Train start time: 2024-12-08_10:58:41 Torch device: cuda Processing dataset... Loaded data: Batch(atomic_numbers=[2688000, 1], batch=[2688000], cell=[6000, 3, 3], edge_cell_shift=[104016464, 3], edge_index=[2, 104016464], forces=[2688000, 3], pbc=[6000, 3], pos=[2688000, 3], ptr=[6001], total_energy=[6000, 1]) processed data size: ~4132.50 MB Cached processed data to disk Done! Successfully loaded the data set of type ASEDataset(6000)... Replace string dataset_per_atom_total_energy_mean to -346.88965814708905 Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -346.889658]. Replace string dataset_forces_rms to 1.218411654125436 Initially outputs are globally scaled by: 1.218411654125436, 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 25.5 1.07 4.05 0.927 1.26 2.39 2.45 0.00535 0.00547 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 7.337 0.005 1.01 6.87 27 0.905 1.22 2.61 3.19 0.00583 0.00713 Wall time: 7.337323782034218 ! Best model 0 27.036 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 86 29.8 0.998 9.85 0.899 1.22 3.18 3.82 0.00709 0.00853 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 22.9 1.06 1.57 0.924 1.26 1.44 1.53 0.00322 0.0034 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 1 163.647 0.005 0.998 9.3 29.3 0.901 1.22 2.97 3.72 0.00664 0.00829 ! Validation 1 163.647 0.005 1 5.72 25.8 0.902 1.22 2.43 2.91 0.00543 0.00651 Wall time: 163.6478587128222 ! Best model 1 25.808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 86 21.8 0.951 2.75 0.872 1.19 1.57 2.02 0.00351 0.00451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 21.2 1 1.17 0.89 1.22 1.24 1.32 0.00277 0.00294 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 2 318.401 0.005 0.987 5.92 25.7 0.893 1.21 2.35 2.96 0.00524 0.00662 ! Validation 2 318.401 0.005 0.952 2.46 21.5 0.873 1.19 1.59 1.91 0.00354 0.00427 Wall time: 318.4011399038136 ! Best model 2 21.503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 86 10.1 0.457 0.965 0.617 0.824 0.941 1.2 0.0021 0.00267 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 13.1 0.465 3.83 0.619 0.831 2.28 2.38 0.0051 0.00532 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 473.177 0.005 0.707 1.92 16.1 0.753 1.02 1.34 1.69 0.003 0.00377 ! Validation 3 473.177 0.005 0.462 5.27 14.5 0.62 0.828 2.47 2.8 0.0055 0.00624 Wall time: 473.17843161383644 ! Best model 3 14.504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 86 6.91 0.274 1.44 0.474 0.637 1.22 1.46 0.00273 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 7.38 0.275 1.87 0.474 0.639 1.43 1.67 0.00319 0.00372 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 4 627.934 0.005 0.36 1.31 8.5 0.543 0.731 1.11 1.39 0.00248 0.00311 ! Validation 4 627.934 0.005 0.294 3.01 8.89 0.492 0.661 1.78 2.11 0.00398 0.00472 Wall time: 627.9346758131869 ! Best model 4 8.893 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 86 5.99 0.21 1.79 0.418 0.559 1.4 1.63 0.00312 0.00363 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 4.73 0.199 0.756 0.406 0.543 0.768 1.06 0.00171 0.00237 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 782.760 0.005 0.243 0.975 5.84 0.447 0.601 0.967 1.2 0.00216 0.00268 ! Validation 5 782.760 0.005 0.216 0.78 5.09 0.424 0.566 0.817 1.08 0.00182 0.0024 Wall time: 782.760742681101 ! Best model 5 5.091 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 86 4.66 0.174 1.18 0.381 0.509 1.12 1.32 0.00251 0.00295 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 3.8 0.163 0.551 0.368 0.491 0.888 0.904 0.00198 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 6 938.082 0.005 0.19 1.09 4.89 0.398 0.532 1.03 1.27 0.00229 0.00284 ! Validation 6 938.082 0.005 0.179 0.53 4.11 0.388 0.516 0.729 0.887 0.00163 0.00198 Wall time: 938.0824940088205 ! Best model 6 4.113 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 86 3.58 0.147 0.638 0.352 0.467 0.794 0.973 0.00177 0.00217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 3.42 0.139 0.637 0.341 0.454 0.877 0.973 0.00196 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 7 1093.931 0.005 0.16 0.793 3.98 0.365 0.487 0.866 1.09 0.00193 0.00242 ! Validation 7 1093.931 0.005 0.154 0.608 3.69 0.361 0.478 0.792 0.95 0.00177 0.00212 Wall time: 1093.9317508400418 ! Best model 7 3.686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 86 3.43 0.135 0.731 0.338 0.447 0.835 1.04 0.00186 0.00233 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 3.13 0.125 0.629 0.324 0.431 0.673 0.966 0.0015 0.00216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 8 1248.784 0.005 0.14 0.854 3.66 0.343 0.456 0.904 1.13 0.00202 0.00251 ! Validation 8 1248.784 0.005 0.139 0.658 3.44 0.343 0.454 0.77 0.989 0.00172 0.00221 Wall time: 1248.7846934641711 ! Best model 8 3.441 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 86 3.31 0.124 0.82 0.323 0.429 0.928 1.1 0.00207 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 3.1 0.114 0.821 0.309 0.411 0.888 1.1 0.00198 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 9 1403.638 0.005 0.129 0.72 3.3 0.329 0.437 0.833 1.03 0.00186 0.00231 ! Validation 9 1403.638 0.005 0.127 1.03 3.58 0.329 0.435 0.999 1.24 0.00223 0.00276 Wall time: 1403.638999441173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 86 2.91 0.114 0.628 0.311 0.412 0.752 0.966 0.00168 0.00216 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 2.57 0.107 0.43 0.3 0.399 0.697 0.799 0.00156 0.00178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 10 1558.484 0.005 0.119 0.77 3.15 0.316 0.42 0.871 1.07 0.00194 0.00239 ! Validation 10 1558.484 0.005 0.119 0.54 2.92 0.319 0.421 0.749 0.896 0.00167 0.002 Wall time: 1558.4842338608578 ! Best model 10 2.924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 86 4.74 0.109 2.55 0.303 0.403 1.84 1.95 0.0041 0.00434 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 2.91 0.101 0.897 0.29 0.386 1 1.15 0.00224 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 11 1713.329 0.005 0.111 0.585 2.81 0.306 0.406 0.743 0.931 0.00166 0.00208 ! Validation 11 1713.329 0.005 0.111 0.848 3.07 0.308 0.406 0.935 1.12 0.00209 0.0025 Wall time: 1713.3292989539914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 86 2.38 0.1 0.377 0.291 0.386 0.611 0.748 0.00136 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 2.6 0.0954 0.692 0.283 0.376 0.857 1.01 0.00191 0.00226 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 12 1868.269 0.005 0.106 0.621 2.73 0.298 0.396 0.767 0.96 0.00171 0.00214 ! Validation 12 1868.269 0.005 0.106 0.714 2.83 0.3 0.396 0.851 1.03 0.0019 0.0023 Wall time: 1868.2706276318058 ! Best model 12 2.826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 86 2.5 0.0956 0.589 0.284 0.377 0.795 0.935 0.00177 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 2.05 0.0919 0.213 0.277 0.369 0.523 0.562 0.00117 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 13 2023.307 0.005 0.0998 0.601 2.6 0.29 0.385 0.765 0.945 0.00171 0.00211 ! Validation 13 2023.307 0.005 0.101 0.364 2.39 0.294 0.388 0.598 0.735 0.00134 0.00164 Wall time: 2023.307005644776 ! Best model 13 2.388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 86 2.23 0.0941 0.349 0.282 0.374 0.553 0.72 0.00123 0.00161 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 2.02 0.0884 0.249 0.272 0.362 0.466 0.608 0.00104 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 14 2178.143 0.005 0.0967 0.705 2.64 0.286 0.379 0.825 1.02 0.00184 0.00228 ! Validation 14 2178.143 0.005 0.0975 0.378 2.33 0.288 0.381 0.576 0.749 0.00129 0.00167 Wall time: 2178.1433197888546 ! Best model 14 2.329 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 86 2.1 0.0909 0.282 0.278 0.367 0.533 0.647 0.00119 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 2.3 0.0847 0.604 0.266 0.355 0.841 0.947 0.00188 0.00211 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 15 2332.986 0.005 0.0926 0.522 2.37 0.28 0.371 0.703 0.88 0.00157 0.00196 ! Validation 15 2332.986 0.005 0.0933 0.725 2.59 0.282 0.372 0.898 1.04 0.002 0.00232 Wall time: 2332.986637291964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 86 2.47 0.0829 0.813 0.265 0.351 0.935 1.1 0.00209 0.00245 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 1.96 0.083 0.298 0.263 0.351 0.503 0.665 0.00112 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 16 2487.813 0.005 0.0899 0.694 2.49 0.275 0.365 0.824 1.01 0.00184 0.00227 ! Validation 16 2487.813 0.005 0.0907 0.377 2.19 0.278 0.367 0.589 0.748 0.00131 0.00167 Wall time: 2487.813927799929 ! Best model 16 2.191 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 86 2.13 0.0822 0.486 0.264 0.349 0.674 0.85 0.00151 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 1.85 0.0794 0.259 0.258 0.343 0.476 0.62 0.00106 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 17 2642.650 0.005 0.0864 0.438 2.17 0.27 0.358 0.637 0.807 0.00142 0.0018 ! Validation 17 2642.650 0.005 0.0869 0.422 2.16 0.272 0.359 0.619 0.791 0.00138 0.00177 Wall time: 2642.6506861839443 ! Best model 17 2.160 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 86 1.9 0.082 0.26 0.264 0.349 0.477 0.621 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 18 100 1.9 0.0779 0.342 0.255 0.34 0.612 0.713 0.00137 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 18 2797.489 0.005 0.0826 0.472 2.12 0.264 0.35 0.66 0.838 0.00147 0.00187 ! Validation 18 2797.489 0.005 0.0849 0.516 2.21 0.269 0.355 0.738 0.876 0.00165 0.00195 Wall time: 2797.489649122115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 86 1.86 0.0788 0.282 0.258 0.342 0.532 0.647 0.00119 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 1.74 0.0747 0.246 0.25 0.333 0.497 0.604 0.00111 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 19 2952.429 0.005 0.0805 0.505 2.11 0.261 0.346 0.695 0.866 0.00155 0.00193 ! Validation 19 2952.429 0.005 0.0815 0.511 2.14 0.264 0.348 0.69 0.871 0.00154 0.00194 Wall time: 2952.42985880794 ! Best model 19 2.142 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 86 1.79 0.0778 0.235 0.255 0.34 0.476 0.591 0.00106 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 1.61 0.0733 0.146 0.247 0.33 0.367 0.466 0.00082 0.00104 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 20 3107.330 0.005 0.0784 0.554 2.12 0.257 0.341 0.72 0.907 0.00161 0.00202 ! Validation 20 3107.330 0.005 0.0794 0.341 1.93 0.26 0.343 0.546 0.711 0.00122 0.00159 Wall time: 3107.3306800080463 ! Best model 20 1.928 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 86 2.22 0.0777 0.669 0.255 0.34 0.851 0.996 0.0019 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 1.47 0.0707 0.0594 0.243 0.324 0.272 0.297 0.000607 0.000663 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 3262.311 0.005 0.0755 0.42 1.93 0.253 0.335 0.639 0.789 0.00143 0.00176 ! Validation 21 3262.311 0.005 0.0768 0.258 1.79 0.256 0.338 0.495 0.619 0.0011 0.00138 Wall time: 3262.311847806908 ! Best model 21 1.793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 86 1.67 0.0702 0.268 0.244 0.323 0.513 0.631 0.00115 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 1.51 0.069 0.131 0.24 0.32 0.337 0.441 0.000752 0.000985 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 22 3417.200 0.005 0.074 0.559 2.04 0.25 0.331 0.734 0.911 0.00164 0.00203 ! Validation 22 3417.200 0.005 0.0751 0.289 1.79 0.253 0.334 0.505 0.654 0.00113 0.00146 Wall time: 3417.2008462110534 ! Best model 22 1.790 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 86 2.37 0.0716 0.942 0.246 0.326 1.06 1.18 0.00236 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 2.37 0.0689 0.996 0.24 0.32 1.18 1.22 0.00263 0.00271 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 23 3572.077 0.005 0.0724 0.588 2.04 0.247 0.328 0.745 0.934 0.00166 0.00209 ! Validation 23 3572.077 0.005 0.0744 1.18 2.66 0.251 0.332 1.18 1.32 0.00264 0.00295 Wall time: 3572.0776894087903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 86 2.39 0.0694 1 0.243 0.321 1.1 1.22 0.00244 0.00272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 1.59 0.0662 0.262 0.235 0.313 0.558 0.624 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 24 3726.954 0.005 0.0705 0.432 1.84 0.244 0.324 0.64 0.8 0.00143 0.00179 ! Validation 24 3726.954 0.005 0.0715 0.398 1.83 0.246 0.326 0.613 0.769 0.00137 0.00172 Wall time: 3726.954570136033 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 86 1.57 0.0702 0.163 0.243 0.323 0.376 0.492 0.00084 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 1.57 0.0645 0.281 0.232 0.309 0.589 0.646 0.00132 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 25 3881.937 0.005 0.0684 0.471 1.84 0.24 0.319 0.675 0.837 0.00151 0.00187 ! Validation 25 3881.937 0.005 0.0696 0.516 1.91 0.243 0.322 0.706 0.875 0.00158 0.00195 Wall time: 3881.9377072718926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 86 1.61 0.0677 0.261 0.239 0.317 0.519 0.623 0.00116 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 1.71 0.0632 0.451 0.23 0.306 0.778 0.818 0.00174 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 26 4036.793 0.005 0.0669 0.459 1.8 0.238 0.315 0.667 0.826 0.00149 0.00184 ! Validation 26 4036.793 0.005 0.0682 0.674 2.04 0.241 0.318 0.862 1 0.00193 0.00223 Wall time: 4036.7937092329375 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 86 1.58 0.0643 0.297 0.233 0.309 0.535 0.664 0.00119 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 1.54 0.0611 0.322 0.226 0.301 0.651 0.691 0.00145 0.00154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 27 4191.642 0.005 0.0651 0.418 1.72 0.235 0.311 0.633 0.787 0.00141 0.00176 ! Validation 27 4191.642 0.005 0.066 0.565 1.89 0.237 0.313 0.746 0.916 0.00167 0.00204 Wall time: 4191.64214767376 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 86 1.47 0.0644 0.185 0.233 0.309 0.435 0.523 0.000971 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 1.23 0.0593 0.0431 0.223 0.297 0.22 0.253 0.000491 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 28 4346.507 0.005 0.0632 0.379 1.64 0.231 0.306 0.603 0.75 0.00135 0.00167 ! Validation 28 4346.507 0.005 0.0642 0.248 1.53 0.234 0.309 0.492 0.607 0.0011 0.00135 Wall time: 4346.507343752775 ! Best model 28 1.532 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 86 1.73 0.062 0.486 0.229 0.303 0.705 0.85 0.00157 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 1.69 0.0578 0.532 0.22 0.293 0.864 0.889 0.00193 0.00198 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 4501.799 0.005 0.0619 0.413 1.65 0.229 0.303 0.618 0.783 0.00138 0.00175 ! Validation 29 4501.799 0.005 0.0626 0.677 1.93 0.231 0.305 0.853 1 0.0019 0.00224 Wall time: 4501.799468117766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 86 1.57 0.0614 0.338 0.228 0.302 0.602 0.708 0.00134 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 1.83 0.0578 0.677 0.22 0.293 0.975 1 0.00218 0.00224 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 30 4656.779 0.005 0.062 0.693 1.93 0.229 0.303 0.824 1.01 0.00184 0.00226 ! Validation 30 4656.779 0.005 0.0625 0.788 2.04 0.231 0.305 0.939 1.08 0.0021 0.00241 Wall time: 4656.779651296791 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 86 1.47 0.0576 0.315 0.221 0.292 0.583 0.684 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 31 100 1.17 0.0559 0.0569 0.217 0.288 0.24 0.291 0.000537 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 31 4811.767 0.005 0.0598 0.375 1.57 0.225 0.298 0.601 0.746 0.00134 0.00167 ! Validation 31 4811.767 0.005 0.0607 0.283 1.5 0.228 0.3 0.524 0.648 0.00117 0.00145 Wall time: 4811.767588474788 ! Best model 31 1.496 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 86 1.62 0.0604 0.408 0.226 0.299 0.632 0.778 0.00141 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 1.25 0.0545 0.163 0.214 0.284 0.447 0.492 0.000998 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 32 4966.872 0.005 0.058 0.357 1.52 0.222 0.293 0.582 0.727 0.0013 0.00162 ! Validation 32 4966.872 0.005 0.0592 0.418 1.6 0.225 0.296 0.657 0.788 0.00147 0.00176 Wall time: 4966.872311934829 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 86 1.39 0.056 0.271 0.218 0.288 0.503 0.634 0.00112 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 1.28 0.0527 0.228 0.21 0.28 0.55 0.581 0.00123 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 33 5122.399 0.005 0.0566 0.398 1.53 0.219 0.29 0.609 0.769 0.00136 0.00172 ! Validation 33 5122.399 0.005 0.0574 0.381 1.53 0.221 0.292 0.604 0.752 0.00135 0.00168 Wall time: 5122.39986494882 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 86 1.48 0.0545 0.389 0.215 0.284 0.624 0.76 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 34 100 1.08 0.0518 0.0441 0.208 0.277 0.196 0.256 0.000437 0.000571 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 34 5277.397 0.005 0.0553 0.459 1.56 0.216 0.286 0.656 0.825 0.00146 0.00184 ! Validation 34 5277.397 0.005 0.0565 0.234 1.37 0.22 0.29 0.468 0.59 0.00105 0.00132 Wall time: 5277.397900721058 ! Best model 34 1.365 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 86 1.23 0.0542 0.149 0.216 0.284 0.367 0.47 0.000818 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 1.36 0.0515 0.328 0.208 0.276 0.674 0.698 0.00151 0.00156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 35 5432.385 0.005 0.0553 0.566 1.67 0.216 0.286 0.726 0.917 0.00162 0.00205 ! Validation 35 5432.385 0.005 0.056 0.37 1.49 0.219 0.288 0.605 0.741 0.00135 0.00165 Wall time: 5432.385422207881 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 86 1.24 0.0537 0.165 0.213 0.282 0.413 0.495 0.000922 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 1.03 0.0489 0.0563 0.203 0.269 0.23 0.289 0.000514 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 36 5587.383 0.005 0.0528 0.286 1.34 0.212 0.28 0.519 0.651 0.00116 0.00145 ! Validation 36 5587.383 0.005 0.0535 0.306 1.38 0.214 0.282 0.552 0.674 0.00123 0.00151 Wall time: 5587.3839044128545 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 86 1.21 0.0499 0.208 0.205 0.272 0.45 0.555 0.00101 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 0.961 0.0473 0.0155 0.2 0.265 0.142 0.152 0.000317 0.000339 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 37 5742.370 0.005 0.0514 0.371 1.4 0.209 0.276 0.595 0.742 0.00133 0.00166 ! Validation 37 5742.370 0.005 0.052 0.208 1.25 0.211 0.278 0.454 0.556 0.00101 0.00124 Wall time: 5742.370812874753 ! Best model 37 1.248 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 86 1.74 0.0532 0.679 0.212 0.281 0.9 1 0.00201 0.00224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 1.89 0.0521 0.851 0.209 0.278 1.11 1.12 0.00248 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 38 5897.484 0.005 0.0503 0.616 1.62 0.207 0.273 0.768 0.956 0.00171 0.00213 ! Validation 38 5897.484 0.005 0.0561 1.41 2.53 0.218 0.289 1.32 1.45 0.00295 0.00323 Wall time: 5897.484356041998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 86 1.11 0.0485 0.139 0.203 0.268 0.356 0.453 0.000795 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 0.919 0.045 0.0185 0.195 0.258 0.159 0.166 0.000355 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 39 6052.469 0.005 0.0502 0.406 1.41 0.206 0.273 0.619 0.777 0.00138 0.00173 ! Validation 39 6052.469 0.005 0.0498 0.201 1.2 0.207 0.272 0.444 0.546 0.000991 0.00122 Wall time: 6052.46921925107 ! Best model 39 1.197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 86 1.07 0.0446 0.178 0.195 0.257 0.415 0.515 0.000926 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 1.03 0.0436 0.161 0.192 0.254 0.464 0.489 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 40 6207.490 0.005 0.0467 0.277 1.21 0.2 0.263 0.51 0.641 0.00114 0.00143 ! Validation 40 6207.490 0.005 0.0484 0.339 1.31 0.204 0.268 0.576 0.709 0.00129 0.00158 Wall time: 6207.4903337047435 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 86 1.01 0.0429 0.157 0.192 0.252 0.388 0.482 0.000865 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 1.1 0.0405 0.29 0.186 0.245 0.637 0.656 0.00142 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 41 6362.479 0.005 0.0447 0.353 1.25 0.196 0.258 0.572 0.724 0.00128 0.00162 ! Validation 41 6362.479 0.005 0.045 0.421 1.32 0.197 0.259 0.655 0.79 0.00146 0.00176 Wall time: 6362.479987835977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 86 1.21 0.0424 0.358 0.191 0.251 0.636 0.729 0.00142 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 1.83 0.0382 1.07 0.181 0.238 1.25 1.26 0.00279 0.00281 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 42 6517.468 0.005 0.0429 0.429 1.29 0.192 0.252 0.647 0.798 0.00144 0.00178 ! Validation 42 6517.468 0.005 0.0429 1.07 1.93 0.193 0.253 1.16 1.26 0.00259 0.00281 Wall time: 6517.468890211079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 86 1.61 0.039 0.832 0.183 0.24 1.01 1.11 0.00224 0.00248 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 2.1 0.036 1.38 0.175 0.231 1.42 1.43 0.00317 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 43 6672.449 0.005 0.0401 0.318 1.12 0.186 0.244 0.549 0.687 0.00122 0.00153 ! Validation 43 6672.449 0.005 0.0409 1.47 2.28 0.188 0.246 1.38 1.47 0.00308 0.00329 Wall time: 6672.449332313146 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 86 0.924 0.0389 0.146 0.182 0.24 0.379 0.465 0.000846 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 1.24 0.0348 0.539 0.172 0.227 0.876 0.895 0.00195 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 44 6827.662 0.005 0.0389 0.432 1.21 0.183 0.24 0.654 0.801 0.00146 0.00179 ! Validation 44 6827.662 0.005 0.0402 0.605 1.41 0.186 0.244 0.826 0.948 0.00184 0.00212 Wall time: 6827.662754037883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 86 1.17 0.0379 0.408 0.18 0.237 0.656 0.779 0.00146 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 0.706 0.0336 0.0339 0.17 0.223 0.172 0.224 0.000385 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 45 6982.746 0.005 0.0379 0.445 1.2 0.18 0.237 0.659 0.813 0.00147 0.00181 ! Validation 45 6982.746 0.005 0.0388 0.186 0.963 0.183 0.24 0.418 0.526 0.000933 0.00117 Wall time: 6982.746427315753 ! Best model 45 0.963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 86 0.832 0.0367 0.0987 0.177 0.233 0.314 0.383 0.000701 0.000855 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 0.684 0.0328 0.0281 0.168 0.221 0.172 0.204 0.000384 0.000456 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 7137.743 0.005 0.037 0.455 1.2 0.178 0.234 0.666 0.822 0.00149 0.00184 ! Validation 46 7137.743 0.005 0.0381 0.196 0.959 0.181 0.238 0.44 0.539 0.000982 0.0012 Wall time: 7137.743426246103 ! Best model 46 0.959 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 86 0.814 0.0339 0.136 0.17 0.224 0.378 0.45 0.000844 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 0.76 0.0314 0.132 0.164 0.216 0.401 0.443 0.000895 0.000989 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 47 7292.748 0.005 0.0358 0.285 1 0.175 0.23 0.523 0.65 0.00117 0.00145 ! Validation 47 7292.748 0.005 0.0367 0.177 0.912 0.178 0.234 0.408 0.512 0.000912 0.00114 Wall time: 7292.748937238939 ! Best model 47 0.912 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 86 0.744 0.0331 0.0826 0.169 0.222 0.261 0.35 0.000581 0.000782 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 0.618 0.0298 0.0213 0.16 0.21 0.162 0.178 0.000363 0.000397 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 48 7447.738 0.005 0.0343 0.224 0.911 0.171 0.226 0.462 0.577 0.00103 0.00129 ! Validation 48 7447.738 0.005 0.0354 0.163 0.87 0.175 0.229 0.402 0.491 0.000897 0.0011 Wall time: 7447.738069618121 ! Best model 48 0.870 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 86 0.811 0.0328 0.155 0.167 0.221 0.398 0.48 0.000889 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 0.687 0.0295 0.0961 0.159 0.209 0.332 0.378 0.000742 0.000843 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 7602.737 0.005 0.0335 0.308 0.977 0.169 0.223 0.546 0.676 0.00122 0.00151 ! Validation 49 7602.737 0.005 0.0349 0.294 0.993 0.173 0.228 0.546 0.661 0.00122 0.00148 Wall time: 7602.7373621258885 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 86 0.745 0.0304 0.136 0.162 0.213 0.351 0.449 0.000784 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 0.574 0.0281 0.0121 0.156 0.204 0.119 0.134 0.000265 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 50 7757.715 0.005 0.0327 0.236 0.889 0.167 0.22 0.469 0.591 0.00105 0.00132 ! Validation 50 7757.715 0.005 0.0337 0.182 0.855 0.17 0.224 0.425 0.52 0.000948 0.00116 Wall time: 7757.71562140109 ! Best model 50 0.855 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 86 1.33 0.0322 0.691 0.166 0.218 0.919 1.01 0.00205 0.00226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 0.624 0.0284 0.0567 0.156 0.205 0.241 0.29 0.000538 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 51 7912.813 0.005 0.0323 0.432 1.08 0.166 0.219 0.641 0.8 0.00143 0.00179 ! Validation 51 7912.813 0.005 0.0338 0.129 0.805 0.17 0.224 0.35 0.437 0.00078 0.000976 Wall time: 7912.813699189108 ! Best model 51 0.805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 86 0.719 0.0316 0.087 0.164 0.217 0.277 0.359 0.000619 0.000802 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 0.641 0.0274 0.0924 0.153 0.202 0.335 0.37 0.000748 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 52 8067.810 0.005 0.0326 0.351 1 0.166 0.22 0.542 0.722 0.00121 0.00161 ! Validation 52 8067.810 0.005 0.0327 0.162 0.816 0.168 0.22 0.395 0.49 0.000881 0.00109 Wall time: 8067.810369859915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 86 1.08 0.0314 0.457 0.163 0.216 0.747 0.823 0.00167 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 0.774 0.0267 0.239 0.151 0.199 0.58 0.596 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 53 8222.801 0.005 0.0309 0.259 0.877 0.162 0.214 0.506 0.62 0.00113 0.00138 ! Validation 53 8222.801 0.005 0.032 0.253 0.893 0.166 0.218 0.506 0.613 0.00113 0.00137 Wall time: 8222.801835034043 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 86 1.04 0.0294 0.451 0.158 0.209 0.741 0.818 0.00166 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 0.56 0.0268 0.0245 0.151 0.199 0.157 0.191 0.000351 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 54 8377.795 0.005 0.0304 0.375 0.984 0.161 0.213 0.613 0.746 0.00137 0.00167 ! Validation 54 8377.795 0.005 0.0319 0.173 0.811 0.165 0.218 0.408 0.507 0.00091 0.00113 Wall time: 8377.79606501013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 86 0.708 0.0295 0.118 0.159 0.209 0.311 0.418 0.000693 0.000934 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 0.574 0.0259 0.0562 0.149 0.196 0.256 0.289 0.000572 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 55 8532.788 0.005 0.0303 0.324 0.931 0.161 0.212 0.563 0.694 0.00126 0.00155 ! Validation 55 8532.788 0.005 0.0311 0.133 0.754 0.163 0.215 0.358 0.444 0.000799 0.00099 Wall time: 8532.788518239744 ! Best model 55 0.754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 86 0.657 0.0287 0.084 0.156 0.206 0.279 0.353 0.000622 0.000788 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 0.559 0.025 0.059 0.146 0.193 0.264 0.296 0.000589 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 56 8687.776 0.005 0.0292 0.202 0.787 0.158 0.208 0.431 0.548 0.000961 0.00122 ! Validation 56 8687.776 0.005 0.0302 0.148 0.753 0.161 0.212 0.381 0.469 0.000851 0.00105 Wall time: 8687.77634955477 ! Best model 56 0.753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 86 0.759 0.0287 0.186 0.156 0.206 0.438 0.525 0.000977 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 0.679 0.025 0.178 0.146 0.193 0.494 0.515 0.0011 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 57 8842.883 0.005 0.0286 0.265 0.836 0.156 0.206 0.514 0.627 0.00115 0.0014 ! Validation 57 8842.883 0.005 0.0298 0.14 0.737 0.16 0.21 0.363 0.456 0.000809 0.00102 Wall time: 8842.883176397998 ! Best model 57 0.737 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 86 0.869 0.0294 0.281 0.157 0.209 0.552 0.646 0.00123 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 0.515 0.0244 0.0265 0.144 0.191 0.15 0.198 0.000335 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 58 8997.885 0.005 0.0285 0.355 0.926 0.156 0.206 0.577 0.726 0.00129 0.00162 ! Validation 58 8997.885 0.005 0.0295 0.205 0.796 0.159 0.209 0.44 0.551 0.000981 0.00123 Wall time: 8997.885461491067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 86 0.701 0.0269 0.162 0.152 0.2 0.403 0.491 0.000899 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 0.947 0.0238 0.471 0.143 0.188 0.828 0.836 0.00185 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 59 9152.874 0.005 0.0277 0.228 0.783 0.153 0.203 0.472 0.582 0.00105 0.0013 ! Validation 59 9152.874 0.005 0.0288 0.41 0.986 0.157 0.207 0.681 0.781 0.00152 0.00174 Wall time: 9152.874144442845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 86 1.11 0.0271 0.573 0.151 0.2 0.839 0.922 0.00187 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 0.504 0.0235 0.0348 0.141 0.187 0.196 0.227 0.000437 0.000507 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 60 9307.872 0.005 0.0269 0.195 0.733 0.151 0.2 0.432 0.537 0.000964 0.0012 ! Validation 60 9307.872 0.005 0.0283 0.222 0.788 0.156 0.205 0.468 0.574 0.00104 0.00128 Wall time: 9307.87241651211 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 86 0.583 0.0253 0.0777 0.147 0.194 0.29 0.34 0.000647 0.000758 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 0.751 0.0232 0.286 0.141 0.186 0.635 0.652 0.00142 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 61 9462.860 0.005 0.0276 0.375 0.928 0.153 0.203 0.593 0.746 0.00132 0.00167 ! Validation 61 9462.860 0.005 0.0282 0.269 0.833 0.156 0.205 0.533 0.632 0.00119 0.00141 Wall time: 9462.860350550152 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 86 1.43 0.0254 0.918 0.146 0.194 1.12 1.17 0.00249 0.00261 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 2.34 0.023 1.88 0.14 0.185 1.67 1.67 0.00372 0.00373 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 62 9617.839 0.005 0.0263 0.237 0.763 0.149 0.198 0.478 0.592 0.00107 0.00132 ! Validation 62 9617.839 0.005 0.0277 1.59 2.15 0.154 0.203 1.47 1.54 0.00329 0.00343 Wall time: 9617.839685585815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 86 1.29 0.0245 0.805 0.144 0.191 1.06 1.09 0.00236 0.00244 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 0.618 0.0223 0.172 0.138 0.182 0.491 0.505 0.0011 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 63 9772.823 0.005 0.026 0.261 0.781 0.148 0.197 0.496 0.622 0.00111 0.00139 ! Validation 63 9772.823 0.005 0.0272 0.419 0.963 0.152 0.201 0.689 0.789 0.00154 0.00176 Wall time: 9772.823666295968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 86 0.683 0.0264 0.156 0.15 0.198 0.38 0.482 0.000849 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 0.457 0.0221 0.0151 0.137 0.181 0.119 0.15 0.000265 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 64 9928.212 0.005 0.0259 0.315 0.833 0.148 0.196 0.558 0.684 0.00125 0.00153 ! Validation 64 9928.212 0.005 0.0267 0.145 0.679 0.151 0.199 0.368 0.464 0.000821 0.00104 Wall time: 9928.212668561842 ! Best