Train start time: 2024-12-07_07:57:27 Torch device: cuda Processing dataset... Loaded data: Batch(atomic_numbers=[2400000, 1], batch=[2400000], cell=[6000, 3, 3], edge_cell_shift=[82543766, 3], edge_index=[2, 82543766], forces=[2400000, 3], pbc=[6000, 3], pos=[2400000, 3], ptr=[6001], total_energy=[6000, 1]) processed data size: ~3295.80 MB Cached processed data to disk Done! Successfully loaded the data set of type ASEDataset(6000)... Replace string dataset_per_atom_total_energy_mean to -347.4150504797695 Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -347.415050]. Replace string dataset_forces_rms to 1.195032445765871 Initially outputs are globally scaled by: 1.195032445765871, 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 23.4 1.01 3.13 0.892 1.2 2.05 2.11 0.00512 0.00529 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 5.991 0.005 0.99 4.51 24.3 0.885 1.19 2.18 2.54 0.00544 0.00634 Wall time: 5.990914256777614 ! Best model 0 24.308 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 24 23.3 0.995 3.39 0.887 1.19 1.87 2.2 0.00469 0.0055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 25.2 1.01 4.88 0.892 1.2 2.59 2.64 0.00647 0.0066 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 44.566 0.005 1 7.6 27.6 0.888 1.19 2.71 3.32 0.00679 0.00829 ! Validation 1 44.566 0.005 0.99 6.17 26 0.885 1.19 2.6 2.97 0.0065 0.00742 Wall time: 44.566242740955204 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 24 25.7 1.02 5.34 0.895 1.21 2.38 2.76 0.00595 0.00691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 22 1.01 1.75 0.891 1.2 1.49 1.58 0.00372 0.00395 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 2 82.597 0.005 1 5.89 25.9 0.888 1.19 2.42 2.9 0.00606 0.00726 ! Validation 2 82.597 0.005 0.989 3.77 23.5 0.885 1.19 1.93 2.32 0.00483 0.0058 Wall time: 82.59724288759753 ! Best model 2 23.548 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 24 25.8 0.998 5.88 0.889 1.19 2.4 2.9 0.00599 0.00724 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 23.4 1.01 3.13 0.891 1.2 2.05 2.11 0.00512 0.00528 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 120.604 0.005 0.999 5.84 25.8 0.888 1.19 2.36 2.89 0.00591 0.00722 ! Validation 3 120.604 0.005 0.989 4.82 24.6 0.885 1.19 2.26 2.62 0.00565 0.00656 Wall time: 120.60453729191795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 24 26.3 1.02 5.93 0.894 1.21 2.47 2.91 0.00617 0.00728 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 22.3 1.01 2.02 0.891 1.2 1.61 1.7 0.00404 0.00424 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 4 158.592 0.005 0.999 5.6 25.6 0.888 1.19 2.35 2.83 0.00587 0.00707 ! Validation 4 158.592 0.005 0.988 4 23.8 0.884 1.19 2.01 2.39 0.00503 0.00597 Wall time: 158.59264158364385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 24 23.8 0.992 3.95 0.885 1.19 1.94 2.38 0.00486 0.00594 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 22.1 1.01 1.91 0.89 1.2 1.56 1.65 0.00391 0.00412 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 196.592 0.005 0.998 5.32 25.3 0.887 1.19 2.27 2.77 0.00568 0.00691 ! Validation 5 196.592 0.005 0.987 3.87 23.6 0.884 1.19 1.97 2.35 0.00494 0.00588 Wall time: 196.59263539500535 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 24 23.8 1.01 3.6 0.891 1.2 1.88 2.27 0.00471 0.00567 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 21.1 1.01 0.938 0.888 1.2 1.03 1.16 0.00259 0.00289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 6 234.597 0.005 0.996 5.04 25 0.886 1.19 2.21 2.69 0.00553 0.00673 ! Validation 6 234.597 0.005 0.983 2.97 22.6 0.881 1.18 1.65 2.06 0.00412 0.00515 Wall time: 234.5971964658238 ! Best model 6 22.627 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 24 20.8 0.937 2.1 0.857 1.16 1.32 1.73 0.0033 0.00433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 24.9 0.957 5.78 0.861 1.17 2.83 2.87 0.00707 0.00718 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 7 273.497 0.005 0.978 3.94 23.5 0.877 1.18 1.92 2.39 0.00481 0.00597 ! Validation 7 273.497 0.005 0.935 5.37 24.1 0.857 1.16 2.44 2.77 0.0061 0.00692 Wall time: 273.4969712346792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 24 16.8 0.8 0.781 0.796 1.07 0.789 1.06 0.00197 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 16.7 0.812 0.507 0.795 1.08 0.74 0.851 0.00185 0.00213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 8 311.476 0.005 0.887 2.14 19.9 0.836 1.13 1.41 1.76 0.00352 0.00441 ! Validation 8 311.476 0.005 0.8 1.26 17.3 0.795 1.07 1.02 1.34 0.00254 0.00335 Wall time: 311.4762620870024 ! Best model 8 17.255 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 24 13.1 0.6 1.06 0.695 0.926 1.01 1.23 0.00253 0.00308 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 12.1 0.58 0.468 0.679 0.91 0.719 0.817 0.0018 0.00204 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 9 349.477 0.005 0.695 1.52 15.4 0.744 0.998 1.17 1.48 0.00292 0.0037 ! Validation 9 349.477 0.005 0.583 1.15 12.8 0.685 0.912 1.01 1.28 0.00251 0.0032 Wall time: 349.4770617308095 ! Best model 9 12.801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 24 11 0.431 2.32 0.586 0.785 1.59 1.82 0.00396 0.00455 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 9.63 0.424 1.15 0.581 0.778 1.2 1.28 0.00301 0.0032 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 10 387.456 0.005 0.507 1.21 11.3 0.637 0.852 1.03 1.3 0.00258 0.00325 ! Validation 10 387.456 0.005 0.421 1.69 10.1 0.582 0.775 1.3 1.55 0.00324 0.00388 Wall time: 387.4561752076261 ! Best model 10 10.103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 24 9.22 0.381 1.6 0.552 0.738 1.31 1.51 0.00328 0.00377 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 7.89 0.367 0.558 0.54 0.724 0.758 0.892 0.00189 0.00223 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 11 426.209 0.005 0.407 2.02 10.2 0.571 0.763 1.41 1.7 0.00353 0.00426 ! Validation 11 426.209 0.005 0.368 0.892 8.25 0.544 0.725 0.916 1.13 0.00229 0.00282 Wall time: 426.20975198596716 ! Best model 11 8.247 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 24 7.62 0.33 1.01 0.511 0.687 0.975 1.2 0.00244 0.00301 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 6.75 0.313 0.495 0.499 0.668 0.709 0.841 0.00177 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 12 464.474 0.005 0.356 0.995 8.12 0.534 0.714 0.946 1.19 0.00237 0.00298 ! Validation 12 464.474 0.005 0.317 0.889 7.23 0.504 0.673 0.896 1.13 0.00224 0.00282 Wall time: 464.47498687775806 ! Best model 12 7.226 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 24 7.04 0.284 1.35 0.479 0.637 1.16 1.39 0.0029 0.00347 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 8.2 0.272 2.76 0.465 0.623 1.93 1.98 0.00483 0.00496 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 13 504.922 0.005 0.307 1.07 7.22 0.496 0.663 0.97 1.23 0.00243 0.00308 ! Validation 13 504.922 0.005 0.278 3.25 8.81 0.472 0.63 1.98 2.15 0.00495 0.00538 Wall time: 504.92204883880913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 24 5.95 0.26 0.757 0.458 0.609 0.806 1.04 0.00202 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 5.57 0.249 0.596 0.447 0.596 0.805 0.923 0.00201 0.00231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 14 542.848 0.005 0.279 1.31 6.88 0.472 0.631 1.11 1.37 0.00278 0.00343 ! Validation 14 542.848 0.005 0.256 0.883 5.99 0.454 0.604 0.904 1.12 0.00226 0.00281 Wall time: 542.8487288309261 ! Best model 14 5.994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 24 6.59 0.259 1.4 0.455 0.608 1.27 1.42 0.00318 0.00354 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 6.01 0.231 1.38 0.431 0.575 1.33 1.4 0.00333 0.0035 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 15 580.734 0.005 0.258 1.29 6.45 0.454 0.606 1.11 1.36 0.00278 0.0034 ! Validation 15 580.734 0.005 0.238 1.74 6.51 0.438 0.583 1.39 1.58 0.00348 0.00394 Wall time: 580.734491806943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 24 5.63 0.235 0.93 0.435 0.579 0.937 1.15 0.00234 0.00288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 4.62 0.215 0.315 0.417 0.554 0.531 0.671 0.00133 0.00168 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 16 618.658 0.005 0.244 1.23 6.11 0.442 0.59 1.07 1.33 0.00266 0.00332 ! Validation 16 618.658 0.005 0.222 0.651 5.09 0.424 0.563 0.797 0.964 0.00199 0.00241 Wall time: 618.6585494927131 ! Best model 16 5.089 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 24 5.06 0.216 0.741 0.417 0.555 0.751 1.03 0.00188 0.00257 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 5.26 0.194 1.37 0.398 0.527 1.34 1.4 0.00334 0.0035 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 17 656.583 0.005 0.224 0.549 5.02 0.424 0.565 0.707 0.882 0.00177 0.0022 ! Validation 17 656.583 0.005 0.201 1.57 5.6 0.404 0.536 1.32 1.5 0.0033 0.00374 Wall time: 656.5837160698138 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 24 4.27 0.195 0.369 0.395 0.528 0.598 0.726 0.0015 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 4.01 0.18 0.404 0.383 0.507 0.637 0.759 0.00159 0.0019 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 18 694.513 0.005 0.206 0.938 5.05 0.407 0.542 0.949 1.17 0.00237 0.00291 ! Validation 18 694.513 0.005 0.187 0.822 4.57 0.389 0.517 0.866 1.08 0.00216 0.00271 Wall time: 694.5137444669381 ! Best model 18 4.570 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 24 4.9 0.19 1.09 0.391 0.521 1.12 1.25 0.00281 0.00312 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 3.72 0.172 0.287 0.373 0.495 0.532 0.641 0.00133 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 19 732.538 0.005 0.194 1.13 5.01 0.394 0.526 1.03 1.27 0.00258 0.00318 ! Validation 19 732.538 0.005 0.179 0.513 4.09 0.379 0.505 0.706 0.856 0.00177 0.00214 Wall time: 732.5381241687573 ! Best model 19 4.088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 24 4.04 0.182 0.409 0.382 0.509 0.601 0.764 0.0015 0.00191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 3.36 0.163 0.11 0.364 0.482 0.336 0.397 0.000839 0.000991 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 20 770.479 0.005 0.185 0.788 4.49 0.385 0.514 0.866 1.07 0.00216 0.00267 ! Validation 20 770.479 0.005 0.169 0.448 3.82 0.369 0.491 0.624 0.799 0.00156 0.002 Wall time: 770.4790020659566 ! Best model 20 3.823 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 24 3.83 0.168 0.466 0.367 0.49 0.614 0.816 0.00153 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 3.53 0.153 0.467 0.351 0.467 0.725 0.817 0.00181 0.00204 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 21 808.400 0.005 0.174 0.594 4.08 0.373 0.499 0.737 0.924 0.00184 0.00231 ! Validation 21 808.400 0.005 0.16 0.507 3.7 0.357 0.478 0.703 0.851 0.00176 0.00213 Wall time: 808.4001605450176 ! Best model 21 3.701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 24 3.92 0.165 0.622 0.363 0.485 0.778 0.943 0.00195 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 5.07 0.148 2.11 0.346 0.46 1.69 1.73 0.00423 0.00434 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 22 846.573 0.005 0.166 0.973 4.3 0.364 0.487 0.956 1.18 0.00239 0.00296 ! Validation 22 846.573 0.005 0.154 1.76 4.84 0.351 0.469 1.43 1.58 0.00358 0.00396 Wall time: 846.5735673247837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 24 3.34 0.154 0.261 0.349 0.469 0.484 0.611 0.00121 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 2.93 0.142 0.1 0.338 0.45 0.339 0.378 0.000848 0.000945 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 23 891.753 0.005 0.16 0.623 3.83 0.357 0.479 0.761 0.95 0.0019 0.00237 ! Validation 23 891.753 0.005 0.147 0.433 3.36 0.342 0.458 0.612 0.786 0.00153 0.00197 Wall time: 891.7536301598884 ! Best model 23 3.364 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 24 3.79 0.155 0.686 0.348 0.471 0.819 0.99 0.00205 0.00247 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 3.22 0.136 0.49 0.331 0.441 0.747 0.837 0.00187 0.00209 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 24 929.733 0.005 0.154 0.699 3.78 0.349 0.469 0.816 0.999 0.00204 0.0025 ! Validation 24 929.733 0.005 0.141 0.487 3.31 0.336 0.449 0.693 0.834 0.00173 0.00209 Wall time: 929.7330515738577 ! Best model 24 3.315 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 24 2.99 0.142 0.152 0.336 0.45 0.392 0.466 0.000979 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 3.29 0.132 0.647 0.326 0.435 0.888 0.961 0.00222 0.0024 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 25 967.725 0.005 0.148 0.658 3.63 0.343 0.461 0.792 0.979 0.00198 0.00245 ! Validation 25 967.725 0.005 0.137 0.555 3.29 0.33 0.442 0.747 0.89 0.00187 0.00223 Wall time: 967.725273525808 ! Best model 25 3.285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 24 4.43 0.14 1.63 0.333 0.447 1.4 1.53 0.00351 0.00382 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 4.19 0.131 1.58 0.324 0.432 1.45 1.5 0.00363 0.00376 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 26 1005.699 0.005 0.145 0.968 3.86 0.338 0.454 0.944 1.17 0.00236 0.00292 ! Validation 26 1005.699 0.005 0.135 2.7 5.39 0.328 0.438 1.82 1.96 0.00455 0.00491 Wall time: 1005.6990061346442 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 24 3.81 0.139 1.03 0.332 0.445 1.1 1.21 0.00274 0.00303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 5.45 0.126 2.92 0.319 0.425 2.01 2.04 0.00503 0.0051 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 27 1043.678 0.005 0.141 0.66 3.49 0.334 0.449 0.762 0.964 0.00191 0.00241 ! Validation 27 1043.678 0.005 0.13 2.24 4.84 0.321 0.43 1.68 1.79 0.00419 0.00448 Wall time: 1043.678589912597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 24 3.17 0.134 0.494 0.325 0.437 0.673 0.84 0.00168 0.0021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 2.55 0.123 0.0842 0.314 0.419 0.286 0.347 0.000715 0.000867 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 1081.659 0.005 0.137 0.664 3.41 0.329 0.443 0.783 0.977 0.00196 0.00244 ! Validation 28 1081.659 0.005 0.126 0.38 2.9 0.317 0.424 0.575 0.737 0.00144 0.00184 Wall time: 1081.6591179547831 ! Best model 28 2.897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 24 3.01 0.134 0.331 0.325 0.438 0.521 0.687 0.0013 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 3.29 0.12 0.899 0.309 0.413 1.08 1.13 0.0027 0.00283 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 29 1119.644 0.005 0.133 0.589 3.25 0.324 0.436 0.754 0.922 0.00189 0.00231 ! Validation 29 1119.644 0.005 0.122 0.608 3.06 0.312 0.418 0.785 0.932 0.00196 0.00233 Wall time: 1119.6449381937273 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 24 3.81 0.13 1.21 0.321 0.431 1.13 1.32 0.00282 0.00329 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 3.07 0.117 0.731 0.305 0.409 0.949 1.02 0.00237 0.00255 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 30 1157.627 0.005 0.129 0.676 3.26 0.319 0.429 0.781 0.973 0.00195 0.00243 ! Validation 30 1157.627 0.005 0.12 1.58 3.98 0.309 0.414 1.36 1.5 0.00339 0.00376 Wall time: 1157.6276429509744 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 24 3.35 0.124 0.863 0.312 0.421 0.986 1.11 0.00246 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 2.89 0.116 0.559 0.304 0.408 0.822 0.893 0.00206 0.00223 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 31 1195.613 0.005 0.127 0.917 3.46 0.317 0.426 0.964 1.15 0.00241 0.00286 ! Validation 31 1195.613 0.005 0.119 0.41 2.79 0.307 0.412 0.632 0.765 0.00158 0.00191 Wall time: 1195.6136009469628 ! Best model 31 2.788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 24 2.65 0.121 0.238 0.309 0.415 0.482 0.582 0.00121 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 2.54 0.113 0.287 0.301 0.401 0.576 0.64 0.00144 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 32 1233.605 0.005 0.125 0.505 3 0.314 0.422 0.692 0.855 0.00173 0.00214 ! Validation 32 1233.605 0.005 0.114 0.303 2.59 0.302 0.404 0.529 0.658 0.00132 0.00165 Wall time: 1233.605419038795 ! Best model 32 2.591 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 24 2.79 0.121 0.371 0.31 0.416 0.593 0.728 0.00148 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 2.34 0.11 0.144 0.296 0.396 0.432 0.454 0.00108 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 33 1271.570 0.005 0.121 0.421 2.84 0.309 0.415 0.628 0.776 0.00157 0.00194 ! Validation 33 1271.570 0.005 0.111 0.288 2.51 0.297 0.398 0.504 0.641 0.00126 0.0016 Wall time: 1271.5704661277123 ! Best model 33 2.505 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 24 3.04 0.12 0.638 0.307 0.414 0.797 0.955 0.00199 0.00239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 2.32 0.107 0.176 0.293 0.391 0.361 0.501 0.000902 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 34 1316.209 0.005 0.118 0.493 2.85 0.305 0.41 0.675 0.836 0.00169 0.00209 ! Validation 34 1316.209 0.005 0.108 0.701 2.87 0.294 0.394 0.838 1 0.00209 0.0025 Wall time: 1316.2097957869992 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 24 2.75 0.113 0.481 0.298 0.402 0.699 0.829 0.00175 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 2.37 0.105 0.268 0.29 0.388 0.508 0.619 0.00127 0.00155 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 35 1354.214 0.005 0.115 0.639 2.95 0.302 0.406 0.789 0.958 0.00197 0.00239 ! Validation 35 1354.214 0.005 0.106 0.868 3 0.291 0.39 0.956 1.11 0.00239 0.00278 Wall time: 1354.2149316309951 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 24 2.73 0.109 0.559 0.293 0.394 0.811 0.893 0.00203 0.00223 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 2.58 0.104 0.493 0.289 0.386 0.762 0.839 0.00191 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 36 1392.233 0.005 0.113 0.717 2.98 0.299 0.402 0.835 1.01 0.00209 0.00254 ! Validation 36 1392.233 0.005 0.106 1.17 3.29 0.29 0.389 1.16 1.29 0.00291 0.00323 Wall time: 1392.2333492808975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 24 2.72 0.112 0.492 0.298 0.399 0.714 0.838 0.00178 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 2.34 0.102 0.295 0.286 0.382 0.586 0.649 0.00147 0.00162 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 37 1430.244 0.005 0.112 0.657 2.89 0.297 0.4 0.813 0.972 0.00203 0.00243 ! Validation 37 1430.244 0.005 0.103 0.273 2.33 0.286 0.383 0.502 0.625 0.00126 0.00156 Wall time: 1430.2442190526053 ! Best model 37 2.330 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 24 2.53 0.107 0.385 0.291 0.392 0.611 0.742 0.00153 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 2.23 0.1 0.222 0.283 0.379 0.526 0.563 0.00131 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 38 1472.571 0.005 0.11 0.628 2.82 0.295 0.396 0.786 0.951 0.00197 0.00238 ! Validation 38 1472.571 0.005 0.101 0.266 2.29 0.284 0.38 0.489 0.617 0.00122 0.00154 Wall time: 1472.5719600138254 ! Best model 38 2.287 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 24 2.65 0.112 0.413 0.298 0.399 0.657 0.768 0.00164 0.00192 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 2.63 0.0982 0.663 0.281 0.375 0.915 0.973 0.00229 0.00243 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 39 1510.560 0.005 0.107 0.406 2.55 0.292 0.391 0.6 0.761 0.0015 0.0019 ! Validation 39 1510.560 0.005 0.0987 0.393 2.37 0.281 0.375 0.618 0.749 0.00155 0.00187 Wall time: 1510.5603098347783 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 24 2.62 0.103 0.573 0.285 0.383 0.731 0.905 0.00183 0.00226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 2.51 0.0964 0.583 0.278 0.371 0.851 0.913 0.00213 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 40 1548.544 0.005 0.105 0.492 2.59 0.289 0.387 0.678 0.837 0.0017 0.00209 ! Validation 40 1548.544 0.005 0.0969 0.341 2.28 0.278 0.372 0.571 0.698 0.00143 0.00174 Wall time: 1548.5447752438486 ! Best model 40 2.279 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 24 2.46 0.102 0.418 0.285 0.382 0.68 0.773 0.0017 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 2 0.0956 0.0885 0.277 0.369 0.308 0.355 0.00077 0.000889 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 41 1586.541 0.005 0.104 0.791 2.87 0.287 0.386 0.898 1.07 0.00225 0.00267 ! Validation 41 1586.541 0.005 0.0959 0.327 2.25 0.277 0.37 0.533 0.684 0.00133 0.00171 Wall time: 1586.5415295278654 ! Best model 41 2.245 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 24 2.76 0.107 0.626 0.292 0.39 0.815 0.946 0.00204 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 2.15 0.0953 0.244 0.277 0.369 0.547 0.59 0.00137 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 42 1624.660 0.005 0.104 0.934 3.01 0.287 0.385 0.989 1.16 0.00247 0.0029 ! Validation 42 1624.660 0.005 0.096 0.278 2.2 0.277 0.37 0.503 0.63 0.00126 0.00157 Wall time: 1624.660711563658 ! Best model 42 2.197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 24 2.25 0.104 0.161 0.288 0.386 0.374 0.479 0.000936 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 1.97 0.0937 0.1 0.274 0.366 0.351 0.378 0.000877 0.000945 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 1662.661 0.005 0.102 0.632 2.67 0.285 0.382 0.794 0.958 0.00198 0.0024 ! Validation 43 1662.661 0.005 0.0947 0.272 2.17 0.275 0.368 0.488 0.623 0.00122 0.00156 Wall time: 1662.6619104086421 ! Best model 43 2.166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 24 2.3 0.095 0.396 0.275 0.368 0.617 0.752 0.00154 0.00188 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 2.2 0.091 0.382 0.271 0.361 0.66 0.739 0.00165 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 44 1700.654 0.005 0.0997 0.268 2.26 0.282 0.378 0.496 0.615 0.00124 0.00154 ! Validation 44 1700.654 0.005 0.0916 0.272 2.1 0.271 0.362 0.505 0.624 0.00126 0.00156 Wall time: 1700.6543710050173 ! Best model 44 2.103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 24 2.33 0.0973 0.386 0.278 0.373 0.592 0.743 0.00148 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 2.97 0.0899 1.17 0.269 0.358 1.25 1.3 0.00313 0.00324 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 1738.644 0.005 0.0976 0.44 2.39 0.279 0.373 0.662 0.794 0.00166 0.00199 ! Validation 45 1738.644 0.005 0.0901 0.727 2.53 0.269 0.359 0.896 1.02 0.00224 0.00255 Wall time: 1738.6441915989853 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 24 2.07 0.0933 0.206 0.274 0.365 0.407 0.543 0.00102 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 1.83 0.0876 0.0787 0.266 0.354 0.249 0.335 0.000622 0.000838 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 46 1776.635 0.005 0.0957 0.339 2.25 0.276 0.37 0.569 0.699 0.00142 0.00175 ! Validation 46 1776.635 0.005 0.0879 0.348 2.11 0.266 0.354 0.561 0.704 0.0014 0.00176 Wall time: 1776.6356780990027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 24 2.79 0.0965 0.861 0.278 0.371 0.994 1.11 0.00248 0.00277 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 1.91 0.0878 0.156 0.266 0.354 0.454 0.472 0.00113 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 47 1814.616 0.005 0.0953 0.872 2.78 0.275 0.369 0.949 1.12 0.00237 0.00279 ! Validation 47 1814.616 0.005 0.0882 0.313 2.08 0.266 0.355 0.531 0.669 0.00133 0.00167 Wall time: 1814.6166415559128 ! Best model 47 2.077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 24 2.26 0.0924 0.409 0.272 0.363 0.627 0.765 0.00157 0.00191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.7 0.0858 0.988 0.263 0.35 1.14 1.19 0.00286 0.00297 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 48 1852.617 0.005 0.0937 0.3 2.17 0.274 0.366 0.515 0.651 0.00129 0.00163 ! Validation 48 1852.617 0.005 0.086 0.579 2.3 0.263 0.35 0.781 0.91 0.00195 0.00227 Wall time: 1852.6172744575888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 24 3.19 0.0894 1.4 0.268 0.357 1.31 1.41 0.00327 0.00353 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.26 0.084 0.584 0.261 0.346 0.852 0.913 0.00213 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 49 1890.605 0.005 0.0917 0.353 2.19 0.271 0.362 0.551 0.684 0.00138 0.00171 ! Validation 49 1890.605 0.005 0.0843 1.31 3 0.261 0.347 1.26 1.37 0.00314 0.00342 Wall time: 1890.605718781706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 24 3.23 0.0952 1.33 0.275 0.369 1.31 1.38 0.00327 0.00345 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.01 0.0828 0.35 0.259 0.344 0.626 0.707 0.00156 0.00177 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 50 1928.588 0.005 0.0904 0.438 2.25 0.268 0.359 0.625 0.771 0.00156 0.00193 ! Validation 50 1928.588 0.005 0.0834 0.907 2.58 0.259 0.345 1.02 1.14 0.00255 0.00285 Wall time: 1928.588785899803 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 24 2.07 0.0905 0.265 0.269 0.359 0.491 0.615 0.00123 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 1.82 0.0816 0.19 0.257 0.341 0.486 0.521 0.00121 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 51 1966.579 0.005 0.0893 0.408 2.19 0.267 0.357 0.626 0.766 0.00156 0.00192 ! Validation 51 1966.579 0.005 0.0819 0.182 1.82 0.257 0.342 0.406 0.509 0.00101 0.00127 Wall time: 1966.5797347356565 ! Best model 51 1.820 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 24 1.85 0.0857 0.131 0.262 0.35 0.369 0.433 0.000922 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 1.82 0.08 0.222 0.255 0.338 0.517 0.563 0.00129 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 52 2004.578 0.005 0.0874 0.21 1.96 0.264 0.353 0.437 0.55 0.00109 0.00138 ! Validation 52 2004.578 0.005 0.0801 0.2 1.8 0.254 0.338 0.427 0.535 0.00107 0.00134 Wall time: 2004.5779938427731 ! Best model 52 1.801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 24 3.05 0.0917 1.22 0.271 0.362 1.24 1.32 0.00311 0.0033 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 1.71 0.0802 0.105 0.255 0.338 0.363 0.387 0.000908 0.000967 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 53 2045.196 0.005 0.0874 0.878 2.63 0.264 0.353 0.933 1.11 0.00233 0.00279 ! Validation 53 2045.196 0.005 0.0807 0.282 1.9 0.255 0.339 0.496 0.635 0.00124 0.00159 Wall time: 2045.196614567656 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 24 2.44 0.0919 0.598 0.271 0.362 0.819 0.924 0.00205 0.00231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 2.85 0.082 1.21 0.257 0.342 1.28 1.32 0.0032 0.00329 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 54 2083.142 0.005 0.0897 1.24 3.03 0.267 0.358 1.14 1.34 0.00284 0.00334 ! Validation 54 2083.142 0.005 0.0823 0.779 2.43 0.257 0.343 0.935 1.05 0.00234 0.00264 Wall time: 2083.14212141186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 24 2.24 0.0864 0.511 0.264 0.351 0.74 0.854 0.00185 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 1.68 0.0804 0.0755 0.255 0.339 0.283 0.328 0.000708 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 55 2121.130 0.005 0.0886 0.714 2.49 0.266 0.356 0.866 1.01 0.00216 0.00253 ! Validation 55 2121.130 0.005 0.0812 0.235 1.86 0.256 0.341 0.461 0.579 0.00115 0.00145 Wall time: 2121.130774831865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 24 2.55 0.0848 0.854 0.26 0.348 1.02 1.1 0.00254 0.00276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 1.69 0.079 0.114 0.253 0.336 0.385 0.403 0.000963 0.00101 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 56 2159.098 0.005 0.0866 0.604 2.34 0.263 0.352 0.778 0.924 0.00195 0.00231 ! Validation 56 2159.098 0.005 0.08 0.197 1.8 0.254 0.338 0.422 0.53 0.00106 0.00133 Wall time: 2159.0986704300158 ! Best model 56 1.798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 24 2.16 0.0867 0.422 0.265 0.352 0.628 0.777 0.00157 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 2.74 0.0775 1.19 0.251 