Train start time: 2024-12-06_23:06:14 Torch device: cuda Processing dataset... Loaded data: Batch(atomic_numbers=[3584000, 1], batch=[3584000], cell=[8000, 3, 3], edge_cell_shift=[138811224, 3], edge_index=[2, 138811224], forces=[3584000, 3], pbc=[8000, 3], pos=[3584000, 3], ptr=[8001], total_energy=[8000, 1]) processed data size: ~5514.67 MB Cached processed data to disk Done! Successfully loaded the data set of type ASEDataset(8000)... Replace string dataset_per_atom_total_energy_mean to -346.8898078190852 Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -346.889808]. Replace string dataset_forces_rms to 1.219386272769641 Initially outputs are globally scaled by: 1.219386272769641, total_energy are globally shifted by None. Successfully built the network... Number of weights: 363624 Number of trainable weights: 363624 ! Starting training ... validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 0 100 33.9 1.03 13.4 0.916 1.23 4.38 4.47 0.00977 0.00997 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 7.373 0.005 0.986 10.3 30 0.898 1.21 3.33 3.92 0.00742 0.00874 Wall time: 7.373131275642663 ! Best model 0 30.038 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 24 32.6 0.99 12.8 0.897 1.21 3.69 4.36 0.00824 0.00974 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 31.3 1.02 10.8 0.915 1.23 3.91 4.01 0.00873 0.00896 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 1 54.710 0.005 1 14.4 34.4 0.902 1.22 3.68 4.63 0.00822 0.0103 ! Validation 1 54.710 0.005 0.986 9.15 28.9 0.897 1.21 3.11 3.69 0.00694 0.00824 Wall time: 54.71052683098242 ! Best model 1 28.869 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 24 27.8 0.976 8.3 0.893 1.2 3 3.51 0.0067 0.00784 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 32.5 1.02 12 0.914 1.23 4.13 4.23 0.00922 0.00943 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 2 101.459 0.005 0.999 10.8 30.8 0.902 1.22 3.21 4.02 0.00717 0.00898 ! Validation 2 101.459 0.005 0.985 8.29 28 0.896 1.21 2.98 3.51 0.00664 0.00784 Wall time: 101.45967078069225 ! Best model 2 27.979 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 24 32.6 1.02 12.2 0.908 1.23 3.51 4.26 0.00784 0.0095 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 32.8 1.02 12.4 0.913 1.23 4.19 4.29 0.00936 0.00958 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 148.201 0.005 0.998 9.26 29.2 0.901 1.22 2.95 3.7 0.00659 0.00825 ! Validation 3 148.201 0.005 0.983 8.51 28.2 0.895 1.21 2.97 3.56 0.00663 0.00794 Wall time: 148.20334526291117 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 24 29.1 0.978 9.53 0.895 1.21 3.19 3.76 0.00711 0.0084 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 30 1.02 9.54 0.911 1.23 3.66 3.77 0.00817 0.00841 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 4 194.933 0.005 0.996 8.25 28.2 0.899 1.22 2.79 3.5 0.00623 0.0078 ! Validation 4 194.933 0.005 0.982 7.71 27.4 0.894 1.21 2.78 3.39 0.00621 0.00756 Wall time: 194.93342707771808 ! Best model 4 27.352 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 24 29.1 1.01 8.84 0.906 1.23 2.84 3.62 0.00635 0.00809 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 39.1 1.02 18.7 0.91 1.23 5.2 5.28 0.0116 0.0118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 242.624 0.005 0.996 7.46 27.4 0.899 1.22 2.64 3.32 0.00588 0.00742 ! Validation 5 242.624 0.005 0.982 9.32 29 0.893 1.21 3.2 3.72 0.00715 0.00831 Wall time: 242.62469789059833 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 24 27.1 0.982 7.48 0.893 1.21 2.83 3.34 0.00631 0.00744 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 31.9 1.02 11.5 0.908 1.23 4.04 4.13 0.00902 0.00923 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 6 289.357 0.005 0.995 6.84 26.7 0.897 1.22 2.48 3.19 0.00553 0.00711 ! Validation 6 289.357 0.005 0.981 6.5 26.1 0.892 1.21 2.61 3.11 0.00583 0.00694 Wall time: 289.35761297494173 ! Best model 6 26.112 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 24 22.7 0.972 3.24 0.885 1.2 1.63 2.19 0.00364 0.0049 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 23 1.01 2.82 0.9 1.22 1.86 2.05 0.00414 0.00457 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 7 336.099 0.005 0.991 5.26 25.1 0.895 1.21 2.15 2.81 0.0048 0.00627 ! Validation 7 336.099 0.005 0.971 4.6 24 0.886 1.2 2 2.62 0.00446 0.00584 Wall time: 336.10018220962957 ! Best model 7 24.028 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 24 20.2 0.937 1.45 0.861 1.18 1.19 1.47 0.00266 0.00328 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 19.8 0.952 0.743 0.867 1.19 0.759 1.05 0.00169 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 8 382.839 0.005 0.966 3.05 22.4 0.88 1.2 1.64 2.14 0.00367 0.00478 ! Validation 8 382.839 0.005 0.922 1.53 20 0.858 1.17 1.19 1.51 0.00266 0.00336 Wall time: 382.83962535392493 ! Best model 8 19.971 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 24 18.6 0.875 1.15 0.832 1.14 0.972 1.31 0.00217 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 18.5 0.884 0.813 0.836 1.15 0.937 1.1 0.00209 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 9 429.577 0.005 0.906 2.07 20.2 0.85 1.16 1.4 1.77 0.00313 0.00394 ! Validation 9 429.577 0.005 0.859 2.12 19.3 0.829 1.13 1.38 1.77 0.00308 0.00396 Wall time: 429.57734711887315 ! Best model 9 19.302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 24 14.8 0.69 1 0.751 1.01 0.985 1.22 0.0022 0.00272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 14.5 0.671 1.1 0.739 0.999 0.99 1.28 0.00221 0.00285 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 10 476.326 0.005 0.796 1.49 17.4 0.801 1.09 1.18 1.5 0.00262 0.00334 ! Validation 10 476.326 0.005 0.678 1.96 15.5 0.744 1 1.41 1.71 0.00315 0.00381 Wall time: 476.3265172187239 ! Best model 10 15.529 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 24 11.4 0.526 0.908 0.656 0.884 0.884 1.16 0.00197 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 10.3 0.485 0.606 0.635 0.849 0.782 0.949 0.00175 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 11 523.179 0.005 0.591 1.71 13.5 0.698 0.938 1.29 1.6 0.00289 0.00358 ! Validation 11 523.179 0.005 0.517 1.6 11.9 0.652 0.877 1.24 1.54 0.00276 0.00344 Wall time: 523.1794811235741 ! Best model 11 11.941 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 24 11.1 0.415 2.84 0.582 0.786 1.76 2.05 0.00393 0.00458 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 11.6 0.386 3.91 0.567 0.757 2.33 2.41 0.0052 0.00538 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 12 570.094 0.005 0.456 1.62 10.7 0.613 0.825 1.24 1.54 0.00276 0.00343 ! Validation 12 570.094 0.005 0.417 3.24 11.6 0.584 0.787 1.92 2.19 0.00429 0.0049 Wall time: 570.0949395429343 ! Best model 12 11.571 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 24 8.43 0.366 1.11 0.548 0.738 0.994 1.29 0.00222 0.00287 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 6.83 0.33 0.232 0.524 0.7 0.435 0.588 0.00097 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 13 616.822 0.005 0.386 1.36 9.08 0.562 0.758 1.14 1.42 0.00254 0.00318 ! Validation 13 616.822 0.005 0.363 1.17 8.43 0.544 0.735 1.04 1.32 0.00232 0.00294 Wall time: 616.8220609487034 ! Best model 13 8.428 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 24 7.3 0.321 0.89 0.513 0.691 0.884 1.15 0.00197 0.00257 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 6.03 0.291 0.204 0.494 0.658 0.354 0.551 0.00079 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 14 663.559 0.005 0.34 0.859 7.67 0.528 0.712 0.91 1.13 0.00203 0.00252 ! Validation 14 663.559 0.005 0.325 1.26 7.75 0.516 0.695 1.08 1.37 0.00241 0.00305 Wall time: 663.5594280716032 ! Best model 14 7.754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 24 6.42 0.292 0.583 0.49 0.659 0.777 0.931 0.00173 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 6.67 0.263 1.4 0.471 0.626 1.39 1.44 0.0031 0.00322 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 15 715.216 0.005 0.308 0.684 6.83 0.502 0.677 0.798 1.01 0.00178 0.00226 ! Validation 15 715.216 0.005 0.296 1.12 7.05 0.493 0.664 1.09 1.29 0.00243 0.00288 Wall time: 715.2167201605625 ! Best model 15 7.046 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 24 6.08 0.28 0.48 0.48 0.645 0.691 0.845 0.00154 0.00189 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 6.22 0.248 1.26 0.455 0.607 1.31 1.37 0.00293 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 16 761.870 0.005 0.285 1.37 7.07 0.483 0.651 1.2 1.44 0.00269 0.00321 ! Validation 16 761.870 0.005 0.279 0.977 6.56 0.477 0.644 1.01 1.21 0.00226 0.00269 Wall time: 761.8702823398635 ! Best model 16 6.555 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 24 5.94 0.25 0.937 0.453 0.61 1.05 1.18 0.00234 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 5.04 0.226 0.512 0.437 0.58 0.803 0.873 0.00179 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 17 808.526 0.005 0.265 0.873 6.17 0.467 0.628 0.904 1.14 0.00202 0.00254 ! Validation 17 808.526 0.005 0.257 0.622 5.76 0.459 0.618 0.788 0.962 0.00176 0.00215 Wall time: 808.5266567096114 ! Best model 17 5.758 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 24 4.86 0.235 0.164 0.439 0.591 0.406 0.494 0.000907 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 5.84 0.209 1.66 0.42 0.557 1.54 1.57 0.00343 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 18 855.183 0.005 0.245 0.806 5.71 0.449 0.604 0.878 1.11 0.00196 0.00247 ! Validation 18 855.183 0.005 0.237 1.15 5.9 0.442 0.594 1.12 1.31 0.0025 0.00292 Wall time: 855.1839333069511 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 24 5.43 0.222 0.988 0.428 0.575 1.07 1.21 0.00238 0.00271 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 3.99 0.194 0.109 0.405 0.537 0.365 0.403 0.000814 0.000899 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 19 901.824 0.005 0.227 1.08 5.62 0.433 0.581 1.04 1.27 0.00233 0.00283 ! Validation 19 901.824 0.005 0.221 0.708 5.14 0.428 0.574 0.796 1.03 0.00178 0.00229 Wall time: 901.8244630736299 ! Best model 19 5.137 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 24 4.94 0.209 0.766 0.414 0.557 0.889 1.07 0.00198 0.00238 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 3.65 0.179 0.0729 0.389 0.516 0.265 0.329 0.000591 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 20 948.482 0.005 0.212 0.627 4.87 0.419 0.562 0.776 0.963 0.00173 0.00215 ! Validation 20 948.482 0.005 0.204 0.979 5.07 0.411 0.551 0.925 1.21 0.00207 0.00269 Wall time: 948.4822563417256 ! Best model 20 5.068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 24 5.63 0.198 1.66 0.405 0.543 1.4 1.57 0.00314 0.00351 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 3.68 0.17 0.28 0.379 0.503 0.558 0.645 0.00125 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 21 995.123 0.005 0.198 0.993 4.94 0.404 0.542 0.976 1.21 0.00218 0.00269 ! Validation 21 995.123 0.005 0.194 1.48 5.35 0.401 0.536 1.22 1.48 0.00272 0.00331 Wall time: 995.1233553700149 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 24 3.84 0.179 0.262 0.387 0.516 0.476 0.624 0.00106 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 3.58 0.159 0.389 0.367 0.487 0.701 0.761 0.00156 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 22 1041.763 0.005 0.187 0.826 4.57 0.394 0.528 0.907 1.12 0.00202 0.00249 ! Validation 22 1041.763 0.005 0.182 0.53 4.17 0.389 0.52 0.716 0.888 0.0016 0.00198 Wall time: 1041.7639522529207 ! Best model 22 4.166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 24 4.41 0.17 1.02 0.376 0.502 1.13 1.23 0.00252 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 3.67 0.149 0.685 0.356 0.471 0.968 1.01 0.00216 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 23 1088.420 0.005 0.176 0.584 4.1 0.382 0.512 0.745 0.923 0.00166 0.00206 ! Validation 23 1088.420 0.005 0.171 0.583 3.99 0.377 0.504 0.766 0.931 0.00171 0.00208 Wall time: 1088.420826242771 ! Best model 23 3.994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 24 4.53 0.161 1.32 0.367 0.489 1.28 1.4 0.00287 0.00312 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 3.48 0.141 0.653 0.346 0.458 0.945 0.985 0.00211 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 24 1135.060 0.005 0.166 0.61 3.94 0.372 0.498 0.75 0.939 0.00167 0.0021 ! Validation 24 1135.060 0.005 0.161 2.23 5.46 0.367 0.49 1.6 1.82 0.00357 0.00407 Wall time: 1135.0600083549507 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 24 3.86 0.15 0.857 0.353 0.472 0.956 1.13 0.00213 0.00252 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 2.78 0.135 0.072 0.338 0.448 0.268 0.327 0.000597 0.00073 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 1181.712 0.005 0.159 0.836 4.02 0.364 0.487 0.916 1.11 0.00204 0.00249 ! Validation 25 1181.712 0.005 0.155 0.793 3.89 0.36 0.48 0.835 1.09 0.00186 0.00242 Wall time: 1181.712044748012 ! Best model 25 3.890 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 24 3.3 0.151 0.276 0.355 0.474 0.542 0.641 0.00121 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 2.8 0.131 0.185 0.332 0.441 0.445 0.525 0.000994 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 26 1228.364 0.005 0.153 0.78 3.84 0.357 0.477 0.872 1.09 0.00195 0.00242 ! Validation 26 1228.364 0.005 0.149 0.576 3.56 0.354 0.471 0.73 0.925 0.00163 0.00207 Wall time: 1228.3652265099809 ! Best model 26 3.565 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 24 3.11 0.138 0.35 0.339 0.453 0.582 0.721 0.0013 0.00161 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 3.21 0.125 0.697 0.326 0.432 0.983 1.02 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 27 1275.012 0.005 0.147 0.508 3.45 0.35 0.468 0.692 0.873 0.00155 0.00195 ! Validation 27 1275.012 0.005 0.143 0.566 3.43 0.346 0.461 0.754 0.917 0.00168 0.00205 Wall time: 1275.0125504028983 ! Best model 27 3.431 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 24 3.17 0.139 0.392 0.342 0.455 0.621 0.763 0.00139 0.0017 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 2.84 0.12 0.439 0.319 0.423 0.761 0.808 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 28 1321.668 0.005 0.141 0.545 3.37 0.344 0.458 0.715 0.903 0.0016 0.00202 ! Validation 28 1321.668 0.005 0.138 0.559 3.31 0.34 0.452 0.732 0.912 0.00163 0.00203 Wall time: 1321.6682605198584 ! Best model 28 3.311 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 24 3.11 0.141 0.294 0.342 0.457 0.519 0.662 0.00116 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 4.01 0.118 1.65 0.316 0.419 1.54 1.57 0.00344 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 29 1368.320 0.005 0.138 0.911 3.66 0.339 0.452 0.966 1.17 0.00216 0.00262 ! Validation 29 1368.320 0.005 0.135 0.967 3.66 0.336 0.448 1.01 1.2 0.00225 0.00268 Wall time: 1368.3207433177158 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 24 2.93 0.132 0.288 0.333 0.444 0.559 0.655 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 30 100 2.34 0.115 0.0526 0.312 0.413 0.244 0.28 0.000544 0.000625 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 30 1414.958 0.005 0.134 0.712 3.39 0.335 0.446 0.847 1.04 0.00189 0.00231 ! Validation 30 1414.958 0.005 0.131 0.838 3.47 0.333 0.442 0.871 1.12 0.00194 0.00249 Wall time: 1414.9595581446774 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 24 2.71 0.122 0.274 0.319 0.425 0.467 0.638 0.00104 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 2.29 0.112 0.0581 0.308 0.407 0.259 0.294 0.000578 0.000656 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 31 1461.604 0.005 0.13 0.612 3.22 0.331 0.44 0.767 0.96 0.00171 0.00214 ! Validation 31 1461.604 0.005 0.128 0.993 3.55 0.328 0.436 0.963 1.22 0.00215 0.00271 Wall time: 1461.604566482827 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 24 2.96 0.132 0.331 0.332 0.442 0.57 0.702 0.00127 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 2.73 0.109 0.552 0.304 0.402 0.864 0.906 0.00193 0.00202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 32 1508.256 0.005 0.128 0.662 3.21 0.327 0.435 0.802 0.998 0.00179 0.00223 ! Validation 32 1508.256 0.005 0.125 0.509 3 0.324 0.431 0.708 0.87 0.00158 0.00194 Wall time: 1508.256317392923 ! Best model 32 3.004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 24 3.52 0.12 1.11 0.32 0.423 1.15 1.29 0.00258 0.00287 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 2.18 0.106 0.0616 0.3 0.397 0.259 0.303 0.000578 0.000675 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 1554.910 0.005 0.124 0.646 3.13 0.323 0.43 0.785 0.971 0.00175 0.00217 ! Validation 33 1554.910 0.005 0.122 1.03 3.47 0.321 0.426 0.981 1.24 0.00219 0.00277 Wall time: 1554.9110840647481 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 24 3.74 0.126 1.23 0.325 0.432 1.12 1.35 0.0025 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 2.47 0.107 0.334 0.3 0.398 0.643 0.705 0.00144 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 34 1601.639 0.005 0.122 0.952 3.4 0.321 0.426 0.972 1.19 0.00217 0.00265 ! Validation 34 1601.639 0.005 0.123 0.578 3.03 0.321 0.427 0.744 0.927 0.00166 0.00207 Wall time: 1601.6393761667423 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 24 3.43 0.124 0.954 0.323 0.429 1.02 1.19 0.00227 0.00266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 4.13 0.103 2.06 0.296 0.392 1.73 1.75 0.00386 0.00391 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 1648.284 0.005 0.121 0.582 2.99 0.319 0.423 0.753 0.923 0.00168 0.00206 ! Validation 35 1648.284 0.005 0.118 1.21 3.58 0.316 0.42 1.14 1.34 0.00255 0.00299 Wall time: 1648.2849893118255 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 24 2.94 0.115 0.639 0.312 0.413 0.8 0.975 0.00178 0.00218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 3.29 0.101 1.27 0.293 0.388 1.35 1.37 0.00301 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 36 1694.930 0.005 0.118 0.696 3.06 0.316 0.419 0.825 1.02 0.00184 0.00227 ! Validation 36 1694.930 0.005 0.116 0.753 3.07 0.313 0.415 0.884 1.06 0.00197 0.00236 Wall time: 1694.9302066527307 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 24 2.5 0.111 0.286 0.307 0.405 0.55 0.652 0.00123 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 2.52 0.0982 0.556 0.289 0.382 0.87 0.909 0.00194 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 37 1741.574 0.005 0.115 0.381 2.69 0.312 0.414 0.596 0.755 0.00133 0.00169 ! Validation 37 1741.574 0.005 0.113 0.493 2.75 0.31 0.41 0.702 0.856 0.00157 0.00191 Wall time: 1741.5748607316054 ! Best model 37 2.754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 24 3.19 0.109 1 0.304 0.403 1.13 1.22 0.00252 0.00273 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 3.24 0.0969 1.3 0.288 0.38 1.36 1.39 0.00304 0.0031 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 38 1788.226 0.005 0.113 0.522 2.78 0.309 0.41 0.704 0.871 0.00157 0.00194 ! Validation 38 1788.226 0.005 0.111 3.17 5.39 0.308 0.406 2.01 2.17 0.00449 0.00485 Wall time: 1788.226605368778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 24 3.54 0.111 1.32 0.306 0.406 1.32 1.4 0.00295 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 1.99 0.0952 0.086 0.285 0.376 0.275 0.358 0.000613 0.000798 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 39 1834.878 0.005 0.111 0.715 2.94 0.307 0.407 0.814 1.02 0.00182 0.00228 ! Validation 39 1834.878 0.005 0.109 0.558 2.74 0.305 0.403 0.715 0.911 0.00159 0.00203 Wall time: 1834.8783758846112 ! Best model 39 2.744 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 24 3.19 0.109 1.02 0.304 0.402 1.02 1.23 0.00229 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 2.03 0.0936 0.16 0.283 0.373 0.412 0.488 0.000919 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 40 1881.538 0.005 0.11 0.537 2.73 0.305 0.404 0.717 0.884 0.0016 0.00197 ! Validation 40 1881.538 0.005 0.108 0.483 2.64 0.303 0.4 0.668 0.848 0.00149 0.00189 Wall time: 1881.53822294157 ! Best model 40 2.636 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 24 2.37 0.101 0.346 0.294 0.388 0.564 0.718 0.00126 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 1.93 0.0919 0.0952 0.28 0.37 0.334 0.376 0.000746 0.00084 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 1928.185 0.005 0.108 0.462 2.62 0.302 0.401 0.667 0.831 0.00149 0.00186 ! Validation 41 1928.185 0.005 0.106 1.09 3.21 0.3 0.397 1.03 1.28 0.00231 0.00285 Wall time: 1928.1853231918067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 24 2.89 0.108 0.727 0.302 0.401 0.924 1.04 0.00206 0.00232 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 1.88 0.0906 0.0649 0.278 0.367 0.265 0.311 0.000592 0.000693 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 42 1974.819 0.005 0.106 0.542 2.66 0.299 0.397 0.726 0.894 0.00162 0.002 ! Validation 42 1974.819 0.005 0.105 0.659 2.75 0.298 0.394 0.776 0.99 0.00173 0.00221 Wall time: 1974.8196067856625 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 24 2.49 0.102 0.45 0.294 0.389 0.66 0.818 0.00147 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 1.93 0.0889 0.15 0.276 0.364 0.399 0.472 0.000891 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 43 2021.460 0.005 0.104 0.423 2.51 0.297 0.394 0.638 0.792 0.00142 0.00177 ! Validation 43 2021.460 0.005 0.102 1.1 3.15 0.295 0.39 1.06 1.28 0.00237 0.00286 Wall time: 2021.4606664245948 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 24 2.74 0.107 0.595 0.301 0.4 0.832 0.941 0.00186 0.0021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 2.01 0.0877 0.26 0.274 0.361 0.558 0.622 0.00125 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 44 2068.108 0.005 0.103 0.563 2.62 0.295 0.391 0.749 0.914 0.00167 0.00204 ! Validation 44 2068.108 0.005 0.101 1.5 3.53 0.294 0.388 1.28 1.49 0.00285 0.00333 Wall time: 2068.108510644641 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 24 3.1 0.105 1.01 0.297 0.395 1.1 1.22 0.00245 0.00273 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 2.78 0.0872 1.03 0.272 0.36 1.21 1.24 0.00269 0.00277 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 45 2114.749 0.005 0.102 0.783 2.82 0.294 0.389 0.91 1.08 0.00203 0.0024 ! Validation 45 2114.749 0.005 0.101 0.661 2.68 0.292 0.387 0.831 0.992 0.00185 0.00221 Wall time: 2114.749950878788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 24 2.31 0.0995 0.316 0.29 0.385 0.581 0.685 0.0013 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 1.89 0.0857 0.173 0.27 0.357 0.425 0.507 0.000948 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 46 2161.394 0.005 0.101 0.412 2.42 0.292 0.387 0.633 0.785 0.00141 0.00175 ! Validation 46 2161.394 0.005 0.099 0.442 2.42 0.29 0.384 0.642 0.811 0.00143 0.00181 Wall time: 2161.394076003693 ! Best model 46 2.422 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 24 2.6 0.0991 0.619 0.289 0.384 0.742 0.959 0.00166 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 2.01 0.0854 0.306 0.27 0.356 0.612 0.674 0.00137 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 47 2208.045 0.005 0.0989 0.576 2.55 0.29 0.384 0.746 0.924 0.00167 0.00206 ! Validation 47 2208.045 0.005 0.0986 1.44 3.41 0.289 0.383 1.25 1.46 0.0028 0.00327 Wall time: 2208.0458731628023 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 24 2.23 0.0935 0.359 0.283 0.373 0.599 0.73 0.00134 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.18 0.0834 0.509 0.267 0.352 0.824 0.87 0.00184 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 48 2254.679 0.005 0.0977 0.374 2.33 0.288 0.381 0.596 0.746 0.00133 0.00166 ! Validation 48 2254.679 0.005 0.0964 0.488 2.42 0.286 0.379 0.702 0.852 0.00157 0.0019 Wall time: 2254.679268806707 ! Best model 48 2.417 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 24 2.68 0.0982 0.711 0.289 0.382 0.921 1.03 0.00206 0.00229 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.79 0.0843 1.1 0.268 0.354 1.24 1.28 0.00278 0.00286 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 49 2301.328 0.005 0.0963 0.752 2.68 0.286 0.378 0.833 1.06 0.00186 0.00236 ! Validation 49 2301.328 0.005 0.097 2.77 4.71 0.287 0.38 1.87 2.03 0.00417 0.00453 Wall time: 2301.328574196901 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 24 2.06 0.0945 0.169 0.284 0.375 0.435 0.501 0.000972 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.05 0.0823 0.409 0.265 0.35 0.728 0.78 0.00162 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 50 2347.962 0.005 0.0969 0.608 2.55 0.287 0.38 0.772 0.959 0.00172 0.00214 ! Validation 50 2347.962 0.005 0.0947 0.421 2.32 0.284 0.375 0.649 0.791 0.00145 0.00177 Wall time: 2347.9626172278076 ! Best model 50 2.316 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 24 2.2 0.0968 0.266 0.286 0.379 0.492 0.629 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 51 100 1.93 0.0803 0.324 0.262 0.345 0.635 0.694 0.00142 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 51 2394.612 0.005 0.0944 0.269 2.16 0.283 0.374 0.502 0.633 0.00112 0.00141 ! Validation 51 2394.612 0.005 0.0928 0.402 2.26 0.281 0.371 0.629 0.773 0.0014 0.00173 Wall time: 2394.612074984703 ! Best model 51 2.257 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 24 2.05 0.0902 0.242 0.276 0.366 0.494 0.6 0.0011 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 2.12 0.0791 0.538 0.26 0.343 0.85 0.895 0.0019 0.002 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 2441.262 0.005 0.0927 0.375 2.23 0.281 0.371 0.598 0.75 0.00134 0.00167 ! Validation 52 2441.262 0.005 0.0914 0.451 2.28 0.279 0.369 0.677 0.819 0.00151 0.00183 Wall time: 2441.2621438307688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 24 2.04 0.0881 0.282 0.274 0.362 0.446 0.647 0.000995 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 1.65 0.0785 0.0773 0.259 0.342 0.3 0.339 0.00067 0.000757 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 53 2487.905 0.005 0.0917 0.588 2.42 0.279 0.369 0.771 0.941 0.00172 0.0021 ! Validation 53 2487.905 0.005 0.0907 0.688 2.5 0.278 0.367 0.808 1.01 0.0018 0.00226 Wall time: 2487.9056172198616 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 24 2.51 0.0831 0.853 0.267 0.352 1.03 1.13 0.00229 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 1.61 0.0776 0.0549 0.258 0.34 0.238 0.286 0.000531 0.000638 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 54 2534.537 0.005 0.0907 0.59 2.4 0.278 0.368 0.773 0.931 0.00173 0.00208 ! Validation 54 2534.537 0.005 0.0899 0.611 2.41 0.277 0.366 0.753 0.953 0.00168 0.00213 Wall time: 2534.53788970178 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 24 2.01 0.0885 0.244 0.274 0.363 0.494 0.602 0.0011 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 2.18 0.0764 0.649 0.256 0.337 0.943 0.982 0.0021 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 55 2581.180 0.005 0.0896 0.328 2.12 0.276 0.365 0.572 0.7 0.00128 0.00156 ! Validation 55 2581.180 0.005 0.0886 0.512 2.28 0.275 0.363 0.729 0.873 0.00163 0.00195 Wall time: 2581.180105597712 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 24 2.35 0.0871 0.606 0.273 0.36 0.842 0.949 0.00188 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 1.56 0.0751 0.0541 0.254 0.334 0.248 0.284 0.000553 0.000633 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 2627.825 0.005 0.0882 0.259 2.02 0.274 0.362 0.483 0.61 0.00108 0.00136 ! Validation 56 2627.825 0.005 0.0869 0.609 2.35 0.272 0.359 0.754 0.952 0.00168 0.00212 Wall time: 2627.825552749913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 24 2.52 0.0897 0.727 0.276 0.365 0.929 1.04 0.00207 0.00232 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 1.6 0.0742 0.119 0.252 0.332 0.375 0.421 0.000836 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 57 2674.563 0.005 0.0868 0.414 2.15 0.272 0.359 0.632 0.778 0.00141 0.00174 ! Validation 57 2674.563 0.005 0.0861 0.82 2.54 0.271 0.358 0.9 1.1 0.00201 0.00246 Wall time: 2674.5640586027876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 24 2.05 0.0889 0.271 0.273 0.364 0.515 0.635 0.00115 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 1.52 0.0733 0.0562 0.251 0.33 0.242 0.289 0.000541 0.000645 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 2721.217 0.005 0.0859 0.436 2.15 0.27 0.357 0.666 0.809 0.00149 0.00181 ! Validation 58 2721.217 0.005 0.085 0.497 2.2 0.269 0.356 0.674 0.859 0.00151 0.00192 Wall time: 2721.2175474697724 ! Best model 58 2.197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 24 1.77 0.0824 0.123 0.265 0.35 0.367 0.427 0.00082 0.000953 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.81 0.0725 0.357 0.249 0.328 0.668 0.729 0.00149 0.00163 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 59 2767.863 0.005 0.0847 0.31 2 0.268 0.355 0.548 0.684 0.00122 0.00153 ! Validation 59 2767.863 0.005 0.0839 0.41 2.09 0.267 0.353 0.644 0.781 0.00144 0.00174 Wall time: 2767.8633029446937 ! Best model 59 2.088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 24 2.3 0.0778 0.739 0.258 0.34 0.975 1.05 0.00218 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 1.77 0.0717 0.336 0.248 0.327 0.649 0.707 0.00145 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 60 2814.522 0.005 0.0835 0.399 2.07 0.267 0.353 0.598 0.762 0.00134 0.0017 ! Validation 60 2814.522 0.005 0.083 0.421 2.08 0.266 0.351 0.651 0.792 0.00145 0.00177 Wall time: 2814.5227071819827 ! Best model 60 2.082 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 24 1.92 0.0802 0.318 0.261 0.345 0.605 0.688 0.00135 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 1.48 0.0704 0.0728 0.246 0.324 0.283 0.329 0.000631 0.000734 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 61 2861.166 0.005 0.0827 0.284 1.94 0.265 0.351 0.519 0.649 0.00116 0.00145 ! Validation 61 2861.166 0.005 0.0817 0.597 2.23 0.264 0.348 0.75 0.942 0.00167 0.0021 Wall time: 2861.1662712539546 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 24 1.96 0.0839 0.28 0.267 0.353 0.549 0.645 0.00122 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 1.62 0.0706 0.21 0.246 0.324 0.478 0.559 0.00107 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 62 2907.814 0.005 0.0816 0.571 2.2 0.263 0.348 0.753 0.927 0.00168 0.00207 ! Validation 62 2907.814 0.005 0.0817 0.364 2 0.264 0.349 0.599 0.736 0.00134 0.00164 Wall time: 2907.814914440736 ! Best model 62 1.998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 24 3.27 0.0805 1.66 0.261 0.346 1.49 1.57 0.00332 0.00351 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 2.71 0.0693 1.32 0.244 0.321 1.37 1.4 0.00306 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 63 2954.471 0.005 0.081 0.376 2 0.262 0.347 0.57 0.716 0.00127 0.0016 ! Validation 63 2954.471 0.005 0.0801 2.55 4.15 0.261 0.345 1.8 1.95 0.00402 0.00435 Wall time: 2954.47169650672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 24 1.78 0.0756 0.271 0.255 0.335 0.514 0.635 0.00115 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 1.45 0.0685 0.081 0.243 0.319 0.265 0.347 0.000592 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 64 3001.101 0.005 0.0802 0.451 2.06 0.261 0.346 0.656 0.822 0.00146 0.00184 ! Validation 64 3001.101 0.005 0.0795 0.346 1.94 0.261 0.344 0.567 0.717 0.00127 0.0016 Wall time: 3001.1019596518017 ! Best model 64 1.936 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 24 1.84 0.08 0.244 0.261 0.345 0.486 0.602 0.00108 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.6 0.0683 0.237 0.243 0.319 0.519 0.593 0.00116 0.00132 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 65 3047.752 0.005 0.0793 0.484 2.07 0.26 0.343 0.693 0.854 0.00155 0.00191 ! Validation 65 3047.752 0.005 0.0791 0.941 2.52 0.26 0.343 0.987 1.18 0.0022 0.00264 Wall time: 3047.752368479967 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 24 1.95 0.0823 0.307 0.264 0.35 0.567 0.676 0.00127 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.49 0.0684 0.127 0.242 0.319 0.382 0.435 0.000852 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 66 3094.401 0.005 0.0792 0.706 2.29 0.259 0.343 0.875 1.03 0.00195 0.0023 ! Validation 66 3094.401 0.005 0.0792 0.727 2.31 0.259 0.343 0.846 1.04 0.00189 0.00232 Wall time: 3094.401871292852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 24 1.79 0.0755 0.283 0.255 0.335 0.505 0.649 0.00113 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.52 0.0675 0.17 0.241 0.317 0.438 0.503 0.000978 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 67 3141.052 0.005 0.0786 0.461 2.03 0.258 0.342 0.675 0.832 0.00151 0.00186 ! Validation 67 3141.052 0.005 0.0781 0.817 2.38 0.258 0.341 0.908 1.1 0.00203 0.00246 Wall time: 3141.0525906449184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 24 1.94 0.0765 0.412 0.255 0.337 0.673 0.783 0.0015 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.4 0.0668 0.0623 0.24 0.315 0.255 0.304 0.000568 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 68 3187.694 0.005 0.0776 0.452 2 0.257 0.34 0.679 0.821 0.00152 0.00183 ! Validation 68 3187.694 0.005 0.0772 0.396 1.94 0.256 0.339 0.603 0.767 0.00135 0.00171 Wall time: 3187.6943796789274 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 24 1.7 0.0758 0.186 0.255 0.336 0.401 0.526 0.000895 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.4 0.0667 0.0669 0.239 0.315 0.258 0.315 0.000577 0.000704 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 69 3234.339 0.005 0.0777 0.69 2.24 0.257 0.34 0.846 1.02 0.00189 0.00228 ! Validation 69 3234.339 0.005 0.0771 0.4 1.94 0.256 0.339 0.607 0.771 0.00136 0.00172 Wall time: 3234.3394695417956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 24 2.21 0.0761 0.693 0.255 0.336 0.925 1.02 0.00207 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 2.03 0.0658 0.717 0.238 0.313 0.989 1.03 0.00221 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 70 3280.993 0.005 0.0767 0.418 1.95 0.255 0.338 0.637 0.782 0.00142 0.00175 ! Validation 70 3280.993 0.005 0.0764 0.615 2.14 0.255 0.337 0.804 0.956 0.00179 0.00213 Wall time: 3280.993809401989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 24 1.87 0.0784 0.3 0.258 0.341 0.48 0.668 0.00107 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 1.36 0.065 0.0573 0.237 0.311 0.243 0.292 0.000542 0.000652 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 71 3327.645 0.005 0.076 0.449 1.97 0.254 0.336 0.676 0.821 0.00151 0.00183 ! Validation 71 3327.645 0.005 0.0752 0.456 1.96 0.253 0.334 0.645 0.823 0.00144 0.00184 Wall time: 3327.645371461753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 24 1.7 0.0782 0.14 0.257 0.341 0.383 0.457 0.000855 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.76 0.0642 0.475 0.235 0.309 0.787 0.84 0.00176 0.00188 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 72 3374.284 0.005 0.0751 0.475 1.98 0.253 0.334 0.698 0.847 0.00156 0.00189 ! Validation 72 3374.284 0.005 0.0747 0.463 1.96 0.252 0.333 0.694 0.83 0.00155 0.00185 Wall time: 3374.2840567617677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 24 1.73 0.0748 0.228 0.252 0.334 0.439 0.583 0.000981 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 1.43 0.0637 0.157 0.235 0.308 0.422 0.484 0.000942 0.00108 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 73 3420.936 0.005 0.0743 0.435 1.92 0.251 0.332 0.675 0.809 0.00151 0.00181 ! Validation 73 3420.936 0.005 0.074 0.692 2.17 0.251 0.332 0.827 1.01 0.00185 0.00226 Wall time: 3420.936280767899 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 24 1.81 0.0752 0.311 0.253 0.334 0.596 0.68 0.00133 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.87 0.0641 0.585 0.235 0.309 0.885 0.933 0.00197 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 74 3467.584 0.005 0.0744 0.721 2.21 0.251 0.332 0.887 1.04 0.00198 0.00233 ! Validation 74 3467.584 0.005 0.0742 0.568 2.05 0.251 0.332 0.766 0.919 0.00171 0.00205 Wall time: 3467.584918266628 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 24 1.74 0.0728 0.289 0.249 0.329 0.521 0.655 0.00116 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.35 0.0629 0.0915 0.233 0.306 0.267 0.369 0.000595 0.000823 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 75 3514.225 0.005 0.0736 0.301 1.77 0.25 0.331 0.544 0.669 0.00122 0.00149 ! Validation 75 3514.225 0.005 0.0729 0.324 1.78 0.25 0.329 0.56 0.694 0.00125 0.00155 Wall time: 3514.2258823625743 ! Best model 75 1.782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 24 1.65 0.073 0.187 0.249 0.329 0.4 0.528 0.000892 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.31 0.0623 0.0601 0.232 0.304 0.244 0.299 0.000545 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 76 3560.881 0.005 0.0725 0.399 1.85 0.248 0.328 0.631 0.776 0.00141 0.00173 ! Validation 76 3560.881 0.005 0.0722 0.384 1.83 0.248 0.328 0.597 0.756 0.00133 0.00169 Wall time: 3560.882144843694 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 24 1.64 0.0707 0.224 0.245 0.324 0.471 0.577 0.00105 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.32 0.0611 0.0937 0.23 0.301 0.281 0.373 0.000626 0.000833 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 3607.525 0.005 0.0715 0.239 1.67 0.247 0.326 0.479 0.597 0.00107 0.00133 ! Validation 77 3607.525 0.005 0.0711 0.319 1.74 0.246 0.325 0.559 0.688 0.00125 0.00154 Wall time: 3607.525730521884 ! Best model 77 1.740 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 24 1.46 0.067 0.12 0.24 0.316 0.355 0.423 0.000792 0.000943 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 1.27 0.0604 0.0614 0.229 0.3 0.244 0.302 0.000544 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 78 3654.179 0.005 0.0705 0.266 1.68 0.245 0.324 0.504 0.633 0.00112 0.00141 ! Validation 78 3654.179 0.005 0.0702 0.337 1.74 0.245 0.323 0.565 0.708 0.00126 0.00158 Wall time: 3654.1796965636313 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 24 1.47 0.0694 0.0801 0.244 0.321 0.25 0.345 0.000558 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 1.28 0.0598 0.0784 0.228 0.298 0.263 0.341 0.000587 0.000762 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 79 3700.812 0.005 0.0697 0.263 1.66 0.244 0.322 0.504 0.63 0.00112 0.00141 ! Validation 79 3700.812 0.005 0.0694 0.337 1.73 0.243 0.321 0.566 0.708 0.00126 0.00158 Wall time: 3700.812375654932 ! Best model 79 1.726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 24 2.06 0.068 0.701 0.241 0.318 0.948 1.02 0.00212 0.00228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 3.09 0.0591 1.9 0.226 0.296 1.66 1.68 0.0037 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 80 3747.570 0.005 0.069 0.429 1.81 0.242 0.32 0.639 0.792 0.00143 0.00177 ! Validation 80 3747.570 0.005 0.0688 1.68 3.06 0.242 0.32 1.43 1.58 0.00319 0.00353 Wall time: 3747.5707909315825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 24 2.02 0.0671 0.677 0.239 0.316 0.879 1 0.00196 0.00224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.26 0.0594 0.0729 0.227 0.297 0.258 0.329 0.000577 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 81 3794.225 0.005 0.0692 0.669 2.05 0.243 0.321 0.835 0.997 0.00186 0.00223 ! Validation 81 3794.225 0.005 0.0689 0.324 1.7 0.242 0.32 0.557 0.694 0.00124 0.00155 Wall time: 3794.225852034986 ! Best model 81 1.701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 24 1.59 0.0688 0.211 0.241 0.32 0.458 0.56 0.00102 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 1.3 0.0586 0.125 0.225 0.295 0.324 0.431 0.000723 0.000963 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 82 3840.885 0.005 0.0684 0.336 1.7 0.241 0.319 0.586 0.71 0.00131 0.00158 ! Validation 82 3840.885 0.005 0.0681 0.313 1.68 0.241 0.318 0.557 0.683 0.00124 0.00152 Wall time: 3840.8853929168545 ! Best model 82 1.676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 24 1.48 0.0666 0.145 0.239 0.315 0.366 0.464 0.000818 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.27 0.0579 0.114 0.224 0.293 0.369 0.412 0.000823 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 83 3887.531 0.005 0.0674 0.204 1.55 0.24 0.317 0.441 0.553 0.000984 0.00123 ! Validation 83 3887.531 0.005 0.0671 0.457 1.8 0.24 0.316 0.657 0.825 0.00147 0.00184 Wall time: 3887.5325678386725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 24 1.5 0.0671 0.161 0.239 0.316 0.397 0.489 0.000886 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 2.54 0.0582 1.37 0.224 0.294 1.4 1.43 0.00312 0.00319 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 3934.179 0.005 0.0669 0.619 1.96 0.239 0.315 0.81 0.968 0.00181 0.00216 ! Validation 84 3934.179 0.005 0.0676 1.16 2.51 0.24 0.317 1.14 1.31 0.00255 0.00293 Wall time: 3934.179319283925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 24 1.74 0.0681 0.377 0.239 0.318 0.646 0.749 0.00144 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 2.12 0.0576 0.963 0.224 0.293 1.16 1.2 0.00259 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 85 3980.823 0.005 0.067 0.367 1.71 0.239 0.315 0.597 0.739 0.00133 0.00165 ! Validation 85 3980.823 0.005 0.0667 1.61 2.94 0.239 0.315 1.39 1.55 0.0031 0.00345 Wall time: 3980.823501846753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 24 1.44 0.0665 0.112 0.238 0.315 0.34 0.409 0.000759 0.000912 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.37 0.0569 0.23 0.223 0.291 0.51 0.585 0.00114 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 86 4027.468 0.005 0.066 0.378 1.7 0.237 0.313 0.611 0.756 0.00136 0.00169 ! Validation 86 4027.468 0.005 0.0658 0.673 1.99 0.237 0.313 0.811 1 0.00181 0.00223 Wall time: 4027.4688571565785 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 24 1.49 0.0671 0.148 0.238 0.316 0.371 0.468 0.000829 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 1.31 0.0563 0.185 0.221 0.289 0.454 0.524 0.00101 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 87 4074.128 0.005 0.0654 0.373 1.68 0.236 0.312 0.62 0.75 0.00138 0.00167 ! Validation 87 4074.128 0.005 0.0653 0.579 1.89 0.236 0.312 0.745 0.927 0.00166 0.00207 Wall time: 4074.1296333717182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 24 1.53 0.0662 0.205 0.238 0.314 0.472 0.552 0.00105 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 1.55 0.0567 0.415 0.222 0.29 0.726 0.786 0.00162 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 88 4120.803 0.005 0.0653 0.603 1.91 0.236 0.312 0.79 0.954 0.00176 0.00213 ! Validation 88 4120.803 0.005 0.0653 0.779 2.08 0.236 0.312 0.897 1.08 0.002 0.0024 Wall time: 4120.803797969595 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 24 2.27 0.0626 1.02 0.231 0.305 1.17 1.23 0.00261 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 2.13 0.0565 1 0.222 0.29 1.19 1.22 0.00265 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 89 4167.457 0.005 0.0652 0.579 1.88 0.236 0.311 0.761 0.919 0.0017 0.00205 ! Validation 89 4167.457 0.005 0.0652 1.58 2.88 0.236 0.311 1.38 1.53 0.00309 0.00342 Wall time: 4167.457685587928 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 24 1.46 0.0675 0.108 0.239 0.317 0.321 0.401 0.000716 0.000896 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 1.17 0.0554 0.0573 0.22 0.287 0.246 0.292 0.00055 0.000652 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 90 4214.111 0.005 0.0646 0.34 1.63 0.234 0.31 0.569 0.717 0.00127 0.0016 ! Validation 90 4214.111 0.005 0.0642 0.315 1.6 0.234 0.309 0.548 0.684 0.00122 0.00153 Wall time: 4214.111376937013 ! Best model 90 1.600 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 24 1.83 0.0644 0.544 0.235 0.309 0.821 0.899 0.00183 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 1.26 0.0547 0.162 0.218 0.285 0.431 0.49 0.000962 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 91 4260.762 0.005 0.0635 0.307 1.58 0.233 0.307 0.54 0.669 0.0012 0.00149 ! Validation 91 4260.762 0.005 0.0634 0.5 1.77 0.233 0.307 0.69 0.862 0.00154 0.00192 Wall time: 4260.762054638006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 24 1.77 0.0604 0.558 0.228 0.3 0.829 0.911 0.00185 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 1.38 0.0545 0.284 0.218 0.285 0.578 0.65 0.00129 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 92 4307.412 0.005 0.0628 0.318 1.57 0.232 0.306 0.552 0.681 0.00123 0.00152 ! Validation 92 4307.412 0.005 0.0631 0.696 1.96 0.232 0.306 0.829 1.02 0.00185 0.00227 Wall time: 4307.412151466589 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 24 1.31 0.0602 0.103 0.226 0.299 0.315 0.392 0.000704 0.000874 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 1.15 0.0541 0.0654 0.217 0.284 0.272 0.312 0.000606 0.000696 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 93 4354.059 0.005 0.0623 0.305 1.55 0.231 0.304 0.549 0.679 0.00123 0.00152 ! Validation 93 4354.059 0.005 0.0626 0.371 1.62 0.231 0.305 0.592 0.743 0.00132 0.00166 Wall time: 4354.059909608681 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 24 1.59 0.0606 0.377 0.228 0.3 0.635 0.749 0.00142 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 1.55 0.0529 0.489 0.215 0.28 0.802 0.852 0.00179 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 94 4400.719 0.005 0.0615 0.178 1.41 0.229 0.303 0.404 0.508 0.000902 0.00113 ! Validation 94 4400.719 0.005 0.0614 0.616 1.84 0.229 0.302 0.808 0.957 0.0018 0.00214 Wall time: 4400.719151302706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 24 1.33 0.0596 0.136 0.225 0.298 0.385 0.45 0.00086 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 1.23 0.0523 0.182 0.213 0.279 0.436 0.52 0.000973 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 95 4447.358 0.005 0.0607 0.289 1.5 0.228 0.301 0.54 0.659 0.00121 0.00147 ! Validation 95 4447.358 0.005 0.0608 0.356 1.57 0.228 0.301 0.606 0.728 0.00135 0.00162 Wall time: 4447.358614422847 ! Best model 95 1.571 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 24 1.43 0.0615 0.197 0.229 0.302 0.465 0.542 0.00104 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 1.14 0.0525 0.0883 0.214 0.279 0.259 0.362 0.000578 0.000809 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 4494.011 0.005 0.0605 0.406 1.62 0.227 0.3 0.645 0.782 0.00144 0.00175 ! Validation 96 4494.011 0.005 0.0605 0.291 1.5 0.228 0.3 0.541 0.657 0.00121 0.00147 Wall time: 4494.011645394843 ! Best model 96 1.501 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 24 1.76 0.0626 0.512 0.231 0.305 0.79 0.872 0.00176 0.00195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 1.09 0.0517 0.0591 0.213 0.277 0.247 0.297 0.000552 0.000662 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 97 4540.654 0.005 0.0598 0.291 1.49 0.226 0.298 0.518 0.652 0.00116 0.00146 ! Validation 97 4540.654 0.005 0.0599 0.285 1.48 0.226 0.298 0.528 0.651 0.00118 0.00145 Wall time: 4540.654443095904 ! Best model 97 1.483 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 24 1.88 0.0628 0.621 0.233 0.305 0.839 0.961 0.00187 0.00215 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 1.59 0.053 0.533 0.214 0.281 0.847 0.89 0.00189 0.00199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 98 4587.299 0.005 0.0613 0.768 1.99 0.228 0.302 0.875 1.07 0.00195 0.00239 ! Validation 98 4587.299 0.005 0.061 0.792 2.01 0.228 0.301 0.916 1.09 0.00204 0.00242 Wall time: 4587.299270394724 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 24 1.4 0.0602 0.197 0.226 0.299 0.435 0.541 0.00097 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 1.14 0.0521 0.103 0.213 0.278 0.368 0.39 0.000821 0.000871 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 4633.936 0.005 0.0602 0.424 1.63 0.226 0.299 0.672 0.799 0.0015 0.00178 ! Validation 99 4633.936 0.005 0.0601 0.363 1.57 0.226 0.299 0.581 0.735 0.0013 0.00164 Wall time: 4633.937374418601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 24 1.51 0.0594 0.324 0.225 0.297 0.585 0.694 0.00131 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 1.1 0.0509 0.0807 0.21 0.275 0.313 0.346 0.000698 0.000773 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 100 4680.577 0.005 0.0591 0.242 1.42 0.225 0.296 0.478 0.597 0.00107 0.00133 ! Validation 100 4680.577 0.005 0.059 0.372 1.55 0.224 0.296 0.593 0.744 0.00132 0.00166 Wall time: 4680.5774355027825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 24 1.36 0.0588 0.186 0.225 0.296 0.374 0.526 0.000835 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 1.1 0.051 0.0831 0.211 0.275 0.325 0.352 0.000726 0.000785 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 101 4727.476 0.005 0.0585 0.431 1.6 0.224 0.295 0.68 0.806 0.00152 0.0018 ! Validation 101 4727.476 0.005 0.0586 0.365 1.54 0.224 0.295 0.579 0.737 0.00129 0.00165 Wall time: 4727.476619694848 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 24 2.69 0.0576 1.53 0.222 0.293 1.44 1.51 0.00322 0.00337 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 2.52 0.0503 1.51 0.209 0.273 1.47 1.5 0.00329 0.00335 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 4774.179 0.005 0.0579 0.264 1.42 0.222 0.293 0.464 0.59 0.00104 0.00132 ! Validation 102 4774.179 0.005 0.058 1.88 3.04 0.223 0.294 1.55 1.67 0.00345 0.00373 Wall time: 4774.179401051719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 24 1.49 0.0596 0.301 0.225 0.298 0.57 0.669 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 103 100 1.09 0.0502 0.0858 0.209 0.273 0.326 0.357 0.000727 0.000797 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 103 4820.974 0.005 0.058 0.399 1.56 0.222 0.294 0.627 0.772 0.0014 0.00172 ! Validation 103 4820.974 0.005 0.0582 0.358 1.52 0.223 0.294 0.583 0.729 0.0013 0.00163 Wall time: 4820.975292024668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 24 1.27 0.0577 0.116 0.222 0.293 0.336 0.415 0.000751 0.000925 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 1.17 0.0507 0.156 0.21 0.275 0.44 0.481 0.000981 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 4867.676 0.005 0.0576 0.544 1.7 0.222 0.293 0.746 0.908 0.00166 0.00203 ! Validation 104 4867.676 0.005 0.0581 0.431 1.59 0.222 0.294 0.634 0.8 0.00141 0.00179 Wall time: 4867.676569455769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 24 1.28 0.0567 0.15 0.22 0.29 0.378 0.472 0.000843 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 1.1 0.0493 0.115 0.207 0.271 0.312 0.414 0.000697 0.000925 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 105 4914.370 0.005 0.0572 0.282 1.43 0.221 0.292 0.534 0.651 0.00119 0.00145 ! Validation 105 4914.370 0.005 0.057 0.315 1.45 0.221 0.291 0.567 0.685 0.00126 0.00153 Wall time: 4914.370785373729 ! Best model 105 1.454 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 24 1.93 0.0571 0.786 0.221 0.292 1.01 1.08 0.00225 0.00241 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 1.07 0.0491 0.0861 0.207 0.27 0.326 0.358 0.000728 0.000799 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 4961.085 0.005 0.0563 0.406 1.53 0.219 0.289 0.627 0.768 0.0014 0.00171 ! Validation 106 4961.085 0.005 0.0566 0.341 1.47 0.22 0.29 0.57 0.712 0.00127 0.00159 Wall time: 4961.085404189769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 24 1.48 0.0575 0.33 0.221 0.292 0.565 0.7 0.00126 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 1.58 0.0495 0.593 0.207 0.271 0.893 0.939 0.00199 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 107 5007.784 0.005 0.0572 0.69 1.83 0.221 0.292 0.833 1.02 0.00186 0.00227 ! Validation 107 5007.784 0.005 0.0571 0.798 1.94 0.221 0.291 0.941 1.09 0.0021 0.00243 Wall time: 5007.78493837174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 24 1.22 0.0571 0.0801 0.22 0.291 0.264 0.345 0.00059 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 1.32 0.0488 0.341 0.206 0.269 0.656 0.712 0.00146 0.00159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 108 5054.482 0.005 0.0567 0.298 1.43 0.22 0.29 0.53 0.671 0.00118 0.0015 ! Validation 108 5054.482 0.005 0.0563 0.673 1.8 0.219 0.289 0.815 1 0.00182 0.00223 Wall time: 5054.482023859862 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 24 1.17 0.0538 0.0898 0.214 0.283 0.306 0.365 0.000682 0.000816 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.09 0.0481 0.131 0.205 0.268 0.405 0.442 0.000905 0.000986 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 5101.197 0.005 0.0554 0.208 1.32 0.218 0.287 0.453 0.56 0.00101 0.00125 ! Validation 109 5101.197 0.005 0.0552 0.388 1.49 0.218 0.287 0.6 0.759 0.00134 0.00169 Wall time: 5101.198034623638 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 24 1.19 0.0549 0.092 0.217 0.286 0.31 0.37 0.000691 0.000825 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.03 0.048 0.0713 0.205 0.267 0.3 0.326 0.000671 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 110 5148.476 0.005 0.0552 0.418 1.52 0.217 0.287 0.618 0.796 0.00138 0.00178 ! Validation 110 5148.476 0.005 0.0554 0.261 1.37 0.218 0.287 0.509 0.622 0.00114 0.00139 Wall time: 5148.4771968899295 ! Best model 110 1.368 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 24 2.36 0.0536 1.29 0.214 0.282 1.35 1.39 0.00302 0.00309 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.8 0.0474 0.851 0.203 0.265 1.09 1.13 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 111 5195.212 0.005 0.0544 0.297 1.39 0.216 0.285 0.509 0.638 0.00114 0.00142 ! Validation 111 5195.212 0.005 0.0545 1.2 2.29 0.216 0.285 1.17 1.33 0.00261 0.00298 Wall time: 5195.213765502907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 24 1.26 0.0554 0.15 0.217 0.287 0.391 0.472 0.000872 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 1.04 0.0477 0.0855 0.204 0.266 0.333 0.356 0.000744 0.000796 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 112 5241.928 0.005 0.0547 0.566 1.66 0.216 0.285 0.774 0.926 0.00173 0.00207 ! Validation 112 5241.928 0.005 0.0547 0.303 1.4 0.216 0.285 0.543 0.671 0.00121 0.0015 Wall time: 5241.928631473798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 24 1.27 0.0554 0.161 0.217 0.287 0.409 0.49 0.000914 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 1.08 0.0466 0.151 0.202 0.263 0.387 0.474 0.000863 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 113 5288.621 0.005 0.0538 0.162 1.24 0.214 0.283 0.391 0.491 0.000872 0.0011 ! Validation 113 5288.621 0.005 0.0535 0.367 1.44 0.214 0.282 0.614 0.739 0.00137 0.00165 Wall time: 5288.621781948954 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 24 1.1 0.0509 0.0822 0.209 0.275 0.298 0.35 0.000666 0.00078 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1 0.0462 0.0747 0.201 0.262 0.245 0.333 0.000547 0.000744 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 5335.334 0.005 0.0531 0.384 1.45 0.213 0.281 0.643 0.763 0.00144 0.0017 ! Validation 114 5335.334 0.005 0.0532 0.291 1.36 0.213 0.281 0.543 0.658 0.00121 0.00147 Wall time: 5335.334668810014 ! Best model 114 1.356 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 24 1.19 0.0544 0.1 0.216 0.284 0.315 0.386 0.000703 0.000861 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 1.39 0.0461 0.47 0.201 0.262 0.788 0.836 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 115 5382.053 0.005 0.0528 0.28 1.34 0.212 0.28 0.529 0.65 0.00118 0.00145 ! Validation 115 5382.053 0.005 0.0528 0.629 1.68 0.213 0.28 0.796 0.967 0.00178 0.00216 Wall time: 5382.053172588814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 24 1.68 0.0551 0.574 0.216 0.286 0.879 0.924 0.00196 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 1.56 0.0456 0.645 0.199 0.26 0.941 0.98 0.0021 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 116 5428.756 0.005 0.0525 0.444 1.49 0.212 0.279 0.685 0.809 0.00153 0.00181 ! Validation 116 5428.756 0.005 0.0529 0.819 1.88 0.212 0.281 0.95 1.1 0.00212 0.00246 Wall time: 5428.756423427723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 24 1.44 0.0484 0.478 0.204 0.268 0.793 0.843 0.00177 0.00188 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 1.52 0.0458 0.606 0.2 0.261 0.908 0.949 0.00203 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 117 5475.449 0.005 0.0521 0.409 1.45 0.211 0.279 0.663 0.778 0.00148 0.00174 ! Validation 117 5475.449 0.005 0.0523 0.777 1.82 0.212 0.279 0.902 1.08 0.00201 0.0024 Wall time: 5475.450000478886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 24 1.57 0.0524 0.527 0.211 0.279 0.779 0.885 0.00174 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.07 0.0471 0.126 0.202 0.265 0.319 0.432 0.000713 0.000965 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 5522.169 0.005 0.0528 0.825 1.88 0.212 0.28 0.952 1.11 0.00213 0.00248 ! Validation 118 5522.169 0.005 0.0538 0.35 1.43 0.214 0.283 0.604 0.721 0.00135 0.00161 Wall time: 5522.169848535676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 24 1.25 0.0507 0.237 0.208 0.274 0.534 0.594 0.00119 0.00133 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 1.17 0.046 0.25 0.2 0.261 0.538 0.61 0.0012 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 119 5568.872 0.005 0.0527 0.337 1.39 0.212 0.28 0.589 0.71 0.00131 0.00159 ! Validation 119 5568.872 0.005 0.0524 0.474 1.52 0.212 0.279 0.669 0.84 0.00149 0.00187 Wall time: 5568.872758532874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 24 1.25 0.0512 0.225 0.21 0.276 0.492 0.579 0.0011 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.969 0.0452 0.0638 0.199 0.259 0.233 0.308 0.000519 0.000687 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 120 5615.583 0.005 0.0514 0.238 1.27 0.21 0.277 0.487 0.596 0.00109 0.00133 ! Validation 120 5615.583 0.005 0.0515 0.283 1.31 0.21 0.277 0.533 0.649 0.00119 0.00145 Wall time: 5615.583853203803 ! Best model 120 1.313 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 24 1.86 0.0518 0.823 0.21 0.277 1.04 1.11 0.00232 0.00247 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 1.15 0.0449 0.254 0.198 0.258 0.55 0.615 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 121 5662.280 0.005 0.0509 0.392 1.41 0.209 0.275 0.621 0.753 0.00139 0.00168 ! Validation 121 5662.280 0.005 0.0512 0.427 1.45 0.21 0.276 0.634 0.797 0.00141 0.00178 Wall time: 5662.280577775557 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 24 1.3 0.0528 0.245 0.213 0.28 0.507 0.603 0.00113 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 1.02 0.0442 0.136 0.196 0.256 0.352 0.45 0.000785 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 122 5708.989 0.005 0.0507 0.243 1.26 0.208 0.274 0.492 0.602 0.0011 0.00134 ! Validation 122 5708.989 0.005 0.0505 0.372 1.38 0.208 0.274 0.619 0.744 0.00138 0.00166 Wall time: 5708.989949947689 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 24 1.1 0.0483 0.138 0.203 0.268 0.37 0.453 0.000827 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.928 0.0436 0.055 0.195 0.255 0.225 0.286 0.000502 0.000638 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 123 5755.681 0.005 0.0499 0.207 1.2 0.207 0.272 0.452 0.557 0.00101 0.00124 ! Validation 123 5755.681 0.005 0.05 0.262 1.26 0.207 0.273 0.513 0.624 0.00114 0.00139 Wall time: 5755.6818064427935 ! Best model 123 1.261 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 24 1.39 0.0495 0.4 0.206 0.271 0.669 0.771 0.00149 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 1.91 0.0434 1.04 0.195 0.254 1.21 1.25 0.00271 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 124 5802.360 0.005 0.0492 0.185 1.17 0.205 0.27 0.411 0.518 0.000917 0.00116 ! Validation 124 5802.360 0.005 0.0493 1.09 2.07 0.206 0.271 1.12 1.27 0.0025 0.00284 Wall time: 5802.360190866981 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 24 1.38 0.0483 0.415 0.204 0.268 0.718 0.786 0.0016 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 1.22 0.0431 0.354 0.194 0.253 0.674 0.725 0.00151 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 125 5849.050 0.005 0.0488 0.406 1.38 0.205 0.269 0.651 0.776 0.00145 0.00173 ! Validation 125 5849.050 0.005 0.0492 0.515 1.5 0.205 0.27 0.703 0.875 0.00157 0.00195 Wall time: 5849.050118485 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 24 1.27 0.0457 0.36 0.199 0.261 0.678 0.731 0.00151 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.907 0.0425 0.0569 0.193 0.251 0.234 0.291 0.000523 0.000649 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 126 5895.844 0.005 0.0484 0.247 1.21 0.204 0.268 0.501 0.602 0.00112 0.00134 ! Validation 126 5895.844 0.005 0.0486 0.273 1.25 0.204 0.269 0.526 