model 64 0.679 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 86 0.597 0.0236 0.125 0.142 0.187 0.355 0.431 0.000792 0.000961 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 0.453 0.0214 0.0245 0.135 0.178 0.151 0.191 0.000337 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 65 10083.238 0.005 0.0251 0.204 0.707 0.146 0.193 0.434 0.55 0.000968 0.00123 ! Validation 65 10083.238 0.005 0.026 0.11 0.63 0.149 0.196 0.324 0.403 0.000724 0.0009 Wall time: 10083.238957969937 ! Best model 65 0.630 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 86 0.553 0.0242 0.0687 0.144 0.19 0.262 0.319 0.000584 0.000713 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 0.518 0.0212 0.0941 0.135 0.177 0.358 0.374 0.000799 0.000834 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 66 10238.218 0.005 0.025 0.297 0.798 0.145 0.193 0.516 0.665 0.00115 0.00148 ! Validation 66 10238.218 0.005 0.0259 0.112 0.63 0.149 0.196 0.33 0.408 0.000736 0.00091 Wall time: 10238.218551284168 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 86 0.88 0.0242 0.396 0.143 0.19 0.668 0.767 0.00149 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 0.92 0.021 0.5 0.133 0.176 0.855 0.862 0.00191 0.00192 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 67 10393.746 0.005 0.0244 0.204 0.692 0.144 0.19 0.425 0.55 0.000949 0.00123 ! Validation 67 10393.746 0.005 0.0255 0.341 0.85 0.147 0.194 0.621 0.711 0.00139 0.00159 Wall time: 10393.746530738194 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 86 0.549 0.0234 0.0803 0.14 0.186 0.274 0.345 0.000612 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 0.584 0.0211 0.163 0.134 0.177 0.477 0.492 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 68 10548.695 0.005 0.0242 0.304 0.788 0.143 0.19 0.551 0.672 0.00123 0.0015 ! Validation 68 10548.695 0.005 0.0255 0.151 0.661 0.148 0.195 0.388 0.473 0.000866 0.00106 Wall time: 10548.695915125776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 86 1.13 0.0241 0.648 0.143 0.189 0.927 0.981 0.00207 0.00219 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 0.791 0.0208 0.375 0.133 0.176 0.742 0.746 0.00166 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 69 10703.662 0.005 0.0238 0.287 0.762 0.142 0.188 0.513 0.652 0.00115 0.00146 ! Validation 69 10703.662 0.005 0.0252 0.272 0.776 0.147 0.193 0.543 0.636 0.00121 0.00142 Wall time: 10703.662223238964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 86 0.696 0.0238 0.22 0.142 0.188 0.485 0.571 0.00108 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 0.68 0.0206 0.268 0.132 0.175 0.619 0.63 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 70 10858.734 0.005 0.0235 0.244 0.713 0.141 0.187 0.484 0.602 0.00108 0.00134 ! Validation 70 10858.734 0.005 0.025 0.642 1.14 0.146 0.192 0.906 0.976 0.00202 0.00218 Wall time: 10858.734693539795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 86 0.99 0.0231 0.528 0.14 0.185 0.84 0.885 0.00188 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 0.423 0.0206 0.0111 0.131 0.175 0.125 0.129 0.00028 0.000287 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 71 11013.917 0.005 0.023 0.212 0.672 0.14 0.185 0.448 0.561 0.001 0.00125 ! Validation 71 11013.917 0.005 0.0247 0.141 0.634 0.145 0.191 0.355 0.458 0.000793 0.00102 Wall time: 11013.917266015895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 86 0.574 0.0244 0.0866 0.144 0.19 0.284 0.358 0.000634 0.0008 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 0.415 0.0203 0.0091 0.132 0.174 0.084 0.116 0.000188 0.00026 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 72 11168.770 0.005 0.023 0.311 0.771 0.139 0.185 0.533 0.68 0.00119 0.00152 ! Validation 72 11168.770 0.005 0.0249 0.122 0.621 0.146 0.192 0.343 0.425 0.000766 0.000949 Wall time: 11168.770195274148 ! Best model 72 0.621 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 86 0.935 0.0223 0.488 0.137 0.182 0.779 0.851 0.00174 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 0.817 0.0195 0.428 0.128 0.17 0.791 0.797 0.00177 0.00178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 73 11323.631 0.005 0.0224 0.188 0.635 0.138 0.182 0.416 0.528 0.000929 0.00118 ! Validation 73 11323.631 0.005 0.0236 0.294 0.765 0.142 0.187 0.572 0.66 0.00128 0.00147 Wall time: 11323.631089602131 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 86 0.632 0.023 0.171 0.14 0.185 0.43 0.504 0.00096 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 0.459 0.0192 0.0746 0.128 0.169 0.317 0.333 0.000708 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 74 11478.587 0.005 0.022 0.245 0.685 0.137 0.181 0.484 0.603 0.00108 0.00135 ! Validation 74 11478.587 0.005 0.0234 0.325 0.793 0.141 0.186 0.607 0.695 0.00136 0.00155 Wall time: 11478.58764017094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 86 0.495 0.0207 0.0811 0.133 0.175 0.309 0.347 0.000689 0.000774 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 0.371 0.0182 0.0058 0.125 0.165 0.0878 0.0928 0.000196 0.000207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 75 11633.728 0.005 0.0215 0.161 0.591 0.135 0.179 0.387 0.489 0.000865 0.00109 ! Validation 75 11633.728 0.005 0.0224 0.141 0.59 0.139 0.182 0.359 0.458 0.000802 0.00102 Wall time: 11633.728349735029 ! Best model 75 0.590 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 86 0.575 0.0217 0.141 0.136 0.18 0.371 0.457 0.000827 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 0.605 0.018 0.245 0.124 0.163 0.597 0.603 0.00133 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 76 11788.597 0.005 0.0212 0.216 0.64 0.134 0.178 0.452 0.566 0.00101 0.00126 ! Validation 76 11788.597 0.005 0.0222 0.211 0.655 0.138 0.182 0.471 0.559 0.00105 0.00125 Wall time: 11788.597735764924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 86 0.49 0.0204 0.0821 0.131 0.174 0.274 0.349 0.000611 0.000779 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 0.443 0.0178 0.087 0.123 0.163 0.347 0.359 0.000775 0.000802 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 77 11943.932 0.005 0.0211 0.269 0.691 0.134 0.177 0.514 0.632 0.00115 0.00141 ! Validation 77 11943.932 0.005 0.0219 0.0929 0.531 0.137 0.18 0.301 0.371 0.000671 0.000829 Wall time: 11943.932365581859 ! Best model 77 0.531 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 86 0.547 0.0221 0.105 0.136 0.181 0.324 0.395 0.000723 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 0.517 0.0179 0.159 0.124 0.163 0.483 0.486 0.00108 0.00108 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 78 12098.841 0.005 0.0207 0.253 0.667 0.132 0.175 0.49 0.613 0.00109 0.00137 ! Validation 78 12098.841 0.005 0.0221 0.2 0.642 0.138 0.181 0.455 0.545 0.00101 0.00122 Wall time: 12098.841809213161 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 86 0.521 0.0203 0.115 0.131 0.174 0.309 0.413 0.00069 0.000923 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 0.743 0.0176 0.39 0.123 0.162 0.757 0.761 0.00169 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 79 12254.077 0.005 0.0204 0.219 0.628 0.131 0.174 0.462 0.57 0.00103 0.00127 ! Validation 79 12254.077 0.005 0.0217 0.265 0.699 0.136 0.179 0.553 0.627 0.00123 0.0014 Wall time: 12254.077393983025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 86 0.603 0.0194 0.215 0.127 0.17 0.49 0.565 0.00109 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 0.387 0.0172 0.0423 0.121 0.16 0.241 0.25 0.000539 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 80 12408.941 0.005 0.0204 0.204 0.611 0.131 0.174 0.439 0.55 0.00098 0.00123 ! Validation 80 12408.941 0.005 0.0211 0.088 0.511 0.135 0.177 0.293 0.361 0.000653 0.000807 Wall time: 12408.941962501965 ! Best model 80 0.511 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 86 1.09 0.0184 0.724 0.125 0.165 0.998 1.04 0.00223 0.00231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 0.656 0.0169 0.317 0.12 0.159 0.68 0.686 0.00152 0.00153 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 81 12563.800 0.005 0.0202 0.225 0.628 0.131 0.173 0.46 0.577 0.00103 0.00129 ! Validation 81 12563.800 0.005 0.0209 0.582 1 0.134 0.176 0.857 0.93 0.00191 0.00207 Wall time: 12563.800743055996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 86 0.539 0.0198 0.143 0.129 0.171 0.386 0.461 0.000862 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 0.424 0.0171 0.0824 0.12 0.159 0.334 0.35 0.000745 0.000781 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 82 12718.652 0.005 0.0199 0.259 0.656 0.13 0.172 0.491 0.62 0.0011 0.00138 ! Validation 82 12718.652 0.005 0.0209 0.276 0.694 0.134 0.176 0.558 0.64 0.00125 0.00143 Wall time: 12718.652820357122 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 86 0.68 0.0191 0.299 0.127 0.168 0.603 0.666 0.00135 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 0.342 0.0164 0.0149 0.118 0.156 0.137 0.149 0.000305 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 83 12873.611 0.005 0.0194 0.191 0.58 0.128 0.17 0.431 0.533 0.000962 0.00119 ! Validation 83 12873.611 0.005 0.0203 0.18 0.586 0.132 0.174 0.424 0.517 0.000945 0.00115 Wall time: 12873.611010611989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 86 0.629 0.0186 0.257 0.126 0.166 0.554 0.618 0.00124 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 0.8 0.0161 0.479 0.117 0.154 0.841 0.843 0.00188 0.00188 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 84 13028.464 0.005 0.019 0.178 0.558 0.127 0.168 0.411 0.514 0.000917 0.00115 ! Validation 84 13028.464 0.005 0.0199 0.382 0.78 0.131 0.172 0.679 0.753 0.00152 0.00168 Wall time: 13028.46475886507 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 86 0.551 0.0213 0.125 0.134 0.178 0.349 0.431 0.000779 0.000963 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 0.535 0.0168 0.199 0.119 0.158 0.54 0.544 0.00121 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 85 13183.340 0.005 0.019 0.263 0.642 0.127 0.168 0.491 0.625 0.0011 0.0014 ! Validation 85 13183.340 0.005 0.0206 0.412 0.825 0.133 0.175 0.689 0.782 0.00154 0.00175 Wall time: 13183.340292711742 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 86 0.421 0.0182 0.0584 0.124 0.164 0.215 0.294 0.000479 0.000657 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 0.332 0.016 0.0114 0.117 0.154 0.116 0.13 0.000258 0.00029 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 13338.209 0.005 0.0194 0.296 0.685 0.128 0.17 0.52 0.663 0.00116 0.00148 ! Validation 86 13338.209 0.005 0.0199 0.136 0.533 0.13 0.172 0.352 0.449 0.000786 0.001 Wall time: 13338.20912008686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 86 0.424 0.0183 0.0582 0.124 0.165 0.242 0.294 0.000539 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.337 0.0158 0.0216 0.116 0.153 0.163 0.179 0.000363 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 87 13493.367 0.005 0.0183 0.131 0.497 0.124 0.165 0.353 0.441 0.000789 0.000984 ! Validation 87 13493.367 0.005 0.0193 0.0718 0.458 0.129 0.169 0.253 0.326 0.000566 0.000729 Wall time: 13493.367894086055 ! Best model 87 0.458 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 86 0.523 0.0181 0.161 0.124 0.164 0.413 0.489 0.000921 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 0.486 0.0158 0.17 0.116 0.153 0.5 0.503 0.00112 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 88 13648.232 0.005 0.0181 0.174 0.535 0.124 0.164 0.404 0.508 0.000901 0.00113 ! Validation 88 13648.232 0.005 0.0196 0.354 0.746 0.13 0.171 0.639 0.725 0.00143 0.00162 Wall time: 13648.232356321998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 86 0.672 0.0181 0.31 0.124 0.164 0.591 0.679 0.00132 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 0.699 0.0153 0.393 0.114 0.151 0.761 0.764 0.0017 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 89 13803.088 0.005 0.0179 0.206 0.564 0.123 0.163 0.449 0.552 0.001 0.00123 ! Validation 89 13803.088 0.005 0.019 0.366 0.746 0.128 0.168 0.653 0.737 0.00146 0.00165 Wall time: 13803.088780902792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 86 0.852 0.0173 0.505 0.121 0.16 0.817 0.866 0.00182 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 0.602 0.0154 0.294 0.114 0.151 0.657 0.661 0.00147 0.00147 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 90 13958.063 0.005 0.0179 0.201 0.559 0.123 0.163 0.437 0.546 0.000975 0.00122 ! Validation 90 13958.063 0.005 0.019 0.584 0.963 0.127 0.168 0.871 0.931 0.00194 0.00208 Wall time: 13958.063011225779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 86 0.397 0.0167 0.062 0.119 0.158 0.239 0.303 0.000532 0.000677 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 0.305 0.0149 0.00703 0.113 0.149 0.0887 0.102 0.000198 0.000228 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 14112.932 0.005 0.0178 0.237 0.592 0.123 0.162 0.488 0.593 0.00109 0.00132 ! Validation 91 14112.932 0.005 0.0185 0.0831 0.453 0.126 0.166 0.277 0.351 0.000618 0.000784 Wall time: 14112.932169642765 ! Best model 91 0.453 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 86 1.03 0.0177 0.682 0.122 0.162 0.965 1.01 0.00215 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 0.997 0.0148 0.702 0.112 0.148 1.02 1.02 0.00228 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 92 14267.804 0.005 0.0172 0.156 0.5 0.121 0.16 0.389 0.481 0.000869 0.00107 ! Validation 92 14267.804 0.005 0.0182 0.531 0.896 0.125 0.165 0.831 0.888 0.00185 0.00198 Wall time: 14267.804608439095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 86 0.383 0.0163 0.0569 0.118 0.156 0.228 0.291 0.00051 0.000649 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 0.406 0.0148 0.11 0.112 0.148 0.398 0.403 0.000888 0.000901 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 93 14422.843 0.005 0.0173 0.22 0.566 0.121 0.16 0.453 0.571 0.00101 0.00127 ! Validation 93 14422.843 0.005 0.0182 0.108 0.472 0.125 0.164 0.33 0.401 0.000738 0.000895 Wall time: 14422.843439238146 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 86 0.804 0.0174 0.457 0.121 0.161 0.764 0.823 0.0017 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 1.33 0.0145 1.04 0.111 0.147 1.24 1.24 0.00277 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 94 14577.708 0.005 0.0168 0.153 0.49 0.119 0.158 0.384 0.476 0.000858 0.00106 ! Validation 94 14577.708 0.005 0.0179 0.766 1.12 0.124 0.163 1.02 1.07 0.00228 0.00238 Wall time: 14577.708454560954 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 86 0.409 0.0166 0.0769 0.118 0.157 0.273 0.338 0.000609 0.000754 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 0.782 0.0145 0.492 0.111 0.147 0.852 0.855 0.0019 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 95 14732.971 0.005 0.0174 0.262 0.611 0.122 0.161 0.499 0.624 0.00111 0.00139 ! Validation 95 14732.971 0.005 0.0178 0.414 0.77 0.123 0.163 0.712 0.784 0.00159 0.00175 Wall time: 14732.971744304989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 86 0.392 0.0167 0.0586 0.119 0.157 0.235 0.295 0.000525 0.000658 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 0.3 0.0142 0.0155 0.11 0.145 0.144 0.152 0.00032 0.000339 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 96 14888.101 0.005 0.0166 0.137 0.468 0.118 0.157 0.361 0.451 0.000806 0.00101 ! Validation 96 14888.101 0.005 0.0177 0.0649 0.419 0.123 0.162 0.241 0.31 0.000539 0.000693 Wall time: 14888.101934347767 ! Best model 96 0.419 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 86 0.414 0.0172 0.0703 0.121 0.16 0.255 0.323 0.00057 0.000721 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 0.752 0.0145 0.462 0.11 0.147 0.825 0.828 0.00184 0.00185 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 15042.965 0.005 0.0166 0.197 0.528 0.118 0.157 0.432 0.54 0.000964 0.00121 ! Validation 97 15042.965 0.005 0.0177 0.382 0.737 0.123 0.162 0.693 0.753 0.00155 0.00168 Wall time: 15042.965259647928 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 86 0.651 0.0171 0.309 0.12 0.159 0.615 0.677 0.00137 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 0.508 0.0143 0.222 0.11 0.146 0.571 0.574 0.00128 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 98 15197.823 0.005 0.0167 0.282 0.615 0.119 0.157 0.537 0.647 0.0012 0.00144 ! Validation 98 15197.823 0.005 0.0176 0.2 0.552 0.123 0.162 0.473 0.545 0.00106 0.00122 Wall time: 15197.823662979063 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 86 0.42 0.016 0.101 0.116 0.154 0.315 0.387 0.000703 0.000864 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 0.296 0.0139 0.0181 0.108 0.144 0.148 0.164 0.000329 0.000366 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 99 15352.768 0.005 0.0161 0.134 0.457 0.117 0.155 0.362 0.447 0.000809 0.000997 ! Validation 99 15352.768 0.005 0.0171 0.129 0.47 0.121 0.159 0.346 0.437 0.000773 0.000975 Wall time: 15352.768729194999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 86 0.376 0.0159 0.0584 0.116 0.154 0.229 0.295 0.00051 0.000657 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 0.306 0.0135 0.0353 0.107 0.142 0.22 0.229 0.000492 0.000511 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 15507.646 0.005 0.0159 0.136 0.454 0.116 0.154 0.354 0.449 0.000791 0.001 ! Validation 100 15507.646 0.005 0.0168 0.0627 0.398 0.12 0.158 0.241 0.305 0.000539 0.000681 Wall time: 15507.646303304005 ! Best model 100 0.398 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 86 0.614 0.0155 0.303 0.114 0.152 0.592 0.671 0.00132 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 0.27 0.0133 0.00343 0.106 0.141 0.061 0.0714 0.000136 0.000159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 101 15662.527 0.005 0.0155 0.119 0.428 0.114 0.152 0.332 0.42 0.000741 0.000937 ! Validation 101 15662.527 0.005 0.0165 0.109 0.439 0.119 0.157 0.313 0.401 0.000698 0.000896 Wall time: 15662.527874221094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 86 0.457 0.0153 0.152 0.114 0.151 0.403 0.475 0.0009 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 0.411 0.0134 0.142 0.107 0.141 0.455 0.46 0.00102 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 102 15817.709 0.005 0.0156 0.219 0.531 0.115 0.152 0.456 0.57 0.00102 0.00127 ! Validation 102 15817.709 0.005 0.0167 0.108 0.442 0.12 0.157 0.33 0.4 0.000736 0.000892 Wall time: 15817.709938725922 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 86 0.467 0.0163 0.142 0.117 0.155 0.377 0.459 0.000842 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 0.283 0.0133 0.0173 0.107 0.141 0.148 0.16 0.000329 0.000357 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 103 15972.813 0.005 0.0153 0.166 0.472 0.114 0.151 0.392 0.496 0.000874 0.00111 ! Validation 103 15972.813 0.005 0.0166 0.0918 0.424 0.119 0.157 0.292 0.369 0.000651 0.000824 Wall time: 15972.813284014817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 86 0.439 0.0151 0.136 0.113 0.15 0.368 0.449 0.000822 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 0.487 0.013 0.227 0.105 0.139 0.578 0.581 0.00129 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 104 16127.793 0.005 0.0153 0.186 0.492 0.114 0.151 0.432 0.525 0.000965 0.00117 ! Validation 104 16127.793 0.005 0.0163 0.207 0.533 0.118 0.156 0.479 0.554 0.00107 0.00124 Wall time: 16127.793446910102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 86 0.379 0.0154 0.0711 0.114 0.151 0.249 0.325 0.000556 0.000725 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 0.563 0.0128 0.306 0.105 0.138 0.673 0.674 0.0015 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 105 16282.759 0.005 0.0151 0.161 0.464 0.113 0.15 0.396 0.489 0.000884 0.00109 ! Validation 105 16282.759 0.005 0.016 0.283 0.603 0.117 0.154 0.589 0.649 0.00131 0.00145 Wall time: 16282.759465770796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 86 0.321 0.0135 0.0498 0.108 0.142 0.225 0.272 0.000502 0.000607 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 0.342 0.0126 0.0893 0.104 0.137 0.36 0.364 0.000805 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 106 16437.747 0.005 0.0152 0.182 0.486 0.113 0.15 0.406 0.52 0.000906 0.00116 ! Validation 106 16437.747 0.005 0.0158 0.0902 0.406 0.116 0.153 0.301 0.366 0.000671 0.000817 Wall time: 16437.74800948985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 86 0.478 0.0146 0.186 0.111 0.147 0.45 0.526 0.001 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 0.473 0.0128 0.217 0.104 0.138 0.564 0.567 0.00126 0.00127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 107 16592.733 0.005 0.0147 0.143 0.437 0.112 0.148 0.367 0.461 0.00082 0.00103 ! Validation 107 16592.733 0.005 0.0158 0.208 0.523 0.116 0.153 0.482 0.556 0.00108 0.00124 Wall time: 16592.733719523996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 86 0.481 0.0144 0.192 0.111 0.146 0.469 0.534 0.00105 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 0.257 0.0126 0.00372 0.104 0.137 0.0682 0.0743 0.000152 0.000166 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 108 16747.812 0.005 0.0146 0.152 0.444 0.111 0.147 0.384 0.475 0.000857 0.00106 ! Validation 108 16747.812 0.005 0.0156 0.0891 0.402 0.116 0.152 0.282 0.364 0.00063 0.000812 Wall time: 16747.81273511192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 86 0.345 0.0138 0.0691 0.108 0.143 0.248 0.32 0.000554 0.000715 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 0.327 0.0127 0.0736 0.104 0.137 0.322 0.331 0.000718 0.000738 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 109 16902.904 0.005 0.0146 0.172 0.463 0.111 0.147 0.407 0.505 0.000909 0.00113 ! Validation 109 16902.904 0.005 0.0156 0.0962 0.409 0.116 0.152 0.311 0.378 0.000694 0.000843 Wall time: 16902.904162622057 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 86 0.506 0.0139 0.228 0.108 0.144 0.528 0.582 0.00118 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 0.884 0.0124 0.636 0.103 0.136 0.97 0.972 0.00216 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 110 17058.259 0.005 0.0142 0.127 0.41 0.11 0.145 0.348 0.434 0.000777 0.000969 ! Validation 110 17058.259 0.005 0.0154 0.512 0.82 0.115 0.151 0.827 0.872 0.00185 0.00195 Wall time: 17058.259752077982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 86 0.449 0.0146 0.156 0.111 0.147 0.417 0.481 0.00093 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 0.338 0.0121 0.0952 0.102 0.134 0.371 0.376 0.000829 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 111 17213.122 0.005 0.0146 0.217 0.509 0.111 0.147 0.459 0.568 0.00103 0.00127 ! Validation 111 17213.122 0.005 0.0151 0.112 0.413 0.114 0.149 0.336 0.407 0.000751 0.000909 Wall time: 17213.122393200174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 86 0.594 0.0143 0.308 0.11 0.146 0.615 0.677 0.00137 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 0.468 0.012 0.229 0.101 0.133 0.58 0.583 0.00129 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 112 17367.984 0.005 0.0141 0.151 0.434 0.11 0.145 0.385 0.473 0.000859 0.00106 ! Validation 112 17367.984 0.005 0.015 0.22 0.52 0.113 0.149 0.506 0.572 0.00113 0.00128 Wall time: 17367.984125229996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 86 0.349 0.0145 0.0597 0.111 0.147 0.251 0.298 0.000559 0.000665 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 0.248 0.012 0.00786 0.101 0.134 0.0866 0.108 0.000193 0.000241 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 113 17522.864 0.005 0.0138 0.133 0.408 0.108 0.143 0.36 0.444 0.000803 0.000992 ! Validation 113 17522.864 0.005 0.0149 0.0588 0.356 0.113 0.149 0.229 0.295 0.00051 0.000659 Wall time: 17522.864651707 ! Best model 113 0.356 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 86 0.309 0.0132 0.0448 0.106 0.14 0.215 0.258 0.00048 0.000576 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 0.25 0.0117 0.0148 0.1 0.132 0.136 0.148 0.000304 0.000331 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 114 17677.791 0.005 0.0138 0.16 0.435 0.108 0.143 0.391 0.488 0.000873 0.00109 ! Validation 114 17677.791 0.005 0.0147 0.0977 0.391 0.112 0.148 0.3 0.381 0.000669 0.00085 Wall time: 17677.791366104037 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 86 0.629 0.0132 0.365 0.106 0.14 0.661 0.736 0.00148 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.581 0.0117 0.347 0.101 0.132 0.715 0.718 0.0016 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 115 17832.699 0.005 0.0137 0.19 0.465 0.108 0.143 0.422 0.531 0.000943 0.00119 ! Validation 115 17832.699 0.005 0.0148 0.4 0.697 0.113 0.148 0.69 0.771 0.00154 0.00172 Wall time: 17832.699961441103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 86 0.352 0.0134 0.0842 0.107 0.141 0.294 0.354 0.000656 0.000789 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 0.4 0.0116 0.168 0.0999 0.131 0.495 0.499 0.0011 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 116 17987.702 0.005 0.0136 0.155 0.427 0.108 0.142 0.392 0.479 0.000874 0.00107 ! Validation 116 17987.702 0.005 0.0144 0.135 0.424 0.111 0.146 0.388 0.448 0.000866 0.001 Wall time: 17987.702505203895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 86 0.311 0.0132 0.0466 0.106 0.14 0.212 0.263 0.000473 0.000587 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 0.234 0.0114 0.00628 0.0985 0.13 0.0706 0.0966 0.000158 0.000216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 117 18142.601 0.005 0.0135 0.184 0.454 0.107 0.141 0.427 0.523 0.000953 0.00117 ! Validation 117 18142.601 0.005 0.0143 0.058 0.343 0.111 0.145 0.234 0.293 0.000522 0.000655 Wall time: 18142.601710097864 ! Best model 117 0.343 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 86 0.379 0.0131 0.117 0.106 0.139 0.358 0.417 0.000799 0.000931 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 0.395 0.0113 0.168 0.0985 0.13 0.494 0.5 0.0011 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 118 18297.497 0.005 0.0131 0.108 0.37 0.106 0.14 0.322 0.4 0.000718 0.000893 ! Validation 118 18297.497 0.005 0.014 0.141 0.421 0.11 0.144 0.396 0.457 0.000884 0.00102 Wall time: 18297.497297709808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 86 0.318 0.013 0.0582 0.105 0.139 0.235 0.294 0.000524 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 0.26 0.0109 0.0418 0.0969 0.127 0.241 0.249 0.000538 0.000556 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 119 18452.383 0.005 0.0129 0.115 0.373 0.105 0.138 0.325 0.413 0.000726 0.000922 ! Validation 119 18452.383 0.005 0.0137 0.0919 0.366 0.109 0.143 0.305 0.369 0.00068 0.000824 Wall time: 18452.383972017094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 86 0.318 0.0131 0.0565 0.105 0.139 0.233 0.29 0.00052 0.000647 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.231 0.0112 0.00759 0.0976 0.129 0.0789 0.106 0.000176 0.000237 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 120 18607.271 0.005 0.0131 0.177 0.438 0.106 0.139 0.396 0.512 0.000885 0.00114 ! Validation 120 18607.271 0.005 0.0138 0.0513 0.328 0.109 0.143 0.217 0.276 0.000484 0.000616 Wall time: 18607.271556871943 ! Best model 120 0.328 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 86 0.368 0.013 0.107 0.105 0.139 0.31 0.399 0.000691 0.00089 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.696 0.0115 0.466 0.0995 0.13 0.829 0.832 0.00185 0.00186 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 121 18762.169 0.005 0.0127 0.14 0.394 0.104 0.137 0.358 0.456 0.000799 0.00102 ! Validation 121 18762.169 0.005 0.0142 0.559 0.843 0.111 0.145 0.837 0.911 0.00187 0.00203 Wall time: 18762.169831158128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 86 0.513 0.0128 0.257 0.104 0.138 0.558 0.618 0.00125 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 0.266 0.0107 0.0522 0.0959 0.126 0.269 0.278 0.000601 0.000622 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 122 18917.440 0.005 0.0127 0.146 0.399 0.104 0.137 0.376 0.465 0.00084 0.00104 ! Validation 122 18917.440 0.005 0.0136 0.14 0.411 0.108 0.142 0.389 0.456 0.000869 0.00102 Wall time: 18917.440419725142 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 86 0.3 0.0126 0.0482 0.104 0.137 0.225 0.267 0.000503 0.000597 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.27 0.0108 0.0545 0.0964 0.127 0.276 0.284 0.000617 0.000635 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 123 19072.440 0.005 0.0126 0.154 0.406 0.104 0.137 0.371 0.478 0.000829 0.00107 ! Validation 123 19072.440 0.005 0.0136 0.0908 0.362 0.108 0.142 0.304 0.367 0.000679 0.000819 Wall time: 19072.44061651919 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 86 0.299 0.0122 0.0541 0.102 0.135 0.237 0.283 0.00053 0.000632 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 0.545 0.0106 0.333 0.0959 0.126 0.7 0.703 0.00156 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 124 19227.431 0.005 0.0125 0.173 0.422 0.103 0.136 0.413 0.507 0.000923 0.00113 ! Validation 124 19227.431 0.005 0.0134 0.267 0.536 0.108 0.141 0.577 0.629 0.00129 0.0014 Wall time: 19227.431531845126 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 86 0.275 0.0118 0.0396 0.101 0.132 0.207 0.242 0.000462 0.000541 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 0.253 0.0103 0.0465 0.0942 0.124 0.254 0.263 0.000566 0.000586 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 19382.446 0.005 0.0123 0.116 0.362 0.102 0.135 0.339 0.415 0.000757 0.000927 ! Validation 125 19382.446 0.005 0.0131 0.127 0.388 0.106 0.139 0.364 0.434 0.000813 0.000969 Wall time: 19382.446412791964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 86 0.295 0.0117 0.0622 0.1 0.132 0.251 0.304 0.00056 0.000678 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.257 0.0103 0.0511 0.0939 0.124 0.265 0.275 0.000591 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 126 19537.961 0.005 0.0121 0.145 0.387 0.102 0.134 0.372 0.464 0.000831 0.00104 ! Validation 126 19537.961 0.005 0.013 0.0749 0.334 0.106 0.139 0.274 0.334 0.000612 0.000744 Wall time: 19537.962024440058 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 86 0.304 0.0115 0.0747 0.0998 0.131 0.283 0.333 0.000632 0.000743 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.205 0.0101 0.00328 0.0934 0.122 0.0572 0.0698 0.000128 0.000156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 127 19692.938 0.005 0.012 0.117 0.356 0.101 0.133 0.343 0.417 0.000765 0.000931 ! Validation 127 19692.938 0.005 0.0128 0.0533 0.31 0.105 0.138 0.222 0.281 0.000496 0.000628 Wall time: 19692.93889775593 ! Best model 127 0.310 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 86 0.288 0.0113 0.0612 0.099 0.13 0.257 0.302 0.000575 0.000673 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.275 0.00997 0.0754 0.0928 0.122 0.326 0.335 0.000729 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 128 19847.821 0.005 0.0119 0.121 0.358 0.101 0.133 0.331 0.424 0.000739 0.000946 ! Validation 128 19847.821 0.005 0.0126 0.0812 0.333 0.104 0.137 0.291 0.347 0.000649 0.000775 Wall time: 19847.821342693176 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 86 0.529 0.0119 0.291 0.101 0.133 0.616 0.657 0.00138 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.315 0.01 0.114 0.0932 0.122 0.405 0.412 0.000904 0.00092 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 129 20002.780 0.005 0.0117 0.149 0.383 0.0998 0.132 0.382 0.47 0.000852 