0.333 1.27 1.31 0.00317 0.00326 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 57 2197.099 0.005 0.0851 0.386 2.09 0.261 0.348 0.603 0.742 0.00151 0.00185 ! Validation 57 2197.099 0.005 0.0782 0.752 2.32 0.251 0.334 0.925 1.04 0.00231 0.00259 Wall time: 2197.09938045172 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 24 2.04 0.0811 0.421 0.254 0.34 0.702 0.775 0.00176 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 1.73 0.0761 0.211 0.249 0.33 0.504 0.549 0.00126 0.00137 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 58 2235.067 0.005 0.0835 0.382 2.05 0.259 0.345 0.611 0.737 0.00153 0.00184 ! Validation 58 2235.067 0.005 0.0768 0.186 1.72 0.249 0.331 0.412 0.515 0.00103 0.00129 Wall time: 2235.0679467716254 ! Best model 58 1.721 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 24 1.77 0.0776 0.213 0.251 0.333 0.424 0.551 0.00106 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.69 0.0751 0.192 0.248 0.327 0.483 0.524 0.00121 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 59 2273.058 0.005 0.0823 0.327 1.97 0.257 0.343 0.555 0.686 0.00139 0.00172 ! Validation 59 2273.058 0.005 0.0754 0.189 1.7 0.247 0.328 0.415 0.519 0.00104 0.0013 Wall time: 2273.058620812837 ! Best model 59 1.696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 24 3.14 0.083 1.48 0.258 0.344 1.39 1.45 0.00347 0.00364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 2.43 0.0742 0.945 0.246 0.326 1.12 1.16 0.0028 0.0029 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 60 2311.034 0.005 0.0809 0.384 2 0.255 0.34 0.57 0.715 0.00142 0.00179 ! Validation 60 2311.034 0.005 0.0749 0.55 2.05 0.246 0.327 0.77 0.887 0.00193 0.00222 Wall time: 2311.0346715906635 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 24 1.75 0.0789 0.172 0.252 0.336 0.422 0.495 0.00105 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 1.89 0.0726 0.435 0.244 0.322 0.725 0.788 0.00181 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 61 2349.019 0.005 0.0801 0.264 1.87 0.253 0.338 0.501 0.617 0.00125 0.00154 ! Validation 61 2349.019 0.005 0.0731 0.257 1.72 0.243 0.323 0.49 0.606 0.00123 0.00152 Wall time: 2349.019956350792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 24 1.77 0.0773 0.226 0.251 0.332 0.45 0.568 0.00113 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 1.97 0.0717 0.533 0.242 0.32 0.819 0.873 0.00205 0.00218 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 62 2386.989 0.005 0.0787 0.304 1.88 0.251 0.335 0.531 0.661 0.00133 0.00165 ! Validation 62 2386.989 0.005 0.072 0.307 1.75 0.241 0.321 0.544 0.662 0.00136 0.00166 Wall time: 2386.988995678723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 24 1.69 0.076 0.175 0.246 0.329 0.399 0.499 0.000998 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 1.57 0.0708 0.153 0.241 0.318 0.353 0.468 0.000883 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 63 2424.964 0.005 0.0776 0.357 1.91 0.25 0.333 0.596 0.719 0.00149 0.0018 ! Validation 63 2424.964 0.005 0.0712 0.596 2.02 0.24 0.319 0.79 0.923 0.00198 0.00231 Wall time: 2424.964968824759 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 24 1.76 0.0758 0.25 0.248 0.329 0.53 0.597 0.00133 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 1.48 0.0698 0.0811 0.239 0.316 0.232 0.34 0.00058 0.000851 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 64 2471.946 0.005 0.0768 0.281 1.82 0.248 0.331 0.509 0.634 0.00127 0.00159 ! Validation 64 2471.946 0.005 0.0703 0.415 1.82 0.239 0.317 0.636 0.77 0.00159 0.00193 Wall time: 2471.9469230510294 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 24 2.3 0.0777 0.749 0.249 0.333 0.876 1.03 0.00219 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 2.53 0.07 1.13 0.239 0.316 1.24 1.27 0.00309 0.00318 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 65 2509.984 0.005 0.0761 0.547 2.07 0.247 0.33 0.709 0.88 0.00177 0.0022 ! Validation 65 2509.984 0.005 0.0708 0.687 2.1 0.239 0.318 0.885 0.991 0.00221 0.00248 Wall time: 2509.984916444868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 24 1.75 0.0772 0.206 0.249 0.332 0.415 0.542 0.00104 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.86 0.0683 0.493 0.237 0.312 0.784 0.839 0.00196 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 66 2547.959 0.005 0.0755 0.353 1.86 0.246 0.328 0.58 0.713 0.00145 0.00178 ! Validation 66 2547.959 0.005 0.0691 0.276 1.66 0.236 0.314 0.512 0.628 0.00128 0.00157 Wall time: 2547.9598760898225 ! Best model 66 1.658 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 24 1.87 0.0773 0.326 0.249 0.332 0.577 0.682 0.00144 0.0017 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.47 0.0676 0.118 0.235 0.311 0.387 0.41 0.000968 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 67 2589.269 0.005 0.0745 0.355 1.84 0.245 0.326 0.589 0.713 0.00147 0.00178 ! Validation 67 2589.269 0.005 0.0682 0.167 1.53 0.235 0.312 0.39 0.489 0.000976 0.00122 Wall time: 2589.269673082046 ! Best model 67 1.531 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 24 1.59 0.0723 0.144 0.242 0.321 0.39 0.454 0.000975 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.82 0.0668 0.482 0.234 0.309 0.777 0.829 0.00194 0.00207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 68 2627.231 0.005 0.0735 0.364 1.83 0.243 0.324 0.599 0.726 0.0015 0.00182 ! Validation 68 2627.231 0.005 0.0674 0.272 1.62 0.234 0.31 0.507 0.623 0.00127 0.00156 Wall time: 2627.231112689711 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 24 1.84 0.0759 0.319 0.247 0.329 0.53 0.675 0.00132 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.91 0.0658 0.597 0.232 0.307 0.877 0.923 0.00219 0.00231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 69 2665.178 0.005 0.0727 0.3 1.75 0.242 0.322 0.515 0.654 0.00129 0.00163 ! Validation 69 2665.178 0.005 0.0666 0.329 1.66 0.232 0.309 0.567 0.685 0.00142 0.00171 Wall time: 2665.178156580776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 24 1.77 0.074 0.286 0.243 0.325 0.546 0.639 0.00136 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 1.85 0.0666 0.516 0.233 0.308 0.8 0.858 0.002 0.00215 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 70 2703.123 0.005 0.0735 0.865 2.33 0.243 0.324 0.942 1.12 0.00236 0.0028 ! Validation 70 2703.123 0.005 0.0677 0.305 1.66 0.234 0.311 0.539 0.66 0.00135 0.00165 Wall time: 2703.123381185811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 24 1.82 0.072 0.385 0.239 0.321 0.588 0.742 0.00147 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 1.37 0.0654 0.061 0.232 0.306 0.224 0.295 0.000559 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 71 2741.055 0.005 0.0725 0.381 1.83 0.241 0.322 0.594 0.738 0.00149 0.00184 ! Validation 71 2741.055 0.005 0.0663 0.286 1.61 0.232 0.308 0.515 0.639 0.00129 0.0016 Wall time: 2741.0550454566255 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 24 2.38 0.0732 0.915 0.242 0.323 1.07 1.14 0.00267 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.39 0.0654 0.0823 0.231 0.306 0.237 0.343 0.000593 0.000857 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 72 2778.990 0.005 0.0719 0.648 2.09 0.24 0.32 0.813 0.957 0.00203 0.00239 ! Validation 72 2778.990 0.005 0.0667 0.337 1.67 0.232 0.309 0.573 0.694 0.00143 0.00174 Wall time: 2778.990019185003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 24 1.52 0.0711 0.103 0.239 0.319 0.319 0.383 0.000799 0.000957 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 2.32 0.0648 1.02 0.23 0.304 1.17 1.21 0.00293 0.00302 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 73 2816.912 0.005 0.0713 0.48 1.91 0.239 0.319 0.686 0.835 0.00171 0.00209 ! Validation 73 2816.912 0.005 0.0659 0.608 1.93 0.231 0.307 0.821 0.932 0.00205 0.00233 Wall time: 2816.9121452686377 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 24 1.71 0.0703 0.306 0.238 0.317 0.606 0.661 0.00151 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.33 0.0633 0.0597 0.229 0.301 0.221 0.292 0.000552 0.00073 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 74 2854.849 0.005 0.0704 0.281 1.69 0.238 0.317 0.512 0.633 0.00128 0.00158 ! Validation 74 2854.849 0.005 0.0642 0.286 1.57 0.229 0.303 0.515 0.639 0.00129 0.0016 Wall time: 2854.849311779719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 24 1.82 0.0673 0.472 0.233 0.31 0.699 0.821 0.00175 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.33 0.0621 0.0843 0.227 0.298 0.246 0.347 0.000614 0.000868 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 2892.784 0.005 0.069 0.197 1.58 0.236 0.314 0.417 0.521 0.00104 0.0013 ! Validation 75 2892.784 0.005 0.063 0.382 1.64 0.226 0.3 0.612 0.738 0.00153 0.00185 Wall time: 2892.784792413935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 24 1.53 0.0689 0.147 0.235 0.314 0.389 0.459 0.000973 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.29 0.0617 0.0557 0.226 0.297 0.234 0.282 0.000585 0.000705 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 2930.992 0.005 0.0685 0.416 1.79 0.235 0.313 0.624 0.777 0.00156 0.00194 ! Validation 76 2930.992 0.005 0.0625 0.222 1.47 0.225 0.299 0.447 0.562 0.00112 0.00141 Wall time: 2930.992622728925 ! Best model 76 1.471 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 24 1.48 0.0661 0.156 0.232 0.307 0.384 0.472 0.000961 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.66 0.0609 0.442 0.224 0.295 0.743 0.794 0.00186 0.00199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 77 2968.948 0.005 0.0676 0.274 1.63 0.233 0.311 0.498 0.628 0.00125 0.00157 ! Validation 77 2968.948 0.005 0.0616 0.258 1.49 0.224 0.297 0.493 0.607 0.00123 0.00152 Wall time: 2968.9480832698755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 24 1.54 0.0684 0.172 0.235 0.312 0.429 0.495 0.00107 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 1.32 0.0615 0.0861 0.226 0.296 0.32 0.351 0.000799 0.000877 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 78 3006.894 0.005 0.0686 0.658 2.03 0.235 0.313 0.757 0.978 0.00189 0.00244 ! Validation 78 3006.894 0.005 0.0623 0.165 1.41 0.225 0.298 0.386 0.485 0.000966 0.00121 Wall time: 3006.8943429528736 ! Best model 78 1.412 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 24 1.44 0.0638 0.163 0.227 0.302 0.398 0.482 0.000994 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 1.54 0.06 0.336 0.223 0.293 0.637 0.693 0.00159 0.00173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 79 3044.853 0.005 0.0669 0.229 1.57 0.233 0.309 0.457 0.574 0.00114 0.00143 ! Validation 79 3044.853 0.005 0.061 0.204 1.42 0.222 0.295 0.432 0.539 0.00108 0.00135 Wall time: 3044.853849938605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 24 1.65 0.0642 0.367 0.228 0.303 0.647 0.724 0.00162 0.00181 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 1.41 0.0592 0.229 0.221 0.291 0.501 0.571 0.00125 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 80 3082.771 0.005 0.0658 0.413 1.73 0.23 0.307 0.617 0.769 0.00154 0.00192 ! Validation 80 3082.771 0.005 0.0603 0.169 1.37 0.221 0.293 0.393 0.491 0.000983 0.00123 Wall time: 3082.7718861079775 ! Best model 80 1.374 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 24 1.49 0.068 0.13 0.234 0.312 0.359 0.431 0.000898 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.31 0.0585 0.142 0.22 0.289 0.402 0.451 0.001 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 81 3120.733 0.005 0.0652 0.213 1.52 0.229 0.305 0.446 0.555 0.00112 0.00139 ! Validation 81 3120.733 0.005 0.0594 0.141 1.33 0.22 0.291 0.362 0.449 0.000904 0.00112 Wall time: 3120.7333528469317 ! Best model 81 1.329 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 24 1.75 0.0635 0.484 0.225 0.301 0.755 0.831 0.00189 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 1.87 0.0578 0.719 0.218 0.287 0.976 1.01 0.00244 0.00253 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 82 3158.673 0.005 0.0643 0.313 1.6 0.228 0.303 0.528 0.664 0.00132 0.00166 ! Validation 82 3158.673 0.005 0.0589 0.414 1.59 0.219 0.29 0.658 0.769 0.00164 0.00192 Wall time: 3158.673748309724 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 24 1.69 0.0664 0.359 0.231 0.308 0.631 0.716 0.00158 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.2 0.0573 0.0558 0.218 0.286 0.212 0.282 0.000529 0.000705 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 3196.626 0.005 0.0642 0.479 1.76 0.227 0.303 0.686 0.83 0.00171 0.00207 ! Validation 83 3196.626 0.005 0.0583 0.31 1.48 0.218 0.289 0.536 0.665 0.00134 0.00166 Wall time: 3196.6264041718096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 24 1.41 0.0629 0.155 0.225 0.3 0.377 0.471 0.000943 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.49 0.0575 0.343 0.218 0.287 0.653 0.7 0.00163 0.00175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 84 3234.560 0.005 0.0638 0.448 1.72 0.227 0.302 0.673 0.806 0.00168 0.00202 ! Validation 84 3234.560 0.005 0.0585 0.236 1.41 0.218 0.289 0.469 0.58 0.00117 0.00145 Wall time: 3234.5609514978714 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 24 1.35 0.0633 0.0835 0.226 0.301 0.273 0.345 0.000683 0.000863 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 1.21 0.057 0.0708 0.218 0.285 0.284 0.318 0.00071 0.000795 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 3272.793 0.005 0.0634 0.444 1.71 0.226 0.301 0.653 0.804 0.00163 0.00201 ! Validation 85 3272.793 0.005 0.0578 0.153 1.31 0.217 0.287 0.371 0.468 0.000928 0.00117 Wall time: 3272.793333564885 ! Best model 85 1.309 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 24 2.26 0.0622 1.02 0.224 0.298 1.12 1.21 0.00281 0.00301 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.27 0.056 0.146 0.216 0.283 0.378 0.457 0.000945 0.00114 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 86 3310.750 0.005 0.0624 0.277 1.52 0.224 0.298 0.473 0.609 0.00118 0.00152 ! Validation 86 3310.750 0.005 0.0569 0.467 1.6 0.215 0.285 0.706 0.816 0.00176 0.00204 Wall time: 3310.750635000877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 24 1.55 0.0628 0.299 0.223 0.299 0.597 0.653 0.00149 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 1.88 0.0569 0.745 0.216 0.285 1 1.03 0.0025 0.00258 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 87 3348.716 0.005 0.0621 0.517 1.76 0.224 0.298 0.717 0.863 0.00179 0.00216 ! Validation 87 3348.716 0.005 0.058 0.452 1.61 0.217 0.288 0.695 0.803 0.00174 0.00201 Wall time: 3348.7162458957173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 24 1.77 0.0615 0.535 0.223 0.296 0.819 0.874 0.00205 0.00219 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 1.25 0.0563 0.126 0.215 0.284 0.373 0.425 0.000931 0.00106 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 88 3386.660 0.005 0.0621 0.517 1.76 0.224 0.298 0.72 0.859 0.0018 0.00215 ! Validation 88 3386.660 0.005 0.0575 0.137 1.29 0.216 0.286 0.356 0.442 0.000889 0.0011 Wall time: 3386.660015170928 ! Best model 88 1.286 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 24 1.74 0.0628 0.483 0.224 0.299 0.757 0.831 0.00189 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 1.26 0.0555 0.152 0.214 0.282 0.386 0.466 0.000964 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 89 3424.725 0.005 0.062 0.573 1.81 0.223 0.298 0.748 0.907 0.00187 0.00227 ! Validation 89 3424.725 0.005 0.0566 0.512 1.64 0.214 0.284 0.73 0.855 0.00183 0.00214 Wall time: 3424.7249748189934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 24 2.69 0.06 1.48 0.221 0.293 1.42 1.46 0.00354 0.00364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 2.35 0.0548 1.25 0.213 0.28 1.32 1.34 0.00329 0.00334 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 90 3462.668 0.005 0.061 0.293 1.51 0.222 0.295 0.477 0.614 0.00119 0.00153 ! Validation 90 3462.668 0.005 0.0557 0.849 1.96 0.213 0.282 1.02 1.1 0.00254 0.00275 Wall time: 3462.6683934419416 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 24 1.31 0.058 0.154 0.217 0.288 0.379 0.469 0.000946 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 1.34 0.054 0.256 0.212 0.278 0.555 0.605 0.00139 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 91 3500.628 0.005 0.0603 0.264 1.47 0.221 0.294 0.495 0.617 0.00124 0.00154 ! Validation 91 3500.628 0.005 0.0549 0.187 1.28 0.212 0.28 0.416 0.517 0.00104 0.00129 Wall time: 3500.628861492034 ! Best model 91 1.284 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 24 1.34 0.0622 0.0942 0.223 0.298 0.299 0.367 0.000747 0.000917 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 1.14 0.0533 0.0722 0.211 0.276 0.282 0.321 0.000706 0.000803 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 92 3538.566 0.005 0.0595 0.224 1.41 0.219 0.291 0.457 0.569 0.00114 0.00142 ! Validation 92 3538.566 0.005 0.0542 0.138 1.22 0.21 0.278 0.357 0.444 0.000891 0.00111 Wall time: 3538.5661555938423 ! Best model 92 1.222 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 24 1.38 0.0576 0.23 0.217 0.287 0.446 0.574 0.00111 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 1.13 0.0527 0.079 0.209 0.274 0.297 0.336 0.000742 0.00084 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 93 3576.505 0.005 0.0588 0.355 1.53 0.218 0.29 0.594 0.715 0.00149 0.00179 ! Validation 93 3576.505 0.005 0.0538 0.144 1.22 0.209 0.277 0.363 0.453 0.000907 0.00113 Wall time: 3576.5053751226515 ! Best model 93 1.220 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 24 1.35 0.0577 0.194 0.216 0.287 0.42 0.526 0.00105 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 1.56 0.0527 0.51 0.21 0.274 0.822 0.853 0.00206 0.00213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 94 3614.484 0.005 0.0586 0.292 1.46 0.217 0.289 0.504 0.648 0.00126 0.00162 ! Validation 94 3614.484 0.005 0.0534 0.334 1.4 0.209 0.276 0.582 0.69 0.00145 0.00173 Wall time: 3614.4843889009207 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 24 1.78 0.0609 0.563 0.22 0.295 0.817 0.897 0.00204 0.00224 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 1.14 0.0539 0.0636 0.212 0.278 0.243 0.301 0.000607 0.000754 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 95 3659.550 0.005 0.061 0.933 2.15 0.222 0.295 0.978 1.16 0.00245 0.0029 ! Validation 95 3659.550 0.005 0.055 0.244 1.34 0.212 0.28 0.486 0.591 0.00121 0.00148 Wall time: 3659.550950586796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 24 1.44 0.0602 0.238 0.221 0.293 0.434 0.583 0.00109 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 1.09 0.0524 0.0444 0.209 0.274 0.227 0.252 0.000567 0.000629 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 96 3697.507 0.005 0.0589 0.27 1.45 0.218 0.29 0.495 0.622 0.00124 0.00155 ! Validation 96 3697.507 0.005 0.0533 0.185 1.25 0.209 0.276 0.409 0.514 0.00102 0.00128 Wall time: 3697.5075004836544 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 24 1.47 0.06 0.266 0.221 0.293 0.522 0.617 0.00131 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 1.26 0.052 0.223 0.209 0.272 0.511 0.564 0.00128 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 97 3735.480 0.005 0.0581 0.367 1.53 0.216 0.288 0.576 0.726 0.00144 0.00182 ! Validation 97 3735.480 0.005 0.0528 0.546 1.6 0.208 0.275 0.778 0.883 0.00194 0.00221 Wall time: 3735.4802404697984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 24 1.28 0.0581 0.114 0.217 0.288 0.335 0.403 0.000837 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 1.11 0.0513 0.0795 0.207 0.271 0.294 0.337 0.000736 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 98 3773.440 0.005 0.0573 0.307 1.45 0.215 0.286 0.548 0.667 0.00137 0.00167 ! Validation 98 3773.440 0.005 0.0521 0.142 1.18 0.207 0.273 0.358 0.45 0.000895 0.00112 Wall time: 3773.4400172079913 ! Best model 98 1.184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 24 1.31 0.0537 0.24 0.209 0.277 0.49 0.586 0.00123 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 1.85 0.0509 0.836 0.205 0.27 1.07 1.09 0.00266 0.00273 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 99 3811.397 0.005 0.0565 0.363 1.49 0.214 0.284 0.591 0.723 0.00148 0.00181 ! Validation 99 3811.397 0.005 0.052 0.56 1.6 0.205 0.272 0.797 0.894 0.00199 0.00224 Wall time: 3811.397601764649 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 24 1.31 0.0571 0.172 0.215 0.286 0.407 0.496 0.00102 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 1.09 0.0507 0.0719 0.205 0.269 0.276 0.32 0.000689 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 100 3862.975 0.005 0.0562 0.296 1.42 0.213 0.283 0.517 0.653 0.00129 0.00163 ! Validation 100 3862.975 0.005 0.0516 0.13 1.16 0.205 0.271 0.35 0.432 0.000876 0.00108 Wall time: 3862.9749912419356 ! Best model 100 1.162 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 24 1.37 0.0573 0.229 0.214 0.286 0.516 0.572 0.00129 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 1.14 0.0508 0.127 0.205 0.269 0.362 0.427 0.000906 0.00107 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 101 3900.935 0.005 0.0559 0.385 1.5 0.212 0.282 0.585 0.745 0.00146 0.00186 ! Validation 101 3900.935 0.005 0.0518 0.135 1.17 0.205 0.272 0.351 0.44 0.000879 0.0011 Wall time: 3900.93590037385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 24 1.23 0.0572 0.0905 0.214 0.286 0.286 0.359 0.000716 0.000899 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 1.15 0.0504 0.14 0.204 0.268 0.373 0.448 0.000934 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 102 3938.878 0.005 0.0555 0.321 1.43 0.212 0.281 0.527 0.683 0.00132 0.00171 ! Validation 102 3938.878 0.005 0.0515 0.397 1.43 0.205 0.271 0.645 0.753 0.00161 0.00188 Wall time: 3938.878793385811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 24 1.21 0.0557 0.0968 0.212 0.282 0.307 0.372 0.000768 0.000929 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 1.12 0.0501 0.117 0.204 0.267 0.34 0.409 0.000849 0.00102 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 103 3976.827 0.005 0.0559 0.615 1.73 0.212 0.282 0.814 0.947 0.00203 0.00237 ! Validation 103 3976.827 0.005 0.0512 0.137 1.16 0.204 0.27 0.356 0.442 0.000889 0.0011 Wall time: 3976.827900873963 ! Best model 103 1.161 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 24 1.62 0.0565 0.494 0.213 0.284 0.731 0.84 0.00183 0.0021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 1.4 0.0499 0.405 0.204 0.267 0.718 0.76 0.0018 0.0019 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 104 4014.784 0.005 0.0556 0.506 1.62 0.212 0.282 0.705 0.851 0.00176 0.00213 ! Validation 104 4014.784 0.005 0.051 0.27 1.29 0.204 0.27 0.511 0.621 0.00128 0.00155 Wall time: 4014.7840915969573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 24 2.27 0.0553 1.17 0.21 0.281 1.26 1.29 0.00315 0.00323 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 1.55 0.0498 0.555 0.204 0.267 0.855 0.89 0.00214 0.00223 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 105 4052.741 0.005 0.0554 0.51 1.62 0.211 0.281 0.709 0.841 0.00177 0.0021 ! Validation 105 4052.741 0.005 0.0508 0.955 1.97 0.204 0.269 1.08 1.17 0.00271 0.00292 Wall time: 4052.7418132256716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 24 1.42 0.0508 0.405 0.203 0.269 0.675 0.76 0.00169 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 1.08 0.0487 0.103 0.201 0.264 0.321 0.383 0.000803 0.000959 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 106 4090.713 0.005 0.0547 0.263 1.36 0.21 0.28 0.484 0.608 0.00121 0.00152 ! Validation 106 4090.713 0.005 0.0497 0.134 1.13 0.201 0.266 0.35 0.438 0.000874 0.00109 Wall time: 4090.7133549470454 ! Best model 106 1.128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 24 1.34 0.0544 0.256 0.209 0.279 0.5 0.605 0.00125 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 1.05 0.0483 0.0788 0.201 0.263 0.283 0.336 0.000708 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 107 4128.675 0.005 0.0539 0.314 1.39 0.209 0.277 0.563 0.671 0.00141 0.00168 ! Validation 107 4128.675 0.005 0.0493 0.129 1.11 0.2 0.265 0.344 0.429 0.000861 0.00107 Wall time: 4128.675399791915 ! Best model 107 1.114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 24 2.25 0.054 1.17 0.209 0.278 1.22 1.29 0.00306 0.00323 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 1.04 0.0496 0.0472 0.203 0.266 0.202 0.26 0.000504 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 108 4166.631 0.005 0.0544 0.789 1.88 0.209 0.279 0.904 1.06 0.00226 0.00264 ! Validation 108 4166.631 0.005 0.0507 0.186 1.2 0.203 0.269 0.419 0.515 0.00105 0.00129 Wall time: 4166.631370531861 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 24 1.13 0.0517 0.0918 0.204 0.272 0.285 0.362 0.000713 0.000905 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.11 0.0484 0.143 0.201 0.263 0.38 0.451 0.000951 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 109 4204.585 0.005 0.0542 0.258 1.34 0.209 0.278 0.501 0.612 0.00125 0.00153 ! Validation 109 4204.585 0.005 0.0493 0.36 1.35 0.201 0.265 0.612 0.717 0.00153 0.00179 Wall time: 4204.585599898826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 24 1.18 0.0536 0.107 0.208 0.277 0.335 0.391 0.000838 0.000977 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.04 0.0477 0.0813 0.2 0.261 0.284 0.341 0.000711 0.000852 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 110 4242.535 0.005 0.0535 0.369 1.44 0.208 0.276 0.595 0.732 0.00149 0.00183 ! Validation 110 4242.535 0.005 0.0486 0.125 1.1 0.199 0.263 0.341 0.423 0.000852 0.00106 Wall time: 4242.5355505039915 ! Best model 110 1.097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 24 1.3 0.0512 0.27 0.205 0.271 0.498 0.621 0.00124 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.03 0.0472 0.088 0.199 0.26 0.29 0.354 0.000724 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 111 4280.489 0.005 0.0528 0.356 1.41 0.207 0.275 0.585 0.716 0.00146 0.00179 ! Validation 111 4280.489 0.005 0.0482 0.286 1.25 0.198 0.262 0.53 0.64 0.00132 0.0016 Wall time: 4280.489329041913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 24 1.32 0.0534 0.253 0.208 0.276 0.507 0.602 0.00127 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 1.19 0.0469 0.249 0.198 0.259 0.549 0.596 0.00137 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 112 4318.564 0.005 0.0523 0.283 1.33 0.206 0.273 0.528 0.637 0.00132 0.00159 ! Validation 112 4318.564 0.005 0.0478 0.197 1.15 0.198 0.261 0.428 0.531 0.00107 0.00133 Wall time: 4318.564931344707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 24 1.24 0.0518 0.201 0.205 0.272 0.457 0.536 0.00114 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 1.1 0.0465 0.166 0.197 0.258 0.428 0.487 0.00107 0.00122 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 113 4356.515 0.005 0.0519 0.352 1.39 0.205 0.272 0.597 0.713 0.00149 0.00178 ! Validation 113 4356.515 0.005 0.0474 0.156 1.1 0.197 0.26 0.376 0.472 0.00094 0.00118 Wall time: 4356.51515057683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 24 1.22 0.0507 0.204 0.202 0.269 0.46 0.54 0.00115 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 0.984 0.0459 0.0667 0.196 0.256 0.248 0.309 0.00062 0.000771 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 114 4394.469 0.005 0.0514 0.179 1.21 0.204 0.271 0.409 0.504 0.00102 0.00126 ! Validation 114 4394.469 0.005 0.0469 0.226 1.16 0.196 0.259 0.469 0.568 0.00117 0.00142 Wall time: 4394.4692447585985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 24 1.08 0.0489 0.0997 0.199 0.264 0.3 0.377 0.00075 0.000944 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.979 0.0453 0.0729 0.194 0.254 0.266 0.323 0.000665 0.000806 