0.638 0.00117 0.00142 Wall time: 5895.844996280968 ! Best model 126 1.245 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 24 1.1 0.0454 0.187 0.198 0.26 0.443 0.527 0.00099 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 2.03 0.0424 1.18 0.193 0.251 1.3 1.33 0.0029 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 127 5942.557 0.005 0.0481 0.298 1.26 0.203 0.268 0.507 0.668 0.00113 0.00149 ! Validation 127 5942.557 0.005 0.0485 1.15 2.12 0.204 0.268 1.17 1.31 0.00261 0.00292 Wall time: 5942.557693986688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 24 1.03 0.0478 0.0719 0.201 0.266 0.266 0.327 0.000594 0.00073 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.977 0.0427 0.122 0.193 0.252 0.402 0.425 0.000898 0.00095 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 128 5989.246 0.005 0.0486 0.549 1.52 0.204 0.269 0.753 0.913 0.00168 0.00204 ! Validation 128 5989.246 0.005 0.0484 0.29 1.26 0.203 0.268 0.52 0.657 0.00116 0.00147 Wall time: 5989.246481056791 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 24 1.07 0.0485 0.103 0.204 0.268 0.309 0.391 0.00069 0.000873 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.883 0.0413 0.0573 0.19 0.248 0.218 0.292 0.000486 0.000651 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 129 6035.943 0.005 0.0474 0.123 1.07 0.202 0.265 0.34 0.428 0.000759 0.000956 ! Validation 129 6035.943 0.005 0.0473 0.286 1.23 0.202 0.265 0.54 0.652 0.0012 0.00146 Wall time: 6035.943300826009 ! Best model 129 1.233 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 24 1.5 0.0452 0.6 0.197 0.259 0.866 0.945 0.00193 0.00211 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.916 0.0419 0.0783 0.191 0.25 0.321 0.341 0.000717 0.000762 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 130 6082.639 0.005 0.0468 0.49 1.43 0.2 0.264 0.704 0.851 0.00157 0.0019 ! Validation 130 6082.639 0.005 0.0475 0.27 1.22 0.202 0.266 0.513 0.633 0.00115 0.00141 Wall time: 6082.639510297682 ! Best model 130 1.220 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 24 1.56 0.0464 0.63 0.2 0.263 0.879 0.968 0.00196 0.00216 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 1.42 0.042 0.576 0.192 0.25 0.882 0.925 0.00197 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 131 6129.375 0.005 0.0474 0.556 1.5 0.201 0.266 0.771 0.908 0.00172 0.00203 ! Validation 131 6129.375 0.005 0.0477 0.712 1.67 0.202 0.266 0.852 1.03 0.0019 0.0023 Wall time: 6129.375526555814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 24 1.47 0.0474 0.526 0.201 0.265 0.84 0.885 0.00187 0.00197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 1.07 0.0414 0.241 0.19 0.248 0.528 0.599 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 132 6176.093 0.005 0.0469 0.368 1.31 0.201 0.264 0.617 0.736 0.00138 0.00164 ! Validation 132 6176.093 0.005 0.0471 0.511 1.45 0.201 0.265 0.74 0.871 0.00165 0.00195 Wall time: 6176.093650607858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 24 1.43 0.0458 0.513 0.198 0.261 0.809 0.873 0.00181 0.00195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 1.33 0.0406 0.514 0.188 0.246 0.833 0.874 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 133 6225.434 0.005 0.0463 0.335 1.26 0.199 0.262 0.587 0.701 0.00131 0.00156 ! Validation 133 6225.434 0.005 0.0463 0.772 1.7 0.199 0.262 0.928 1.07 0.00207 0.00239 Wall time: 6225.435221999884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 24 1.07 0.0446 0.18 0.196 0.258 0.394 0.517 0.00088 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.969 0.0405 0.16 0.188 0.245 0.41 0.488 0.000916 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 134 6272.143 0.005 0.0459 0.341 1.26 0.198 0.261 0.592 0.716 0.00132 0.0016 ! Validation 134 6272.143 0.005 0.0459 0.431 1.35 0.199 0.261 0.673 0.8 0.0015 0.00179 Wall time: 6272.143303602934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 24 1.02 0.0472 0.0729 0.202 0.265 0.275 0.329 0.000613 0.000735 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.922 0.0399 0.124 0.187 0.244 0.394 0.429 0.000878 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 135 6318.848 0.005 0.0454 0.255 1.16 0.197 0.26 0.494 0.621 0.0011 0.00139 ! Validation 135 6318.848 0.005 0.0454 0.245 1.15 0.198 0.26 0.488 0.604 0.00109 0.00135 Wall time: 6318.84885829268 ! Best model 135 1.152 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 24 1.26 0.0438 0.38 0.194 0.255 0.71 0.752 0.00158 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 1.26 0.0393 0.476 0.186 0.242 0.795 0.842 0.00178 0.00188 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 136 6365.559 0.005 0.0445 0.14 1.03 0.196 0.257 0.356 0.447 0.000794 0.000997 ! Validation 136 6365.559 0.005 0.0445 0.534 1.42 0.196 0.257 0.722 0.891 0.00161 0.00199 Wall time: 6365.559767674655 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 24 0.98 0.0449 0.0828 0.195 0.258 0.289 0.351 0.000644 0.000783 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.855 0.039 0.0743 0.185 0.241 0.307 0.332 0.000686 0.000742 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 137 6412.255 0.005 0.044 0.192 1.07 0.195 0.256 0.419 0.539 0.000936 0.0012 ! Validation 137 6412.255 0.005 0.0442 0.235 1.12 0.195 0.256 0.486 0.591 0.00108 0.00132 Wall time: 6412.255520522594 ! Best model 137 1.119 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 24 1.08 0.0466 0.151 0.199 0.263 0.396 0.473 0.000883 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 1.84 0.0413 1.02 0.189 0.248 1.19 1.23 0.00267 0.00275 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 138 6458.952 0.005 0.0451 0.707 1.61 0.197 0.259 0.826 1.04 0.00184 0.00231 ! Validation 138 6458.952 0.005 0.0464 1.24 2.16 0.199 0.263 1.2 1.36 0.00267 0.00303 Wall time: 6458.952829568647 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 24 0.934 0.0432 0.0702 0.193 0.253 0.267 0.323 0.000596 0.000721 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 1.07 0.0393 0.282 0.186 0.242 0.585 0.648 0.00131 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 139 6505.651 0.005 0.0451 0.377 1.28 0.197 0.259 0.629 0.756 0.0014 0.00169 ! Validation 139 6505.651 0.005 0.0444 0.368 1.26 0.195 0.257 0.587 0.74 0.00131 0.00165 Wall time: 6505.651164683979 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 24 0.959 0.0409 0.141 0.188 0.247 0.41 0.458 0.000914 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.892 0.0383 0.125 0.184 0.239 0.397 0.431 0.000885 0.000962 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 6552.353 0.005 0.0433 0.136 1 0.193 0.254 0.362 0.45 0.000809 0.001 ! Validation 140 6552.353 0.005 0.0433 0.257 1.12 0.193 0.254 0.5 0.619 0.00111 0.00138 Wall time: 6552.353813814931 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 24 0.986 0.0433 0.12 0.194 0.254 0.367 0.422 0.000819 0.000941 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 0.892 0.0385 0.123 0.184 0.239 0.399 0.428 0.00089 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 141 6599.045 0.005 0.043 0.432 1.29 0.192 0.253 0.647 0.809 0.00145 0.00181 ! Validation 141 6599.045 0.005 0.0433 0.263 1.13 0.193 0.254 0.501 0.625 0.00112 0.0014 Wall time: 6599.046242449898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 24 1.06 0.0443 0.17 0.195 0.257 0.416 0.502 0.00093 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.82 0.0375 0.0697 0.182 0.236 0.297 0.322 0.000663 0.000718 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 6645.723 0.005 0.0425 0.134 0.984 0.191 0.251 0.361 0.446 0.000806 0.000995 ! Validation 142 6645.723 0.005 0.0424 0.241 1.09 0.191 0.251 0.486 0.598 0.00108 0.00134 Wall time: 6645.723260717001 ! Best model 142 1.089 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 24 1.19 0.0452 0.282 0.196 0.259 0.575 0.648 0.00128 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.792 0.0372 0.0491 0.18 0.235 0.222 0.27 0.000496 0.000603 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 6692.420 0.005 0.0418 0.291 1.13 0.19 0.249 0.546 0.658 0.00122 0.00147 ! Validation 143 6692.420 0.005 0.042 0.245 1.09 0.19 0.25 0.498 0.604 0.00111 0.00135 Wall time: 6692.420104847755 ! Best model 143 1.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 24 0.989 0.0418 0.153 0.19 0.249 0.421 0.476 0.00094 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.893 0.0368 0.156 0.18 0.234 0.439 0.481 0.000979 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 144 6739.114 0.005 0.0413 0.141 0.967 0.189 0.248 0.366 0.457 0.000818 0.00102 ! Validation 144 6739.114 0.005 0.0415 0.27 1.1 0.189 0.248 0.502 0.633 0.00112 0.00141 Wall time: 6739.114054644946 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 24 1.39 0.0403 0.582 0.187 0.245 0.875 0.93 0.00195 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.812 0.0373 0.0655 0.181 0.236 0.234 0.312 0.000522 0.000696 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 145 6785.800 0.005 0.0407 0.234 1.05 0.188 0.246 0.44 0.579 0.000981 0.00129 ! Validation 145 6785.800 0.005 0.0422 0.309 1.15 0.19 0.251 0.558 0.678 0.00125 0.00151 Wall time: 6785.800993944984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 24 0.961 0.0418 0.124 0.189 0.249 0.37 0.429 0.000825 0.000958 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 1.02 0.0358 0.302 0.177 0.231 0.617 0.67 0.00138 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 146 6832.491 0.005 0.0408 0.276 1.09 0.188 0.246 0.523 0.645 0.00117 0.00144 ! Validation 146 6832.491 0.005 0.0408 0.688 1.5 0.187 0.246 0.875 1.01 0.00195 0.00226 Wall time: 6832.49131375365 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 24 1 0.0389 0.224 0.184 0.241 0.509 0.577 0.00114 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 1.29 0.0358 0.578 0.177 0.231 0.889 0.927 0.00198 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 147 6879.189 0.005 0.0408 0.279 1.09 0.187 0.246 0.514 0.645 0.00115 0.00144 ! Validation 147 6879.189 0.005 0.0405 0.507 1.32 0.187 0.245 0.711 0.869 0.00159 0.00194 Wall time: 6879.189242081717 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 24 1.05 0.0424 0.198 0.191 0.251 0.49 0.543 0.00109 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.759 0.0355 0.0501 0.176 0.23 0.231 0.273 0.000515 0.000609 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 6925.870 0.005 0.0398 0.29 1.09 0.185 0.243 0.539 0.66 0.0012 0.00147 ! Validation 148 6925.870 0.005 0.0403 0.236 1.04 0.186 0.245 0.487 0.593 0.00109 0.00132 Wall time: 6925.870195215568 ! Best model 148 1.043 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 24 0.893 0.0379 0.134 0.181 0.238 0.357 0.447 0.000797 0.000997 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.893 0.0353 0.187 0.176 0.229 0.47 0.527 0.00105 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 149 6972.667 0.005 0.0396 0.327 1.12 0.185 0.243 0.573 0.702 0.00128 0.00157 ! Validation 149 6972.667 0.005 0.0398 0.262 1.06 0.185 0.243 0.494 0.625 0.0011 0.00139 Wall time: 6972.667277330998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 24 1.15 0.0381 0.386 0.182 0.238 0.709 0.758 0.00158 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 1.56 0.0349 0.858 0.175 0.228 1.1 1.13 0.00245 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 150 7019.363 0.005 0.0393 0.239 1.03 0.184 0.242 0.482 0.592 0.00108 0.00132 ! Validation 150 7019.363 0.005 0.0394 0.708 1.5 0.184 0.242 0.872 1.03 0.00195 0.00229 Wall time: 7019.363168944605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 24 0.883 0.0391 0.1 0.184 0.241 0.306 0.386 0.000684 0.000862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.729 0.034 0.0485 0.173 0.225 0.211 0.269 0.000471 0.000599 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 7066.051 0.005 0.0387 0.126 0.9 0.183 0.24 0.34 0.433 0.000759 0.000967 ! Validation 151 7066.051 0.005 0.0388 0.264 1.04 0.183 0.24 0.522 0.627 0.00117 0.0014 Wall time: 7066.051876946818 ! Best model 151 1.041 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 24 1.65 0.0424 0.797 0.19 0.251 1.05 1.09 0.00234 0.00243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 2.33 0.0366 1.59 0.178 0.233 1.51 1.54 0.00338 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 152 7112.757 0.005 0.0387 0.569 1.34 0.182 0.24 0.782 0.916 0.00175 0.00204 ! Validation 152 7112.757 0.005 0.0415 1.58 2.41 0.188 0.248 1.41 1.53 0.00314 0.00342 Wall time: 7112.757821669802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 24 1.4 0.0393 0.61 0.184 0.242 0.871 0.952 0.00194 0.00213 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.744 0.0343 0.058 0.173 0.226 0.243 0.294 0.000541 0.000655 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 153 7159.463 0.005 0.0393 0.337 1.12 0.184 0.242 0.581 0.701 0.0013 0.00156 ! Validation 153 7159.463 0.005 0.0391 0.297 1.08 0.183 0.241 0.558 0.665 0.00124 0.00148 Wall time: 7159.464144425001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 24 0.897 0.039 0.118 0.183 0.241 0.356 0.419 0.000794 0.000936 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 1.02 0.0339 0.344 0.172 0.224 0.666 0.715 0.00149 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 154 7206.167 0.005 0.0384 0.192 0.96 0.182 0.239 0.434 0.537 0.00097 0.0012 ! Validation 154 7206.167 0.005 0.0384 0.357 1.13 0.182 0.239 0.576 0.728 0.00129 0.00163 Wall time: 7206.16759124957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 24 1.3 0.0357 0.589 0.176 0.231 0.877 0.935 0.00196 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 1.03 0.0331 0.37 0.17 0.222 0.692 0.741 0.00154 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 155 7252.856 0.005 0.0376 0.169 0.921 0.18 0.237 0.401 0.486 0.000894 0.00108 ! Validation 155 7252.856 0.005 0.0379 0.768 1.53 0.181 0.238 0.94 1.07 0.0021 0.00239 Wall time: 7252.856684945989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 24 0.847 0.037 0.107 0.179 0.234 0.322 0.4 0.000719 0.000892 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.757 0.0331 0.0952 0.17 0.222 0.35 0.376 0.000781 0.00084 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 7299.563 0.005 0.0376 0.397 1.15 0.18 0.237 0.662 0.775 0.00148 0.00173 ! Validation 156 7299.563 0.005 0.0378 0.218 0.973 0.18 0.237 0.457 0.569 0.00102 0.00127 Wall time: 7299.563197297975 ! Best model 156 0.973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 24 1.14 0.0381 0.374 0.182 0.238 0.671 0.746 0.0015 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.714 0.0329 0.0567 0.169 0.221 0.261 0.29 0.000583 0.000648 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 157 7346.267 0.005 0.0372 0.228 0.971 0.179 0.235 0.458 0.577 0.00102 0.00129 ! Validation 157 7346.267 0.005 0.0374 0.22 0.968 0.179 0.236 0.466 0.571 0.00104 0.00128 Wall time: 7346.267609227914 ! Best model 157 0.968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 24 0.969 0.0367 0.235 0.178 0.234 0.507 0.591 0.00113 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 0.852 0.0332 0.188 0.17 0.222 0.463 0.528 0.00103 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 158 7392.976 0.005 0.0371 0.433 1.18 0.179 0.235 0.687 0.806 0.00153 0.0018 ! Validation 158 7392.976 0.005 0.0376 0.258 1.01 0.179 0.236 0.489 0.619 0.00109 0.00138 Wall time: 7392.976838590577 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 24 0.806 0.0349 0.108 0.173 0.228 0.303 0.4 0.000677 0.000893 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 0.895 0.0323 0.248 0.168 0.219 0.55 0.607 0.00123 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 159 7439.661 0.005 0.0368 0.168 0.905 0.178 0.234 0.402 0.503 0.000897 0.00112 ! Validation 159 7439.661 0.005 0.0369 0.29 1.03 0.178 0.234 0.517 0.657 0.00115 0.00147 Wall time: 7439.6620006226 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 24 1.43 0.0354 0.724 0.175 0.229 0.967 1.04 0.00216 0.00232 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 2.04 0.0322 1.4 0.168 0.219 1.42 1.44 0.00317 0.00322 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 7486.355 0.005 0.0362 0.164 0.889 0.177 0.232 0.383 0.473 0.000856 0.00106 ! Validation 160 7486.355 0.005 0.0367 1.24 1.98 0.177 0.233 1.23 1.36 0.00275 0.00303 Wall time: 7486.355848575011 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 24 1.19 0.0397 0.397 0.184 0.243 0.663 0.768 0.00148 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.761 0.0323 0.116 0.167 0.219 0.383 0.416 0.000855 0.000928 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 7533.047 0.005 0.0363 0.407 1.13 0.176 0.232 0.651 0.778 0.00145 0.00174 ! Validation 161 7533.047 0.005 0.0367 0.235 0.969 0.177 0.234 0.468 0.591 0.00105 0.00132 Wall time: 7533.048030842561 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 24 0.89 0.0355 0.179 0.175 0.23 0.439 0.516 0.00098 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 0.704 0.032 0.065 0.167 0.218 0.282 0.311 0.00063 0.000694 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 162 7579.741 0.005 0.0363 0.394 1.12 0.177 0.233 0.659 0.77 0.00147 0.00172 ! Validation 162 7579.741 0.005 0.0365 0.203 0.932 0.177 0.233 0.45 0.549 0.001 0.00123 Wall time: 7579.741264537908 ! Best model 162 0.932 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 24 0.751 0.0356 0.0382 0.175 0.23 0.193 0.238 0.000432 0.000532 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 0.697 0.0314 0.0679 0.166 0.216 0.294 0.318 0.000656 0.000709 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 163 7626.439 0.005 0.0358 0.0981 0.814 0.176 0.231 0.304 0.385 0.00068 0.000859 ! Validation 163 7626.439 0.005 0.0359 0.2 0.918 0.176 0.231 0.447 0.545 0.000997 0.00122 Wall time: 7626.439259970561 ! Best model 163 0.918 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 24 0.775 0.0331 0.114 0.17 0.222 0.329 0.411 0.000734 0.000918 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 0.663 0.0311 0.041 0.165 0.215 0.205 0.247 0.000458 0.000551 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 164 7673.137 0.005 0.0352 0.131 0.835 0.174 0.229 0.352 0.442 0.000786 0.000986 ! Validation 164 7673.137 0.005 0.0355 0.241 0.95 0.175 0.23 0.494 0.598 0.0011 0.00134 Wall time: 7673.137809576001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 24 1.02 0.0353 0.319 0.174 0.229 0.604 0.689 0.00135 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 0.678 0.0313 0.0515 0.165 0.216 0.208 0.277 0.000464 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 165 7719.835 0.005 0.0353 0.411 1.12 0.174 0.229 0.657 0.783 0.00147 0.00175 ! Validation 165 7719.835 0.005 0.0356 0.297 1.01 0.175 0.23 0.546 0.664 0.00122 0.00148 Wall time: 7719.835240625776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 24 0.85 0.0346 0.158 0.172 0.227 0.431 0.484 0.000961 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 0.658 0.0307 0.0441 0.164 0.214 0.211 0.256 0.000472 0.000572 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 7766.524 0.005 0.0352 0.175 0.878 0.174 0.229 0.408 0.51 0.00091 0.00114 ! Validation 166 7766.524 0.005 0.0352 0.262 0.967 0.174 0.229 0.524 0.624 0.00117 0.00139 Wall time: 7766.524469248019 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 24 0.796 0.0363 0.0705 0.176 0.232 0.237 0.324 0.000529 0.000723 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 0.67 0.0301 0.0674 0.162 0.212 0.233 0.317 0.00052 0.000707 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 7813.224 0.005 0.0346 0.157 0.849 0.172 0.227 0.4 0.486 0.000894 0.00109 ! Validation 167 7813.224 0.005 0.0348 0.315 1.01 0.173 0.227 0.579 0.685 0.00129 0.00153 Wall time: 7813.224674526602 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 24 0.999 0.0376 0.247 0.179 0.236 0.516 0.606 0.00115 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 0.831 0.0308 0.215 0.164 0.214 0.51 0.566 0.00114 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 168 7859.906 0.005 0.036 0.574 1.29 0.175 0.231 0.763 0.93 0.0017 0.00208 ! Validation 168 7859.906 0.005 0.0356 0.248 0.96 0.175 0.23 0.477 0.608 0.00106 0.00136 Wall time: 7859.906865751836 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 24 1.03 0.0343 0.345 0.173 0.226 0.663 0.716 0.00148 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 0.823 0.0304 0.214 0.163 0.213 0.498 0.564 0.00111 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 169 7906.587 0.005 0.0346 0.172 0.865 0.172 0.227 0.406 0.5 0.000907 0.00112 ! Validation 169 7906.587 0.005 0.0348 0.299 0.995 0.173 0.227 0.518 0.667 0.00116 0.00149 Wall time: 7906.58790153591 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 24 1.06 0.0349 0.363 0.173 0.228 0.686 0.734 0.00153 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 1.74 0.0301 1.14 0.162 0.211 1.28 1.3 0.00285 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 170 7953.278 0.005 0.0342 0.224 0.907 0.171 0.225 0.453 0.572 0.00101 0.00128 ! Validation 170 7953.278 0.005 0.0345 0.935 1.62 0.172 0.226 1.05 1.18 0.00235 0.00263 Wall time: 7953.278878598008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 24 0.697 0.0315 0.0668 0.165 0.216 0.256 0.315 0.000572 0.000703 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 0.661 0.0295 0.0706 0.16 0.21 0.294 0.324 0.000657 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 171 7999.965 0.005 0.0338 0.24 0.917 0.171 0.225 0.496 0.603 0.00111 0.00135 ! Validation 171 7999.965 0.005 0.0341 0.208 0.89 0.171 0.225 0.45 0.556 0.00101 0.00124 Wall time: 7999.9657451887615 ! Best model 171 0.890 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 24 1.81 0.0314 1.18 0.165 0.216 1.3 1.33 0.0029 0.00296 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 1.41 0.0294 0.82 0.16 0.209 1.08 1.1 0.0024 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 172 8046.764 0.005 0.0335 0.223 0.893 0.17 0.223 0.437 0.545 0.000974 0.00122 ! Validation 172 8046.764 0.005 0.034 1.2 1.88 0.17 0.225 1.21 1.33 0.00271 0.00298 Wall time: 8046.764521291014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 24 0.727 0.0335 0.0575 0.169 0.223 0.24 0.292 0.000535 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 0.867 0.0294 0.279 0.16 0.209 0.598 0.644 0.00133 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 173 8093.454 0.005 0.0339 0.397 1.07 0.17 0.225 0.666 0.776 0.00149 0.00173 ! Validation 173 8093.454 0.005 0.0341 0.287 0.969 0.171 0.225 0.511 0.653 0.00114 0.00146 Wall time: 8093.454496950842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 24 0.797 0.0333 0.132 0.169 0.222 0.366 0.443 0.000818 0.000988 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 0.722 0.0291 0.14 0.159 0.208 0.405 0.456 0.000904 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 174 8140.153 0.005 0.0334 0.144 0.811 0.169 0.223 0.37 0.463 0.000825 0.00103 ! Validation 174 8140.153 0.005 0.0335 0.243 0.913 0.169 0.223 0.472 0.602 0.00105 0.00134 Wall time: 8140.153758335859 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 24 0.857 0.0313 0.231 0.164 0.216 0.526 0.586 0.00117 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 1.24 0.0292 0.661 0.159 0.208 0.959 0.992 0.00214 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 175 8186.842 0.005 0.0331 0.327 0.989 0.169 0.222 0.595 0.7 0.00133 0.00156 ! Validation 175 8186.842 0.005 0.0337 0.641 1.31 0.17 0.224 0.813 0.977 0.00181 0.00218 Wall time: 8186.842693887651 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 24 0.936 0.0317 0.301 0.165 0.217 0.614 0.669 0.00137 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 0.667 0.0288 0.0905 0.158 0.207 0.334 0.367 0.000745 0.000819 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 176 8233.513 0.005 0.0329 0.213 0.871 0.168 0.221 0.459 0.56 0.00102 0.00125 ! Validation 176 8233.513 0.005 0.0331 0.194 0.856 0.168 0.222 0.434 0.537 0.000968 0.0012 Wall time: 8233.513658426702 ! Best model 176 0.856 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 24 1.15 0.0329 0.494 0.168 0.221 0.81 0.857 0.00181 0.00191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 1.13 0.0286 0.556 0.158 0.206 0.878 0.909 0.00196 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 177 8280.204 0.005 0.0328 0.256 0.911 0.167 0.221 0.494 0.609 0.0011 0.00136 ! Validation 177 8280.204 0.005 0.033 0.948 1.61 0.168 0.222 1.07 1.19 0.00239 0.00265 Wall time: 8280.204487502575 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 24 0.725 0.0331 0.062 0.168 0.222 0.252 0.304 0.000561 0.000678 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 0.637 0.0288 0.0605 0.158 0.207 0.272 0.3 0.000607 0.00067 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 8326.893 0.005 0.0335 0.303 0.973 0.169 0.223 0.516 0.678 0.00115 0.00151 ! Validation 178 8326.893 0.005 0.0332 0.189 0.854 0.169 0.222 0.431 0.531 0.000961 0.00118 Wall time: 8326.893722597975 ! Best model 178 0.854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 24 0.845 0.0305 0.236 0.162 0.213 0.532 0.592 0.00119 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 0.907 0.0286 0.335 0.158 0.206 0.661 0.706 0.00148 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 179 8373.595 0.005 0.0323 0.255 0.901 0.166 0.219 0.5 0.617 0.00112 0.00138 ! Validation 179 8373.595 0.005 0.0328 0.386 1.04 0.167 0.221 0.593 0.758 0.00132 0.00169 Wall time: 8373.595152662601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 24 0.747 0.033 0.0862 0.168 0.222 0.3 0.358 0.00067 0.000799 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 0.705 0.0287 0.13 0.158 0.207 0.383 0.44 0.000854 0.000981 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 8420.276 0.005 0.0331 0.499 1.16 0.168 0.222 0.711 0.87 0.00159 0.00194 ! Validation 180 8420.276 0.005 0.0331 0.226 0.888 0.168 0.222 0.456 0.579 0.00102 0.00129 Wall time: 8420.277431366965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 24 0.768 0.0324 0.12 0.167 0.219 0.329 0.423 0.000735 0.000945 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 0.626 0.0279 0.0676 0.156 0.204 0.221 0.317 0.000493 0.000708 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 8466.970 0.005 0.0323 0.0898 0.736 0.166 0.219 0.296 0.364 0.000661 0.000812 ! Validation 181 8466.970 0.005 0.0323 0.303 0.949 0.166 0.219 0.57 0.671 0.00127 0.0015 Wall time: 8466.970141681843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 24 0.733 0.0315 0.104 0.164 0.216 0.333 0.394 0.000743 0.000878 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 0.678 0.0279 0.119 0.157 0.204 0.373 0.421 0.000834 0.000939 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 8513.654 0.005 0.0318 0.171 0.807 0.165 0.217 0.418 0.507 0.000934 0.00113 ! Validation 182 8513.654 0.005 0.0321 0.211 0.852 0.166 0.218 0.443 0.56 0.000988 0.00125 Wall time: 8513.654876672663 ! Best model 182 0.852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 24 0.849 0.0325 0.198 0.167 0.22 0.497 0.542 0.00111 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 0.602 0.0279 0.0449 0.156 0.204 0.208 0.258 0.000465 0.000577 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 183 8560.336 0.005 0.0316 0.246 0.878 0.164 0.217 0.507 0.606 0.00113 0.00135 ! Validation 183 8560.336 0.005 0.032 0.237 0.877 0.165 0.218 0.496 0.593 0.00111 0.00132 Wall time: 8560.336187744979 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 24 0.884 0.0312 0.26 0.164 0.215 0.561 0.622 0.00125 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 1.25 0.0273 0.707 0.154 0.201 0.998 1.03 0.00223 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 184 8607.022 0.005 0.0313 0.121 0.747 0.164 0.216 0.339 0.419 0.000757 0.000935 ! Validation 184 8607.022 0.005 0.0315 0.615 1.24 0.164 0.216 0.807 0.956 0.0018 0.00213 Wall time: 8607.022930542938 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 24 0.806 0.0324 0.159 0.165 0.219 0.385 0.486 0.000859 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 0.743 0.0281 0.181 0.157 0.204 0.467 0.519 0.00104 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 185 8653.712 0.005 0.0315 0.439 1.07 0.164 0.217 0.664 0.814 0.00148 0.00182 ! Validation 185 8653.712 0.005 0.0322 0.242 0.885 0.166 0.219 0.468 0.6 0.00105 0.00134 Wall time: 8653.712605217006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 24 0.72 0.0327 0.0654 0.168 0.221 0.257 0.312 0.000573 0.000696 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 0.579 0.027 0.0389 0.153 0.2 0.207 0.24 0.000463 0.000537 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 8700.405 0.005 0.0313 0.149 0.775 0.164 0.216 0.382 0.474 0.000852 0.00106 ! Validation 186 8700.405 0.005 0.0313 0.197 0.822 0.163 0.216 0.444 0.541 0.000991 0.00121 Wall time: 8700.40519652795 ! Best model 186 0.822 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 24 0.676 0.031 0.056 0.163 0.215 0.232 0.288 0.000517 0.000644 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 0.658 0.0268 0.122 0.153 0.2 0.375 0.426 0.000836 0.000952 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 187 8747.096 0.005 0.0307 0.125 0.74 0.162 0.214 0.351 0.434 0.000783 0.000968 ! Validation 187 8747.096 0.005 0.0309 0.205 0.823 0.163 0.214 0.435 0.552 0.000971 0.00123 Wall time: 8747.096598399803 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 24 0.761 0.0294 0.173 0.159 0.209 0.429 0.507 0.000957 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.626 0.0266 0.0948 0.152 0.199 0.334 0.375 0.000746 0.000838 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 188 8793.772 0.005 0.0303 0.128 0.734 0.161 0.212 0.339 0.434 0.000756 0.000968 ! Validation 188 8793.772 0.005 0.0309 0.207 0.824 0.162 0.214 0.439 0.554 0.00098 0.00124 Wall time: 8793.77251833072 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 24 0.772 0.0311 0.15 0.163 0.215 0.404 0.472 0.000903 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.785 0.0279 0.227 0.155 0.204 0.525 0.582 0.00117 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 189 8840.458 0.005 0.0317 0.614 1.25 0.165 0.217 0.797 0.964 0.00178 0.00215 ! Validation 189 8840.458 0.005 0.0323 0.279 0.926 0.166 0.219 0.497 0.645 0.00111 0.00144 Wall time: 8840.458794230595 