0.24 0.496 0.576 0.00124 0.00144 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 444 100 1.39 0.0378 0.635 0.178 0.232 0.913 0.952 0.00228 0.00238 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 444 14689.611 0.005 0.0418 0.284 1.12 0.185 0.244 0.52 0.637 0.0013 0.00159 +! Validation 444 14689.611 0.005 0.0379 0.95 1.71 0.177 0.233 1.09 1.17 0.00272 0.00291 +Wall time: 14689.610943472944 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 47 1.41 0.0404 0.597 0.182 0.24 0.81 0.924 0.00202 0.00231 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 445 100 1.81 0.038 1.05 0.179 0.233 1.19 1.23 0.00298 0.00306 + + + 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 14722.676 0.005 0.0422 0.688 1.53 0.186 0.246 0.845 0.991 0.00211 0.00248 +! Validation 445 14722.676 0.005 0.0381 0.836 1.6 0.177 0.233 1.01 1.09 0.00253 0.00273 +Wall time: 14722.676156688016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 47 1.11 0.0401 0.31 0.182 0.239 0.59 0.665 0.00147 0.00166 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 446 100 1.53 0.0376 0.778 0.178 0.232 1.02 1.05 0.00255 0.00263 + + + 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 14755.683 0.005 0.0424 0.696 1.54 0.187 0.246 0.809 0.998 0.00202 0.00249 +! Validation 446 14755.683 0.005 0.0381 0.689 1.45 0.178 0.233 0.905 0.992 0.00226 0.00248 +Wall time: 14755.682998509146 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 47 1.3 0.0448 0.405 0.191 0.253 0.671 0.761 0.00168 0.0019 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 447 100 1.03 0.0404 0.223 0.184 0.24 0.5 0.564 0.00125 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 447 14788.685 0.005 0.0427 0.896 1.75 0.187 0.247 0.939 1.13 0.00235 0.00283 +! Validation 447 14788.685 0.005 0.0409 0.398 1.22 0.184 0.242 0.647 0.754 0.00162 0.00189 +Wall time: 14788.685572728049 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 47 0.851 0.0369 0.112 0.175 0.23 0.313 0.4 0.000782 0.000999 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 448 100 1.11 0.0371 0.365 0.177 0.23 0.68 0.722 0.0017 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 448 14821.696 0.005 0.0413 0.359 1.19 0.184 0.243 0.583 0.716 0.00146 0.00179 +! Validation 448 14821.696 0.005 0.0378 0.382 1.14 0.176 0.232 0.635 0.738 0.00159 0.00185 +Wall time: 14821.69685721118 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 47 2.46 0.0417 1.63 0.186 0.244 1.46 1.53 0.00366 0.00381 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 449 100 1.08 0.0382 0.317 0.179 0.233 0.622 0.673 0.00156 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 449 14854.701 0.005 0.0404 0.619 1.43 0.182 0.24 0.764 0.938 0.00191 0.00234 +! Validation 449 14854.701 0.005 0.0387 0.327 1.1 0.179 0.235 0.578 0.684 0.00145 0.00171 +Wall time: 14854.701678297948 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 47 0.86 0.0392 0.0761 0.179 0.237 0.261 0.33 0.000653 0.000824 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 450 100 1.35 0.0367 0.618 0.175 0.229 0.906 0.939 0.00226 0.00235 + + + 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 14887.719 0.005 0.0401 0.415 1.22 0.182 0.239 0.639 0.771 0.0016 0.00193 +! Validation 450 14887.719 0.005 0.0372 0.934 1.68 0.175 0.231 1.08 1.15 0.00269 0.00289 +Wall time: 14887.719394473359 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 47 1.39 0.0387 0.619 0.178 0.235 0.875 0.94 0.00219 0.00235 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 451 100 0.852 0.0351 0.15 0.172 0.224 0.386 0.462 0.000966 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 451 14920.728 0.005 0.0392 0.395 1.18 0.179 0.237 0.615 0.751 0.00154 0.00188 +! Validation 451 14920.728 0.005 0.0355 0.384 1.09 0.171 0.225 0.632 0.74 0.00158 0.00185 +Wall time: 14920.728486489039 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 47 1.22 0.0404 0.411 0.182 0.24 0.685 0.766 0.00171 0.00192 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 452 100 0.961 