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 4432.428 0.005 0.0508 0.243 1.26 0.203 0.269 0.482 0.593 0.00121 0.00148 ! Validation 115 4432.428 0.005 0.0464 0.121 1.05 0.194 0.257 0.337 0.416 0.000843 0.00104 Wall time: 4432.428736923728 ! Best model 115 1.049 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 24 1.19 0.0508 0.178 0.202 0.269 0.421 0.504 0.00105 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 0.943 0.0451 0.0402 0.194 0.254 0.221 0.24 0.000552 0.000599 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 116 4474.689 0.005 0.0505 0.328 1.34 0.202 0.268 0.575 0.688 0.00144 0.00172 ! Validation 116 4474.689 0.005 0.0462 0.134 1.06 0.194 0.257 0.356 0.438 0.000889 0.0011 Wall time: 4474.689437177964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 24 1.12 0.051 0.0966 0.203 0.27 0.321 0.371 0.000802 0.000928 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 1.75 0.0455 0.841 0.195 0.255 1.07 1.1 0.00267 0.00274 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 117 4512.643 0.005 0.0507 0.552 1.56 0.202 0.269 0.764 0.896 0.00191 0.00224 ! Validation 117 4512.643 0.005 0.0466 0.636 1.57 0.194 0.258 0.857 0.953 0.00214 0.00238 Wall time: 4512.643398885615 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 24 1.02 0.0487 0.0478 0.199 0.264 0.212 0.261 0.000531 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1 0.0448 0.108 0.194 0.253 0.319 0.392 0.000797 0.00098 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 118 4550.592 0.005 0.0503 0.201 1.21 0.202 0.268 0.425 0.541 0.00106 0.00135 ! Validation 118 4550.592 0.005 0.0458 0.129 1.04 0.193 0.256 0.345 0.429 0.000863 0.00107 Wall time: 4550.592787005939 ! Best model 118 1.044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 24 1.48 0.05 0.479 0.201 0.267 0.721 0.827 0.0018 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 0.935 0.0446 0.0434 0.193 0.252 0.226 0.249 0.000565 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 119 4588.561 0.005 0.0497 0.502 1.5 0.2 0.266 0.727 0.847 0.00182 0.00212 ! Validation 119 4588.561 0.005 0.0457 0.126 1.04 0.193 0.256 0.345 0.424 0.000863 0.00106 Wall time: 4588.5610911180265 ! Best model 119 1.040 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 24 1.16 0.05 0.158 0.202 0.267 0.392 0.475 0.00098 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.928 0.0441 0.0466 0.192 0.251 0.233 0.258 0.000583 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 120 4626.518 0.005 0.0495 0.163 1.15 0.2 0.266 0.389 0.482 0.000974 0.00121 ! Validation 120 4626.518 0.005 0.045 0.125 1.03 0.192 0.254 0.344 0.423 0.000861 0.00106 Wall time: 4626.518013593741 ! Best model 120 1.025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 24 1.07 0.0475 0.122 0.196 0.26 0.309 0.418 0.000773 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.99 0.0442 0.107 0.192 0.251 0.318 0.39 0.000794 0.000975 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 4664.479 0.005 0.0491 0.421 1.4 0.199 0.265 0.617 0.782 0.00154 0.00195 ! Validation 121 4664.479 0.005 0.0452 0.319 1.22 0.192 0.254 0.569 0.675 0.00142 0.00169 Wall time: 4664.4793869969435 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 24 1.26 0.0513 0.237 0.202 0.271 0.504 0.582 0.00126 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 2.35 0.0445 1.46 0.192 0.252 1.42 1.44 0.00356 0.00361 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 122 4702.446 0.005 0.049 0.358 1.34 0.199 0.264 0.599 0.718 0.0015 0.0018 ! Validation 122 4702.446 0.005 0.0455 1.18 2.09 0.192 0.255 1.23 1.3 0.00308 0.00325 Wall time: 4702.4469547919 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 24 1.2 0.0472 0.252 0.196 0.26 0.555 0.599 0.00139 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 1.12 0.0435 0.249 0.19 0.249 0.547 0.597 0.00137 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 123 4740.374 0.005 0.0491 0.467 1.45 0.199 0.265 0.698 0.821 0.00175 0.00205 ! Validation 123 4740.374 0.005 0.0447 0.22 1.11 0.191 0.253 0.456 0.56 0.00114 0.0014 Wall time: 4740.374648250639 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 24 1.02 0.0455 0.111 0.192 0.255 0.306 0.398 0.000765 0.000995 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 0.968 0.043 0.108 0.19 0.248 0.316 0.393 0.00079 0.000982 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 4778.316 0.005 0.0483 0.21 1.18 0.198 0.263 0.452 0.55 0.00113 0.00138 ! Validation 124 4778.316 0.005 0.0439 0.129 1.01 0.19 0.25 0.345 0.429 0.000862 0.00107 Wall time: 4778.316063725855 ! Best model 124 1.008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 24 2.6 0.0499 1.6 0.201 0.267 1.47 1.51 0.00367 0.00378 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 1.94 0.0441 1.06 0.191 0.251 1.21 1.23 0.00302 0.00307 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 125 4816.251 0.005 0.0478 0.469 1.42 0.197 0.261 0.613 0.795 0.00153 0.00199 ! Validation 125 4816.251 0.005 0.0451 0.823 1.73 0.191 0.254 1 1.08 0.00251 0.00271 Wall time: 4816.251315817703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 24 1.32 0.0495 0.327 0.2 0.266 0.579 0.683 0.00145 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.947 0.0443 0.0617 0.192 0.251 0.253 0.297 0.000632 0.000742 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 126 4854.181 0.005 0.0498 0.706 1.7 0.2 0.267 0.866 1.01 0.00217 0.00253 ! Validation 126 4854.181 0.005 0.0453 0.128 1.03 0.192 0.254 0.351 0.427 0.000877 0.00107 Wall time: 4854.1810905789025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 24 1 0.0468 0.0668 0.195 0.259 0.254 0.309 0.000635 0.000772 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.926 0.0427 0.0731 0.189 0.247 0.26 0.323 0.000649 0.000808 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 127 4892.107 0.005 0.0483 0.104 1.07 0.198 0.263 0.311 0.387 0.000777 0.000968 ! Validation 127 4892.107 0.005 0.0435 0.119 0.99 0.189 0.249 0.336 0.412 0.000839 0.00103 Wall time: 4892.107619164046 ! Best model 127 0.990 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 24 0.999 0.0456 0.0858 0.193 0.255 0.284 0.35 0.000709 0.000875 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 1.07 0.0417 0.233 0.187 0.244 0.526 0.577 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 128 4930.034 0.005 0.047 0.114 1.05 0.195 0.259 0.32 0.405 0.000801 0.00101 ! Validation 128 4930.034 0.005 0.0427 0.201 1.05 0.187 0.247 0.434 0.535 0.00108 0.00134 Wall time: 4930.034357682802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 24 1.54 0.0462 0.615 0.194 0.257 0.848 0.937 0.00212 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 1.66 0.0436 0.789 0.191 0.25 1.03 1.06 0.00259 0.00265 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 129 4967.966 0.005 0.0487 0.918 1.89 0.198 0.264 0.983 1.15 0.00246 0.00287 ! Validation 129 4967.966 0.005 0.0447 1.03 1.92 0.191 0.253 1.14 1.21 0.00284 0.00303 Wall time: 4967.966863259673 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 24 1.04 0.0477 0.0819 0.197 0.261 0.293 0.342 0.000732 0.000855 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.922 0.0421 0.0796 0.188 0.245 0.277 0.337 0.000694 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 130 5005.911 0.005 0.0478 0.22 1.18 0.197 0.261 0.449 0.565 0.00112 0.00141 ! Validation 130 5005.911 0.005 0.0431 0.237 1.1 0.188 0.248 0.476 0.581 0.00119 0.00145 Wall time: 5005.911332896911 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 24 1.04 0.0443 0.159 0.19 0.251 0.357 0.476 0.000892 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 1.18 0.0418 0.346 0.187 0.244 0.666 0.702 0.00166 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 131 5043.836 0.005 0.0468 0.371 1.31 0.195 0.259 0.595 0.733 0.00149 0.00183 ! Validation 131 5043.836 0.005 0.0429 0.297 1.16 0.187 0.248 0.547 0.651 0.00137 0.00163 Wall time: 5043.836334818043 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 24 1.19 0.0472 0.244 0.196 0.26 0.529 0.591 0.00132 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.862 0.0412 0.0378 0.186 0.243 0.216 0.232 0.000539 0.000581 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 132 5082.224 0.005 0.0464 0.219 1.15 0.194 0.257 0.455 0.559 0.00114 0.0014 ! Validation 132 5082.224 0.005 0.0422 0.12 0.963 0.186 0.245 0.335 0.413 0.000838 0.00103 Wall time: 5082.224751687609 ! Best model 132 0.963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 24 1.1 0.0466 0.172 0.194 0.258 0.418 0.495 0.00104 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.892 0.0408 0.0755 0.185 0.241 0.262 0.328 0.000655 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 133 5120.175 0.005 0.0459 0.245 1.16 0.193 0.256 0.49 0.593 0.00123 0.00148 ! Validation 133 5120.175 0.005 0.0418 0.118 0.955 0.185 0.244 0.335 0.411 0.000838 0.00103 Wall time: 5120.175220178906 ! Best model 133 0.955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 24 1.23 0.044 0.346 0.19 0.251 0.615 0.703 0.00154 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 1.55 0.041 0.735 0.185 0.242 1 1.02 0.00251 0.00256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 134 5158.143 0.005 0.0458 0.358 1.27 0.193 0.256 0.601 0.715 0.0015 0.00179 ! Validation 134 5158.143 0.005 0.0419 0.631 1.47 0.185 0.245 0.867 0.95 0.00217 0.00237 Wall time: 5158.1434814678505 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 24 1.17 0.0466 0.234 0.194 0.258 0.511 0.578 0.00128 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.956 0.0405 0.147 0.184 0.24 0.397 0.458 0.000992 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 135 5196.086 0.005 0.0455 0.355 1.27 0.192 0.255 0.605 0.715 0.00151 0.00179 ! Validation 135 5196.086 0.005 0.0414 0.155 0.983 0.184 0.243 0.377 0.47 0.000942 0.00118 Wall time: 5196.086275185924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 24 0.988 0.0436 0.116 0.188 0.249 0.327 0.407 0.000817 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.855 0.0398 0.0578 0.183 0.239 0.239 0.287 0.000597 0.000718 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 136 5234.144 0.005 0.0448 0.0856 0.982 0.191 0.253 0.276 0.348 0.000689 0.00087 ! Validation 136 5234.144 0.005 0.0408 0.11 0.925 0.183 0.241 0.324 0.396 0.00081 0.000991 Wall time: 5234.144256342668 ! Best model 136 0.925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 24 2.13 0.0434 1.26 0.188 0.249 1.28 1.34 0.00321 0.00335 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 2.82 0.0397 2.03 0.183 0.238 1.69 1.7 0.00421 0.00425 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 5272.095 0.005 0.0445 0.414 1.3 0.19 0.252 0.637 0.75 0.00159 0.00187 ! Validation 137 5272.095 0.005 0.0407 2.44 3.25 0.183 0.241 1.82 1.87 0.00455 0.00466 Wall time: 5272.095817702822 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 24 1.05 0.0473 0.104 0.195 0.26 0.324 0.385 0.00081 0.000963 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 1.18 0.0413 0.35 0.186 0.243 0.67 0.707 0.00168 0.00177 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 138 5310.038 0.005 0.0465 0.732 1.66 0.194 0.258 0.872 1.03 0.00218 0.00258 ! Validation 138 5310.038 0.005 0.0422 0.537 1.38 0.186 0.246 0.781 0.876 0.00195 0.00219 Wall time: 5310.038651326671 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 24 1.28 0.0442 0.392 0.189 0.251 0.654 0.749 0.00163 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 1.5 0.0404 0.694 0.184 0.24 0.971 0.995 0.00243 0.00249 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 139 5347.981 0.005 0.0453 0.369 1.28 0.191 0.254 0.611 0.726 0.00153 0.00181 ! Validation 139 5347.981 0.005 0.0414 0.591 1.42 0.184 0.243 0.832 0.919 0.00208 0.0023 Wall time: 5347.981479889713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 24 1.39 0.045 0.488 0.191 0.253 0.783 0.835 0.00196 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 1.37 0.0394 0.582 0.182 0.237 0.883 0.911 0.00221 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 140 5385.919 0.005 0.0446 0.238 1.13 0.19 0.252 0.47 0.576 0.00117 0.00144 ! Validation 140 5385.919 0.005 0.0405 0.507 1.32 0.182 0.24 0.759 0.851 0.0019 0.00213 Wall time: 5385.919527214952 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 24 0.989 0.0441 0.107 0.189 0.251 0.307 0.391 0.000767 0.000978 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 0.875 0.0391 0.0936 0.181 0.236 0.29 0.366 0.000725 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 141 5423.853 0.005 0.0438 0.236 1.11 0.189 0.25 0.456 0.585 0.00114 0.00146 ! Validation 141 5423.853 0.005 0.0402 0.122 0.926 0.181 0.24 0.338 0.417 0.000844 0.00104 Wall time: 5423.853604788892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 24 1.62 0.0447 0.725 0.191 0.253 0.979 1.02 0.00245 0.00254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.825 0.0395 0.0357 0.181 0.237 0.206 0.226 0.000515 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 142 5461.804 0.005 0.0435 0.305 1.17 0.188 0.249 0.51 0.649 0.00127 0.00162 ! Validation 142 5461.804 0.005 0.0406 0.126 0.937 0.182 0.241 0.347 0.424 0.000868 0.00106 Wall time: 5461.804821508937 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 24 0.966 0.043 0.107 0.186 0.248 0.318 0.39 0.000796 0.000976 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.828 0.0389 0.0491 0.181 0.236 0.234 0.265 0.000584 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 143 5499.754 0.005 0.0438 0.338 1.21 0.188 0.25 0.598 0.7 0.00149 0.00175 ! Validation 143 5499.754 0.005 0.0399 0.107 0.904 0.18 0.239 0.32 0.391 0.000801 0.000977 Wall time: 5499.754909012001 ! Best model 143 0.904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 24 0.958 0.0445 0.0676 0.19 0.252 0.256 0.311 0.000641 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.804 0.0382 0.0402 0.179 0.234 0.219 0.24 0.000548 0.000599 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 144 5537.713 0.005 0.0431 0.225 1.09 0.187 0.248 0.464 0.572 0.00116 0.00143 ! Validation 144 5537.713 0.005 0.0392 0.11 0.894 0.179 0.237 0.324 0.397 0.00081 0.000991 Wall time: 5537.713020040654 ! Best model 144 0.894 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 24 1.37 0.0439 0.495 0.188 0.25 0.774 0.841 0.00193 0.0021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.815 0.0385 0.0452 0.179 0.234 0.233 0.254 0.000582 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 145 5575.658 0.005 0.0427 0.352 1.21 0.186 0.247 0.583 0.705 0.00146 0.00176 ! Validation 145 5575.658 0.005 0.0396 0.11 0.902 0.18 0.238 0.329 0.397 0.000822 0.000992 Wall time: 5575.6585215386 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 24 1.62 0.0431 0.757 0.188 0.248 1.01 1.04 0.00252 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 1.54 0.0382 0.773 0.179 0.234 1.03 1.05 0.00256 0.00263 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 146 5613.589 0.005 0.0429 0.382 1.24 0.186 0.248 0.597 0.73 0.00149 0.00182 ! Validation 146 5613.589 0.005 0.0392 0.7 1.48 0.179 0.237 0.921 1 0.0023 0.0025 Wall time: 5613.589754911605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 24 0.917 0.0424 0.07 0.186 0.246 0.247 0.316 0.000618 0.00079 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.829 0.0374 0.081 0.177 0.231 0.264 0.34 0.00066 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 147 5652.331 0.005 0.0424 0.149 0.996 0.185 0.246 0.37 0.464 0.000925 0.00116 ! Validation 147 5652.331 0.005 0.0384 0.12 0.888 0.177 0.234 0.334 0.415 0.000835 0.00104 Wall time: 5652.331510623917 ! Best model 147 0.888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 24 2.19 0.0471 1.25 0.194 0.259 1.3 1.33 0.00325 0.00334 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 1.47 0.0401 0.665 0.183 0.239 0.942 0.975 0.00235 0.00244 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 5693.598 0.005 0.0433 0.753 1.62 0.187 0.248 0.79 1.03 0.00198 0.00257 ! Validation 148 5693.598 0.005 0.0414 0.954 1.78 0.183 0.243 1.09 1.17 0.00273 0.00292 Wall time: 5693.598437582608 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 24 0.977 0.0443 0.0911 0.189 0.252 0.292 0.361 0.00073 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.88 0.0388 0.105 0.18 0.235 0.32 0.387 0.000801 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 149 5731.574 0.005 0.0436 0.465 1.34 0.188 0.249 0.697 0.823 0.00174 0.00206 ! Validation 149 5731.574 0.005 0.0398 0.229 1.03 0.18 0.238 0.474 0.572 0.00118 0.00143 Wall time: 5731.5743545559235 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 24 0.946 0.0412 0.122 0.183 0.243 0.317 0.418 0.000793 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.854 0.0372 0.109 0.177 0.231 0.327 0.395 0.000817 0.000987 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 150 5769.556 0.005 0.0424 0.155 1 0.186 0.246 0.385 0.471 0.000961 0.00118 ! Validation 150 5769.556 0.005 0.0383 0.138 0.903 0.177 0.234 0.355 0.444 0.000887 0.00111 Wall time: 5769.556526185013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 24 1.05 0.0413 0.222 0.183 0.243 0.514 0.564 0.00129 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.778 0.0366 0.0458 0.175 0.229 0.204 0.256 0.000509 0.000639 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 151 5807.531 0.005 0.0414 0.14 0.967 0.183 0.243 0.361 0.444 0.000902 0.00111 ! Validation 151 5807.531 0.005 0.0377 0.142 0.895 0.176 0.232 0.368 0.45 0.000921 0.00112 Wall time: 5807.531473189592 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 24 1.21 0.041 0.387 0.182 0.242 0.688 0.743 0.00172 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.781 0.0371 0.0387 0.176 0.23 0.186 0.235 0.000466 0.000588 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 152 5845.503 0.005 0.041 0.348 1.17 0.182 0.242 0.583 0.704 0.00146 0.00176 ! Validation 152 5845.503 0.005 0.0382 0.129 0.894 0.177 0.234 0.351 0.43 0.000878 0.00108 Wall time: 5845.503114675637 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 24 1.76 0.043 0.904 0.186 0.248 1.1 1.14 0.00274 0.00284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 1.75 0.0375 1 0.177 0.231 1.17 1.2 0.00293 0.00299 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 153 5883.491 0.005 0.0424 0.666 1.51 0.185 0.246 0.774 0.971 0.00194 0.00243 ! Validation 153 5883.491 0.005 0.0389 0.93 1.71 0.178 0.236 1.08 1.15 0.00271 0.00288 Wall time: 5883.491824666038 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 24 1.16 0.0421 0.313 0.185 0.245 0.593 0.669 0.00148 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.827 0.0372 0.0827 0.177 0.231 0.288 0.344 0.00072 0.000859 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 154 5921.442 0.005 0.0421 0.338 1.18 0.185 0.245 0.558 0.695 0.00139 0.00174 ! Validation 154 5921.442 0.005 0.0382 0.194 0.958 0.177 0.234 0.43 0.526 0.00107 0.00132 Wall time: 5921.44222797174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 24 0.947 0.0428 0.09 0.186 0.247 0.295 0.359 0.000738 0.000896 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.835 0.0371 0.0933 0.176 0.23 0.304 0.365 0.000759 0.000913 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 155 5959.430 0.005 0.0415 0.491 1.32 0.183 0.243 0.711 0.846 0.00178 0.00211 ! Validation 155 5959.430 0.005 0.0382 0.212 0.977 0.177 0.234 0.452 0.551 0.00113 0.00138 Wall time: 5959.430350324605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 24 1.25 0.0391 0.471 0.179 0.236 0.759 0.82 0.0019 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 1.4 0.0362 0.681 0.174 0.227 0.962 0.986 0.00241 0.00247 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 156 5997.403 0.005 0.0409 0.146 0.963 0.182 0.242 0.359 0.445 0.000899 0.00111 ! Validation 156 5997.403 0.005 0.0371 0.624 1.37 0.175 0.23 0.864 0.944 0.00216 0.00236 Wall time: 5997.403842638712 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 24 0.824 0.0377 0.0694 0.175 0.232 0.253 0.315 0.000632 0.000787 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.746 0.0356 0.035 0.173 0.225 0.181 0.224 0.000452 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 157 6035.374 0.005 0.0402 0.166 0.97 0.181 0.24 0.389 0.49 0.000973 0.00122 ! Validation 157 6035.374 0.005 0.0365 0.116 0.847 0.173 0.228 0.333 0.408 0.000832 0.00102 Wall time: 6035.374280506745 ! Best model 157 0.847 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 24 0.837 0.0394 0.049 0.179 0.237 0.219 0.265 0.000546 0.000662 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 0.767 0.035 0.0672 0.172 0.224 0.24 0.31 0.000601 0.000774 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 158 6073.361 0.005 0.0397 0.127 0.92 0.18 0.238 0.34 0.428 0.00085 0.00107 ! Validation 158 6073.361 0.005 0.036 0.114 0.835 0.172 0.227 0.327 0.404 0.000819 0.00101 Wall time: 6073.361383728683 ! Best model 158 0.835 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 24 1.42 0.048 0.459 0.196 0.262 0.749 0.809 0.00187 0.00202 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 2.74 0.0424 1.9 0.187 0.246 1.63 1.65 0.00408 0.00411 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 159 6111.431 0.005 0.0413 0.89 1.72 0.183 0.242 0.854 1.13 0.00213 0.00283 ! Validation 159 6111.431 0.005 0.0436 1.82 2.69 0.188 0.249 1.56 1.61 0.0039 0.00403 Wall time: 6111.431652315892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 24 1.05 0.0407 0.235 0.182 0.241 0.513 0.58 0.00128 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 1.48 0.0382 0.721 0.178 0.234 0.986 1.01 0.00246 0.00254 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 160 6149.407 0.005 0.0432 0.651 1.52 0.187 0.249 0.839 0.971 0.0021 0.00243 ! Validation 160 6149.407 0.005 0.0393 0.849 1.63 0.179 0.237 1.03 1.1 0.00257 0.00275 Wall time: 6149.407131664921 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 24 0.939 0.0395 0.15 0.179 0.237 0.4 0.463 0.001 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.884 0.0358 0.169 0.173 0.226 0.438 0.491 0.0011 0.00123 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 161 6187.392 0.005 0.0411 0.249 1.07 0.183 0.243 0.493 0.599 0.00123 0.0015 ! Validation 161 6187.392 0.005 0.0369 0.205 0.943 0.174 0.23 0.439 0.542 0.0011 0.00135 Wall time: 6187.392395309638 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 24 0.871 0.0397 0.0776 0.18 0.238 0.281 0.333 0.000703 0.000832 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 0.896 0.0353 0.19 0.172 0.225 0.474 0.521 0.00119 0.0013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 162 6225.384 0.005 0.0398 0.232 1.03 0.18 0.238 0.473 0.58 0.00118 0.00145 ! Validation 162 6225.384 0.005 0.0364 0.209 0.937 0.173 0.228 0.445 0.547 0.00111 0.00137 Wall time: 6225.384241530672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 24 0.922 0.0383 0.155 0.177 0.234 0.419 0.471 0.00105 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 1.53 0.0349 0.83 0.171 0.223 1.07 1.09 0.00266 0.00272 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 163 6264.069 0.005 0.0392 0.224 1.01 0.179 0.237 0.478 0.568 0.0012 0.00142 ! Validation 163 6264.069 0.005 0.036 1 1.72 0.172 0.227 1.14 1.2 0.00284 0.00299 Wall time: 6264.06952435663 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 24 1.47 0.0403 0.663 0.18 0.24 0.922 0.973 0.00231 0.00243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 1.51 0.035 0.808 0.171 0.224 1.05 1.07 0.00263 0.00269 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 164 6302.051 0.005 0.0395 0.461 1.25 0.179 0.237 0.692 0.807 0.00173 0.00202 ! Validation 164 6302.051 0.005 0.0361 0.751 1.47 0.172 0.227 0.963 1.04 0.00241 0.00259 Wall time: 6302.051490467973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 24 0.843 0.0384 0.0741 0.176 0.234 0.249 0.325 0.000621 0.000813 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 0.779 0.0345 0.0885 0.17 0.222 0.282 0.356 0.000706 0.000889 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 165 6340.039 0.005 0.0392 0.24 1.02 0.178 0.237 0.479 0.591 0.0012 0.00148 ! Validation 165 6340.039 0.005 0.0355 0.13 0.841 0.171 0.225 0.346 0.432 0.000864 0.00108 Wall time: 6340.039026896935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 24 0.884 0.0393 0.0982 0.179 0.237 0.31 0.374 0.000775 0.000936 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 0.804 0.0339 0.126 0.169 0.22 0.36 0.424 0.0009 0.00106 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 166 6377.999 0.005 0.0386 0.27 1.04 0.177 0.235 0.523 0.626 0.00131 0.00156 ! Validation 166 6377.999 0.005 0.035 0.236 0.937 0.17 0.224 0.486 0.581 0.00122 0.00145 Wall time: 6377.99904112285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 24 1.39 0.0386 0.62 0.176 0.235 0.894 0.941 0.00223 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 1.39 0.0339 0.717 0.168 0.22 0.986 1.01 0.00247 0.00253 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 167 6415.991 0.005 0.0382 0.241 1.01 0.176 0.234 0.475 0.576 0.00119 0.00144 ! Validation 167 6415.991 0.005 0.0351 0.868 1.57 0.17 0.224 1.05 1.11 0.00262 0.00278 Wall time: 6415.991138395853 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 24 0.994 0.0382 0.23 0.176 0.234 0.518 0.573 0.00129 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 1.29 0.0344 0.6 0.17 0.222 0.902 0.925 0.00225 0.00231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 168 6453.972 0.005 0.0384 0.458 1.23 0.176 0.234 0.703 0.813 0.00176 0.00203 ! Validation 168 6453.972 0.005 0.0354 0.595 1.3 0.17 0.225 0.844 0.921 0.00211 0.0023 Wall time: 6453.972501770593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 24 0.93 0.0398 0.134 0.18 0.239 0.354 0.437 0.000886 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 0.714 0.0338 0.0378 0.168 0.22 0.212 0.232 0.00053 0.000581 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 169 6491.956 0.005 0.0382 0.3 1.06 0.176 0.233 0.549 0.659 0.00137 0.00165 ! Validation 169 6491.956 0.005 0.0349 0.104 0.802 0.169 0.223 0.315 0.385 0.000786 0.000962 Wall time: 6491.956425942946 ! Best model 169 0.802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 24 0.922 0.0391 0.14 0.178 0.236 0.397 0.447 0.000993 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 0.881 0.0332 0.217 0.167 0.218 0.516 0.557 0.00129 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 170 6529.958 0.005 0.0377 0.14 0.893 0.175 0.232 0.36 0.447 0.000901 0.00112 ! Validation 170 6529.958 0.005 0.0342 0.262 0.946 0.168 0.221 0.512 0.611 0.00128 0.00153 Wall time: 6529.9583466676995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 24 0.884 0.0376 0.133 0.175 0.232 0.375 0.436 0.000937 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 0.726 0.0331 0.065 0.167 0.217 0.255 0.305 0.000638 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 171 6567.923 0.005 0.0375 0.349 1.1 0.175 0.231 0.591 0.711 0.00148 0.00178 ! Validation 171 6567.923 0.005 0.0343 0.136 0.821 0.167 0.221 0.361 0.441 0.000904 0.0011 Wall time: 6567.92361249961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 24 1.18 0.0377 0.427 0.175 0.232 0.717 0.781 0.00179 0.00195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 1.18 0.0327 0.53 0.166 0.216 0.842 0.87 0.0021 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 172 6605.917 0.005 0.0371 0.113 0.855 0.174 0.23 0.31 0.389 0.000775 0.000972 ! Validation 172 6605.917 0.005 0.0338 0.62 1.3 0.167 0.22 0.866 0.941 0.00217 0.00235 Wall time: 6605.917816988658 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 24 0.924 0.0374 0.175 0.174 0.231 0.435 0.501 0.00109 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 0.738 0.0333 0.072 0.167 0.218 0.268 