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 24 0.759 0.0315 0.129 0.165 0.216 0.381 0.438 0.00085 0.000977 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.613 0.0269 0.0755 0.153 0.2 0.314 0.335 0.000702 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 190 8887.143 0.005 0.0309 0.159 0.777 0.162 0.214 0.398 0.488 0.000888 0.00109 ! Validation 190 8887.143 0.005 0.0309 0.201 0.819 0.162 0.214 0.434 0.546 0.000969 0.00122 Wall time: 8887.144002825953 ! Best model 190 0.819 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 24 0.652 0.0292 0.0691 0.159 0.208 0.27 0.32 0.000602 0.000715 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 0.558 0.026 0.038 0.151 0.197 0.205 0.238 0.000458 0.00053 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 8933.836 0.005 0.0303 0.117 0.722 0.161 0.212 0.33 0.419 0.000736 0.000935 ! Validation 191 8933.836 0.005 0.0304 0.192 0.801 0.161 0.213 0.441 0.535 0.000985 0.00119 Wall time: 8933.836835288908 ! Best model 191 0.801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 24 1.13 0.0316 0.497 0.164 0.217 0.79 0.86 0.00176 0.00192 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 1.26 0.0267 0.73 0.152 0.199 1.01 1.04 0.00226 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 192 8980.529 0.005 0.0301 0.327 0.928 0.16 0.211 0.595 0.693 0.00133 0.00155 ! Validation 192 8980.529 0.005 0.0306 0.683 1.3 0.162 0.213 0.861 1.01 0.00192 0.00225 Wall time: 8980.529187795706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 24 1.06 0.0308 0.443 0.162 0.214 0.753 0.812 0.00168 0.00181 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.604 0.0264 0.0754 0.151 0.198 0.299 0.335 0.000667 0.000747 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 193 9027.225 0.005 0.03 0.284 0.883 0.16 0.211 0.52 0.645 0.00116 0.00144 ! Validation 193 9027.225 0.005 0.0305 0.224 0.833 0.161 0.213 0.449 0.577 0.001 0.00129 Wall time: 9027.225867761765 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 24 0.644 0.0293 0.0573 0.158 0.209 0.243 0.292 0.000542 0.000652 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.702 0.0264 0.173 0.152 0.198 0.452 0.508 0.00101 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 194 9073.912 0.005 0.0301 0.383 0.985 0.16 0.212 0.655 0.762 0.00146 0.0017 ! Validation 194 9073.912 0.005 0.0307 0.444 1.06 0.162 0.214 0.707 0.813 0.00158 0.00181 Wall time: 9073.912970824633 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 24 0.817 0.0295 0.227 0.158 0.209 0.537 0.581 0.0012 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.898 0.0264 0.37 0.152 0.198 0.708 0.741 0.00158 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 195 9120.686 0.005 0.0301 0.272 0.873 0.16 0.211 0.543 0.637 0.00121 0.00142 ! Validation 195 9120.686 0.005 0.0303 0.365 0.97 0.161 0.212 0.578 0.736 0.00129 0.00164 Wall time: 9120.68659307668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 24 1.11 0.0292 0.531 0.158 0.208 0.855 0.888 0.00191 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.707 0.0256 0.194 0.149 0.195 0.488 0.537 0.00109 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 196 9167.358 0.005 0.0296 0.12 0.711 0.159 0.21 0.321 0.404 0.000716 0.000902 ! Validation 196 9167.358 0.005 0.0298 0.463 1.06 0.159 0.21 0.72 0.83 0.00161 0.00185 Wall time: 9167.35885696765 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 24 0.894 0.0315 0.264 0.163 0.216 0.574 0.626 0.00128 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 1.41 0.0256 0.895 0.149 0.195 1.13 1.15 0.00253 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 197 9214.038 0.005 0.0294 0.215 0.802 0.158 0.209 0.474 0.563 0.00106 0.00126 ! Validation 197 9214.038 0.005 0.0296 0.822 1.41 0.159 0.21 0.973 1.11 0.00217 0.00247 Wall time: 9214.038805132732 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 24 1.03 0.0291 0.446 0.158 0.208 0.761 0.814 0.0017 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.909 0.0254 0.4 0.149 0.194 0.741 0.771 0.00165 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 198 9261.597 0.005 0.0294 0.29 0.877 0.158 0.209 0.546 0.653 0.00122 0.00146 ! Validation 198 9261.597 0.005 0.0298 0.73 1.33 0.159 0.211 0.929 1.04 0.00207 0.00233 Wall time: 9261.59705623798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 24 0.814 0.0281 0.252 0.156 0.204 0.558 0.612 0.00124 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.558 0.0254 0.0496 0.149 0.194 0.209 0.271 0.000467 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 199 9308.264 0.005 0.0292 0.155 0.738 0.158 0.208 0.374 0.477 0.000836 0.00106 ! Validation 199 9308.264 0.005 0.0294 0.255 0.842 0.158 0.209 0.523 0.616 0.00117 0.00137 Wall time: 9308.264366141986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 24 0.702 0.029 0.122 0.158 0.208 0.369 0.426 0.000823 0.00095 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.713 0.0257 0.199 0.149 0.195 0.496 0.544 0.00111 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 200 9354.921 0.005 0.029 0.412 0.992 0.157 0.208 0.675 0.79 0.00151 0.00176 ! Validation 200 9354.921 0.005 0.0298 0.267 0.862 0.159 0.21 0.482 0.63 0.00108 0.00141 Wall time: 9354.921032754704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 24 1.16 0.0288 0.585 0.157 0.207 0.879 0.933 0.00196 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.744 0.0252 0.241 0.148 0.193 0.559 0.598 0.00125 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 201 9401.577 0.005 0.0291 0.289 0.871 0.158 0.208 0.543 0.647 0.00121 0.00144 ! Validation 201 9401.577 0.005 0.0294 0.507 1.1 0.158 0.209 0.764 0.868 0.00171 0.00194 Wall time: 9401.577381099574 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 24 0.656 0.0263 0.13 0.151 0.198 0.387 0.439 0.000864 0.00098 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.619 0.0251 0.117 0.148 0.193 0.354 0.417 0.00079 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 202 9448.234 0.005 0.0288 0.168 0.744 0.157 0.207 0.411 0.501 0.000918 0.00112 ! Validation 202 9448.234 0.005 0.0292 0.365 0.948 0.158 0.208 0.636 0.737 0.00142 0.00164 Wall time: 9448.234703016002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 24 0.854 0.0287 0.281 0.157 0.206 0.608 0.646 0.00136 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 1.75 0.0249 1.26 0.147 0.192 1.35 1.37 0.00301 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 203 9494.892 0.005 0.0286 0.161 0.733 0.156 0.206 0.393 0.485 0.000876 0.00108 ! Validation 203 9494.892 0.005 0.0288 1.13 1.7 0.157 0.207 1.19 1.29 0.00265 0.00289 Wall time: 9494.892563314643 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 24 0.643 0.0285 0.0726 0.156 0.206 0.275 0.329 0.000615 0.000733 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.547 0.0246 0.0558 0.147 0.191 0.212 0.288 0.000474 0.000643 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 9541.551 0.005 0.0284 0.199 0.767 0.156 0.206 0.418 0.547 0.000932 0.00122 ! Validation 204 9541.551 0.005 0.0286 0.27 0.843 0.156 0.206 0.539 0.634 0.0012 0.00141 Wall time: 9541.551545092836 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 24 0.657 0.0265 0.126 0.151 0.199 0.342 0.433 0.000764 0.000967 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.526 0.0245 0.0361 0.146 0.191 0.188 0.232 0.00042 0.000517 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 205 9588.217 0.005 0.0281 0.18 0.742 0.155 0.204 0.428 0.52 0.000956 0.00116 ! Validation 205 9588.217 0.005 0.0284 0.225 0.793 0.156 0.205 0.487 0.578 0.00109 0.00129 Wall time: 9588.218017429579 ! Best model 205 0.793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 24 0.702 0.0288 0.126 0.157 0.207 0.35 0.433 0.000781 0.000966 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.533 0.0245 0.0422 0.147 0.191 0.218 0.25 0.000486 0.000559 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 9634.883 0.005 0.028 0.179 0.739 0.155 0.204 0.42 0.518 0.000938 0.00116 ! Validation 206 9634.883 0.005 0.0283 0.206 0.772 0.156 0.205 0.439 0.553 0.000981 0.00123 Wall time: 9634.883255886845 ! Best model 206 0.772 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 24 0.727 0.029 0.146 0.157 0.208 0.418 0.466 0.000933 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.522 0.0241 0.0409 0.145 0.189 0.199 0.247 0.000445 0.000551 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 207 9681.554 0.005 0.0278 0.11 0.666 0.154 0.203 0.321 0.403 0.000717 0.000899 ! Validation 207 9681.554 0.005 0.028 0.255 0.814 0.154 0.204 0.521 0.616 0.00116 0.00138 Wall time: 9681.555211106781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 24 0.611 0.0263 0.0847 0.15 0.198 0.293 0.355 0.000654 0.000792 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.542 0.0244 0.0542 0.146 0.19 0.208 0.284 0.000464 0.000634 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 9728.213 0.005 0.0276 0.329 0.881 0.153 0.203 0.595 0.706 0.00133 0.00158 ! Validation 208 9728.213 0.005 0.0282 0.253 0.817 0.155 0.205 0.522 0.613 0.00116 0.00137 Wall time: 9728.213252246846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 24 1.73 0.0286 1.15 0.156 0.206 1.28 1.31 0.00287 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 1.1 0.0245 0.611 0.146 0.191 0.927 0.953 0.00207 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 209 9774.874 0.005 0.0278 0.373 0.929 0.154 0.203 0.616 0.726 0.00137 0.00162 ! Validation 209 9774.874 0.005 0.0285 0.878 1.45 0.156 0.206 1.03 1.14 0.0023 0.00255 Wall time: 9774.874135822989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 24 0.761 0.0307 0.146 0.161 0.214 0.338 0.466 0.000754 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.542 0.025 0.0423 0.147 0.193 0.22 0.251 0.00049 0.00056 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 9821.540 0.005 0.0285 0.449 1.02 0.155 0.206 0.692 0.824 0.00155 0.00184 ! Validation 210 9821.540 0.005 0.0286 0.179 0.751 0.156 0.206 0.413 0.516 0.000923 0.00115 Wall time: 9821.540827612858 ! Best model 210 0.751 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 24 0.671 0.0265 0.14 0.151 0.199 0.39 0.456 0.000871 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.648 0.0238 0.171 0.144 0.188 0.459 0.504 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 211 9868.210 0.005 0.0276 0.094 0.647 0.154 0.203 0.3 0.372 0.00067 0.000829 ! Validation 211 9868.210 0.005 0.0278 0.258 0.814 0.154 0.203 0.474 0.62 0.00106 0.00138 Wall time: 9868.211444645654 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 24 0.589 0.0264 0.0598 0.15 0.198 0.228 0.298 0.000509 0.000665 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.503 0.0237 0.0301 0.144 0.188 0.169 0.211 0.000377 0.000472 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 212 9914.872 0.005 0.0272 0.0906 0.634 0.152 0.201 0.298 0.369 0.000664 0.000823 ! Validation 212 9914.872 0.005 0.0275 0.197 0.747 0.153 0.202 0.45 0.541 0.00101 0.00121 Wall time: 9914.872972603887 ! Best model 212 0.747 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 24 0.713 0.0299 0.116 0.159 0.211 0.338 0.415 0.000755 0.000926 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.543 0.0243 0.0574 0.145 0.19 0.262 0.292 0.000584 0.000652 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 213 9961.860 0.005 0.0272 0.371 0.915 0.152 0.201 0.625 0.748 0.0014 0.00167 ! Validation 213 9961.860 0.005 0.0279 0.2 0.759 0.154 0.204 0.426 0.545 0.00095 0.00122 Wall time: 9961.861029137857 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 24 0.672 0.0297 0.0782 0.158 0.21 0.292 0.341 0.000651 0.000761 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.549 0.0237 0.0744 0.144 0.188 0.292 0.333 0.000653 0.000743 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 10008.512 0.005 0.0273 0.181 0.727 0.152 0.201 0.428 0.522 0.000955 0.00117 ! Validation 214 10008.512 0.005 0.0275 0.193 0.742 0.153 0.202 0.417 0.535 0.000931 0.0012 Wall time: 10008.51255548466 ! Best model 214 0.742 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 24 0.65 0.0255 0.14 0.148 0.195 0.402 0.456 0.000896 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.821 0.0233 0.354 0.143 0.186 0.696 0.726 0.00155 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 215 10055.184 0.005 0.0268 0.0831 0.618 0.151 0.2 0.276 0.349 0.000617 0.000778 ! Validation 215 10055.184 0.005 0.027 0.368 0.908 0.152 0.2 0.588 0.739 0.00131 0.00165 Wall time: 10055.184906113893 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 24 1.08 0.0267 0.545 0.152 0.199 0.865 0.901 0.00193 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.648 0.0239 0.171 0.144 0.188 0.456 0.504 0.00102 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 216 10101.846 0.005 0.0268 0.385 0.92 0.151 0.2 0.639 0.752 0.00143 0.00168 ! Validation 216 10101.846 0.005 0.0276 0.294 0.846 0.153 0.203 0.5 0.661 0.00112 0.00147 Wall time: 10101.846143068746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 24 0.58 0.0265 0.0491 0.15 0.199 0.233 0.27 0.000521 0.000603 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.81 0.0237 0.337 0.144 0.188 0.675 0.707 0.00151 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 217 10148.505 0.005 0.027 0.262 0.803 0.152 0.201 0.534 0.631 0.00119 0.00141 ! Validation 217 10148.505 0.005 0.0273 0.387 0.933 0.152 0.201 0.604 0.759 0.00135 0.00169 Wall time: 10148.505264224019 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 24 0.612 0.0258 0.0966 0.149 0.196 0.313 0.379 0.000699 0.000846 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.564 0.0232 0.0998 0.142 0.186 0.332 0.385 0.000741 0.00086 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 218 10195.261 0.005 0.0267 0.204 0.737 0.151 0.199 0.463 0.555 0.00103 0.00124 ! Validation 218 10195.261 0.005 0.027 0.193 0.732 0.151 0.2 0.416 0.536 0.000929 0.0012 Wall time: 10195.261559283827 ! Best model 218 0.732 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 24 0.624 0.0291 0.0416 0.158 0.208 0.199 0.249 0.000445 0.000555 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.617 0.0232 0.153 0.143 0.186 0.433 0.477 0.000967 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 219 10241.922 0.005 0.0265 0.17 0.7 0.15 0.198 0.398 0.507 0.000888 0.00113 ! Validation 219 10241.922 0.005 0.0268 0.212 0.748 0.151 0.199 0.431 0.562 0.000961 0.00125 Wall time: 10241.922635927796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 24 0.586 0.0255 0.0754 0.148 0.195 0.265 0.335 0.000591 0.000747 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.555 0.0228 0.0983 0.141 0.184 0.325 0.382 0.000726 0.000853 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 220 10288.579 0.005 0.0262 0.228 0.753 0.15 0.197 0.474 0.587 0.00106 0.00131 ! Validation 220 10288.579 0.005 0.0266 0.214 0.746 0.15 0.199 0.431 0.564 0.000962 0.00126 Wall time: 10288.579435595777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 24 0.617 0.0282 0.0533 0.155 0.205 0.24 0.282 0.000536 0.000629 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.54 0.0228 0.0846 0.141 0.184 0.314 0.355 0.000701 0.000792 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 221 10335.234 0.005 0.0262 0.156 0.679 0.149 0.197 0.394 0.485 0.00088 0.00108 ! Validation 221 10335.234 0.005 0.0265 0.215 0.744 0.15 0.198 0.435 0.565 0.000971 0.00126 Wall time: 10335.235029947013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 24 0.667 0.0263 0.14 0.149 0.198 0.403 0.457 0.000899 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.511 0.0226 0.0586 0.14 0.183 0.269 0.295 0.0006 0.000659 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 222 10381.890 0.005 0.0261 0.177 0.699 0.149 0.197 0.423 0.514 0.000945 0.00115 ! Validation 222 10381.890 0.005 0.0263 0.181 0.708 0.15 0.198 0.409 0.519 0.000912 0.00116 Wall time: 10381.890779058915 ! Best model 222 0.708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 24 0.713 0.0256 0.201 0.148 0.195 0.482 0.547 0.00108 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.716 0.0227 0.263 0.141 0.184 0.594 0.625 0.00133 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 223 10428.555 0.005 0.0262 0.298 0.821 0.149 0.197 0.551 0.668 0.00123 0.00149 ! Validation 223 10428.555 0.005 0.0264 0.344 0.872 0.15 0.198 0.559 0.715 0.00125 0.0016 Wall time: 10428.55553813884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 24 0.618 0.0277 0.0645 0.152 0.203 0.254 0.31 0.000568 0.000692 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.552 0.0229 0.0935 0.141 0.185 0.312 0.373 0.000696 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 224 10475.215 0.005 0.0262 0.302 0.827 0.149 0.197 0.564 0.677 0.00126 0.00151 ! Validation 224 10475.215 0.005 0.0267 0.29 0.823 0.15 0.199 0.567 0.656 0.00127 0.00146 Wall time: 10475.215949540958 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 24 0.575 0.0258 0.0587 0.149 0.196 0.241 0.296 0.000537 0.00066 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.519 0.0224 0.0707 0.14 0.183 0.287 0.324 0.000641 0.000724 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 225 10521.869 0.005 0.0258 0.0833 0.6 0.148 0.196 0.283 0.353 0.000633 0.000788 ! Validation 225 10521.869 0.005 0.026 0.174 0.693 0.149 0.196 0.398 0.508 0.000888 0.00113 Wall time: 10521.869802225847 ! Best model 225 0.693 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 24 0.587 0.0265 0.0569 0.15 0.198 0.246 0.291 0.000549 0.000649 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.583 0.022 0.142 0.139 0.181 0.417 0.46 0.000932 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 226 10568.539 0.005 0.0254 0.101 0.609 0.147 0.194 0.318 0.39 0.000709 0.000871 ! Validation 226 10568.539 0.005 0.0256 0.223 0.735 0.148 0.195 0.437 0.575 0.000976 0.00128 Wall time: 10568.53941808967 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 24 0.567 0.0251 0.0654 0.146 0.193 0.262 0.312 0.000584 0.000696 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.663 0.0223 0.217 0.139 0.182 0.531 0.568 0.00119 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 227 10615.200 0.005 0.0251 0.122 0.624 0.146 0.193 0.339 0.428 0.000757 0.000956 ! Validation 227 10615.200 0.005 0.0259 0.327 0.845 0.148 0.196 0.532 0.698 0.00119 0.00156 Wall time: 10615.200565135572 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 24 0.577 0.0248 0.0814 0.147 0.192 0.293 0.348 0.000655 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.489 0.0219 0.0505 0.139 0.181 0.25 0.274 0.000558 0.000612 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 10661.862 0.005 0.0252 0.263 0.767 0.147 0.194 0.504 0.63 0.00112 0.00141 ! Validation 228 10661.862 0.005 0.0255 0.163 0.672 0.147 0.195 0.397 0.492 0.000886 0.0011 Wall time: 10661.86206871178 ! Best model 228 0.672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 24 1.12 0.025 0.621 0.146 0.193 0.914 0.961 0.00204 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 2.03 0.0218 1.59 0.138 0.18 1.53 1.54 0.00341 0.00343 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 229 10708.531 0.005 0.0249 0.17 0.669 0.146 0.193 0.403 0.487 0.0009 0.00109 ! Validation 229 10708.531 0.005 0.0253 1.38 1.89 0.147 0.194 1.35 1.44 0.003 0.0032 Wall time: 10708.531758596655 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 24 0.648 0.0242 0.163 0.144 0.19 0.413 0.493 0.000921 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.954 0.022 0.513 0.139 0.181 0.853 0.873 0.0019 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 230 10755.181 0.005 0.0254 0.377 0.884 0.147 0.194 0.642 0.753 0.00143 0.00168 ! Validation 230 10755.181 0.005 0.0257 0.547 1.06 0.148 0.195 0.736 0.902 0.00164 0.00201 Wall time: 10755.181720984634 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 24 1.13 0.0255 0.618 0.147 0.195 0.929 0.958 0.00207 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 1.65 0.0218 1.22 0.138 0.18 1.33 1.35 0.00298 0.00301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 231 10801.836 0.005 0.025 0.156 0.657 0.146 0.193 0.373 0.465 0.000832 0.00104 ! Validation 231 10801.836 0.005 0.0252 1.11 1.62 0.147 0.194 1.19 1.29 0.00265 0.00287 Wall time: 10801.836910885759 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 24 0.619 0.0234 0.152 0.142 0.186 0.4 0.476 0.000893 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.711 0.0222 0.267 0.139 0.182 0.606 0.63 0.00135 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 232 10848.492 0.005 0.0255 0.367 0.877 0.147 0.195 0.62 0.744 0.00138 0.00166 ! Validation 232 10848.492 0.005 0.0258 0.368 0.885 0.148 0.196 0.576 0.74 0.00129 0.00165 Wall time: 10848.492554309778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 24 0.665 0.0265 0.136 0.15 0.198 0.384 0.449 0.000857 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.504 0.0217 0.0709 0.138 0.18 0.291 0.325 0.00065 0.000725 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 233 10895.145 0.005 0.025 0.21 0.71 0.146 0.193 0.477 0.561 0.00106 0.00125 ! Validation 233 10895.145 0.005 0.0252 0.187 0.691 0.146 0.194 0.409 0.527 0.000913 0.00118 Wall time: 10895.14558062097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 24 0.71 0.026 0.19 0.148 0.197 0.448 0.531 0.001 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 1.15 0.0214 0.725 0.137 0.178 1.02 1.04 0.00228 0.00232 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 234 10941.806 0.005 0.0247 0.108 0.602 0.145 0.191 0.316 0.397 0.000706 0.000887 ! Validation 234 10941.806 0.005 0.0249 0.704 1.2 0.145 0.192 0.891 1.02 0.00199 0.00228 Wall time: 10941.806470262818 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 24 0.525 0.0241 0.0441 0.144 0.189 0.191 0.256 0.000426 0.000572 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.516 0.0214 0.0885 0.137 0.178 0.315 0.363 0.000703 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 235 10988.469 0.005 0.0247 0.237 0.732 0.145 0.192 0.484 0.599 0.00108 0.00134 ! Validation 235 10988.469 0.005 0.0249 0.167 0.666 0.146 0.193 0.39 0.499 0.00087 0.00111 Wall time: 10988.469521388877 ! Best model 235 0.666 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 24 0.581 0.0254 0.0738 0.146 0.194 0.261 0.331 0.000583 0.000739 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.453 0.021 0.0318 0.136 0.177 0.162 0.218 0.000362 0.000486 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 11035.136 0.005 0.0243 0.0967 0.583 0.144 0.19 0.307 0.38 0.000686 0.000849 ! Validation 236 11035.136 0.005 0.0246 0.208 0.7 0.145 0.191 0.471 0.557 0.00105 0.00124 Wall time: 11035.136612423696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 24 0.586 0.024 0.106 0.143 0.189 0.312 0.397 0.000696 0.000887 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.722 0.0212 0.298 0.136 0.178 0.636 0.666 0.00142 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 237 11081.793 0.005 0.0246 0.253 0.746 0.145 0.191 0.526 0.618 0.00117 0.00138 ! Validation 237 11081.793 0.005 0.0247 0.515 1.01 0.145 0.192 0.78 0.875 0.00174 0.00195 Wall time: 11081.793762799818 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 24 0.766 0.0265 0.236 0.149 0.198 0.545 0.593 0.00122 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 1.22 0.0217 0.787 0.137 0.18 1.07 1.08 0.00238 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 238 11128.447 0.005 0.0249 0.492 0.99 0.145 0.192 0.739 0.86 0.00165 0.00192 ! Validation 238 11128.447 0.005 0.0255 0.882 1.39 0.147 0.195 1 1.14 0.00224 0.00256 Wall time: 11128.44785570493 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 24 0.575 0.0256 0.0633 0.146 0.195 0.242 0.307 0.000539 0.000685 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.605 0.0216 0.173 0.137 0.179 0.475 0.507 0.00106 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 239 11175.110 0.005 0.025 0.281 0.78 0.146 0.193 0.546 0.652 0.00122 0.00145 ! Validation 239 11175.110 0.005 0.025 0.273 0.774 0.146 0.193 0.48 0.637 0.00107 0.00142 Wall time: 11175.11043636268 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 24 0.536 0.0239 0.0567 0.144 0.189 0.219 0.29 0.00049 0.000648 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.507 0.021 0.0872 0.135 0.177 0.311 0.36 0.000695 0.000804 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 240 11221.760 0.005 0.0243 0.121 0.606 0.144 0.19 0.35 0.426 0.000781 0.000952 ! Validation 240 11221.760 0.005 0.0245 0.206 0.696 0.144 0.191 0.42 0.553 0.000938 0.00123 Wall time: 11221.7603396778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 24 0.52 0.0236 0.0475 0.142 0.187 0.223 0.266 0.000497 0.000594 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.637 0.0209 0.22 0.135 0.176 0.542 0.571 0.00121 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 241 11268.507 0.005 0.024 0.117 0.596 0.143 0.189 0.334 0.42 0.000745 0.000937 ! Validation 241 11268.507 0.005 0.0243 0.269 0.754 0.144 0.19 0.482 0.633 0.00108 0.00141 Wall time: 11268.508113927674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 24 0.794 0.024 0.315 0.143 0.189 0.64 0.684 0.00143 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.549 0.0205 0.14 0.134 0.174 0.423 0.456 0.000944 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 242 11315.163 0.005 0.0237 0.103 0.578 0.142 0.188 0.312 0.382 0.000696 0.000853 ! Validation 242 11315.163 0.005 0.0241 0.396 0.878 0.143 0.189 0.675 0.767 0.00151 0.00171 Wall time: 11315.163732231595 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 24 0.62 0.0231 0.158 0.141 0.185 0.42 0.484 0.000936 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.473 0.0207 0.059 0.134 0.175 0.261 0.296 0.000583 0.000661 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 243 11361.818 0.005 0.0239 0.308 0.786 0.143 0.189 0.567 0.68 0.00127 0.00152 ! Validation 243 11361.818 0.005 0.0243 0.166 0.652 0.144 0.19 0.391 0.497 0.000873 0.00111 Wall time: 11361.818756036926 ! Best model 243 0.652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 24 0.582 0.0228 0.127 0.139 0.184 0.383 0.434 0.000855 0.00097 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.474 0.0206 0.0622 0.134 0.175 0.269 0.304 0.000601 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 244 11408.483 0.005 0.0237 0.163 0.637 0.142 0.188 0.406 0.494 0.000906 0.0011 ! Validation 244 11408.483 0.005 0.0239 0.195 0.674 0.143 0.189 0.412 0.538 0.00092 0.0012 Wall time: 11408.483527136967 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 24 0.528 0.0227 0.0744 0.139 0.184 0.286 0.333 0.000638 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.434 0.0204 0.027 0.133 0.174 0.175 0.2 0.000392 0.000447 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 11455.136 0.005 0.0234 0.1 0.569 0.141 0.187 0.313 0.388 0.000699 0.000865 ! Validation 245 11455.136 0.005 0.0237 0.164 0.639 0.142 0.188 0.402 0.495 0.000898 0.0011 Wall time: 11455.136933299713 ! Best model 245 0.639 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 24 0.582 0.023 0.121 0.14 0.185 0.346 0.424 0.000772 0.000947 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 1.18 0.0204 0.768 0.134 0.174 1.05 1.07 0.00235 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 246 11501.805 0.005 0.0234 0.204 0.672 0.141 0.187 0.471 0.554 0.00105 0.00124 ! Validation 246 11501.805 0.005 0.0237 0.73 1.2 0.142 0.188 0.918 1.04 0.00205 0.00233 Wall time: 11501.805562315974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 24 0.765 0.0257 0.25 0.148 0.196 0.566 0.61 0.00126 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.608 0.0214 0.181 0.137 0.178 0.484 0.519 0.00108 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 247 11548.456 0.005 0.0241 0.553 1.04 0.143 0.189 0.792 0.912 0.00177 0.00204 ! Validation 247 11548.456 0.005 0.0245 0.436 0.926 0.144 0.191 0.712 0.805 0.00159 0.0018 Wall time: 11548.456667452585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 24 0.512 0.0225 0.0615 0.138 0.183 0.232 0.303 0.000519 0.000675 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.441 0.0205 0.0308 0.134 0.175 0.195 0.214 0.000436 0.000478 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 248 11595.114 0.005 0.0238 0.156 0.632 0.142 0.188 0.4 0.486 0.000892 0.00108 ! Validation 248 11595.114 0.005 0.0239 0.166 0.644 0.142 0.188 0.402 0.497 0.000898 0.00111 Wall time: 11595.11405492993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 24 0.869 0.0228 0.412 0.14 0.184 0.748 0.783 0.00167 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 1.12 0.0203 0.713 0.133 0.174 1.01 1.03 0.00226 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 249 11641.770 0.005 0.0233 0.101 0.567 0.141 0.186 0.296 0.373 0.000661 0.000834 ! Validation 249 11641.770 0.005 0.0237 0.686 1.16 0.142 0.188 0.88 1.01 0.00196 0.00225 Wall time: 11641.771054546814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 24 0.926 0.0247 0.431 0.145 0.192 0.729 0.801 0.00163 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 0.52 0.0215 0.0904 0.137 0.179 0.298 0.367 0.000665 0.000819 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 250 11688.432 0.005 0.0235 0.356 0.827 0.141 0.187 0.622 0.726 0.00139 0.00162 ! Validation 250 11688.432 0.005 0.0244 0.289 0.776 0.144 0.19 0.556 0.655 0.00124 0.00146 Wall time: 11688.432731923647 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 24 0.535 0.0243 0.0488 0.144 0.19 0.209 0.269 0.000467 0.000601 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 0.534 0.0202 0.129 0.133 0.173 0.399 0.438 0.000891 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 251 11735.083 0.005 0.0238 0.165 0.641 0.142 0.188 0.414 0.499 0.000924 0.00111 ! Validation 251 11735.083 0.005 0.0237 0.329 0.803 0.142 0.188 0.613 0.699 0.00137 0.00156 Wall time: 11735.08335393481 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 24 0.539 0.0223 0.0926 0.138 0.182 0.311 0.371 0.000695 0.000828 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 0.996 0.0204 0.589 0.133 0.174 0.919 0.936 0.00205 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 252 11781.732 0.005 0.0232 0.234 0.697 0.14 0.186 0.508 0.594 0.00113 0.00133 ! Validation 252 11781.732 0.005 0.0235 0.61 1.08 0.141 