0.00105 ! Validation 129 20002.780 0.005 0.0127 0.191 0.445 0.105 0.137 0.472 0.532 0.00105 0.00119 Wall time: 20002.78098947089 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 86 0.274 0.0114 0.0465 0.0983 0.13 0.204 0.263 0.000455 0.000586 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.246 0.00983 0.0496 0.0923 0.121 0.263 0.271 0.000588 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 130 20157.654 0.005 0.0117 0.13 0.363 0.0999 0.132 0.361 0.439 0.000806 0.00098 ! Validation 130 20157.654 0.005 0.0125 0.102 0.353 0.104 0.136 0.324 0.39 0.000724 0.00087 Wall time: 20157.65400627302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 86 0.303 0.0113 0.0773 0.0979 0.129 0.287 0.339 0.00064 0.000756 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 0.329 0.01 0.128 0.0932 0.122 0.429 0.436 0.000957 0.000973 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 20312.519 0.005 0.0116 0.142 0.374 0.0995 0.131 0.369 0.46 0.000823 0.00103 ! Validation 131 20312.519 0.005 0.0125 0.122 0.372 0.104 0.136 0.372 0.425 0.000829 0.000948 Wall time: 20312.51926462585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 86 0.257 0.0108 0.0424 0.0961 0.126 0.189 0.251 0.000421 0.00056 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.21 0.00985 0.0128 0.0922 0.121 0.124 0.138 0.000276 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 132 20467.374 0.005 0.0114 0.16 0.388 0.0988 0.13 0.402 0.487 0.000898 0.00109 ! Validation 132 20467.374 0.005 0.0124 0.0614 0.31 0.103 0.136 0.236 0.302 0.000527 0.000674 Wall time: 20467.374378005043 ! Best model 132 0.310 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 86 0.309 0.0114 0.0803 0.0988 0.13 0.274 0.345 0.000612 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.373 0.00946 0.184 0.0907 0.119 0.515 0.522 0.00115 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 133 20622.913 0.005 0.0112 0.0986 0.322 0.0977 0.129 0.31 0.383 0.000691 0.000854 ! Validation 133 20622.913 0.005 0.0121 0.249 0.491 0.102 0.134 0.544 0.608 0.00121 0.00136 Wall time: 20622.913070823066 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 86 0.341 0.0108 0.125 0.096 0.127 0.377 0.431 0.000842 0.000962 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.285 0.00937 0.0976 0.0898 0.118 0.371 0.381 0.000827 0.00085 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 134 20777.976 0.005 0.0112 0.143 0.368 0.0978 0.129 0.366 0.461 0.000817 0.00103 ! Validation 134 20777.976 0.005 0.0119 0.193 0.432 0.101 0.133 0.48 0.536 0.00107 0.0012 Wall time: 20777.976630863734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 86 0.328 0.0126 0.0752 0.104 0.137 0.269 0.334 0.0006 0.000746 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.606 0.0117 0.372 0.101 0.132 0.737 0.744 0.00165 0.00166 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 135 20933.108 0.005 0.0111 0.195 0.418 0.0974 0.128 0.417 0.539 0.00093 0.0012 ! Validation 135 20933.108 0.005 0.0141 0.505 0.787 0.11 0.145 0.818 0.866 0.00182 0.00193 Wall time: 20933.10846620379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 86 0.261 0.0109 0.0435 0.0965 0.127 0.207 0.254 0.000463 0.000567 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.19 0.00924 0.00517 0.0895 0.117 0.0645 0.0876 0.000144 0.000196 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 21088.122 0.005 0.0113 0.128 0.354 0.0982 0.13 0.343 0.435 0.000767 0.000972 ! Validation 136 21088.122 0.005 0.0117 0.0451 0.279 0.1 0.132 0.203 0.259 0.000452 0.000577 Wall time: 21088.122581635136 ! Best model 136 0.279 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 86 0.294 0.0104 0.0866 0.0941 0.124 0.305 0.358 0.000681 0.0008 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.329 0.00929 0.143 0.0898 0.117 0.453 0.461 0.00101 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 137 21243.473 0.005 0.0107 0.101 0.316 0.0957 0.126 0.313 0.388 0.000699 0.000866 ! Validation 137 21243.473 0.005 0.0118 0.207 0.443 0.101 0.132 0.491 0.555 0.0011 0.00124 Wall time: 21243.47326155193 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 86 0.369 0.0123 0.122 0.102 0.135 0.36 0.426 0.000804 0.000952 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.51 0.0101 0.308 0.0937 0.122 0.671 0.677 0.0015 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 138 21398.489 0.005 0.011 0.179 0.399 0.0969 0.128 0.423 0.516 0.000944 0.00115 ! Validation 138 21398.489 0.005 0.0127 0.301 0.554 0.105 0.137 0.602 0.669 0.00134 0.00149 Wall time: 21398.489693445154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 86 0.291 0.0103 0.084 0.0936 0.124 0.299 0.353 0.000667 0.000788 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.279 0.00881 0.102 0.0873 0.114 0.379 0.39 0.000846 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 139 21553.508 0.005 0.0107 0.0949 0.308 0.0954 0.126 0.298 0.375 0.000665 0.000838 ! Validation 139 21553.508 0.005 0.0113 0.0932 0.319 0.0985 0.129 0.321 0.372 0.000716 0.00083 Wall time: 21553.508671924006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 86 0.367 0.0108 0.15 0.096 0.127 0.411 0.472 0.000917 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.275 0.00902 0.0943 0.0883 0.116 0.362 0.374 0.000807 0.000835 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 140 21708.522 0.005 0.0104 0.113 0.32 0.0941 0.124 0.324 0.409 0.000722 0.000912 ! Validation 140 21708.522 0.005 0.0115 0.0851 0.314 0.0992 0.13 0.302 0.355 0.000674 0.000793 Wall time: 21708.522354782093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 86 0.266 0.00952 0.0757 0.0907 0.119 0.277 0.335 0.000619 0.000748 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 0.437 0.0086 0.265 0.0863 0.113 0.622 0.627 0.00139 0.0014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 141 21863.543 0.005 0.0103 0.109 0.315 0.0938 0.124 0.331 0.401 0.000739 0.000896 ! Validation 141 21863.543 0.005 0.0111 0.249 0.471 0.0977 0.128 0.556 0.608 0.00124 0.00136 Wall time: 21863.543687537778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 86 0.315 0.0106 0.103 0.0947 0.125 0.339 0.392 0.000757 0.000874 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.234 0.00853 0.0633 0.0863 0.113 0.294 0.307 0.000657 0.000684 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 142 22018.656 0.005 0.0102 0.102 0.306 0.0933 0.123 0.314 0.389 0.000701 0.000868 ! Validation 142 22018.656 0.005 0.011 0.0704 0.291 0.0973 0.128 0.271 0.323 0.000604 0.000722 Wall time: 22018.656745385844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 86 0.223 0.00962 0.0303 0.0909 0.119 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 143 100 0.329 0.00861 0.156 0.0865 0.113 0.471 0.482 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 143 22173.679 0.005 0.01 0.0969 0.297 0.0923 0.122 0.296 0.379 0.000661 0.000847 ! Validation 143 22173.679 0.005 0.011 0.196 0.416 0.0972 0.128 0.48 0.539 0.00107 0.0012 Wall time: 22173.679126827046 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 86 0.223 0.00958 0.0315 0.0906 0.119 0.174 0.216 0.000387 0.000482 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.239 0.00838 0.0716 0.0852 0.112 0.313 0.326 0.0007 0.000728 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 144 22328.703 0.005 0.0107 0.196 0.411 0.0956 0.126 0.423 0.539 0.000945 0.0012 ! Validation 144 22328.703 0.005 0.0109 0.122 0.339 0.0965 0.127 0.366 0.426 0.000818 0.00095 Wall time: 22328.703678784892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 86 0.255 0.0104 0.0478 0.0934 0.124 0.215 0.266 0.000481 0.000595 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.177 0.00827 0.0119 0.0847 0.111 0.113 0.133 0.000252 0.000297 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 22483.738 0.005 0.00982 0.0839 0.28 0.0914 0.121 0.284 0.353 0.000633 0.000788 ! Validation 145 22483.738 0.005 0.0107 0.0522 0.266 0.0957 0.126 0.217 0.278 0.000485 0.000621 Wall time: 22483.738953434862 ! Best model 145 0.266 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 86 0.219 0.00966 0.0256 0.0908 0.12 0.151 0.195 0.000338 0.000435 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.188 0.00848 0.0182 0.0855 0.112 0.143 0.164 0.000319 0.000367 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 146 22638.775 0.005 0.00978 0.103 0.298 0.0913 0.121 0.316 0.391 0.000705 0.000872 ! Validation 146 22638.775 0.005 0.0107 0.0427 0.257 0.0959 0.126 0.204 0.252 0.000456 0.000562 Wall time: 22638.77588318009 ! Best model 146 0.257 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 86 0.251 0.0106 0.0399 0.095 0.125 0.201 0.243 0.000449 0.000544 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.384 0.00836 0.216 0.0858 0.111 0.56 0.567 0.00125 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 147 22793.809 0.005 0.00958 0.0741 0.266 0.0903 0.119 0.267 0.332 0.000597 0.000741 ! Validation 147 22793.809 0.005 0.011 0.203 0.422 0.0973 0.128 0.505 0.549 0.00113 0.00123 Wall time: 22793.80923604779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 86 0.355 0.00856 0.184 0.0857 0.113 0.476 0.522 0.00106 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.21 0.00793 0.0509 0.0829 0.109 0.255 0.275 0.000569 0.000614 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 148 22948.948 0.005 0.00973 0.136 0.33 0.091 0.12 0.36 0.449 0.000803 0.001 ! Validation 148 22948.948 0.005 0.0104 0.0886 0.297 0.0944 0.124 0.288 0.363 0.000644 0.00081 Wall time: 22948.94832985988 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 86 0.221 0.00894 0.0417 0.0874 0.115 0.195 0.249 0.000436 0.000555 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.177 0.00774 0.0224 0.082 0.107 0.159 0.182 0.000354 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 149 23103.960 0.005 0.00933 0.0772 0.264 0.089 0.118 0.272 0.339 0.000607 0.000756 ! Validation 149 23103.960 0.005 0.0101 0.0451 0.247 0.0929 0.122 0.212 0.259 0.000472 0.000577 Wall time: 23103.960306424182 ! Best model 149 0.247 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 86 0.214 0.00931 0.0274 0.089 0.118 0.167 0.202 0.000373 0.00045 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.165 0.00781 0.00922 0.0826 0.108 0.104 0.117 0.000231 0.000261 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 150 23258.995 0.005 0.00957 0.114 0.305 0.0902 0.119 0.309 0.411 0.000689 0.000918 ! Validation 150 23258.995 0.005 0.0102 0.0348 0.239 0.0935 0.123 0.181 0.227 0.000404 0.000507 Wall time: 23258.995330357924 ! Best model 150 0.239 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 86 0.269 0.0094 0.0807 0.089 0.118 0.296 0.346 0.00066 0.000773 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.164 0.00778 0.00875 0.0821 0.107 0.102 0.114 0.000228 0.000254 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 23414.022 0.005 0.00956 0.168 0.359 0.0901 0.119 0.4 0.499 0.000893 0.00111 ! Validation 151 23414.022 0.005 0.0102 0.0382 0.242 0.0934 0.123 0.19 0.238 0.000425 0.000531 Wall time: 23414.022176031023 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 86 0.226 0.00912 0.0433 0.0882 0.116 0.204 0.254 0.000456 0.000566 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.216 0.00749 0.0664 0.0807 0.105 0.297 0.314 0.000662 0.000701 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 23569.035 0.005 0.00908 0.0675 0.249 0.0878 0.116 0.255 0.317 0.000569 0.000707 ! Validation 152 23569.035 0.005 0.00989 0.0878 0.286 0.092 0.121 0.31 0.361 0.000692 0.000806 Wall time: 23569.03522408195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 86 0.281 0.00923 0.0963 0.0883 0.117 0.329 0.378 0.000734 0.000844 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.248 0.00794 0.089 0.083 0.109 0.35 0.363 0.000781 0.000811 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 23724.051 0.005 0.00918 0.126 0.31 0.0882 0.117 0.356 0.433 0.000795 0.000967 ! Validation 153 23724.051 0.005 0.0102 0.132 0.337 0.0936 0.123 0.393 0.443 0.000877 0.00099 Wall time: 23724.051871486008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 86 0.212 0.00907 0.0303 0.0872 0.116 0.175 0.212 0.000391 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.18 0.00739 0.0319 0.0802 0.105 0.195 0.218 0.000436 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 154 23879.204 0.005 0.00893 0.0861 0.265 0.087 0.115 0.286 0.358 0.000639 0.000798 ! Validation 154 23879.204 0.005 0.00975 0.0609 0.256 0.0912 0.12 0.25 0.301 0.000559 0.000671 Wall time: 23879.20455606701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 86 0.442 0.00989 0.244 0.0914 0.121 0.543 0.601 0.00121 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.178 0.00768 0.0241 0.0815 0.107 0.169 0.189 0.000377 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 155 24034.234 0.005 0.00891 0.114 0.292 0.0869 0.115 0.312 0.411 0.000697 0.000918 ! Validation 155 24034.234 0.005 0.00997 0.063 0.263 0.0923 0.122 0.257 0.306 0.000574 0.000683 Wall time: 24034.234810987953 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 86 0.225 0.00908 0.0435 0.0878 0.116 0.201 0.254 0.000448 0.000567 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.169 0.00747 0.0196 0.0806 0.105 0.149 0.171 0.000333 0.000381 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 156 24189.486 0.005 0.00904 0.147 0.328 0.0875 0.116 0.383 0.468 0.000854 0.00104 ! Validation 156 24189.486 0.005 0.00985 0.0457 0.243 0.0918 0.121 0.215 0.26 0.000479 0.000581 Wall time: 24189.486885590013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 86 0.227 0.00816 0.0639 0.0832 0.11 0.254 0.308 0.000568 0.000688 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.213 0.00728 0.0671 0.0795 0.104 0.296 0.316 0.000661 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 157 24344.494 0.005 0.00875 0.0834 0.258 0.0861 0.114 0.286 0.352 0.000638 0.000786 ! Validation 157 24344.494 0.005 0.00958 0.0764 0.268 0.0904 0.119 0.284 0.337 0.000633 0.000752 Wall time: 24344.494771197904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 86 0.271 0.00879 0.0954 0.0868 0.114 0.333 0.376 0.000744 0.00084 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 0.151 0.00718 0.00721 0.079 0.103 0.091 0.103 0.000203 0.000231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 158 24499.515 0.005 0.00876 0.11 0.285 0.0861 0.114 0.331 0.404 0.00074 0.000902 ! Validation 158 24499.515 0.005 0.00954 0.0417 0.233 0.0902 0.119 0.195 0.249 0.000436 0.000555 Wall time: 24499.515403951053 ! Best model 158 0.233 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 86 0.199 0.00792 0.0408 0.0821 0.108 0.209 0.246 0.000467 0.00055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 0.19 0.00719 0.0463 0.0791 0.103 0.243 0.262 0.000543 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 159 24654.522 0.005 0.00848 0.0746 0.244 0.0847 0.112 0.268 0.333 0.000598 0.000743 ! Validation 159 24654.522 0.005 0.00955 0.0529 0.244 0.0901 0.119 0.233 0.28 0.000519 0.000626 Wall time: 24654.52218349604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 86 0.221 0.00825 0.0563 0.0837 0.111 0.241 0.289 0.000537 0.000646 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 0.182 0.00704 0.0415 0.0782 0.102 0.223 0.248 0.000497 0.000554 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 160 24809.525 0.005 0.00847 0.0921 0.261 0.0846 0.112 0.286 0.37 0.000637 0.000825 ! Validation 160 24809.525 0.005 0.00925 0.0665 0.252 0.0888 0.117 0.252 0.314 0.000562 0.000702 Wall time: 24809.525819066912 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 86 0.255 0.00775 0.1 0.0809 0.107 0.35 0.386 0.000782 0.000862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.249 0.0068 0.113 0.077 0.1 0.396 0.409 0.000884 0.000914 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 24964.631 0.005 0.00844 0.101 0.269 0.0845 0.112 0.313 0.386 0.000698 0.000863 ! Validation 161 24964.631 0.005 0.00907 0.14 0.322 0.0879 0.116 0.403 0.457 0.000901 0.00102 Wall time: 24964.63200520305 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 86 0.214 0.00789 0.056 0.0815 0.108 0.256 0.288 0.000572 0.000644 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 0.148 0.00674 0.013 0.0765 0.1 0.126 0.139 0.000282 0.00031 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 25119.638 0.005 0.00826 0.0746 0.24 0.0835 0.111 0.267 0.333 0.000597 0.000743 ! Validation 162 25119.638 0.005 0.00899 0.049 0.229 0.0874 0.116 0.225 0.27 0.000501 0.000602 Wall time: 25119.638610535767 ! Best model 162 0.229 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 86 0.255 0.00841 0.087 0.0843 0.112 0.308 0.359 0.000688 0.000802 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 0.185 0.00702 0.0444 0.078 0.102 0.235 0.257 0.000524 0.000573 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 25274.651 0.005 0.00846 0.114 0.283 0.0845 0.112 0.334 0.411 0.000745 0.000918 ! Validation 163 25274.651 0.005 0.00915 0.0723 0.255 0.0883 0.117 0.282 0.328 0.00063 0.000731 Wall time: 25274.651943047065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 86 0.184 0.00789 0.026 0.0818 0.108 0.146 0.196 0.000325 0.000438 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 0.139 0.00665 0.00638 0.0761 0.0993 0.0812 0.0973 0.000181 0.000217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 164 25429.654 0.005 0.0082 0.0997 0.264 0.0832 0.11 0.295 0.385 0.000658 0.000859 ! Validation 164 25429.654 0.005 0.00891 0.0309 0.209 0.0871 0.115 0.169 0.214 0.000377 0.000478 Wall time: 25429.65499898698 ! Best model 164 0.209 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 86 0.211 0.00828 0.0452 0.0836 0.111 0.216 0.259 0.000483 0.000578 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 0.169 0.00683 0.0322 0.0771 0.101 0.191 0.219 0.000427 0.000488 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 165 25584.658 0.005 0.008 0.0734 0.233 0.0821 0.109 0.266 0.33 0.000594 0.000737 ! Validation 165 25584.658 0.005 0.00895 0.0514 0.231 0.0873 0.115 0.232 0.276 0.000518 0.000617 Wall time: 25584.658173060045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 86 0.265 0.00776 0.11 0.0813 0.107 0.362 0.404 0.000808 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 0.161 0.0068 0.0251 0.0769 0.101 0.166 0.193 0.00037 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 166 25739.664 0.005 0.00818 0.13 0.294 0.0831 0.11 0.364 0.44 0.000812 0.000981 ! Validation 166 25739.664 0.005 0.00903 0.0757 0.256 0.0876 0.116 0.286 0.335 0.000639 0.000748 Wall time: 25739.66466736188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 86 0.199 0.00861 0.0264 0.0851 0.113 0.164 0.198 0.000367 0.000442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 0.199 0.00682 0.0625 0.077 0.101 0.291 0.305 0.000648 0.00068 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 25894.764 0.005 0.00785 0.0674 0.224 0.0813 0.108 0.242 0.316 0.000539 0.000706 ! Validation 167 25894.764 0.005 0.00905 0.08 0.261 0.0878 0.116 0.291 0.345 0.00065 0.000769 Wall time: 25894.76433382416 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 86 0.458 0.0081 0.296 0.0821 0.11 0.628 0.663 0.0014 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 0.207 0.00679 0.071 0.077 0.1 0.309 0.325 0.000691 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 168 26049.775 0.005 0.00803 0.129 0.289 0.0823 0.109 0.362 0.437 0.000809 0.000975 ! Validation 168 26049.775 0.005 0.00901 0.101 0.282 0.0875 0.116 0.337 0.388 0.000753 0.000866 Wall time: 26049.775283951778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 86 0.179 0.00729 0.0334 0.0786 0.104 0.178 0.223 0.000397 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 0.168 0.00644 0.0388 0.075 0.0978 0.223 0.24 0.000498 0.000536 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 169 26204.781 0.005 0.00807 0.122 0.284 0.0825 0.109 0.347 0.426 0.000774 0.000951 ! Validation 169 26204.781 0.005 0.00865 0.0889 0.262 0.0858 0.113 0.309 0.363 0.00069 0.000811 Wall time: 26204.781907844823 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 86 0.314 0.00726 0.169 0.0784 0.104 0.461 0.501 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 170 100 0.3 0.00659 0.168 0.0756 0.0989 0.49 0.499 0.00109 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 170 26359.782 0.005 0.00773 0.0794 0.234 0.0807 0.107 0.28 0.343 0.000625 0.000766 ! Validation 170 26359.782 0.005 0.00881 0.213 0.389 0.0866 0.114 0.514 0.562 0.00115 0.00126 Wall time: 26359.782882283907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 86 0.179 0.00706 0.038 0.0779 0.102 0.2 0.238 0.000447 0.00053 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 0.158 0.00639 0.0302 0.0748 0.0974 0.188 0.212 0.000421 0.000473 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 171 26514.775 0.005 0.00765 0.0449 0.198 0.0803 0.107 0.206 0.258 0.00046 0.000576 ! Validation 171 26514.775 0.005 0.00859 0.0398 0.211 0.0856 0.113 0.204 0.243 0.000455 0.000542 Wall time: 26514.775503823068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 86 0.22 0.00731 0.0736 0.0787 0.104 0.293 0.331 0.000654 0.000738 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 0.134 0.00623 0.00994 0.0736 0.0961 0.0888 0.121 0.000198 0.000271 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 172 26669.769 0.005 0.00766 0.0964 0.25 0.0803 0.107 0.302 0.378 0.000674 0.000844 ! Validation 172 26669.769 0.005 0.00835 0.0376 0.205 0.0842 0.111 0.188 0.236 0.000419 0.000528 Wall time: 26669.76993044 ! Best model 172 0.205 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 86 0.246 0.00773 0.0915 0.0805 0.107 0.329 0.369 0.000734 0.000823 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 0.305 0.00625 0.18 0.0738 0.0964 0.505 0.517 0.00113 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 173 26824.820 0.005 0.00749 0.0603 0.21 0.0794 0.105 0.243 0.299 0.000543 0.000668 ! Validation 173 26824.820 0.005 0.00836 0.222 0.389 0.0843 0.111 0.536 0.574 0.0012 0.00128 Wall time: 26824.820107949898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 86 0.206 0.00762 0.0537 0.08 0.106 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 174 100 0.16 0.00623 0.0354 0.0736 0.0961 0.2 0.229 0.000446 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 174 26979.924 0.005 0.00762 0.092 0.244 0.0801 0.106 0.297 0.37 0.000664 0.000825 ! Validation 174 26979.924 0.005 0.0083 0.0725 0.239 0.084 0.111 0.279 0.328 0.000622 0.000732 Wall time: 26979.924098840915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 86 0.23 0.00932 0.0432 0.0881 0.118 0.21 0.253 0.00047 0.000565 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 0.269 0.00717 0.126 0.0789 0.103 0.421 0.432 0.000939 0.000964 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 27134.927 0.005 0.00746 0.107 0.257 0.0792 0.105 0.324 0.4 0.000723 0.000892 ! Validation 175 27134.927 0.005 0.00908 0.102 0.283 0.0878 0.116 0.34 0.388 0.000758 0.000867 Wall time: 27134.927789721172 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 86 0.168 0.00733 0.0215 0.0784 0.104 0.144 0.179 0.000322 0.000398 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 0.135 0.00601 0.0148 0.0723 0.0945 0.133 0.148 0.000298 0.000331 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 176 27289.914 0.005 0.00748 0.0721 0.222 0.0793 0.105 0.253 0.327 0.000565 0.000731 ! Validation 176 27289.914 0.005 0.0081 0.0417 0.204 0.0828 0.11 0.207 0.249 0.000462 0.000555 Wall time: 27289.914719522 ! Best model 176 0.204 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 86 0.175 0.00721 0.0313 0.0783 0.103 0.184 0.215 0.000412 0.000481 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 0.287 0.00628 0.162 0.074 0.0965 0.481 0.49 0.00107 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 177 27444.919 0.005 0.00735 0.105 0.252 0.0786 0.104 0.289 0.394 0.000646 0.00088 ! Validation 177 27444.919 0.005 0.00847 0.241 0.41 0.0849 0.112 0.554 0.598 0.00124 0.00134 Wall time: 27444.920005028136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 86 0.164 0.00713 0.0215 0.0772 0.103 0.134 0.179 0.000299 0.000399 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 0.162 0.00606 0.0406 0.0727 0.0949 0.227 0.245 0.000508 0.000548 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 27599.924 0.005 0.00747 0.0841 0.233 0.0793 0.105 0.284 0.353 0.000633 0.000789 ! Validation 178 27599.924 0.005 0.00813 0.0732 0.236 0.083 0.11 0.286 0.33 0.000639 0.000736 Wall time: 27599.92437101109 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 86 0.211 0.00703 0.0706 0.077 0.102 0.292 0.324 0.000653 0.000723 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 0.146 0.00621 0.0213 0.0736 0.096 0.157 0.178 0.000349 0.000397 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 179 27754.910 0.005 0.00722 0.0683 0.213 0.0779 0.104 0.255 0.318 0.000569 0.000711 ! Validation 179 27754.910 0.005 0.00823 0.048 0.213 0.0835 0.111 0.225 0.267 0.000503 0.000596 Wall time: 27754.910679631867 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 86 0.161 0.00707 0.0196 0.0767 0.102 0.138 0.171 0.000308 0.000381 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 0.124 0.0058 0.0084 0.0711 0.0928 0.0988 0.112 0.000221 0.000249 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 27910.455 0.005 0.00728 0.0974 0.243 0.0783 0.104 0.292 0.38 0.000652 0.000849 ! Validation 180 27910.455 0.005 0.00782 0.0313 0.188 0.0814 0.108 0.177 0.216 0.000395 0.000481 Wall time: 27910.455811324995 ! Best model 180 0.188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 86 0.169 0.00721 0.0248 0.0778 0.103 0.155 0.192 0.000346 0.000429 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 0.129 0.00587 0.0115 0.0714 0.0933 0.114 0.131 0.000254 0.000292 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 28065.445 0.005 0.00722 0.104 0.248 0.0779 0.104 0.31 0.393 0.000692 0.000877 ! Validation 181 28065.445 0.005 0.00794 0.0344 0.193 0.0821 0.109 0.19 0.226 0.000424 0.000504 Wall time: 28065.44513511611 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 86 0.168 0.00687 0.0307 0.0765 0.101 0.18 0.213 0.000403 0.000476 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 0.162 0.00579 0.0463 0.071 0.0927 0.243 0.262 0.000542 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 182 28220.406 0.005 0.0071 0.0711 0.213 0.0772 0.103 0.267 0.325 0.000596 0.000725 ! Validation 182 28220.406 0.005 0.00785 0.0688 0.226 0.0816 0.108 0.279 0.319 0.000622 0.000713 Wall time: 28220.40642453404 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 86 0.158 0.00679 0.0226 0.0753 0.1 0.151 0.183 0.000337 0.000409 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 0.16 0.00564 0.0473 0.0701 0.0915 0.25 0.265 0.000559 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 183 28375.378 0.005 0.00714 0.0776 0.22 0.0774 0.103 0.261 0.339 0.000583 0.000758 ! Validation 183 28375.378 0.005 0.00762 0.0688 0.221 0.0803 0.106 0.28 0.32 0.000624 0.000713 Wall time: 28375.37854432175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 86 0.748 0.00816 0.585 0.0824 0.11 0.909 0.932 0.00203 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 0.65 0.00724 0.505 0.0792 0.104 0.857 0.866 0.00191 0.00193 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 184 28530.350 0.005 0.00693 0.1 0.239 0.0763 0.101 0.304 0.385 0.000678 0.00086 ! Validation 184 28530.350 0.005 0.00924 0.737 0.922 0.0884 0.117 1.01 1.05 0.00226 0.00233 Wall time: 28530.350160119124 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 86 0.146 0.0065 0.0155 0.0741 0.0983 0.121 0.152 0.00027 0.000338 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 0.122 0.00562 0.00931 0.0699 0.0913 0.084 0.118 0.000187 0.000262 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 185 28685.323 0.005 0.00728 0.0767 0.222 0.0782 0.104 0.249 0.338 0.000555 0.000754 ! Validation 185 28685.323 0.005 0.00757 0.0266 0.178 0.08 0.106 0.159 0.199 0.000355 0.000443 Wall time: 28685.32353238389 ! Best model 185 0.178 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 86 0.181 0.00618 0.0572 0.0726 0.0958 0.236 0.291 0.000528 0.00065 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 0.214 0.00556 0.102 0.0698 0.0909 0.38 0.39 0.000847 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 186 28840.290 0.005 0.00681 0.0436 0.18 0.0756 0.101 0.202 0.254 0.00045 0.000568 ! Validation 186 28840.290 0.005 0.00757 0.12 0.272 0.0801 0.106 0.378 0.422 0.000844 0.000943 Wall time: 28840.290958784055 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 86 0.18 0.00703 0.0395 0.0767 0.102 0.203 0.242 0.000454 0.00054 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 0.117 0.00553 0.00652 0.0693 0.0906 0.0836 0.0984 0.000187 0.00022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 187 28995.404 0.005 0.00685 0.0746 0.212 0.0758 0.101 0.27 0.333 0.000603 0.000743 ! Validation 187 28995.404 0.005 0.00756 0.0282 0.179 0.08 0.106 0.159 0.204 0.000354 0.000456 Wall time: 28995.40505517507 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 86 0.33 0.00665 0.197 0.0744 0.0993 0.518 0.541 0.00116 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.297 0.00573 0.183 0.0712 0.0923 0.516 0.521 0.00115 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 188 29150.370 0.005 0.00671 0.062 0.196 0.075 0.0998 0.242 0.303 0.00054 0.000677 ! Validation 188 29150.370 0.005 0.00772 0.203 0.357 0.0809 0.107 0.513 0.549 0.00115 0.00122 Wall time: 29150.37094747182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 86 0.508 0.00703 0.367 0.0769 0.102 0.719 0.739 0.00161 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.213 0.00581 0.0971 0.0713 0.0929 0.37 0.38 0.000826 0.000847 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 29305.341 0.005 0.00688 0.125 0.263 0.076 0.101 0.353 0.431 0.000788 0.000962 ! Validation 189 29305.341 0.005 0.00775 0.126 0.281 0.0811 0.107 0.376 0.432 0.000839 0.000965 Wall time: 29305.341273379978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 86 0.218 0.00616 0.0948 0.072 0.0957 0.329 0.375 0.000735 0.000837 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.135 0.00537 0.0274 0.0684 0.0893 0.184 0.202 0.000411 0.00045 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 29460.317 0.005 0.00676 0.0708 0.206 0.0753 0.1 0.261 0.324 0.000582 0.000724 ! Validation 190 29460.317 0.005 0.00736 0.0395 0.187 0.0789 0.105 0.205 0.242 0.000458 0.00054 Wall time: 29460.317842205055 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 86 0.155 0.00644 0.0262 0.0737 0.0977 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 191 100 0.134 0.00552 0.0236 0.0696 0.0905 0.162 0.187 0.000362 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 191 29615.295 0.005 0.00654 0.053 0.184 0.074 0.0985 0.222 0.281 0.000496 0.000626 ! Validation 191 29615.295 0.005 0.00746 0.042 0.191 0.0795 0.105 0.21 0.25 0.000469 0.000557 Wall time: 29615.295093202032 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 86 0.155 0.00629 0.0295 0.0729 0.0967 0.174 0.209 0.000388 0.000467 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.148 0.00555 0.0369 0.0695 0.0908 0.217 0.234 0.000484 0.000522 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 29770.285 0.005 0.0067 0.0986 0.233 0.075 0.0997 0.319 0.383 0.000711 0.000854 ! Validation 