0.321 0.000671 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 173 6643.879 0.005 0.0376 0.557 1.31 0.174 0.232 0.782 0.899 0.00196 0.00225 ! Validation 173 6643.879 0.005 0.0344 0.142 0.83 0.168 0.222 0.37 0.45 0.000924 0.00112 Wall time: 6643.8792744488455 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 24 0.881 0.038 0.122 0.175 0.233 0.345 0.418 0.000862 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 0.792 0.0323 0.145 0.165 0.215 0.398 0.455 0.000996 0.00114 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 174 6681.861 0.005 0.0371 0.217 0.959 0.174 0.23 0.443 0.559 0.00111 0.0014 ! Validation 174 6681.861 0.005 0.0336 0.179 0.85 0.166 0.219 0.407 0.506 0.00102 0.00126 Wall time: 6681.861592659727 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 24 2.11 0.0389 1.34 0.178 0.236 1.35 1.38 0.00338 0.00345 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 1.09 0.0339 0.41 0.169 0.22 0.733 0.765 0.00183 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 175 6719.851 0.005 0.0366 0.456 1.19 0.173 0.229 0.61 0.788 0.00153 0.00197 ! Validation 175 6719.851 0.005 0.0351 0.477 1.18 0.169 0.224 0.733 0.826 0.00183 0.00206 Wall time: 6719.851062675938 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 24 0.844 0.0369 0.107 0.173 0.229 0.289 0.391 0.000722 0.000978 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 0.702 0.0323 0.0558 0.165 0.215 0.233 0.282 0.000583 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 176 6757.829 0.005 0.0371 0.197 0.939 0.174 0.23 0.435 0.533 0.00109 0.00133 ! Validation 176 6757.829 0.005 0.0334 0.109 0.777 0.166 0.218 0.319 0.395 0.000797 0.000987 Wall time: 6757.82992823096 ! Best model 176 0.777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 24 1.9 0.0362 1.18 0.172 0.227 1.25 1.3 0.00313 0.00325 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 1.48 0.0319 0.846 0.164 0.213 1.08 1.1 0.00269 0.00275 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 177 6795.812 0.005 0.0361 0.255 0.976 0.171 0.227 0.457 0.577 0.00114 0.00144 ! Validation 177 6795.812 0.005 0.033 0.946 1.61 0.165 0.217 1.1 1.16 0.00276 0.00291 Wall time: 6795.812214420643 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 24 0.788 0.0359 0.0711 0.171 0.226 0.265 0.319 0.000662 0.000797 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 1.01 0.0333 0.34 0.167 0.218 0.659 0.697 0.00165 0.00174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 178 6845.774 0.005 0.0373 0.712 1.46 0.174 0.231 0.888 1.02 0.00222 0.00255 ! Validation 178 6845.774 0.005 0.0344 0.43 1.12 0.168 0.222 0.694 0.783 0.00174 0.00196 Wall time: 6845.774895356037 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 24 1.13 0.0382 0.365 0.175 0.234 0.671 0.722 0.00168 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 0.967 0.032 0.327 0.164 0.214 0.652 0.683 0.00163 0.00171 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 179 6886.752 0.005 0.037 0.284 1.02 0.173 0.23 0.524 0.635 0.00131 0.00159 ! Validation 179 6886.752 0.005 0.0331 0.38 1.04 0.165 0.218 0.645 0.737 0.00161 0.00184 Wall time: 6886.752763922792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 24 0.889 0.0369 0.151 0.174 0.23 0.362 0.464 0.000906 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 0.897 0.0321 0.255 0.164 0.214 0.561 0.603 0.0014 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 180 6924.738 0.005 0.0365 0.498 1.23 0.172 0.228 0.727 0.85 0.00182 0.00213 ! Validation 180 6924.738 0.005 0.0332 0.318 0.982 0.165 0.218 0.579 0.674 0.00145 0.00169 Wall time: 6924.738448065706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 24 0.798 0.0346 0.107 0.168 0.222 0.307 0.391 0.000768 0.000979 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 0.666 0.0314 0.0382 0.162 0.212 0.184 0.234 0.000459 0.000584 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 6962.723 0.005 0.0359 0.143 0.861 0.171 0.226 0.361 0.454 0.000903 0.00113 ! Validation 181 6962.723 0.005 0.0325 0.104 0.753 0.164 0.215 0.316 0.385 0.000789 0.000962 Wall time: 6962.723497071769 ! Best model 181 0.753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 24 1.77 0.0347 1.08 0.168 0.223 1.21 1.24 0.00302 0.00311 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.01 0.0312 0.387 0.162 0.211 0.709 0.743 0.00177 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 182 7000.802 0.005 0.0352 0.269 0.973 0.169 0.224 0.476 0.597 0.00119 0.00149 ! Validation 182 7000.802 0.005 0.0324 0.463 1.11 0.163 0.215 0.729 0.813 0.00182 0.00203 Wall time: 7000.802589281928 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 24 0.782 0.0348 0.0864 0.169 0.223 0.261 0.351 0.000654 0.000878 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 0.773 0.0311 0.151 0.162 0.211 0.414 0.464 0.00104 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 183 7038.780 0.005 0.0354 0.307 1.01 0.17 0.225 0.543 0.668 0.00136 0.00167 ! Validation 183 7038.780 0.005 0.0321 0.186 0.828 0.163 0.214 0.42 0.515 0.00105 0.00129 Wall time: 7038.780710121617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 24 0.921 0.0363 0.196 0.171 0.228 0.454 0.529 0.00113 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 0.643 0.0305 0.0322 0.16 0.209 0.19 0.215 0.000474 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 184 7076.746 0.005 0.035 0.21 0.909 0.169 0.223 0.444 0.547 0.00111 0.00137 ! Validation 184 7076.746 0.005 0.0316 0.0931 0.725 0.161 0.213 0.3 0.365 0.000751 0.000911 Wall time: 7076.746969922911 ! Best model 184 0.725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 24 1.28 0.0351 0.575 0.17 0.224 0.872 0.907 0.00218 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 1.47 0.0313 0.841 0.162 0.211 1.08 1.1 0.0027 0.00274 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 185 7114.735 0.005 0.035 0.425 1.13 0.169 0.224 0.654 0.776 0.00164 0.00194 ! Validation 185 7114.735 0.005 0.0323 0.848 1.49 0.163 0.215 1.04 1.1 0.0026 0.00275 Wall time: 7114.735182620585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 24 1.23 0.0342 0.546 0.168 0.221 0.831 0.883 0.00208 0.00221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 0.844 0.0305 0.235 0.16 0.209 0.537 0.579 0.00134 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 186 7152.717 0.005 0.0348 0.22 0.915 0.168 0.223 0.444 0.551 0.00111 0.00138 ! Validation 186 7152.717 0.005 0.0316 0.313 0.945 0.161 0.212 0.581 0.669 0.00145 0.00167 Wall time: 7152.717501251958 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 24 0.818 0.0356 0.106 0.17 0.225 0.298 0.389 0.000746 0.000974 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 0.71 0.0298 0.114 0.158 0.206 0.338 0.404 0.000846 0.00101 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 187 7190.689 0.005 0.0343 0.169 0.856 0.167 0.221 0.403 0.494 0.00101 0.00124 ! Validation 187 7190.689 0.005 0.031 0.159 0.779 0.16 0.21 0.383 0.477 0.000957 0.00119 Wall time: 7190.689231810626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 24 0.878 0.0369 0.14 0.174 0.23 0.375 0.447 0.000938 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.711 0.0335 0.04 0.168 0.219 0.198 0.239 0.000495 0.000597 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 188 7228.676 0.005 0.0382 0.909 1.67 0.176 0.234 0.882 1.15 0.0022 0.00288 ! Validation 188 7228.676 0.005 0.0345 0.14 0.831 0.169 0.222 0.357 0.448 0.000892 0.00112 Wall time: 7228.676530896686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 24 0.767 0.0334 0.1 0.166 0.218 0.313 0.378 0.000783 0.000945 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.656 0.0302 0.0521 0.16 0.208 0.229 0.273 0.000573 0.000682 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 189 7266.639 0.005 0.0355 0.154 0.864 0.17 0.225 0.385 0.47 0.000962 0.00118 ! Validation 189 7266.639 0.005 0.0314 0.113 0.741 0.161 0.212 0.325 0.401 0.000811 0.001 Wall time: 7266.639561852906 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 24 0.814 0.0344 0.125 0.167 0.222 0.328 0.422 0.00082 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.869 0.0297 0.276 0.158 0.206 0.587 0.628 0.00147 0.00157 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 190 7304.612 0.005 0.0341 0.261 0.942 0.167 0.221 0.509 0.614 0.00127 0.00154 ! Validation 190 7304.612 0.005 0.0309 0.379 0.998 0.16 0.21 0.651 0.736 0.00163 0.00184 Wall time: 7304.612414635718 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 24 1.01 0.0338 0.332 0.166 0.22 0.621 0.689 0.00155 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 0.736 0.0294 0.147 0.157 0.205 0.405 0.458 0.00101 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 191 7342.571 0.005 0.0338 0.269 0.945 0.166 0.22 0.521 0.619 0.0013 0.00155 ! Validation 191 7342.571 0.005 0.0306 0.196 0.809 0.159 0.209 0.432 0.529 0.00108 0.00132 Wall time: 7342.571027986705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 24 0.814 0.0339 0.137 0.167 0.22 0.368 0.442 0.00092 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.723 0.029 0.142 0.156 0.204 0.401 0.45 0.001 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 192 7380.559 0.005 0.0333 0.171 0.836 0.165 0.218 0.406 0.495 0.00102 0.00124 ! Validation 192 7380.559 0.005 0.0302 0.198 0.803 0.158 0.208 0.433 0.532 0.00108 0.00133 Wall time: 7380.559875541832 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 24 0.703 0.0327 0.0482 0.164 0.216 0.205 0.262 0.000512 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.682 0.029 0.102 0.156 0.203 0.318 0.381 0.000796 0.000953 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 7418.516 0.005 0.0332 0.366 1.03 0.165 0.218 0.596 0.731 0.00149 0.00183 ! Validation 193 7418.516 0.005 0.0303 0.155 0.761 0.158 0.208 0.39 0.471 0.000975 0.00118 Wall time: 7418.516664153896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 24 1.24 0.0305 0.63 0.158 0.209 0.923 0.949 0.00231 0.00237 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 1.12 0.029 0.54 0.157 0.204 0.853 0.878 0.00213 0.0022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 194 7461.806 0.005 0.033 0.268 0.928 0.164 0.217 0.519 0.609 0.0013 0.00152 ! Validation 194 7461.806 0.005 0.0302 0.622 1.22 0.158 0.208 0.872 0.942 0.00218 0.00236 Wall time: 7461.8070015278645 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 24 0.765 0.0323 0.118 0.163 0.215 0.34 0.41 0.000849 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.603 0.0285 0.0336 0.155 0.202 0.172 0.219 0.000431 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 195 7501.180 0.005 0.0328 0.193 0.848 0.164 0.216 0.435 0.527 0.00109 0.00132 ! Validation 195 7501.180 0.005 0.0296 0.0945 0.687 0.156 0.206 0.301 0.367 0.000753 0.000918 Wall time: 7501.180890467018 ! Best model 195 0.687 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 24 1.73 0.0326 1.07 0.163 0.216 1.18 1.24 0.00296 0.0031 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.708 0.0298 0.111 0.157 0.206 0.324 0.399 0.00081 0.000998 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 7539.285 0.005 0.0325 0.555 1.21 0.163 0.216 0.733 0.88 0.00183 0.0022 ! Validation 196 7539.285 0.005 0.0313 0.179 0.804 0.16 0.211 0.408 0.505 0.00102 0.00126 Wall time: 7539.285073086619 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 24 0.695 0.0321 0.0534 0.162 0.214 0.229 0.276 0.000571 0.000691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.653 0.0289 0.0741 0.156 0.203 0.272 0.325 0.000679 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 197 7577.318 0.005 0.0336 0.237 0.909 0.166 0.219 0.459 0.587 0.00115 0.00147 ! Validation 197 7577.318 0.005 0.0301 0.136 0.737 0.157 0.207 0.359 0.44 0.000897 0.0011 Wall time: 7577.318847737741 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 24 0.962 0.0327 0.308 0.164 0.216 0.627 0.663 0.00157 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 1.2 0.0281 0.642 0.154 0.2 0.933 0.957 0.00233 0.00239 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 198 7615.336 0.005 0.0324 0.232 0.879 0.163 0.215 0.478 0.573 0.0012 0.00143 ! Validation 198 7615.336 0.005 0.0292 0.708 1.29 0.156 0.204 0.943 1.01 0.00236 0.00251 Wall time: 7615.336449127644 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 24 0.673 0.0311 0.0503 0.159 0.211 0.223 0.268 0.000558 0.00067 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.613 0.0282 0.0491 0.154 0.201 0.213 0.265 0.000532 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 199 7653.341 0.005 0.0322 0.362 1.01 0.162 0.215 0.616 0.727 0.00154 0.00182 ! Validation 199 7653.341 0.005 0.0293 0.109 0.694 0.156 0.204 0.316 0.394 0.000791 0.000986 Wall time: 7653.341390481684 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 24 0.764 0.0309 0.145 0.159 0.21 0.386 0.456 0.000964 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.643 0.0275 0.0923 0.152 0.198 0.304 0.363 0.000759 0.000908 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 200 7691.353 0.005 0.0316 0.0986 0.731 0.161 0.213 0.301 0.373 0.000752 0.000933 ! Validation 200 7691.353 0.005 0.0287 0.152 0.726 0.154 0.202 0.386 0.467 0.000965 0.00117 Wall time: 7691.353798915632 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 24 1.71 0.032 1.07 0.162 0.214 1.21 1.23 0.00304 0.00309 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 2.43 0.0272 1.89 0.151 0.197 1.63 1.64 0.00407 0.0041 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 201 7729.359 0.005 0.0312 0.236 0.861 0.16 0.211 0.461 0.556 0.00115 0.00139 ! Validation 201 7729.359 0.005 0.0284 1.88 2.45 0.153 0.202 1.6 1.64 0.004 0.00409 Wall time: 7729.359331403859 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 24 0.738 0.033 0.0783 0.164 0.217 0.255 0.334 0.000638 0.000836 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.789 0.0293 0.203 0.157 0.204 0.497 0.539 0.00124 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 202 7767.360 0.005 0.0341 0.792 1.47 0.167 0.221 0.91 1.08 0.00228 0.00269 ! Validation 202 7767.360 0.005 0.0303 0.266 0.871 0.158 0.208 0.519 0.616 0.0013 0.00154 Wall time: 7767.360539994668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 24 0.688 0.0317 0.0535 0.161 0.213 0.216 0.276 0.00054 0.000691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.577 0.0275 0.0274 0.153 0.198 0.17 0.198 0.000425 0.000495 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 203 7805.332 0.005 0.0321 0.172 0.814 0.162 0.214 0.408 0.5 0.00102 0.00125 ! Validation 203 7805.332 0.005 0.0287 0.0878 0.661 0.154 0.202 0.291 0.354 0.000727 0.000885 Wall time: 7805.332699606661 ! Best model 203 0.661 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 24 0.823 0.0308 0.207 0.159 0.21 0.491 0.543 0.00123 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.671 0.0266 0.138 0.15 0.195 0.396 0.444 0.000989 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 204 7843.358 0.005 0.031 0.118 0.737 0.159 0.21 0.328 0.406 0.000821 0.00101 ! Validation 204 7843.358 0.005 0.028 0.184 0.743 0.152 0.2 0.417 0.512 0.00104 0.00128 Wall time: 7843.3583344738 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 24 0.962 0.0315 0.332 0.161 0.212 0.643 0.688 0.00161 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.605 0.0276 0.0539 0.152 0.198 0.225 0.278 0.000564 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 205 7881.447 0.005 0.0306 0.261 0.873 0.158 0.209 0.511 0.609 0.00128 0.00152 ! Validation 205 7881.447 0.005 0.0288 0.113 0.688 0.154 0.203 0.324 0.402 0.00081 0.001 Wall time: 7881.44775685668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 24 0.965 0.0309 0.347 0.158 0.21 0.632 0.704 0.00158 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.581 0.027 0.0409 0.15 0.196 0.194 0.242 0.000484 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 206 7919.452 0.005 0.0309 0.366 0.984 0.159 0.21 0.6 0.723 0.0015 0.00181 ! Validation 206 7919.452 0.005 0.0282 0.0912 0.655 0.153 0.201 0.296 0.361 0.000739 0.000902 Wall time: 7919.452803628985 ! Best model 206 0.655 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 24 0.649 0.0293 0.0617 0.155 0.205 0.246 0.297 0.000616 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.633 0.0264 0.105 0.149 0.194 0.326 0.387 0.000815 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 207 7957.447 0.005 0.0306 0.28 0.891 0.158 0.209 0.543 0.638 0.00136 0.00159 ! Validation 207 7957.447 0.005 0.0276 0.148 0.701 0.151 0.199 0.37 0.46 0.000926 0.00115 Wall time: 7957.447710304987 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 24 0.821 0.0304 0.213 0.158 0.208 0.495 0.552 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 208 100 0.698 0.0263 0.172 0.149 0.194 0.447 0.495 0.00112 0.00124 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 208 7995.456 0.005 0.0302 0.23 0.835 0.157 0.208 0.476 0.574 0.00119 0.00144 ! Validation 208 7995.456 0.005 0.0275 0.211 0.762 0.151 0.198 0.467 0.549 0.00117 0.00137 Wall time: 7995.456058177631 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 24 0.71 0.0318 0.0734 0.16 0.213 0.255 0.324 0.000638 0.000809 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.802 0.0268 0.265 0.149 0.196 0.577 0.615 0.00144 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 209 8033.470 0.005 0.0299 0.337 0.936 0.156 0.207 0.552 0.7 0.00138 0.00175 ! Validation 209 8033.470 0.005 0.0282 0.326 0.89 0.152 0.201 0.6 0.682 0.0015 0.00171 Wall time: 8033.470339921769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 24 0.69 0.0286 0.119 0.152 0.202 0.34 0.413 0.000851 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.687 0.026 0.168 0.147 0.193 0.444 0.49 0.00111 0.00122 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 210 8071.469 0.005 0.0301 0.224 0.825 0.157 0.207 0.455 0.568 0.00114 0.00142 ! Validation 210 8071.469 0.005 0.0272 0.212 0.756 0.15 0.197 0.456 0.55 0.00114 0.00138 Wall time: 8071.469493239652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 24 1.09 0.0301 0.488 0.157 0.207 0.767 0.835 0.00192 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 1.06 0.0255 0.549 0.146 0.191 0.86 0.886 0.00215 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 211 8109.472 0.005 0.0298 0.35 0.947 0.156 0.206 0.571 0.704 0.00143 0.00176 ! Validation 211 8109.472 0.005 0.0269 0.55 1.09 0.149 0.196 0.811 0.886 0.00203 0.00221 Wall time: 8109.4727176986635 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 24 0.633 0.0284 0.0646 0.153 0.202 0.246 0.304 0.000615 0.000759 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.542 0.0252 0.0381 0.146 0.19 0.208 0.233 0.000521 0.000583 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 212 8151.556 0.005 0.0295 0.184 0.774 0.155 0.205 0.427 0.517 0.00107 0.00129 ! Validation 212 8151.556 0.005 0.0266 0.0852 0.617 0.148 0.195 0.287 0.349 0.000717 0.000872 Wall time: 8151.556361924857 ! Best model 212 0.617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 24 0.926 0.0297 0.333 0.156 0.206 0.633 0.689 0.00158 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.847 0.0249 0.348 0.145 0.189 0.679 0.705 0.0017 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 213 8189.535 0.005 0.029 0.127 0.706 0.154 0.203 0.34 0.418 0.000849 0.00104 ! Validation 213 8189.535 0.005 0.0262 0.384 0.907 0.147 0.193 0.662 0.74 0.00165 0.00185 Wall time: 8189.535803569015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 24 2.26 0.0325 1.61 0.163 0.216 1.49 1.51 0.00372 0.00379 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 2.2 0.0278 1.64 0.153 0.199 1.51 1.53 0.00377 0.00383 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 214 8227.501 0.005 0.0321 0.88 1.52 0.161 0.214 0.906 1.11 0.00227 0.00277 ! Validation 214 8227.501 0.005 0.0295 1.64 2.23 0.156 0.205 1.49 1.53 0.00373 0.00383 Wall time: 8227.50182581693 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 24 0.665 0.0298 0.0697 0.157 0.206 0.272 0.315 0.00068 0.000789 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.971 0.0265 0.44 0.149 0.195 0.764 0.793 0.00191 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 215 8265.490 0.005 0.0316 0.364 0.995 0.161 0.213 0.593 0.728 0.00148 0.00182 ! Validation 215 8265.490 0.005 0.0277 0.429 0.983 0.152 0.199 0.708 0.783 0.00177 0.00196 Wall time: 8265.49089760799 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 24 0.752 0.0319 0.114 0.161 0.213 0.339 0.403 0.000846 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.685 0.0255 0.174 0.146 0.191 0.455 0.499 0.00114 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 216 8303.476 0.005 0.0295 0.269 0.859 0.155 0.205 0.52 0.624 0.0013 0.00156 ! Validation 216 8303.476 0.005 0.027 0.227 0.768 0.149 0.196 0.478 0.57 0.0012 0.00142 Wall time: 8303.476739403792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 24 0.891 0.0289 0.312 0.154 0.203 0.619 0.667 0.00155 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.738 0.0248 0.241 0.145 0.188 0.549 0.587 0.00137 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 217 8341.463 0.005 0.029 0.157 0.738 0.154 0.204 0.383 0.469 0.000958 0.00117 ! Validation 217 8341.463 0.005 0.0262 0.299 0.822 0.147 0.193 0.576 0.653 0.00144 0.00163 Wall time: 8341.463315160945 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 24 0.66 0.0291 0.0783 0.154 0.204 0.268 0.334 0.00067 0.000836 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.617 0.0246 0.124 0.144 0.188 0.374 0.421 0.000936 0.00105 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 218 8379.434 0.005 0.0285 0.16 0.73 0.153 0.202 0.404 0.481 0.00101 0.0012 ! Validation 218 8379.434 0.005 0.0259 0.165 0.683 0.146 0.192 0.396 0.486 0.000989 0.00121 Wall time: 8379.434749747626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 24 0.713 0.0297 0.118 0.156 0.206 0.325 0.411 0.000812 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.839 0.0246 0.348 0.144 0.187 0.676 0.705 0.00169 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 219 8417.410 0.005 0.0285 0.306 0.875 0.152 0.202 0.557 0.666 0.00139 0.00166 ! Validation 219 8417.410 0.005 0.0257 0.368 0.883 0.146 0.192 0.648 0.725 0.00162 0.00181 Wall time: 8417.410342399031 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 24 0.595 0.0273 0.0476 0.15 0.198 0.214 0.261 0.000536 0.000652 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.514 0.0243 0.0276 0.143 0.186 0.166 0.199 0.000414 0.000496 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 8455.527 0.005 0.0284 0.285 0.854 0.153 0.202 0.536 0.645 0.00134 0.00161 ! Validation 220 8455.527 0.005 0.0256 0.0779 0.59 0.146 0.191 0.274 0.334 0.000685 0.000834 Wall time: 8455.527718584985 ! Best model 220 0.590 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 24 0.647 0.0275 0.0973 0.151 0.198 0.327 0.373 0.000816 0.000932 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.647 0.0241 0.165 0.142 0.186 0.451 0.486 0.00113 0.00121 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 221 8493.566 0.005 0.0278 0.122 0.678 0.151 0.199 0.34 0.419 0.000849 0.00105 ! Validation 221 8493.566 0.005 0.0253 0.202 0.707 0.145 0.19 0.448 0.537 0.00112 0.00134 Wall time: 8493.5665820227 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 24 0.672 0.0261 0.15 0.146 0.193 0.397 0.463 0.000993 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.56 0.0237 0.0873 0.141 0.184 0.293 0.353 0.000733 0.000883 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 222 8531.549 0.005 0.0274 0.144 0.692 0.15 0.198 0.364 0.452 0.000909 0.00113 ! Validation 222 8531.549 0.005 0.0249 0.156 0.653 0.144 0.188 0.39 0.472 0.000975 0.00118 Wall time: 8531.549398378003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 24 0.823 0.027 0.283 0.149 0.196 0.582 0.636 0.00145 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.771 0.0246 0.278 0.144 0.188 0.593 0.63 0.00148 0.00158 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 223 8569.554 0.005 0.0282 0.605 1.17 0.152 0.201 0.782 0.936 0.00196 0.00234 ! Validation 223 8569.554 0.005 0.026 0.317 0.837 0.147 0.193 0.593 0.673 0.00148 0.00168 Wall time: 8569.554151248652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 24 0.586 0.0266 0.0526 0.148 0.195 0.232 0.274 0.00058 0.000685 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.524 0.0236 0.0515 0.141 0.184 0.229 0.271 0.000572 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 224 8607.585 0.005 0.0279 0.122 0.68 0.151 0.2 0.342 0.42 0.000855 0.00105 ! Validation 224 8607.585 0.005 0.0249 0.105 0.604 0.144 0.189 0.313 0.387 0.000781 0.000967 Wall time: 8607.58589303866 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 24 0.678 0.0259 0.16 0.146 0.192 0.416 0.477 0.00104 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.675 0.0233 0.208 0.14 0.183 0.506 0.545 0.00127 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 225 8645.629 0.005 0.0271 0.163 0.706 0.149 0.197 0.401 0.482 0.001 0.00121 ! Validation 225 8645.629 0.005 0.0245 0.219 0.71 0.143 0.187 0.474 0.56 0.00119 0.0014 Wall time: 8645.629735329654 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 24 0.586 0.0271 0.0443 0.149 0.197 0.209 0.251 0.000524 0.000629 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.503 0.023 0.0436 0.139 0.181 0.208 0.249 0.000521 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 226 8683.663 0.005 0.0268 0.108 0.645 0.148 0.196 0.325 0.396 0.000813 0.000991 ! Validation 226 8683.663 0.005 0.0243 0.0962 0.581 0.142 0.186 0.299 0.371 0.000747 0.000927 Wall time: 8683.663773625623 ! Best model 226 0.581 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 24 0.733 0.0271 0.19 0.149 0.197 0.453 0.521 0.00113 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.707 0.0244 0.219 0.142 0.187 0.528 0.559 0.00132 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 227 8721.678 0.005 0.0273 0.492 1.04 0.149 0.198 0.724 0.844 0.00181 0.00211 ! Validation 227 8721.678 0.005 0.0256 0.236 0.747 0.145 0.191 0.493 0.58 0.00123 0.00145 Wall time: 8721.67838512361 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 24 0.691 0.029 0.111 0.154 0.203 0.348 0.398 0.000871 0.000995 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.98 0.0232 0.517 0.14 0.182 0.833 0.859 0.00208 0.00215 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 228 8759.718 0.005 0.0274 0.267 0.815 0.15 0.198 0.5 0.622 0.00125 0.00156 ! Validation 228 8759.718 0.005 0.0244 0.59 1.08 0.142 0.187 0.859 0.918 0.00215 0.0023 Wall time: 8759.718546879012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 24 0.614 0.0248 0.118 0.142 0.188 0.356 0.411 0.000891 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.494 0.0229 0.0356 0.139 0.181 0.176 0.226 0.00044 0.000564 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 229 8797.891 0.005 0.027 0.303 0.844 0.149 0.197 0.551 0.663 0.00138 0.00166 ! Validation 229 8797.891 0.005 0.0242 0.0813 0.566 0.142 0.186 0.278 0.341 0.000694 0.000852 Wall time: 8797.891112634912 ! Best model 229 0.566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 24 0.635 0.0261 0.113 0.147 0.193 0.325 0.402 0.000812 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.511 0.0227 0.0569 0.138 0.18 0.238 0.285 0.000595 0.000713 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 230 8835.891 0.005 0.0265 0.174 0.703 0.147 0.194 0.413 0.5 0.00103 0.00125 ! Validation 230 8835.891 0.005 0.0239 0.111 0.589 0.141 0.185 0.322 0.397 0.000804 0.000993 Wall time: 8835.891263102647 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 24 0.63 0.0281 0.0667 0.152 0.2 0.249 0.309 0.000623 0.000772 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.74 