0.0358 0.246 0.174 0.226 0.536 0.593 0.00134 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 452 14953.709 0.005 0.0393 0.629 1.41 0.18 0.237 0.794 0.948 0.00199 0.00237 +! Validation 452 14953.709 0.005 0.0358 0.211 0.928 0.172 0.226 0.458 0.549 0.00115 0.00137 +Wall time: 14953.709349872079 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 47 3.51 0.0427 2.66 0.188 0.247 1.92 1.95 0.00481 0.00487 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 453 100 1.36 0.0365 0.626 0.175 0.228 0.912 0.945 0.00228 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 453 14986.685 0.005 0.0394 0.819 1.61 0.18 0.237 0.901 1.08 0.00225 0.0027 +! Validation 453 14986.685 0.005 0.0368 0.629 1.37 0.175 0.229 0.84 0.948 0.0021 0.00237 +Wall time: 14986.685262545943 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 47 0.9 0.0408 0.0833 0.183 0.241 0.262 0.345 0.000656 0.000862 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 454 100 0.83 0.0357 0.116 0.174 0.226 0.384 0.407 0.000959 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 454 15019.751 0.005 0.0405 0.635 1.44 0.182 0.24 0.773 0.953 0.00193 0.00238 +! Validation 454 15019.751 0.005 0.0362 0.14 0.863 0.173 0.227 0.364 0.447 0.000909 0.00112 +Wall time: 15019.751851796173 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 47 2.62 0.0391 1.84 0.179 0.236 1.58 1.62 0.00394 0.00405 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 455 100 2.4 0.0369 1.66 0.176 0.229 1.52 1.54 0.0038 0.00385 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 455 15052.779 0.005 0.0387 0.556 1.33 0.178 0.235 0.741 0.888 0.00185 0.00222 +! Validation 455 15052.779 0.005 0.0369 1.97 2.71 0.175 0.23 1.62 1.68 0.00406 0.0042 +Wall time: 15052.779445264023 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 47 1.12 0.0373 0.379 0.175 0.231 0.609 0.736 0.00152 0.00184 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 456 100 1.12 0.0347 0.427 0.171 0.223 0.742 0.781 0.00186 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 456 15085.805 0.005 0.0389 0.481 1.26 0.179 0.236 0.67 0.829 0.00168 0.00207 +! Validation 456 15085.805 0.005 0.0351 0.295 0.996 0.17 0.224 0.548 0.649 0.00137 0.00162 +Wall time: 15085.805769090075 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 47 0.867 0.035 0.166 0.17 0.224 0.446 0.487 0.00111 0.00122 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 457 100 0.725 0.0337 0.0517 0.168 0.219 0.26 0.272 0.00065 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 457 15118.835 0.005 0.0372 0.364 1.11 0.175 0.231 0.589 0.721 0.00147 0.0018 +! Validation 457 15118.835 0.005 0.0338 0.122 0.798 0.167 0.22 0.326 0.417 0.000816 0.00104 +Wall time: 15118.835262028035 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 47 2.95 0.0386 2.18 0.178 0.235 1.73 1.77 0.00434 0.00441 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 458 100 0.891 0.0372 0.147 0.177 0.23 0.39 0.459 0.000975 0.00115 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 458 15151.870 0.005 0.0369 0.669 1.41 0.174 0.23 0.778 0.974 0.00194 0.00244 +! Validation 458 15151.870 0.005 0.0372 0.356 1.1 0.176 0.23 0.607 0.713 0.00152 0.00178 +Wall time: 15151.870055282954 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 47 0.855 0.0367 0.122 0.174 0.229 0.34 0.417 0.000849 0.00104 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 459 100 0.733 0.0341 0.0519 0.169 0.221 0.268 0.272 0.000671 0.000681 + + + 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 15184.901 0.005 0.0388 0.611 1.39 0.178 0.235 0.747 0.935 0.00187 0.00234 +! Validation 459 15184.901 0.005 0.0342 0.11 0.794 0.168 0.221 0.312 0.396 0.00078 0.000989 +Wall time: 15184.901803260203 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 47 0.876 0.035 0.175 0.17 0.224 0.398 0.501 0.000995 0.00125 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 460 100 0.991 0.0331 0.329 0.167 0.217 0.639 0.685 0.0016 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 