0.187 0.814 0.953 0.00182 0.00213 Wall time: 11781.732068117708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 24 0.532 0.0243 0.0455 0.144 0.19 0.216 0.26 0.000482 0.000581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 0.575 0.0202 0.171 0.133 0.173 0.468 0.504 0.00104 0.00113 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 253 11828.390 0.005 0.023 0.157 0.617 0.14 0.185 0.393 0.487 0.000877 0.00109 ! Validation 253 11828.390 0.005 0.0234 0.26 0.728 0.141 0.187 0.469 0.622 0.00105 0.00139 Wall time: 11828.390257834923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 24 0.575 0.0238 0.0997 0.142 0.188 0.309 0.385 0.00069 0.000859 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 0.462 0.0198 0.0654 0.132 0.172 0.278 0.312 0.00062 0.000696 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 254 11875.046 0.005 0.0229 0.175 0.632 0.139 0.184 0.431 0.512 0.000961 0.00114 ! Validation 254 11875.046 0.005 0.0231 0.17 0.633 0.14 0.185 0.389 0.502 0.000869 0.00112 Wall time: 11875.046111386735 ! Best model 254 0.633 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 24 0.587 0.0224 0.139 0.138 0.182 0.393 0.455 0.000877 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.419 0.0199 0.0216 0.132 0.172 0.152 0.179 0.000339 0.0004 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 255 11921.704 0.005 0.0228 0.205 0.661 0.139 0.184 0.453 0.554 0.00101 0.00124 ! Validation 255 11921.704 0.005 0.0231 0.177 0.639 0.14 0.185 0.424 0.513 0.000946 0.00114 Wall time: 11921.704639008734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 24 0.481 0.0222 0.0379 0.137 0.182 0.2 0.237 0.000446 0.00053 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 0.494 0.0196 0.102 0.131 0.171 0.352 0.39 0.000785 0.00087 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 11968.365 0.005 0.0226 0.125 0.578 0.139 0.184 0.342 0.435 0.000763 0.000971 ! Validation 256 11968.365 0.005 0.0229 0.194 0.651 0.139 0.184 0.406 0.537 0.000906 0.0012 Wall time: 11968.365321362857 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 24 0.574 0.0224 0.126 0.138 0.182 0.397 0.434 0.000886 0.000968 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 0.433 0.0195 0.0424 0.131 0.17 0.231 0.251 0.000515 0.00056 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 12015.016 0.005 0.0224 0.178 0.626 0.138 0.183 0.408 0.516 0.00091 0.00115 ! Validation 257 12015.016 0.005 0.0228 0.216 0.672 0.139 0.184 0.43 0.567 0.000961 0.00127 Wall time: 12015.017029979732 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 24 0.459 0.0201 0.0566 0.132 0.173 0.239 0.29 0.000534 0.000648 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 0.532 0.0195 0.142 0.131 0.17 0.427 0.46 0.000954 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 258 12061.667 0.005 0.0224 0.186 0.634 0.138 0.183 0.441 0.53 0.000984 0.00118 ! Validation 258 12061.667 0.005 0.0229 0.23 0.688 0.14 0.185 0.44 0.585 0.000983 0.00131 Wall time: 12061.667588821612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 24 0.494 0.0216 0.0621 0.136 0.179 0.249 0.304 0.000556 0.000678 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.621 0.0194 0.233 0.13 0.17 0.562 0.588 0.00125 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 259 12108.319 0.005 0.0223 0.174 0.62 0.138 0.182 0.436 0.513 0.000973 0.00115 ! Validation 259 12108.319 0.005 0.0226 0.248 0.7 0.138 0.183 0.464 0.608 0.00104 0.00136 Wall time: 12108.319381297566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 24 0.5 0.0235 0.0302 0.141 0.187 0.173 0.212 0.000387 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.743 0.0194 0.355 0.13 0.17 0.705 0.726 0.00157 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 260 12154.969 0.005 0.0221 0.124 0.566 0.137 0.181 0.355 0.434 0.000792 0.000968 ! Validation 260 12154.969 0.005 0.0225 0.401 0.85 0.138 0.183 0.615 0.772 0.00137 0.00172 Wall time: 12154.96984615596 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 24 0.993 0.0208 0.577 0.133 0.176 0.898 0.926 0.002 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.666 0.0193 0.281 0.13 0.169 0.618 0.646 0.00138 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 261 12201.624 0.005 0.022 0.195 0.635 0.137 0.181 0.447 0.526 0.000997 0.00117 ! Validation 261 12201.624 0.005 0.0224 0.527 0.975 0.138 0.183 0.789 0.885 0.00176 0.00198 Wall time: 12201.624736997765 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 24 0.507 0.0211 0.0855 0.134 0.177 0.297 0.356 0.000663 0.000796 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 0.546 0.0197 0.153 0.131 0.171 0.437 0.477 0.000975 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 262 12248.280 0.005 0.0224 0.335 0.783 0.138 0.183 0.606 0.712 0.00135 0.00159 ! Validation 262 12248.280 0.005 0.0227 0.209 0.664 0.139 0.184 0.424 0.558 0.000947 0.00125 Wall time: 12248.28063162975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 24 0.738 0.0215 0.307 0.135 0.179 0.633 0.676 0.00141 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.995 0.0191 0.614 0.129 0.168 0.94 0.956 0.0021 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 263 12294.937 0.005 0.022 0.118 0.558 0.137 0.181 0.335 0.41 0.000747 0.000915 ! Validation 263 12294.937 0.005 0.0223 0.556 1 0.137 0.182 0.774 0.91 0.00173 0.00203 Wall time: 12294.938039888628 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 24 0.56 0.0228 0.103 0.139 0.184 0.325 0.391 0.000725 0.000873 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.407 0.0191 0.0236 0.13 0.169 0.142 0.187 0.000317 0.000418 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 264 12341.685 0.005 0.0218 0.133 0.57 0.136 0.18 0.366 0.446 0.000818 0.000996 ! Validation 264 12341.685 0.005 0.0222 0.177 0.62 0.137 0.182 0.428 0.512 0.000955 0.00114 Wall time: 12341.685400217772 ! Best model 264 0.620 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 24 0.827 0.0215 0.397 0.135 0.179 0.723 0.768 0.00161 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 1.48 0.0191 1.1 0.129 0.169 1.27 1.28 0.00283 0.00285 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 265 12388.349 0.005 0.0217 0.147 0.58 0.136 0.179 0.381 0.458 0.00085 0.00102 ! Validation 265 12388.349 0.005 0.0222 1.14 1.58 0.137 0.182 1.2 1.3 0.00268 0.00291 Wall time: 12388.350116403773 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 24 0.489 0.0212 0.0639 0.135 0.178 0.269 0.308 0.000601 0.000688 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.638 0.0192 0.254 0.13 0.169 0.594 0.615 0.00132 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 266 12435.009 0.005 0.0221 0.286 0.729 0.137 0.182 0.54 0.658 0.00121 0.00147 ! Validation 266 12435.009 0.005 0.0223 0.297 0.743 0.138 0.182 0.509 0.665 0.00114 0.00148 Wall time: 12435.01000642078 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 24 0.572 0.021 0.153 0.134 0.177 0.422 0.476 0.000942 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.765 0.0186 0.393 0.128 0.166 0.748 0.765 0.00167 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 267 12481.670 0.005 0.0216 0.112 0.544 0.136 0.179 0.334 0.406 0.000744 0.000906 ! Validation 267 12481.670 0.005 0.0219 0.418 0.856 0.136 0.18 0.636 0.788 0.00142 0.00176 Wall time: 12481.670615139883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 24 1.6 0.0241 1.12 0.143 0.189 1.23 1.29 0.00274 0.00288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 1.04 0.0209 0.618 0.135 0.176 0.943 0.959 0.0021 0.00214 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 12528.337 0.005 0.0221 0.52 0.962 0.137 0.181 0.69 0.867 0.00154 0.00194 ! Validation 268 12528.337 0.005 0.024 0.792 1.27 0.142 0.189 0.931 1.09 0.00208 0.00242 Wall time: 12528.33833987359 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 24 0.489 0.0218 0.0525 0.137 0.18 0.237 0.28 0.000528 0.000624 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.416 0.0194 0.0288 0.13 0.17 0.149 0.207 0.000332 0.000462 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 269 12574.992 0.005 0.023 0.318 0.778 0.14 0.185 0.585 0.695 0.00131 0.00155 ! Validation 269 12574.992 0.005 0.0225 0.199 0.648 0.138 0.183 0.46 0.544 0.00103 0.00121 Wall time: 12574.993463875726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 24 1.28 0.0217 0.846 0.136 0.18 1.1 1.12 0.00245 0.0025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 1.29 0.0194 0.898 0.129 0.17 1.14 1.16 0.00255 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 270 12621.652 0.005 0.0218 0.209 0.646 0.136 0.18 0.446 0.537 0.000995 0.0012 ! Validation 270 12621.652 0.005 0.0225 0.929 1.38 0.138 0.183 1.06 1.18 0.00238 0.00262 Wall time: 12621.65270823799 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 24 0.524 0.022 0.0843 0.136 0.181 0.274 0.354 0.000611 0.00079 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.422 0.0191 0.041 0.129 0.168 0.237 0.247 0.00053 0.000551 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 271 12668.311 0.005 0.0219 0.175 0.612 0.136 0.18 0.424 0.513 0.000946 0.00114 ! Validation 271 12668.311 0.005 0.022 0.163 0.604 0.137 0.181 0.386 0.493 0.000862 0.0011 Wall time: 12668.31161991274 ! Best model 271 0.604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 24 0.519 0.0199 0.121 0.131 0.172 0.38 0.423 0.000849 0.000945 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.806 0.0185 0.436 0.128 0.166 0.788 0.805 0.00176 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 272 12714.974 0.005 0.0214 0.0739 0.501 0.135 0.178 0.267 0.329 0.000596 0.000734 ! Validation 272 12714.974 0.005 0.0216 0.41 0.843 0.136 0.179 0.641 0.781 0.00143 0.00174 Wall time: 12714.974576917011 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 24 0.618 0.0208 0.201 0.133 0.176 0.497 0.547 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 273 100 0.737 0.0184 0.37 0.127 0.165 0.723 0.741 0.00161 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 273 12761.631 0.005 0.0212 0.0982 0.522 0.134 0.178 0.31 0.377 0.000692 0.000842 ! Validation 273 12761.631 0.005 0.0214 0.409 0.837 0.135 0.178 0.629 0.78 0.0014 0.00174 Wall time: 12761.631968823727 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 24 0.624 0.0227 0.17 0.138 0.184 0.463 0.503 0.00103 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.956 0.0185 0.586 0.127 0.166 0.922 0.934 0.00206 0.00208 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 274 12808.276 0.005 0.0212 0.204 0.629 0.134 0.178 0.453 0.551 0.00101 0.00123 ! Validation 274 12808.276 0.005 0.0217 0.567 1 0.136 0.179 0.786 0.918 0.00176 0.00205 Wall time: 12808.276135762688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 24 0.442 0.0203 0.0359 0.132 0.174 0.191 0.231 0.000427 0.000515 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.389 0.0182 0.0241 0.126 0.165 0.175 0.189 0.00039 0.000422 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 275 12854.926 0.005 0.021 0.102 0.522 0.134 0.177 0.313 0.393 0.000699 0.000877 ! Validation 275 12854.926 0.005 0.0212 0.154 0.579 0.134 0.178 0.38 0.479 0.000848 0.00107 Wall time: 12854.926789192948 ! Best model 275 0.579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 24 0.721 0.0199 0.322 0.131 0.172 0.657 0.692 0.00147 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.409 0.0184 0.0404 0.127 0.166 0.186 0.245 0.000416 0.000547 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 276 12901.591 0.005 0.0209 0.191 0.608 0.133 0.176 0.446 0.528 0.000996 0.00118 ! Validation 276 12901.591 0.005 0.0214 0.218 0.647 0.135 0.179 0.485 0.57 0.00108 0.00127 Wall time: 12901.591537567787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 24 0.426 0.0198 0.0302 0.13 0.172 0.169 0.212 0.000378 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.387 0.0181 0.0245 0.126 0.164 0.178 0.191 0.000397 0.000426 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 12948.248 0.005 0.0208 0.138 0.554 0.133 0.176 0.371 0.457 0.000829 0.00102 ! Validation 277 12948.248 0.005 0.0211 0.156 0.578 0.134 0.177 0.382 0.482 0.000853 0.00108 Wall time: 12948.248753602616 ! Best model 277 0.578 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 24 0.496 0.0213 0.069 0.135 0.178 0.277 0.32 0.000618 0.000715 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.507 0.0178 0.151 0.125 0.163 0.448 0.473 0.001 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 278 12994.904 0.005 0.0206 0.0879 0.5 0.132 0.175 0.298 0.363 0.000665 0.000809 ! Validation 278 12994.904 0.005 0.0209 0.231 0.65 0.133 0.176 0.438 0.586 0.000978 0.00131 Wall time: 12994.90492496267 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 24 1.35 0.0222 0.902 0.138 0.182 1.11 1.16 0.00247 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 2.25 0.0198 1.85 0.131 0.172 1.65 1.66 0.00368 0.0037 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 279 13041.559 0.005 0.0212 0.469 0.893 0.134 0.177 0.689 0.826 0.00154 0.00184 ! Validation 279 13041.559 0.005 0.0223 1.61 2.06 0.137 0.182 1.47 1.55 0.00328 0.00346 Wall time: 13041.559865688905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 24 0.779 0.0203 0.373 0.132 0.174 0.71 0.745 0.00159 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 1.15 0.019 0.771 0.129 0.168 1.06 1.07 0.00236 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 280 13088.213 0.005 0.0224 0.318 0.765 0.138 0.183 0.569 0.686 0.00127 0.00153 ! Validation 280 13088.213 0.005 0.0219 0.786 1.22 0.136 0.18 0.959 1.08 0.00214 0.00241 Wall time: 13088.213062683586 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 24 0.601 0.0217 0.166 0.136 0.18 0.435 0.497 0.000972 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.688 0.0182 0.323 0.127 0.165 0.675 0.693 0.00151 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 281 13134.862 0.005 0.0215 0.178 0.608 0.135 0.179 0.417 0.515 0.000932 0.00115 ! Validation 281 13134.862 0.005 0.0213 0.363 0.789 0.135 0.178 0.584 0.735 0.0013 0.00164 Wall time: 13134.863016238902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 24 0.436 0.0196 0.0444 0.129 0.171 0.22 0.257 0.00049 0.000574 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.414 0.0179 0.0559 0.125 0.163 0.259 0.288 0.000578 0.000644 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 282 13181.517 0.005 0.0207 0.111 0.524 0.133 0.175 0.342 0.409 0.000764 0.000912 ! Validation 282 13181.517 0.005 0.0209 0.186 0.604 0.133 0.176 0.394 0.525 0.00088 0.00117 Wall time: 13181.51731657656 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 24 0.563 0.02 0.164 0.13 0.172 0.426 0.494 0.000951 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.417 0.0178 0.0612 0.125 0.163 0.267 0.302 0.000596 0.000673 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 13228.169 0.005 0.0204 0.118 0.526 0.132 0.174 0.319 0.417 0.000712 0.000931 ! Validation 283 13228.169 0.005 0.0208 0.156 0.573 0.133 0.176 0.371 0.482 0.000828 0.00108 Wall time: 13228.16950883856 ! Best model 283 0.573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 24 0.672 0.0207 0.258 0.133 0.175 0.577 0.62 0.00129 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.616 0.0179 0.259 0.125 0.163 0.602 0.62 0.00134 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 284 13274.825 0.005 0.0204 0.139 0.547 0.132 0.174 0.366 0.45 0.000818 0.001 ! Validation 284 13274.825 0.005 0.0209 0.386 0.804 0.133 0.176 0.587 0.757 0.00131 0.00169 Wall time: 13274.825098400936 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 24 0.438 0.02 0.0379 0.131 0.173 0.181 0.238 0.000404 0.00053 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.414 0.0177 0.059 0.125 0.162 0.263 0.296 0.000587 0.000661 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 285 13321.475 0.005 0.0204 0.215 0.624 0.132 0.174 0.464 0.571 0.00104 0.00127 ! Validation 285 13321.475 0.005 0.0208 0.165 0.581 0.133 0.176 0.377 0.495 0.00084 0.00111 Wall time: 13321.47570645297 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 24 0.546 0.0184 0.178 0.126 0.165 0.446 0.515 0.000996 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.49 0.0173 0.143 0.123 0.161 0.437 0.462 0.000974 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 286 13368.123 0.005 0.0201 0.0539 0.455 0.131 0.173 0.218 0.275 0.000486 0.000614 ! Validation 286 13368.123 0.005 0.0203 0.363 0.769 0.131 0.174 0.652 0.735 0.00145 0.00164 Wall time: 13368.123185569886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 24 0.458 0.02 0.0589 0.13 0.172 0.233 0.296 0.000519 0.00066 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.369 0.0175 0.0185 0.124 0.161 0.141 0.166 0.000314 0.00037 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 13414.870 0.005 0.0201 0.203 0.605 0.131 0.173 0.431 0.554 0.000962 0.00124 ! Validation 287 13414.870 0.005 0.0204 0.155 0.563 0.131 0.174 0.387 0.48 0.000863 0.00107 Wall time: 13414.87057173159 ! Best model 287 0.563 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 24 0.608 0.0207 0.194 0.133 0.176 0.493 0.537 0.0011 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.404 0.0189 0.0265 0.128 0.168 0.18 0.198 0.000401 0.000443 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 288 13461.530 0.005 0.0201 0.232 0.633 0.131 0.173 0.499 0.588 0.00111 0.00131 ! Validation 288 13461.530 0.005 0.0219 0.218 0.656 0.136 0.18 0.421 0.569 0.00094 0.00127 Wall time: 13461.530792805832 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 24 0.47 0.0201 0.068 0.13 0.173 0.286 0.318 0.000639 0.00071 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.4 0.0175 0.051 0.124 0.161 0.248 0.275 0.000554 0.000614 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 289 13508.179 0.005 0.0204 0.146 0.554 0.132 0.174 0.38 0.47 0.000849 0.00105 ! Validation 289 13508.179 0.005 0.0204 0.168 0.575 0.131 0.174 0.38 0.5 0.000847 0.00112 Wall time: 13508.17936330568 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 24 0.455 0.0195 0.0641 0.129 0.17 0.24 0.309 0.000536 0.000689 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.41 0.0176 0.0586 0.124 0.162 0.252 0.295 0.000562 0.000659 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 290 13554.823 0.005 0.0201 0.307 0.708 0.131 0.173 0.58 0.682 0.00129 0.00152 ! Validation 290 13554.823 0.005 0.0204 0.23 0.639 0.132 0.174 0.506 0.585 0.00113 0.00131 Wall time: 13554.823675264604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 24 0.505 0.0204 0.0976 0.132 0.174 0.333 0.381 0.000744 0.00085 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.652 0.0172 0.307 0.123 0.16 0.661 0.676 0.00147 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 291 13601.470 0.005 0.0199 0.0734 0.472 0.13 0.172 0.264 0.329 0.000589 0.000734 ! Validation 291 13601.470 0.005 0.0202 0.367 0.772 0.131 0.173 0.588 0.739 0.00131 0.00165 Wall time: 13601.470515449997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 24 0.851 0.0212 0.427 0.134 0.178 0.779 0.797 0.00174 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.726 0.0175 0.376 0.123 0.161 0.73 0.747 0.00163 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 292 13648.123 0.005 0.0203 0.312 0.718 0.131 0.174 0.582 0.678 0.0013 0.00151 ! Validation 292 13648.123 0.005 0.0204 0.458 0.865 0.131 0.174 0.675 0.825 0.00151 0.00184 Wall time: 13648.123530689627 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 24 0.441 0.0191 0.0593 0.128 0.169 0.237 0.297 0.000529 0.000663 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.412 0.0173 0.0665 0.123 0.16 0.274 0.314 0.000611 0.000702 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 293 13694.769 0.005 0.0199 0.101 0.499 0.13 0.172 0.32 0.39 0.000715 0.000871 ! Validation 293 13694.769 0.005 0.0201 0.164 0.567 0.131 0.173 0.375 0.494 0.000837 0.0011 Wall time: 13694.76977803465 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 24 0.442 0.0194 0.0542 0.129 0.17 0.228 0.284 0.000508 0.000634 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.442 0.0173 0.0949 0.123 0.161 0.342 0.376 0.000763 0.000839 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 13741.418 0.005 0.0196 0.186 0.578 0.129 0.171 0.434 0.53 0.000968 0.00118 ! Validation 294 13741.418 0.005 0.0202 0.272 0.675 0.131 0.173 0.555 0.636 0.00124 0.00142 Wall time: 13741.41875404166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 24 0.694 0.0208 0.278 0.133 0.176 0.602 0.643 0.00134 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.386 0.0169 0.0471 0.122 0.159 0.219 0.265 0.000488 0.000591 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 295 13788.071 0.005 0.0197 0.187 0.581 0.129 0.171 0.447 0.524 0.000999 0.00117 ! Validation 295 13788.071 0.005 0.02 0.226 0.626 0.13 0.172 0.498 0.58 0.00111 0.00129 Wall time: 13788.07147204876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 24 0.451 0.0184 0.0828 0.126 0.165 0.299 0.351 0.000668 0.000783 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.585 0.0169 0.247 0.122 0.159 0.588 0.606 0.00131 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 296 13834.722 0.005 0.0195 0.106 0.496 0.129 0.17 0.317 0.397 0.000708 0.000887 ! Validation 296 13834.722 0.005 0.0198 0.413 0.808 0.129 0.172 0.617 0.783 0.00138 0.00175 Wall time: 13834.723334505688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 24 0.59 0.0189 0.213 0.127 0.168 0.516 0.562 0.00115 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.586 0.0171 0.244 0.122 0.159 0.584 0.602 0.0013 0.00134 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 297 13881.372 0.005 0.0194 0.174 0.562 0.128 0.17 0.429 0.508 0.000957 0.00113 ! Validation 297 13881.372 0.005 0.0202 0.335 0.739 0.131 0.173 0.545 0.706 0.00122 0.00158 Wall time: 13881.372687341645 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 24 0.451 0.0207 0.0366 0.132 0.176 0.199 0.233 0.000444 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.363 0.0171 0.0206 0.123 0.159 0.133 0.175 0.000296 0.000391 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 298 13928.020 0.005 0.0199 0.235 0.633 0.13 0.172 0.499 0.597 0.00111 0.00133 ! Validation 298 13928.020 0.005 0.0198 0.168 0.564 0.13 0.172 0.419 0.499 0.000935 0.00111 Wall time: 13928.02066046279 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 24 0.469 0.0182 0.104 0.125 0.165 0.351 0.394 0.000784 0.000879 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.631 0.0168 0.296 0.121 0.158 0.647 0.663 0.00144 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 299 13974.670 0.005 0.0194 0.133 0.522 0.129 0.17 0.363 0.446 0.000809 0.000995 ! Validation 299 13974.670 0.005 0.0196 0.39 0.782 0.129 0.171 0.608 0.761 0.00136 0.0017 Wall time: 13974.67061769683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 24 1.3 0.0185 0.934 0.126 0.166 1.15 1.18 0.00256 0.00263 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 1 0.017 0.661 0.122 0.159 0.978 0.991 0.00218 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 300 14021.319 0.005 0.0192 0.235 0.62 0.128 0.169 0.472 0.57 0.00105 0.00127 ! Validation 300 14021.319 0.005 0.0197 0.902 1.3 0.129 0.171 1.06 1.16 0.00237 0.00258 Wall time: 14021.319678112864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 24 0.675 0.0196 0.282 0.129 0.171 0.601 0.648 0.00134 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.578 0.0175 0.228 0.124 0.161 0.56 0.582 0.00125 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 301 14067.971 0.005 0.0206 0.468 0.88 0.132 0.175 0.701 0.838 0.00156 0.00187 ! Validation 301 14067.971 0.005 0.0205 0.442 0.851 0.131 0.174 0.722 0.811 0.00161 0.00181 Wall time: 14067.971363856923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 24 0.43 0.0192 0.0459 0.128 0.169 0.221 0.261 0.000492 0.000583 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.509 0.0169 0.17 0.122 0.159 0.477 0.503 0.00106 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 302 14114.611 0.005 0.0197 0.1 0.494 0.129 0.171 0.304 0.388 0.000678 0.000866 ! Validation 302 14114.611 0.005 0.0198 0.251 0.646 0.129 0.171 0.46 0.611 0.00103 0.00136 Wall time: 14114.611830548849 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 24 0.474 0.0197 0.0791 0.129 0.171 0.281 0.343 0.000626 0.000765 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.707 0.0169 0.37 0.121 0.158 0.725 0.742 0.00162 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 303 14161.256 0.005 0.0193 0.241 0.627 0.128 0.17 0.497 0.603 0.00111 0.00135 ! Validation 303 14161.256 0.005 0.0198 0.586 0.983 0.129 0.172 0.843 0.934 0.00188 0.00208 Wall time: 14161.257230663672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 24 0.725 0.0189 0.347 0.127 0.168 0.66 0.718 0.00147 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 1.16 0.017 0.817 0.122 0.159 1.09 1.1 0.00244 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 304 14207.907 0.005 0.0194 0.205 0.593 0.128 0.17 0.462 0.547 0.00103 0.00122 ! Validation 304 14207.907 0.005 0.0198 0.882 1.28 0.13 0.172 1.03 1.14 0.0023 0.00256 Wall time: 14207.907595157623 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 24 0.506 0.0188 0.13 0.127 0.167 0.391 0.439 0.000872 0.00098 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.363 0.0167 0.0284 0.121 0.158 0.143 0.206 0.00032 0.000459 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 305 14254.559 0.005 0.0194 0.177 0.565 0.128 0.17 0.433 0.515 0.000967 0.00115 ! Validation 305 14254.559 0.005 0.0196 0.185 0.576 0.129 0.171 0.439 0.524 0.000981 0.00117 Wall time: 14254.559818377718 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 24 0.385 0.0182 0.0205 0.125 0.165 0.145 0.174 0.000324 0.000389 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.467 0.0166 0.136 0.12 0.157 0.421 0.449 0.00094 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 306 14301.213 0.005 0.0191 0.0959 0.477 0.127 0.168 0.312 0.381 0.000695 0.000851 ! Validation 306 14301.213 0.005 0.0193 0.219 0.606 0.128 0.17 0.426 0.571 0.00095 0.00127 Wall time: 14301.213774357922 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 24 0.395 0.0179 0.036 0.124 0.163 0.183 0.232 0.00041 0.000517 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.396 0.0163 0.0696 0.12 0.156 0.291 0.322 0.000649 0.000718 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 14347.860 0.005 0.0188 0.108 0.485 0.127 0.167 0.325 0.404 0.000724 0.000901 ! Validation 307 14347.860 0.005 0.0192 0.166 0.55 0.127 0.169 0.372 0.497 0.000831 0.00111 Wall time: 14347.86057060957 ! Best model 307 0.550 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 24 0.419 0.0188 0.044 0.126 0.167 0.204 0.256 0.000456 0.000571 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.339 0.0162 0.015 0.119 0.155 0.121 0.149 0.000271 0.000334 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 308 14394.520 0.005 0.0187 0.0972 0.471 0.126 0.167 0.321 0.383 0.000717 0.000854 ! Validation 308 14394.520 0.005 0.019 0.156 0.536 0.127 0.168 0.394 0.482 0.000879 0.00108 Wall time: 14394.520709218923 ! Best model 308 0.536 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 24 0.612 0.0206 0.2 0.132 0.175 0.464 0.545 0.00104 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.35 0.016 0.0301 0.118 0.154 0.159 0.212 0.000354 0.000472 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 309 14441.180 0.005 0.0186 0.112 0.484 0.126 0.166 0.328 0.405 0.000732 0.000903 ! Validation 309 14441.180 0.005 0.0189 0.198 0.577 0.127 0.168 0.462 0.543 0.00103 0.00121 Wall time: 14441.180101734586 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 24 0.697 0.0175 0.346 0.122 0.162 0.685 0.717 0.00153 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.433 0.0161 0.111 0.119 0.155 0.379 0.405 0.000846 0.000905 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 14487.912 0.005 0.0185 0.144 0.513 0.125 0.166 0.38 0.454 0.000848 0.00101 ! Validation 310 14487.912 0.005 0.0188 0.295 0.672 0.126 0.167 0.586 0.662 0.00131 0.00148 Wall time: 14487.912837999873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 24 0.416 0.0185 0.0463 0.125 0.166 0.193 0.263 0.000431 0.000586 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.337 0.0161 0.0154 0.119 0.155 0.129 0.151 0.000287 0.000337 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 311 14534.556 0.005 0.0186 0.207 0.579 0.126 0.166 0.479 0.56 0.00107 0.00125 ! Validation 311 14534.556 0.005 0.0188 0.201 0.578 0.126 0.167 0.443 0.547 0.000989 0.00122 Wall time: 14534.556704932824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 24 0.431 0.0176 0.0792 0.122 0.162 0.302 0.343 0.000675 0.000766 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.496 0.0159 0.177 0.118 0.154 0.491 0.513 0.0011 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 312 14581.202 0.005 0.0186 0.104 0.475 0.126 0.166 0.325 0.394 0.000726 0.000879 ! Validation 312 14581.202 0.005 0.0187 0.242 0.616 0.126 0.167 0.457 0.6 0.00102 0.00134 Wall time: 14581.202910633758 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 24 0.414 0.0179 0.057 0.124 0.163 0.242 0.291 0.000539 0.00065 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.611 0.0168 0.275 0.121 0.158 0.622 0.639 0.00139 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 313 14627.850 0.005 0.0191 0.328 0.71 0.127 0.169 0.581 0.705 0.0013 0.00157 ! Validation 313 14627.850 0.005 0.0197 0.403 0.797 0.129 0.171 0.608 0.774 0.00136 0.00173 Wall time: 14627.850756619591 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 24 0.438 0.0188 0.0606 0.126 0.167 0.259 0.3 0.000579 0.00067 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.339 0.0162 0.0147 0.119 0.155 0.115 0.148 0.000256 0.00033 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 314 14674.500 0.005 0.0188 0.297 0.673 0.126 0.167 0.581 0.671 0.0013 0.0015 ! Validation 314 14674.500 0.005 0.0191 0.162 0.544 0.127 0.169 0.392 0.491 0.000875 0.0011 Wall time: 14674.500153531786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 24 0.465 0.0196 0.0734 0.129 0.171 0.271 0.33 0.000606 0.000737 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.362 0.016 0.0421 0.118 0.154 0.222 0.25 0.000496 0.000559 