0.0225 0.29 0.137 0.179 0.613 0.643 0.00153 0.00161 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 231 8873.901 0.005 0.0261 0.175 0.697 0.146 0.193 0.407 0.504 0.00102 0.00126 ! Validation 231 8873.901 0.005 0.0237 0.291 0.766 0.14 0.184 0.565 0.645 0.00141 0.00161 Wall time: 8873.901203869842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 24 0.878 0.0263 0.351 0.147 0.194 0.65 0.708 0.00163 0.00177 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 1.26 0.0236 0.786 0.14 0.184 1.03 1.06 0.00258 0.00265 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 232 8911.884 0.005 0.027 0.578 1.12 0.148 0.197 0.795 0.913 0.00199 0.00228 ! Validation 232 8911.884 0.005 0.025 0.943 1.44 0.143 0.189 1.11 1.16 0.00278 0.0029 Wall time: 8911.884669306688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 24 0.641 0.0265 0.111 0.148 0.195 0.339 0.398 0.000847 0.000995 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.497 0.023 0.036 0.139 0.181 0.19 0.227 0.000474 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 233 8949.899 0.005 0.027 0.277 0.818 0.149 0.196 0.525 0.634 0.00131 0.00158 ! Validation 233 8949.899 0.005 0.0242 0.0779 0.562 0.142 0.186 0.273 0.334 0.000683 0.000834 Wall time: 8949.899003492668 ! Best model 233 0.562 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 24 0.843 0.0252 0.34 0.144 0.19 0.659 0.696 0.00165 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.512 0.0227 0.0584 0.137 0.18 0.238 0.289 0.000596 0.000722 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 8987.897 0.005 0.0261 0.177 0.699 0.146 0.193 0.395 0.497 0.000989 0.00124 ! Validation 234 8987.897 0.005 0.024 0.0926 0.573 0.141 0.185 0.296 0.364 0.00074 0.000909 Wall time: 8987.897707494907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 24 0.526 0.0243 0.0397 0.142 0.186 0.198 0.238 0.000494 0.000595 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.503 0.0225 0.0529 0.137 0.179 0.229 0.275 0.000574 0.000687 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 235 9025.898 0.005 0.0261 0.223 0.744 0.146 0.193 0.446 0.569 0.00111 0.00142 ! Validation 235 9025.898 0.005 0.0236 0.111 0.584 0.14 0.184 0.323 0.399 0.000808 0.000996 Wall time: 9025.898901890032 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 24 0.596 0.0247 0.102 0.143 0.188 0.329 0.382 0.000823 0.000956 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.53 0.0219 0.0927 0.135 0.177 0.306 0.364 0.000765 0.000909 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 236 9063.907 0.005 0.0256 0.0699 0.581 0.145 0.191 0.25 0.314 0.000625 0.000786 ! Validation 236 9063.907 0.005 0.0231 0.111 0.573 0.138 0.182 0.323 0.399 0.000807 0.000996 Wall time: 9063.907643617596 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 24 1.22 0.0259 0.704 0.145 0.192 0.971 1 0.00243 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 1.15 0.0225 0.703 0.137 0.179 0.98 1 0.00245 0.0025 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 237 9101.929 0.005 0.0255 0.334 0.845 0.144 0.191 0.537 0.681 0.00134 0.0017 ! Validation 237 9101.929 0.005 0.0238 0.793 1.27 0.14 0.185 1.02 1.06 0.00254 0.00266 Wall time: 9101.92955831904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 24 0.622 0.0267 0.0866 0.148 0.195 0.302 0.352 0.000755 0.000879 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.758 0.0221 0.316 0.136 0.178 0.644 0.672 0.00161 0.00168 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 238 9139.923 0.005 0.0258 0.258 0.774 0.145 0.192 0.509 0.612 0.00127 0.00153 ! Validation 238 9139.923 0.005 0.0233 0.31 0.777 0.139 0.182 0.582 0.666 0.00146 0.00166 Wall time: 9139.923772620969 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 24 0.657 0.0263 0.132 0.147 0.194 0.375 0.434 0.000937 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.818 0.0218 0.383 0.135 0.176 0.711 0.739 0.00178 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 239 9177.923 0.005 0.0254 0.12 0.629 0.144 0.191 0.324 0.413 0.000809 0.00103 ! Validation 239 9177.923 0.005 0.023 0.457 0.917 0.138 0.181 0.748 0.808 0.00187 0.00202 Wall time: 9177.923584180884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 24 0.593 0.0259 0.0744 0.146 0.193 0.267 0.326 0.000667 0.000815 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.525 0.0225 0.0758 0.137 0.179 0.268 0.329 0.000669 0.000822 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 9215.920 0.005 0.0268 0.481 1.02 0.148 0.196 0.69 0.837 0.00173 0.00209 ! Validation 240 9215.920 0.005 0.0236 0.1 0.572 0.139 0.183 0.307 0.379 0.000767 0.000947 Wall time: 9215.920683528762 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 24 0.565 0.0253 0.0589 0.144 0.19 0.236 0.29 0.000591 0.000725 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.483 0.0215 0.0529 0.134 0.175 0.231 0.275 0.000577 0.000687 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 241 9255.749 0.005 0.0253 0.102 0.607 0.144 0.19 0.311 0.384 0.000777 0.000959 ! Validation 241 9255.749 0.005 0.0227 0.0964 0.55 0.137 0.18 0.3 0.371 0.00075 0.000927 Wall time: 9255.749295106623 ! Best model 241 0.550 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 24 0.709 0.0249 0.21 0.143 0.189 0.494 0.548 0.00124 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.507 0.0216 0.0753 0.134 0.176 0.271 0.328 0.000677 0.00082 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 242 9293.749 0.005 0.025 0.296 0.795 0.143 0.189 0.559 0.653 0.0014 0.00163 ! Validation 242 9293.749 0.005 0.0228 0.109 0.564 0.137 0.18 0.32 0.394 0.000799 0.000985 Wall time: 9293.749687139876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 24 0.658 0.0257 0.145 0.145 0.191 0.42 0.454 0.00105 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.53 0.0213 0.105 0.133 0.174 0.338 0.387 0.000845 0.000967 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 243 9331.759 0.005 0.0248 0.0924 0.588 0.142 0.188 0.291 0.361 0.000727 0.000902 ! Validation 243 9331.759 0.005 0.0224 0.175 0.624 0.136 0.179 0.422 0.501 0.00106 0.00125 Wall time: 9331.759741790593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 24 0.989 0.0251 0.487 0.144 0.189 0.803 0.834 0.00201 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 1.07 0.0217 0.638 0.135 0.176 0.932 0.954 0.00233 0.00239 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 244 9369.743 0.005 0.025 0.39 0.889 0.143 0.189 0.633 0.744 0.00158 0.00186 ! Validation 244 9369.743 0.005 0.0229 0.698 1.15 0.138 0.181 0.948 0.998 0.00237 0.0025 Wall time: 9369.743358281907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 24 0.488 0.0232 0.0244 0.139 0.182 0.154 0.187 0.000385 0.000467 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.487 0.0213 0.0605 0.134 0.174 0.244 0.294 0.00061 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 245 9407.751 0.005 0.025 0.213 0.713 0.143 0.189 0.468 0.557 0.00117 0.00139 ! Validation 245 9407.751 0.005 0.0224 0.0869 0.536 0.136 0.179 0.286 0.352 0.000715 0.00088 Wall time: 9407.751906073652 ! Best model 245 0.536 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 24 0.576 0.0262 0.0519 0.147 0.193 0.228 0.272 0.00057 0.000681 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.831 0.0229 0.373 0.138 0.181 0.705 0.73 0.00176 0.00182 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 246 9445.777 0.005 0.0247 0.281 0.774 0.142 0.188 0.503 0.64 0.00126 0.0016 ! Validation 246 9445.777 0.005 0.024 0.484 0.965 0.141 0.185 0.77 0.831 0.00193 0.00208 Wall time: 9445.777919868007 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 24 1.42 0.0253 0.919 0.144 0.19 1.13 1.15 0.00281 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 1.03 0.0225 0.582 0.137 0.179 0.892 0.912 0.00223 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 247 9483.769 0.005 0.0254 0.427 0.934 0.144 0.19 0.667 0.77 0.00167 0.00192 ! Validation 247 9483.769 0.005 0.0236 0.579 1.05 0.14 0.184 0.854 0.909 0.00214 0.00227 Wall time: 9483.769081818871 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 24 0.819 0.0254 0.31 0.144 0.19 0.621 0.666 0.00155 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.585 0.0214 0.158 0.134 0.175 0.44 0.474 0.0011 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 248 9521.767 0.005 0.025 0.194 0.694 0.143 0.189 0.434 0.522 0.00109 0.00131 ! Validation 248 9521.767 0.005 0.0225 0.22 0.671 0.137 0.179 0.485 0.561 0.00121 0.0014 Wall time: 9521.767020728905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 24 0.769 0.0249 0.271 0.143 0.189 0.554 0.622 0.00138 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 0.565 0.0214 0.137 0.134 0.175 0.409 0.442 0.00102 0.0011 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 249 9559.775 0.005 0.0245 0.185 0.675 0.142 0.187 0.431 0.511 0.00108 0.00128 ! Validation 249 9559.775 0.005 0.0225 0.183 0.632 0.137 0.179 0.427 0.511 0.00107 0.00128 Wall time: 9559.775081743952 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 24 0.778 0.0243 0.293 0.142 0.186 0.605 0.647 0.00151 0.00162 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 0.696 0.0208 0.28 0.132 0.172 0.606 0.632 0.00152 0.00158 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 250 9597.753 0.005 0.0243 0.131 0.616 0.141 0.186 0.358 0.426 0.000894 0.00107 ! Validation 250 9597.753 0.005 0.022 0.298 0.737 0.135 0.177 0.582 0.652 0.00145 0.00163 Wall time: 9597.753006078769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 24 0.558 0.0252 0.0549 0.142 0.19 0.231 0.28 0.000578 0.0007 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 0.597 0.0209 0.18 0.132 0.173 0.466 0.506 0.00116 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 251 9635.759 0.005 0.0241 0.254 0.736 0.14 0.185 0.517 0.608 0.00129 0.00152 ! Validation 251 9635.759 0.005 0.0222 0.177 0.62 0.135 0.178 0.422 0.503 0.00105 0.00126 Wall time: 9635.759206101764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 24 0.695 0.0234 0.228 0.139 0.183 0.535 0.57 0.00134 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 0.56 0.0207 0.146 0.132 0.172 0.412 0.457 0.00103 0.00114 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 252 9673.877 0.005 0.0241 0.256 0.738 0.141 0.186 0.518 0.605 0.0013 0.00151 ! Validation 252 9673.877 0.005 0.0218 0.161 0.597 0.134 0.176 0.399 0.48 0.000998 0.0012 Wall time: 9673.877328568604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 24 0.579 0.0243 0.0924 0.141 0.186 0.324 0.363 0.000811 0.000908 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 0.562 0.0203 0.155 0.13 0.17 0.433 0.471 0.00108 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 253 9711.850 0.005 0.0238 0.136 0.613 0.14 0.184 0.363 0.443 0.000907 0.00111 ! Validation 253 9711.850 0.005 0.0215 0.165 0.595 0.133 0.175 0.406 0.486 0.00101 0.00121 Wall time: 9711.850759922992 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 24 1.87 0.023 1.41 0.137 0.181 1.4 1.42 0.00349 0.00355 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 1.29 0.0205 0.881 0.13 0.171 1.11 1.12 0.00277 0.0028 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 254 9749.836 0.005 0.0235 0.274 0.744 0.139 0.183 0.47 0.594 0.00117 0.00148 ! Validation 254 9749.836 0.005 0.0218 0.807 1.24 0.134 0.176 1.03 1.07 0.00257 0.00268 Wall time: 9749.836047627963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 24 0.566 0.0238 0.0903 0.14 0.184 0.323 0.359 0.000807 0.000898 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.466 0.0205 0.0554 0.131 0.171 0.23 0.281 0.000576 0.000703 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 255 9787.826 0.005 0.0241 0.267 0.75 0.14 0.186 0.516 0.622 0.00129 0.00155 ! Validation 255 9787.826 0.005 0.0216 0.0847 0.518 0.134 0.176 0.284 0.348 0.00071 0.000869 Wall time: 9787.826306891628 ! Best model 255 0.518 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 24 1.25 0.0235 0.779 0.138 0.183 1.02 1.05 0.00255 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 0.828 0.0203 0.423 0.13 0.17 0.756 0.777 0.00189 0.00194 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 256 9825.817 0.005 0.0235 0.157 0.627 0.139 0.183 0.345 0.451 0.000861 0.00113 ! Validation 256 9825.817 0.005 0.0215 0.497 0.927 0.133 0.175 0.786 0.843 0.00197 0.00211 Wall time: 9825.817335254978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 24 0.55 0.0234 0.0817 0.138 0.183 0.294 0.342 0.000735 0.000854 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 0.425 0.0201 0.0235 0.129 0.169 0.141 0.183 0.000354 0.000458 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 257 9866.337 0.005 0.0234 0.142 0.609 0.138 0.183 0.369 0.452 0.000923 0.00113 ! Validation 257 9866.337 0.005 0.0211 0.0727 0.495 0.132 0.174 0.259 0.322 0.000648 0.000806 Wall time: 9866.337577019818 ! Best model 257 0.495 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 24 1 0.0217 0.568 0.134 0.176 0.868 0.9 0.00217 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 0.742 0.0205 0.333 0.13 0.171 0.666 0.689 0.00166 0.00172 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 258 9904.346 0.005 0.023 0.166 0.627 0.137 0.182 0.383 0.473 0.000958 0.00118 ! Validation 258 9904.346 0.005 0.0216 0.409 0.84 0.133 0.175 0.702 0.764 0.00176 0.00191 Wall time: 9904.346713013947 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 24 1.18 0.0236 0.709 0.138 0.184 0.969 1.01 0.00242 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.635 0.0199 0.238 0.129 0.168 0.557 0.583 0.00139 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 259 9942.349 0.005 0.0233 0.248 0.713 0.138 0.182 0.484 0.581 0.00121 0.00145 ! Validation 259 9942.349 0.005 0.021 0.279 0.7 0.132 0.173 0.556 0.631 0.00139 0.00158 Wall time: 9942.349757379852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 24 0.72 0.0226 0.267 0.136 0.18 0.577 0.618 0.00144 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.558 0.0201 0.156 0.13 0.169 0.444 0.471 0.00111 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 260 9980.317 0.005 0.0231 0.151 0.612 0.137 0.181 0.372 0.46 0.000931 0.00115 ! Validation 260 9980.317 0.005 0.0211 0.172 0.594 0.132 0.174 0.417 0.495 0.00104 0.00124 Wall time: 9980.317777978722 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 24 0.513 0.0229 0.054 0.137 0.181 0.219 0.278 0.000547 0.000694 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.48 0.0205 0.071 0.131 0.171 0.278 0.318 0.000695 0.000796 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 261 10018.330 0.005 0.024 0.358 0.837 0.14 0.185 0.557 0.722 0.00139 0.0018 ! Validation 261 10018.330 0.005 0.0215 0.116 0.545 0.133 0.175 0.329 0.406 0.000822 0.00102 Wall time: 10018.33061765274 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 24 0.897 0.0247 0.404 0.142 0.188 0.726 0.759 0.00182 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 1.11 0.0199 0.709 0.129 0.169 0.995 1.01 0.00249 0.00252 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 262 10056.309 0.005 0.0231 0.0953 0.557 0.137 0.182 0.286 0.354 0.000716 0.000886 ! Validation 262 10056.309 0.005 0.0209 0.735 1.15 0.132 0.173 0.98 1.02 0.00245 0.00256 Wall time: 10056.30931940768 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 24 0.718 0.0244 0.23 0.141 0.187 0.488 0.573 0.00122 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.439 0.0207 0.0248 0.131 0.172 0.167 0.188 0.000417 0.000471 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 10094.290 0.005 0.0234 0.496 0.964 0.138 0.183 0.739 0.847 0.00185 0.00212 ! Validation 263 10094.290 0.005 0.0218 0.0678 0.504 0.134 0.177 0.257 0.311 0.000643 0.000778 Wall time: 10094.29080239404 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 24 0.642 0.0233 0.175 0.138 0.183 0.443 0.501 0.00111 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.477 0.0197 0.0829 0.129 0.168 0.3 0.344 0.000749 0.00086 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 264 10132.264 0.005 0.0232 0.127 0.591 0.138 0.182 0.348 0.425 0.000871 0.00106 ! Validation 264 10132.264 0.005 0.0207 0.113 0.527 0.131 0.172 0.325 0.401 0.000812 0.001 Wall time: 10132.264924879652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 24 0.715 0.0233 0.249 0.138 0.182 0.562 0.596 0.00141 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.79 0.0194 0.401 0.127 0.167 0.737 0.757 0.00184 0.00189 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 265 10170.266 0.005 0.0228 0.161 0.617 0.137 0.18 0.399 0.477 0.000997 0.00119 ! Validation 265 10170.266 0.005 0.0205 0.385 0.795 0.13 0.171 0.682 0.741 0.0017 0.00185 Wall time: 10170.266152611934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 24 0.745 0.0221 0.304 0.135 0.177 0.614 0.659 0.00154 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.843 0.0195 0.453 0.128 0.167 0.784 0.804 0.00196 0.00201 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 266 10208.221 0.005 0.0227 0.329 0.783 0.136 0.18 0.587 0.686 0.00147 0.00171 ! Validation 266 10208.221 0.005 0.0205 0.436 0.847 0.13 0.171 0.729 0.789 0.00182 0.00197 Wall time: 10208.22145509487 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 24 0.576 0.0221 0.134 0.135 0.178 0.369 0.437 0.000924 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.648 0.0193 0.261 0.127 0.166 0.585 0.611 0.00146 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 267 10246.200 0.005 0.0226 0.228 0.679 0.136 0.18 0.479 0.573 0.0012 0.00143 ! Validation 267 10246.200 0.005 0.0205 0.261 0.67 0.13 0.171 0.539 0.611 0.00135 0.00153 Wall time: 10246.200179061852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 24 0.633 0.0216 0.201 0.134 0.176 0.467 0.536 0.00117 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.673 0.0193 0.288 0.127 0.166 0.617 0.641 0.00154 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 268 10284.160 0.005 0.0224 0.141 0.588 0.135 0.179 0.371 0.447 0.000928 0.00112 ! Validation 268 10284.160 0.005 0.0203 0.272 0.678 0.13 0.17 0.554 0.623 0.00138 0.00156 Wall time: 10284.160745001864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 24 0.487 0.0226 0.035 0.136 0.18 0.186 0.224 0.000464 0.000559 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.404 0.019 0.0248 0.126 0.165 0.166 0.188 0.000414 0.000471 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 10322.148 0.005 0.0222 0.134 0.578 0.135 0.178 0.358 0.441 0.000896 0.0011 ! Validation 269 10322.148 0.005 0.02 0.0613 0.462 0.129 0.169 0.243 0.296 0.000607 0.00074 Wall time: 10322.1481709769 ! Best model 269 0.462 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 24 2.58 0.0264 2.05 0.146 0.194 1.69 1.71 0.00421 0.00428 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 1.09 0.0224 0.647 0.137 0.179 0.944 0.961 0.00236 0.0024 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 270 10360.129 0.005 0.0227 0.457 0.91 0.136 0.18 0.568 0.773 0.00142 0.00193 ! Validation 270 10360.129 0.005 0.0237 0.822 1.3 0.14 0.184 1.04 1.08 0.00259 0.00271 Wall time: 10360.129459486809 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 24 0.915 0.0231 0.453 0.138 0.182 0.749 0.804 0.00187 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.842 0.0201 0.441 0.129 0.169 0.772 0.793 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 271 10398.102 0.005 0.0242 0.466 0.951 0.141 0.186 0.687 0.816 0.00172 0.00204 ! Validation 271 10398.102 0.005 0.0212 0.486 0.91 0.132 0.174 0.776 0.833 0.00194 0.00208 Wall time: 10398.102925566025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 24 0.506 0.0219 0.0685 0.134 0.177 0.253 0.313 0.000632 0.000782 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.405 0.0192 0.0199 0.127 0.166 0.13 0.169 0.000326 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 272 10436.077 0.005 0.0227 0.154 0.608 0.136 0.18 0.389 0.473 0.000971 0.00118 ! Validation 272 10436.077 0.005 0.0203 0.0679 0.474 0.13 0.17 0.25 0.311 0.000626 0.000778 Wall time: 10436.077282753773 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 24 0.477 0.0223 0.0308 0.135 0.179 0.177 0.21 0.000443 0.000524 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.397 0.019 0.0177 0.126 0.165 0.139 0.159 0.000348 0.000398 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 10480.294 0.005 0.0222 0.179 0.623 0.135 0.178 0.423 0.511 0.00106 0.00128 ! Validation 273 10480.294 0.005 0.0201 0.0599 0.461 0.129 0.169 0.236 0.292 0.000591 0.000731 Wall time: 10480.294258487877 ! Best model 273 0.461 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 24 0.548 0.0233 0.0829 0.138 0.182 0.268 0.344 0.000669 0.00086 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.393 0.0187 0.0189 0.125 0.164 0.147 0.164 0.000367 0.000411 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 274 10527.428 0.005 0.0219 0.078 0.516 0.134 0.177 0.267 0.334 0.000667 0.000834 ! Validation 274 10527.428 0.005 0.0198 0.0584 0.455 0.128 0.168 0.237 0.289 0.000593 0.000722 Wall time: 10527.428670420777 ! Best model 274 0.455 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 24 0.596 0.0215 0.166 0.133 0.175 0.462 0.488 0.00116 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.464 0.0187 0.0906 0.125 0.163 0.321 0.36 0.000804 0.000899 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 275 10565.517 0.005 0.0218 0.125 0.56 0.133 0.176 0.331 0.42 0.000828 0.00105 ! Validation 275 10565.517 0.005 0.0197 0.158 0.551 0.128 0.168 0.4 0.475 0.001 0.00119 Wall time: 10565.517857581843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 24 0.626 0.0215 0.195 0.133 0.175 0.463 0.528 0.00116 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.425 0.0194 0.0381 0.127 0.166 0.195 0.233 0.000488 0.000583 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 276 10603.461 0.005 0.0221 0.329 0.771 0.134 0.178 0.571 0.689 0.00143 0.00172 ! Validation 276 10603.461 0.005 0.0202 0.102 0.507 0.13 0.17 0.309 0.382 0.000773 0.000956 Wall time: 10603.461208072957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 24 0.517 0.0224 0.0695 0.135 0.179 0.247 0.315 0.000617 0.000788 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.412 0.0185 0.0415 0.125 0.163 0.196 0.243 0.000491 0.000608 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 10641.397 0.005 0.0217 0.118 0.553 0.133 0.176 0.342 0.413 0.000856 0.00103 ! Validation 277 10641.397 0.005 0.0195 0.0769 0.467 0.127 0.167 0.271 0.331 0.000679 0.000829 Wall time: 10641.397788284812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 24 1 0.0229 0.546 0.136 0.181 0.841 0.883 0.0021 0.00221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 1.09 0.0184 0.727 0.124 0.162 1.01 1.02 0.00252 0.00255 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 278 10679.377 0.005 0.0214 0.149 0.576 0.132 0.174 0.354 0.447 0.000884 0.00112 ! Validation 278 10679.377 0.005 0.0194 0.652 1.04 0.127 0.167 0.917 0.965 0.00229 0.00241 Wall time: 10679.377017640043 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 24 0.507 0.0231 0.0446 0.137 0.182 0.189 0.252 0.000474 0.000631 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.482 0.019 0.103 0.125 0.165 0.346 0.383 0.000864 0.000957 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 279 10717.375 0.005 0.0221 0.423 0.865 0.134 0.177 0.681 0.786 0.0017 0.00196 ! Validation 279 10717.375 0.005 0.0201 0.176 0.577 0.129 0.169 0.427 0.502 0.00107 0.00125 Wall time: 10717.375170348678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 24 0.862 0.0209 0.445 0.131 0.173 0.767 0.797 0.00192 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.865 0.0186 0.494 0.125 0.163 0.827 0.84 0.00207 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 280 10755.376 0.005 0.0219 0.288 0.725 0.134 0.177 0.545 0.637 0.00136 0.00159 ! Validation 280 10755.376 0.005 0.0196 0.497 0.89 0.128 0.167 0.792 0.843 0.00198 0.00211 Wall time: 10755.376182615757 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 24 0.459 0.0205 0.0499 0.13 0.171 0.203 0.267 0.000507 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.387 0.0184 0.019 0.124 0.162 0.131 0.165 0.000329 0.000412 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 281 10793.334 0.005 0.0215 0.149 0.579 0.133 0.175 0.389 0.465 0.000973 0.00116 ! Validation 281 10793.334 0.005 0.0195 0.0712 0.461 0.127 0.167 0.255 0.319 0.000637 0.000797 Wall time: 10793.334318106994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 24 0.608 0.0204 0.2 0.129 0.171 0.465 0.534 0.00116 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.59 0.0181 0.229 0.123 0.161 0.551 0.571 0.00138 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 282 10831.317 0.005 0.0211 0.0689 0.491 0.132 0.174 0.247 0.306 0.000619 0.000766 ! Validation 282 10831.317 0.005 0.0191 0.233 0.615 0.126 0.165 0.509 0.577 0.00127 0.00144 Wall time: 10831.317718926817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 24 0.502 0.0209 0.0834 0.13 0.173 0.318 0.345 0.000795 0.000863 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.456 0.0183 0.0911 0.124 0.161 0.325 0.361 0.000812 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 283 10869.275 0.005 0.0211 0.19 0.612 0.131 0.174 0.414 0.524 0.00103 0.00131 ! Validation 283 10869.275 0.005 0.0192 0.149 0.533 0.126 0.165 0.387 0.461 0.000967 0.00115 Wall time: 10869.27586797392 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 24 0.503 0.0218 0.0667 0.133 0.176 0.265 0.309 0.000663 0.000772 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.422 0.0178 0.0657 0.122 0.16 0.263 0.306 0.000657 0.000766 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 284 10907.336 0.005 0.021 0.105 0.525 0.131 0.173 0.31 0.388 0.000775 0.00097 ! Validation 284 10907.336 0.005 0.0189 0.128 0.505 0.125 0.164 0.354 0.427 0.000884 0.00107 Wall time: 10907.33693467686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 24 0.549 0.0223 0.103 0.135 0.178 0.313 0.383 0.000782 0.000958 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.422 0.0186 0.0504 0.125 0.163 0.216 0.268 0.00054 0.00067 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 285 10945.288 0.005 0.0218 0.413 0.848 0.133 0.176 0.653 0.775 0.00163 0.00194 ! Validation 285 10945.288 0.005 0.0196 0.0928 0.485 0.127 0.167 0.299 0.364 0.000749 0.00091 Wall time: 10945.288647404872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 24 0.716 0.0224 0.269 0.135 0.179 0.563 0.619 0.00141 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.748 0.0181 0.386 0.123 0.161 0.726 0.743 0.00182 0.00186 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 286 10988.206 0.005 0.0214 0.14 0.567 0.132 0.175 0.366 0.442 0.000915 0.0011 ! Validation 286 10988.206 0.005 0.0192 0.461 0.845 0.126 0.166 0.758 0.811 0.0019 0.00203 Wall time: 10988.20643136464 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 24 0.584 0.0204 0.176 0.129 0.171 0.463 0.501 0.00116 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.483 0.0178 0.128 0.122 0.159 0.398 0.428 0.000994 0.00107 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 287 11026.160 0.005 0.021 0.171 0.591 0.131 0.173 0.419 0.494 0.00105 0.00123 ! Validation 287 11026.160 0.005 0.0188 0.138 0.514 0.125 0.164 0.369 0.445 0.000923 0.00111 Wall time: 11026.16049973201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 24 0.755 0.0228 0.298 0.136 0.181 0.614 0.653 0.00153 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 1.6 0.0187 1.23 0.125 0.163 1.31 1.33 0.00329 0.00331 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 11072.494 0.005 0.0214 0.362 0.79 0.132 0.175 0.57 0.72 0.00143 0.0018 ! Validation 288 11072.494 0.005 0.0198 1.1 1.5 0.128 0.168 1.22 1.25 