460 15217.932 0.005 0.0364 0.373 1.1 0.173 0.228 0.583 0.73 0.00146 0.00183 +! Validation 460 15217.932 0.005 0.0334 0.258 0.925 0.166 0.218 0.516 0.607 0.00129 0.00152 +Wall time: 15217.931955641136 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 47 4.14 0.0378 3.38 0.177 0.232 2.17 2.2 0.00543 0.0055 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 461 100 2.24 0.0356 1.52 0.173 0.225 1.45 1.48 0.00363 0.00369 + + + 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 15251.122 0.005 0.0365 0.666 1.4 0.173 0.228 0.762 0.97 0.0019 0.00243 +! Validation 461 15251.122 0.005 0.0354 1.11 1.82 0.171 0.225 1.16 1.26 0.0029 0.00315 +Wall time: 15251.122577710077 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 47 0.85 0.0362 0.125 0.172 0.227 0.328 0.423 0.000819 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 462 100 0.732 0.0335 0.0626 0.168 0.219 0.296 0.299 0.000739 0.000748 + + + 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 15284.148 0.005 0.0386 0.635 1.41 0.178 0.235 0.757 0.954 0.00189 0.00238 +! Validation 462 15284.148 0.005 0.0336 0.111 0.783 0.167 0.219 0.317 0.398 0.000793 0.000995 +Wall time: 15284.14819950331 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 47 1.05 0.0373 0.304 0.175 0.231 0.591 0.659 0.00148 0.00165 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 463 100 0.86 0.0327 0.207 0.165 0.216 0.503 0.544 0.00126 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 463 15317.152 0.005 0.0358 0.326 1.04 0.171 0.226 0.525 0.683 0.00131 0.00171 +! Validation 463 15317.152 0.005 0.0329 0.191 0.849 0.164 0.217 0.43 0.522 0.00108 0.00131 +Wall time: 15317.152733308263 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 47 0.913 0.0358 0.198 0.172 0.226 0.417 0.532 0.00104 0.00133 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 464 100 0.712 0.0332 0.0474 0.167 0.218 0.254 0.26 0.000635 0.00065 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 464 15350.146 0.005 0.0354 0.456 1.16 0.17 0.225 0.621 0.808 0.00155 0.00202 +! Validation 464 15350.146 0.005 0.0335 0.143 0.812 0.166 0.219 0.356 0.452 0.000891 0.00113 +Wall time: 15350.146196664311 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 47 1.85 0.0364 1.12 0.174 0.228 1.18 1.26 0.00295 0.00316 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 465 100 1.83 0.0347 1.13 0.17 0.223 1.25 1.27 0.00312 0.00318 + + + 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 15383.134 0.005 0.0367 0.707 1.44 0.173 0.229 0.801 1 0.002 0.00251 +! Validation 465 15383.134 0.005 0.0347 1.79 2.48 0.169 0.223 1.53 1.6 0.00382 0.004 +Wall time: 15383.134161469061 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 47 1.06 0.0357 0.346 0.172 0.226 0.604 0.703 0.00151 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 466 100 0.725 0.0333 0.0591 0.167 0.218 0.286 0.291 0.000714 0.000727 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 466 15416.124 0.005 0.0363 0.53 1.26 0.172 0.228 0.7 0.871 0.00175 0.00218 +! Validation 466 15416.124 0.005 0.0335 0.119 0.79 0.166 0.219 0.326 0.413 0.000816 0.00103 +Wall time: 15416.124598722905 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 47 1.4 0.0362 0.677 0.171 0.227 0.922 0.984 0.00231 0.00246 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 467 100 1.13 0.0333 0.461 0.167 0.218 0.78 0.811 0.00195 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 467 15449.104 0.005 0.0359 0.63 1.35 0.171 0.226 0.778 0.949 0.00194 0.00237 +! Validation 467 15449.104 0.005 0.0333 0.771 1.44 0.166 0.218 0.959 1.05 0.0024 0.00262 +Wall time: 15449.104122160934 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 47 0.891 0.0359 0.173 0.172 0.226 0.411 0.497 0.00103 0.00124 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 468 100 3.27 0.0334 2.6 0.167 0.218 1.91 1.93 0.00479 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 468 15482.083 0.005 0.0351 0.561 1.26 0.169 0.224 0.733 0.896 0.00183 0.00224 +! Validation 468 