192 29770.285 0.005 0.00752 0.0477 0.198 0.0799 0.106 0.208 0.266 0.000465 0.000594 Wall time: 29770.285758379847 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 86 0.199 0.00664 0.0659 0.0746 0.0993 0.265 0.313 0.000591 0.000698 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.118 0.00555 0.00737 0.0696 0.0907 0.0923 0.105 0.000206 0.000233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 193 29925.416 0.005 0.00673 0.0852 0.22 0.0752 0.0999 0.295 0.356 0.000658 0.000794 ! Validation 193 29925.416 0.005 0.00744 0.0407 0.19 0.0793 0.105 0.205 0.246 0.000457 0.000549 Wall time: 29925.416275253054 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 86 0.197 0.0065 0.0672 0.074 0.0983 0.276 0.316 0.000616 0.000705 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.197 0.00553 0.0862 0.0694 0.0906 0.349 0.358 0.000779 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 194 30080.409 0.005 0.0066 0.0751 0.207 0.0744 0.099 0.267 0.334 0.000596 0.000746 ! Validation 194 30080.409 0.005 0.00737 0.122 0.269 0.079 0.105 0.385 0.425 0.000859 0.000948 Wall time: 30080.4093408701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 86 0.154 0.00663 0.0212 0.0744 0.0992 0.141 0.178 0.000316 0.000396 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.109 0.0052 0.00529 0.0675 0.0878 0.0803 0.0886 0.000179 0.000198 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 195 30235.402 0.005 0.00643 0.0443 0.173 0.0734 0.0977 0.208 0.256 0.000465 0.000573 ! Validation 195 30235.402 0.005 0.00702 0.0247 0.165 0.077 0.102 0.158 0.191 0.000353 0.000427 Wall time: 30235.402334863786 ! Best model 195 0.165 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 86 0.151 0.00604 0.0302 0.0712 0.0947 0.178 0.212 0.000397 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.118 0.0054 0.0102 0.0688 0.0895 0.111 0.123 0.000247 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 196 30390.400 0.005 0.00666 0.0902 0.223 0.0747 0.0994 0.283 0.366 0.000633 0.000817 ! Validation 196 30390.400 0.005 0.00716 0.0299 0.173 0.0778 0.103 0.177 0.211 0.000394 0.00047 Wall time: 30390.400249598082 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 86 0.164 0.0065 0.0345 0.0733 0.0982 0.18 0.226 0.000402 0.000505 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.157 0.0058 0.0407 0.0711 0.0928 0.237 0.246 0.00053 0.000549 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 30545.388 0.005 0.00642 0.0683 0.197 0.0733 0.0976 0.261 0.318 0.000583 0.000711 ! Validation 197 30545.388 0.005 0.00766 0.0872 0.24 0.0805 0.107 0.309 0.36 0.00069 0.000803 Wall time: 30545.388518644962 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 86 0.164 0.00639 0.036 0.0732 0.0974 0.191 0.231 0.000427 0.000516 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.211 0.00524 0.106 0.0678 0.0882 0.386 0.397 0.000862 0.000886 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 198 30700.376 0.005 0.00642 0.0842 0.213 0.0733 0.0976 0.276 0.354 0.000617 0.000789 ! Validation 198 30700.376 0.005 0.00711 0.167 0.309 0.0775 0.103 0.451 0.498 0.00101 0.00111 Wall time: 30700.37684885692 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 86 0.171 0.00664 0.0381 0.0747 0.0993 0.204 0.238 0.000455 0.000531 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.111 0.00528 0.00563 0.0679 0.0885 0.0809 0.0914 0.000181 0.000204 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 30855.372 0.005 0.00627 0.0585 0.184 0.0725 0.0965 0.234 0.295 0.000522 0.000658 ! Validation 199 30855.372 0.005 0.00718 0.0278 0.171 0.0781 0.103 0.167 0.203 0.000374 0.000454 Wall time: 30855.372398884967 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 86 0.17 0.00665 0.0366 0.0736 0.0993 0.19 0.233 0.000424 0.00052 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.104 0.00498 0.00427 0.066 0.086 0.0662 0.0797 0.000148 0.000178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 200 31010.472 0.005 0.00624 0.0542 0.179 0.0723 0.0963 0.225 0.284 0.000503 0.000633 ! Validation 200 31010.472 0.005 0.00683 0.0225 0.159 0.0759 0.101 0.148 0.183 0.000331 0.000408 Wall time: 31010.47271494707 ! Best model 200 0.159 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 86 0.196 0.00682 0.0592 0.0759 0.101 0.25 0.297 0.000558 0.000662 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.123 0.00539 0.015 0.0688 0.0895 0.131 0.149 0.000292 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 201 31165.494 0.005 0.00628 0.0737 0.199 0.0725 0.0965 0.257 0.331 0.000573 0.000738 ! Validation 201 31165.494 0.005 0.00726 0.0994 0.245 0.0785 0.104 0.332 0.384 0.000742 0.000857 Wall time: 31165.494968562853 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 86 0.172 0.00616 0.0484 0.0717 0.0956 0.224 0.268 0.0005 0.000598 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.151 0.00493 0.0525 0.0656 0.0856 0.267 0.279 0.000597 0.000623 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 31320.491 0.005 0.00625 0.0499 0.175 0.0724 0.0963 0.218 0.272 0.000487 0.000608 ! Validation 202 31320.491 0.005 0.00675 0.0686 0.204 0.0754 0.1 0.279 0.319 0.000622 0.000712 Wall time: 31320.491720215883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 86 0.187 0.00605 0.0657 0.0712 0.0948 0.286 0.312 0.000638 0.000697 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.159 0.00502 0.0585 0.0663 0.0863 0.284 0.295 0.000633 0.000658 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 203 31475.849 0.005 0.00607 0.0501 0.172 0.0713 0.095 0.219 0.273 0.000488 0.000609 ! Validation 203 31475.849 0.005 0.00672 0.098 0.232 0.0754 0.0999 0.346 0.381 0.000771 0.000851 Wall time: 31475.849235023838 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 86 0.173 0.00639 0.0454 0.0733 0.0974 0.214 0.26 0.000478 0.00058 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.155 0.00511 0.0524 0.0666 0.0871 0.267 0.279 0.000597 0.000623 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 31630.823 0.005 0.00644 0.127 0.256 0.0734 0.0977 0.343 0.435 0.000766 0.000971 ! Validation 204 31630.823 0.005 0.00682 0.0571 0.193 0.0759 0.101 0.24 0.291 0.000536 0.00065 Wall time: 31630.823346645106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 86 0.183 0.00647 0.0536 0.0736 0.098 0.241 0.282 0.000538 0.00063 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.135 0.00505 0.0336 0.0665 0.0866 0.203 0.223 0.000454 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 205 31785.805 0.005 0.00627 0.0935 0.219 0.0725 0.0965 0.307 0.373 0.000685 0.000832 ! Validation 205 31785.805 0.005 0.00686 0.0434 0.181 0.0761 0.101 0.196 0.254 0.000436 0.000566 Wall time: 31785.805148156825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 86 0.128 0.00562 0.0156 0.0689 0.0913 0.123 0.152 0.000275 0.000339 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.11 0.00478 0.0148 0.0646 0.0842 0.117 0.148 0.000262 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 206 31940.895 0.005 0.00598 0.033 0.153 0.0707 0.0942 0.176 0.221 0.000393 0.000494 ! Validation 206 31940.895 0.005 0.00655 0.0232 0.154 0.0743 0.0986 0.146 0.186 0.000326 0.000414 Wall time: 31940.895272584166 ! Best model 206 0.154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 86 0.177 0.0051 0.0753 0.0663 0.087 0.289 0.334 0.000644 0.000746 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.28 0.00488 0.182 0.0652 0.0851 0.514 0.52 0.00115 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 207 32095.895 0.005 0.00597 0.0636 0.183 0.0707 0.0942 0.252 0.307 0.000561 0.000686 ! Validation 207 32095.895 0.005 0.00672 0.174 0.309 0.0753 0.0999 0.469 0.509 0.00105 0.00114 Wall time: 32095.895729774144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 86 0.159 0.00609 0.037 0.0713 0.0951 0.187 0.234 0.000418 0.000523 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.189 0.00491 0.0912 0.0655 0.0853 0.359 0.368 0.000801 0.000821 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 208 32250.877 0.005 0.00616 0.0863 0.21 0.0719 0.0956 0.296 0.358 0.00066 0.000799 ! Validation 208 32250.877 0.005 0.00676 0.101 0.237 0.0756 0.1 0.343 0.388 0.000766 0.000866 Wall time: 32250.877655280754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 86 0.135 0.00567 0.022 0.0692 0.0917 0.148 0.181 0.00033 0.000403 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.107 0.00491 0.00888 0.0654 0.0854 0.105 0.115 0.000235 0.000256 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 32405.871 0.005 0.00595 0.0566 0.176 0.0706 0.094 0.236 0.29 0.000526 0.000647 ! Validation 209 32405.871 0.005 0.00656 0.0336 0.165 0.0745 0.0987 0.19 0.223 0.000423 0.000499 Wall time: 32405.87141675502 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 86 0.307 0.00623 0.182 0.0725 0.0962 0.499 0.52 0.00111 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.15 0.00553 0.0394 0.0697 0.0906 0.232 0.242 0.000519 0.00054 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 210 32560.867 0.005 0.00591 0.0723 0.19 0.0703 0.0936 0.256 0.328 0.000571 0.000731 ! Validation 210 32560.867 0.005 0.00741 0.0557 0.204 0.0795 0.105 0.25 0.288 0.000558 0.000642 Wall time: 32560.867554848082 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 86 0.136 0.00582 0.019 0.0703 0.093 0.13 0.168 0.000289 0.000375 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.104 0.00482 0.00735 0.0648 0.0846 0.0933 0.104 0.000208 0.000233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 211 32716.368 0.005 0.00609 0.0816 0.203 0.0714 0.0951 0.288 0.348 0.000642 0.000777 ! Validation 211 32716.368 0.005 0.00656 0.0331 0.164 0.0743 0.0987 0.188 0.222 0.000421 0.000495 Wall time: 32716.36900151195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 86 0.177 0.00585 0.0595 0.0699 0.0932 0.259 0.297 0.000578 0.000664 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.147 0.00477 0.0511 0.0646 0.0841 0.263 0.276 0.000587 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 212 32871.228 0.005 0.00583 0.0648 0.181 0.0698 0.093 0.242 0.31 0.000541 0.000692 ! Validation 212 32871.228 0.005 0.0065 0.0515 0.182 0.074 0.0983 0.222 0.276 0.000496 0.000617 Wall time: 32871.228992530145 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 86 0.191 0.00589 0.0734 0.0703 0.0935 0.299 0.33 0.000667 0.000737 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.138 0.00479 0.0422 0.0646 0.0843 0.239 0.25 0.000533 0.000558 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 33026.165 0.005 0.00574 0.0468 0.162 0.0692 0.0923 0.211 0.263 0.000472 0.000588 ! Validation 213 33026.165 0.005 0.00653 0.0294 0.16 0.0742 0.0984 0.167 0.209 0.000373 0.000466 Wall time: 33026.16554061696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 86 0.162 0.00538 0.0542 0.0673 0.0893 0.242 0.284 0.000541 0.000633 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.122 0.00458 0.0302 0.0633 0.0824 0.196 0.212 0.000437 0.000473 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 214 33181.009 0.005 0.00569 0.0483 0.162 0.069 0.0919 0.208 0.268 0.000464 0.000598 ! Validation 214 33181.009 0.005 0.0063 0.0334 0.159 0.0728 0.0967 0.175 0.223 0.000391 0.000497 Wall time: 33181.00989474589 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 86 0.155 0.00571 0.0411 0.0691 0.0921 0.195 0.247 0.000435 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.179 0.00463 0.0868 0.0635 0.0829 0.352 0.359 0.000785 0.000801 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 33336.429 0.005 0.00585 0.0859 0.203 0.07 0.0932 0.285 0.357 0.000636 0.000797 ! Validation 215 33336.429 0.005 0.00637 0.142 0.269 0.0733 0.0972 0.426 0.459 0.000951 0.00103 Wall time: 33336.42959357379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 86 0.143 0.00527 0.0377 0.067 0.0885 0.2 0.237 0.000445 0.000528 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.102 0.00446 0.0125 0.0626 0.0814 0.117 0.136 0.00026 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 216 33491.404 0.005 0.00561 0.0375 0.15 0.0685 0.0913 0.187 0.236 0.000418 0.000527 ! Validation 216 33491.404 0.005 0.0062 0.0411 0.165 0.0723 0.0959 0.212 0.247 0.000473 0.000551 Wall time: 33491.40468462277 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 86 0.414 0.00649 0.285 0.0734 0.0982 0.608 0.65 0.00136 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.162 0.00474 0.0673 0.0643 0.0839 0.302 0.316 0.000674 0.000706 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 33646.444 0.005 0.00564 0.0781 0.191 0.0687 0.0915 0.267 0.34 0.000595 0.000759 ! Validation 217 33646.444 0.005 0.00655 0.0602 0.191 0.0744 0.0986 0.227 0.299 0.000506 0.000667 Wall time: 33646.44463344477 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 86 0.281 0.00525 0.176 0.0666 0.0883 0.494 0.511 0.0011 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.278 0.00483 0.182 0.0649 0.0846 0.511 0.519 0.00114 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 218 33801.651 0.005 0.00586 0.0651 0.182 0.07 0.0932 0.244 0.311 0.000545 0.000694 ! Validation 218 33801.651 0.005 0.00645 0.247 0.376 0.0738 0.0978 0.581 0.606 0.0013 0.00135 Wall time: 33801.6515775891 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 86 0.234 0.00523 0.13 0.0666 0.0881 0.401 0.438 0.000896 0.000979 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.0979 0.00462 0.00551 0.0637 0.0828 0.0866 0.0905 0.000193 0.000202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 219 33956.731 0.005 0.00559 0.0555 0.167 0.0684 0.0911 0.227 0.287 0.000506 0.00064 ! Validation 219 33956.731 0.005 0.00622 0.0255 0.15 0.0725 0.0961 0.16 0.194 0.000357 0.000434 Wall time: 33956.731465091 ! Best model 219 0.150 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 86 0.129 0.00526 0.0242 0.0664 0.0884 0.157 0.19 0.00035 0.000423 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.0948 0.00444 0.00606 0.0622 0.0812 0.0911 0.0948 0.000203 0.000212 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 220 34111.712 0.005 0.00561 0.0653 0.178 0.0685 0.0913 0.251 0.311 0.00056 0.000695 ! Validation 220 34111.712 0.005 0.00612 0.0372 0.16 0.0718 0.0953 0.201 0.235 0.000448 0.000524 Wall time: 34111.71240803599 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 86 0.218 0.00595 0.0989 0.0711 0.094 0.339 0.383 0.000756 0.000855 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.0954 0.00459 0.00363 0.0633 0.0825 0.0699 0.0735 0.000156 0.000164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 221 34266.699 0.005 0.00553 0.0676 0.178 0.068 0.0906 0.252 0.317 0.000562 0.000707 ! Validation 221 34266.699 0.005 0.0063 0.0236 0.15 0.0728 0.0967 0.155 0.187 0.000345 0.000418 Wall time: 34266.699881929904 ! Best model 221 0.150 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 86 0.127 0.00546 0.0173 0.0673 0.09 0.136 0.16 0.000304 0.000358 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.108 0.00448 0.0178 0.0627 0.0816 0.144 0.163 0.000321 0.000363 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 222 34422.049 0.005 0.00558 0.0521 0.164 0.0683 0.091 0.229 0.278 0.00051 0.000621 ! Validation 222 34422.049 0.005 0.00606 0.066 0.187 0.0714 0.0948 0.272 0.313 0.000608 0.000699 Wall time: 34422.04995854618 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 86 0.137 0.00547 0.0278 0.0672 0.0901 0.173 0.203 0.000386 0.000453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.103 0.00436 0.0155 0.0618 0.0805 0.135 0.152 0.000302 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 223 34577.059 0.005 0.00545 0.0563 0.165 0.0675 0.09 0.235 0.289 0.000525 0.000645 ! Validation 223 34577.059 0.005 0.00598 0.0203 0.14 0.071 0.0942 0.135 0.174 0.000302 0.000388 Wall time: 34577.059245666955 ! Best model 223 0.140 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 86 0.134 0.0055 0.0242 0.0675 0.0903 0.154 0.19 0.000344 0.000423 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.105 0.00437 0.0181 0.0618 0.0805 0.142 0.164 0.000318 0.000366 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 224 34732.045 0.005 0.00538 0.0514 0.159 0.067 0.0894 0.226 0.276 0.000503 0.000617 ! Validation 224 34732.045 0.005 0.00606 0.0294 0.15 0.0714 0.0948 0.171 0.209 0.000382 0.000466 Wall time: 34732.04577961704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 86 0.148 0.00517 0.0448 0.0659 0.0876 0.232 0.258 0.000517 0.000576 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.14 0.00436 0.0525 0.0618 0.0805 0.269 0.279 0.000601 0.000623 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 34887.016 0.005 0.00538 0.0419 0.15 0.067 0.0894 0.2 0.249 0.000447 0.000557 ! Validation 225 34887.016 0.005 0.00599 0.106 0.225 0.0711 0.0943 0.363 0.396 0.000809 0.000884 Wall time: 34887.016106809024 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 86 0.366 0.00531 0.259 0.0672 0.0888 0.596 0.621 0.00133 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.39 0.00449 0.3 0.0628 0.0816 0.663 0.668 0.00148 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 226 35042.632 0.005 0.00565 0.112 0.225 0.0688 0.0916 0.33 0.407 0.000737 0.000908 ! Validation 226 35042.632 0.005 0.00607 0.215 0.336 0.0716 0.0949 0.534 0.565 0.00119 0.00126 Wall time: 35042.63207825087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 86 0.126 0.0055 0.0157 0.0673 0.0904 0.111 0.153 0.000247 0.000341 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.0931 0.00433 0.00647 0.0615 0.0802 0.0853 0.098 0.00019 0.000219 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 227 35197.598 0.005 0.0055 0.0661 0.176 0.0679 0.0904 0.251 0.313 0.00056 0.0007 ! Validation 227 35197.598 0.005 0.00596 0.0396 0.159 0.0708 0.094 0.206 0.242 0.00046 0.000541 Wall time: 35197.598570876755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 86 0.12 0.00509 0.0187 0.0655 0.0869 0.144 0.167 0.000322 0.000372 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.0897 0.00431 0.00342 0.0613 0.08 0.0543 0.0712 0.000121 0.000159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 228 35352.581 0.005 0.00527 0.0439 0.149 0.0663 0.0884 0.208 0.255 0.000464 0.00057 ! Validation 228 35352.581 0.005 0.00592 0.0227 0.141 0.0707 0.0938 0.154 0.184 0.000343 0.00041 Wall time: 35352.58200473385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 86 0.144 0.00501 0.0438 0.0648 0.0863 0.206 0.255 0.00046 0.000569 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.106 0.00409 0.0243 0.06 0.0779 0.175 0.19 0.00039 0.000424 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 35507.564 0.005 0.00518 0.0286 0.132 0.0657 0.0877 0.166 0.206 0.000371 0.00046 ! Validation 229 35507.564 0.005 0.00579 0.0313 0.147 0.0699 0.0927 0.162 0.216 0.000361 0.000481 Wall time: 35507.564343840815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 86 0.291 0.00524 0.186 0.0659 0.0882 0.505 0.525 0.00113 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.149 0.00432 0.0627 0.0615 0.0801 0.301 0.305 0.000671 0.000681 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 230 35663.041 0.005 0.00535 0.0835 0.19 0.0669 0.0891 0.296 0.352 0.000661 0.000786 ! Validation 230 35663.041 0.005 0.006 0.0489 0.169 0.0711 0.0944 0.218 0.27 0.000487 0.000602 Wall time: 35663.04150472814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 86 0.124 0.00473 0.0299 0.0633 0.0838 0.171 0.211 0.000382 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.167 0.00413 0.0844 0.0601 0.0783 0.347 0.354 0.000774 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 231 35818.044 0.005 0.00517 0.0348 0.138 0.0657 0.0876 0.182 0.227 0.000406 0.000508 ! Validation 231 35818.044 0.005 0.00571 0.0552 0.169 0.0693 0.0921 0.239 0.286 0.000533 0.000639 Wall time: 35818.04433498299 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 86 0.13 0.00484 0.0335 0.0639 0.0848 0.191 0.223 0.000427 0.000498 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.134 0.00424 0.0493 0.061 0.0793 0.26 0.271 0.000579 0.000604 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 35973.185 0.005 0.00522 0.0608 0.165 0.066 0.0881 0.245 0.301 0.000547 0.000671 ! Validation 232 35973.185 0.005 0.00577 0.0309 0.146 0.0697 0.0926 0.165 0.214 0.000369 0.000478 Wall time: 35973.185671668965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 86 0.116 0.00462 0.0232 0.0625 0.0828 0.151 0.186 0.000338 0.000414 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.0904 0.00423 0.00586 0.0608 0.0792 0.0798 0.0933 0.000178 0.000208 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 36128.207 0.005 0.00513 0.0395 0.142 0.0654 0.0872 0.191 0.242 0.000426 0.000541 ! Validation 233 36128.207 0.005 0.00572 0.0285 0.143 0.0694 0.0921 0.176 0.206 0.000392 0.000459 Wall time: 36128.2077147821 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 86 0.129 0.00537 0.0217 0.0671 0.0893 0.143 0.179 0.00032 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.0967 0.00428 0.0112 0.061 0.0797 0.115 0.129 0.000256 0.000287 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 234 36283.372 0.005 0.00575 0.114 0.229 0.0692 0.0924 0.32 0.411 0.000714 0.000917 ! Validation 234 36283.372 0.005 0.00577 0.0209 0.136 0.0698 0.0925 0.144 0.176 0.000322 0.000393 Wall time: 36283.37242002785 ! Best model 234 0.136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 86 0.111 0.00493 0.0124 0.0641 0.0855 0.108 0.136 0.000242 0.000303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.0888 0.00409 0.0071 0.0598 0.0779 0.0745 0.103 0.000166 0.000229 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 235 36438.298 0.005 0.00512 0.0351 0.137 0.0653 0.0872 0.185 0.228 0.000413 0.00051 ! Validation 235 36438.298 0.005 0.00559 0.019 0.131 0.0686 0.0911 0.137 0.168 0.000305 0.000375 Wall time: 36438.29856583616 ! Best model 235 0.131 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 86 0.126 0.00501 0.0254 0.0652 0.0863 0.156 0.194 0.000348 0.000434 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.116 0.00417 0.0329 0.0602 0.0786 0.208 0.221 0.000464 0.000493 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 236 36593.192 0.005 0.00504 0.0529 0.154 0.0648 0.0865 0.221 0.28 0.000494 0.000625 ! Validation 236 36593.192 0.005 0.00569 0.0912 0.205 0.0692 0.0919 0.331 0.368 0.000738 0.000822 Wall time: 36593.19288372388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 86 0.167 0.00501 0.0673 0.0645 0.0862 0.28 0.316 0.000625 0.000705 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.0957 0.00404 0.0149 0.0596 0.0774 0.14 0.149 0.000313 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 237 36748.076 0.005 0.00506 0.049 0.15 0.065 0.0867 0.219 0.27 0.000489 0.000602 ! Validation 237 36748.076 0.005 0.00561 0.0186 0.131 0.0688 0.0912 0.133 0.166 0.000296 0.000371 Wall time: 36748.0764740468 ! Best model 237 0.131 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 86 0.119 0.00481 0.023 0.0635 0.0845 0.152 0.185 0.000339 0.000413 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.0902 0.00408 0.00857 0.0599 0.0778 0.0874 0.113 0.000195 0.000252 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 36903.213 0.005 0.00566 0.121 0.234 0.0687 0.0917 0.346 0.423 0.000773 0.000945 ! Validation 238 36903.213 0.005 0.00565 0.0337 0.147 0.069 0.0916 0.187 0.224 0.000417 0.000499 Wall time: 36903.213243064005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 86 0.112 0.00486 0.0148 0.0639 0.0849 0.12 0.148 0.000268 0.000331 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.0973 0.00402 0.0168 0.0594 0.0773 0.138 0.158 0.000308 0.000353 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 239 37058.352 0.005 0.005 0.0254 0.125 0.0646 0.0862 0.154 0.194 0.000344 0.000434 ! Validation 239 37058.352 0.005 0.00556 0.0193 0.13 0.0685 0.0908 0.133 0.169 0.000296 0.000377 Wall time: 37058.35240841005 ! Best model 239 0.130 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 86 0.155 0.00512 0.0526 0.0656 0.0872 0.243 0.279 0.000542 0.000623 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.109 0.00444 0.0203 0.0625 0.0812 0.159 0.173 0.000355 0.000387 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 240 37213.367 0.005 0.00504 0.083 0.184 0.0649 0.0865 0.273 0.351 0.00061 0.000784 ! Validation 240 37213.367 0.005 0.00596 0.0836 0.203 0.0711 0.094 0.31 0.352 0.000693 0.000786 Wall time: 37213.36712025106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 86 0.312 0.00547 0.202 0.0676 0.0901 0.529 0.548 0.00118 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.256 0.00419 0.172 0.0606 0.0789 0.498 0.505 0.00111 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 241 37368.320 0.005 0.00508 0.0626 0.164 0.0651 0.0868 0.238 0.304 0.000531 0.00068 ! Validation 241 37368.320 0.005 0.00566 0.12 0.233 0.0691 0.0917 0.389 0.421 0.000868 0.00094 Wall time: 37368.320518924855 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 86 0.112 0.00459 0.0204 0.0623 0.0826 0.137 0.174 0.000305 0.000388 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.0869 0.00403 0.00634 0.0595 0.0773 0.0739 0.097 0.000165 0.000217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 242 37523.566 0.005 0.00512 0.0493 0.152 0.0654 0.0872 0.215 0.271 0.00048 0.000604 ! Validation 242 37523.566 0.005 0.00548 0.019 0.129 0.0679 0.0902 0.136 0.168 0.000305 0.000375 Wall time: 37523.56627273606 ! Best model 242 0.129 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 86 0.253 0.00493 0.154 0.0642 0.0855 0.461 0.478 0.00103 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.205 0.00393 0.127 0.0587 0.0764 0.426 0.434 0.00095 0.000968 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 243 37678.438 0.005 0.00483 0.0321 0.129 0.0634 0.0847 0.175 0.218 0.00039 0.000487 ! Validation 243 37678.438 0.005 0.00543 0.109 0.217 0.0676 0.0898 0.359 0.402 0.000801 0.000896 Wall time: 37678.438626878895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 86 0.115 0.00504 0.0142 0.0647 0.0865 0.117 0.145 0.000262 0.000325 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.0984 0.00392 0.02 0.0586 0.0763 0.16 0.172 0.000358 0.000385 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 244 37833.439 0.005 0.00498 0.06 0.16 0.0645 0.086 0.243 0.298 0.000542 0.000666 ! Validation 244 37833.439 0.005 0.0054 0.075 0.183 0.0674 0.0896 0.299 0.334 0.000667 0.000745 Wall time: 37833.43987074494 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 86 0.15 0.00546 0.0407 0.0675 0.09 0.217 0.246 0.000483 0.000549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.128 0.00401 0.0483 0.0593 0.0771 0.257 0.268 0.000574 0.000598 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 245 37988.539 0.005 0.00497 0.0634 0.163 0.0644 0.0859 0.242 0.307 0.00054 0.000685 ! Validation 245 37988.539 0.005 0.0055 0.0961 0.206 0.0682 0.0904 0.337 0.378 0.000752 0.000843 Wall time: 37988.53987948503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 86 0.295 0.00475 0.2 0.0624 0.084 0.525 0.544 0.00117 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.0886 0.00414 0.00579 0.0603 0.0784 0.0816 0.0927 0.000182 0.000207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 246 38143.484 0.005 0.00487 0.0565 0.154 0.0637 0.085 0.225 0.289 0.000503 0.000646 ! Validation 246 38143.484 0.005 0.0057 0.0308 0.145 0.0696 0.092 0.182 0.214 0.000407 0.000477 Wall time: 38143.48494410096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 86 0.132 0.00476 0.0366 0.0631 0.0841 0.195 0.233 0.000435 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.102 0.00387 0.0244 0.058 0.0758 0.18 0.19 0.000402 0.000425 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 247 38298.413 0.005 0.00487 0.0497 0.147 0.0637 0.085 0.221 0.272 0.000494 0.000606 ! Validation 247 38298.413 0.005 0.00539 0.0229 0.131 0.0673 0.0894 0.138 0.185 0.000308 0.000412 Wall time: 38298.41310139885 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 86 0.128 0.00504 0.0269 0.065 0.0865 0.152 0.2 0.00034 0.000446 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.0993 0.00406 0.0182 0.0596 0.0776 0.137 0.164 0.000306 0.000366 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 248 38453.342 0.005 0.00488 0.0445 0.142 0.0638 0.0851 0.211 0.257 0.00047 0.000574 ! Validation 248 38453.342 0.005 0.00553 0.0275 0.138 0.0682 0.0906 0.161 0.202 0.00036 0.000451 Wall time: 38453.34210021701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 86 0.113 0.00491 0.0153 0.0634 0.0854 0.116 0.151 0.000259 0.000336 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 0.113 0.00377 0.0374 0.0574 0.0748 0.225 0.236 0.000503 0.000526 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 38608.591 0.005 0.00475 0.033 0.128 0.0629 0.084 0.177 0.221 0.000395 0.000494 ! Validation 249 38608.591 0.005 0.00524 0.0336 0.139 0.0664 0.0882 0.171 0.223 0.000382 0.000499 Wall time: 38608.591396435164 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 86 0.364 0.0056 0.252 0.0689 0.0912 0.595 0.612 0.00133 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 0.674 0.00449 0.584 0.0621 0.0816 0.927 0.931 0.00207 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 250 38763.482 0.005 0.00478 0.0739 0.17 0.0631 0.0842 0.273 0.331 0.00061 0.000739 ! Validation 250 38763.482 0.005 0.00598 0.4 0.519 0.0708 0.0942 0.751 0.771 0.00168 0.00172 Wall time: 38763.482790776994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 86 0.109 0.00474 0.0139 0.0628 0.0839 0.114 0.144 0.000254 0.000321 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 0.133 0.00404 0.0527 0.0592 0.0774 0.272 0.28 0.000606 0.000624 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 38918.467 0.005 0.00505 0.0539 0.155 0.065 0.0866 0.216 0.283 0.000482 0.000631 ! Validation 251 38918.467 0.005 0.00541 0.0247 0.133 0.0673 0.0896 0.148 0.191 0.000329 0.000427 Wall time: 38918.46774509875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 86 0.168 0.00459 0.0763 0.062 0.0826 0.312 0.336 0.000696 0.000751 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 0.15 0.0037 0.0759 0.057 0.0741 0.331 0.336 0.000739 0.000749 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 39073.398 0.005 0.00491 0.0598 0.158 0.0641 0.0854 0.232 0.298 0.000517 0.000665 ! Validation 252 39073.398 0.005 0.00513 0.0412 0.144 0.0658 0.0873 0.198 0.247 0.000442 0.000552 Wall time: 39073.39882032387 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 86 0.163 0.00482 0.0663 0.0635 0.0846 0.289 0.314 0.000645 0.0007 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 0.0976 0.00386 0.0203 0.0584 0.0757 0.152 0.173 0.00034 0.000387 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 253 39228.494 0.005 0.00461 0.0344 0.127 0.062 0.0827 0.178 0.226 0.000398 0.000504 ! Validation 253 39228.494 0.005 0.00527 0.0732 0.179 0.0667 0.0885 0.291 0.33 0.00065 0.000736 Wall time: 39228.494484730065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 86 0.145 0.00467 0.0513 0.0625 0.0833 0.245 0.276 0.000546 0.000616 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 0.0796 0.00382 0.0032 0.0578 0.0753 0.0649 0.0689 0.000145 0.000154 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 39383.475 0.005 0.00474 0.0524 0.147 0.0629 0.0839 0.224 0.279 0.0005 0.000623 ! Validation 254 39383.475 0.005 0.00522 0.0417 0.146 0.0664 0.0881 0.212 0.249 0.000473 0.000556 