0.00304 0.00313 Wall time: 11072.494014868978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 24 0.874 0.0215 0.444 0.133 0.175 0.777 0.796 0.00194 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.397 0.0184 0.0291 0.124 0.162 0.168 0.204 0.000419 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 289 11110.506 0.005 0.0216 0.374 0.807 0.133 0.176 0.634 0.729 0.00158 0.00182 ! Validation 289 11110.506 0.005 0.0195 0.0872 0.477 0.127 0.167 0.282 0.353 0.000704 0.000882 Wall time: 11110.506688109599 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 24 0.44 0.0206 0.0289 0.13 0.171 0.154 0.203 0.000385 0.000508 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.378 0.018 0.0188 0.123 0.16 0.133 0.164 0.000332 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 290 11149.211 0.005 0.0211 0.18 0.602 0.131 0.174 0.418 0.512 0.00105 0.00128 ! Validation 290 11149.211 0.005 0.019 0.0625 0.443 0.126 0.165 0.238 0.299 0.000595 0.000747 Wall time: 11149.211056028958 ! Best model 290 0.443 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 24 0.52 0.0206 0.108 0.13 0.172 0.338 0.394 0.000844 0.000984 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.386 0.0175 0.0366 0.121 0.158 0.192 0.228 0.000481 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 291 11187.196 0.005 0.0207 0.104 0.517 0.13 0.172 0.319 0.385 0.000797 0.000961 ! Validation 291 11187.196 0.005 0.0185 0.0967 0.467 0.124 0.163 0.3 0.372 0.000749 0.000929 Wall time: 11187.19607845461 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 24 0.629 0.0204 0.222 0.129 0.171 0.508 0.563 0.00127 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.456 0.0174 0.109 0.12 0.157 0.366 0.395 0.000914 0.000987 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 292 11225.167 0.005 0.0204 0.132 0.539 0.129 0.171 0.358 0.43 0.000894 0.00108 ! Validation 292 11225.167 0.005 0.0184 0.118 0.485 0.123 0.162 0.339 0.41 0.000847 0.00103 Wall time: 11225.167822850868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 24 0.968 0.0189 0.59 0.125 0.164 0.898 0.918 0.00224 0.00229 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.716 0.0178 0.359 0.121 0.16 0.701 0.716 0.00175 0.00179 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 293 11263.124 0.005 0.0204 0.274 0.683 0.129 0.171 0.535 0.617 0.00134 0.00154 ! Validation 293 11263.124 0.005 0.0189 0.362 0.741 0.125 0.164 0.661 0.719 0.00165 0.0018 Wall time: 11263.124263477977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 24 0.432 0.0198 0.037 0.128 0.168 0.188 0.23 0.000471 0.000574 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.393 0.0173 0.0463 0.12 0.157 0.217 0.257 0.000542 0.000643 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 294 11301.072 0.005 0.0205 0.151 0.561 0.129 0.171 0.384 0.469 0.000959 0.00117 ! Validation 294 11301.072 0.005 0.0183 0.0755 0.442 0.123 0.162 0.268 0.328 0.000671 0.000821 Wall time: 11301.07210428873 ! Best model 294 0.442 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 24 0.488 0.0224 0.0398 0.134 0.179 0.189 0.238 0.000472 0.000596 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.75 0.0176 0.398 0.121 0.159 0.738 0.754 0.00184 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 295 11339.051 0.005 0.0204 0.301 0.709 0.129 0.171 0.554 0.662 0.00138 0.00166 ! Validation 295 11339.051 0.005 0.0187 0.365 0.739 0.124 0.163 0.664 0.722 0.00166 0.0018 Wall time: 11339.051828722004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 24 0.992 0.0205 0.582 0.13 0.171 0.875 0.912 0.00219 0.00228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.501 0.0174 0.154 0.12 0.157 0.445 0.468 0.00111 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 296 11377.003 0.005 0.0204 0.256 0.665 0.129 0.171 0.506 0.596 0.00126 0.00149 ! Validation 296 11377.003 0.005 0.0184 0.164 0.532 0.123 0.162 0.41 0.484 0.00103 0.00121 Wall time: 11377.003043781966 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 24 0.423 0.0197 0.0304 0.127 0.168 0.167 0.208 0.000418 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.36 0.0172 0.0161 0.12 0.157 0.137 0.152 0.000342 0.000379 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 297 11414.961 0.005 0.0203 0.143 0.549 0.129 0.17 0.374 0.456 0.000935 0.00114 ! Validation 297 11414.961 0.005 0.0182 0.056 0.42 0.123 0.161 0.232 0.283 0.00058 0.000707 Wall time: 11414.961406009737 ! Best model 297 0.420 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 24 0.481 0.0194 0.094 0.126 0.166 0.295 0.366 0.000737 0.000916 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.442 0.0168 0.105 0.119 0.155 0.363 0.387 0.000907 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 298 11453.038 0.005 0.0199 0.0488 0.447 0.128 0.169 0.209 0.261 0.000522 0.000652 ! Validation 298 11453.038 0.005 0.0179 0.117 0.474 0.122 0.16 0.339 0.409 0.000848 0.00102 Wall time: 11453.038577769883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 24 0.455 0.0189 0.0759 0.125 0.164 0.275 0.329 0.000687 0.000823 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.603 0.0168 0.267 0.119 0.155 0.601 0.617 0.0015 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 299 11490.994 0.005 0.0198 0.137 0.532 0.127 0.168 0.383 0.445 0.000957 0.00111 ! Validation 299 11490.994 0.005 0.0178 0.259 0.616 0.122 0.16 0.543 0.608 0.00136 0.00152 Wall time: 11490.994316313881 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 24 0.453 0.0212 0.0286 0.131 0.174 0.164 0.202 0.00041 0.000505 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.586 0.0169 0.248 0.118 0.155 0.579 0.595 0.00145 0.00149 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 300 11529.725 0.005 0.0201 0.257 0.659 0.128 0.169 0.515 0.612 0.00129 0.00153 ! Validation 300 11529.725 0.005 0.018 0.254 0.614 0.122 0.16 0.538 0.603 0.00134 0.00151 Wall time: 11529.724984782748 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 24 0.448 0.0184 0.0797 0.123 0.162 0.261 0.337 0.000651 0.000843 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.369 0.0172 0.0247 0.12 0.157 0.157 0.188 0.000393 0.000469 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 301 11567.680 0.005 0.0201 0.331 0.732 0.128 0.169 0.6 0.694 0.0015 0.00174 ! Validation 301 11567.680 0.005 0.0183 0.0921 0.459 0.123 0.162 0.29 0.363 0.000726 0.000907 Wall time: 11567.680239965674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 24 0.501 0.0202 0.0967 0.128 0.17 0.317 0.372 0.000793 0.000929 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.478 0.0176 0.126 0.121 0.158 0.403 0.424 0.00101 0.00106 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 302 11605.628 0.005 0.0203 0.321 0.727 0.129 0.17 0.59 0.683 0.00148 0.00171 ! Validation 302 11605.628 0.005 0.0186 0.156 0.528 0.124 0.163 0.4 0.472 0.000999 0.00118 Wall time: 11605.628572894726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 24 0.887 0.02 0.487 0.128 0.169 0.802 0.834 0.00201 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.628 0.0176 0.276 0.121 0.159 0.615 0.628 0.00154 0.00157 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 303 11643.569 0.005 0.0205 0.375 0.784 0.129 0.171 0.635 0.73 0.00159 0.00182 ! Validation 303 11643.569 0.005 0.0186 0.353 0.724 0.124 0.163 0.647 0.71 0.00162 0.00177 Wall time: 11643.56967062084 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 24 0.684 0.0199 0.286 0.128 0.169 0.585 0.639 0.00146 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.564 0.0168 0.228 0.119 0.155 0.553 0.57 0.00138 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 304 11682.739 0.005 0.0201 0.162 0.563 0.128 0.169 0.391 0.476 0.000978 0.00119 ! Validation 304 11682.739 0.005 0.0179 0.215 0.572 0.122 0.16 0.486 0.554 0.00121 0.00138 Wall time: 11682.73983634077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 24 0.546 0.0197 0.151 0.127 0.168 0.431 0.465 0.00108 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.578 0.0166 0.247 0.117 0.154 0.577 0.594 0.00144 0.00148 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 305 11721.723 0.005 0.0196 0.0868 0.479 0.127 0.167 0.281 0.349 0.000703 0.000872 ! Validation 305 11721.723 0.005 0.0177 0.248 0.601 0.121 0.159 0.531 0.595 0.00133 0.00149 Wall time: 11721.723723144736 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 24 0.865 0.0197 0.47 0.127 0.168 0.797 0.819 0.00199 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 1.07 0.0167 0.735 0.118 0.155 1.02 1.02 0.00254 0.00256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 306 11764.516 0.005 0.0195 0.19 0.58 0.126 0.167 0.437 0.511 0.00109 0.00128 ! Validation 306 11764.516 0.005 0.0177 0.879 1.23 0.121 0.159 1.08 1.12 0.00271 0.0028 Wall time: 11764.516439039726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 24 0.507 0.0198 0.111 0.127 0.168 0.347 0.399 0.000867 0.000996 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.401 0.0167 0.0681 0.118 0.154 0.274 0.312 0.000686 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 307 11802.521 0.005 0.0198 0.258 0.654 0.127 0.168 0.511 0.611 0.00128 0.00153 ! Validation 307 11802.521 0.005 0.0177 0.0859 0.439 0.121 0.159 0.287 0.35 0.000718 0.000875 Wall time: 11802.52121234173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 24 0.504 0.0177 0.15 0.121 0.159 0.421 0.463 0.00105 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.344 0.0164 0.0158 0.117 0.153 0.14 0.15 0.000349 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 308 11840.518 0.005 0.0194 0.156 0.544 0.126 0.167 0.397 0.472 0.000993 0.00118 ! Validation 308 11840.518 0.005 0.0175 0.0651 0.415 0.12 0.158 0.244 0.305 0.000611 0.000763 Wall time: 11840.518636062741 ! Best model 308 0.415 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 24 0.468 0.0203 0.0625 0.128 0.17 0.255 0.299 0.000637 0.000747 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.462 0.017 0.122 0.119 0.156 0.389 0.417 0.000974 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 309 11883.147 0.005 0.0196 0.269 0.66 0.126 0.167 0.532 0.625 0.00133 0.00156 ! Validation 309 11883.147 0.005 0.018 0.18 0.54 0.122 0.16 0.437 0.508 0.00109 0.00127 Wall time: 11883.147454638034 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 24 0.448 0.0198 0.0516 0.127 0.168 0.211 0.271 0.000527 0.000678 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.345 0.0163 0.0196 0.117 0.153 0.135 0.167 0.000337 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 310 11921.146 0.005 0.0196 0.115 0.506 0.126 0.167 0.329 0.407 0.000823 0.00102 ! Validation 310 11921.146 0.005 0.0174 0.0699 0.418 0.12 0.158 0.25 0.316 0.000626 0.00079 Wall time: 11921.146347418893 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 24 0.445 0.0185 0.076 0.122 0.162 0.285 0.329 0.000714 0.000824 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.338 0.0161 0.0157 0.116 0.152 0.139 0.15 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 311 11959.172 0.005 0.0191 0.111 0.493 0.125 0.165 0.312 0.4 0.000779 0.000999 ! Validation 311 11959.172 0.005 0.0172 0.0548 0.399 0.119 0.157 0.227 0.28 0.000567 0.000699 Wall time: 11959.172736022621 ! Best model 311 0.399 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 24 0.481 0.0193 0.0944 0.126 0.166 0.324 0.367 0.000811 0.000918 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.351 0.0166 0.0192 0.117 0.154 0.151 0.165 0.000378 0.000413 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 312 11997.172 0.005 0.0197 0.363 0.758 0.127 0.168 0.596 0.727 0.00149 0.00182 ! Validation 312 11997.172 0.005 0.0176 0.0641 0.417 0.12 0.159 0.246 0.303 0.000616 0.000757 Wall time: 11997.17209136486 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 24 0.445 0.0191 0.0616 0.125 0.165 0.27 0.297 0.000675 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.345 0.0161 0.0229 0.116 0.152 0.15 0.181 0.000375 0.000452 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 313 12035.148 0.005 0.0192 0.122 0.506 0.125 0.166 0.349 0.421 0.000872 0.00105 ! Validation 313 12035.148 0.005 0.0172 0.0719 0.416 0.119 0.157 0.256 0.32 0.000641 0.000801 Wall time: 12035.148199007846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 24 0.47 0.0202 0.0665 0.128 0.17 0.265 0.308 0.000663 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.347 0.0161 0.0252 0.116 0.152 0.145 0.19 0.000363 0.000474 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 12073.141 0.005 0.019 0.0967 0.476 0.124 0.164 0.303 0.373 0.000758 0.000933 ! Validation 314 12073.141 0.005 0.017 0.0583 0.399 0.119 0.156 0.237 0.289 0.000593 0.000721 Wall time: 12073.141867063008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 24 0.626 0.0206 0.215 0.129 0.171 0.525 0.554 0.00131 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.472 0.0168 0.136 0.118 0.155 0.418 0.441 0.00104 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 315 12111.122 0.005 0.0191 0.287 0.668 0.125 0.165 0.55 0.642 0.00138 0.0016 ! Validation 315 12111.122 0.005 0.0178 0.144 0.5 0.121 0.159 0.383 0.454 0.000958 0.00114 Wall time: 12111.12255237298 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 24 0.389 0.0166 0.0568 0.118 0.154 0.245 0.285 0.000612 0.000712 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.527 0.0159 0.209 0.115 0.151 0.529 0.546 0.00132 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 316 12149.109 0.005 0.0191 0.123 0.505 0.125 0.165 0.339 0.422 0.000848 0.00106 ! Validation 316 12149.109 0.005 0.0169 0.277 0.616 0.118 0.155 0.57 0.629 0.00143 0.00157 Wall time: 12149.109449708834 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 24 0.443 0.0188 0.0671 0.124 0.164 0.254 0.309 0.000634 0.000774 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 0.343 0.0162 0.0187 0.116 0.152 0.134 0.163 0.000335 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 317 12195.161 0.005 0.0189 0.263 0.641 0.124 0.164 0.521 0.618 0.0013 0.00154 ! Validation 317 12195.161 0.005 0.0172 0.0713 0.415 0.119 0.157 0.255 0.319 0.000638 0.000798 Wall time: 12195.161901175044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 24 0.405 0.0188 0.0293 0.124 0.164 0.159 0.205 0.000397 0.000512 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.434 0.016 0.113 0.116 0.151 0.386 0.402 0.000965 0.001 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 318 12233.164 0.005 0.0188 0.122 0.499 0.124 0.164 0.35 0.422 0.000874 0.00105 ! Validation 318 12233.164 0.005 0.0169 0.12 0.459 0.118 0.156 0.347 0.415 0.000867 0.00104 Wall time: 12233.164210612886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 24 0.427 0.0194 0.0386 0.126 0.166 0.187 0.235 0.000467 0.000587 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.349 0.0157 0.0356 0.115 0.15 0.191 0.225 0.000477 0.000564 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 319 12271.176 0.005 0.0186 0.0827 0.454 0.123 0.163 0.279 0.346 0.000697 0.000865 ! Validation 319 12271.176 0.005 0.0166 0.0622 0.395 0.117 0.154 0.245 0.298 0.000612 0.000745 Wall time: 12271.176966667641 ! Best model 319 0.395 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 24 1.14 0.0189 0.759 0.124 0.164 1.02 1.04 0.00254 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.925 0.0161 0.602 0.116 0.152 0.92 0.927 0.0023 0.00232 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 12309.194 0.005 0.0184 0.164 0.533 0.122 0.162 0.379 0.463 0.000947 0.00116 ! Validation 320 12309.194 0.005 0.0171 0.764 1.1 0.119 0.156 1.01 1.04 0.00252 0.00261 Wall time: 12309.19478977099 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 24 0.438 0.02 0.0384 0.128 0.169 0.194 0.234 0.000484 0.000586 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.535 0.0181 0.173 0.123 0.161 0.489 0.498 0.00122 0.00124 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 321 12347.193 0.005 0.0194 0.397 0.785 0.126 0.166 0.672 0.761 0.00168 0.0019 ! Validation 321 12347.193 0.005 0.0189 0.22 0.598 0.125 0.164 0.485 0.561 0.00121 0.0014 Wall time: 12347.193576159887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 24 0.701 0.019 0.321 0.125 0.165 0.655 0.677 0.00164 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.494 0.0159 0.176 0.115 0.151 0.489 0.502 0.00122 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 322 12385.290 0.005 0.0193 0.21 0.596 0.126 0.166 0.467 0.544 0.00117 0.00136 ! Validation 322 12385.290 0.005 0.0168 0.171 0.507 0.118 0.155 0.423 0.494 0.00106 0.00124 Wall time: 12385.290987299755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 24 0.399 0.0183 0.0327 0.122 0.162 0.184 0.216 0.00046 0.00054 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.33 0.0156 0.0188 0.114 0.149 0.131 0.164 0.000327 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 323 12423.312 0.005 0.0184 0.0919 0.46 0.123 0.162 0.298 0.365 0.000746 0.000913 ! Validation 323 12423.312 0.005 0.0166 0.0739 0.406 0.117 0.154 0.259 0.325 0.000647 0.000812 Wall time: 12423.312493782956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 24 0.428 0.0184 0.0596 0.122 0.162 0.252 0.292 0.000629 0.000729 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.325 0.0156 0.0126 0.114 0.149 0.11 0.134 0.000274 0.000336 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 324 12461.312 0.005 0.0183 0.17 0.536 0.122 0.162 0.423 0.496 0.00106 0.00124 ! Validation 324 12461.312 0.005 0.0165 0.0627 0.393 0.117 0.154 0.238 0.299 0.000595 0.000748 Wall time: 12461.312336172909 ! Best model 324 0.393 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 24 0.481 0.0189 0.104 0.123 0.164 0.337 0.385 0.000844 0.000962 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.543 0.0154 0.235 0.114 0.148 0.566 0.58 0.00141 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 325 12499.329 0.005 0.0183 0.131 0.497 0.122 0.162 0.359 0.434 0.000898 0.00109 ! Validation 325 12499.329 0.005 0.0164 0.21 0.538 0.116 0.153 0.484 0.548 0.00121 0.00137 Wall time: 12499.32998421602 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 24 0.577 0.0197 0.184 0.126 0.168 0.458 0.512 0.00115 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.541 0.0161 0.218 0.116 0.152 0.544 0.558 0.00136 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 326 12537.323 0.005 0.0187 0.369 0.743 0.123 0.163 0.628 0.731 0.00157 0.00183 ! Validation 326 12537.323 0.005 0.0171 0.214 0.557 0.119 0.156 0.485 0.552 0.00121 0.00138 Wall time: 12537.323662068695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 24 0.415 0.0183 0.0496 0.122 0.162 0.198 0.266 0.000494 0.000665 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.364 0.0154 0.0564 0.114 0.148 0.251 0.284 0.000627 0.00071 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 12575.337 0.005 0.0186 0.0706 0.443 0.123 0.163 0.255 0.319 0.000637 0.000797 ! Validation 327 12575.337 0.005 0.0164 0.0762 0.405 0.117 0.153 0.269 0.33 0.000674 0.000825 Wall time: 12575.337483271025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 24 0.408 0.0185 0.0371 0.122 0.163 0.204 0.23 0.00051 0.000575 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.348 0.0152 0.0448 0.113 0.147 0.223 0.253 0.000557 0.000632 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 12613.327 0.005 0.0181 0.0909 0.454 0.122 0.161 0.296 0.363 0.00074 0.000907 ! Validation 328 12613.327 0.005 0.0161 0.0679 0.39 0.115 0.152 0.255 0.311 0.000636 0.000779 Wall time: 12613.32749771094 ! Best model 328 0.390 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 24 0.582 0.0179 0.225 0.12 0.16 0.527 0.567 0.00132 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.342 0.0155 0.0321 0.114 0.149 0.173 0.214 0.000432 0.000535 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 329 12651.344 0.005 0.0179 0.158 0.516 0.121 0.16 0.384 0.472 0.00096 0.00118 ! Validation 329 12651.344 0.005 0.0165 0.0985 0.429 0.117 0.154 0.306 0.375 0.000764 0.000938 Wall time: 12651.344944844954 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 24 0.852 0.0188 0.475 0.124 0.164 0.761 0.824 0.0019 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.656 0.0162 0.332 0.116 0.152 0.676 0.688 0.00169 0.00172 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 330 12689.339 0.005 0.0183 0.293 0.658 0.122 0.161 0.521 0.642 0.0013 0.0016 ! Validation 330 12689.339 0.005 0.0172 0.478 0.823 0.119 0.157 0.773 0.826 0.00193 0.00207 Wall time: 12689.33940697601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 24 0.755 0.0189 0.377 0.125 0.164 0.708 0.734 0.00177 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.399 0.0161 0.0761 0.116 0.152 0.309 0.33 0.000772 0.000824 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 331 12727.346 0.005 0.0186 0.282 0.654 0.123 0.163 0.545 0.632 0.00136 0.00158 ! Validation 331 12727.346 0.005 0.017 0.0964 0.436 0.119 0.156 0.305 0.371 0.000762 0.000928 Wall time: 12727.346238696948 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 24 0.513 0.0184 0.145 0.123 0.162 0.406 0.456 0.00102 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.323 0.0157 0.00914 0.115 0.15 0.0929 0.114 0.000232 0.000286 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 332 12765.349 0.005 0.0185 0.238 0.608 0.123 0.162 0.504 0.586 0.00126 0.00146 ! Validation 332 12765.349 0.005 0.0165 0.0588 0.389 0.117 0.154 0.233 0.29 0.000582 0.000724 Wall time: 12765.349089199677 ! Best model 332 0.389 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 24 0.392 0.0182 0.0268 0.122 0.161 0.164 0.196 0.000411 0.000489 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.372 0.015 0.0716 0.112 0.146 0.288 0.32 0.000721 0.000799 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 333 12803.385 0.005 0.0182 0.162 0.525 0.122 0.161 0.408 0.485 0.00102 0.00121 ! Validation 333 12803.385 0.005 0.0161 0.0822 0.405 0.115 0.152 0.28 0.343 0.0007 0.000857 Wall time: 12803.385689799674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 24 0.449 0.0184 0.0808 0.122 0.162 0.294 0.34 0.000736 0.000849 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.363 0.0149 0.0647 0.112 0.146 0.283 0.304 0.000708 0.00076 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 334 12841.400 0.005 0.0178 0.0905 0.447 0.121 0.16 0.299 0.36 0.000747 0.0009 ! Validation 334 12841.400 0.005 0.0159 0.0792 0.397 0.115 0.151 0.276 0.336 0.000689 0.000841 Wall time: 12841.40009039687 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 24 0.59 0.0183 0.224 0.122 0.162 0.525 0.566 0.00131 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.563 0.0147 0.268 0.111 0.145 0.606 0.619 0.00152 0.00155 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 335 12880.742 0.005 0.0176 0.0744 0.426 0.12 0.158 0.257 0.318 0.000643 0.000795 ! Validation 335 12880.742 0.005 0.0158 0.233 0.548 0.114 0.15 0.515 0.577 0.00129 0.00144 Wall time: 12880.742627604865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 24 1.12 0.0183 0.759 0.122 0.162 1.01 1.04 0.00253 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.93 0.0156 0.619 0.114 0.149 0.933 0.94 0.00233 0.00235 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 336 12918.764 0.005 0.0179 0.39 0.748 0.121 0.16 0.61 0.737 0.00152 0.00184 ! Validation 336 12918.764 0.005 0.0166 0.546 0.877 0.117 0.154 0.832 0.883 0.00208 0.00221 Wall time: 12918.76465160679 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 24 0.642 0.0184 0.273 0.123 0.162 0.596 0.625 0.00149 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.594 0.0152 0.291 0.113 0.147 0.634 0.644 0.00158 0.00161 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 337 12956.776 0.005 0.0182 0.16 0.524 0.122 0.161 0.396 0.473 0.000989 0.00118 ! Validation 337 12956.776 0.005 0.0162 0.402 0.726 0.116 0.152 0.704 0.757 0.00176 0.00189 Wall time: 12956.77639107965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 24 0.485 0.017 0.146 0.118 0.156 0.414 0.456 0.00103 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.555 0.0149 0.258 0.112 0.146 0.596 0.606 0.00149 0.00152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 338 12994.791 0.005 0.0178 0.193 0.549 0.12 0.159 0.448 0.527 0.00112 0.00132 ! Validation 338 12994.791 0.005 0.0158 0.256 0.573 0.114 0.15 0.546 0.605 0.00137 0.00151 Wall time: 12994.791412476916 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 24 0.545 0.0181 0.184 0.121 0.161 0.453 0.513 0.00113 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.423 0.0148 0.128 0.111 0.145 0.409 0.428 0.00102 0.00107 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 339 13032.800 0.005 0.0177 0.138 0.491 0.12 0.159 0.371 0.442 0.000927 0.00111 ! Validation 339 13032.800 0.005 0.0158 0.135 0.45 0.114 0.15 0.372 0.439 0.000929 0.0011 Wall time: 13032.800509159919 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 24 0.384 0.0175 0.0341 0.119 0.158 0.173 0.221 0.000433 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.356 0.0146 0.0643 0.111 0.144 0.281 0.303 0.000701 0.000758 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 13070.827 0.005 0.0174 0.0716 0.42 0.119 0.158 0.263 0.322 0.000658 0.000804 ! Validation 340 13070.827 0.005 0.0155 0.078 0.388 0.113 0.149 0.274 0.334 0.000684 0.000834 Wall time: 13070.827652979642 ! Best model 340 0.388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 24 0.554 0.0172 0.211 0.118 0.157 0.496 0.549 0.00124 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.432 0.0146 0.14 0.11 0.145 0.43 0.447 0.00108 0.00112 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 341 13108.843 0.005 0.0172 0.105 0.45 0.119 0.157 0.319 0.382 0.000797 0.000955 ! Validation 341 13108.843 0.005 0.0156 0.132 0.444 0.114 0.149 0.366 0.434 0.000914 0.00108 Wall time: 13108.843083326705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 24 0.862 0.0169 0.523 0.117 0.156 0.844 0.864 0.00211 0.00216 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.583 0.0156 0.271 0.114 0.149 0.607 0.622 0.00152 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 342 13146.864 0.005 0.0174 0.173 0.52 0.119 0.157 0.391 0.484 0.000977 0.00121 ! Validation 342 13146.864 0.005 0.0166 0.398 0.731 0.117 0.154 0.702 0.754 0.00175 0.00189 Wall time: 13146.864681317005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 24 0.419 0.0178 0.0624 0.12 0.16 0.234 0.298 0.000585 0.000746 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.324 0.0153 0.0177 0.113 0.148 0.136 0.159 0.00034 0.000398 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 13184.851 0.005 0.0182 0.32 0.684 0.122 0.161 0.564 0.683 0.00141 0.00171 ! Validation 343 13184.851 0.005 0.0163 0.0598 0.385 0.116 0.152 0.237 0.292 0.000593 0.000731 Wall time: 13184.851533002686 ! Best model 343 0.385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 24 0.355 0.0162 0.0305 0.115 0.152 0.177 0.209 0.000442 0.000522 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.3 0.0145 0.0103 0.11 0.144 0.107 0.121 0.000267 0.000303 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 344 13222.862 0.005 0.0175 0.0949 0.445 0.12 0.158 0.303 0.371 0.000757 0.000928 ! Validation 344 13222.862 0.005 0.0154 0.0587 0.368 0.113 0.149 0.232 0.29 0.00058 0.000724 Wall time: 13222.862563859671 ! Best model 344 0.368 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 24 0.616 0.0179 0.258 0.121 0.16 0.549 0.607 0.00137 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.341 0.0145 0.0504 0.11 0.144 0.24 0.268 0.0006 0.00067 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 345 13265.339 0.005 0.0171 0.115 0.456 0.118 0.156 0.321 0.399 0.000801 0.000996 ! Validation 345 13265.339 0.005 0.0155 