15482.083 0.005 0.0338 2.25 2.92 0.167 0.22 1.73 1.79 0.00433 0.00448 +Wall time: 15482.083521506283 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 47 1.02 0.0362 0.297 0.172 0.227 0.563 0.651 0.00141 0.00163 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 469 100 0.843 0.0326 0.191 0.165 0.216 0.477 0.522 0.00119 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 469 15515.063 0.005 0.0359 0.595 1.31 0.172 0.226 0.754 0.922 0.00189 0.00231 +! Validation 469 15515.063 0.005 0.0328 0.171 0.827 0.165 0.217 0.406 0.494 0.00102 0.00124 +Wall time: 15515.063844128978 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 470 47 0.932 0.0339 0.254 0.167 0.22 0.536 0.603 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 + 470 100 0.752 0.0313 0.125 0.162 0.212 0.362 0.423 0.000905 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 470 15548.042 0.005 0.0345 0.387 1.08 0.168 0.222 0.611 0.744 0.00153 0.00186 +! Validation 470 15548.042 0.005 0.0314 0.307 0.935 0.161 0.212 0.561 0.662 0.0014 0.00166 +Wall time: 15548.041997697204 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 47 1.04 0.0335 0.375 0.166 0.219 0.633 0.732 0.00158 0.00183 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 471 100 0.997 0.0303 0.391 0.16 0.208 0.713 0.747 0.00178 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 471 15581.031 0.005 0.0335 0.292 0.962 0.165 0.219 0.53 0.645 0.00132 0.00161 +! Validation 471 15581.031 0.005 0.0302 0.31 0.914 0.158 0.208 0.575 0.665 0.00144 0.00166 +Wall time: 15581.031643910334 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 47 1.14 0.0354 0.432 0.17 0.225 0.684 0.786 0.00171 0.00196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 472 100 0.688 0.0319 0.0499 0.164 0.213 0.264 0.267 0.00066 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 472 15614.028 0.005 0.0331 0.455 1.12 0.165 0.218 0.652 0.807 0.00163 0.00202 +! Validation 472 15614.028 0.005 0.0321 0.159 0.8 0.163 0.214 0.38 0.476 0.000949 0.00119 +Wall time: 15614.02886218112 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 47 0.875 0.0328 0.22 0.163 0.216 0.467 0.56 0.00117 0.0014 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 473 100 0.708 0.03 0.108 0.159 0.207 0.361 0.392 0.000903 0.00098 + + + 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 15647.020 0.005 0.0337 0.372 1.05 0.166 0.219 0.574 0.729 0.00144 0.00182 +! Validation 473 15647.020 0.005 0.0298 0.122 0.718 0.157 0.206 0.338 0.417 0.000845 0.00104 +Wall time: 15647.020783371292 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 47 1.12 0.0387 0.345 0.177 0.235 0.612 0.702 0.00153 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 474 100 1.7 0.036 0.983 0.173 0.227 1.17 1.18 0.00292 0.00296 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 474 15680.008 0.005 0.0335 0.666 1.34 0.166 0.219 0.779 0.976 0.00195 0.00244 +! Validation 474 15680.008 0.005 0.0363 0.895 1.62 0.173 0.228 1.04 1.13 0.00261 0.00283 +Wall time: 15680.007922430057 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 47 0.764 0.0329 0.106 0.164 0.217 0.312 0.389 0.000779 0.000972 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 475 100 0.647 0.0298 0.0509 0.158 0.206 0.265 0.27 0.000663 0.000674 + + + 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 15712.988 0.005 0.0341 0.396 1.08 0.167 0.221 0.606 0.753 0.00151 0.00188 +! Validation 475 15712.988 0.005 0.03 0.0993 0.699 0.157 0.207 0.3 0.377 0.000751 0.000942 +Wall time: 15712.98867242923 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 47 2.5 0.031 1.88 0.16 0.21 1.6 1.64 0.004 0.00409 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 476 100 3.34 0.0308 2.73 0.162 0.21 1.96 1.97 0.0049 0.00493 + + + 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 15745.976 0.005 0.0322 0.301 0.946 0.162 0.215 0.509 0.651 0.00127 0.00163 +! Validation 476 15745.976 0.005 0.0302 3.17 3.77 0.159 0.208 2.09 2.13 0.00522 0.00532 +Wall time: 15745.97638265416 