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 14721.146 0.005 0.0187 0.167 0.541 0.126 0.167 0.413 0.502 0.000922 0.00112 ! Validation 315 14721.146 0.005 0.0188 0.149 0.526 0.126 0.167 0.359 0.471 0.000802 0.00105 Wall time: 14721.146517384797 ! Best model 315 0.526 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 24 0.539 0.018 0.18 0.124 0.163 0.482 0.517 0.00108 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.349 0.016 0.0282 0.118 0.154 0.175 0.205 0.00039 0.000457 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 316 14767.807 0.005 0.0184 0.129 0.497 0.125 0.165 0.359 0.436 0.000801 0.000973 ! Validation 316 14767.807 0.005 0.0189 0.201 0.58 0.127 0.168 0.401 0.546 0.000894 0.00122 Wall time: 14767.807747690938 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 24 0.483 0.0181 0.12 0.124 0.164 0.384 0.423 0.000857 0.000944 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 0.568 0.0163 0.242 0.119 0.156 0.579 0.6 0.00129 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 317 14814.451 0.005 0.0186 0.203 0.574 0.126 0.166 0.467 0.552 0.00104 0.00123 ! Validation 317 14814.451 0.005 0.0188 0.324 0.699 0.126 0.167 0.538 0.694 0.0012 0.00155 Wall time: 14814.451412166934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 24 0.399 0.0179 0.0401 0.124 0.163 0.193 0.244 0.000431 0.000545 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.442 0.0156 0.13 0.117 0.152 0.417 0.44 0.000931 0.000983 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 14861.095 0.005 0.0182 0.0467 0.41 0.124 0.164 0.212 0.264 0.000472 0.000589 ! Validation 318 14861.095 0.005 0.0184 0.228 0.595 0.125 0.165 0.433 0.582 0.000967 0.0013 Wall time: 14861.095648758579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 24 0.455 0.0176 0.103 0.123 0.162 0.335 0.391 0.000747 0.000872 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.347 0.0156 0.0336 0.117 0.153 0.197 0.224 0.000439 0.000499 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 14907.742 0.005 0.018 0.119 0.48 0.124 0.164 0.343 0.421 0.000766 0.00094 ! Validation 319 14907.742 0.005 0.0185 0.173 0.544 0.125 0.166 0.382 0.507 0.000854 0.00113 Wall time: 14907.743478919845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 24 0.743 0.0178 0.387 0.123 0.163 0.725 0.759 0.00162 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.447 0.0163 0.12 0.119 0.156 0.389 0.423 0.000868 0.000943 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 14954.390 0.005 0.0181 0.241 0.603 0.124 0.164 0.512 0.594 0.00114 0.00133 ! Validation 320 14954.390 0.005 0.0188 0.292 0.667 0.126 0.167 0.575 0.658 0.00128 0.00147 Wall time: 14954.39042954659 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 24 0.781 0.019 0.401 0.126 0.168 0.751 0.772 0.00168 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.441 0.0157 0.127 0.117 0.153 0.412 0.435 0.00092 0.000971 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 321 15001.037 0.005 0.0182 0.172 0.535 0.124 0.164 0.416 0.497 0.000928 0.00111 ! Validation 321 15001.037 0.005 0.0184 0.323 0.691 0.125 0.165 0.615 0.693 0.00137 0.00155 Wall time: 15001.038015648723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 24 0.467 0.0175 0.118 0.122 0.161 0.354 0.419 0.000791 0.000934 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.326 0.0155 0.0153 0.117 0.152 0.129 0.151 0.000287 0.000336 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 322 15047.691 0.005 0.0181 0.168 0.529 0.124 0.164 0.428 0.501 0.000956 0.00112 ! Validation 322 15047.691 0.005 0.0183 0.171 0.538 0.125 0.165 0.417 0.505 0.00093 0.00113 Wall time: 15047.691929542925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 24 0.508 0.0175 0.158 0.122 0.161 0.449 0.484 0.001 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.359 0.0155 0.0495 0.116 0.152 0.233 0.271 0.000521 0.000605 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 323 15094.339 0.005 0.0179 0.0932 0.45 0.123 0.163 0.306 0.369 0.000684 0.000824 ! Validation 323 15094.339 0.005 0.0181 0.214 0.577 0.124 0.164 0.49 0.565 0.00109 0.00126 Wall time: 15094.340208929963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 24 1.15 0.0195 0.756 0.128 0.17 1.02 1.06 0.00227 0.00237 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.939 0.0167 0.605 0.121 0.158 0.939 0.949 0.0021 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 324 15140.985 0.005 0.0184 0.361 0.728 0.125 0.165 0.601 0.723 0.00134 0.00161 ! Validation 324 15140.985 0.005 0.0193 0.79 1.18 0.128 0.17 0.99 1.08 0.00221 0.00242 Wall time: 15140.985434090719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 24 0.424 0.0184 0.0562 0.125 0.165 0.24 0.289 0.000535 0.000646 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.345 0.0158 0.0293 0.117 0.153 0.194 0.209 0.000434 0.000466 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 15187.635 0.005 0.0186 0.238 0.609 0.126 0.166 0.496 0.6 0.00111 0.00134 ! Validation 325 15187.635 0.005 0.0184 0.17 0.539 0.125 0.166 0.379 0.503 0.000845 0.00112 Wall time: 15187.635120825842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 24 0.363 0.0168 0.0261 0.12 0.158 0.164 0.197 0.000367 0.00044 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.802 0.0158 0.487 0.118 0.153 0.84 0.851 0.00188 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 326 15234.278 0.005 0.018 0.179 0.538 0.124 0.164 0.441 0.521 0.000984 0.00116 ! Validation 326 15234.278 0.005 0.0185 0.494 0.864 0.125 0.166 0.719 0.857 0.0016 0.00191 Wall time: 15234.278551644646 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 24 0.85 0.0176 0.497 0.123 0.162 0.818 0.86 0.00183 0.00192 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.374 0.0172 0.029 0.122 0.16 0.159 0.208 0.000354 0.000464 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 15280.930 0.005 0.0184 0.342 0.71 0.125 0.165 0.621 0.709 0.00139 0.00158 ! Validation 327 15280.930 0.005 0.0198 0.185 0.581 0.129 0.172 0.425 0.524 0.000949 0.00117 Wall time: 15280.931847861968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 24 0.442 0.0186 0.0691 0.126 0.166 0.251 0.321 0.00056 0.000716 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.413 0.016 0.0923 0.118 0.154 0.338 0.371 0.000754 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 328 15327.578 0.005 0.0186 0.23 0.601 0.126 0.166 0.502 0.589 0.00112 0.00132 ! Validation 328 15327.578 0.005 0.0187 0.208 0.582 0.126 0.167 0.411 0.557 0.000917 0.00124 Wall time: 15327.578769858927 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 24 0.432 0.0175 0.0809 0.122 0.161 0.312 0.347 0.000697 0.000774 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.35 0.0154 0.0428 0.116 0.151 0.221 0.252 0.000492 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 329 15374.228 0.005 0.0179 0.086 0.444 0.123 0.163 0.292 0.358 0.000652 0.000799 ! Validation 329 15374.228 0.005 0.0183 0.161 0.526 0.124 0.165 0.369 0.489 0.000823 0.00109 Wall time: 15374.228224409744 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 24 0.408 0.0188 0.0324 0.126 0.167 0.185 0.219 0.000413 0.00049 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.363 0.0155 0.0533 0.116 0.152 0.249 0.281 0.000556 0.000628 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 330 15420.879 0.005 0.0178 0.0751 0.43 0.123 0.162 0.269 0.336 0.000599 0.000751 ! Validation 330 15420.879 0.005 0.018 0.163 0.523 0.123 0.164 0.368 0.492 0.000822 0.0011 Wall time: 15420.879662931897 ! Best model 330 0.523 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 24 0.418 0.0188 0.0415 0.126 0.167 0.179 0.248 0.000399 0.000554 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.377 0.0152 0.073 0.115 0.15 0.295 0.329 0.00066 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 331 15467.543 0.005 0.0175 0.104 0.454 0.122 0.161 0.316 0.395 0.000705 0.000882 ! Validation 331 15467.543 0.005 0.0178 0.232 0.588 0.123 0.163 0.512 0.587 0.00114 0.00131 Wall time: 15467.543212362565 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 24 0.63 0.0187 0.256 0.125 0.167 0.564 0.617 0.00126 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.402 0.0152 0.0988 0.115 0.15 0.355 0.383 0.000793 0.000855 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 15514.185 0.005 0.0176 0.199 0.551 0.122 0.162 0.468 0.543 0.00104 0.00121 ! Validation 332 15514.185 0.005 0.018 0.295 0.654 0.123 0.163 0.585 0.662 0.00131 0.00148 Wall time: 15514.1850270438 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 24 0.387 0.0169 0.048 0.12 0.159 0.209 0.267 0.000467 0.000596 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.48 0.0153 0.174 0.116 0.151 0.487 0.508 0.00109 0.00113 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 333 15560.913 0.005 0.0176 0.179 0.53 0.122 0.162 0.443 0.52 0.000988 0.00116 ! Validation 333 15560.913 0.005 0.0179 0.292 0.65 0.123 0.163 0.495 0.659 0.00111 0.00147 Wall time: 15560.913590029813 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 24 1.41 0.0178 1.06 0.123 0.163 1.24 1.25 0.00277 0.0028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 1.07 0.0158 0.75 0.117 0.153 1.04 1.06 0.00233 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 334 15607.592 0.005 0.0175 0.256 0.605 0.122 0.161 0.502 0.593 0.00112 0.00132 ! Validation 334 15607.592 0.005 0.0186 0.906 1.28 0.125 0.166 1.06 1.16 0.00237 0.00259 Wall time: 15607.592821387574 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 24 0.438 0.0172 0.0941 0.121 0.16 0.321 0.374 0.000717 0.000835 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.356 0.0154 0.048 0.116 0.151 0.217 0.267 0.000485 0.000596 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 335 15654.238 0.005 0.0184 0.233 0.601 0.125 0.165 0.478 0.593 0.00107 0.00132 ! Validation 335 15654.238 0.005 0.0181 0.212 0.574 0.124 0.164 0.482 0.562 0.00108 0.00125 Wall time: 15654.2384119099 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 24 0.412 0.019 0.0316 0.126 0.168 0.173 0.217 0.000385 0.000484 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.39 0.0151 0.0879 0.115 0.15 0.328 0.361 0.000732 0.000807 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 336 15700.882 0.005 0.0175 0.0845 0.434 0.122 0.161 0.292 0.357 0.000652 0.000797 ! Validation 336 15700.882 0.005 0.0177 0.183 0.537 0.122 0.162 0.387 0.522 0.000863 0.00116 Wall time: 15700.882252429612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 24 0.505 0.0176 0.154 0.122 0.162 0.424 0.478 0.000945 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.668 0.0149 0.369 0.114 0.149 0.727 0.741 0.00162 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 337 15747.527 0.005 0.0172 0.0456 0.389 0.121 0.16 0.201 0.253 0.000448 0.000564 ! Validation 337 15747.527 0.005 0.0174 0.419 0.768 0.121 0.161 0.649 0.789 0.00145 0.00176 Wall time: 15747.527519328985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 24 1.01 0.0184 0.646 0.125 0.165 0.956 0.98 0.00213 0.00219 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 1.11 0.0151 0.812 0.115 0.15 1.09 1.1 0.00243 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 338 15794.177 0.005 0.0173 0.193 0.538 0.121 0.16 0.429 0.52 0.000958 0.00116 ! Validation 338 15794.177 0.005 0.0177 0.954 1.31 0.122 0.162 1.09 1.19 0.00243 0.00266 Wall time: 15794.177445099689 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 24 0.424 0.0181 0.0624 0.123 0.164 0.251 0.304 0.00056 0.00068 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.389 0.0152 0.085 0.116 0.15 0.326 0.356 0.000727 0.000794 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 15840.825 0.005 0.0174 0.194 0.542 0.122 0.161 0.45 0.541 0.001 0.00121 ! Validation 339 15840.825 0.005 0.0176 0.165 0.517 0.122 0.162 0.369 0.495 0.000823 0.00111 Wall time: 15840.82554734964 ! Best model 339 0.517 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 24 0.395 0.0165 0.065 0.119 0.157 0.256 0.311 0.000571 0.000694 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.308 0.0147 0.0135 0.114 0.148 0.114 0.142 0.000254 0.000317 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 15887.489 0.005 0.0171 0.0489 0.391 0.121 0.159 0.209 0.269 0.000466 0.000599 ! Validation 340 15887.489 0.005 0.0173 0.158 0.504 0.121 0.16 0.397 0.485 0.000887 0.00108 Wall time: 15887.490004490595 ! Best model 340 0.504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 24 0.483 0.0168 0.148 0.119 0.158 0.43 0.469 0.000961 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.317 0.015 0.0177 0.114 0.149 0.118 0.162 0.000264 0.000362 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 341 15934.150 0.005 0.017 0.187 0.527 0.12 0.159 0.437 0.528 0.000974 0.00118 ! Validation 341 15934.150 0.005 0.0174 0.156 0.505 0.121 0.161 0.399 0.481 0.000892 0.00107 Wall time: 15934.150572377723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 24 0.402 0.018 0.0411 0.124 0.164 0.189 0.247 0.000422 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.308 0.0146 0.0152 0.113 0.148 0.136 0.15 0.000303 0.000336 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 342 15980.798 0.005 0.017 0.081 0.42 0.12 0.159 0.283 0.349 0.000633 0.000779 ! Validation 342 15980.798 0.005 0.0171 0.146 0.489 0.12 0.16 0.366 0.465 0.000817 0.00104 Wall time: 15980.798707230948 ! Best model 342 0.489 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 24 0.344 0.0161 0.0217 0.117 0.155 0.141 0.18 0.000315 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.458 0.0144 0.17 0.112 0.146 0.484 0.503 0.00108 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 343 16027.451 0.005 0.0167 0.0443 0.377 0.119 0.157 0.208 0.258 0.000464 0.000576 ! Validation 343 16027.451 0.005 0.017 0.224 0.565 0.12 0.159 0.432 0.578 0.000965 0.00129 Wall time: 16027.451707824599 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 24 0.518 0.0173 0.173 0.12 0.16 0.453 0.506 0.00101 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.407 0.0145 0.116 0.112 0.147 0.392 0.416 0.000875 0.000929 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 16074.102 0.005 0.0167 0.166 0.5 0.119 0.157 0.425 0.497 0.000949 0.00111 ! Validation 344 16074.102 0.005 0.0171 0.217 0.559 0.12 0.159 0.424 0.568 0.000947 0.00127 Wall time: 16074.102806361858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 24 0.38 0.0173 0.0352 0.121 0.16 0.183 0.229 0.000409 0.000511 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.306 0.0147 0.0121 0.113 0.148 0.104 0.134 0.000232 0.0003 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 16120.748 0.005 0.0169 0.191 0.529 0.12 0.158 0.446 0.538 0.000996 0.0012 ! Validation 345 16120.748 0.005 0.0173 0.148 0.493 0.121 0.16 0.384 0.47 0.000857 0.00105 Wall time: 16120.748847185634 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 24 0.375 0.0173 0.0297 0.121 0.16 0.169 0.21 0.000378 0.000469 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.306 0.0145 0.0154 0.113 0.147 0.124 0.151 0.000276 0.000338 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 346 16167.403 0.005 0.0172 0.254 0.597 0.121 0.16 0.528 0.621 0.00118 0.00139 ! Validation 346 16167.403 0.005 0.0172 0.156 0.5 0.121 0.16 0.399 0.482 0.00089 0.00107 Wall time: 16167.4038994289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 24 0.41 0.0172 0.0665 0.12 0.16 0.254 0.314 0.000568 0.000702 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.331 0.0148 0.0356 0.113 0.148 0.209 0.23 0.000467 0.000513 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 347 16214.043 0.005 0.0172 0.164 0.508 0.121 0.16 0.414 0.498 0.000924 0.00111 ! Validation 347 16214.043 0.005 0.0172 0.151 0.495 0.121 0.16 0.356 0.474 0.000795 0.00106 Wall time: 16214.043325271923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 24 0.374 0.0168 0.0385 0.119 0.158 0.2 0.239 0.000446 0.000534 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.337 0.0143 0.0505 0.112 0.146 0.243 0.274 0.000542 0.000612 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 16260.683 0.005 0.0166 0.0412 0.373 0.119 0.157 0.197 0.248 0.00044 0.000553 ! Validation 348 16260.683 0.005 0.0168 0.155 0.492 0.119 0.158 0.359 0.48 0.0008 0.00107 Wall time: 16260.683417963795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 24 0.478 0.0166 0.146 0.119 0.157 0.414 0.466 0.000925 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.307 0.0146 0.0149 0.113 0.147 0.12 0.149 0.000269 0.000333 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 349 16307.331 0.005 0.0165 0.152 0.483 0.118 0.157 0.408 0.476 0.000912 0.00106 ! Validation 349 16307.331 0.005 0.0171 0.148 0.489 0.12 0.159 0.375 0.469 0.000837 0.00105 Wall time: 16307.331856733654 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 24 0.421 0.0183 0.0551 0.125 0.165 0.247 0.286 0.000551 0.000639 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.513 0.0149 0.216 0.113 0.149 0.546 0.567 0.00122 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 350 16353.977 0.005 0.017 0.234 0.573 0.12 0.159 0.511 0.595 0.00114 0.00133 ! Validation 350 16353.977 0.005 0.0174 0.38 0.728 0.121 0.161 0.593 0.752 0.00132 0.00168 Wall time: 16353.978049969766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 24 0.339 0.016 0.0195 0.117 0.154 0.134 0.17 0.000299 0.00038 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.309 0.0142 0.0253 0.111 0.145 0.141 0.194 0.000315 0.000433 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 16400.626 0.005 0.0168 0.111 0.446 0.119 0.158 0.339 0.41 0.000757 0.000915 ! Validation 351 16400.626 0.005 0.0168 0.172 0.508 0.119 0.158 0.424 0.505 0.000947 0.00113 Wall time: 16400.62638492463 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 24 0.428 0.0158 0.112 0.116 0.153 0.364 0.407 0.000812 0.000909 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.316 0.014 0.0356 0.111 0.144 0.187 0.23 0.000417 0.000513 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 352 16447.276 0.005 0.0163 0.0722 0.399 0.118 0.156 0.261 0.325 0.000583 0.000726 ! Validation 352 16447.276 0.005 0.0166 0.182 0.513 0.118 0.157 0.446 0.52 0.000995 0.00116 Wall time: 16447.27653568471 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 24 0.386 0.0146 0.0937 0.112 0.147 0.338 0.373 0.000755 0.000833 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.538 0.0139 0.261 0.11 0.144 0.61 0.622 0.00136 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 353 16493.923 0.005 0.0161 0.0536 0.375 0.117 0.155 0.225 0.28 0.000502 0.000624 ! Validation 353 16493.923 0.005 0.0164 0.315 0.643 0.118 0.156 0.538 0.684 0.0012 0.00153 Wall time: 16493.92332299892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 24 0.383 0.0161 0.06 0.117 0.155 0.244 0.299 0.000545 0.000667 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.353 0.0151 0.0498 0.115 0.15 0.215 0.272 0.000481 0.000607 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 16540.567 0.005 0.0165 0.27 0.599 0.118 0.156 0.559 0.64 0.00125 0.00143 ! Validation 354 16540.567 0.005 0.0175 0.176 0.526 0.122 0.161 0.426 0.511 0.000952 0.00114 Wall time: 16540.567630347796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 24 0.578 0.0167 0.245 0.118 0.157 0.567 0.603 0.00127 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.333 0.0145 0.0428 0.113 0.147 0.218 0.252 0.000486 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 355 16587.210 0.005 0.0171 0.269 0.611 0.121 0.16 0.525 0.633 0.00117 0.00141 ! Validation 355 16587.210 0.005 0.017 0.15 0.489 0.12 0.159 0.354 0.472 0.00079 0.00105 Wall time: 16587.210560546722 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 24 0.562 0.0154 0.254 0.114 0.151 0.593 0.614 0.00132 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.758 0.0144 0.471 0.112 0.146 0.824 0.837 0.00184 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 356 16633.936 0.005 0.0169 0.218 0.556 0.12 0.159 0.463 0.569 0.00103 0.00127 ! Validation 356 16633.936 0.005 0.0168 0.528 0.864 0.119 0.158 0.761 0.886 0.0017 0.00198 Wall time: 16633.93662629975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 24 0.421 0.0158 0.105 0.116 0.153 0.337 0.396 0.000752 0.000883 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.311 0.0142 0.0277 0.111 0.145 0.148 0.203 0.000331 0.000453 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 357 16680.582 0.005 0.0164 0.0921 0.419 0.118 0.156 0.29 0.369 0.000647 0.000824 ! Validation 357 16680.582 0.005 0.0165 0.172 0.502 0.118 0.157 0.426 0.505 0.000951 0.00113 Wall time: 16680.582665717695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 24 0.701 0.0171 0.359 0.12 0.159 0.7 0.731 0.00156 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.368 0.0142 0.0842 0.111 0.145 0.32 0.354 0.000714 0.00079 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 16727.231 0.005 0.0162 0.161 0.485 0.117 0.155 0.412 0.482 0.00092 0.00108 ! Validation 358 16727.231 0.005 0.0167 0.249 0.582 0.119 0.158 0.532 0.608 0.00119 0.00136 Wall time: 16727.23160118889 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 24 0.807 0.0164 0.48 0.117 0.156 0.824 0.845 0.00184 0.00189 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.542 0.0138 0.265 0.11 0.143 0.615 0.628 0.00137 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 359 16773.881 0.005 0.0161 0.109 0.431 0.117 0.155 0.312 0.386 0.000696 0.000862 ! Validation 359 16773.881 0.005 0.0165 0.474 0.803 0.118 0.156 0.761 0.839 0.0017 0.00187 Wall time: 16773.88125617383 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 24 0.561 0.0165 0.232 0.118 0.156 0.555 0.588 0.00124 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.447 0.014 0.167 0.111 0.144 0.481 0.498 0.00107 0.00111 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 360 16820.534 0.005 0.0162 0.15 0.473 0.117 0.155 0.376 0.469 0.000839 0.00105 ! Validation 360 16820.534 0.005 0.0165 0.329 0.659 0.118 0.157 0.626 0.699 0.0014 0.00156 Wall time: 16820.53468555864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 24 0.404 0.0148 0.108 0.112 0.148 0.373 0.401 0.000832 0.000895 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.535 0.0138 0.258 0.11 0.143 0.603 0.62 0.00135 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 361 16867.182 0.005 0.016 0.109 0.428 0.117 0.154 0.337 0.403 0.000753 0.000899 ! Validation 361 16867.182 0.005 0.0162 0.333 0.658 0.117 0.155 0.557 0.704 0.00124 0.00157 Wall time: 16867.182402317878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 24 0.507 0.0147 0.213 0.112 0.148 0.521 0.563 0.00116 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.462 0.0139 0.184 0.11 0.144 0.504 0.522 0.00112 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 362 16913.828 0.005 0.0158 0.108 0.424 0.116 0.153 0.336 0.397 0.00075 0.000885 ! Validation 362 16913.828 0.005 0.0162 0.299 0.624 0.117 0.155 0.509 0.666 0.00114 0.00149 Wall time: 16913.828305266798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 24 0.677 0.0169 0.339 0.12 0.158 0.66 0.71 0.00147 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.361 0.0155 0.0503 0.116 0.152 0.252 0.274 0.000562 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 363 16960.477 0.005 0.0164 0.385 0.713 0.118 0.156 0.64 0.758 0.00143 0.00169 ! Validation 363 16960.477 0.005 0.0178 0.15 0.506 0.123 0.163 0.36 0.472 0.000803 0.00105 Wall time: 16960.477887494955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 24 0.861 0.0167 0.527 0.12 0.158 0.862 0.885 0.00193 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.351 0.0145 0.0611 0.112 0.147 0.268 0.301 0.000598 0.000673 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 17007.125 0.005 0.0169 0.286 0.624 0.12 0.159 0.554 0.645 0.00124 0.00144 ! Validation 364 17007.125 0.005 0.0172 0.233 0.577 0.121 0.16 0.52 0.588 0.00116 0.00131 Wall time: 17007.12602816196 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 24 0.348 0.0148 0.0519 0.113 0.148 0.242 0.278 0.000541 0.00062 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.316 0.014 0.0354 0.111 0.144 0.207 0.229 0.000462 0.000512 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 365 17053.776 0.005 0.0164 0.118 0.447 0.118 0.157 0.331 0.422 0.000739 0.000943 ! Validation 365 17053.776 0.005 0.0165 0.151 0.481 0.118 0.156 0.352 0.474 0.000785 0.00106 Wall time: 17053.77621762175 ! Best model 365 0.481 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 24 0.469 0.0171 0.126 0.12 0.16 0.394 0.433 0.00088 0.000967 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.459 0.014 0.179 0.111 0.144 0.499 0.516 0.00111 0.00115 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 366 17100.440 0.005 0.0161 0.182 0.504 0.117 0.154 0.44 0.523 0.000981 0.00117 ! Validation 366 17100.440 0.005 0.0164 0.246 0.573 0.118 0.156 0.459 0.605 0.00102 0.00135 Wall time: 17100.440187512897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 24 0.435 0.0166 0.103 0.119 0.157 0.344 0.392 0.000767 0.000875 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.294 0.0136 0.0215 0.109 0.142 0.133 0.179 0.000297 0.000399 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 367 17147.128 0.005 0.0159 0.0851 0.403 0.116 0.154 0.295 0.355 0.000659 0.000792 ! Validation 367 17147.128 0.005 0.0162 0.175 0.499 0.117 0.155 0.424 0.51 0.000947 0.00114 Wall time: 17147.12824854767 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 24 0.363 0.0148 0.0679 0.112 0.148 0.282 0.318 0.00063 0.000709 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.356 0.0136 0.0849 0.109 0.142 0.329 0.355 0.000734 0.000793 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 368 17193.775 0.005 0.0157 0.0633 0.377 0.116 0.153 0.246 0.306 0.00055 0.000684 ! Validation 368 17193.775 0.005 0.016 0.238 0.559 0.116 0.154 0.527 0.595 0.00118 0.00133 Wall time: 17193.776023947634 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 24 0.835 0.0154 0.527 0.115 0.151 0.87 0.885 0.00194 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.577 0.0137 0.303 0.109 0.143 0.656 0.671 0.00146 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 369 17240.429 0.005 0.0158 0.165 0.481 0.116 0.153 0.403 0.482 0.0009 0.00108 ! Validation 369 17240.429 0.005 0.0161 0.57 0.893 0.117 0.155 0.834 0.92 0.00186 0.00205 Wall time: 17240.42909218883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 24 0.34 0.0156 0.0277 0.115 0.152 0.157 0.203 0.00035 0.000453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.435 0.0138 0.16 0.11 0.143 0.467 0.488 0.00104 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 370 17287.077 0.005 0.0159 0.174 0.493 0.116 0.154 0.421 0.514 0.00094 0.00115 ! Validation 370 17287.077 0.005 0.0162 0.223 0.546 0.117 0.155 0.43 0.576 0.00096 0.00128 Wall time: 17287.077144161798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 24 0.41 0.0164 0.082 0.118 0.156 0.301 0.349 0.000671 0.000779 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.386 0.0136 0.115 0.109 0.142 0.387 0.413 0.000864 0.000922 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 371 17333.722 0.005 0.0156 0.052 0.364 0.115 0.152 0.223 0.276 0.000498 0.000616 ! Validation 371 17333.722 0.005 0.0159 0.195 0.514 0.116 0.154 0.401 0.539 0.000896 0.0012 Wall time: 17333.72270629974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 24 0.414 0.0166 0.0821 0.119 0.157 0.279 0.349 0.000623 0.00078 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.307 0.0142 0.0225 0.111 0.145 0.135 0.183 0.000302 0.000408 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 17380.375 0.005 0.0159 0.228 0.547 0.116 0.154 0.494 0.587 0.0011 0.00131 ! Validation 372 17380.375 0.005 0.0163 0.162 0.489 0.118 0.156 0.4 0.491 0.000892 0.0011 Wall time: 17380.37578527769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 24 1.05 0.0149 0.75 0.113 0.149 1.04 1.06 0.00232 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.761 0.0134 0.493 0.108 0.141 0.845 0.856 0.00189 0.00191 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 373 17427.028 0.005 0.0158 0.103 0.418 0.116 0.153 0.277 0.361 0.000619 0.000806 ! Validation 373 17427.028 0.005 0.0159 0.73 1.05 0.116 0.154 0.954 1.04 0.00213 0.00233 Wall time: 17427.02865368873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 24 0.406 0.0144 0.118 0.111 0.146 0.371 0.418 0.000828 0.000933 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.291 0.0137 0.017 0.109 0.143 0.147 0.159 0.000328 0.000355 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 374 17473.681 0.005 0.0159 0.247 0.565 0.116 0.154 0.505 0.61 0.00113 0.00136 ! Validation 374 17473.681 0.005 0.0162 0.135 0.458 0.117 0.155 0.344 0.448 0.000768 0.000999 Wall time: 17473.68152931286 ! Best model 374 0.458 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 24 0.449 0.0152 0.145 0.114 0.15 0.424 0.464 0.000947 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.708 0.0135 0.438 0.108 0.141 0.796 0.807 0.00178 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 375 17520.339 0.005 0.0156 0.122 0.434 0.115 0.152 0.355 0.425 0.000793 0.000949 ! Validation 375 17520.339 0.005 0.0159 0.445 0.762 0.116 0.154 0.689 0.814 0.00154 0.00182 Wall time: 17520.339423647616 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 24 0.465 0.0154 0.157 0.113 0.151 0.451 0.483 0.00101 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.363 0.0136 0.0901 0.109 0.142 0.332 0.366 0.000742 0.000817 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 17566.993 0.005 0.0155 0.219 0.53 0.115 0.152 0.497 0.573 0.00111 0.00128 ! Validation 376 17566.993 0.005 0.016 0.184 0.505 0.116 0.154 0.383 0.523 0.000855 0.00117 Wall time: 17566.993420691695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 24 0.685 0.0149 0.388 0.113 0.149 0.735 0.76 0.00164 0.0017 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.36 0.0141 0.077 0.111 0.145 0.304 0.338 0.000678 0.000755 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 17613.648 0.005 0.0157 0.226 0.54 0.115 0.153 0.479 0.575 0.00107 0.00128 ! Validation 377 17613.648 0.005 0.0167 0.21 0.544 0.119 0.157 0.405 0.559 0.000905 0.00125 Wall time: 17613.649356862996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 