0.114 0.423 0.113 0.149 0.335 0.404 0.000836 0.00101 Wall time: 13265.339217104949 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 24 0.349 0.0159 0.0308 0.114 0.151 0.156 0.21 0.000389 0.000524 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.312 0.0141 0.0287 0.109 0.142 0.167 0.202 0.000417 0.000506 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 346 13303.591 0.005 0.017 0.083 0.424 0.118 0.156 0.273 0.347 0.000682 0.000867 ! Validation 346 13303.591 0.005 0.0151 0.0556 0.358 0.112 0.147 0.23 0.282 0.000574 0.000704 Wall time: 13303.59130744962 ! Best model 346 0.358 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 24 0.532 0.0183 0.167 0.122 0.161 0.446 0.488 0.00111 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.334 0.0154 0.0252 0.113 0.149 0.157 0.19 0.000391 0.000474 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 13341.625 0.005 0.0174 0.283 0.63 0.119 0.157 0.538 0.639 0.00134 0.0016 ! Validation 347 13341.625 0.005 0.0163 0.0605 0.387 0.116 0.153 0.239 0.294 0.000597 0.000735 Wall time: 13341.625240152702 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 24 0.437 0.0172 0.0918 0.119 0.157 0.312 0.362 0.00078 0.000905 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.404 0.0142 0.12 0.109 0.142 0.394 0.414 0.000986 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 348 13379.625 0.005 0.0173 0.106 0.452 0.119 0.157 0.311 0.389 0.000777 0.000973 ! Validation 348 13379.625 0.005 0.0153 0.187 0.493 0.112 0.148 0.451 0.517 0.00113 0.00129 Wall time: 13379.625324342865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 24 0.409 0.0165 0.0783 0.116 0.154 0.291 0.334 0.000728 0.000836 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.443 0.0147 0.149 0.111 0.145 0.448 0.462 0.00112 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 349 13417.668 0.005 0.017 0.183 0.523 0.118 0.156 0.439 0.515 0.0011 0.00129 ! Validation 349 13417.668 0.005 0.0155 0.152 0.463 0.113 0.149 0.4 0.465 0.001 0.00116 Wall time: 13417.66885570297 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 24 0.514 0.0187 0.141 0.123 0.163 0.359 0.449 0.000897 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 1.1 0.0148 0.806 0.112 0.145 1.07 1.07 0.00267 0.00268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 350 13455.747 0.005 0.0174 0.292 0.641 0.119 0.158 0.56 0.65 0.0014 0.00162 ! Validation 350 13455.747 0.005 0.0156 0.748 1.06 0.114 0.149 0.992 1.03 0.00248 0.00258 Wall time: 13455.74798650667 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 24 0.78 0.0186 0.408 0.123 0.163 0.726 0.763 0.00182 0.00191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 1.13 0.0156 0.818 0.114 0.149 1.08 1.08 0.00269 0.0027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 351 13493.745 0.005 0.0189 0.499 0.877 0.124 0.164 0.73 0.846 0.00182 0.00212 ! Validation 351 13493.745 0.005 0.0165 0.793 1.12 0.117 0.154 1.03 1.06 0.00257 0.00266 Wall time: 13493.7453888827 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 24 0.445 0.0166 0.113 0.117 0.154 0.34 0.401 0.00085 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.309 0.0147 0.0152 0.111 0.145 0.117 0.147 0.000291 0.000368 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 13531.698 0.005 0.0178 0.219 0.575 0.121 0.16 0.459 0.563 0.00115 0.00141 ! Validation 352 13531.698 0.005 0.0156 0.0599 0.371 0.114 0.149 0.235 0.292 0.000587 0.000731 Wall time: 13531.69803913869 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 24 0.747 0.0181 0.384 0.122 0.161 0.714 0.74 0.00178 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.465 0.0145 0.174 0.11 0.144 0.487 0.498 0.00122 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 353 13572.595 0.005 0.0173 0.19 0.536 0.119 0.157 0.43 0.515 0.00107 0.00129 ! Validation 353 13572.595 0.005 0.0156 0.174 0.485 0.113 0.149 0.432 0.498 0.00108 0.00124 Wall time: 13572.595615527593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 24 0.581 0.017 0.242 0.118 0.156 0.562 0.587 0.0014 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.657 0.0144 0.369 0.11 0.143 0.718 0.726 0.00179 0.00182 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 354 13610.628 0.005 0.0171 0.168 0.51 0.118 0.156 0.412 0.488 0.00103 0.00122 ! Validation 354 13610.628 0.005 0.0153 0.337 0.643 0.112 0.148 0.634 0.694 0.00159 0.00174 Wall time: 13610.628397091758 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 24 0.44 0.0157 0.125 0.114 0.15 0.387 0.423 0.000968 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.383 0.0142 0.1 0.109 0.142 0.363 0.378 0.000907 0.000945 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 13648.647 0.005 0.0169 0.078 0.415 0.117 0.155 0.267 0.331 0.000667 0.000828 ! Validation 355 13648.647 0.005 0.0151 0.171 0.473 0.112 0.147 0.428 0.495 0.00107 0.00124 Wall time: 13648.647823528852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 24 0.362 0.0165 0.0311 0.116 0.154 0.171 0.211 0.000427 0.000527 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.291 0.0142 0.00754 0.109 0.142 0.0931 0.104 0.000233 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 356 13686.675 0.005 0.0167 0.0786 0.412 0.117 0.154 0.28 0.337 0.000701 0.000844 ! Validation 356 13686.675 0.005 0.015 0.0516 0.351 0.111 0.146 0.22 0.271 0.000551 0.000679 Wall time: 13686.675300057046 ! Best model 356 0.351 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 24 0.35 0.0159 0.0319 0.114 0.151 0.158 0.213 0.000395 0.000534 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.293 0.0138 0.0163 0.108 0.141 0.123 0.152 0.000308 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 357 13724.681 0.005 0.0168 0.126 0.463 0.117 0.155 0.341 0.427 0.000852 0.00107 ! Validation 357 13724.681 0.005 0.0148 0.0781 0.374 0.111 0.145 0.267 0.334 0.000668 0.000835 Wall time: 13724.68164963182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 24 0.35 0.0163 0.0241 0.115 0.153 0.134 0.186 0.000336 0.000464 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.289 0.0139 0.0105 0.109 0.141 0.112 0.123 0.00028 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 358 13762.701 0.005 0.0166 0.168 0.501 0.116 0.154 0.412 0.495 0.00103 0.00124 ! Validation 358 13762.701 0.005 0.0149 0.0513 0.349 0.111 0.146 0.219 0.271 0.000547 0.000677 Wall time: 13762.70195146976 ! Best model 358 0.349 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 24 0.74 0.0169 0.402 0.118 0.155 0.73 0.758 0.00182 0.00189 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.633 0.014 0.353 0.108 0.142 0.702 0.71 0.00175 0.00177 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 359 13800.759 0.005 0.0165 0.119 0.45 0.116 0.154 0.327 0.401 0.000818 0.001 ! Validation 359 13800.759 0.005 0.015 0.32 0.621 0.111 0.147 0.623 0.677 0.00156 0.00169 Wall time: 13800.759928399697 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 24 0.381 0.0173 0.0352 0.119 0.157 0.188 0.224 0.00047 0.000561 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.288 0.0139 0.0102 0.108 0.141 0.0988 0.121 0.000247 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 360 13838.775 0.005 0.0167 0.167 0.502 0.117 0.154 0.41 0.494 0.00102 0.00123 ! Validation 360 13838.775 0.005 0.0147 0.0574 0.352 0.11 0.145 0.229 0.286 0.000572 0.000716 Wall time: 13838.7759703896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 24 0.426 0.0179 0.0672 0.12 0.16 0.264 0.31 0.00066 0.000775 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.603 0.0142 0.32 0.109 0.142 0.666 0.676 0.00166 0.00169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 361 13876.805 0.005 0.0166 0.192 0.524 0.116 0.154 0.408 0.527 0.00102 0.00132 ! Validation 361 13876.805 0.005 0.015 0.312 0.613 0.112 0.147 0.612 0.668 0.00153 0.00167 Wall time: 13876.805904728826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 24 0.368 0.0165 0.0391 0.116 0.153 0.195 0.236 0.000486 0.000591 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.325 0.0137 0.0503 0.108 0.14 0.246 0.268 0.000614 0.00067 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 362 13914.803 0.005 0.0167 0.111 0.445 0.117 0.154 0.319 0.402 0.000797 0.001 ! Validation 362 13914.803 0.005 0.0146 0.116 0.408 0.11 0.144 0.338 0.407 0.000845 0.00102 Wall time: 13914.803213527892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 24 0.473 0.0168 0.136 0.117 0.155 0.412 0.441 0.00103 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.513 0.0138 0.237 0.108 0.14 0.573 0.582 0.00143 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 363 13952.790 0.005 0.0163 0.137 0.463 0.115 0.153 0.34 0.442 0.00085 0.00111 ! Validation 363 13952.790 0.005 0.0147 0.241 0.535 0.11 0.145 0.528 0.587 0.00132 0.00147 Wall time: 13952.79067881871 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 24 0.366 0.0172 0.0228 0.118 0.157 0.138 0.18 0.000344 0.000451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.3 0.014 0.0193 0.109 0.142 0.133 0.166 0.000332 0.000415 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 364 13990.780 0.005 0.0166 0.267 0.599 0.116 0.154 0.524 0.624 0.00131 0.00156 ! Validation 364 13990.780 0.005 0.0149 0.0606 0.358 0.111 0.146 0.242 0.294 0.000604 0.000735 Wall time: 13990.780158726033 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 24 0.77 0.0165 0.441 0.116 0.153 0.765 0.793 0.00191 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.433 0.0137 0.159 0.107 0.14 0.464 0.477 0.00116 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 365 14030.897 0.005 0.0164 0.093 0.421 0.116 0.153 0.265 0.348 0.000664 0.000869 ! Validation 365 14030.897 0.005 0.0146 0.272 0.564 0.11 0.144 0.558 0.623 0.0014 0.00156 Wall time: 14030.897111988626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 24 0.516 0.0189 0.137 0.123 0.164 0.396 0.442 0.00099 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.477 0.0138 0.2 0.108 0.141 0.52 0.534 0.0013 0.00134 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 366 14071.351 0.005 0.0167 0.291 0.624 0.116 0.154 0.561 0.649 0.0014 0.00162 ! Validation 366 14071.351 0.005 0.0149 0.219 0.517 0.111 0.146 0.492 0.56 0.00123 0.0014 Wall time: 14071.351374398917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 24 0.394 0.0159 0.0761 0.114 0.151 0.279 0.33 0.000698 0.000824 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.472 0.0134 0.204 0.106 0.138 0.528 0.539 0.00132 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 367 14109.358 0.005 0.0164 0.102 0.429 0.116 0.153 0.315 0.383 0.000788 0.000958 ! Validation 367 14109.358 0.005 0.0144 0.184 0.471 0.109 0.143 0.45 0.513 0.00113 0.00128 Wall time: 14109.358372626826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 24 0.383 0.0155 0.0728 0.112 0.149 0.27 0.323 0.000674 0.000806 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.281 0.0134 0.0125 0.107 0.139 0.107 0.134 0.000269 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 368 14147.487 0.005 0.016 0.0868 0.407 0.114 0.151 0.292 0.353 0.000731 0.000882 ! Validation 368 14147.487 0.005 0.0143 0.0714 0.357 0.109 0.143 0.255 0.319 0.000637 0.000798 Wall time: 14147.487178626936 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 24 0.703 0.0167 0.37 0.116 0.154 0.701 0.726 0.00175 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.599 0.0133 0.332 0.106 0.138 0.68 0.689 0.0017 0.00172 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 369 14185.470 0.005 0.016 0.155 0.474 0.114 0.151 0.378 0.462 0.000944 0.00116 ! Validation 369 14185.470 0.005 0.0143 0.293 0.578 0.109 0.143 0.591 0.647 0.00148 0.00162 Wall time: 14185.470446157735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 24 0.387 0.0168 0.052 0.117 0.155 0.222 0.273 0.000554 0.000681 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.28 0.0133 0.0136 0.106 0.138 0.11 0.139 0.000274 0.000348 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 370 14223.494 0.005 0.0161 0.145 0.467 0.114 0.151 0.393 0.459 0.000983 0.00115 ! Validation 370 14223.494 0.005 0.0143 0.0581 0.344 0.109 0.143 0.231 0.288 0.000577 0.00072 Wall time: 14223.494571138639 ! Best model 370 0.344 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 24 0.336 0.0148 0.0406 0.111 0.145 0.208 0.241 0.00052 0.000602 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.293 0.0134 0.0244 0.106 0.138 0.148 0.187 0.000371 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 371 14261.510 0.005 0.0162 0.233 0.557 0.115 0.152 0.485 0.582 0.00121 0.00146 ! Validation 371 14261.510 0.005 0.0143 0.0791 0.366 0.109 0.143 0.269 0.336 0.000673 0.00084 Wall time: 14261.51046793582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 24 0.437 0.0165 0.107 0.115 0.154 0.34 0.391 0.000851 0.000977 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.609 0.0132 0.344 0.106 0.138 0.694 0.701 0.00173 0.00175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 372 14299.509 0.005 0.016 0.107 0.426 0.114 0.151 0.324 0.39 0.00081 0.000975 ! Validation 372 14299.509 0.005 0.0141 0.315 0.598 0.108 0.142 0.617 0.671 0.00154 0.00168 Wall time: 14299.509167145006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 24 0.439 0.0161 0.117 0.115 0.152 0.38 0.408 0.000951 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.312 0.014 0.0316 0.108 0.142 0.19 0.212 0.000474 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 373 14338.624 0.005 0.0164 0.321 0.649 0.116 0.153 0.588 0.682 0.00147 0.00171 ! Validation 373 14338.624 0.005 0.0149 0.067 0.366 0.111 0.146 0.253 0.309 0.000632 0.000773 Wall time: 14338.624429402873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 24 0.375 0.0155 0.0653 0.113 0.149 0.263 0.305 0.000657 0.000764 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.305 0.0134 0.0367 0.106 0.138 0.206 0.229 0.000516 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 374 14376.645 0.005 0.0162 0.15 0.475 0.115 0.152 0.386 0.466 0.000964 0.00117 ! Validation 374 14376.645 0.005 0.0142 0.0694 0.354 0.109 0.143 0.256 0.315 0.000641 0.000787 Wall time: 14376.645959533751 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 24 0.366 0.0171 0.0246 0.118 0.156 0.154 0.187 0.000386 0.000469 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.335 0.0137 0.061 0.107 0.14 0.27 0.295 0.000675 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 375 14414.650 0.005 0.0162 0.284 0.608 0.115 0.152 0.542 0.644 0.00136 0.00161 ! Validation 375 14414.650 0.005 0.0146 0.144 0.436 0.11 0.144 0.376 0.453 0.00094 0.00113 Wall time: 14414.650737377815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 24 0.465 0.0167 0.13 0.117 0.155 0.37 0.431 0.000925 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.344 0.0132 0.0796 0.106 0.137 0.32 0.337 0.0008 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 376 14452.658 0.005 0.016 0.0762 0.396 0.114 0.151 0.266 0.327 0.000666 0.000818 ! Validation 376 14452.658 0.005 0.0141 0.161 0.444 0.108 0.142 0.412 0.48 0.00103 0.0012 Wall time: 14452.658191124909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 24 0.385 0.0164 0.0557 0.116 0.153 0.227 0.282 0.000567 0.000705 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.466 0.0131 0.204 0.105 0.137 0.53 0.54 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 377 14490.667 0.005 0.0158 0.0909 0.406 0.113 0.15 0.292 0.362 0.000729 0.000905 ! Validation 377 14490.667 0.005 0.014 0.194 0.473 0.107 0.141 0.466 0.526 0.00116 0.00132 Wall time: 14490.667328223586 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 24 0.343 0.0154 0.0343 0.112 0.148 0.188 0.221 0.000471 0.000554 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.28 0.013 0.0186 0.105 0.137 0.139 0.163 0.000347 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 378 14528.665 0.005 0.0157 0.097 0.411 0.113 0.15 0.306 0.375 0.000765 0.000938 ! Validation 378 14528.665 0.005 0.0139 0.0508 0.329 0.107 0.141 0.22 0.269 0.000549 0.000674 Wall time: 14528.665890094824 ! Best model 378 0.329 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 24 0.947 0.0168 0.611 0.116 0.155 0.912 0.934 0.00228 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.792 0.0132 0.528 0.105 0.137 0.861 0.868 0.00215 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 379 14566.665 0.005 0.0158 0.243 0.558 0.113 0.15 0.495 0.578 0.00124 0.00145 ! Validation 379 14566.665 0.005 0.0142 0.465 0.749 0.108 0.142 0.771 0.815 0.00193 0.00204 Wall time: 14566.665124973748 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 24 0.339 0.0146 0.0468 0.109 0.144 0.204 0.259 0.000509 0.000646 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.299 0.0131 0.0377 0.105 0.137 0.202 0.232 0.000506 0.00058 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 380 14604.693 0.005 0.016 0.172 0.491 0.114 0.151 0.424 0.499 0.00106 0.00125 ! Validation 380 14604.693 0.005 0.014 0.11 0.39 0.108 0.141 0.324 0.397 0.00081 0.000993 Wall time: 14604.693283865694 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 24 0.395 0.0157 0.0811 0.113 0.15 0.286 0.34 0.000714 0.000851 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.289 0.0132 0.0251 0.106 0.137 0.163 0.189 0.000408 0.000474 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 14642.707 0.005 0.016 0.217 0.537 0.114 0.151 0.462 0.561 0.00116 0.0014 ! Validation 381 14642.707 0.005 0.0141 0.0543 0.335 0.108 0.142 0.227 0.278 0.000568 0.000696 Wall time: 14642.707833965775 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 24 0.325 0.015 0.0245 0.111 0.146 0.148 0.187 0.000369 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.27 0.0131 0.00793 0.105 0.137 0.0955 0.106 0.000239 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 382 14681.220 0.005 0.0159 0.167 0.485 0.114 0.151 0.38 0.494 0.00095 0.00123 ! Validation 382 14681.220 0.005 0.0139 0.0468 0.325 0.107 0.141 0.21 0.259 0.000526 0.000646 Wall time: 14681.22031674767 ! Best model 382 0.325 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 24 0.338 0.0148 0.0424 0.11 0.145 0.21 0.246 0.000524 0.000615 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.277 0.0129 0.0194 0.104 0.136 0.133 0.167 0.000333 0.000416 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 383 14719.222 0.005 0.0155 0.0431 0.353 0.112 0.149 0.198 0.248 0.000495 0.000621 ! Validation 383 14719.222 0.005 0.0138 0.0724 0.348 0.107 0.14 0.259 0.321 0.000647 0.000804 Wall time: 14719.222253928892 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 24 0.371 0.0166 0.0379 0.116 0.154 0.19 0.233 0.000475 0.000582 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.268 0.0128 0.0132 0.104 0.135 0.111 0.138 0.000277 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 384 14757.214 0.005 0.0154 0.117 0.426 0.112 0.148 0.339 0.413 0.000849 0.00103 ! Validation 384 14757.214 0.005 0.0136 0.0667 0.339 0.106 0.14 0.247 0.309 0.000617 0.000771 Wall time: 14757.214900192805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 24 0.755 0.0157 0.441 0.113 0.15 0.775 0.794 0.00194 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.535 0.0133 0.268 0.106 0.138 0.608 0.618 0.00152 0.00155 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 385 14795.214 0.005 0.0153 0.178 0.485 0.112 0.148 0.416 0.496 0.00104 0.00124 ! Validation 385 14795.214 0.005 0.0142 0.385 0.67 0.109 0.142 0.693 0.742 0.00173 0.00185 Wall time: 14795.214780287817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 24 0.311 0.0143 0.0247 0.109 0.143 0.154 0.188 0.000384 0.000469 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.26 0.0127 0.00694 0.104 0.134 0.0848 0.0996 0.000212 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 386 14833.214 0.005 0.0155 0.111 0.421 0.113 0.149 0.331 0.402 0.000828 0.001 ! Validation 386 14833.214 0.005 0.0135 0.0474 0.318 0.106 0.139 0.211 0.26 0.000529 0.00065 Wall time: 14833.214643778745 ! Best model 386 0.318 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 24 0.379 0.0156 0.0662 0.113 0.149 0.269 0.307 0.000672 0.000768 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.273 0.0128 0.017 0.104 0.135 0.124 0.156 0.00031 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 387 14871.208 0.005 0.0151 0.125 0.428 0.111 0.147 0.32 0.424 0.000799 0.00106 ! Validation 387 14871.208 0.005 0.0136 0.0692 0.342 0.106 0.14 0.254 0.314 0.000634 0.000786 Wall time: 14871.208935165778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 24 0.675 0.0144 0.387 0.109 0.143 0.718 0.744 0.00179 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.746 0.0132 0.482 0.105 0.137 0.823 0.83 0.00206 0.00207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 388 14909.195 0.005 0.0156 0.275 0.587 0.113 0.149 0.544 0.624 0.00136 0.00156 ! Validation 388 14909.195 0.005 0.0141 0.555 0.837 0.108 0.142 0.849 0.89 0.00212 0.00222 Wall time: 14909.195181093644 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 24 0.345 0.0155 0.0356 0.112 0.149 0.175 0.225 0.000436 0.000563 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.445 0.013 0.184 0.105 0.136 0.501 0.513 0.00125 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 389 14947.197 0.005 0.0159 0.284 0.602 0.114 0.151 0.558 0.644 0.0014 0.00161 ! Validation 389 14947.197 0.005 0.0138 0.186 0.462 0.107 0.141 0.442 0.515 0.00111 0.00129 Wall time: 14947.197470732965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 24 0.318 0.0142 0.0335 0.108 0.143 0.183 0.219 0.000457 0.000547 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.337 0.0127 0.0821 0.104 0.135 0.327 0.342 0.000817 0.000856 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 390 14985.192 0.005 0.0154 0.0957 0.403 0.112 0.148 0.306 0.373 0.000766 0.000931 ! Validation 390 14985.192 0.005 0.0136 0.164 0.436 0.106 0.139 0.416 0.484 0.00104 0.00121 Wall time: 14985.192854458932 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 24 0.314 0.015 0.0143 0.111 0.146 0.116 0.143 0.000289 0.000358 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.262 0.0126 0.00928 0.103 0.134 0.0933 0.115 0.000233 0.000288 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 391 15023.294 0.005 0.0151 0.117 0.42 0.111 0.147 0.333 0.413 0.000833 0.00103 ! Validation 391 15023.294 0.005 0.0135 0.057 0.328 0.106 0.139 0.229 0.285 0.000573 0.000713 Wall time: 15023.294490509667 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 24 0.381 0.0144 0.0929 0.109 0.143 0.329 0.364 0.000821 0.00091 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.263 0.0125 0.0126 0.103 0.134 0.104 0.134 0.000261 0.000335 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 392 15061.296 0.005 0.0151 0.108 0.41 0.111 0.147 0.321 0.393 0.000803 0.000982 ! Validation 392 15061.296 0.005 0.0134 0.0635 0.332 0.105 0.138 0.241 0.301 0.000603 0.000753 Wall time: 15061.296225714032 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 24 1.1 0.0158 0.781 0.113 0.15 1.04 1.06 0.0026 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 1.07 0.0134 0.803 0.106 0.139 1.06 1.07 0.00266 0.00268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 393 15099.299 0.005 0.0153 0.232 0.538 0.112 0.148 0.409 0.559 0.00102 0.0014 ! Validation 393 15099.299 0.005 0.0144 1.01 1.3 0.109 0.144 1.17 1.2 0.00292 0.003 Wall time: 15099.299918706995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 24 0.361 0.0152 0.0572 0.112 0.147 0.225 0.286 0.000563 0.000714 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.396 0.0129 0.138 0.105 0.136 0.433 0.445 0.00108 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 394 15137.282 0.005 0.0159 0.219 0.537 0.114 0.151 0.462 0.564 0.00115 0.00141 ! Validation 394 15137.282 0.005 0.0137 0.215 0.488 0.107 0.14 0.492 0.554 0.00123 0.00139 Wall time: 15137.282068205997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 24 0.368 0.0141 0.0864 0.108 0.142 0.312 0.351 0.000779 0.000878 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.346 0.0126 0.0936 0.103 0.134 0.351 0.366 0.000877 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 395 15175.522 0.005 0.0153 0.172 0.478 0.112 0.148 0.402 0.498 0.00101 0.00125 ! Validation 395 15175.522 0.005 0.0134 0.113 0.381 0.105 0.138 0.338 0.402 0.000844 0.001 Wall time: 15175.522574124858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 24 0.372 0.0147 0.0793 0.109 0.145 0.286 0.336 0.000716 0.000841 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.522 0.0125 0.273 0.103 0.133 0.615 0.624 0.00154 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 396 15213.523 0.005 0.015 0.116 0.417 0.111 0.147 0.345 0.408 0.000864 0.00102 ! Validation 396 15213.523 0.005 0.0133 0.254 0.52 0.105 0.138 0.546 0.602 0.00137 0.0015 Wall time: 15213.52359086601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 24 0.39 0.0155 0.0794 0.112 0.149 0.305 0.337 0.000763 0.000842 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.261 0.0127 0.0082 0.104 0.134 0.0927 0.108 0.000232 0.00027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 397 15252.887 0.005 0.0151 0.212 0.515 0.111 0.147 0.481 0.554 0.0012 0.00138 ! Validation 397 15252.887 0.005 0.0135 0.0493 0.319 0.106 0.139 0.216 0.265 0.000539 0.000663 Wall time: 15252.887745743617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 24 0.317 0.0143 0.0314 0.108 0.143 0.172 0.212 0.000429 0.000529 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.257 0.0125 0.00738 0.103 0.133 0.082 0.103 0.000205 0.000257 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 398 15294.352 0.005 0.015 0.0676 0.367 0.111 0.146 0.251 0.313 0.000627 0.000782 ! Validation 398 15294.352 0.005 0.0132 0.0542 0.319 0.105 0.138 0.225 0.278 0.000562 0.000695 Wall time: 15294.352606354747 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 24 0.358 0.0159 0.0395 0.114 0.151 0.206 0.238 0.000515 0.000594 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.263 0.0126 0.0122 0.103 0.134 0.112 0.132 0.00028 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 399 15332.358 0.005 0.0149 0.195 0.493 0.11 0.146 0.45 0.532 0.00113 0.00133 ! Validation 399 15332.358 0.005 0.0135 0.0474 0.317 0.105 0.139 0.213 0.26 0.000533 0.00065 Wall time: 15332.358285830822 ! Best model 399 0.317 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 24 0.41 0.0159 0.0911 0.114 0.151 0.311 0.361 0.000777 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.303 0.0121 0.0605 0.101 0.131 0.272 0.294 0.00068 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 400 15370.382 0.005 0.0148 0.0429 0.34 0.11 0.145 0.197 0.244 0.000493 0.000611 ! Validation 400 15370.382 0.005 0.013 0.129 0.39 0.104 0.136 0.364 0.429 0.000909 0.00107 Wall time: 15370.382161946036 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 24 0.553 0.0137 0.279 0.106 0.14 0.601 0.631 0.0015 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.568 0.0123 0.322 0.102 0.132 0.672 0.679 0.00168 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 401 15408.361 0.005 0.0147 0.13 0.424 0.11 0.145 0.35 0.425 0.000876 0.00106 ! Validation 401 15408.361 0.005 0.0131 0.409 0.67 0.104 0.137 0.72 0.764 0.0018 0.00191 Wall time: 15408.361714551691 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 24 0.42 0.0146 0.128 0.11 0.144 0.377 0.428 0.000942 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.48 0.0125 0.229 0.104 0.134 0.564 0.572 0.00141 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 402 15446.360 0.005 0.015 0.217 0.518 0.111 0.147 0.472 0.56 0.00118 0.0014 ! Validation 402 15446.360 0.005 0.0133 0.213 0.479 0.105 0.138 0.493 0.552 0.00123 0.00138 Wall time: 