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 47 0.765 0.0328 0.11 0.164 0.216 0.298 0.396 0.000744 0.00099 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 477 100 1.19 0.0311 0.566 0.162 0.211 0.871 0.899 0.00218 0.00225 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 477 15778.967 0.005 0.0349 0.859 1.56 0.17 0.223 0.915 1.11 0.00229 0.00277 +! Validation 477 15778.967 0.005 0.0311 0.872 1.49 0.16 0.211 1.05 1.12 0.00262 0.00279 +Wall time: 15778.967209812254 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 47 1.44 0.031 0.825 0.159 0.21 0.985 1.09 0.00246 0.00271 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 478 100 0.624 0.0294 0.0356 0.157 0.205 0.133 0.225 0.000333 0.000563 + + + 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 15811.954 0.005 0.0326 0.326 0.979 0.163 0.216 0.544 0.681 0.00136 0.0017 +! Validation 478 15811.954 0.005 0.0293 0.246 0.833 0.156 0.205 0.481 0.592 0.0012 0.00148 +Wall time: 15811.953911137301 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 47 1.06 0.0324 0.408 0.163 0.215 0.614 0.763 0.00153 0.00191 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 479 100 1.03 0.0296 0.435 0.158 0.206 0.762 0.788 0.00191 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 479 15844.925 0.005 0.0315 0.336 0.965 0.16 0.212 0.553 0.692 0.00138 0.00173 +! Validation 479 15844.925 0.005 0.0296 0.816 1.41 0.156 0.205 0.99 1.08 0.00248 0.0027 +Wall time: 15844.925890399143 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 47 0.77 0.0322 0.126 0.163 0.215 0.348 0.424 0.000871 0.00106 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 480 100 0.685 0.0289 0.107 0.156 0.203 0.346 0.392 0.000865 0.000979 + + + 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 15878.330 0.005 0.0322 0.43 1.07 0.162 0.215 0.639 0.784 0.0016 0.00196 +! Validation 480 15878.330 0.005 0.0288 0.331 0.907 0.154 0.203 0.58 0.688 0.00145 0.00172 +Wall time: 15878.330083569977 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 47 0.711 0.0299 0.112 0.156 0.207 0.299 0.401 0.000747 0.001 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 481 100 0.939 0.0285 0.369 0.155 0.202 0.691 0.726 0.00173 0.00182 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 481 15911.509 0.005 0.0311 0.31 0.931 0.159 0.211 0.55 0.666 0.00137 0.00166 +! Validation 481 15911.509 0.005 0.0281 0.27 0.833 0.153 0.2 0.534 0.621 0.00133 0.00155 +Wall time: 15911.50912200706 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 47 1.68 0.0347 0.983 0.17 0.223 1.07 1.18 0.00267 0.00296 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 482 100 1.28 0.0324 0.631 0.165 0.215 0.931 0.95 0.00233 0.00237 + + + 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 15944.534 0.005 0.0317 0.707 1.34 0.161 0.213 0.839 1 0.0021 0.00251 +! Validation 482 15944.534 0.005 0.0324 0.954 1.6 0.164 0.215 1.09 1.17 0.00274 0.00292 +Wall time: 15944.534624997992 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 47 0.694 0.0305 0.0836 0.158 0.209 0.273 0.346 0.000684 0.000864 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 483 100 0.677 0.0282 0.113 0.154 0.201 0.37 0.401 0.000926 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 483 15977.561 0.005 0.0321 0.417 1.06 0.162 0.214 0.604 0.773 0.00151 0.00193 +! Validation 483 15977.561 0.005 0.0279 0.119 0.678 0.152 0.2 0.331 0.413 0.000826 0.00103 +Wall time: 15977.561074872967 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 47 0.772 0.0295 0.181 0.155 0.205 0.43 0.509 0.00108 0.00127 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 484 100 0.919 0.0279 0.362 0.153 0.199 0.693 0.719 0.00173 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 484 16010.577 0.005 0.0317 0.398 1.03 0.161 0.213 0.572 0.755 0.00143 0.00189 +! Validation 484 16010.577 0.005 0.0276 0.67 1.22 0.151 0.198 0.897 0.978 0.00224 0.00245 +Wall time: 16010.577735101338 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 47 0.739 0.0303 