24 0.347 0.0146 0.0545 0.112 0.148 0.223 0.285 0.000498 0.000635 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.394 0.0136 0.122 0.109 0.142 0.402 0.426 0.000898 0.000951 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 378 17660.301 0.005 0.0158 0.222 0.538 0.116 0.153 0.502 0.579 0.00112 0.00129 ! Validation 378 17660.301 0.005 0.0159 0.205 0.524 0.116 0.154 0.408 0.552 0.00091 0.00123 Wall time: 17660.301320271567 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 24 0.4 0.0154 0.0907 0.115 0.152 0.316 0.367 0.000705 0.00082 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.282 0.0133 0.0173 0.108 0.14 0.144 0.16 0.000322 0.000358 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 379 17707.035 0.005 0.0155 0.0515 0.361 0.115 0.152 0.222 0.274 0.000495 0.000612 ! Validation 379 17707.035 0.005 0.0157 0.136 0.449 0.115 0.153 0.347 0.45 0.000775 0.001 Wall time: 17707.035147284623 ! Best model 379 0.449 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 24 1.2 0.0177 0.844 0.122 0.162 1.09 1.12 0.00244 0.0025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.851 0.015 0.55 0.115 0.15 0.893 0.905 0.00199 0.00202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 380 17753.703 0.005 0.0158 0.339 0.655 0.116 0.153 0.542 0.697 0.00121 0.00156 ! Validation 380 17753.703 0.005 0.0176 0.794 1.15 0.123 0.162 0.992 1.09 0.00222 0.00243 Wall time: 17753.70349433692 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 24 0.342 0.0154 0.0347 0.114 0.151 0.192 0.227 0.000428 0.000507 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.732 0.0137 0.459 0.109 0.143 0.815 0.826 0.00182 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 381 17800.357 0.005 0.0163 0.178 0.504 0.118 0.156 0.435 0.52 0.000971 0.00116 ! Validation 381 17800.357 0.005 0.0162 0.532 0.857 0.117 0.155 0.76 0.89 0.0017 0.00199 Wall time: 17800.357620592695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 24 0.355 0.0154 0.0482 0.114 0.151 0.2 0.268 0.000446 0.000598 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.29 0.0134 0.0223 0.108 0.141 0.158 0.182 0.000352 0.000406 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 382 17847.011 0.005 0.0157 0.169 0.484 0.116 0.153 0.417 0.506 0.000931 0.00113 ! Validation 382 17847.011 0.005 0.0159 0.139 0.456 0.116 0.154 0.346 0.455 0.000773 0.00101 Wall time: 17847.0118888258 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 24 0.314 0.0143 0.0274 0.11 0.146 0.156 0.202 0.000348 0.000451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.34 0.0131 0.078 0.107 0.14 0.314 0.341 0.000702 0.00076 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 383 17893.663 0.005 0.0153 0.0379 0.344 0.114 0.151 0.193 0.238 0.00043 0.000532 ! Validation 383 17893.663 0.005 0.0155 0.184 0.495 0.114 0.152 0.383 0.524 0.000855 0.00117 Wall time: 17893.663675821852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 24 0.431 0.0154 0.122 0.114 0.152 0.373 0.427 0.000833 0.000952 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.29 0.0137 0.0154 0.109 0.143 0.131 0.151 0.000292 0.000338 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 384 17940.316 0.005 0.0152 0.157 0.46 0.113 0.15 0.403 0.484 0.000899 0.00108 ! Validation 384 17940.316 0.005 0.016 0.161 0.481 0.116 0.154 0.376 0.49 0.000839 0.00109 Wall time: 17940.317097991705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 24 0.549 0.0149 0.252 0.113 0.149 0.574 0.612 0.00128 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.449 0.0135 0.18 0.108 0.142 0.496 0.517 0.00111 0.00115 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 385 17986.970 0.005 0.0154 0.232 0.54 0.114 0.152 0.504 0.586 0.00112 0.00131 ! Validation 385 17986.970 0.005 0.0157 0.256 0.57 0.115 0.153 0.468 0.617 0.00104 0.00138 Wall time: 17986.970339933876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 24 0.649 0.0146 0.357 0.112 0.147 0.687 0.729 0.00153 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.773 0.0132 0.51 0.107 0.14 0.861 0.871 0.00192 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 386 18033.625 0.005 0.0153 0.0904 0.396 0.114 0.151 0.287 0.354 0.000641 0.000789 ! Validation 386 18033.625 0.005 0.0155 0.576 0.885 0.114 0.152 0.799 0.925 0.00178 0.00207 Wall time: 18033.6260066377 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 24 0.439 0.0146 0.148 0.111 0.147 0.436 0.469 0.000973 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.287 0.0132 0.0224 0.107 0.14 0.163 0.182 0.000364 0.000407 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 387 18080.274 0.005 0.0152 0.099 0.402 0.114 0.15 0.313 0.381 0.000698 0.000851 ! Validation 387 18080.274 0.005 0.0154 0.15 0.458 0.114 0.151 0.351 0.472 0.000784 0.00105 Wall time: 18080.274390642997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 24 0.316 0.0144 0.029 0.111 0.146 0.172 0.208 0.000383 0.000463 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.294 0.013 0.0338 0.107 0.139 0.191 0.224 0.000427 0.000501 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 18126.921 0.005 0.015 0.0566 0.357 0.113 0.15 0.227 0.292 0.000507 0.000651 ! Validation 388 18126.921 0.005 0.0153 0.135 0.442 0.114 0.151 0.334 0.449 0.000746 0.001 Wall time: 18126.921863322612 ! Best model 388 0.442 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 24 0.359 0.0148 0.0631 0.112 0.148 0.271 0.306 0.000604 0.000684 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.559 0.0131 0.298 0.107 0.139 0.652 0.665 0.00145 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 389 18173.583 0.005 0.0149 0.121 0.42 0.113 0.149 0.353 0.426 0.000789 0.000951 ! Validation 389 18173.583 0.005 0.0153 0.356 0.663 0.114 0.151 0.588 0.727 0.00131 0.00162 Wall time: 18173.58379518101 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 24 0.368 0.0157 0.0536 0.116 0.153 0.245 0.282 0.000547 0.00063 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.272 0.0131 0.0102 0.107 0.139 0.107 0.123 0.000238 0.000275 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 390 18220.234 0.005 0.0152 0.234 0.538 0.114 0.15 0.506 0.595 0.00113 0.00133 ! Validation 390 18220.234 0.005 0.0156 0.154 0.467 0.115 0.152 0.378 0.479 0.000845 0.00107 Wall time: 18220.234866430983 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 24 1.32 0.0148 1.02 0.113 0.149 1.22 1.23 0.00272 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.523 0.0131 0.261 0.107 0.14 0.609 0.622 0.00136 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 391 18266.893 0.005 0.015 0.209 0.51 0.113 0.149 0.422 0.531 0.000942 0.00119 ! Validation 391 18266.893 0.005 0.0156 0.467 0.779 0.115 0.152 0.756 0.834 0.00169 0.00186 Wall time: 18266.89332932001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 24 0.332 0.0154 0.0242 0.115 0.151 0.148 0.19 0.000331 0.000423 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.28 0.013 0.0211 0.106 0.139 0.13 0.177 0.00029 0.000395 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 392 18313.539 0.005 0.0152 0.155 0.46 0.114 0.151 0.398 0.485 0.000888 0.00108 ! Validation 392 18313.539 0.005 0.0154 0.154 0.462 0.114 0.151 0.396 0.479 0.000885 0.00107 Wall time: 18313.539309383836 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 24 0.33 0.0148 0.0347 0.113 0.148 0.187 0.227 0.000418 0.000507 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.275 0.0129 0.0175 0.106 0.138 0.133 0.162 0.000298 0.000361 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 393 18360.189 0.005 0.0149 0.0787 0.377 0.113 0.149 0.285 0.344 0.000635 0.000769 ! Validation 393 18360.189 0.005 0.0153 0.141 0.448 0.114 0.151 0.346 0.459 0.000772 0.00102 Wall time: 18360.189290213864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 24 0.328 0.0136 0.0557 0.108 0.142 0.222 0.288 0.000494 0.000643 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.266 0.0128 0.0105 0.106 0.138 0.108 0.125 0.000242 0.000279 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 394 18406.842 0.005 0.0148 0.114 0.409 0.112 0.148 0.343 0.414 0.000765 0.000923 ! Validation 394 18406.842 0.005 0.0151 0.137 0.439 0.113 0.15 0.357 0.451 0.000797 0.00101 Wall time: 18406.842910408974 ! Best model 394 0.439 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 24 0.427 0.0143 0.141 0.11 0.146 0.398 0.458 0.000887 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.293 0.0127 0.0389 0.105 0.138 0.205 0.241 0.000457 0.000537 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 18453.497 0.005 0.0147 0.117 0.412 0.112 0.148 0.348 0.417 0.000777 0.00093 ! Validation 395 18453.497 0.005 0.0151 0.183 0.484 0.113 0.15 0.447 0.522 0.000998 0.00117 Wall time: 18453.497675957624 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 24 0.37 0.0138 0.0937 0.108 0.143 0.312 0.373 0.000697 0.000833 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.272 0.0126 0.0193 0.105 0.137 0.121 0.169 0.000271 0.000378 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 396 18500.150 0.005 0.0147 0.0758 0.37 0.112 0.148 0.279 0.335 0.000623 0.000747 ! Validation 396 18500.150 0.005 0.0149 0.153 0.451 0.112 0.149 0.4 0.476 0.000893 0.00106 Wall time: 18500.15016321186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 24 0.356 0.0152 0.0524 0.114 0.15 0.224 0.279 0.000501 0.000623 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.278 0.0131 0.0154 0.107 0.14 0.103 0.151 0.000231 0.000337 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 397 18546.806 0.005 0.0152 0.193 0.498 0.114 0.15 0.445 0.541 0.000994 0.00121 ! Validation 397 18546.806 0.005 0.0154 0.151 0.458 0.114 0.151 0.4 0.473 0.000892 0.00106 Wall time: 18546.80627021473 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 24 0.319 0.0143 0.0323 0.11 0.146 0.198 0.219 0.000443 0.000489 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.27 0.0127 0.0163 0.105 0.137 0.119 0.156 0.000265 0.000348 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 18593.463 0.005 0.0147 0.0631 0.357 0.112 0.148 0.244 0.308 0.000544 0.000688 ! Validation 398 18593.463 0.005 0.0148 0.148 0.445 0.112 0.149 0.388 0.469 0.000866 0.00105 Wall time: 18593.463843745645 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 24 0.376 0.0154 0.0675 0.115 0.151 0.248 0.317 0.000553 0.000707 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.498 0.013 0.238 0.106 0.139 0.579 0.594 0.00129 0.00133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 399 18640.120 0.005 0.0148 0.31 0.607 0.112 0.148 0.568 0.685 0.00127 0.00153 ! Validation 399 18640.120 0.005 0.0153 0.436 0.742 0.114 0.151 0.728 0.805 0.00163 0.0018 Wall time: 18640.12064718781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 24 0.364 0.0158 0.0474 0.116 0.153 0.199 0.265 0.000445 0.000592 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.345 0.0129 0.0882 0.106 0.138 0.337 0.362 0.000752 0.000808 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 400 18686.779 0.005 0.0152 0.19 0.493 0.114 0.15 0.45 0.536 0.001 0.0012 ! Validation 400 18686.779 0.005 0.0151 0.161 0.464 0.113 0.15 0.36 0.489 0.000804 0.00109 Wall time: 18686.77929028077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 24 0.484 0.0156 0.171 0.115 0.152 0.474 0.505 0.00106 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.602 0.0127 0.349 0.105 0.137 0.707 0.72 0.00158 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 401 18733.429 0.005 0.0147 0.103 0.396 0.112 0.148 0.324 0.388 0.000724 0.000867 ! Validation 401 18733.429 0.005 0.0149 0.379 0.677 0.112 0.149 0.622 0.751 0.00139 0.00168 Wall time: 18733.430017533712 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 24 0.386 0.0148 0.0904 0.112 0.148 0.33 0.367 0.000736 0.000818 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.277 0.0127 0.0228 0.106 0.137 0.159 0.184 0.000356 0.000411 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 18780.165 0.005 0.0146 0.145 0.437 0.111 0.147 0.4 0.466 0.000894 0.00104 ! Validation 402 18780.165 0.005 0.0152 0.153 0.457 0.114 0.15 0.349 0.477 0.000778 0.00107 Wall time: 18780.165097169578 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 24 0.478 0.0143 0.192 0.111 0.146 0.513 0.535 0.00114 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.392 0.0125 0.141 0.105 0.136 0.439 0.458 0.000981 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 403 18826.814 0.005 0.0147 0.165 0.459 0.112 0.148 0.417 0.495 0.00093 0.0011 ! Validation 403 18826.814 0.005 0.0148 0.261 0.558 0.112 0.148 0.458 0.623 0.00102 0.00139 Wall time: 18826.8145399387 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 24 0.334 0.0141 0.0525 0.11 0.145 0.246 0.28 0.000548 0.000624 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.322 0.0125 0.0718 0.105 0.136 0.301 0.327 0.000671 0.000729 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 404 18873.469 0.005 0.0148 0.163 0.46 0.112 0.149 0.414 0.496 0.000925 0.00111 ! Validation 404 18873.469 0.005 0.0149 0.167 0.464 0.112 0.149 0.364 0.498 0.000812 0.00111 Wall time: 18873.47058571363 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 24 0.359 0.0152 0.0543 0.113 0.151 0.223 0.284 0.000498 0.000634 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.277 0.0124 0.0288 0.104 0.136 0.162 0.207 0.000362 0.000462 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 405 18920.125 0.005 0.0145 0.0782 0.369 0.111 0.147 0.282 0.342 0.000629 0.000764 ! Validation 405 18920.125 0.005 0.0147 0.158 0.452 0.111 0.148 0.413 0.485 0.000923 0.00108 Wall time: 18920.125904711895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 24 0.347 0.0149 0.0496 0.113 0.149 0.228 0.271 0.00051 0.000606 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.417 0.0126 0.166 0.105 0.137 0.478 0.497 0.00107 0.00111 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 406 18966.774 0.005 0.0147 0.175 0.47 0.112 0.148 0.422 0.515 0.000941 0.00115 ! Validation 406 18966.774 0.005 0.0148 0.276 0.571 0.112 0.148 0.486 0.641 0.00108 0.00143 Wall time: 18966.774478040636 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 24 0.292 0.0138 0.0156 0.108 0.143 0.124 0.152 0.000277 0.00034 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.267 0.0122 0.0231 0.103 0.135 0.164 0.185 0.000366 0.000414 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 407 19013.422 0.005 0.0143 0.0397 0.325 0.11 0.146 0.197 0.245 0.00044 0.000546 ! Validation 407 19013.422 0.005 0.0145 0.141 0.431 0.111 0.147 0.339 0.459 0.000757 0.00102 Wall time: 19013.423443158623 ! Best model 407 0.431 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 24 0.421 0.0142 0.137 0.11 0.145 0.393 0.451 0.000877 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.559 0.0124 0.312 0.104 0.136 0.67 0.681 0.0015 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 408 19060.084 0.005 0.0142 0.121 0.405 0.11 0.145 0.345 0.424 0.00077 0.000946 ! Validation 408 19060.084 0.005 0.0147 0.329 0.623 0.112 0.148 0.573 0.699 0.00128 0.00156 Wall time: 19060.084559169598 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 24 0.438 0.0144 0.15 0.111 0.146 0.426 0.473 0.000952 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.302 0.0126 0.0501 0.105 0.137 0.235 0.273 0.000524 0.000609 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 19106.741 0.005 0.0144 0.155 0.444 0.111 0.146 0.405 0.481 0.000905 0.00107 ! Validation 409 19106.741 0.005 0.0148 0.188 0.485 0.112 0.149 0.46 0.529 0.00103 0.00118 Wall time: 19106.74143343279 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 24 0.391 0.0137 0.118 0.109 0.143 0.371 0.419 0.000828 0.000934 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.513 0.0121 0.27 0.103 0.134 0.622 0.633 0.00139 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 410 19153.394 0.005 0.0142 0.0512 0.335 0.11 0.145 0.216 0.272 0.000482 0.000606 ! Validation 410 19153.394 0.005 0.0144 0.329 0.616 0.11 0.146 0.563 0.699 0.00126 0.00156 Wall time: 19153.394687708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 24 0.356 0.0154 0.0487 0.114 0.151 0.197 0.269 0.000441 0.000601 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.328 0.0127 0.0731 0.105 0.138 0.293 0.33 0.000655 0.000736 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 19200.045 0.005 0.0144 0.243 0.532 0.111 0.146 0.509 0.607 0.00114 0.00136 ! Validation 411 19200.045 0.005 0.0149 0.209 0.508 0.112 0.149 0.49 0.557 0.00109 0.00124 Wall time: 19200.046427193563 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 24 0.326 0.0155 0.0151 0.115 0.152 0.118 0.15 0.000263 0.000335 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.455 0.0128 0.199 0.106 0.138 0.531 0.544 0.00119 0.00122 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 412 19246.705 0.005 0.015 0.164 0.464 0.113 0.149 0.428 0.499 0.000955 0.00111 ! Validation 412 19246.705 0.005 0.0153 0.259 0.565 0.114 0.151 0.476 0.621 0.00106 0.00139 Wall time: 19246.705619466957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 24 0.354 0.0148 0.0592 0.111 0.148 0.261 0.297 0.000582 0.000662 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.278 0.0122 0.0331 0.103 0.135 0.188 0.222 0.000419 0.000495 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 413 19293.362 0.005 0.0144 0.102 0.389 0.111 0.146 0.333 0.392 0.000743 0.000875 ! Validation 413 19293.362 0.005 0.0145 0.149 0.439 0.111 0.147 0.344 0.471 0.000768 0.00105 Wall time: 19293.362826185767 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 24 0.326 0.0133 0.0605 0.106 0.14 0.277 0.3 0.000617 0.000669 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.26 0.012 0.021 0.102 0.133 0.141 0.177 0.000315 0.000395 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 414 19340.015 0.005 0.014 0.0456 0.325 0.109 0.144 0.21 0.259 0.000469 0.000579 ! Validation 414 19340.015 0.005 0.0143 0.149 0.434 0.11 0.146 0.393 0.47 0.000876 0.00105 Wall time: 19340.015211954713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 24 0.405 0.014 0.124 0.109 0.144 0.379 0.43 0.000846 0.00096 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.484 0.012 0.243 0.102 0.134 0.588 0.601 0.00131 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 415 19386.665 0.005 0.0139 0.116 0.393 0.108 0.144 0.356 0.415 0.000794 0.000927 ! Validation 415 19386.665 0.005 0.0142 0.32 0.604 0.11 0.145 0.549 0.69 0.00123 0.00154 Wall time: 19386.666315458715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 24 0.806 0.0135 0.537 0.107 0.141 0.872 0.893 0.00195 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.512 0.0122 0.268 0.103 0.135 0.618 0.632 0.00138 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 416 19433.319 0.005 0.0138 0.159 0.436 0.108 0.143 0.402 0.472 0.000897 0.00105 ! Validation 416 19433.319 0.005 0.0143 0.37 0.657 0.11 0.146 0.672 0.742 0.0015 0.00166 Wall time: 19433.319822213612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 24 0.321 0.0129 0.0631 0.106 0.138 0.268 0.306 0.000598 0.000684 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.397 0.0119 0.158 0.102 0.133 0.47 0.485 0.00105 0.00108 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 417 19479.964 0.005 0.0141 0.1 0.382 0.11 0.145 0.316 0.388 0.000705 0.000866 ! Validation 417 19479.964 0.005 0.0142 0.259 0.544 0.11 0.145 0.466 0.621 0.00104 0.00139 Wall time: 19479.965016613714 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 24 0.416 0.014 0.136 0.109 0.144 0.429 0.449 0.000957 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.282 0.0118 0.0462 0.102 0.132 0.233 0.262 0.000521 0.000585 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 19526.617 0.005 0.0138 0.0676 0.343 0.108 0.143 0.252 0.313 0.000562 0.000699 ! Validation 418 19526.617 0.005 0.014 0.184 0.464 0.109 0.144 0.452 0.523 0.00101 0.00117 Wall time: 19526.61710529076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 24 0.363 0.0145 0.0726 0.111 0.147 0.29 0.329 0.000648 0.000733 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.379 0.0119 0.141 0.102 0.133 0.44 0.458 0.000982 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 419 19573.270 0.005 0.0137 0.0672 0.341 0.108 0.142 0.264 0.316 0.000589 0.000705 ! Validation 419 19573.270 0.005 0.014 0.238 0.517 0.109 0.144 0.447 0.595 0.000998 0.00133 Wall time: 19573.27076942101 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 24 1.21 0.0154 0.903 0.115 0.151 1.14 1.16 0.00255 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 1.5 0.0135 1.23 0.109 0.142 1.35 1.35 0.00301 0.00302 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 19619.931 0.005 0.0143 0.316 0.601 0.11 0.145 0.564 0.67 0.00126 0.0015 ! Validation 420 19619.931 0.005 0.0158 1.34 1.65 0.116 0.153 1.32 1.41 0.00294 0.00315 Wall time: 19619.931999591645 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 24 0.311 0.0133 0.0443 0.107 0.141 0.223 0.257 0.000498 0.000573 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.434 0.0125 0.184 0.104 0.136 0.506 0.524 0.00113 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 421 19666.585 0.005 0.0154 0.311 0.618 0.115 0.151 0.586 0.687 0.00131 0.00153 ! Validation 421 19666.585 0.005 0.0147 0.285 0.58 0.112 0.148 0.498 0.651 0.00111 0.00145 Wall time: 19666.585925183725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 24 0.313 0.0136 0.0409 0.108 0.142 0.2 0.247 0.000447 0.00055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.26 0.0122 0.0148 0.103 0.135 0.129 0.149 0.000287 0.000332 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 19713.238 0.005 0.0142 0.147 0.432 0.11 0.146 0.399 0.472 0.000891 0.00105 ! Validation 422 19713.238 0.005 0.0143 0.125 0.412 0.11 0.146 0.336 0.432 0.00075 0.000963 Wall time: 19713.23901444301 ! Best model 422 0.412 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 24 0.329 0.0125 0.0793 0.104 0.136 0.306 0.343 0.000684 0.000767 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.258 0.0119 0.0212 0.102 0.133 0.134 0.177 0.000298 0.000396 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 19759.896 0.005 0.0138 0.0516 0.328 0.109 0.144 0.221 0.275 0.000494 0.000615 ! Validation 423 19759.896 0.005 0.0141 0.153 0.434 0.109 0.145 0.396 0.477 0.000884 0.00106 Wall time: 19759.89672493376 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 24 0.406 0.0136 0.133 0.107 0.142 0.396 0.445 0.000883 0.000993 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.246 0.0118 0.0101 0.101 0.132 0.104 0.122 0.000232 0.000273 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 19806.541 0.005 0.0137 0.113 0.387 0.108 0.143 0.346 0.41 0.000773 0.000915 ! Validation 424 19806.541 0.005 0.014 0.129 0.408 0.109 0.144 0.338 0.438 0.000754 0.000977 Wall time: 19806.541353038978 ! Best model 424 0.408 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 24 0.265 0.0122 0.0217 0.102 0.134 0.157 0.18 0.00035 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.324 0.0116 0.0918 0.101 0.131 0.35 0.369 0.000782 0.000825 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 425 19853.286 0.005 0.0135 0.0477 0.318 0.107 0.142 0.215 0.268 0.00048 0.000598 ! Validation 425 19853.286 0.005 0.0139 0.175 0.452 0.108 0.144 0.373 0.51 0.000833 0.00114 Wall time: 19853.286849404685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 24 1.44 0.0138 1.17 0.108 0.143 1.3 1.32 0.0029 0.00294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 1.44 0.0126 1.19 0.104 0.137 1.32 1.33 0.00295 0.00297 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 426 19899.939 0.005 0.0135 0.14 0.409 0.107 0.142 0.315 0.413 0.000703 0.000922 ! Validation 426 19899.939 0.005 0.0147 1.22 1.52 0.111 0.148 1.26 1.35 0.00281 0.00301 Wall time: 19899.93904788699 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 24 0.337 0.0138 0.0607 0.108 0.143 0.266 0.301 0.000594 0.000671 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.601 0.012 0.361 0.102 0.134 0.721 0.733 0.00161 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 427 19946.594 0.005 0.0144 0.283 0.571 0.111 0.147 0.534 0.654 0.00119 0.00146 ! Validation 427 19946.594 0.005 0.0143 0.426 0.712 0.11 0.146 0.666 0.795 0.00149 0.00178 Wall time: 19946.59417717764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 24 0.312 0.0141 0.0299 0.11 0.145 0.169 0.211 0.000378 0.000471 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.323 0.0117 0.0893 0.101 0.132 0.342 0.364 0.000763 0.000813 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 19993.246 0.005 0.0138 0.116 0.391 0.108 0.143 0.355 0.419 0.000792 0.000934 ! Validation 428 19993.246 0.005 0.0139 0.176 0.454 0.108 0.144 0.374 0.512 0.000836 0.00114 Wall time: 19993.24606065778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 24 0.303 0.014 0.0219 0.109 0.145 0.136 0.18 0.000303 0.000403 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.253 0.012 0.0134 0.102 0.133 0.12 0.141 0.000268 0.000315 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 429 20039.891 0.005 0.0138 0.163 0.438 0.108 0.143 0.417 0.497 0.000931 0.00111 ! Validation 429 20039.891 0.005 0.0141 0.144 0.427 0.109 0.145 0.367 0.463 0.00082 0.00103 Wall time: 20039.891964803915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 24 0.477 0.0145 0.187 0.111 0.147 0.482 0.527 0.00107 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.408 0.0117 0.175 0.101 0.132 0.495 0.509 0.0011 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 430 20086.540 0.005 0.0136 0.105 0.377 0.108 0.142 0.332 0.391 0.000741 0.000873 ! Validation 430 20086.540 0.005 0.0138 0.346 0.622 0.108 0.143 0.65 0.717 0.00145 0.0016 Wall time: 20086.540303149726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 24 0.378 0.0137 0.105 0.108 0.143 0.373 0.395 0.000832 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.368 0.0118 0.132 0.102 0.133 0.423 0.443 0.000945 0.00099 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 20133.193 0.005 0.0137 0.156 0.43 0.108 0.143 0.41 0.484 0.000914 0.00108 ! Validation 431 20133.193 0.005 0.0139 0.22 0.498 0.108 0.144 0.426 0.572 0.00095 0.00128 Wall time: 20133.19478817098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 24 0.316 0.014 0.0357 0.109 0.144 0.187 0.23 0.000417 0.000514 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.244 0.0116 0.0124 0.101 0.131 0.114 0.136 0.000255 0.000303 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 432 20179.844 0.005 0.0136 0.139 0.411 0.107 0.142 0.394 0.459 0.00088 0.00103 ! Validation 432 20179.844 0.005 0.0138 0.14 0.416 0.108 0.143 0.347 0.457 0.000774 0.00102 Wall time: 20179.844574944582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 24 0.454 0.0156 0.143 0.114 0.152 0.429 0.462 0.000958 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.585 0.0119 0.348 0.101 0.133 0.707 0.719 0.00158 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 433 20226.493 0.005 0.0136 0.147 0.418 0.107 0.142 0.39 0.468 0.00087 0.00104 ! Validation 433 20226.493 0.005 0.0141 0.459 0.741 0.109 0.145 0.694 0.826 0.00155 0.00184 Wall time: 20226.493251510896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 24 0.314 0.0127 0.059 0.104 0.138 0.255 0.296 0.000569 0.000661 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.351 0.0117 0.118 0.101 0.132 0.397 0.419 0.000887 0.000935 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 434 20273.144 0.005 0.0135 0.0791 0.349 0.107 0.142 0.275 0.344 0.000613 0.000768 ! Validation 434 20273.144 0.005 0.0137 0.196 0.47 0.108 0.143 0.4 0.54 0.000893 0.00121 Wall time: 20273.144267037977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 24 0.31 0.0136 0.0374 0.108 0.142 0.185 0.236 0.000413 0.000527 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.336 0.0118 0.101 0.101 0.132 0.362 0.388 0.000808 0.000866 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 20319.788 0.005 0.0134 0.0945 0.362 0.107 0.141 0.322 0.378 0.000718 0.000843 ! Validation 435 20319.788 0.005 0.0138 0.222 0.497 0.108 0.143 0.412 0.574 0.00092 0.00128 Wall time: 20319.788873238955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 24 0.289 0.0126 0.0376 0.104 0.137 0.185 0.237 0.000412 0.000528 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.297 0.0114 0.0677 0.0998 0.13 0.292 0.317 0.000651 0.000708 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 20366.439 0.005 0.0133 0.0872 0.354 0.107 0.141 0.295 0.363 0.000658 0.000809 ! Validation 436 20366.439 0.005 0.0136 0.147 0.418 0.107 0.142 0.34 0.467 0.000759 0.00104 Wall time: 20366.439559630584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 24 0.419 0.0146 0.127 0.112 0.147 0.38 0.435 0.000848 0.000972 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.258 0.0121 0.016 0.102 0.134 0.12 0.154 0.000267 0.000344 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 20413.083 0.005 0.0137 0.26 0.533 0.108 0.142 0.529 0.625 0.00118 0.0014 ! Validation 437 20413.083 0.005 0.014 0.142 0.423 0.109 0.144 0.375 0.46 0.000837 0.00103 Wall time: 20413.08339371858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 24 0.308 0.0137 0.0334 0.108 0.143 0.188 0.223 0.000419 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.251 0.0115 0.0205 0.1 0.131 0.145 0.174 0.000324 0.000389 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 438 20459.733 0.005 0.0137 0.16 0.434 0.108 0.143 0.404 0.493 0.000902 0.0011 ! Validation 438 20459.733 0.005 0.0138 0.163 0.439 0.108 0.143 0.354 0.493 0.000789 0.0011 Wall time: 20459.73367244797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 24 0.286 0.0132 0.0217 0.106 0.14 0.14 0.18 0.000312 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.276 0.0115 0.0454 0.101 0.131 0.237 0.26 0.000529 0.00058 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 20506.384 0.005 0.0135 0.118 0.389 0.107 0.142 0.334 0.423 0.000746 0.000944 ! Validation 439 20506.384 0.005 0.0138 0.16 0.436 0.108 0.143 0.354 0.488 0.000789 0.00109 Wall time: 20506.384760679677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 24 0.31 0.013 0.0513 0.105 0.139 0.231 0.276 0.000517 0.000616 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.293 