Wall time: 39383.47561888117 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 86 0.2 0.00468 0.106 0.0624 0.0833 0.376 0.397 0.000839 0.000885 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.18 0.00368 0.106 0.0569 0.0739 0.392 0.397 0.000874 0.000887 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 255 39538.458 0.005 0.0046 0.0352 0.127 0.0619 0.0826 0.185 0.229 0.000414 0.00051 ! Validation 255 39538.458 0.005 0.00511 0.0687 0.171 0.0657 0.0871 0.28 0.319 0.000625 0.000713 Wall time: 39538.458137721755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 86 0.174 0.0047 0.0796 0.0621 0.0835 0.326 0.344 0.000728 0.000767 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 0.208 0.00376 0.132 0.0572 0.0747 0.437 0.443 0.000975 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 256 39693.452 0.005 0.00456 0.0311 0.122 0.0616 0.0823 0.172 0.215 0.000383 0.000479 ! Validation 256 39693.452 0.005 0.00517 0.196 0.299 0.0658 0.0876 0.513 0.539 0.00115 0.0012 Wall time: 39693.452545745764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 86 0.162 0.00512 0.0595 0.0645 0.0872 0.264 0.297 0.00059 0.000663 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 0.153 0.00389 0.0748 0.0582 0.076 0.326 0.333 0.000727 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 257 39849.220 0.005 0.00475 0.0556 0.151 0.063 0.084 0.235 0.287 0.000525 0.000641 ! Validation 257 39849.220 0.005 0.00534 0.0613 0.168 0.0669 0.089 0.254 0.302 0.000566 0.000673 Wall time: 39849.22065340495 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 86 0.134 0.00474 0.039 0.0627 0.0839 0.213 0.241 0.000476 0.000537 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 0.119 0.00361 0.0464 0.0561 0.0732 0.253 0.262 0.000564 0.000586 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 40004.307 0.005 0.0045 0.034 0.124 0.0612 0.0817 0.184 0.225 0.000411 0.000501 ! Validation 258 40004.307 0.005 0.00502 0.0985 0.199 0.0651 0.0864 0.354 0.382 0.000789 0.000853 Wall time: 40004.307686586864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 86 0.136 0.00429 0.0498 0.0603 0.0798 0.24 0.272 0.000536 0.000607 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.0837 0.00372 0.00927 0.0573 0.0743 0.1 0.117 0.000224 0.000262 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 259 40159.277 0.005 0.00458 0.0511 0.143 0.0618 0.0825 0.223 0.276 0.000498 0.000615 ! Validation 259 40159.277 0.005 0.00514 0.0815 0.184 0.066 0.0874 0.303 0.348 0.000676 0.000776 Wall time: 40159.27735213796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 86 0.343 0.00716 0.2 0.0774 0.103 0.515 0.544 0.00115 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.141 0.00641 0.0125 0.0746 0.0976 0.136 0.136 0.000303 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 260 40314.202 0.005 0.00485 0.11 0.207 0.0633 0.0849 0.303 0.404 0.000676 0.000902 ! Validation 260 40314.202 0.005 0.00771 0.0404 0.194 0.0811 0.107 0.19 0.245 0.000423 0.000547 Wall time: 40314.202590416186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 86 0.126 0.00474 0.0314 0.0625 0.0839 0.181 0.216 0.000403 0.000482 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.0958 0.00355 0.0247 0.056 0.0726 0.177 0.191 0.000395 0.000427 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 40469.426 0.005 0.00488 0.0359 0.134 0.0639 0.0851 0.178 0.231 0.000397 0.000516 ! Validation 261 40469.426 0.005 0.00495 0.0235 0.123 0.0645 0.0857 0.14 0.187 0.000312 0.000417 Wall time: 40469.42626891192 ! Best model 261 0.123 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 86 0.0976 0.00429 0.0117 0.06 0.0798 0.103 0.132 0.000231 0.000295 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 0.0808 0.00372 0.00644 0.0569 0.0743 0.0726 0.0978 0.000162 0.000218 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 262 40624.320 0.005 0.00455 0.0457 0.137 0.0616 0.0822 0.208 0.26 0.000465 0.000581 ! Validation 262 40624.320 0.005 0.00504 0.0211 0.122 0.0651 0.0865 0.149 0.177 0.000333 0.000395 Wall time: 40624.3205929338 ! Best model 262 0.122 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 86 0.12 0.00435 0.0334 0.0604 0.0804 0.193 0.223 0.000432 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.0753 0.00356 0.0041 0.056 0.0727 0.0746 0.078 0.000167 0.000174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 263 40779.273 0.005 0.00445 0.0413 0.13 0.0609 0.0813 0.203 0.248 0.000453 0.000553 ! Validation 263 40779.273 0.005 0.00492 0.0368 0.135 0.0644 0.0855 0.202 0.234 0.00045 0.000522 Wall time: 40779.27369911317 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 86 0.0969 0.00426 0.0117 0.0594 0.0795 0.106 0.132 0.000237 0.000294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.106 0.00371 0.0318 0.0572 0.0743 0.208 0.217 0.000465 0.000485 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 40934.226 0.005 0.00436 0.0254 0.112 0.0602 0.0804 0.156 0.194 0.000348 0.000433 ! Validation 264 40934.226 0.005 0.00503 0.0195 0.12 0.0651 0.0864 0.136 0.17 0.000303 0.00038 Wall time: 40934.22690979205 ! Best model 264 0.120 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 86 0.112 0.00454 0.0215 0.0616 0.0821 0.146 0.179 0.000325 0.000399 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.0791 0.0038 0.00318 0.0575 0.0751 0.0539 0.0687 0.00012 0.000153 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 41089.571 0.005 0.00457 0.0617 0.153 0.0618 0.0824 0.254 0.303 0.000568 0.000676 ! Validation 265 41089.571 0.005 0.00512 0.0364 0.139 0.0657 0.0872 0.194 0.232 0.000434 0.000519 Wall time: 41089.571853803005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 86 0.112 0.00476 0.0169 0.0638 0.0841 0.13 0.158 0.000291 0.000353 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.166 0.00391 0.0877 0.0585 0.0762 0.357 0.361 0.000798 0.000805 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 266 41244.535 0.005 0.00467 0.0691 0.162 0.0624 0.0832 0.253 0.32 0.000564 0.000715 ! Validation 266 41244.535 0.005 0.00526 0.0502 0.155 0.0667 0.0883 0.219 0.273 0.000488 0.000609 Wall time: 41244.53578462498 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 86 0.0942 0.00409 0.0123 0.0585 0.0779 0.102 0.135 0.000228 0.000302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.0722 0.00343 0.00364 0.0548 0.0714 0.0585 0.0735 0.000131 0.000164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 267 41399.516 0.005 0.00444 0.0248 0.114 0.0608 0.0812 0.153 0.192 0.00034 0.000428 ! Validation 267 41399.516 0.005 0.00476 0.0299 0.125 0.0633 0.0841 0.182 0.211 0.000407 0.00047 Wall time: 41399.51696074102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 86 0.112 0.0043 0.0258 0.0599 0.0799 0.169 0.196 0.000377 0.000437 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.0784 0.00348 0.00879 0.0551 0.0719 0.0975 0.114 0.000218 0.000255 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 41554.496 0.005 0.00442 0.0527 0.141 0.0607 0.081 0.229 0.28 0.000511 0.000625 ! Validation 268 41554.496 0.005 0.00481 0.0631 0.159 0.0637 0.0845 0.273 0.306 0.000609 0.000683 Wall time: 41554.496424613986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 86 0.119 0.00425 0.0337 0.06 0.0794 0.188 0.224 0.000419 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.135 0.00351 0.0651 0.0554 0.0722 0.304 0.311 0.000678 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 269 41709.765 0.005 0.00437 0.0454 0.133 0.0603 0.0805 0.198 0.26 0.000441 0.00058 ! Validation 269 41709.765 0.005 0.00484 0.0356 0.132 0.0639 0.0848 0.181 0.23 0.000403 0.000513 Wall time: 41709.765292361844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 86 0.122 0.00429 0.0358 0.0598 0.0798 0.204 0.231 0.000454 0.000515 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.136 0.00341 0.0674 0.0547 0.0711 0.306 0.316 0.000683 0.000706 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 41864.753 0.005 0.00427 0.0336 0.119 0.0596 0.0796 0.182 0.223 0.000405 0.000499 ! Validation 270 41864.753 0.005 0.00475 0.036 0.131 0.0633 0.0839 0.183 0.231 0.000409 0.000516 Wall time: 41864.75306907995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 86 0.0956 0.0043 0.00967 0.0595 0.0799 0.102 0.12 0.000228 0.000267 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.0731 0.00336 0.00588 0.0543 0.0706 0.0766 0.0934 0.000171 0.000209 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 271 42019.881 0.005 0.00424 0.0375 0.122 0.0594 0.0794 0.189 0.236 0.000421 0.000527 ! Validation 271 42019.881 0.005 0.0047 0.0243 0.118 0.0629 0.0835 0.161 0.19 0.000359 0.000424 Wall time: 42019.88111107703 ! Best model 271 0.118 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 86 0.213 0.00429 0.127 0.0599 0.0798 0.416 0.434 0.000928 0.00097 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.177 0.00347 0.107 0.0552 0.0718 0.393 0.399 0.000877 0.00089 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 42174.929 0.005 0.00428 0.0414 0.127 0.0597 0.0797 0.204 0.248 0.000455 0.000553 ! Validation 272 42174.929 0.005 0.00486 0.207 0.304 0.0641 0.085 0.534 0.554 0.00119 0.00124 Wall time: 42174.92979149381 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 86 0.199 0.0043 0.113 0.0603 0.0799 0.382 0.409 0.000852 0.000914 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.131 0.00356 0.0596 0.0559 0.0727 0.292 0.298 0.000652 0.000664 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 273 42329.949 0.005 0.00442 0.0543 0.143 0.0607 0.081 0.231 0.284 0.000516 0.000634 ! Validation 273 42329.949 0.005 0.00484 0.036 0.133 0.0638 0.0848 0.182 0.231 0.000405 0.000516 Wall time: 42329.94934704015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 86 0.272 0.00395 0.193 0.0578 0.0765 0.521 0.536 0.00116 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.159 0.00387 0.0819 0.0581 0.0758 0.344 0.349 0.000768 0.000778 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 274 42485.008 0.005 0.00428 0.049 0.135 0.0598 0.0797 0.224 0.269 0.0005 0.000601 ! Validation 274 42485.008 0.005 0.00519 0.184 0.288 0.0663 0.0878 0.493 0.522 0.0011 0.00117 Wall time: 42485.00876006903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 86 0.103 0.00443 0.0147 0.0605 0.0811 0.123 0.148 0.000275 0.00033 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.076 0.00361 0.00382 0.0562 0.0732 0.0622 0.0753 0.000139 0.000168 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 42640.026 0.005 0.00434 0.0452 0.132 0.0602 0.0802 0.212 0.259 0.000473 0.000579 ! Validation 275 42640.026 0.005 0.00488 0.039 0.137 0.0641 0.0851 0.206 0.241 0.000459 0.000537 Wall time: 42640.026635495014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 86 0.161 0.0045 0.0704 0.0616 0.0818 0.299 0.323 0.000668 0.000722 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.243 0.00379 0.167 0.0573 0.075 0.494 0.498 0.0011 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 276 42795.625 0.005 0.00444 0.07 0.159 0.0609 0.0812 0.251 0.322 0.00056 0.00072 ! Validation 276 42795.625 0.005 0.00511 0.116 0.218 0.0657 0.0871 0.383 0.414 0.000854 0.000925 Wall time: 42795.62562930584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 86 0.12 0.00413 0.0376 0.0585 0.0783 0.216 0.236 0.000483 0.000527 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.0897 0.00331 0.0235 0.0538 0.0701 0.174 0.187 0.000388 0.000417 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 42950.789 0.005 0.00422 0.0325 0.117 0.0593 0.0792 0.169 0.22 0.000377 0.00049 ! Validation 277 42950.789 0.005 0.00458 0.015 0.107 0.0621 0.0824 0.116 0.149 0.00026 0.000333 Wall time: 42950.789693870116 ! Best model 277 0.107 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 86 0.119 0.00418 0.0351 0.0592 0.0787 0.195 0.228 0.000435 0.000509 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.0761 0.00354 0.00535 0.0559 0.0725 0.0638 0.0891 0.000142 0.000199 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 43105.745 0.005 0.00413 0.0321 0.115 0.0586 0.0783 0.179 0.218 0.0004 0.000488 ! Validation 278 43105.745 0.005 0.00479 0.0271 0.123 0.0636 0.0844 0.171 0.2 0.000381 0.000447 Wall time: 43105.74587180698 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 86 0.101 0.00435 0.0141 0.0601 0.0804 0.118 0.145 0.000264 0.000323 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.0872 0.00334 0.0204 0.0541 0.0704 0.163 0.174 0.000363 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 279 43260.618 0.005 0.00433 0.0546 0.141 0.0601 0.0802 0.228 0.285 0.000508 0.000636 ! Validation 279 43260.618 0.005 0.00459 0.0148 0.107 0.0621 0.0826 0.12 0.148 0.000267 0.000331 Wall time: 43260.61895337608 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 86 0.144 0.00492 0.0451 0.0643 0.0855 0.218 0.259 0.000486 0.000578 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.141 0.00413 0.0583 0.0603 0.0783 0.287 0.294 0.00064 0.000657 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 43415.786 0.005 0.00423 0.0457 0.13 0.0594 0.0792 0.207 0.26 0.000462 0.000581 ! Validation 280 43415.786 0.005 0.00533 0.186 0.292 0.0674 0.089 0.502 0.525 0.00112 0.00117 Wall time: 43415.78646217706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 86 0.191 0.00401 0.11 0.0578 0.0772 0.378 0.405 0.000845 0.000904 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.103 0.00371 0.0289 0.0572 0.0742 0.195 0.207 0.000435 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 281 43570.755 0.005 0.00432 0.0521 0.139 0.0601 0.0801 0.225 0.278 0.000503 0.00062 ! Validation 281 43570.755 0.005 0.00508 0.0969 0.198 0.0658 0.0869 0.343 0.379 0.000765 0.000846 Wall time: 43570.75540872989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 86 0.105 0.00406 0.0233 0.0581 0.0777 0.163 0.186 0.000365 0.000415 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.0705 0.00327 0.0051 0.0536 0.0697 0.0786 0.087 0.000176 0.000194 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 43725.770 0.005 0.00409 0.0229 0.105 0.0584 0.0779 0.15 0.185 0.000334 0.000412 ! Validation 282 43725.770 0.005 0.00455 0.0489 0.14 0.0619 0.0822 0.238 0.269 0.00053 0.000601 Wall time: 43725.77091979282 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 86 0.0957 0.00399 0.0159 0.0575 0.077 0.13 0.153 0.00029 0.000342 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.102 0.00334 0.0352 0.0539 0.0704 0.217 0.228 0.000484 0.00051 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 283 43880.734 0.005 0.00407 0.0341 0.116 0.0582 0.0777 0.18 0.225 0.000401 0.000503 ! Validation 283 43880.734 0.005 0.00459 0.0168 0.109 0.0622 0.0826 0.118 0.158 0.000264 0.000352 Wall time: 43880.73445609398 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 86 0.171 0.00417 0.0875 0.0591 0.0787 0.346 0.36 0.000772 0.000805 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.182 0.00324 0.117 0.0534 0.0694 0.41 0.418 0.000916 0.000932 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 44036.157 0.005 0.00408 0.035 0.117 0.0583 0.0778 0.184 0.228 0.00041 0.000509 ! Validation 284 44036.157 0.005 0.00447 0.0455 0.135 0.0614 0.0815 0.22 0.26 0.000492 0.00058 Wall time: 44036.15777592175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 86 0.128 0.0042 0.0435 0.0594 0.079 0.202 0.254 0.000451 0.000568 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.0774 0.00328 0.0118 0.0536 0.0698 0.12 0.133 0.000268 0.000296 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 285 44191.136 0.005 0.00405 0.0406 0.122 0.0581 0.0776 0.201 0.246 0.000449 0.000548 ! Validation 285 44191.136 0.005 0.00457 0.0146 0.106 0.0621 0.0824 0.12 0.147 0.000268 0.000329 Wall time: 44191.13629184291 ! Best model 285 0.106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 86 0.114 0.00467 0.0206 0.0619 0.0833 0.14 0.175 0.000312 0.00039 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.0866 0.00348 0.0171 0.0556 0.0718 0.133 0.159 0.000296 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 286 44346.174 0.005 0.00508 0.0998 0.201 0.0651 0.0868 0.318 0.385 0.000709 0.000859 ! Validation 286 44346.174 0.005 0.00475 0.0833 0.178 0.0634 0.084 0.312 0.352 0.000697 0.000785 Wall time: 44346.17408245616 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 86 0.114 0.00394 0.0356 0.0575 0.0765 0.203 0.23 0.000453 0.000513 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.121 0.0032 0.0568 0.0529 0.0689 0.283 0.29 0.000631 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 287 44501.159 0.005 0.00407 0.0358 0.117 0.0582 0.0777 0.19 0.231 0.000423 0.000515 ! Validation 287 44501.159 0.005 0.00444 0.0319 0.121 0.0611 0.0811 0.166 0.218 0.00037 0.000486 Wall time: 44501.15933306096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 86 0.161 0.00387 0.0837 0.0567 0.0758 0.325 0.352 0.000726 0.000787 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.0803 0.00345 0.0113 0.0551 0.0715 0.114 0.13 0.000255 0.000289 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 44656.814 0.005 0.00395 0.0258 0.105 0.0573 0.0766 0.157 0.195 0.00035 0.000436 ! Validation 288 44656.814 0.005 0.00466 0.0856 0.179 0.0627 0.0831 0.317 0.356 0.000708 0.000796 Wall time: 44656.81458284985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 86 0.0822 0.00354 0.0113 0.0545 0.0725 0.103 0.13 0.000229 0.000289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.0722 0.00311 0.00993 0.0524 0.068 0.0925 0.121 0.000207 0.000271 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 289 44811.774 0.005 0.00394 0.0232 0.102 0.0572 0.0765 0.149 0.185 0.000333 0.000414 ! Validation 289 44811.774 0.005 0.00435 0.0186 0.106 0.0605 0.0803 0.139 0.166 0.000309 0.000371 Wall time: 44811.77430449007 ! Best model 289 0.106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 86 0.0909 0.00387 0.0134 0.0569 0.0758 0.114 0.141 0.000255 0.000315 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.1 0.0031 0.0381 0.0523 0.0678 0.224 0.238 0.000501 0.000531 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 290 44966.887 0.005 0.00394 0.03 0.109 0.0573 0.0765 0.169 0.211 0.000378 0.000471 ! Validation 290 44966.887 0.005 0.00434 0.0156 0.102 0.0605 0.0803 0.119 0.152 0.000266 0.00034 Wall time: 44966.887093964964 ! Best model 290 0.102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 86 0.0936 0.00404 0.0129 0.0581 0.0774 0.11 0.138 0.000247 0.000309 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.0906 0.00321 0.0264 0.0532 0.069 0.187 0.198 0.000417 0.000442 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 291 45121.920 0.005 0.00402 0.0529 0.133 0.0579 0.0773 0.23 0.28 0.000512 0.000625 ! Validation 291 45121.920 0.005 0.0044 0.0135 0.102 0.061 0.0808 0.113 0.142 0.000252 0.000316 Wall time: 45121.92014222592 ! Best model 291 0.102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 86 0.0894 0.00389 0.0116 0.0565 0.076 0.105 0.131 0.000234 0.000293 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.0653 0.00306 0.00408 0.0519 0.0674 0.0562 0.0778 0.000126 0.000174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 292 45277.345 0.005 0.00402 0.0393 0.12 0.058 0.0773 0.188 0.242 0.000419 0.00054 ! Validation 292 45277.345 0.005 0.00427 0.0224 0.108 0.0599 0.0796 0.155 0.182 0.000346 0.000407 Wall time: 45277.34511679504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 86 0.177 0.00438 0.0896 0.0601 0.0807 0.35 0.365 0.000781 0.000814 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.206 0.00327 0.141 0.0537 0.0697 0.454 0.457 0.00101 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 293 45432.430 0.005 0.00384 0.0284 0.105 0.0565 0.0755 0.161 0.205 0.00036 0.000458 ! Validation 293 45432.430 0.005 0.00447 0.0982 0.188 0.0613 0.0814 0.343 0.382 0.000766 0.000852 Wall time: 45432.43018480716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 86 0.0927 0.00373 0.0181 0.0561 0.0744 0.14 0.164 0.000312 0.000366 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.11 0.00313 0.0478 0.0523 0.0682 0.258 0.266 0.000575 0.000594 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 45587.463 0.005 0.0041 0.054 0.136 0.0585 0.078 0.223 0.283 0.000498 0.000632 ! Validation 294 45587.463 0.005 0.0043 0.027 0.113 0.0602 0.0799 0.153 0.2 0.000341 0.000447 Wall time: 45587.46339265397 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 86 0.809 0.0097 0.615 0.0881 0.12 0.873 0.955 0.00195 0.00213 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.629 0.00423 0.544 0.0615 0.0793 0.896 0.899 0.002 0.00201 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 295 45742.555 0.005 0.00404 0.067 0.148 0.0577 0.0774 0.223 0.314 0.000498 0.000702 ! Validation 295 45742.555 0.005 0.00559 0.37 0.482 0.0695 0.0911 0.665 0.741 0.00148 0.00165 Wall time: 45742.555876486935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 86 0.155 0.00384 0.0779 0.0564 0.0755 0.324 0.34 0.000724 0.000759 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.0963 0.00313 0.0337 0.0525 0.0682 0.216 0.224 0.000483 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 296 45897.960 0.005 0.00486 0.0407 0.138 0.0629 0.0849 0.184 0.246 0.00041 0.000549 ! Validation 296 45897.960 0.005 0.00433 0.138 0.225 0.0604 0.0802 0.428 0.453 0.000955 0.00101 Wall time: 45897.96008151397 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 86 0.0832 0.00359 0.0114 0.0551 0.073 0.107 0.13 0.000239 0.00029 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.127 0.00324 0.0622 0.0532 0.0693 0.295 0.304 0.000659 0.000678 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 46052.926 0.005 0.00385 0.0334 0.11 0.0567 0.0756 0.18 0.223 0.000402 0.000497 ! Validation 297 46052.926 0.005 0.0044 0.0215 0.109 0.0608 0.0808 0.142 0.179 0.000316 0.000399 Wall time: 46052.92649699096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 86 0.129 0.00403 0.0489 0.0578 0.0773 0.226 0.269 0.000504 0.000601 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.137 0.003 0.0774 0.0513 0.0667 0.332 0.339 0.000742 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 298 46207.781 0.005 0.00383 0.0362 0.113 0.0565 0.0754 0.184 0.232 0.000412 0.000518 ! Validation 298 46207.781 0.005 0.00421 0.0301 0.114 0.0595 0.079 0.171 0.211 0.000381 0.000472 Wall time: 46207.78111677291 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 86 0.0936 0.00405 0.0126 0.0584 0.0775 0.115 0.137 0.000258 0.000305 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.0661 0.00314 0.00321 0.0526 0.0683 0.0652 0.069 0.000146 0.000154 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 46362.614 0.005 0.00391 0.0363 0.115 0.057 0.0762 0.191 0.232 0.000427 0.000518 ! Validation 299 46362.614 0.005 0.00434 0.0349 0.122 0.0605 0.0803 0.196 0.228 0.000437 0.000508 Wall time: 46362.61415679986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 86 0.102 0.00409 0.0203 0.0592 0.0779 0.142 0.173 0.000318 0.000387 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.0975 0.0037 0.0235 0.057 0.0741 0.181 0.187 0.000405 0.000417 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 46517.441 0.005 0.00396 0.063 0.142 0.0573 0.0766 0.231 0.306 0.000515 0.000683 ! Validation 300 46517.441 0.005 0.00499 0.0245 0.124 0.065 0.086 0.16 0.191 0.000358 0.000426 Wall time: 46517.441540174186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 86 0.117 0.00375 0.0423 0.0558 0.0746 0.222 0.251 0.000495 0.000559 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.0783 0.00315 0.0153 0.0527 0.0684 0.141 0.151 0.000314 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 301 46672.215 0.005 0.00385 0.0281 0.105 0.0566 0.0756 0.164 0.204 0.000367 0.000456 ! Validation 301 46672.215 0.005 0.00434 0.0223 0.109 0.0605 0.0803 0.154 0.182 0.000343 0.000406 Wall time: 46672.21543742204 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 86 0.0973 0.00437 0.01 0.06 0.0805 0.0994 0.122 0.000222 0.000272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.079 0.00318 0.0155 0.0529 0.0687 0.139 0.152 0.00031 0.000338 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 302 46827.090 0.005 0.0038 0.0332 0.109 0.0562 0.0751 0.181 0.222 0.000405 0.000496 ! Validation 302 46827.090 0.005 0.00432 0.08 0.166 0.0604 0.0801 0.317 0.345 0.000708 0.000769 Wall time: 46827.09020735603 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 86 0.108 0.00393 0.0294 0.0569 0.0763 0.177 0.209 0.000395 0.000466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.0671 0.00304 0.00634 0.0518 0.0672 0.0824 0.097 0.000184 0.000217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 303 46981.929 0.005 0.00443 0.073 0.162 0.0607 0.0811 0.261 0.329 0.000582 0.000735 ! Validation 303 46981.929 0.005 0.00421 0.0573 0.141 0.0595 0.079 0.262 0.292 0.000584 0.000651 Wall time: 46981.92925324477 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 86 0.0873 0.00377 0.0119 0.0563 0.0748 0.103 0.133 0.000231 0.000297 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.0646 0.00297 0.00519 0.0512 0.0664 0.0658 0.0878 0.000147 0.000196 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 47137.109 0.005 0.00369 0.0148 0.0885 0.0554 0.074 0.118 0.148 0.000264 0.00033 ! Validation 304 47137.109 0.005 0.00412 0.0203 0.103 0.059 0.0782 0.147 0.174 0.000328 0.000387 Wall time: 47137.10958249401 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 86 0.179 0.00419 0.0947 0.0589 0.0789 0.34 0.375 0.000759 0.000837 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.0696 0.00325 0.0046 0.0535 0.0695 0.065 0.0826 0.000145 0.000184 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 305 47292.216 0.005 0.00374 0.0397 0.114 0.0557 0.0745 0.2 0.243 0.000447 0.000541 ! Validation 305 47292.216 0.005 0.00447 0.036 0.125 0.0615 0.0815 0.202 0.231 0.000451 0.000516 Wall time: 47292.216816534754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 86 0.085 0.00371 0.0108 0.0554 0.0742 0.104 0.127 0.000233 0.000283 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.0659 0.00297 0.00647 0.0511 0.0664 0.0781 0.098 0.000174 0.000219 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 306 47447.336 0.005 0.00373 0.0228 0.0974 0.0557 0.0744 0.146 0.184 0.000326 0.00041 ! Validation 306 47447.336 0.005 0.00412 0.018 0.1 0.0588 0.0782 0.138 0.163 0.000307 0.000364 Wall time: 47447.33674228378 ! Best model 306 0.100 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 86 0.114 0.0037 0.0402 0.0559 0.0741 0.22 0.244 0.00049 0.000545 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.112 0.00296 0.0523 0.0512 0.0663 0.265 0.279 0.000592 0.000622 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 307 47602.504 0.005 0.00372 0.0425 0.117 0.0557 0.0743 0.209 0.251 0.000467 0.000561 ! Validation 307 47602.504 0.005 0.00414 0.0176 0.1 0.0592 0.0784 0.124 0.162 0.000276 0.000361 Wall time: 47602.5044240891 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 86 0.0918 0.00367 0.0183 0.0551 0.0738 0.138 0.165 0.000307 0.000368 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.128 0.00291 0.0702 0.0506 0.0657 0.316 0.323 0.000706 0.00072 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 308 47757.355 0.005 0.00363 0.0188 0.0914 0.0549 0.0734 0.134 0.167 0.000298 0.000373 ! Validation 308 47757.355 0.005 0.00405 0.0214 0.102 0.0584 0.0775 0.142 0.178 0.000317 0.000397 Wall time: 47757.355070795864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 86 0.0962 0.00358 0.0246 0.0549 0.0729 0.162 0.191 0.000362 0.000426 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.0659 0.0031 0.00384 0.052 0.0679 0.0721 0.0755 0.000161 0.000169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 309 47912.442 0.005 0.00369 0.0493 0.123 0.0555 0.074 0.225 0.271 0.000501 0.000604 ! Validation 309 47912.442 0.005 0.00425 0.0309 0.116 0.0598 0.0794 0.186 0.214 0.000416 0.000478 Wall time: 47912.44224251388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 86 0.22 0.0036 0.148 0.0552 0.0731 0.456 0.468 0.00102 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.217 0.00359 0.145 0.0559 0.073 0.461 0.464 0.00103 0.00104 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 310 48067.529 0.005 0.00381 0.0409 0.117 0.0563 0.0752 0.188 0.246 0.000419 0.000549 ! Validation 310 48067.529 0.005 0.00476 0.238 0.334 0.0634 0.0841 0.573 0.595 0.00128 0.00133 Wall time: 48067.52945533395 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 86 0.122 0.00384 0.0453 0.0563 0.0755 0.226 0.259 0.000505 0.000579 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.0754 0.00338 0.00771 0.0545 0.0709 0.0862 0.107 0.000192 0.000239 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 48223.036 0.005 0.00393 0.0499 0.129 0.0573 0.0764 0.222 0.272 0.000495 0.000608 ! Validation 311 48223.036 0.005 0.00443 0.0526 0.141 0.0613 0.0811 0.251 0.28 0.00056 0.000624 Wall time: 48223.03696857393 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 86 0.0818 0.00353 0.0111 0.0544 0.0724 0.101 0.128 0.000226 0.000287 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.0698 0.00288 0.0123 0.0502 0.0654 0.123 0.135 0.000274 0.000301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 312 48377.783 0.005 0.00362 0.0264 0.0988 0.0549 0.0733 0.156 0.198 0.000349 0.000442 ! Validation 312 48377.783 0.005 0.00402 0.0168 0.0972 0.0582 0.0773 0.131 0.158 0.000291 0.000352 Wall time: 48377.7831664579 ! Best model 312 0.097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 86 0.0964 0.00379 0.0205 0.0564 0.075 0.146 0.175 0.000326 0.00039 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.123 0.0032 0.059 0.0529 0.069 0.283 0.296 0.000632 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 313 48532.517 0.005 0.00381 0.0552 0.131 0.0563 0.0752 0.23 0.286 0.000513 0.000639 ! Validation 313 48532.517 0.005 0.00426 0.0169 0.102 0.0599 0.0796 0.122 0.158 0.000272 0.000354 Wall time: 48532.51714975294 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 86 0.0848 0.00355 0.0138 0.0545 0.0725 0.123 0.143 0.000274 0.00032 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.0596 0.00283 0.00303 0.0499 0.0648 0.0617 0.0671 0.000138 0.00015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 314 48687.243 0.005 0.00354 0.017 0.0879 0.0543 0.0725 0.127 0.159 0.000283 0.000355 ! Validation 314 48687.243 0.005 0.00398 0.0384 0.118 0.0579 0.0769 0.211 0.239 0.00047 0.000533 Wall time: 48687.243543692864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 86 0.0915 0.00353 0.021 0.0543 0.0724 0.151 0.176 0.000338 0.000394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.0732 0.00285 0.0163 0.05 0.065 0.138 0.155 0.000307 0.000347 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 48841.969 0.005 0.00372 0.042 0.116 0.0557 0.0743 0.199 0.25 0.000444 0.000557 ! Validation 315 48841.969 0.005 0.00397 0.0149 0.0943 0.0577 0.0768 0.122 0.149 0.000271 0.000332 Wall time: 48841.96990601299 ! Best model 315 0.094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 86 0.163 0.00394 0.084 0.057 0.0765 0.327 0.353 0.00073 0.000788 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.0869 0.00312 0.0245 0.0522 0.0681 0.176 0.191 0.000392 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 316 48996.853 0.005 0.00352 0.0377 0.108 0.0541 0.0723 0.181 0.237 0.000404 0.000528 ! Validation 316 48996.853 0.005 0.00426 0.168 