0.134 0.158 0.208 0.343 0.437 0.000858 0.00109 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 485 100 0.743 0.0283 0.177 0.154 0.201 0.466 0.503 0.00117 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 485 16043.599 0.005 0.031 0.474 1.09 0.159 0.21 0.661 0.823 0.00165 0.00206 +! Validation 485 16043.599 0.005 0.0284 0.417 0.984 0.153 0.201 0.676 0.772 0.00169 0.00193 +Wall time: 16043.599073855206 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 47 0.695 0.0303 0.0888 0.158 0.208 0.282 0.356 0.000705 0.00089 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 486 100 0.599 0.0284 0.0312 0.156 0.201 0.172 0.211 0.00043 0.000528 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 486 16076.610 0.005 0.0308 0.389 1 0.159 0.21 0.615 0.746 0.00154 0.00187 +! Validation 486 16076.610 0.005 0.0283 0.127 0.693 0.154 0.201 0.338 0.426 0.000846 0.00106 +Wall time: 16076.610339887906 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 47 1.26 0.0309 0.643 0.159 0.21 0.846 0.958 0.00212 0.0024 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 487 100 0.6 0.0288 0.0232 0.155 0.203 0.145 0.182 0.000363 0.000455 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 487 16109.644 0.005 0.0306 0.49 1.1 0.158 0.209 0.672 0.836 0.00168 0.00209 +! Validation 487 16109.644 0.005 0.0284 0.128 0.696 0.153 0.202 0.342 0.427 0.000854 0.00107 +Wall time: 16109.644599901047 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 47 0.789 0.0325 0.14 0.164 0.215 0.39 0.447 0.000976 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 488 100 0.603 0.0287 0.0291 0.156 0.202 0.138 0.204 0.000345 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 488 16142.660 0.005 0.0324 0.537 1.18 0.163 0.215 0.693 0.876 0.00173 0.00219 +! Validation 488 16142.660 0.005 0.0284 0.121 0.689 0.154 0.201 0.333 0.415 0.000833 0.00104 +Wall time: 16142.659921787214 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 47 0.672 0.0301 0.0699 0.157 0.207 0.253 0.316 0.000632 0.00079 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 489 100 1.11 0.0274 0.564 0.152 0.198 0.876 0.897 0.00219 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 489 16175.688 0.005 0.03 0.324 0.924 0.156 0.207 0.566 0.681 0.00142 0.0017 +! Validation 489 16175.688 0.005 0.0272 0.43 0.973 0.15 0.197 0.69 0.784 0.00172 0.00196 +Wall time: 16175.688098225277 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 47 0.676 0.0287 0.101 0.153 0.203 0.312 0.38 0.00078 0.00095 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 490 100 0.708 0.0273 0.161 0.151 0.198 0.441 0.48 0.0011 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 490 16208.714 0.005 0.03 0.403 1 0.156 0.207 0.626 0.76 0.00156 0.0019 +! Validation 490 16208.714 0.005 0.0268 0.154 0.69 0.148 0.196 0.388 0.469 0.00097 0.00117 +Wall time: 16208.714286317118 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 47 0.699 0.0284 0.132 0.152 0.201 0.351 0.433 0.000878 0.00108 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 491 100 0.64 0.0267 0.105 0.15 0.195 0.353 0.387 0.000883 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 491 16241.770 0.005 0.029 0.303 0.883 0.153 0.203 0.545 0.659 0.00136 0.00165 +! Validation 491 16241.770 0.005 0.0264 0.118 0.646 0.147 0.194 0.328 0.411 0.00082 0.00103 +Wall time: 16241.770759457257 +! Best model 491 0.646 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 47 0.749 0.0304 0.141 0.157 0.208 0.357 0.448 0.000894 0.00112 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 492 100 0.886 0.0268 0.35 0.15 0.196 0.679 0.707 0.0017 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 492 16274.810 0.005 0.0297 0.54 1.13 0.156 0.206 0.734 0.879 0.00183 0.0022 +! Validation 492 16274.810 0.005 0.0266 0.598 1.13 0.148 0.195 0.842 0.924 0.0021 0.00231 +Wall time: 16274.810852251016 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 47 1.01 0.0298 0.412 0.154 0.206 0.689 0.767 0.00172 0.00192 