0.0116 0.0601 0.101 0.132 0.273 0.299 0.000609 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 440 20553.038 0.005 0.0132 0.091 0.356 0.106 0.14 0.304 0.37 0.000678 0.000825 ! Validation 440 20553.038 0.005 0.0137 0.146 0.421 0.108 0.143 0.342 0.466 0.000764 0.00104 Wall time: 20553.03854345763 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 24 0.637 0.0136 0.366 0.108 0.142 0.701 0.737 0.00156 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.819 0.0114 0.591 0.0996 0.13 0.93 0.938 0.00208 0.00209 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 441 20599.688 0.005 0.0132 0.1 0.365 0.106 0.14 0.312 0.374 0.000697 0.000834 ! Validation 441 20599.688 0.005 0.0134 0.73 0.998 0.107 0.141 0.938 1.04 0.00209 0.00232 Wall time: 20599.688561707735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 24 0.317 0.0129 0.0597 0.105 0.138 0.252 0.298 0.000562 0.000665 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.345 0.0117 0.112 0.101 0.132 0.39 0.408 0.000871 0.00091 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 20646.341 0.005 0.0135 0.226 0.496 0.107 0.142 0.512 0.585 0.00114 0.00131 ! Validation 442 20646.341 0.005 0.0137 0.213 0.487 0.108 0.143 0.412 0.563 0.00092 0.00126 Wall time: 20646.341870472766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 24 0.371 0.0137 0.0961 0.108 0.143 0.339 0.378 0.000757 0.000844 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.281 0.0116 0.0484 0.1 0.132 0.235 0.268 0.000525 0.000599 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 20693.001 0.005 0.0133 0.102 0.367 0.106 0.14 0.323 0.39 0.00072 0.000871 ! Validation 443 20693.001 0.005 0.0138 0.143 0.418 0.108 0.143 0.34 0.461 0.000759 0.00103 Wall time: 20693.001662347 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 24 0.37 0.0137 0.095 0.108 0.143 0.348 0.376 0.000777 0.000839 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.243 0.0117 0.0101 0.101 0.132 0.0975 0.123 0.000218 0.000274 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 444 20739.650 0.005 0.0132 0.0818 0.345 0.106 0.14 0.281 0.348 0.000628 0.000777 ! Validation 444 20739.650 0.005 0.0138 0.136 0.412 0.108 0.143 0.345 0.45 0.00077 0.001 Wall time: 20739.6509238719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 24 0.347 0.0143 0.0603 0.11 0.146 0.246 0.299 0.000549 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.248 0.0111 0.0251 0.0988 0.129 0.157 0.193 0.000351 0.000431 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 445 20786.292 0.005 0.0131 0.0803 0.343 0.106 0.14 0.287 0.347 0.000642 0.000774 ! Validation 445 20786.292 0.005 0.0133 0.147 0.414 0.106 0.141 0.392 0.468 0.000876 0.00105 Wall time: 20786.292778018862 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 24 0.431 0.0134 0.163 0.107 0.141 0.459 0.492 0.00103 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.25 0.0118 0.0133 0.101 0.133 0.123 0.141 0.000274 0.000314 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 446 20832.938 0.005 0.013 0.138 0.398 0.105 0.139 0.384 0.453 0.000858 0.00101 ! Validation 446 20832.938 0.005 0.0139 0.164 0.441 0.108 0.144 0.359 0.494 0.000802 0.0011 Wall time: 20832.93940373184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 24 1.31 0.0131 1.05 0.106 0.14 1.24 1.25 0.00276 0.00279 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.93 0.0114 0.702 0.0993 0.13 1.01 1.02 0.00226 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 447 20879.595 0.005 0.0134 0.225 0.492 0.107 0.141 0.458 0.552 0.00102 0.00123 ! Validation 447 20879.595 0.005 0.0136 0.832 1.1 0.107 0.142 1.02 1.11 0.00229 0.00248 Wall time: 20879.595749281812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 24 0.309 0.0133 0.044 0.106 0.14 0.189 0.256 0.000423 0.000571 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.325 0.0123 0.079 0.104 0.135 0.311 0.343 0.000694 0.000765 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 20926.329 0.005 0.0137 0.271 0.545 0.108 0.143 0.553 0.641 0.00123 0.00143 ! Validation 448 20926.329 0.005 0.0142 0.216 0.5 0.11 0.145 0.5 0.566 0.00112 0.00126 Wall time: 20926.329213650897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 24 0.39 0.014 0.111 0.109 0.144 0.377 0.407 0.000841 0.000908 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.421 0.0114 0.194 0.0998 0.13 0.524 0.537 0.00117 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 449 20972.984 0.005 0.0134 0.113 0.381 0.107 0.141 0.35 0.41 0.000782 0.000915 ! Validation 449 20972.984 0.005 0.0135 0.267 0.537 0.107 0.142 0.49 0.63 0.00109 0.00141 Wall time: 20972.98497529188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 24 0.377 0.0129 0.119 0.104 0.138 0.371 0.42 0.000829 0.000938 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.249 0.0114 0.0207 0.0995 0.13 0.138 0.175 0.000308 0.000391 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 21019.640 0.005 0.0131 0.0758 0.338 0.106 0.14 0.281 0.333 0.000627 0.000744 ! Validation 450 21019.640 0.005 0.0134 0.148 0.417 0.106 0.141 0.39 0.47 0.000871 0.00105 Wall time: 21019.641028179787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 24 0.25 0.0118 0.0138 0.102 0.132 0.112 0.143 0.000251 0.000319 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.253 0.0111 0.0316 0.0985 0.128 0.182 0.217 0.000405 0.000484 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 451 21066.293 0.005 0.013 0.0783 0.338 0.105 0.139 0.282 0.345 0.00063 0.000769 ! Validation 451 21066.293 0.005 0.0133 0.153 0.419 0.106 0.14 0.347 0.478 0.000774 0.00107 Wall time: 21066.293151337653 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 24 0.274 0.0127 0.0193 0.104 0.138 0.14 0.169 0.000313 0.000378 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.272 0.011 0.052 0.0978 0.128 0.249 0.278 0.000556 0.00062 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 452 21112.948 0.005 0.0128 0.0612 0.317 0.104 0.138 0.234 0.304 0.000522 0.000679 ! Validation 452 21112.948 0.005 0.013 0.149 0.41 0.105 0.139 0.339 0.471 0.000757 0.00105 Wall time: 21112.948912513908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 24 0.351 0.0124 0.103 0.103 0.136 0.343 0.39 0.000765 0.000872 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.417 0.0111 0.195 0.0985 0.128 0.523 0.539 0.00117 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 453 21159.607 0.005 0.0126 0.0451 0.298 0.104 0.137 0.205 0.255 0.000457 0.000569 ! Validation 453 21159.607 0.005 0.0131 0.289 0.551 0.105 0.139 0.505 0.656 0.00113 0.00146 Wall time: 21159.607302804943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 24 0.456 0.0164 0.128 0.118 0.156 0.398 0.436 0.000888 0.000972 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.394 0.0136 0.121 0.109 0.142 0.388 0.424 0.000866 0.000947 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 21206.263 0.005 0.015 0.482 0.781 0.113 0.149 0.745 0.854 0.00166 0.00191 ! Validation 454 21206.263 0.005 0.0154 0.292 0.6 0.114 0.151 0.583 0.659 0.0013 0.00147 Wall time: 21206.264030186925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 24 0.447 0.0142 0.164 0.11 0.145 0.465 0.493 0.00104 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.276 0.0125 0.0268 0.104 0.136 0.181 0.2 0.000404 0.000445 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 455 21252.921 0.005 0.0143 0.217 0.503 0.111 0.146 0.475 0.57 0.00106 0.00127 ! Validation 455 21252.921 0.005 0.0144 0.134 0.421 0.111 0.146 0.332 0.446 0.00074 0.000995 Wall time: 21252.921123307664 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 24 0.315 0.0131 0.052 0.106 0.14 0.241 0.278 0.000538 0.000621 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.241 0.0111 0.0187 0.0985 0.128 0.139 0.167 0.000311 0.000372 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 21299.577 0.005 0.0133 0.0626 0.328 0.106 0.141 0.251 0.306 0.000561 0.000682 ! Validation 456 21299.577 0.005 0.0132 0.121 0.385 0.106 0.14 0.32 0.424 0.000715 0.000947 Wall time: 21299.577421331778 ! Best model 456 0.385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 24 0.304 0.0131 0.0419 0.106 0.139 0.217 0.25 0.000485 0.000557 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.229 0.0109 0.0109 0.0976 0.127 0.111 0.128 0.000248 0.000285 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 21346.241 0.005 0.0128 0.0478 0.304 0.104 0.138 0.218 0.267 0.000486 0.000596 ! Validation 457 21346.241 0.005 0.013 0.134 0.395 0.105 0.139 0.361 0.446 0.000806 0.000997 Wall time: 21346.24146622978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 24 0.359 0.0133 0.0923 0.107 0.141 0.342 0.371 0.000764 0.000827 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.268 0.011 0.0481 0.0977 0.128 0.235 0.267 0.000525 0.000597 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 458 21392.893 0.005 0.0128 0.116 0.372 0.104 0.138 0.35 0.416 0.000782 0.000929 ! Validation 458 21392.893 0.005 0.0131 0.159 0.421 0.105 0.14 0.351 0.487 0.000784 0.00109 Wall time: 21392.893469137605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 24 0.34 0.0133 0.0734 0.107 0.141 0.273 0.33 0.00061 0.000738 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.246 0.0109 0.0278 0.0979 0.127 0.163 0.203 0.000365 0.000454 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 459 21439.552 0.005 0.0129 0.074 0.331 0.105 0.138 0.26 0.332 0.000581 0.000741 ! Validation 459 21439.552 0.005 0.013 0.168 0.428 0.105 0.139 0.422 0.499 0.000942 0.00111 Wall time: 21439.552995974664 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 24 0.544 0.0133 0.278 0.106 0.141 0.619 0.642 0.00138 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.791 0.0108 0.575 0.0973 0.127 0.917 0.925 0.00205 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 460 21486.204 0.005 0.0126 0.0618 0.313 0.103 0.137 0.239 0.29 0.000533 0.000648 ! Validation 460 21486.204 0.005 0.0128 0.662 0.918 0.104 0.138 0.895 0.992 0.002 0.00221 Wall time: 21486.20477804495 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 24 0.508 0.0132 0.244 0.107 0.14 0.589 0.603 0.00132 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.477 0.0111 0.255 0.0983 0.128 0.598 0.616 0.00133 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 461 21532.861 0.005 0.013 0.23 0.49 0.105 0.139 0.511 0.585 0.00114 0.00131 ! Validation 461 21532.861 0.005 0.0131 0.436 0.698 0.105 0.14 0.734 0.805 0.00164 0.0018 Wall time: 21532.86139679188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 24 0.555 0.0126 0.302 0.104 0.137 0.641 0.67 0.00143 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.275 0.0115 0.0456 0.0999 0.131 0.226 0.26 0.000505 0.000581 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 21579.514 0.005 0.013 0.248 0.508 0.105 0.139 0.53 0.606 0.00118 0.00135 ! Validation 462 21579.514 0.005 0.0134 0.169 0.437 0.107 0.141 0.362 0.501 0.000809 0.00112 Wall time: 21579.5152155729 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 24 0.283 0.0129 0.0238 0.106 0.139 0.149 0.188 0.000332 0.00042 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.227 0.0109 0.00987 0.0973 0.127 0.113 0.121 0.000252 0.00027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 463 21626.167 0.005 0.0129 0.0867 0.345 0.105 0.139 0.297 0.362 0.000663 0.000809 ! Validation 463 21626.167 0.005 0.0131 0.152 0.414 0.105 0.139 0.365 0.476 0.000814 0.00106 Wall time: 21626.167834159918 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 24 0.285 0.0125 0.0345 0.103 0.137 0.185 0.227 0.000414 0.000506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.263 0.011 0.043 0.0979 0.128 0.219 0.253 0.000488 0.000565 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 21672.814 0.005 0.0126 0.0853 0.338 0.104 0.137 0.301 0.359 0.000671 0.0008 ! Validation 464 21672.814 0.005 0.0129 0.155 0.413 0.104 0.139 0.345 0.48 0.000769 0.00107 Wall time: 21672.814954208676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 24 0.267 0.0125 0.0171 0.103 0.136 0.14 0.159 0.000313 0.000356 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.234 0.0106 0.0221 0.0966 0.126 0.154 0.181 0.000344 0.000404 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 21719.470 0.005 0.0124 0.0332 0.282 0.103 0.136 0.18 0.223 0.000403 0.000498 ! Validation 465 21719.470 0.005 0.0127 0.121 0.374 0.104 0.137 0.316 0.424 0.000706 0.000946 Wall time: 21719.470810219646 ! Best model 465 0.374 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 24 0.308 0.0122 0.064 0.103 0.135 0.255 0.309 0.00057 0.000689 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.261 0.0109 0.0427 0.0976 0.128 0.224 0.252 0.0005 0.000562 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 466 21766.133 0.005 0.0123 0.0722 0.318 0.102 0.135 0.251 0.328 0.00056 0.000732 ! Validation 466 21766.133 0.005 0.0128 0.148 0.405 0.104 0.138 0.336 0.469 0.000749 0.00105 Wall time: 21766.13389135059 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 24 0.49 0.012 0.25 0.101 0.134 0.588 0.609 0.00131 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.419 0.0105 0.209 0.0962 0.125 0.546 0.557 0.00122 0.00124 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 467 21812.783 0.005 0.0124 0.0702 0.318 0.103 0.136 0.261 0.313 0.000583 0.000699 ! Validation 467 21812.783 0.005 0.0126 0.363 0.615 0.104 0.137 0.668 0.735 0.00149 0.00164 Wall time: 21812.78326618066 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 24 0.349 0.0121 0.106 0.102 0.134 0.368 0.397 0.000822 0.000887 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.23 0.0109 0.0127 0.0979 0.127 0.12 0.137 0.000269 0.000307 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 21859.436 0.005 0.0131 0.182 0.445 0.106 0.14 0.42 0.523 0.000937 0.00117 ! Validation 468 21859.436 0.005 0.0129 0.132 0.39 0.105 0.138 0.357 0.443 0.000797 0.00099 Wall time: 21859.436507273 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 24 0.574 0.0118 0.337 0.0997 0.133 0.666 0.708 0.00149 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.447 0.0104 0.238 0.0953 0.125 0.582 0.595 0.0013 0.00133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 469 21906.085 0.005 0.0124 0.0786 0.327 0.103 0.136 0.257 0.328 0.000573 0.000732 ! Validation 469 21906.085 0.005 0.0126 0.394 0.645 0.103 0.137 0.697 0.766 0.00156 0.00171 Wall time: 21906.08617845364 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 24 0.298 0.012 0.0583 0.101 0.134 0.252 0.294 0.000563 0.000657 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.226 0.0106 0.0145 0.0958 0.125 0.119 0.147 0.000266 0.000328 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 21952.738 0.005 0.0126 0.181 0.434 0.104 0.137 0.446 0.523 0.000996 0.00117 ! Validation 470 21952.738 0.005 0.0126 0.124 0.376 0.103 0.137 0.324 0.429 0.000723 0.000957 Wall time: 21952.73904763162 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 24 0.282 0.0121 0.0399 0.102 0.134 0.186 0.244 0.000416 0.000544 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.323 0.0107 0.109 0.0963 0.126 0.381 0.403 0.000851 0.000899 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 21999.476 0.005 0.0125 0.0786 0.328 0.103 0.136 0.285 0.344 0.000635 0.000768 ! Validation 471 21999.476 0.005 0.0127 0.232 0.486 0.104 0.137 0.435 0.588 0.000972 0.00131 Wall time: 21999.476552544627 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 24 0.292 0.0126 0.0404 0.103 0.137 0.199 0.245 0.000444 0.000547 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.496 0.0104 0.288 0.095 0.125 0.643 0.654 0.00143 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 472 22046.132 0.005 0.0123 0.1 0.346 0.102 0.135 0.314 0.389 0.0007 0.000867 ! Validation 472 22046.132 0.005 0.0126 0.348 0.6 0.103 0.137 0.591 0.72 0.00132 0.00161 Wall time: 22046.132814643905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 24 0.436 0.0116 0.204 0.1 0.131 0.532 0.551 0.00119 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.658 0.0106 0.445 0.0962 0.126 0.803 0.814 0.00179 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 473 22092.788 0.005 0.0123 0.162 0.409 0.103 0.136 0.406 0.49 0.000907 0.00109 ! Validation 473 22092.788 0.005 0.0125 0.461 0.712 0.103 0.137 0.717 0.828 0.0016 0.00185 Wall time: 22092.78923565289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 24 0.59 0.0129 0.333 0.105 0.138 0.684 0.704 0.00153 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.405 0.0109 0.187 0.0972 0.127 0.509 0.528 0.00114 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 474 22139.445 0.005 0.0121 0.132 0.374 0.102 0.134 0.359 0.434 0.000801 0.000969 ! Validation 474 22139.445 0.005 0.0128 0.32 0.576 0.104 0.138 0.539 0.69 0.0012 0.00154 Wall time: 22139.445328183938 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 24 0.385 0.0133 0.12 0.106 0.14 0.39 0.422 0.000871 0.000942 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.3 0.0105 0.09 0.0956 0.125 0.341 0.366 0.000761 0.000817 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 22186.619 0.005 0.0123 0.114 0.36 0.102 0.135 0.35 0.411 0.000782 0.000916 ! Validation 475 22186.619 0.005 0.0125 0.234 0.484 0.103 0.136 0.528 0.59 0.00118 0.00132 Wall time: 22186.619270643685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 24 0.251 0.0111 0.0294 0.0973 0.128 0.179 0.209 0.000399 0.000466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.282 0.0103 0.0763 0.0949 0.124 0.314 0.337 0.0007 0.000752 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 476 22233.219 0.005 0.0121 0.06 0.301 0.102 0.134 0.227 0.301 0.000507 0.000671 ! Validation 476 22233.219 0.005 0.0123 0.18 0.426 0.102 0.135 0.37 0.517 0.000825 0.00115 Wall time: 22233.219463090878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 24 0.791 0.013 0.531 0.105 0.139 0.868 0.889 0.00194 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.517 0.0107 0.304 0.0965 0.126 0.66 0.672 0.00147 0.0015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 477 22279.818 0.005 0.0121 0.159 0.401 0.101 0.134 0.373 0.473 0.000832 0.00106 ! Validation 477 22279.818 0.005 0.0126 0.511 0.763 0.103 0.137 0.778 0.872 0.00174 0.00195 Wall time: 22279.81934715761 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 24 0.271 0.012 0.03 0.102 0.134 0.164 0.211 0.000366 0.000471 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.244 0.0105 0.0346 0.0954 0.125 0.187 0.227 0.000417 0.000506 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 478 22326.415 0.005 0.0127 0.125 0.378 0.104 0.137 0.352 0.434 0.000785 0.000969 ! Validation 478 22326.415 0.005 0.0127 0.177 0.431 0.104 0.137 0.365 0.514 0.000815 0.00115 Wall time: 22326.41526892176 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 24 0.538 0.0127 0.283 0.103 0.138 0.614 0.649 0.00137 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.522 0.0105 0.311 0.0956 0.125 0.668 0.68 0.00149 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 479 22373.015 0.005 0.0121 0.0767 0.319 0.102 0.134 0.253 0.327 0.000565 0.000729 ! Validation 479 22373.015 0.005 0.0124 0.456 0.704 0.103 0.136 0.692 0.824 0.00154 0.00184 Wall time: 22373.015601643827 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 24 0.279 0.0111 0.0575 0.0982 0.128 0.228 0.292 0.000509 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.305 0.0104 0.0979 0.0953 0.124 0.36 0.381 0.000805 0.000851 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 22419.615 0.005 0.0123 0.158 0.403 0.102 0.135 0.405 0.488 0.000905 0.00109 ! Validation 480 22419.615 0.005 0.0125 0.177 0.427 0.103 0.136 0.38 0.513 0.000847 0.00115 Wall time: 22419.615843050648 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 24 0.261 0.0118 0.0263 0.101 0.132 0.153 0.198 0.000343 0.000441 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.213 0.0101 0.011 0.094 0.123 0.107 0.128 0.000238 0.000285 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 481 22466.206 0.005 0.012 0.0297 0.269 0.101 0.134 0.166 0.211 0.000371 0.00047 ! Validation 481 22466.206 0.005 0.0122 0.12 0.365 0.102 0.135 0.318 0.422 0.000711 0.000942 Wall time: 22466.20678866189 ! Best model 481 0.365 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 24 0.415 0.0122 0.171 0.102 0.135 0.473 0.505 0.00105 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.584 0.01 0.383 0.0934 0.122 0.745 0.755 0.00166 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 482 22512.809 0.005 0.0118 0.0505 0.286 0.1 0.132 0.212 0.266 0.000473 0.000594 ! Validation 482 22512.809 0.005 0.0121 0.472 0.714 0.101 0.134 0.723 0.838 0.00161 0.00187 Wall time: 22512.80963616958 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 24 0.535 0.0126 0.284 0.103 0.137 0.622 0.649 0.00139 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.671 0.0108 0.454 0.0967 0.127 0.81 0.822 0.00181 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 483 22559.405 0.005 0.0121 0.196 0.438 0.102 0.134 0.477 0.537 0.00107 0.0012 ! Validation 483 22559.405 0.005 0.013 0.56 0.819 0.105 0.139 0.795 0.913 0.00178 0.00204 Wall time: 22559.405761402566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 24 0.36 0.0129 0.101 0.105 0.139 0.347 0.387 0.000776 0.000864 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.349 0.0119 0.111 0.102 0.133 0.383 0.406 0.000855 0.000905 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 22606.005 0.005 0.0132 0.353 0.616 0.106 0.14 0.632 0.73 0.00141 0.00163 ! Validation 484 22606.005 0.005 0.0141 0.241 0.522 0.11 0.145 0.533 0.598 0.00119 0.00134 Wall time: 22606.005593826994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 24 0.421 0.0121 0.179 0.102 0.134 0.493 0.516 0.0011 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.239 0.0105 0.0279 0.0957 0.125 0.175 0.204 0.000391 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 485 22652.600 0.005 0.0125 0.101 0.352 0.104 0.137 0.317 0.385 0.000708 0.000859 ! Validation 485 22652.600 0.005 0.0124 0.149 0.398 0.103 0.136 0.34 0.471 0.00076 0.00105 Wall time: 22652.600089484826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 24 0.277 0.0123 0.0299 0.102 0.135 0.169 0.211 0.000376 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.285 0.0103 0.0795 0.0948 0.124 0.32 0.344 0.000715 0.000767 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 486 22699.196 0.005 0.012 0.089 0.33 0.101 0.134 0.308 0.367 0.000688 0.000818 ! Validation 486 22699.196 0.005 0.0122 0.209 0.454 0.102 0.135 0.407 0.558 0.000908 0.00125 Wall time: 22699.19690520782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 24 0.315 0.0113 0.0885 0.0988 0.13 0.328 0.363 0.000731 0.00081 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.368 0.0102 0.164 0.0944 0.123 0.476 0.494 0.00106 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 487 22745.797 0.005 0.0118 0.064 0.301 0.101 0.133 0.258 0.307 0.000577 0.000686 ! Validation 487 22745.797 0.005 0.0121 0.282 0.525 0.101 0.134 0.49 0.648 0.00109 0.00145 Wall time: 22745.797823021654 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 24 0.25 0.0116 0.018 0.0994 0.131 0.133 0.164 0.000297 0.000365 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.296 0.0101 0.0948 0.0938 0.122 0.354 0.376 0.000789 0.000838 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 488 22792.397 0.005 0.0117 0.068 0.303 0.1 0.132 0.259 0.321 0.000579 0.000716 ! Validation 488 22792.397 0.005 0.012 0.188 0.429 0.101 0.134 0.381 0.529 0.00085 0.00118 Wall time: 22792.397152598016 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 24 0.294 0.013 0.0337 0.105 0.139 0.192 0.224 0.000428 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.36 0.0105 0.151 0.0954 0.125 0.454 0.474 0.00101 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 489 22838.998 0.005 0.0121 0.172 0.414 0.102 0.134 0.424 0.511 0.000947 0.00114 ! Validation 489 22838.998 0.005 0.0124 0.244 0.491 0.102 0.136 0.453 0.602 0.00101 0.00134 Wall time: 22838.99877186073 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 24 0.298 0.012 0.0569 0.101 0.134 0.257 0.291 0.000573 0.000649 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.213 0.01 0.0126 0.0934 0.122 0.111 0.137 0.000248 0.000305 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 490 22885.599 0.005 0.0118 0.0709 0.307 0.1 0.132 0.267 0.325 0.000595 0.000727 ! Validation 490 22885.599 0.005 0.012 0.13 0.369 0.101 0.133 0.361 0.439 0.000807 0.00098 Wall time: 22885.599055381957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 24 0.512 0.0115 0.281 0.0996 0.131 0.627 0.646 0.0014 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.398 0.0106 0.186 0.0957 0.125 0.507 0.526 0.00113 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 491 22932.203 0.005 0.0116 0.122 0.354 0.0994 0.131 0.328 0.419 0.000731 0.000934 ! Validation 491 22932.203 0.005 0.0125 0.325 0.574 0.103 0.136 0.54 0.695 0.00121 0.00155 Wall time: 22932.203931544907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 24 0.251 0.0107 0.0359 0.0966 0.126 0.177 0.231 0.000395 0.000516 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.218 0.00989 0.0198 0.0928 0.121 0.129 0.172 0.000287 0.000383 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 22978.798 0.005 0.0118 0.0685 0.304 0.1 0.132 0.263 0.321 0.000587 0.000716 ! Validation 492 22978.798 0.005 0.0119 0.14 0.378 0.101 0.133 0.381 0.456 0.00085 0.00102 Wall time: 22978.798009085935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 24 0.36 0.0117 0.127 0.1 0.132 0.371 0.434 0.000829 0.000968 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.267 0.0108 0.0511 0.0978 0.127 0.248 0.276 0.000554 0.000615 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 23025.392 0.005 0.012 0.196 0.436 0.101 0.134 0.444 0.542 0.000991 0.00121 ! Validation 493 23025.392 0.005 0.0127 0.108 0.362 0.104 0.138 0.3 0.4 0.000669 0.000893 Wall time: 23025.392997721676 ! Best model 493 0.362 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 24 0.304 0.0112 0.0799 0.098 0.129 0.293 0.345 0.000653 0.000769 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.273 0.0104 0.0639 0.0954 0.125 0.277 0.308 0.000618 0.000688 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 23072.078 0.005 0.0128 0.167 0.424 0.105 0.138 0.415 0.501 0.000926 0.00112 ! Validation 494 23072.078 0.005 0.0124 0.142 0.389 0.103 0.136 0.332 0.459 0.000741 0.00102 Wall time: 23072.078762754798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 24 0.235 0.0107 0.0212 0.096 0.126 0.135 0.178 0.000302 0.000397 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.269 0.00992 0.0711 0.0932 0.121 0.303 0.325 0.000677 0.000726 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 23118.677 0.005 0.0117 0.0382 0.272 0.1 0.132 0.195 0.24 0.000434 0.000535 ! Validation 495 23118.677 0.005 0.0118 0.157 0.393 0.1 0.133 0.349 0.483 0.000778 0.00108 Wall time: 23118.677595087793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 24 0.498 0.0118 0.263 0.101 0.132 0.592 0.625 0.00132 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.429 0.0101 0.226 0.0937 0.123 0.563 0.58 0.00126 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 496 23165.285 0.005 0.0115 0.0805 0.31 0.0989 0.131 0.273 0.337 0.000608 0.000751 ! Validation 496 23165.285 0.005 0.0121 0.379 0.621 0.101 0.134 0.601 0.75 0.00134 0.00167 Wall time: 23165.28531791363 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 24 0.249 0.0107 0.0355 0.0959 0.126 0.188 0.23 0.000419 0.000513 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.239 0.00979 0.0434 0.0927 0.121 0.225 0.254 0.000501 0.000567 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 23211.880 0.005 0.0115 0.0391 0.269 0.0993 0.131 0.194 0.241 0.000433 0.000539 ! Validation 497 23211.880 0.005 0.0117 0.165 0.399 0.0996 0.132 0.349 0.496 0.00078 0.00111 Wall time: 23211.880493120756 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 24 0.452 0.0118 0.216 0.1 0.133 0.534 0.566 0.00119 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.242 0.0102 0.0384 0.0939 0.123 0.199 0.239 0.000444 0.000533 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 23258.476 0.005 0.0115 0.121 0.351 0.0991 0.131 0.358 0.419 0.000799 0.000936 ! Validation 498 23258.476 0.005 0.012 0.162 0.403 0.101 0.134 0.416 0.491 0.00093 0.0011 Wall time: 23258.476343736984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 24 0.573 0.0131 0.311 0.105 0.14 0.637 0.68 0.00142 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.22 0.0104 0.0125 0.0948 0.124 0.124 0.136 0.000276 0.000304 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 499 23305.073 0.005 0.0117 0.155 0.389 0.0999 0.132 0.378 0.474 0.000844 0.00106 ! Validation 499 23305.073 0.005 0.0123 0.14 0.386 0.102 0.135 0.344 0.456 0.000769 0.00102 Wall time: 23305.073688612785 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 24 0.402 0.0129 0.144 0.105 0.139 0.426 0.463 0.000951 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.466 0.0106 0.255 0.0956 0.125 0.6 0.615 0.00134 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 500 23351.674 0.005 0.0128 0.326 0.581 0.104 0.138 0.584 0.701 0.0013 0.00156 ! Validation 500 23351.674 0.005 0.0124 0.317 0.564 0.103 0.136 0.553 0.686 0.00123 0.00153 Wall time: 23351.674495811574 ! Stop training: max epochs Wall time: 23351.698283562902 Cumulative wall time: 23351.698283562902 Using device: cuda Please note that _all_ machine learning models running on CUDA hardware are generally somewhat nondeterministic and that this can manifest in small, generally unimportant variation in the final test errors. Loading model... loaded model Loading dataset... Processing dataset... Done! Loaded dataset specified in test_config.yaml. Using all frames from the specified test dataset, yielding a test set size of 500 frames. Starting... --- Final result: --- f_mae = 0.100997 f_rmse = 0.132077 e_mae = 0.250859 e_rmse = 0.324648 e/N_mae = 0.000560 e/N_rmse = 0.000725 f_mae = 0.100997 f_rmse = 0.132077 e_mae = 0.250859 e_rmse = 0.324648 e/N_mae = 0.000560 e/N_rmse = 0.000725 Train end time: 2024-12-07_05:40:06 Training duration: 6h 33m 52s