0.253 0.06 0.0795 0.476 0.5 0.00106 0.00111 Wall time: 48996.853519627824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 86 0.373 0.00461 0.281 0.0626 0.0827 0.629 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 317 100 0.368 0.00298 0.309 0.0514 0.0665 0.675 0.677 0.00151 0.00151 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 317 49151.568 0.005 0.00371 0.0542 0.128 0.0557 0.0742 0.225 0.283 0.000502 0.000632 ! Validation 317 49151.568 0.005 0.00418 0.195 0.278 0.0593 0.0787 0.51 0.538 0.00114 0.0012 Wall time: 49151.56849478092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 86 0.0894 0.0039 0.0115 0.0567 0.076 0.111 0.131 0.000248 0.000292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.0704 0.00291 0.0122 0.0506 0.0657 0.107 0.134 0.000238 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 318 49306.269 0.005 0.00374 0.038 0.113 0.0559 0.0745 0.193 0.238 0.000431 0.00053 ! Validation 318 49306.269 0.005 0.00399 0.0165 0.0964 0.0579 0.077 0.131 0.156 0.000292 0.000349 Wall time: 49306.26943031605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 86 0.0851 0.00372 0.0106 0.0556 0.0743 0.0923 0.126 0.000206 0.00028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.0701 0.00278 0.0145 0.0496 0.0642 0.12 0.147 0.000267 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 319 49460.997 0.005 0.0035 0.0235 0.0935 0.0539 0.0721 0.149 0.187 0.000333 0.000417 ! Validation 319 49460.997 0.005 0.00389 0.0137 0.0915 0.0573 0.076 0.118 0.143 0.000264 0.000318 Wall time: 49460.99766127486 ! Best model 319 0.092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 86 0.101 0.00358 0.0299 0.0545 0.0729 0.186 0.211 0.000416 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.128 0.00277 0.0724 0.0495 0.0641 0.32 0.328 0.000714 0.000732 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 49615.729 0.005 0.00351 0.0255 0.0957 0.054 0.0722 0.155 0.195 0.000346 0.000435 ! Validation 320 49615.729 0.005 0.00389 0.0259 0.104 0.0572 0.076 0.156 0.196 0.000348 0.000438 Wall time: 49615.7293362678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 86 0.155 0.00378 0.0795 0.0565 0.0749 0.325 0.344 0.000726 0.000767 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.0886 0.00348 0.0189 0.0553 0.0719 0.156 0.168 0.000348 0.000374 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 321 49770.445 0.005 0.00355 0.0463 0.117 0.0544 0.0726 0.213 0.262 0.000476 0.000585 ! Validation 321 49770.445 0.005 0.00453 0.0192 0.11 0.0619 0.082 0.14 0.169 0.000314 0.000377 Wall time: 49770.445692122914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 86 0.0852 0.0037 0.0113 0.0549 0.0741 0.0988 0.13 0.000221 0.000289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.0766 0.00286 0.0193 0.0503 0.0652 0.151 0.169 0.000338 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 322 49925.144 0.005 0.00362 0.0458 0.118 0.055 0.0733 0.211 0.261 0.000471 0.000582 ! Validation 322 49925.144 0.005 0.00396 0.0161 0.0954 0.0578 0.0767 0.128 0.155 0.000285 0.000345 Wall time: 49925.144699635915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 86 0.0764 0.00335 0.00937 0.0527 0.0705 0.0875 0.118 0.000195 0.000263 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.0564 0.0027 0.00238 0.0488 0.0633 0.0503 0.0595 0.000112 0.000133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 323 50080.625 0.005 0.00344 0.0162 0.0849 0.0534 0.0714 0.125 0.155 0.000279 0.000346 ! Validation 323 50080.625 0.005 0.00382 0.0327 0.109 0.0566 0.0753 0.188 0.22 0.00042 0.000492 Wall time: 50080.62573446473 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 86 0.104 0.00372 0.0299 0.0558 0.0743 0.177 0.211 0.000394 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.124 0.00324 0.0597 0.0527 0.0693 0.29 0.298 0.000646 0.000664 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 324 50235.466 0.005 0.00343 0.0358 0.104 0.0534 0.0714 0.184 0.23 0.000411 0.000514 ! Validation 324 50235.466 0.005 0.00425 0.124 0.209 0.0596 0.0794 0.402 0.429 0.000898 0.000958 Wall time: 50235.46663034288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 86 0.0956 0.00369 0.0217 0.0554 0.074 0.145 0.18 0.000323 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.113 0.00276 0.0581 0.0493 0.064 0.284 0.294 0.000635 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 325 50390.592 0.005 0.00379 0.0551 0.131 0.0563 0.075 0.225 0.286 0.000503 0.000638 ! Validation 325 50390.592 0.005 0.00391 0.017 0.0953 0.0574 0.0762 0.125 0.159 0.000279 0.000355 Wall time: 50390.59287209902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 86 0.0775 0.00355 0.00659 0.0544 0.0726 0.0816 0.0989 0.000182 0.000221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.0636 0.00274 0.0088 0.0492 0.0638 0.0879 0.114 0.000196 0.000255 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 50545.410 0.005 0.00341 0.02 0.0883 0.0533 0.0712 0.139 0.172 0.000311 0.000385 ! Validation 326 50545.410 0.005 0.00379 0.0263 0.102 0.0564 0.075 0.168 0.198 0.000375 0.000441 Wall time: 50545.41025665309 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 86 0.114 0.00336 0.0468 0.0527 0.0707 0.24 0.264 0.000536 0.000588 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.172 0.00281 0.116 0.0496 0.0646 0.409 0.414 0.000912 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 327 50700.230 0.005 0.00339 0.0181 0.0859 0.0531 0.0709 0.133 0.164 0.000297 0.000366 ! Validation 327 50700.230 0.005 0.00392 0.0642 0.143 0.0574 0.0763 0.267 0.309 0.000596 0.000689 Wall time: 50700.230133580044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 86 0.206 0.00355 0.135 0.0542 0.0726 0.434 0.447 0.000968 0.000998 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.105 0.00366 0.0322 0.0567 0.0737 0.211 0.219 0.000471 0.000488 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 328 50855.055 0.005 0.00348 0.044 0.113 0.0538 0.0718 0.203 0.255 0.000454 0.00057 ! Validation 328 50855.055 0.005 0.00458 0.108 0.199 0.0623 0.0824 0.357 0.4 0.000798 0.000894 Wall time: 50855.055141287856 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 86 0.137 0.00345 0.0677 0.0541 0.0715 0.29 0.317 0.000646 0.000708 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.11 0.0033 0.0442 0.0545 0.07 0.246 0.256 0.000549 0.000571 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 329 51009.990 0.005 0.00357 0.0456 0.117 0.0547 0.0728 0.217 0.26 0.000484 0.000581 ! Validation 329 51009.990 0.005 0.0043 0.0977 0.184 0.0608 0.0799 0.352 0.381 0.000787 0.00085 Wall time: 51009.99064246286 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 86 0.126 0.00365 0.0528 0.0551 0.0736 0.26 0.28 0.000581 0.000625 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.134 0.00276 0.0785 0.0496 0.0641 0.336 0.341 0.00075 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 330 51164.811 0.005 0.00358 0.0379 0.109 0.0546 0.0729 0.187 0.237 0.000417 0.000529 ! Validation 330 51164.811 0.005 0.00382 0.0346 0.111 0.0568 0.0753 0.179 0.227 0.000399 0.000506 Wall time: 51164.811962390784 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 86 0.144 0.00312 0.0811 0.0513 0.0681 0.329 0.347 0.000733 0.000775 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.0879 0.00294 0.0291 0.0512 0.0661 0.197 0.208 0.000439 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 331 51319.948 0.005 0.00338 0.0246 0.0922 0.053 0.0708 0.152 0.191 0.00034 0.000427 ! Validation 331 51319.948 0.005 0.00401 0.0719 0.152 0.0584 0.0772 0.294 0.327 0.000657 0.000729 Wall time: 51319.94893724285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 86 0.127 0.00355 0.0562 0.0542 0.0726 0.261 0.289 0.000582 0.000644 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.171 0.00287 0.113 0.0503 0.0652 0.4 0.41 0.000894 0.000916 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 332 51474.780 0.005 0.00354 0.0354 0.106 0.0543 0.0725 0.186 0.229 0.000414 0.000511 ! Validation 332 51474.780 0.005 0.00391 0.0521 0.13 0.0574 0.0761 0.24 0.278 0.000536 0.000621 Wall time: 51474.78046260914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 86 0.0654 0.00286 0.00808 0.0492 0.0652 0.0896 0.11 0.0002 0.000244 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.0849 0.00263 0.0324 0.0481 0.0625 0.206 0.219 0.00046 0.000489 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 51629.611 0.005 0.00345 0.0249 0.0939 0.0537 0.0716 0.155 0.192 0.000347 0.000429 ! Validation 333 51629.611 0.005 0.00369 0.0132 0.0871 0.0557 0.0741 0.111 0.14 0.000248 0.000312 Wall time: 51629.61180618778 ! Best model 333 0.087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 86 0.167 0.00335 0.0998 0.0526 0.0705 0.375 0.385 0.000837 0.000859 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.0779 0.00281 0.0217 0.0495 0.0646 0.167 0.179 0.000373 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 334 51784.448 0.005 0.00339 0.0323 0.1 0.0532 0.071 0.17 0.219 0.000379 0.000489 ! Validation 334 51784.448 0.005 0.0038 0.0951 0.171 0.0566 0.0751 0.35 0.376 0.000781 0.000839 Wall time: 51784.448284069076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 86 0.0768 0.00336 0.00973 0.0527 0.0706 0.0961 0.12 0.000214 0.000268 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.0655 0.00268 0.012 0.0485 0.063 0.111 0.133 0.000247 0.000298 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 335 51939.554 0.005 0.00332 0.0301 0.0965 0.0525 0.0702 0.172 0.212 0.000385 0.000472 ! Validation 335 51939.554 0.005 0.00371 0.0155 0.0897 0.0559 0.0742 0.125 0.152 0.00028 0.000339 Wall time: 51939.55426491611 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 86 0.0808 0.00326 0.0156 0.052 0.0696 0.129 0.152 0.000288 0.00034 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.0566 0.00266 0.00341 0.0484 0.0628 0.0677 0.0711 0.000151 0.000159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 336 52094.335 0.005 0.00336 0.039 0.106 0.0529 0.0707 0.186 0.241 0.000414 0.000537 ! Validation 336 52094.335 0.005 0.00366 0.0362 0.109 0.0555 0.0738 0.203 0.232 0.000452 0.000517 Wall time: 52094.335581292864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 86 0.125 0.00324 0.0606 0.0519 0.0694 0.285 0.3 0.000635 0.000669 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.0633 0.00306 0.00217 0.0518 0.0673 0.0487 0.0567 0.000109 0.000127 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 52249.030 0.005 0.00334 0.0305 0.0973 0.0527 0.0704 0.178 0.213 0.000396 0.000475 ! Validation 337 52249.030 0.005 0.00403 0.0283 0.109 0.0583 0.0774 0.175 0.205 0.00039 0.000457 Wall time: 52249.0299938228 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 86 0.0941 0.00354 0.0234 0.054 0.0725 0.152 0.186 0.000339 0.000416 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.0809 0.00301 0.0206 0.0513 0.0669 0.162 0.175 0.000361 0.00039 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 52404.155 0.005 0.00338 0.0292 0.0969 0.0531 0.0709 0.163 0.208 0.000363 0.000465 ! Validation 338 52404.155 0.005 0.00393 0.013 0.0917 0.0577 0.0764 0.113 0.139 0.000252 0.000311 Wall time: 52404.15497268876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 86 0.0717 0.00301 0.0114 0.0502 0.0669 0.107 0.13 0.000239 0.000291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.0578 0.0027 0.00384 0.0486 0.0633 0.0611 0.0755 0.000136 0.000169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 339 52558.866 0.005 0.00346 0.0315 0.101 0.0537 0.0717 0.177 0.216 0.000395 0.000483 ! Validation 339 52558.866 0.005 0.00372 0.0332 0.108 0.0561 0.0743 0.189 0.222 0.000423 0.000496 Wall time: 52558.86693446711 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 86 0.0758 0.00331 0.00959 0.0524 0.0701 0.0985 0.119 0.00022 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.0944 0.00261 0.0423 0.0478 0.0622 0.241 0.251 0.000538 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 340 52713.612 0.005 0.00339 0.0439 0.112 0.0532 0.071 0.198 0.255 0.000441 0.00057 ! Validation 340 52713.612 0.005 0.00362 0.0177 0.09 0.0552 0.0733 0.127 0.162 0.000284 0.000362 Wall time: 52713.61212586705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 86 0.0695 0.00319 0.00573 0.0513 0.0688 0.0723 0.0923 0.000161 0.000206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.0953 0.00258 0.0437 0.0476 0.0619 0.245 0.255 0.000548 0.000569 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 52868.298 0.005 0.00324 0.0254 0.0903 0.0519 0.0694 0.157 0.194 0.00035 0.000434 ! Validation 341 52868.298 0.005 0.00361 0.0145 0.0867 0.055 0.0732 0.113 0.147 0.000253 0.000328 Wall time: 52868.2986821509 ! Best model 341 0.087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 86 0.0884 0.00363 0.0157 0.0551 0.0734 0.123 0.153 0.000276 0.000341 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.108 0.00273 0.053 0.0492 0.0637 0.275 0.28 0.000614 0.000626 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 342 53023.084 0.005 0.00317 0.0199 0.0833 0.0513 0.0686 0.136 0.172 0.000304 0.000384 ! Validation 342 53023.084 0.005 0.00372 0.0146 0.0889 0.0561 0.0743 0.111 0.147 0.000248 0.000329 Wall time: 53023.084642674774 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 86 0.354 0.0128 0.098 0.106 0.138 0.315 0.381 0.000703 0.000851 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.262 0.0119 0.0242 0.102 0.133 0.176 0.19 0.000394 0.000423 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 343 53177.816 0.005 0.0417 1.6 2.43 0.161 0.249 1.03 1.54 0.00229 0.00344 ! Validation 343 53177.816 0.005 0.0134 0.182 0.45 0.109 0.141 0.438 0.52 0.000977 0.00116 Wall time: 53177.81631039688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 86 0.135 0.00557 0.0235 0.0692 0.091 0.155 0.187 0.000345 0.000417 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.103 0.00483 0.00614 0.0654 0.0847 0.0712 0.0954 0.000159 0.000213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 344 53332.562 0.005 0.00735 0.0438 0.191 0.0788 0.104 0.197 0.255 0.000441 0.00057 ! Validation 344 53332.562 0.005 0.00585 0.039 0.156 0.0711 0.0932 0.195 0.24 0.000436 0.000537 Wall time: 53332.56239599874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 86 0.0989 0.00399 0.0192 0.0585 0.0769 0.138 0.169 0.000309 0.000377 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.0949 0.0037 0.0209 0.0576 0.0741 0.154 0.176 0.000344 0.000393 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 345 53487.325 0.005 0.00476 0.0214 0.117 0.0638 0.0841 0.142 0.178 0.000316 0.000398 ! Validation 345 53487.325 0.005 0.00474 0.0195 0.114 0.0638 0.0839 0.138 0.17 0.000307 0.00038 Wall time: 53487.325757503044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 86 0.0899 0.0039 0.0118 0.0574 0.0761 0.109 0.133 0.000243 0.000296 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.0725 0.00327 0.00696 0.0542 0.0697 0.0822 0.102 0.000184 0.000227 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 346 53642.041 0.005 0.00405 0.0188 0.0998 0.0585 0.0776 0.133 0.167 0.000298 0.000373 ! Validation 346 53642.041 0.005 0.00432 0.0371 0.124 0.0606 0.0801 0.197 0.235 0.00044 0.000524 Wall time: 53642.041607426014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 86 0.0885 0.00393 0.0099 0.0574 0.0764 0.0979 0.121 0.000219 0.000271 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.0905 0.00305 0.0294 0.0521 0.0673 0.186 0.209 0.000416 0.000467 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 347 53796.762 0.005 0.00376 0.019 0.0942 0.0561 0.0747 0.133 0.168 0.000298 0.000375 ! Validation 347 53796.762 0.005 0.00413 0.0161 0.0987 0.0591 0.0783 0.124 0.154 0.000278 0.000345 Wall time: 53796.762424652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 86 0.0746 0.00325 0.00966 0.0524 0.0694 0.0969 0.12 0.000216 0.000267 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.0643 0.00293 0.00567 0.051 0.066 0.087 0.0917 0.000194 0.000205 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 348 53951.604 0.005 0.00361 0.0132 0.0855 0.0549 0.0732 0.112 0.14 0.00025 0.000313 ! Validation 348 53951.604 0.005 0.004 0.0463 0.126 0.0581 0.077 0.23 0.262 0.000512 0.000586 Wall time: 53951.604911618866 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 86 0.0773 0.00355 0.0063 0.0544 0.0726 0.0764 0.0967 0.00017 0.000216 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.118 0.00285 0.0608 0.0503 0.0651 0.288 0.301 0.000643 0.000671 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 349 54106.319 0.005 0.00351 0.0107 0.0808 0.054 0.0721 0.1 0.126 0.000223 0.000281 ! Validation 349 54106.319 0.005 0.00392 0.0206 0.0989 0.0574 0.0763 0.138 0.175 0.000307 0.00039 Wall time: 54106.31917492207 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 86 0.0797 0.00344 0.0109 0.0533 0.0714 0.105 0.127 0.000233 0.000284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.0745 0.00277 0.0192 0.0495 0.0641 0.151 0.169 0.000336 0.000376 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 350 54261.050 0.005 0.00345 0.0132 0.0822 0.0535 0.0716 0.112 0.14 0.00025 0.000313 ! Validation 350 54261.050 0.005 0.00385 0.0142 0.0912 0.0569 0.0756 0.117 0.145 0.000262 0.000324 Wall time: 54261.05037010973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 86 0.0783 0.00344 0.00945 0.0533 0.0715 0.0956 0.118 0.000213 0.000264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.061 0.00273 0.00639 0.0491 0.0637 0.0881 0.0974 0.000197 0.000217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 351 54415.772 0.005 0.00339 0.0136 0.0814 0.053 0.0709 0.114 0.142 0.000255 0.000317 ! Validation 351 54415.772 0.005 0.0038 0.0542 0.13 0.0566 0.0751 0.256 0.284 0.000571 0.000633 Wall time: 54415.772128473036 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 86 0.0856 0.00349 0.0157 0.0539 0.072 0.129 0.153 0.000288 0.000341 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.116 0.00269 0.0617 0.0487 0.0632 0.293 0.303 0.000653 0.000676 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 54570.537 0.005 0.00335 0.0137 0.0807 0.0528 0.0705 0.113 0.143 0.000253 0.000318 ! Validation 352 54570.537 0.005 0.00374 0.0191 0.094 0.0561 0.0745 0.131 0.169 0.000293 0.000376 Wall time: 54570.537330023944 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 86 0.0777 0.00353 0.007 0.0538 0.0724 0.0826 0.102 0.000184 0.000228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.0667 0.00266 0.0134 0.0485 0.0629 0.122 0.141 0.000272 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 353 54725.250 0.005 0.00331 0.0128 0.079 0.0524 0.0701 0.11 0.138 0.000246 0.000308 ! Validation 353 54725.250 0.005 0.0037 0.0168 0.0909 0.0558 0.0741 0.13 0.158 0.000291 0.000352 Wall time: 54725.250540582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 86 0.0768 0.00347 0.00732 0.0532 0.0718 0.0842 0.104 0.000188 0.000233 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.0574 0.00265 0.00437 0.0483 0.0627 0.0757 0.0805 0.000169 0.00018 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 54879.944 0.005 0.00327 0.0129 0.0783 0.0521 0.0696 0.111 0.139 0.000249 0.000309 ! Validation 354 54879.944 0.005 0.00368 0.0476 0.121 0.0557 0.0739 0.237 0.266 0.000529 0.000593 Wall time: 54879.944798287004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 86 0.0687 0.00316 0.00538 0.0515 0.0685 0.0702 0.0894 0.000157 0.0002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.0589 0.00262 0.00648 0.048 0.0624 0.0783 0.0981 0.000175 0.000219 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 355 55034.730 0.005 0.00325 0.0131 0.0781 0.0519 0.0694 0.112 0.14 0.000249 0.000312 ! Validation 355 55034.730 0.005 0.00365 0.0227 0.0957 0.0554 0.0736 0.154 0.183 0.000344 0.000409 Wall time: 55034.73048192775 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 86 0.0715 0.00314 0.0087 0.0509 0.0683 0.0909 0.114 0.000203 0.000254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.067 0.00258 0.0154 0.0477 0.0619 0.134 0.151 0.0003 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 356 55189.464 0.005 0.00321 0.0119 0.0761 0.0516 0.069 0.107 0.133 0.000238 0.000297 ! Validation 356 55189.464 0.005 0.00361 0.0137 0.0858 0.0551 0.0732 0.116 0.143 0.000258 0.000318 Wall time: 55189.464257260785 ! Best model 356 0.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 86 0.0834 0.00332 0.017 0.0523 0.0702 0.135 0.159 0.000301 0.000355 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.118 0.00258 0.0665 0.0476 0.0618 0.307 0.314 0.000685 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 357 55344.166 0.005 0.00319 0.0106 0.0743 0.0514 0.0688 0.0996 0.125 0.000222 0.00028 ! Validation 357 55344.166 0.005 0.00358 0.0275 0.0992 0.0549 0.0729 0.158 0.202 0.000352 0.000451 Wall time: 55344.16669604881 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 86 0.0733 0.0033 0.00742 0.0524 0.07 0.0843 0.105 0.000188 0.000234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.0804 0.00256 0.0292 0.0475 0.0616 0.2 0.208 0.000445 0.000465 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 55498.860 0.005 0.00317 0.0121 0.0755 0.0513 0.0686 0.106 0.134 0.000237 0.000299 ! Validation 358 55498.860 0.005 0.00357 0.0141 0.0856 0.0548 0.0728 0.112 0.145 0.000249 0.000323 Wall time: 55498.860953363124 ! Best model 358 0.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 86 0.0738 0.00327 0.00847 0.0519 0.0697 0.092 0.112 0.000205 0.00025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.0724 0.00253 0.0219 0.0472 0.0612 0.168 0.18 0.000375 0.000402 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 359 55653.622 0.005 0.00317 0.0185 0.0819 0.0513 0.0686 0.13 0.166 0.000289 0.00037 ! Validation 359 55653.622 0.005 0.00354 0.0119 0.0827 0.0545 0.0725 0.106 0.133 0.000236 0.000297 Wall time: 55653.622541890945 ! Best model 359 0.083 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 86 0.0798 0.00294 0.0211 0.0496 0.066 0.155 0.177 0.000347 0.000395 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.133 0.00254 0.0818 0.0475 0.0614 0.344 0.349 0.000767 0.000778 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 360 55808.337 0.005 0.00312 0.0173 0.0798 0.0509 0.0681 0.129 0.16 0.000287 0.000358 ! Validation 360 55808.337 0.005 0.00353 0.0214 0.0919 0.0545 0.0724 0.142 0.178 0.000316 0.000397 Wall time: 55808.33794306498 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 86 0.0723 0.00293 0.0137 0.0498 0.066 0.122 0.143 0.000272 0.000319 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.0613 0.00253 0.0107 0.0472 0.0612 0.108 0.126 0.00024 0.000282 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 55963.153 0.005 0.00313 0.0208 0.0834 0.051 0.0682 0.144 0.176 0.000322 0.000393 ! Validation 361 55963.153 0.005 0.00353 0.0167 0.0873 0.0545 0.0724 0.13 0.158 0.000291 0.000352 Wall time: 55963.15372396493 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 86 0.0745 0.00319 0.0107 0.0514 0.0688 0.1 0.126 0.000224 0.000281 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.0841 0.0025 0.0342 0.0468 0.0609 0.218 0.225 0.000486 0.000503 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 362 56117.873 0.005 0.00312 0.0202 0.0826 0.0509 0.0681 0.141 0.173 0.000315 0.000387 ! Validation 362 56117.873 0.005 0.00351 0.0124 0.0826 0.0543 0.0722 0.106 0.136 0.000237 0.000303 Wall time: 56117.873768663034 ! Best model 362 0.083 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 86 0.0762 0.00316 0.0131 0.0508 0.0684 0.119 0.139 0.000266 0.000311 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.0543 0.00252 0.00391 0.0471 0.0612 0.0596 0.0762 0.000133 0.00017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 363 56272.782 0.005 0.0031 0.0236 0.0857 0.0507 0.0679 0.146 0.187 0.000326 0.000418 ! Validation 363 56272.782 0.005 0.00351 0.0263 0.0966 0.0544 0.0722 0.17 0.198 0.000379 0.000441 Wall time: 56272.782872359734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 86 0.146 0.00325 0.0815 0.0519 0.0694 0.336 0.348 0.000749 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.138 0.00264 0.0848 0.0483 0.0626 0.352 0.355 0.000786 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 364 56427.733 0.005 0.0031 0.0245 0.0865 0.0507 0.0678 0.155 0.191 0.000347 0.000425 ! Validation 364 56427.733 0.005 0.00362 0.0466 0.119 0.0552 0.0733 0.21 0.263 0.000468 0.000587 Wall time: 56427.73336459091 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 86 0.0686 0.0029 0.0105 0.0494 0.0656 0.103 0.125 0.000229 0.000279 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.0694 0.00247 0.02 0.0466 0.0605 0.163 0.172 0.000365 0.000385 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 365 56582.558 0.005 0.0031 0.0165 0.0785 0.0507 0.0678 0.123 0.157 0.000274 0.00035 ! Validation 365 56582.558 0.005 0.00345 0.0145 0.0835 0.0538 0.0715 0.116 0.147 0.000258 0.000328 Wall time: 56582.5580400289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 86 0.182 0.00299 0.122 0.0499 0.0667 0.416 0.425 0.000928 0.000949 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.111 0.0025 0.0609 0.0469 0.0609 0.293 0.301 0.000654 0.000671 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 366 56737.578 0.005 0.00307 0.025 0.0864 0.0505 0.0675 0.155 0.192 0.000346 0.00043 ! Validation 366 56737.578 0.005 0.00347 0.153 0.223 0.054 0.0717 0.454 0.477 0.00101 0.00106 Wall time: 56737.57821152173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 86 0.0697 0.00296 0.0106 0.0498 0.0663 0.101 0.125 0.000225 0.00028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.0522 0.00247 0.00286 0.0466 0.0605 0.0518 0.0652 0.000116 0.000146 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 367 56892.374 0.005 0.00307 0.0202 0.0815 0.0504 0.0675 0.137 0.173 0.000305 0.000386 ! Validation 367 56892.374 0.005 0.00344 0.0272 0.0961 0.0538 0.0715 0.174 0.201 0.000387 0.000449 Wall time: 56892.37444048887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 86 0.111 0.00319 0.0474 0.0511 0.0688 0.238 0.265 0.000532 0.000592 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.115 0.0025 0.0651 0.047 0.0609 0.304 0.311 0.000678 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 368 57047.322 0.005 0.0031 0.036 0.0981 0.0508 0.0679 0.191 0.231 0.000427 0.000516 ! Validation 368 57047.322 0.005 0.00347 0.0196 0.0889 0.054 0.0717 0.132 0.171 0.000294 0.000381 Wall time: 57047.3224553098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 86 0.0766 0.003 0.0167 0.0497 0.0667 0.14 0.158 0.000312 0.000352 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.0798 0.00252 0.0294 0.0472 0.0612 0.2 0.209 0.000446 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 369 57202.900 0.005 0.00303 0.0148 0.0754 0.0502 0.0671 0.121 0.148 0.00027 0.00033 ! Validation 369 57202.900 0.005 0.00348 0.0123 0.0819 0.0541 0.0719 0.108 0.135 0.00024 0.000302 Wall time: 57202.90104537504 ! Best model 369 0.082 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 86 0.144 0.00295 0.0851 0.0498 0.0662 0.343 0.355 0.000766 0.000793 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.117 0.00252 0.0663 0.0471 0.0612 0.308 0.314 0.000687 0.0007 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 370 57357.786 0.005 0.00307 0.0349 0.0963 0.0505 0.0675 0.187 0.227 0.000417 0.000508 ! Validation 370 57357.786 0.005 0.00346 0.159 0.228 0.0539 0.0717 0.466 0.486 0.00104 0.00108 Wall time: 57357.786618526094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 86 0.173 0.00289 0.115 0.0491 0.0655 0.404 0.413 0.000901 0.000921 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.105 0.00243 0.0561 0.0463 0.06 0.281 0.289 0.000626 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 371 57512.647 0.005 0.00308 0.0253 0.087 0.0506 0.0677 0.157 0.194 0.00035 0.000432 ! Validation 371 57512.647 0.005 0.00343 0.16 0.228 0.0537 0.0713 0.468 0.487 0.00104 0.00109 Wall time: 57512.6478713369 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 86 0.0852 0.00321 0.021 0.0516 0.069 0.159 0.177 0.000355 0.000394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.101 0.00247 0.0515 0.0467 0.0606 0.27 0.276 0.000604 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 372 57667.517 0.005 0.00308 0.0339 0.0955 0.0506 0.0676 0.187 0.224 0.000417 0.000501 ! Validation 372 57667.517 0.005 0.00342 0.0231 0.0914 0.0536 0.0712 0.141 0.185 0.000316 0.000413 Wall time: 57667.517172953114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 86 0.0642 0.00288 0.00665 0.0491 0.0654 0.0804 0.0993 0.000179 0.000222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.0509 0.00241 0.00266 0.0461 0.0598 0.0492 0.0629 0.00011 0.00014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 373 57822.385 0.005 0.00304 0.0205 0.0814 0.0503 0.0672 0.137 0.175 0.000306 0.00039 ! Validation 373 57822.385 0.005 0.0034 0.0233 0.0914 0.0535 0.0711 0.159 0.186 0.000356 0.000415 Wall time: 57822.385982050095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 86 0.0656 0.00301 0.00543 0.0498 0.0668 0.0696 0.0898 0.000155 0.0002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.0531 0.00247 0.00377 0.0466 0.0605 0.0614 0.0748 0.000137 0.000167 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 374 57977.325 0.005 0.00303 0.0285 0.0891 0.0502 0.0671 0.171 0.206 0.000383 0.00046 ! Validation 374 57977.325 0.005 0.00342 0.0201 0.0886 0.0536 0.0713 0.147 0.173 0.000327 0.000386 Wall time: 57977.32579260506 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 86 0.185 0.00304 0.125 0.0504 0.0671 0.405 0.43 0.000903 0.00096 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.0598 0.00271 0.00563 0.0488 0.0634 0.069 0.0914 0.000154 0.000204 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 58132.176 0.005 0.003 0.0245 0.0846 0.0499 0.0668 0.148 0.191 0.00033 0.000425 ! Validation 375 58132.176 0.005 0.00363 0.0527 0.125 0.0554 0.0734 0.25 0.28 0.000558 0.000624 Wall time: 58132.17663452402 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 86 0.112 0.00312 0.0501 0.0507 0.068 0.26 0.273 0.000581 0.000609 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.28 0.00245 0.231 0.0465 0.0603 0.583 0.586 0.0013 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 376 58287.035 0.005 0.00307 0.0337 0.095 0.0505 0.0675 0.176 0.224 0.000392 0.000499 ! Validation 376 58287.035 0.005 0.0034 0.122 0.19 0.0535 0.071 0.404 0.426 0.000901 0.000952 Wall time: 58287.03564601811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 86 0.0656 0.00295 0.00654 0.0497 0.0662 0.0814 0.0985 0.000182 0.00022 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.0627 0.00239 0.0149 0.0458 0.0596 0.137 0.149 0.000306 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 377 58442.736 0.005 0.00306 0.0329 0.094 0.0504 0.0674 0.172 0.221 0.000384 0.000493 ! Validation 377 58442.736 0.005 0.00334 0.0141 0.0809 0.053 0.0704 0.116 0.145 0.00026 0.000323 Wall time: 58442.73645069683 ! Best model 377 0.081 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 86 0.0668 0.00273 0.0122 0.0479 0.0636 0.114 0.135 0.000255 0.000301 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.1 0.00252 0.0501 0.0472 0.0611 0.268 0.273 0.000598 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 378 58597.646 0.005 0.00298 0.0198 0.0793 0.0497 0.0665 0.135 0.171 