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 493 100 0.578 0.0266 0.0453 0.149 0.195 0.249 0.254 0.000623 0.000636 + + + 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 16307.833 0.005 0.029 0.268 0.847 0.153 0.203 0.506 0.618 0.00126 0.00154 +! Validation 493 16307.833 0.005 0.0262 0.106 0.629 0.147 0.193 0.306 0.389 0.000764 0.000972 +Wall time: 16307.83294807095 +! Best model 493 0.629 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 47 0.943 0.0298 0.346 0.155 0.206 0.592 0.703 0.00148 0.00176 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 494 100 0.625 0.0266 0.0923 0.15 0.195 0.312 0.363 0.000781 0.000908 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 494 16340.868 0.005 0.0292 0.489 1.07 0.154 0.204 0.692 0.836 0.00173 0.00209 +! Validation 494 16340.868 0.005 0.0265 0.213 0.742 0.147 0.194 0.449 0.551 0.00112 0.00138 +Wall time: 16340.867990298197 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 47 1.12 0.0283 0.555 0.151 0.201 0.803 0.891 0.00201 0.00223 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 495 100 1.89 0.0258 1.38 0.147 0.192 1.39 1.4 0.00348 0.00351 + + + 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 16373.886 0.005 0.029 0.305 0.885 0.154 0.203 0.515 0.66 0.00129 0.00165 +! Validation 495 16373.886 0.005 0.0254 1.1 1.61 0.144 0.19 1.19 1.25 0.00298 0.00313 +Wall time: 16373.886122819968 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 47 1.44 0.0277 0.883 0.15 0.199 1.04 1.12 0.00261 0.00281 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 496 100 2.02 0.0258 1.51 0.147 0.192 1.45 1.47 0.00363 0.00367 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 496 16406.894 0.005 0.0283 0.38 0.945 0.152 0.201 0.598 0.735 0.0015 0.00184 +! Validation 496 16406.894 0.005 0.0253 1.28 1.78 0.144 0.19 1.3 1.35 0.00325 0.00338 +Wall time: 16406.893945455085 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 47 1.7 0.0287 1.12 0.153 0.203 1.23 1.27 0.00307 0.00317 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 497 100 0.857 0.0263 0.331 0.149 0.194 0.655 0.688 0.00164 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 497 16439.871 0.005 0.0292 0.467 1.05 0.154 0.204 0.68 0.815 0.0017 0.00204 +! Validation 497 16439.871 0.005 0.0259 0.529 1.05 0.146 0.192 0.779 0.869 0.00195 0.00217 +Wall time: 16439.87097410299 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 47 0.698 0.0306 0.0853 0.157 0.209 0.296 0.349 0.000741 0.000872 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 498 100 0.547 0.0264 0.02 0.149 0.194 0.149 0.169 0.000371 0.000423 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 498 16472.960 0.005 0.0285 0.423 0.993 0.152 0.202 0.621 0.778 0.00155 0.00195 +! Validation 498 16472.960 0.005 0.026 0.0973 0.618 0.146 0.193 0.299 0.373 0.000748 0.000932 +Wall time: 16472.960685977247 +! Best model 498 0.618 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 47 1.34 0.0284 0.773 0.152 0.201 0.996 1.05 0.00249 0.00263 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 499 100 0.954 0.0295 0.363 0.159 0.205 0.697 0.72 0.00174 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 499 16506.278 0.005 0.0281 0.342 0.904 0.151 0.2 0.569 0.698 0.00142 0.00175 +! Validation 499 16506.278 0.005 0.0294 0.26 0.849 0.157 0.205 0.514 0.61 0.00129 0.00152 +Wall time: 16506.277883656323 + +training +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 47 1.05 0.0308 0.431 0.16 0.21 0.728 0.784 0.00182 0.00196 + +validation +# Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse + 500 100 0.634 0.0301 0.0321 0.16 0.207 0.16 0.214 0.0004 0.000536 + + + Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse +! Train 500 16539.256 0.005 0.0292 0.482 1.07 0.154 0.204 0.683 0.83 0.00171 0.00207 +! Validation 500 16539.256 0.005 0.0295 0.153 0.743 0.157 0.205 0.377 0.468 0.000941 0.00117 +Wall time: 16539.256135649048 +! Stop training: max epochs +Wall time: 16539.29577727802 +Cumulative wall time: 16539.29577727802