0.0003 0.000383 ! Validation 378 58597.646 0.005 0.00342 0.0167 0.0852 0.0538 0.0713 0.119 0.157 0.000266 0.000351 Wall time: 58597.646696972195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 86 0.0635 0.00295 0.00456 0.0493 0.0661 0.0655 0.0823 0.000146 0.000184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.0542 0.00239 0.00645 0.0461 0.0595 0.0812 0.0979 0.000181 0.000218 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 379 58752.480 0.005 0.00304 0.0229 0.0838 0.0503 0.0672 0.15 0.185 0.000334 0.000412 ! Validation 379 58752.480 0.005 0.00334 0.0203 0.0871 0.053 0.0704 0.146 0.174 0.000327 0.000387 Wall time: 58752.480545515195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 86 0.06 0.00275 0.00504 0.0481 0.0639 0.0683 0.0865 0.000153 0.000193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.066 0.00239 0.0181 0.046 0.0596 0.153 0.164 0.000342 0.000366 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 380 58907.305 0.005 0.00302 0.0373 0.0977 0.0501 0.067 0.188 0.235 0.00042 0.000525 ! Validation 380 58907.305 0.005 0.00334 0.0125 0.0793 0.053 0.0704 0.109 0.136 0.000243 0.000304 Wall time: 58907.30597666791 ! Best model 380 0.079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 86 0.0815 0.00297 0.0221 0.0498 0.0664 0.164 0.181 0.000365 0.000404 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.106 0.00237 0.0585 0.0458 0.0593 0.288 0.295 0.000642 0.000658 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 381 59062.233 0.005 0.00295 0.0203 0.0794 0.0495 0.0662 0.141 0.174 0.000315 0.000388 ! Validation 381 59062.233 0.005 0.00332 0.0227 0.0891 0.0528 0.0702 0.146 0.184 0.000325 0.00041 Wall time: 59062.23401205614 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 86 0.127 0.00334 0.0598 0.0528 0.0704 0.239 0.298 0.000535 0.000665 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.0763 0.00311 0.014 0.0513 0.068 0.128 0.144 0.000286 0.000322 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 382 59217.063 0.005 0.00295 0.0261 0.0851 0.0495 0.0662 0.149 0.197 0.000332 0.000439 ! Validation 382 59217.063 0.005 0.00396 0.0124 0.0917 0.0571 0.0767 0.11 0.136 0.000246 0.000303 Wall time: 59217.06346571911 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 86 0.0656 0.0028 0.00959 0.0483 0.0645 0.1 0.119 0.000224 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.108 0.00242 0.0593 0.0462 0.06 0.291 0.297 0.00065 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 383 59372.132 0.005 0.00308 0.0333 0.095 0.0506 0.0677 0.171 0.222 0.000381 0.000497 ! Validation 383 59372.132 0.005 0.00333 0.0161 0.0826 0.0529 0.0703 0.119 0.154 0.000266 0.000345 Wall time: 59372.13247870002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 86 0.0725 0.00283 0.0159 0.0482 0.0648 0.136 0.154 0.000304 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.125 0.00233 0.0782 0.0454 0.0588 0.337 0.341 0.000751 0.000761 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 59526.969 0.005 0.0029 0.0118 0.0699 0.0491 0.0656 0.106 0.132 0.000236 0.000296 ! Validation 384 59526.969 0.005 0.00326 0.0283 0.0935 0.0523 0.0695 0.167 0.205 0.000373 0.000458 Wall time: 59526.969234002754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 86 0.0758 0.00292 0.0173 0.0493 0.0659 0.132 0.16 0.000296 0.000358 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.0576 0.00272 0.00315 0.0487 0.0635 0.054 0.0684 0.00012 0.000153 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 59682.006 0.005 0.00296 0.03 0.0892 0.0496 0.0663 0.162 0.211 0.000363 0.000471 ! Validation 385 59682.006 0.005 0.00355 0.0385 0.109 0.0546 0.0726 0.203 0.239 0.000452 0.000533 Wall time: 59682.00698128296 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 86 0.0729 0.00286 0.0158 0.049 0.0651 0.128 0.153 0.000285 0.000342 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.0547 0.00239 0.00698 0.0459 0.0595 0.0831 0.102 0.000185 0.000227 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 386 59836.844 0.005 0.00304 0.0408 0.101 0.0503 0.0671 0.199 0.246 0.000443 0.000549 ! Validation 386 59836.844 0.005 0.00332 0.0225 0.0889 0.0529 0.0702 0.152 0.183 0.00034 0.000408 Wall time: 59836.84446910117 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 86 0.165 0.00354 0.0946 0.0544 0.0725 0.353 0.375 0.000787 0.000837 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.0795 0.00302 0.0191 0.0521 0.067 0.154 0.168 0.000344 0.000376 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 387 59991.744 0.005 0.00306 0.0566 0.118 0.0505 0.0674 0.235 0.29 0.000524 0.000647 ! Validation 387 59991.744 0.005 0.00394 0.0371 0.116 0.0584 0.0765 0.204 0.235 0.000456 0.000524 Wall time: 59991.744825167116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 86 0.0752 0.00296 0.0159 0.0496 0.0663 0.131 0.154 0.000292 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.138 0.00233 0.0911 0.0454 0.0588 0.364 0.368 0.000812 0.000821 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 388 60146.575 0.005 0.00303 0.0187 0.0793 0.0502 0.0671 0.132 0.167 0.000294 0.000372 ! Validation 388 60146.575 0.005 0.00325 0.03 0.0951 0.0523 0.0695 0.175 0.211 0.00039 0.000471 Wall time: 60146.57513821218 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 86 0.0988 0.00285 0.0417 0.0489 0.0651 0.229 0.249 0.000511 0.000556 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.0525 0.00232 0.00597 0.0453 0.0587 0.0707 0.0941 0.000158 0.00021 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 60301.444 0.005 0.00289 0.0164 0.0742 0.049 0.0655 0.126 0.156 0.000282 0.000348 ! Validation 389 60301.444 0.005 0.00327 0.0588 0.124 0.0524 0.0697 0.265 0.296 0.000592 0.00066 Wall time: 60301.44405162288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 86 0.07 0.00295 0.011 0.0496 0.0662 0.107 0.128 0.000238 0.000286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.105 0.00249 0.0554 0.047 0.0609 0.278 0.287 0.000621 0.00064 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 390 60456.271 0.005 0.0029 0.0229 0.0809 0.0491 0.0656 0.139 0.184 0.000311 0.000412 ! Validation 390 60456.271 0.005 0.00344 0.0219 0.0906 0.0539 0.0714 0.148 0.18 0.00033 0.000402 Wall time: 60456.271180408075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 86 0.0743 0.00296 0.0152 0.0498 0.0663 0.124 0.15 0.000276 0.000335 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.131 0.00263 0.0782 0.0484 0.0624 0.337 0.341 0.000751 0.000761 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 60611.099 0.005 0.00304 0.0422 0.103 0.0504 0.0672 0.212 0.25 0.000472 0.000559 ! Validation 391 60611.099 0.005 0.0035 0.0377 0.108 0.0546 0.0721 0.194 0.237 0.000434 0.000528 Wall time: 60611.0993758128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 86 0.0701 0.00301 0.00982 0.0501 0.0669 0.105 0.121 0.000234 0.000269 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.054 0.00236 0.00676 0.0457 0.0592 0.0772 0.1 0.000172 0.000224 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 392 60765.938 0.005 0.00293 0.0188 0.0774 0.0494 0.0659 0.128 0.167 0.000286 0.000373 ! Validation 392 60765.938 0.005 0.00329 0.0639 0.13 0.0526 0.0699 0.284 0.308 0.000633 0.000688 Wall time: 60765.93806967791 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 86 0.102 0.003 0.0424 0.0498 0.0667 0.235 0.251 0.000524 0.00056 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.069 0.00232 0.0225 0.0453 0.0587 0.172 0.183 0.000384 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 393 60921.275 0.005 0.00285 0.0181 0.0752 0.0487 0.0651 0.131 0.164 0.000293 0.000366 ! Validation 393 60921.275 0.005 0.00325 0.145 0.21 0.0522 0.0695 0.434 0.464 0.000969 0.00103 Wall time: 60921.275699547026 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 86 0.0617 0.00265 0.00867 0.0472 0.0627 0.0871 0.113 0.000194 0.000253 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.0561 0.00232 0.0097 0.0452 0.0587 0.106 0.12 0.000236 0.000268 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 61076.163 0.005 0.00299 0.026 0.0859 0.0499 0.0667 0.15 0.196 0.000335 0.000438 ! Validation 394 61076.163 0.005 0.00322 0.069 0.134 0.052 0.0692 0.294 0.32 0.000657 0.000714 Wall time: 61076.163506058045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 86 0.0741 0.00272 0.0198 0.0478 0.0635 0.154 0.171 0.000344 0.000382 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.0557 0.00245 0.00665 0.0465 0.0604 0.0783 0.0994 0.000175 0.000222 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 395 61230.982 0.005 0.00302 0.0405 0.101 0.0502 0.0669 0.191 0.245 0.000426 0.000548 ! Validation 395 61230.982 0.005 0.00329 0.0716 0.137 0.0526 0.0699 0.297 0.326 0.000662 0.000728 Wall time: 61230.9821396051 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 86 0.064 0.00287 0.00671 0.0486 0.0652 0.0833 0.0998 0.000186 0.000223 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.0476 0.00225 0.00263 0.0445 0.0578 0.052 0.0625 0.000116 0.00014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 396 61385.796 0.005 0.00284 0.0176 0.0745 0.0486 0.065 0.13 0.162 0.000291 0.000361 ! Validation 396 61385.796 0.005 0.00315 0.0361 0.0992 0.0514 0.0684 0.199 0.232 0.000444 0.000517 Wall time: 61385.79683847679 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 86 0.0665 0.00288 0.00899 0.0491 0.0653 0.0947 0.116 0.000211 0.000258 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.0495 0.00238 0.00185 0.0459 0.0595 0.05 0.0524 0.000112 0.000117 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 61540.615 0.005 0.00283 0.0243 0.0809 0.0485 0.0649 0.155 0.19 0.000347 0.000424 ! Validation 397 61540.615 0.005 0.00324 0.0355 0.1 0.0523 0.0694 0.198 0.23 0.000443 0.000512 Wall time: 61540.61550320219 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 86 0.0712 0.00288 0.0135 0.0488 0.0654 0.115 0.142 0.000258 0.000316 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.0814 0.00235 0.0344 0.0455 0.0591 0.221 0.226 0.000494 0.000505 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 61695.421 0.005 0.00348 0.061 0.131 0.0535 0.0719 0.217 0.301 0.000483 0.000672 ! Validation 398 61695.421 0.005 0.00322 0.0145 0.079 0.0521 0.0691 0.112 0.147 0.000251 0.000328 Wall time: 61695.42120244214 ! Best model 398 0.079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 86 0.0641 0.00283 0.00751 0.0485 0.0648 0.0849 0.106 0.00019 0.000236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.152 0.00225 0.107 0.0445 0.0578 0.394 0.399 0.00088 0.000891 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 61850.251 0.005 0.00278 0.0104 0.066 0.048 0.0642 0.0989 0.125 0.000221 0.000278 ! Validation 399 61850.251 0.005 0.00315 0.0447 0.108 0.0514 0.0684 0.219 0.258 0.000488 0.000575 Wall time: 61850.25187221402 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 86 0.0616 0.00281 0.00547 0.0484 0.0645 0.0748 0.0901 0.000167 0.000201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.0923 0.00222 0.048 0.0442 0.0574 0.263 0.267 0.000587 0.000596 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 400 62005.192 0.005 0.00285 0.0197 0.0767 0.0487 0.065 0.139 0.171 0.000311 0.000382 ! Validation 400 62005.192 0.005 0.00312 0.0237 0.0862 0.0512 0.0681 0.142 0.188 0.000318 0.000419 Wall time: 62005.192954625 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 86 0.145 0.00314 0.0819 0.0509 0.0683 0.332 0.349 0.000742 0.000778 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.0539 0.00242 0.00545 0.0462 0.06 0.0753 0.0899 0.000168 0.000201 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 401 62160.016 0.005 0.0029 0.0387 0.0968 0.0492 0.0657 0.181 0.24 0.000404 0.000535 ! Validation 401 62160.016 0.005 0.00337 0.073 0.14 0.0534 0.0707 0.303 0.329 0.000676 0.000735 Wall time: 62160.01698048506 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 86 0.0601 0.00276 0.00494 0.0478 0.064 0.0709 0.0856 0.000158 0.000191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.0488 0.00224 0.00404 0.0445 0.0577 0.0646 0.0774 0.000144 0.000173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 402 62314.829 0.005 0.00281 0.0191 0.0754 0.0484 0.0646 0.135 0.169 0.000302 0.000376 ! Validation 402 62314.829 0.005 0.00312 0.0223 0.0846 0.0512 0.068 0.156 0.182 0.000349 0.000406 Wall time: 62314.8298128508 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 86 0.0648 0.00279 0.00907 0.0478 0.0643 0.0821 0.116 0.000183 0.000259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.0916 0.00223 0.047 0.0443 0.0575 0.258 0.264 0.000576 0.00059 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 403 62469.643 0.005 0.00279 0.0215 0.0774 0.0482 0.0644 0.147 0.179 0.000327 0.000399 ! Validation 403 62469.643 0.005 0.00308 0.0131 0.0748 0.0509 0.0677 0.108 0.14 0.000241 0.000312 Wall time: 62469.64354073489 ! Best model 403 0.075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 86 0.0626 0.00286 0.00546 0.0488 0.0651 0.0742 0.09 0.000166 0.000201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.0656 0.00232 0.0193 0.0454 0.0587 0.162 0.169 0.000361 0.000377 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 62624.489 0.005 0.003 0.0369 0.0969 0.0501 0.0667 0.187 0.234 0.000418 0.000523 ! Validation 404 62624.489 0.005 0.00319 0.0128 0.0766 0.052 0.0688 0.112 0.138 0.00025 0.000308 Wall time: 62624.48939990904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 86 0.0735 0.0028 0.0175 0.0485 0.0645 0.137 0.161 0.000306 0.00036 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.132 0.00244 0.0828 0.046 0.0601 0.343 0.351 0.000765 0.000783 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 405 62779.311 0.005 0.00279 0.0235 0.0792 0.0482 0.0643 0.152 0.187 0.00034 0.000417 ! Validation 405 62779.311 0.005 0.0033 0.0249 0.0909 0.0526 0.07 0.157 0.192 0.00035 0.000429 Wall time: 62779.31186024286 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 86 0.0585 0.00257 0.00708 0.0462 0.0617 0.0841 0.103 0.000188 0.000229 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.0774 0.00219 0.0337 0.0439 0.057 0.219 0.224 0.000489 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 406 62934.133 0.005 0.00275 0.0135 0.0684 0.0478 0.0639 0.114 0.142 0.000254 0.000316 ! Validation 406 62934.133 0.005 0.00306 0.0145 0.0756 0.0507 0.0674 0.109 0.147 0.000243 0.000327 Wall time: 62934.13328355877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 86 0.0593 0.0027 0.0053 0.0475 0.0633 0.0701 0.0887 0.000156 0.000198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.124 0.00236 0.0766 0.0454 0.0592 0.335 0.337 0.000747 0.000753 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 63089.029 0.005 0.00279 0.0246 0.0804 0.0482 0.0644 0.157 0.191 0.00035 0.000427 ! Validation 407 63089.029 0.005 0.00322 0.0231 0.0875 0.0519 0.0691 0.143 0.185 0.00032 0.000413 Wall time: 63089.02925103111 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 86 0.148 0.00324 0.0833 0.0522 0.0694 0.334 0.352 0.000745 0.000785 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.117 0.00228 0.0715 0.045 0.0581 0.32 0.326 0.000715 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 408 63243.863 0.005 0.00284 0.0349 0.0917 0.0486 0.0649 0.188 0.228 0.00042 0.000508 ! Validation 408 63243.863 0.005 0.00313 0.0181 0.0807 0.0514 0.0682 0.136 0.164 0.000303 0.000366 Wall time: 63243.863357455935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 86 0.0566 0.00256 0.00538 0.0464 0.0616 0.0725 0.0894 0.000162 0.000199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.0491 0.00223 0.00452 0.0444 0.0575 0.0679 0.0819 0.000152 0.000183 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 409 63398.695 0.005 0.00296 0.0442 0.103 0.0498 0.0663 0.211 0.256 0.000472 0.000572 ! Validation 409 63398.695 0.005 0.0031 0.0307 0.0928 0.0511 0.0679 0.186 0.214 0.000415 0.000477 Wall time: 63398.69542782614 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 86 0.0661 0.00288 0.00851 0.0488 0.0654 0.0942 0.112 0.00021 0.000251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.0473 0.00222 0.00295 0.0441 0.0574 0.0582 0.0662 0.00013 0.000148 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 410 63553.480 0.005 0.00271 0.0146 0.0689 0.0475 0.0634 0.115 0.147 0.000256 0.000329 ! Validation 410 63553.480 0.005 0.00308 0.0438 0.105 0.0508 0.0676 0.222 0.255 0.000496 0.000569 Wall time: 63553.480075764935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 86 0.0603 0.00265 0.00719 0.0469 0.0628 0.0869 0.103 0.000194 0.000231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.0711 0.00216 0.0278 0.0437 0.0567 0.197 0.203 0.000439 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 411 63708.243 0.005 0.00272 0.0159 0.0703 0.0476 0.0636 0.125 0.154 0.000279 0.000343 ! Validation 411 63708.243 0.005 0.00301 0.0109 0.0711 0.0502 0.0668 0.098 0.127 0.000219 0.000284 Wall time: 63708.24356336007 ! Best model 411 0.071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 86 0.0565 0.00252 0.00611 0.0459 0.0612 0.0793 0.0952 0.000177 0.000213 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.0525 0.00222 0.00815 0.0442 0.0574 0.0996 0.11 0.000222 0.000245 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 412 63863.004 0.005 0.00271 0.018 0.0722 0.0475 0.0634 0.134 0.163 0.0003 0.000365 ! Validation 412 63863.004 0.005 0.00305 0.0154 0.0764 0.0506 0.0673 0.126 0.151 0.000281 0.000338 Wall time: 63863.004791586194 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 86 0.208 0.00444 0.119 0.0612 0.0812 0.354 0.421 0.000791 0.000939 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.0724 0.00278 0.0167 0.0495 0.0643 0.144 0.158 0.000321 0.000352 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 413 64017.853 0.005 0.00399 0.0899 0.17 0.0558 0.0769 0.281 0.365 0.000627 0.000815 ! Validation 413 64017.853 0.005 0.00375 0.0191 0.0942 0.0567 0.0747 0.138 0.169 0.000307 0.000376 Wall time: 64017.853379364125 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 86 0.0772 0.00279 0.0213 0.0479 0.0644 0.159 0.178 0.000354 0.000397 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.0948 0.00218 0.0511 0.0439 0.0569 0.269 0.275 0.000602 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 414 64172.620 0.005 0.00292 0.016 0.0744 0.0491 0.0658 0.12 0.154 0.000268 0.000344 ! Validation 414 64172.620 0.005 0.00302 0.022 0.0823 0.0504 0.067 0.136 0.181 0.000303 0.000403 Wall time: 64172.620670433156 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 86 0.064 0.00275 0.00897 0.0479 0.0639 0.0887 0.115 0.000198 0.000258 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.0579 0.00223 0.0132 0.0443 0.0576 0.135 0.14 0.000301 0.000313 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 415 64327.382 0.005 0.00276 0.0225 0.0777 0.048 0.064 0.149 0.183 0.000332 0.000408 ! Validation 415 64327.382 0.005 0.00311 0.0133 0.0754 0.0512 0.0679 0.113 0.14 0.000253 0.000313 Wall time: 64327.38241147483 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 86 0.0594 0.00269 0.0055 0.0475 0.0632 0.0751 0.0903 0.000168 0.000202 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.0522 0.00217 0.00876 0.0437 0.0568 0.11 0.114 0.000245 0.000255 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 64482.158 0.005 0.00266 0.0146 0.0677 0.047 0.0628 0.118 0.147 0.000263 0.000328 ! Validation 416 64482.158 0.005 0.00305 0.016 0.0769 0.0507 0.0672 0.128 0.154 0.000286 0.000344 Wall time: 64482.15812885901 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 86 0.0919 0.00255 0.041 0.0463 0.0615 0.229 0.247 0.00051 0.00055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.0472 0.00225 0.00214 0.0446 0.0578 0.0463 0.0563 0.000103 0.000126 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 417 64636.913 0.005 0.00266 0.0173 0.0704 0.047 0.0628 0.13 0.16 0.000291 0.000357 ! Validation 417 64636.913 0.005 0.00312 0.0453 0.108 0.0513 0.0681 0.228 0.259 0.000509 0.000579 Wall time: 64636.913432816975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 86 0.0576 0.00263 0.00511 0.0465 0.0624 0.0657 0.0871 0.000147 0.000194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.0693 0.0022 0.0252 0.0439 0.0572 0.187 0.194 0.000417 0.000432 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 418 64791.674 0.005 0.0027 0.0265 0.0805 0.0474 0.0633 0.161 0.198 0.000358 0.000443 ! Validation 418 64791.674 0.005 0.00305 0.0112 0.0723 0.0506 0.0673 0.102 0.129 0.000228 0.000288 Wall time: 64791.67492057383 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 86 0.0546 0.00241 0.0064 0.045 0.0598 0.0769 0.0974 0.000172 0.000217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.0436 0.00209 0.00183 0.0428 0.0557 0.048 0.0522 0.000107 0.000116 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 64946.442 0.005 0.00262 0.015 0.0674 0.0467 0.0623 0.122 0.149 0.000271 0.000333 ! Validation 419 64946.442 0.005 0.00292 0.0319 0.0902 0.0495 0.0658 0.19 0.217 0.000424 0.000485 Wall time: 64946.44299049396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 86 0.0633 0.00272 0.00887 0.0476 0.0636 0.0893 0.115 0.000199 0.000256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.0559 0.00238 0.00834 0.0455 0.0594 0.1 0.111 0.000224 0.000248 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 420 65101.284 0.005 0.00267 0.0256 0.079 0.0472 0.0629 0.157 0.195 0.00035 0.000435 ! Validation 420 65101.284 0.005 0.00325 0.0255 0.0906 0.0525 0.0695 0.166 0.195 0.000371 0.000435 Wall time: 65101.284345066175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 86 0.126 0.00246 0.0764 0.0454 0.0605 0.326 0.337 0.000728 0.000752 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.0892 0.00217 0.0458 0.0438 0.0568 0.256 0.261 0.000572 0.000582 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 421 65256.049 0.005 0.00266 0.0206 0.0737 0.047 0.0628 0.141 0.175 0.000315 0.00039 ! Validation 421 65256.049 0.005 0.00299 0.179 0.239 0.0502 0.0666 0.489 0.516 0.00109 0.00115 Wall time: 65256.049495587125 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 86 0.0666 0.00275 0.0116 0.0475 0.0639 0.107 0.131 0.00024 0.000293 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.0504 0.00217 0.00708 0.0436 0.0567 0.0932 0.102 0.000208 0.000229 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 65410.816 0.005 0.00284 0.0402 0.0971 0.0487 0.065 0.2 0.244 0.000447 0.000546 ! Validation 422 65410.816 0.005 0.00296 0.0528 0.112 0.0499 0.0663 0.254 0.28 0.000566 0.000625 Wall time: 65410.81608674908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 86 0.0974 0.00256 0.0461 0.0462 0.0617 0.249 0.262 0.000555 0.000584 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.0447 0.00208 0.00314 0.0428 0.0556 0.0558 0.0683 0.000125 0.000152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 65565.582 0.005 0.0026 0.015 0.067 0.0465 0.0621 0.12 0.149 0.000268 0.000332 ! Validation 423 65565.582 0.005 0.00293 0.0308 0.0894 0.0496 0.0659 0.187 0.214 0.000418 0.000477 Wall time: 65565.58293957682 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 86 0.052 0.00238 0.00436 0.0446 0.0595 0.0637 0.0804 0.000142 0.00018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.0598 0.00208 0.0183 0.0428 0.0555 0.156 0.165 0.000349 0.000367 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 424 65720.347 0.005 0.00259 0.0185 0.0703 0.0464 0.062 0.134 0.166 0.0003 0.00037 ! Validation 424 65720.347 0.005 0.0029 0.0126 0.0706 0.0494 0.0656 0.111 0.137 0.000247 0.000305 Wall time: 65720.34774109209 ! Best model 424 0.071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 86 0.129 0.00287 0.0715 0.049 0.0653 0.312 0.326 0.000695 0.000727 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.11 0.00221 0.0653 0.044 0.0573 0.308 0.311 0.000687 0.000695 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 425 65875.125 0.005 0.0029 0.046 0.104 0.0492 0.0656 0.213 0.261 0.000475 0.000583 ! Validation 425 65875.125 0.005 0.00303 0.0163 0.077 0.0505 0.0671 0.121 0.156 0.000271 0.000347 Wall time: 65875.12551738787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 86 0.139 0.00283 0.0821 0.0479 0.0649 0.329 0.349 0.000734 0.000779 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.0854 0.00256 0.0342 0.0472 0.0617 0.217 0.225 0.000483 0.000503 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 426 66029.962 0.005 0.00264 0.0247 0.0775 0.0469 0.0626 0.157 0.191 0.00035 0.000427 ! Validation 426 66029.962 0.005 0.00337 0.213 0.28 0.0533 0.0708 0.532 0.562 0.00119 0.00125 Wall time: 66029.96268240316 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 86 0.0607 0.00279 0.00491 0.0478 0.0643 0.0673 0.0854 0.00015 0.000191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.0543 0.0021 0.0123 0.0433 0.0558 0.129 0.135 0.000287 0.000301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 427 66184.718 0.005 0.00271 0.0195 0.0736 0.0475 0.0634 0.131 0.17 0.000292 0.00038 ! Validation 427 66184.718 0.005 0.00288 0.0131 0.0707 0.0492 0.0654 0.113 0.139 0.000252 0.000311 Wall time: 66184.7184845279 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 86 0.0576 0.00246 0.00833 0.0453 0.0605 0.0915 0.111 0.000204 0.000248 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.0439 0.00205 0.00303 0.0423 0.0551 0.0526 0.0671 0.000117 0.00015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 428 66339.482 0.005 0.00257 0.0169 0.0684 0.0463 0.0618 0.125 0.158 0.000279 0.000353 ! Validation 428 66339.482 0.005 0.00287 0.0526 0.11 0.0491 0.0653 0.251 0.279 0.000561 0.000624 Wall time: 66339.48279845994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 86 0.0521 0.00242 0.00378 0.0449 0.0599 0.0614 0.0749 0.000137 0.000167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.0503 0.00206 0.00906 0.0427 0.0553 0.102 0.116 0.000227 0.000259 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 429 66494.247 0.005 0.0026 0.0179 0.0698 0.0465 0.0621 0.127 0.163 0.000283 0.000363 ! Validation 429 66494.247 0.005 0.00286 0.0146 0.0718 0.049 0.0651 0.122 0.147 0.000272 0.000329 Wall time: 66494.24740944896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 86 0.139 0.00298 0.0798 0.0508 0.0665 0.327 0.344 0.000729 0.000768 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.175 0.00358 0.103 0.0556 0.0729 0.385 0.391 0.000859 0.000872 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 66649.003 0.005 0.00264 0.0379 0.0907 0.0468 0.0626 0.182 0.237 0.000405 0.00053 ! Validation 430 66649.003 0.005 0.00443 0.291 0.38 0.0616 0.0811 0.628 0.657 0.0014 0.00147 Wall time: 66649.00383045012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 86 0.0793 0.0027 0.0253 0.0475 0.0633 0.176 0.194 0.000393 0.000432 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.0472 0.00225 0.0022 0.0444 0.0578 0.0493 0.0571 0.00011 0.000128 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 431 66803.759 0.005 0.00363 0.0511 0.124 0.0546 0.0734 0.203 0.276 0.000454 0.000615 ! Validation 431 66803.759 0.005 0.00305 0.0438 0.105 0.0507 0.0673 0.221 0.255 0.000494 0.000569 Wall time: 66803.75915222708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 86 0.0813 0.00243 0.0326 0.0452 0.0601 0.196 0.22 0.000439 0.000491 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.0439 0.00208 0.00234 0.0427 0.0555 0.0477 0.059 0.000107 0.000132 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 432 66958.520 0.005 0.00251 0.00997 0.0601 0.0456 0.061 0.0961 0.122 0.000215 0.000271 ! Validation 432 66958.520 0.005 0.00291 0.0224 0.0805 0.0495 0.0657 0.156 0.182 0.000349 0.000407 Wall time: 66958.52097505797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 86 0.064 0.00279 0.0082 0.0481 0.0644 0.0899 0.11 0.000201 0.000246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.0485 0.00206 0.0073 0.0428 0.0553 0.1 0.104 0.000223 0.000232 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 67113.354 0.005 0.00263 0.023 0.0756 0.0469 0.0625 0.148 0.185 0.00033 0.000413 ! Validation 433 67113.354 0.005 0.00285 0.0161 0.0731 0.049 0.0651 0.128 0.154 0.000286 0.000345 Wall time: 67113.35481848894 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 86 0.0779 0.00283 0.0213 0.0485 0.0649 0.157 0.178 0.000351 0.000397 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.111 0.00208 0.069 0.0427 0.0556 0.316 0.32 0.000706 0.000714 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 434 67268.107 0.005 0.00257 0.0182 0.0695 0.0463 0.0617 0.134 0.164 0.0003 0.000367 ! Validation 434 67268.107 0.005 0.00288 0.0197 0.0773 0.0492 0.0654 0.133 0.171 0.000296 0.000381 Wall time: 67268.10731194215 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 86 0.0595 0.00242 0.0111 0.045 0.0599 0.108 0.128 0.00024 0.000286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.041 0.00199 0.00117 0.0419 0.0544 0.0325 0.0416 7.24e-05 9.29e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 435 67422.869 0.005 0.00249 0.0161 0.0658 0.0455 0.0608 0.126 0.155 0.000281 0.000345 ! Validation 435 67422.869 0.005 0.00281 0.0283 0.0846 0.0486 0.0646 0.179 0.205 0.000399 0.000457 Wall time: 67422.86987061193 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 86 0.0534 0.00244 0.00457 0.045 0.0602 0.0646 0.0824 0.000144 0.000184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.0486 0.00201 0.00837 0.0422 0.0547 0.0999 0.111 0.000223 0.000249 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 67577.621 0.005 0.00251 0.0211 0.0713 0.0457 0.0611 0.14 0.177 0.000313 0.000395 ! Validation 436 67577.621 0.005 0.0028 0.0154 0.0714 0.0485 0.0645 0.123 0.151 0.000275 0.000338 Wall time: 67577.6219091909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 86 0.0582 0.00242 0.0098 0.0452 0.0599 0.104 0.121 0.000233 0.000269 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.0436 0.00209 0.00184 0.0428 0.0557 0.0481 0.0522 0.000107 0.000117 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 67732.374 0.005 0.00248 0.0137 0.0633 0.0454 0.0607 0.114 0.143 0.000254 0.000318 ! Validation 437 67732.374 0.005 0.00285 0.0383 0.0952 0.0488 0.065 0.213 0.239 0.000476 0.000532 Wall time: 67732.37414483819 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 86 0.0529 0.00242 0.00453 0.0449 0.0599 0.0686 0.082 0.000153 0.000183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.0669 0.00211 0.0247 0.0434 0.0559 0.187 0.192 0.000416 0.000428 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 438 67887.122 0.005 0.00256 0.0257 0.0769 0.0462 0.0616 0.163 0.195 0.000364 0.000436 ! Validation 438 67887.122 0.005 0.00287 0.0118 0.0693 0.0493 0.0653 0.101 0.132 0.000227 0.000296 Wall time: 67887.12287501199 ! Best model 438 0.069 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 86 0.0587 0.00243 0.01 0.045 0.0601 0.0966 0.122 0.000216 0.000272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.0807 0.00202 0.0403 0.0422 0.0548 0.241 0.244 0.000537 0.000546 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 439 68041.966 0.005 0.00264 0.0332 0.0859 0.0469 0.0626 0.162 0.222 0.000361 0.000496 ! Validation 439 68041.966 0.005 0.00279 0.0178 0.0736 0.0485 0.0644 0.12 0.163 0.000269 0.000363 Wall time: 68041.96641898016 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 86 0.0644 0.00259 0.0125 0.0466 0.062 0.119 0.136 0.000265 0.000305 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.124 0.00202 0.0834 0.0424 0.0548 0.351 0.352 0.000782 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 440 68196.720 0.005 0.00265 0.0312 0.0841 0.047 0.0627 0.171 0.215 0.000382 0.00048 ! Validation 440 68196.720 0.005 0.00281 0.0424 0.0986 0.0487 0.0646 0.211 0.251 0.000471 0.00056 Wall time: 68196.72065563593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 86 0.0537 0.0024 0.00566 0.0449 0.0597 0.0739 0.0916 0.000165 0.000205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.0417 0.00199 0.00196 0.0419 0.0543 0.0446 0.054 9.96e-05 0.00012 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 441 68351.473 0.005 0.00252 0.0216 0.072 0.0459 0.0612 0.146 0.179 0.000326 0.0004 ! Validation 441 68351.473 0.005 0.0028 0.0347 0.0906 0.0485 0.0645 0.194 0.227 0.000434 0.000506 Wall time: 68351.4732599738 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 86 0.0804 0.00254 0.0296 0.0461 0.0614 0.194 0.21 0.000434 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0562 0.00216 0.0131 0.0436 0.0566 0.134 0.139 0.000298 0.000311 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 442 68506.231 0.005 0.00268 0.0316 0.0853 0.0473 0.0631 0.172 0.217 0.000384 0.000484 ! Validation 442 68506.231 0.005 0.00291 0.0642 0.122 0.0497 0.0658 0.282 0.309 0.000629 0.000689 Wall time: 68506.23147590784 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 86 0.0621 0.00248 0.0125 0.0456 0.0607 0.114 0.136 0.000255 0.000304 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.0707 0.00199 0.0308 0.0419 0.0544 0.212 0.214 0.000473 0.000477 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 443 68660.971 0.005 0.00248 0.0172 0.0669 0.0455 0.0607 0.133 0.16 0.000296 0.000357 ! Validation 443 68660.971 0.005 0.00279 0.0124 0.0682 0.0484 0.0643 0.105 0.136 0.000234 0.000303 Wall time: 68660.97163437493 ! Best model 443 0.068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 86 0.119 0.00259 0.0675 0.0463 0.0619 0.296 0.317 0.00066 0.000707 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.053 0.002 0.0129 0.042 0.0545 0.129 0.138 0.000287 0.000309 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 444 68815.736 0.005 0.00243 0.0126 0.0612 0.0449 0.06 0.108 0.137 0.000241 0.000305 ! Validation 444 68815.736 0.005 0.00282 0.108 0.164 0.0487 0.0646 0.354 0.4 0.000791 0.000892 Wall time: 68815.73650616081 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 86 0.0538 0.00243 0.00527 0.045 0.06 0.0649 0.0885 0.000145 0.000197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.0696 0.00197 0.0302 0.0417 0.0541 0.204 0.212 0.000456 0.000473 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 445 68970.608 0.005 0.00258 0.0259 0.0775 0.0464 0.0619 0.16 0.196 0.000358 0.000438 ! Validation 445 68970.608 0.005 0.00275 0.012 0.067 0.048 0.0639 0.105 0.133 0.000235 0.000298 Wall time: 68970.60811340017 ! Best model 445 0.067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 86 0.124 0.00256 0.0729 0.0462 0.0616 0.311 0.329 0.000695 0.000734 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.0557 0.00244 0.00693 0.0462 0.0602 0.0846 0.101 0.000189 0.000226 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 69125.402 0.005 0.00247 0.0254 0.0748 0.0453 0.0605 0.157 0.194 0.000351 0.000433 ! Validation 446 69125.402 0.005 0.00314 0.07 0.133 0.0515 0.0682 0.286 0.322 0.000637 0.00072 Wall time: 69125.40248941211 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 86 0.0597 0.00255 0.0086 0.0458 0.0616 0.0943 0.113 0.000211 0.000252 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.0522 0.00207 0.0108 0.0426 0.0554 0.114 0.127 0.000254 0.000283 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 447 69280.145 0.005 0.00258 0.0302 0.0817 0.0464 0.0618 0.175 0.212 0.000392 0.000473 ! Validation 447 69280.145 0.005 0.00282 0.0163 0.0727 0.0487 0.0647 0.127 0.156 0.000284 0.000347 Wall time: 69280.14543625107 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 86 0.0629 0.00253 0.0123 0.0458 0.0613 0.117 0.135 0.000261 0.000302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.0541 0.00197 0.0146 0.0417 0.0541 0.142 0.147 0.000318 0.000329 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 448 69434.886 0.005 0.00257 0.0221 0.0736 0.0463 0.0618 0.144 0.181 0.00032 0.000405 ! Validation 448 69434.886 0.005 0.00274 0.0119 0.0668 0.048 0.0638 0.106 0.133 0.000237 0.000296 Wall time: 69434.88678737218 ! Best model 448 0.067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 86 0.0664 0.00279 0.0106 0.0483 0.0643 0.104 0.126 0.000233 0.000281 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.0898 0.00204 0.0489 0.0424 0.055 0.264 0.27 0.000588 0.000602 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 69589.637 0.005 0.00241 0.013 0.0613 0.0448 0.0599 0.11 0.139 0.000245 0.00031 ! Validation 449 69589.637 0.005 0.00281 0.0194 0.0756 0.0487 0.0646 0.136 0.17 0.000303 0.000379 Wall time: 69589.63797870092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 86 0.0749 0.00234 0.0282 0.0441 0.0589 0.188 0.205 0.000421 0.000457 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.0466 0.00198 0.00707 0.0417 0.0542 0.0953 0.102 0.000213 0.000229 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 69744.370 0.005 0.00245 0.0218 0.0708 0.0452 0.0603 0.148 0.18 0.000331 0.000401 ! Validation 450 69744.370 0.005 0.00272 0.0713 0.126 0.0479 0.0636 0.302 0.325 0.000674 0.000726 Wall time: 69744.37006108183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 86 0.0527 0.00225 0.00767 0.0436 0.0578 0.0815 0.107 0.000182 0.000238 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.0458 0.00204 0.00503 0.0424 0.055 0.0719 0.0865 0.000161 0.000193 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 69899.265 0.005 0.00248 0.0266 0.0763 0.0455 0.0607 0.155 0.199 0.000347 0.000444 ! Validation 451 69899.265 0.005 0.00276 0.0504 0.105 0.0482 0.064 0.249 0.273 0.000557 0.00061 Wall time: 69899.26514615305 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 86 0.0791 0.00244 0.0303 0.0453 0.0602 0.199 0.212 0.000444 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.129 0.002 0.0891 0.0419 0.0544 0.361 0.364 0.000806 0.000812 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 70054.089 0.005 0.00282 0.0388 0.0952 0.0483 0.0647 0.2 0.24 0.000447 0.000536 ! Validation 452 70054.089 0.005 0.00275 0.0267 0.0816 0.0481 0.0638 0.168 0.199 0.000374 0.000444 Wall time: 70054.08982663788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 86 0.0534 0.00235 0.00645 0.0443 0.059 0.0778 0.0979 0.000174 0.000218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.0574 0.00195 0.0185 0.0413 0.0538 0.162 0.166 0.000361 0.000369 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 453 70208.828 0.005 0.0024 0.013 0.0609 0.0447 0.0596 0.11 0.139 0.000246 0.00031 ! Validation 453 70208.828 0.005 0.00273 0.0113 0.0658 0.0479 0.0636 0.103 0.129 0.000229 0.000289 Wall time: 70208.82858263282 ! Best model 453 0.066 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 86 0.0574 0.00251 0.00716 0.0458 0.0611 0.0882 0.103 0.000197 0.00023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.0619 0.00196 0.0227 0.0415 0.054 0.18 0.184 0.000402 0.00041 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 454 70363.584 0.005 0.00239 0.0129 0.0607 0.0446 0.0595 0.109 0.138 0.000242 0.000309 ! Validation 454 70363.584 0.005 0.00274 0.011 0.0658 0.0481 0.0638 0.0997 0.128 0.000223 0.000285 Wall time: 70363.58449173393 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 86 0.0775 0.0026 0.0256 0.0465 0.0621 0.177 0.195 0.000396 0.000435 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.0924 0.00233 0.0459 0.045 0.0588 0.259 0.261 0.000577 0.000582 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 455 70518.323 0.005 0.00238 0.0209 0.0686 0.0445 0.0595 0.138 0.176 0.000308 0.000394 ! Validation 455 70518.323 0.005 0.00303 0.148 0.209 0.0505 0.067 0.452 0.469 0.00101 0.00105 Wall time: 70518.32394136582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 86 0.0787 0.00241 0.0304 0.045 0.0599 0.183 0.213 0.000409 0.000474 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.0469 0.00196 0.00768 0.0418 0.054 0.103 0.107 0.000229 0.000238 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 456 70673.077 0.005 0.00259 0.0296 0.0813 0.0465 0.062 0.165 0.209 0.000369 0.000468 ! Validation 456 70673.077 0.005 0.00272 0.0196 0.0739 0.0479 0.0635 0.141 0.171 0.000315 0.000381 Wall time: 70673.07798493793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 86 0.128 0.00232 0.0814 0.0443 0.0587 0.335 0.348 0.000748 0.000776 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.153 0.00196 0.114 0.0415 0.054 0.408 0.411 0.000911 0.000916 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 70827.813 0.005 0.00239 0.0173 0.065 0.0446 0.0595 0.123 0.16 0.000275 0.000357 ! Validation 457 70827.813 0.005 0.00269 0.0355 0.0893 0.0476 0.0632 0.195 0.229 0.000435 0.000512 Wall time: 70827.8137965328 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 86 0.0514 0.00236 0.00417 0.0445 0.0592 0.0588 0.0787 0.000131 0.000176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.0482 0.00189 0.0105 0.0407 0.0529 0.122 0.125 0.000272 0.000278 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 70982.642 0.005 0.00245 0.0274 0.0764 0.0452 0.0603 0.167 0.202 0.000372 0.00045 ! Validation 458 70982.642 0.005 0.00263 0.0126 0.0652 0.047 0.0625 0.108 0.137 0.000242 0.000306 Wall time: 70982.64210253116 ! Best model 458 0.065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 86 0.105 0.00259 0.0532 0.0467 0.0621 0.263 0.281 0.000587 0.000628 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.0482 0.00193 0.00956 0.0412 0.0535 0.116 0.119 0.000259 0.000266 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 459 71137.391 0.005 0.00244 0.0208 0.0696 0.0451 0.0602 0.136 0.176 0.000304 0.000392 ! Validation 459 71137.391 0.005 0.0027 0.0159 0.0699 0.0476 0.0633 0.128 0.154 0.000285 0.000343 Wall time: 71137.39127857797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 86 0.0661 0.00229 0.0203 0.044 0.0583 0.153 0.174 0.000341 0.000388 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.0426 0.00198 0.00293 0.0418 0.0543 0.0534 0.0659 0.000119 0.000147 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 460 71292.128 0.005 0.00247 0.0264 0.0758 0.0454 0.0606 0.162 0.198 0.000361 0.000442 ! Validation 460 71292.128 0.005 0.00275 0.0228 0.0778 0.0481 0.0639 0.16 0.184 0.000357 0.00041 Wall time: 71292.12867844291 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 86 0.155 0.0022 0.111 0.0429 0.0572 0.401 0.406 0.000894 0.000905 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.0835 0.002 0.0434 0.0419 0.0545 0.251 0.254 0.000561 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 461 71446.866 0.005 0.00237 0.0153 0.0628 0.0445 0.0594 0.118 0.15 0.000264 0.000336 ! Validation 461 71446.866 0.005 0.00278 0.114 0.17 0.0484 0.0643 0.386 0.411 0.000863 0.000918 Wall time: 71446.86606718879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 86 0.0629 0.00231 0.0166 0.0439 0.0586 0.144 0.157 0.000321 0.000351 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.0403 0.00192 0.00183 0.0411 0.0534 0.0416 0.0521 9.3e-05 0.000116 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 71601.607 0.005 0.0024 0.0221 0.0702 0.0448 0.0597 0.142 0.181 0.000317 0.000404 ! Validation 462 71601.607 0.005 0.00262 0.0383 0.0907 0.0469 0.0624 0.214 0.238 0.000477 0.000532 Wall time: 71601.60781604378 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 86 0.0735 0.0022 0.0294 0.0428 0.0572 0.187 0.209 0.000418 0.000466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.0861 0.00185 0.0492 0.0403 0.0523 0.267 0.27 0.000596 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 463 71756.343 0.005 0.00231 0.0137 0.0598 0.0438 0.0585 0.113 0.142 0.000253 0.000318 ! Validation 463 71756.343 0.005 0.00261 0.0156 0.0678 0.0468 0.0622 0.12 0.152 0.000269 0.00034 Wall time: 71756.34369669994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 86 0.0716 0.0023 0.0255 0.044 0.0585 0.179 0.195 0.000401 0.000434 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.234 0.00206 0.193 0.0426 0.0553 0.533 0.535 0.00119 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 464 71911.074 0.005 0.00236 0.0222 0.0693 0.0443 0.0592 0.145 0.181 0.000323 0.000405 ! Validation 464 71911.074 0.005 0.00281 0.0796 0.136 0.0487 0.0646 0.312 0.344 0.000696 0.000767 Wall time: 71911.07455634093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 86 0.0532 0.00227 0.00785 0.0437 0.058 0.0906 0.108 0.000202 0.000241 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.0634 0.00185 0.0265 0.0403 0.0524 0.194 0.198 0.000434 0.000442 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 465 72065.897 0.005 0.00247 0.0284 0.0778 0.0455 0.0606 0.165 0.205 0.000369 0.000458 ! Validation 465 72065.897 0.005 0.00259 0.0124 0.0642 0.0466 0.062 0.102 0.136 0.000228 0.000303 Wall time: 72065.89748570789 ! Best model 465 0.064 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 86 0.112 0.00236 0.0647 0.0444 0.0592 0.3 0.31 0.000669 0.000692 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.15 0.00186 0.113 0.0406 0.0525 0.408 0.41 0.00091 0.000914 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 72220.650 0.005 0.00229 0.0145 0.0603 0.0437 0.0583 0.111 0.147 0.000248 0.000327 ! Validation 466 72220.650 0.005 0.00259 0.0656 0.117 0.0466 0.062 0.279 0.312 0.000622 0.000697 Wall time: 72220.65048879385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 86 0.0586 0.0024 0.0106 0.0447 0.0597 0.0961 0.126 0.000215 0.00028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.0613 0.00208 0.0198 0.0427 0.0555 0.167 0.171 0.000374 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 467 72375.393 0.005 0.00255 0.0327 0.0837 0.0461 0.0615 0.179 0.22 0.000399 0.000492 ! Validation 467 72375.393 0.005 0.00276 0.0125 0.0677 0.0481 0.064 0.108 0.136 0.000241 0.000304 Wall time: 72375.393412414 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 86 0.155 0.00355 0.0843 0.054 0.0726 0.315 0.354 0.000704 0.00079 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.218 0.00377 0.142 0.0571 0.0748 0.454 0.459 0.00101 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 468 72530.122 0.005 0.00263 0.0416 0.0942 0.0462 0.0625 0.164 0.248 0.000366 0.000554 ! Validation 468 72530.122 0.005 0.00472 0.191 0.285 0.0634 0.0837 0.487 0.532 0.00109 0.00119 Wall time: 72530.12234099396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 86 0.0516 0.00235 0.00474 0.0439 0.059 0.0665 0.0839 0.000148 0.000187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.0381 0.00186 0.000941 0.0404 0.0525 0.0309 0.0374 6.9e-05 8.34e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 469 72684.865 0.005 0.00274 0.0294 0.0843 0.0476 0.0638 0.16 0.209 0.000357 0.000467 ! Validation 469 72684.865 0.005 0.00257 0.0416 0.0931 0.0465 0.0618 0.217 0.249 0.000485 0.000555 Wall time: 72684.86601058999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 86 0.0534 0.00227 0.00793 0.0434 0.0581 0.0905 0.109 0.000202 0.000242 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.0491 0.00187 0.0118 0.0405 0.0526 0.121 0.132 0.00027 0.000295 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 470 72839.606 0.005 0.00226 0.00942 0.0547 0.0434 0.058 0.0965 0.118 0.000215 0.000264 ! Validation 470 72839.606 0.005 0.0026 0.0138 0.0657 0.0467 0.0621 0.117 0.143 0.000261 0.000319 Wall time: 72839.60618705489 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 86 0.0475 0.00209 0.00563 0.042 0.0558 0.0757 0.0914 0.000169 0.000204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.068 0.00185 0.0311 0.0403 0.0524 0.212 0.215 0.000473 0.000479 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 471 72994.420 0.005 0.00226 0.0127 0.058 0.0434 0.058 0.109 0.137 0.000244 0.000306 ! Validation 471 72994.420 0.005 0.00256 0.0105 0.0617 0.0464 0.0616 0.0972 0.125 0.000217 0.000279 Wall time: 72994.4204232539 ! Best model 471 0.062 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 86 0.118 0.00253 0.0669 0.0461 0.0613 0.304 0.315 0.000678 0.000704 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.159 0.00208 0.118 0.0429 0.0556 0.415 0.418 0.000926 0.000933 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 472 73149.165 0.005 0.00234 0.0168 0.0636 0.0441 0.0589 0.127 0.158 0.000283 0.000352 ! Validation 472 73149.165 0.005 0.00275 0.0543 0.109 0.0482 0.0639 0.246 0.284 0.00055 0.000634 Wall time: 73149.16511260811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 86 0.0518 0.00233 0.00526 0.0436 0.0588 0.0742 0.0883 0.000166 0.000197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.0602 0.0018 0.0241 0.0399 0.0517 0.185 0.189 0.000413 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 473 73303.907 0.005 0.00231 0.0174 0.0635 0.0439 0.0585 0.133 0.161 0.000296 0.000359 ! Validation 473 73303.907 0.005 0.00254 0.0119 0.0626 0.0462 0.0614 0.106 0.133 0.000237 0.000296 Wall time: 73303.90755070094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 86 0.0665 0.00227 0.021 0.0432 0.0581 0.16 0.177 0.000358 0.000395 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.0472 0.00199 0.00744 0.0418 0.0543 0.0924 0.105 0.000206 0.000235 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 474 73458.640 0.005 0.0025 0.0412 0.0912 0.0457 0.0609 0.208 0.247 0.000465 0.000552 ! Validation 474 73458.640 0.005 0.00267 0.0192 0.0726 0.0474 0.0629 0.139 0.169 0.000309 0.000377 Wall time: 73458.64057938289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 86 0.0691 0.00224 0.0242 0.0431 0.0577 0.165 0.19 0.000368 0.000423 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.0535 0.00182 0.0171 0.04 0.052 0.157 0.159 0.00035 0.000356 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 73613.373 0.005 0.00231 0.0187 0.0648 0.0438 0.0585 0.13 0.166 0.00029 0.000372 ! Validation 475 73613.373 0.005 0.00251 0.012 0.0622 0.0459 0.061 0.105 0.133 0.000234 0.000297 Wall time: 73613.37309844093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 86 0.0562 0.00227 0.0109 0.0434 0.058 0.105 0.127 0.000234 0.000284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.0386 0.0018 0.00273 0.0399 0.0516 0.0542 0.0637 0.000121 0.000142 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 476 73768.101 0.005 0.00235 0.0255 0.0724 0.0443 0.059 0.16 0.194 0.000358 0.000434 ! Validation 476 73768.101 0.005 0.00253 0.0238 0.0744 0.0461 0.0613 0.161 0.188 0.00036 0.000419 Wall time: 73768.10136329988 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 86 0.0521 0.00221 0.00779 0.0429 0.0573 0.0896 0.108 0.0002 0.00024 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.0387 0.00188 0.00107 0.0407 0.0528 0.0357 0.0399 7.96e-05 8.91e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 477 73922.827 0.005 0.00222 0.0114 0.0558 0.043 0.0574 0.103 0.13 0.00023 0.000291 ! Validation 477 73922.827 0.005 0.00256 0.0264 0.0776 0.0465 0.0617 0.171 0.198 0.000381 0.000442 Wall time: 73922.8274793718 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 86 0.0478 0.00214 0.0049 0.0424 0.0564 0.0723 0.0853 0.000161 0.00019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.0629 0.00188 0.0253 0.0409 0.0528 0.19 0.194 0.000424 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 478 74077.971 0.005 0.0023 0.0193 0.0652 0.0438 0.0584 0.133 0.169 0.000298 0.000378 ! Validation 478 74077.971 0.005 0.00259 0.0119 0.0636 0.0467 0.062 0.102 0.133 0.000227 0.000296 Wall time: 74077.97144826408 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 86 0.0565 0.00237 0.00905 0.0441 0.0593 0.1 0.116 0.000224 0.000259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.0515 0.00201 0.0112 0.0424 0.0547 0.126 0.129 0.000281 0.000287 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 479 74232.707 0.005 0.00248 0.0301 0.0798 0.0456 0.0607 0.173 0.211 0.000385 0.000472 ! Validation 479 74232.707 0.005 0.0027 0.0133 0.0673 0.0479 0.0633 0.114 0.14 0.000254 0.000314 Wall time: 74232.70712622907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 86 0.0802 0.00236 0.0329 0.0445 0.0592 0.207 0.221 0.000461 0.000493 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.0968 0.00198 0.0573 0.0416 0.0542 0.29 0.292 0.000646 0.000651 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 480 74387.433 0.005 0.00224 0.00988 0.0547 0.0432 0.0577 0.0951 0.121 0.000212 0.00027 ! Validation 480 74387.433 0.005 0.00271 0.0312 0.0854 0.0479 0.0635 0.174 0.215 0.000388 0.00048 Wall time: 74387.43342114706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 86 0.0748 0.00205 0.0338 0.0415 0.0551 0.209 0.224 0.000467 0.0005 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.118 0.00181 0.0822 0.0401 0.0519 0.344 0.349 0.000769 0.00078 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 74542.167 0.005 0.00227 0.0175 0.0629 0.0435 0.0581 0.133 0.161 0.000298 0.000359 ! Validation 481 74542.167 0.005 0.0025 0.0237 0.0737 0.0459 0.061 0.153 0.187 0.000342 0.000418 Wall time: 74542.167350139 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 86 0.0532 0.00236 0.00595 0.0441 0.0592 0.0727 0.094 0.000162 0.00021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.0887 0.00198 0.0492 0.0417 0.0542 0.265 0.27 0.000592 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 482 74696.927 0.005 0.00223 0.018 0.0625 0.0431 0.0575 0.132 0.163 0.000295 0.000365 ! Validation 482 74696.927 0.005 0.00262 0.0128 0.0652 0.047 0.0624 0.109 0.138 0.000244 0.000307 Wall time: 74696.92724628886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 86 0.0515 0.00192 0.0131 0.0403 0.0534 0.121 0.139 0.000269 0.000311 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.104 0.0018 0.0683 0.04 0.0517 0.317 0.318 0.000708 0.000711 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 483 74851.676 0.005 0.00242 0.0288 0.0773 0.045 0.06 0.16 0.207 0.000357 0.000462 ! Validation 483 74851.676 0.005 0.0025 0.0227 0.0728 0.0459 0.061 0.149 0.184 0.000333 0.00041 Wall time: 74851.67644715076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 86 0.0871 0.00231 0.041 0.044 0.0585 0.233 0.247 0.000521 0.000551 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.154 0.00176 0.118 0.0395 0.0512 0.418 0.419 0.000932 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 484 75006.505 0.005 0.00225 0.0137 0.0587 0.0433 0.0578 0.115 0.143 0.000257 0.000318 ! Validation 484 75006.505 0.005 0.00245 0.049 0.098 0.0453 0.0603 0.24 0.27 0.000535 0.000602 Wall time: 75006.50553839002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 86 4.58 0.158 1.43 0.361 0.484 1.19 1.45 0.00266 0.00325 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 14.1 0.173 10.6 0.38 0.507 3.97 3.97 0.00886 0.00887 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 75161.261 0.005 0.0156 1.11 1.42 0.0803 0.152 0.622 1.28 0.00139 0.00286 ! Validation 485 75161.261 0.005 0.163 7.63 10.9 0.369 0.492 3.05 3.37 0.00681 0.00751 Wall time: 75161.26121306187 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 86 0.126 0.00527 0.0205 0.0674 0.0884 0.143 0.174 0.000319 0.000389 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.0984 0.00478 0.00283 0.0649 0.0842 0.0542 0.0649 0.000121 0.000145 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 486 75316.049 0.005 0.0307 0.669 1.28 0.14 0.214 0.628 0.997 0.0014 0.00223 ! Validation 486 75316.049 0.005 0.00575 0.0737 0.189 0.0705 0.0924 0.262 0.331 0.000585 0.000738 Wall time: 75316.0498965811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 86 0.0785 0.00337 0.011 0.0533 0.0707 0.103 0.128 0.000231 0.000286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.0772 0.00293 0.0187 0.0511 0.0659 0.16 0.166 0.000358 0.000371 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 487 75470.803 0.005 0.00416 0.0192 0.102 0.0594 0.0785 0.132 0.169 0.000296 0.000377 ! Validation 487 75470.803 0.005 0.00382 0.0207 0.0971 0.0571 0.0753 0.138 0.175 0.000307 0.000391 Wall time: 75470.80312865088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 86 0.0657 0.00272 0.0112 0.048 0.0636 0.107 0.129 0.00024 0.000288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.0728 0.00245 0.0239 0.0464 0.0603 0.179 0.188 0.000399 0.00042 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 75625.568 0.005 0.00314 0.0108 0.0737 0.0514 0.0683 0.101 0.127 0.000225 0.000283 ! Validation 488 75625.568 0.005 0.0033 0.0182 0.0842 0.0527 0.07 0.13 0.165 0.000291 0.000367 Wall time: 75625.56877283705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 86 0.0724 0.00278 0.0168 0.0481 0.0643 0.137 0.158 0.000305 0.000352 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0518 0.00223 0.00718 0.0442 0.0575 0.088 0.103 0.000196 0.00023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 489 75780.322 0.005 0.0028 0.01 0.0661 0.0483 0.0645 0.0979 0.122 0.000219 0.000272 ! Validation 489 75780.322 0.005 0.00305 0.0285 0.0896 0.0507 0.0673 0.169 0.206 0.000377 0.000459 Wall time: 75780.32288187603 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 86 0.064 0.0028 0.00804 0.0476 0.0645 0.0844 0.109 0.000188 0.000244 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.0639 0.00212 0.0215 0.0432 0.0561 0.167 0.179 0.000373 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 490 75935.101 0.005 0.00264 0.00878 0.0617 0.0469 0.0627 0.0905 0.114 0.000202 0.000255 ! Validation 490 75935.101 0.005 0.00293 0.0145 0.0731 0.0496 0.0659 0.117 0.147 0.000261 0.000328 Wall time: 75935.10179165984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 86 0.0535 0.00251 0.00336 0.0458 0.061 0.0561 0.0707 0.000125 0.000158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.0688 0.00205 0.0277 0.0424 0.0552 0.194 0.203 0.000433 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 491 76089.934 0.005 0.00255 0.00643 0.0573 0.046 0.0615 0.0775 0.0977 0.000173 0.000218 ! Validation 491 76089.934 0.005 0.00284 0.0131 0.0699 0.0489 0.0649 0.108 0.139 0.000242 0.000311 Wall time: 76089.93458716106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 86 0.0563 0.00248 0.00677 0.0453 0.0606 0.081 0.1 0.000181 0.000224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.0614 0.002 0.0213 0.042 0.0545 0.167 0.178 0.000373 0.000397 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 492 76244.693 0.005 0.00247 0.00561 0.055 0.0453 0.0606 0.0727 0.0912 0.000162 0.000204 ! Validation 492 76244.693 0.005 0.00278 0.013 0.0687 0.0484 0.0643 0.11 0.139 0.000245 0.00031 Wall time: 76244.69348881999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 86 0.0551 0.00254 0.00431 0.0456 0.0614 0.0651 0.08 0.000145 0.000179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.0464 0.00195 0.00741 0.0414 0.0538 0.089 0.105 0.000199 0.000234 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 493 76399.444 0.005 0.00242 0.00668 0.0551 0.0448 0.06 0.0798 0.0996 0.000178 0.000222 ! Validation 493 76399.444 0.005 0.00272 0.0206 0.075 0.0478 0.0635 0.144 0.175 0.000322 0.00039 Wall time: 76399.44447950413 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 86 0.0531 0.00242 0.00461 0.0449 0.06 0.0664 0.0827 0.000148 0.000185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.0508 0.00192 0.0124 0.041 0.0534 0.126 0.135 0.000281 0.000302 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 494 76554.195 0.005 0.00238 0.00592 0.0535 0.0444 0.0594 0.0749 0.0938 0.000167 0.000209 ! Validation 494 76554.195 0.005 0.00268 0.0139 0.0676 0.0475 0.0631 0.116 0.144 0.000259 0.000321 Wall time: 76554.1953369989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 86 0.0504 0.00224 0.0056 0.0432 0.0577 0.0677 0.0911 0.000151 0.000203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.0757 0.00188 0.038 0.0406 0.0528 0.233 0.238 0.000519 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 495 76708.964 0.005 0.00234 0.00578 0.0526 0.0441 0.0589 0.0739 0.0926 0.000165 0.000207 ! Validation 495 76708.964 0.005 0.00265 0.0144 0.0674 0.0472 0.0627 0.111 0.146 0.000248 0.000326 Wall time: 76708.96491956012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 86 0.0495 0.00216 0.00642 0.0426 0.0566 0.0819 0.0977 0.000183 0.000218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.0613 0.00186 0.0242 0.0404 0.0525 0.183 0.189 0.000409 0.000423 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 496 76863.721 0.005 0.00231 0.00643 0.0526 0.0438 0.0586 0.0787 0.0977 0.000176 0.000218 ! Validation 496 76863.721 0.005 0.00261 0.0116 0.0639 0.0468 0.0623 0.102 0.132 0.000228 0.000294 Wall time: 76863.72147041699 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 86 0.052 0.0024 0.00404 0.0445 0.0597 0.0594 0.0774 0.000132 0.000173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.0528 0.00184 0.016 0.0403 0.0523 0.147 0.154 0.000327 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 497 77019.017 0.005 0.00228 0.00538 0.051 0.0435 0.0582 0.0718 0.0894 0.00016 0.000199 ! Validation 497 77019.017 0.005 0.00258 0.0124 0.0641 0.0466 0.0619 0.108 0.136 0.000241 0.000303 Wall time: 77019.01743368618 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 86 0.0455 0.00216 0.00242 0.0424 0.0566 0.0471 0.06 0.000105 0.000134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.0436 0.00182 0.00717 0.04 0.052 0.092 0.103 0.000205 0.00023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 498 77173.779 0.005 0.00226 0.0068 0.052 0.0433 0.0579 0.0802 0.1 0.000179 0.000224 ! Validation 498 77173.779 0.005 0.00256 0.0171 0.0683 0.0463 0.0616 0.131 0.159 0.000293 0.000356 Wall time: 77173.77991215792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 86 0.0526 0.00221 0.00842 0.0431 0.0572 0.0825 0.112 0.000184 0.00025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.0449 0.00182 0.00856 0.04 0.0519 0.104 0.113 0.000231 0.000252 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 77328.547 0.005 0.00224 0.00641 0.0512 0.0431 0.0577 0.0777 0.0975 0.000173 0.000218 ! Validation 499 77328.547 0.005 0.00255 0.0161 0.0672 0.0463 0.0616 0.127 0.155 0.000284 0.000345 Wall time: 77328.54763632175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 86 0.0513 0.00222 0.0069 0.0428 0.0574 0.0838 0.101 0.000187 0.000226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.0457 0.0018 0.00982 0.0398 0.0516 0.112 0.121 0.00025 0.000269 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 500 77483.326 0.005 0.00223 0.00655 0.0512 0.043 0.0575 0.0799 0.0986 0.000178 0.00022 ! Validation 500 77483.326 0.005 0.00252 0.0144 0.0648 0.046 0.0612 0.118 0.146 0.000263 0.000326 Wall time: 77483.32702891901 ! Stop training: max epochs Wall time: 77483.3479291671 Cumulative wall time: 77483.3479291671 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 = 0.041113 f_rmse = 0.054286 e_mae = 0.173495 e_rmse = 0.207967 e/N_mae = 0.000387 e/N_rmse = 0.000464 f_mae = 0.041113 f_rmse = 0.054286 e_mae = 0.173495 e_rmse = 0.207967 e/N_mae = 0.000387 e/N_rmse = 0.000464 Train end time: 2024-12-09_08:33:46 Training duration: 21h 35m 5s