15446.360986744985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 24 0.509 0.0146 0.217 0.109 0.144 0.533 0.557 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 403 100 0.406 0.0121 0.165 0.101 0.131 0.475 0.486 0.00119 0.00121 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 403 15484.333 0.005 0.0148 0.0867 0.382 0.11 0.145 0.278 0.345 0.000695 0.000863 ! Validation 403 15484.333 0.005 0.0129 0.156 0.415 0.104 0.136 0.411 0.472 0.00103 0.00118 Wall time: 15484.333208284806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 24 0.331 0.0156 0.0195 0.112 0.149 0.137 0.167 0.000341 0.000418 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.292 0.013 0.0324 0.105 0.136 0.193 0.215 0.000481 0.000538 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 404 15522.318 0.005 0.0151 0.3 0.601 0.111 0.147 0.567 0.661 0.00142 0.00165 ! Validation 404 15522.318 0.005 0.0137 0.0865 0.36 0.107 0.14 0.287 0.351 0.000717 0.000879 Wall time: 15522.318647603039 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 24 0.315 0.0144 0.0279 0.109 0.143 0.139 0.2 0.000348 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.253 0.0121 0.0109 0.102 0.131 0.0991 0.125 0.000248 0.000312 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 405 15560.301 0.005 0.0148 0.0689 0.365 0.11 0.146 0.253 0.316 0.000633 0.00079 ! Validation 405 15560.301 0.005 0.013 0.0557 0.315 0.104 0.136 0.227 0.282 0.000568 0.000705 Wall time: 15560.301644828636 ! Best model 405 0.315 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 24 0.741 0.0142 0.457 0.108 0.142 0.776 0.808 0.00194 0.00202 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.766 0.0119 0.528 0.101 0.13 0.861 0.868 0.00215 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 406 15598.315 0.005 0.0145 0.11 0.4 0.109 0.144 0.303 0.381 0.000758 0.000952 ! Validation 406 15598.315 0.005 0.0128 0.47 0.726 0.103 0.135 0.772 0.819 0.00193 0.00205 Wall time: 15598.31532838801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 24 0.409 0.0146 0.118 0.109 0.144 0.37 0.41 0.000925 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.252 0.0122 0.0071 0.102 0.132 0.0798 0.101 0.0002 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 407 15636.289 0.005 0.0148 0.14 0.436 0.11 0.145 0.365 0.449 0.000912 0.00112 ! Validation 407 15636.289 0.005 0.013 0.056 0.316 0.104 0.136 0.227 0.283 0.000568 0.000707 Wall time: 15636.289887384046 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 24 0.35 0.0139 0.0728 0.107 0.141 0.277 0.322 0.000692 0.000806 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.288 0.012 0.0481 0.101 0.131 0.242 0.262 0.000604 0.000655 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 408 15674.275 0.005 0.0144 0.0826 0.371 0.109 0.144 0.283 0.344 0.000708 0.00086 ! Validation 408 15674.275 0.005 0.0128 0.105 0.361 0.103 0.135 0.322 0.387 0.000804 0.000969 Wall time: 15674.275072171818 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 24 1.39 0.0138 1.11 0.106 0.14 1.25 1.26 0.00313 0.00315 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 1.74 0.0121 1.5 0.101 0.132 1.46 1.46 0.00365 0.00366 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 15712.236 0.005 0.0143 0.144 0.429 0.108 0.143 0.312 0.415 0.000781 0.00104 ! Validation 409 15712.236 0.005 0.0131 1.36 1.63 0.104 0.137 1.37 1.4 0.00343 0.00349 Wall time: 15712.23626778461 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 24 0.874 0.0159 0.556 0.115 0.151 0.869 0.891 0.00217 0.00223 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.783 0.0136 0.511 0.107 0.14 0.845 0.854 0.00211 0.00213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 410 15750.190 0.005 0.016 0.471 0.791 0.114 0.151 0.717 0.819 0.00179 0.00205 ! Validation 410 15750.190 0.005 0.0145 0.606 0.896 0.109 0.144 0.881 0.93 0.0022 0.00233 Wall time: 15750.190097088926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 24 0.364 0.0148 0.0674 0.11 0.145 0.24 0.31 0.0006 0.000775 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.356 0.0121 0.113 0.102 0.132 0.39 0.402 0.000976 0.00101 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 411 15794.128 0.005 0.0153 0.124 0.43 0.112 0.148 0.323 0.424 0.000807 0.00106 ! Validation 411 15794.128 0.005 0.013 0.173 0.432 0.104 0.136 0.432 0.497 0.00108 0.00124 Wall time: 15794.128114223946 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 24 0.326 0.0151 0.0247 0.111 0.147 0.14 0.188 0.00035 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.262 0.0121 0.0203 0.101 0.131 0.151 0.17 0.000377 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 412 15832.113 0.005 0.0146 0.106 0.398 0.109 0.144 0.33 0.392 0.000824 0.000981 ! Validation 412 15832.113 0.005 0.0129 0.0533 0.311 0.103 0.136 0.224 0.276 0.000561 0.00069 Wall time: 15832.113914020825 ! Best model 412 0.311 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 24 0.285 0.0127 0.0311 0.103 0.135 0.149 0.211 0.000372 0.000526 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.242 0.0117 0.00742 0.0999 0.129 0.0869 0.103 0.000217 0.000257 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 413 15878.661 0.005 0.0143 0.0548 0.341 0.108 0.143 0.227 0.281 0.000567 0.000703 ! Validation 413 15878.661 0.005 0.0126 0.0516 0.303 0.102 0.134 0.22 0.271 0.00055 0.000678 Wall time: 15878.661979751661 ! Best model 413 0.303 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 24 0.365 0.0158 0.0498 0.113 0.15 0.216 0.267 0.00054 0.000667 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.292 0.0121 0.0507 0.101 0.131 0.245 0.269 0.000613 0.000673 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 414 15916.634 0.005 0.0146 0.213 0.505 0.109 0.144 0.467 0.556 0.00117 0.00139 ! Validation 414 15916.634 0.005 0.0129 0.072 0.329 0.103 0.136 0.261 0.321 0.000652 0.000802 Wall time: 15916.634682049975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 24 0.452 0.0132 0.189 0.104 0.137 0.473 0.519 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 415 100 0.657 0.0118 0.421 0.0999 0.13 0.768 0.775 0.00192 0.00194 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 415 15954.713 0.005 0.0143 0.15 0.437 0.108 0.143 0.373 0.461 0.000933 0.00115 ! Validation 415 15954.713 0.005 0.0126 0.396 0.648 0.102 0.134 0.698 0.752 0.00174 0.00188 Wall time: 15954.713343632873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 24 0.329 0.0141 0.0467 0.107 0.142 0.211 0.258 0.000527 0.000646 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.26 0.0118 0.0234 0.1 0.13 0.158 0.183 0.000396 0.000457 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 416 15992.671 0.005 0.0145 0.0986 0.388 0.109 0.144 0.302 0.378 0.000754 0.000944 ! Validation 416 15992.671 0.005 0.0126 0.0527 0.305 0.102 0.134 0.222 0.274 0.000555 0.000686 Wall time: 15992.671292057727 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 24 0.36 0.015 0.0594 0.11 0.146 0.222 0.291 0.000556 0.000728 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.428 0.0122 0.183 0.102 0.132 0.502 0.511 0.00125 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 417 16030.621 0.005 0.0142 0.153 0.437 0.108 0.142 0.385 0.47 0.000964 0.00118 ! Validation 417 16030.621 0.005 0.013 0.185 0.444 0.104 0.136 0.45 0.513 0.00113 0.00128 Wall time: 16030.621843645815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 24 0.309 0.0143 0.024 0.108 0.143 0.138 0.185 0.000346 0.000463 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.265 0.0118 0.0284 0.1 0.13 0.171 0.201 0.000426 0.000504 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 418 16068.579 0.005 0.0147 0.242 0.536 0.11 0.145 0.477 0.594 0.00119 0.00148 ! Validation 418 16068.579 0.005 0.0127 0.0715 0.326 0.103 0.135 0.258 0.32 0.000645 0.000799 Wall time: 16068.579628723674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 24 0.319 0.0139 0.04 0.107 0.141 0.201 0.239 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 419 100 0.247 0.0117 0.0136 0.0998 0.129 0.126 0.139 0.000314 0.000348 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 419 16106.536 0.005 0.0141 0.0392 0.321 0.107 0.142 0.189 0.236 0.000473 0.000591 ! Validation 419 16106.536 0.005 0.0124 0.0448 0.293 0.102 0.133 0.206 0.253 0.000514 0.000633 Wall time: 16106.53612902062 ! Best model 419 0.293 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 24 0.66 0.0148 0.365 0.109 0.145 0.675 0.722 0.00169 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.259 0.0122 0.0143 0.101 0.132 0.109 0.143 0.000272 0.000358 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 420 16144.508 0.005 0.0141 0.195 0.476 0.107 0.142 0.436 0.522 0.00109 0.0013 ! Validation 420 16144.508 0.005 0.0132 0.0856 0.349 0.104 0.137 0.28 0.35 0.0007 0.000874 Wall time: 16144.50808378961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 24 0.36 0.0142 0.0752 0.107 0.143 0.29 0.328 0.000726 0.00082 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.258 0.0115 0.0269 0.0991 0.128 0.17 0.196 0.000425 0.00049 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 421 16182.470 0.005 0.0143 0.118 0.404 0.108 0.143 0.32 0.413 0.000801 0.00103 ! Validation 421 16182.470 0.005 0.0124 0.0537 0.302 0.102 0.133 0.226 0.277 0.000564 0.000692 Wall time: 16182.470167848747 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 24 0.775 0.0131 0.514 0.104 0.137 0.84 0.856 0.0021 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 1.01 0.0113 0.78 0.0981 0.127 1.05 1.06 0.00263 0.00264 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 16220.447 0.005 0.0139 0.0845 0.362 0.107 0.141 0.247 0.325 0.000617 0.000814 ! Validation 422 16220.447 0.005 0.0122 0.705 0.949 0.101 0.132 0.97 1 0.00242 0.00251 Wall time: 16220.44721095683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 24 0.46 0.014 0.18 0.107 0.141 0.463 0.508 0.00116 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.344 0.0122 0.101 0.101 0.132 0.361 0.38 0.000902 0.00095 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 16258.399 0.005 0.0146 0.336 0.628 0.109 0.145 0.596 0.696 0.00149 0.00174 ! Validation 423 16258.399 0.005 0.0131 0.153 0.414 0.104 0.137 0.4 0.467 0.000999 0.00117 Wall time: 16258.399264414795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 24 0.327 0.0151 0.0252 0.111 0.147 0.141 0.19 0.000353 0.000474 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.456 0.0114 0.228 0.0983 0.127 0.56 0.571 0.0014 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 424 16296.350 0.005 0.0142 0.071 0.354 0.107 0.142 0.259 0.321 0.000648 0.000802 ! Validation 424 16296.350 0.005 0.0123 0.226 0.473 0.101 0.133 0.512 0.568 0.00128 0.00142 Wall time: 16296.35049470095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 24 0.326 0.0146 0.0341 0.109 0.144 0.187 0.221 0.000467 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.252 0.0121 0.00998 0.102 0.131 0.0884 0.119 0.000221 0.000299 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 425 16334.315 0.005 0.0143 0.205 0.49 0.108 0.143 0.458 0.547 0.00115 0.00137 ! Validation 425 16334.315 0.005 0.0128 0.0639 0.321 0.103 0.135 0.243 0.302 0.000608 0.000755 Wall time: 16334.31556785293 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 24 0.36 0.0144 0.0733 0.108 0.143 0.276 0.323 0.00069 0.000809 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.236 0.0114 0.00808 0.0986 0.128 0.0871 0.107 0.000218 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 426 16372.847 0.005 0.014 0.114 0.395 0.107 0.142 0.345 0.405 0.000863 0.00101 ! Validation 426 16372.847 0.005 0.0122 0.0526 0.297 0.101 0.132 0.222 0.274 0.000555 0.000685 Wall time: 16372.847326984629 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 24 0.298 0.0141 0.0162 0.107 0.142 0.118 0.152 0.000294 0.00038 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.232 0.0112 0.00697 0.098 0.127 0.0795 0.0998 0.000199 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 427 16419.180 0.005 0.0138 0.0683 0.344 0.106 0.14 0.259 0.315 0.000648 0.000788 ! Validation 427 16419.180 0.005 0.0121 0.0506 0.292 0.1 0.131 0.217 0.269 0.000543 0.000672 Wall time: 16419.18103437964 ! Best model 427 0.292 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 24 0.449 0.0136 0.177 0.106 0.139 0.459 0.503 0.00115 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.329 0.0118 0.0929 0.101 0.13 0.351 0.364 0.000877 0.000911 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 16458.933 0.005 0.0141 0.259 0.541 0.107 0.142 0.52 0.61 0.0013 0.00153 ! Validation 428 16458.933 0.005 0.0126 0.14 0.392 0.102 0.134 0.383 0.448 0.000957 0.00112 Wall time: 16458.933580043726 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 24 0.526 0.0154 0.217 0.112 0.148 0.528 0.557 0.00132 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.26 0.012 0.0206 0.101 0.131 0.151 0.172 0.000378 0.000429 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 429 16496.870 0.005 0.0145 0.2 0.491 0.109 0.144 0.443 0.534 0.00111 0.00134 ! Validation 429 16496.870 0.005 0.0128 0.0608 0.317 0.103 0.135 0.24 0.295 0.0006 0.000737 Wall time: 16496.870507848915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 24 0.295 0.013 0.0345 0.103 0.136 0.174 0.222 0.000435 0.000555 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.258 0.0112 0.0339 0.0979 0.127 0.19 0.22 0.000474 0.00055 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 430 16534.803 0.005 0.0139 0.074 0.352 0.107 0.141 0.271 0.327 0.000677 0.000818 ! Validation 430 16534.803 0.005 0.0121 0.0817 0.324 0.1 0.132 0.277 0.342 0.000693 0.000854 Wall time: 16534.803772090934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 24 0.344 0.015 0.0439 0.11 0.146 0.214 0.25 0.000535 0.000626 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.242 0.0111 0.0208 0.0974 0.126 0.145 0.172 0.000362 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 431 16572.736 0.005 0.0136 0.0428 0.316 0.105 0.139 0.2 0.247 0.0005 0.000618 ! Validation 431 16572.736 0.005 0.0119 0.07 0.309 0.0996 0.131 0.254 0.316 0.000636 0.00079 Wall time: 16572.736399793997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 24 0.385 0.0147 0.0911 0.109 0.145 0.319 0.361 0.000796 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.439 0.0116 0.207 0.0996 0.129 0.533 0.544 0.00133 0.00136 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 432 16610.674 0.005 0.014 0.281 0.561 0.107 0.141 0.559 0.639 0.0014 0.0016 ! Validation 432 16610.674 0.005 0.0123 0.286 0.533 0.101 0.133 0.584 0.639 0.00146 0.0016 Wall time: 16610.67417768063 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 24 0.457 0.0134 0.19 0.104 0.138 0.486 0.521 0.00122 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.283 0.0115 0.0536 0.0989 0.128 0.255 0.277 0.000638 0.000692 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 16648.616 0.005 0.0138 0.168 0.444 0.106 0.141 0.423 0.488 0.00106 0.00122 ! Validation 433 16648.616 0.005 0.0124 0.104 0.351 0.101 0.133 0.317 0.385 0.000794 0.000964 Wall time: 16648.61633098265 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 24 0.431 0.0132 0.166 0.104 0.137 0.43 0.487 0.00108 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.378 0.0113 0.152 0.0983 0.127 0.45 0.465 0.00112 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 434 16694.624 0.005 0.0138 0.158 0.434 0.106 0.14 0.396 0.475 0.000989 0.00119 ! Validation 434 16694.624 0.005 0.0122 0.146 0.391 0.101 0.132 0.393 0.456 0.000983 0.00114 Wall time: 16694.624581990764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 24 0.346 0.0135 0.0758 0.105 0.139 0.296 0.329 0.000739 0.000823 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.247 0.0111 0.0236 0.0978 0.126 0.158 0.184 0.000395 0.000459 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 435 16733.146 0.005 0.0137 0.0892 0.363 0.106 0.14 0.289 0.358 0.000721 0.000894 ! Validation 435 16733.146 0.005 0.012 0.0763 0.316 0.0998 0.131 0.266 0.33 0.000664 0.000825 Wall time: 16733.146584908944 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 24 0.358 0.0129 0.0991 0.103 0.136 0.354 0.376 0.000885 0.000941 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.315 0.0112 0.0899 0.0982 0.127 0.345 0.358 0.000861 0.000896 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 16784.425 0.005 0.0135 0.133 0.404 0.105 0.139 0.359 0.437 0.000898 0.00109 ! Validation 436 16784.425 0.005 0.012 0.156 0.397 0.0999 0.131 0.408 0.472 0.00102 0.00118 Wall time: 16784.42582880659 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 24 0.461 0.0126 0.21 0.102 0.134 0.515 0.548 0.00129 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.601 0.0109 0.382 0.0968 0.125 0.732 0.739 0.00183 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 437 16822.469 0.005 0.0134 0.0567 0.325 0.105 0.138 0.221 0.275 0.000551 0.000688 ! Validation 437 16822.469 0.005 0.0118 0.463 0.699 0.0988 0.13 0.773 0.813 0.00193 0.00203 Wall time: 16822.469684562646 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 24 0.317 0.0137 0.0439 0.106 0.14 0.214 0.251 0.000536 0.000626 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.237 0.0113 0.0111 0.0983 0.127 0.0975 0.126 0.000244 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 438 16860.541 0.005 0.014 0.306 0.586 0.107 0.142 0.565 0.667 0.00141 0.00167 ! Validation 438 16860.541 0.005 0.012 0.067 0.308 0.1 0.131 0.248 0.309 0.000621 0.000773 Wall time: 16860.54145870777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 24 0.311 0.0138 0.0339 0.106 0.141 0.177 0.22 0.000443 0.00055 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.242 0.0109 0.0229 0.0968 0.125 0.16 0.181 0.000401 0.000452 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 439 16898.506 0.005 0.0135 0.0556 0.325 0.105 0.139 0.234 0.283 0.000585 0.000708 ! Validation 439 16898.506 0.005 0.0117 0.0576 0.292 0.0988 0.129 0.234 0.287 0.000585 0.000717 Wall time: 16898.50606599776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 24 0.533 0.0134 0.265 0.105 0.138 0.593 0.615 0.00148 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.448 0.0109 0.229 0.0966 0.125 0.561 0.572 0.0014 0.00143 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 440 16936.460 0.005 0.0135 0.153 0.423 0.105 0.139 0.391 0.463 0.000979 0.00116 ! Validation 440 16936.460 0.005 0.0118 0.231 0.466 0.0989 0.13 0.515 0.574 0.00129 0.00144 Wall time: 16936.46084433701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 24 0.381 0.0128 0.125 0.102 0.135 0.385 0.422 0.000961 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.54 0.011 0.319 0.0971 0.126 0.668 0.675 0.00167 0.00169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 441 16974.429 0.005 0.0135 0.166 0.436 0.105 0.139 0.411 0.489 0.00103 0.00122 ! Validation 441 16974.429 0.005 0.0119 0.395 0.633 0.0997 0.13 0.708 0.751 0.00177 0.00188 Wall time: 16974.42958837282 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 24 0.299 0.0129 0.0408 0.103 0.136 0.186 0.242 0.000464 0.000604 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.246 0.0109 0.0285 0.0967 0.125 0.173 0.202 0.000432 0.000504 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 442 17012.378 0.005 0.0133 0.0916 0.358 0.104 0.138 0.297 0.364 0.000743 0.00091 ! Validation 442 17012.378 0.005 0.0117 0.0729 0.307 0.0988 0.129 0.261 0.323 0.000653 0.000807 Wall time: 17012.37801440293 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 24 0.369 0.0119 0.131 0.0994 0.13 0.415 0.433 0.00104 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.445 0.0107 0.231 0.0957 0.124 0.566 0.575 0.00141 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 443 17050.403 0.005 0.0131 0.0661 0.328 0.104 0.137 0.252 0.304 0.00063 0.000759 ! Validation 443 17050.403 0.005 0.0115 0.214 0.444 0.0978 0.128 0.49 0.553 0.00123 0.00138 Wall time: 17050.403739097994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 24 0.433 0.0136 0.161 0.106 0.139 0.441 0.48 0.0011 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.782 0.0114 0.554 0.099 0.128 0.883 0.889 0.00221 0.00222 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 444 17088.387 0.005 0.0134 0.164 0.431 0.105 0.138 0.392 0.484 0.000981 0.00121 ! Validation 444 17088.387 0.005 0.0122 0.467 0.711 0.101 0.132 0.774 0.817 0.00194 0.00204 Wall time: 17088.38704378996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 24 0.531 0.0131 0.269 0.103 0.137 0.589 0.619 0.00147 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.636 0.0117 0.403 0.0995 0.129 0.751 0.758 0.00188 0.0019 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 445 17126.370 0.005 0.0139 0.261 0.539 0.107 0.141 0.532 0.61 0.00133 0.00153 ! Validation 445 17126.370 0.005 0.0124 0.491 0.739 0.102 0.133 0.795 0.837 0.00199 0.00209 Wall time: 17126.370638418943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 24 0.323 0.0128 0.0661 0.103 0.135 0.273 0.307 0.000683 0.000768 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.258 0.0108 0.0417 0.0964 0.124 0.223 0.244 0.000558 0.00061 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 17164.321 0.005 0.0134 0.0947 0.364 0.105 0.139 0.297 0.369 0.000743 0.000922 ! Validation 446 17164.321 0.005 0.0116 0.0968 0.329 0.0982 0.129 0.305 0.372 0.000763 0.00093 Wall time: 17164.321683623828 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 24 0.338 0.0138 0.0614 0.106 0.14 0.253 0.296 0.000632 0.00074 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.575 0.0108 0.359 0.0962 0.124 0.709 0.716 0.00177 0.00179 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 447 17202.307 0.005 0.0131 0.0642 0.326 0.103 0.137 0.242 0.303 0.000606 0.000757 ! Validation 447 17202.307 0.005 0.0115 0.336 0.567 0.0979 0.128 0.646 0.693 0.00161 0.00173 Wall time: 17202.307109043002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 24 0.295 0.0115 0.0649 0.0979 0.128 0.27 0.304 0.000674 0.000761 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.449 0.0109 0.232 0.0969 0.125 0.569 0.575 0.00142 0.00144 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 448 17240.262 0.005 0.0132 0.165 0.43 0.104 0.138 0.413 0.488 0.00103 0.00122 ! Validation 448 17240.262 0.005 0.0116 0.238 0.469 0.0982 0.129 0.527 0.582 0.00132 0.00146 Wall time: 17240.262340966612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 24 0.425 0.0134 0.157 0.105 0.138 0.445 0.473 0.00111 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.242 0.011 0.0212 0.0974 0.125 0.151 0.174 0.000377 0.000435 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 17278.225 0.005 0.0132 0.162 0.427 0.104 0.137 0.396 0.481 0.000991 0.0012 ! Validation 449 17278.225 0.005 0.0117 0.0546 0.288 0.0986 0.129 0.227 0.279 0.000568 0.000698 Wall time: 17278.225841411855 ! Best model 449 0.288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 24 0.4 0.0129 0.142 0.103 0.136 0.419 0.45 0.00105 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.655 0.0109 0.437 0.0967 0.125 0.782 0.79 0.00195 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 17316.199 0.005 0.0132 0.158 0.422 0.104 0.138 0.404 0.475 0.00101 0.00119 ! Validation 450 17316.199 0.005 0.0118 0.498 0.733 0.0992 0.13 0.804 0.843 0.00201 0.00211 Wall time: 17316.199937030673 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 24 0.654 0.0138 0.377 0.106 0.141 0.707 0.734 0.00177 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.584 0.0113 0.359 0.0979 0.127 0.71 0.716 0.00177 0.00179 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 451 17354.169 0.005 0.0134 0.217 0.485 0.105 0.138 0.468 0.552 0.00117 0.00138 ! Validation 451 17354.169 0.005 0.0121 0.389 0.63 0.1 0.131 0.7 0.745 0.00175 0.00186 Wall time: 17354.169771280605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 24 0.298 0.0137 0.0229 0.105 0.14 0.148 0.181 0.000369 0.000453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.272 0.0109 0.0531 0.0969 0.125 0.259 0.275 0.000648 0.000689 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 452 17394.228 0.005 0.0135 0.199 0.469 0.105 0.139 0.454 0.539 0.00113 0.00135 ! Validation 452 17394.228 0.005 0.0116 0.0816 0.314 0.0982 0.129 0.282 0.341 0.000704 0.000853 Wall time: 17394.22860597493 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 24 0.312 0.014 0.0327 0.107 0.141 0.166 0.216 0.000416 0.000541 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.221 0.0106 0.00862 0.0955 0.123 0.0852 0.111 0.000213 0.000277 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 453 17432.192 0.005 0.0131 0.0954 0.358 0.104 0.137 0.306 0.372 0.000766 0.00093 ! Validation 453 17432.192 0.005 0.0114 0.052 0.28 0.0973 0.128 0.22 0.273 0.000549 0.000681 Wall time: 17432.19283981202 ! Best model 453 0.280 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 24 0.361 0.0137 0.0871 0.105 0.14 0.318 0.353 0.000794 0.000882 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.347 0.0105 0.137 0.0949 0.122 0.43 0.443 0.00107 0.00111 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 454 17470.153 0.005 0.0129 0.0953 0.353 0.103 0.136 0.295 0.369 0.000738 0.000923 ! Validation 454 17470.153 0.005 0.0113 0.197 0.424 0.097 0.127 0.47 0.53 0.00117 0.00133 Wall time: 17470.153149733786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 24 0.299 0.0131 0.0371 0.104 0.137 0.183 0.23 0.000456 0.000575 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.253 0.0105 0.0434 0.0949 0.122 0.23 0.249 0.000574 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 455 17508.091 0.005 0.0129 0.108 0.366 0.103 0.136 0.331 0.395 0.000829 0.000988 ! Validation 455 17508.091 0.005 0.0113 0.093 0.318 0.0968 0.127 0.299 0.365 0.000747 0.000911 Wall time: 17508.091926021036 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 24 0.268 0.0122 0.0227 0.1 0.132 0.141 0.18 0.000353 0.00045 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.215 0.0104 0.0072 0.0946 0.122 0.0859 0.101 0.000215 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 456 17546.027 0.005 0.0128 0.0734 0.329 0.102 0.135 0.268 0.327 0.000671 0.000816 ! Validation 456 17546.027 0.005 0.0112 0.0442 0.268 0.0965 0.126 0.206 0.251 0.000515 0.000628 Wall time: 17546.0274065868 ! Best model 456 0.268 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 24 0.92 0.0122 0.677 0.101 0.132 0.963 0.983 0.00241 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.769 0.011 0.549 0.0967 0.125 0.88 0.886 0.0022 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 17583.988 0.005 0.0126 0.0983 0.351 0.102 0.134 0.26 0.347 0.000649 0.000868 ! Validation 457 17583.988 0.005 0.0118 0.522 0.758 0.0991 0.13 0.823 0.863 0.00206 0.00216 Wall time: 17583.98864579061 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 24 0.314 0.013 0.0539 0.103 0.136 0.238 0.277 0.000596 0.000694 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.219 0.0104 0.0113 0.0944 0.122 0.113 0.127 0.000283 0.000318 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 458 17621.942 0.005 0.0132 0.134 0.398 0.104 0.137 0.366 0.44 0.000914 0.0011 ! Validation 458 17621.942 0.005 0.0112 0.0491 0.273 0.0965 0.126 0.217 0.265 0.000542 0.000662 Wall time: 17621.942173002753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 24 0.399 0.0122 0.156 0.0999 0.132 0.439 0.472 0.0011 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.316 0.0103 0.11 0.0943 0.121 0.385 0.396 0.000964 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 459 17659.852 0.005 0.0127 0.0954 0.349 0.102 0.135 0.303 0.366 0.000757 0.000916 ! Validation 459 17659.852 0.005 0.0111 0.12 0.341 0.0959 0.126 0.352 0.415 0.00088 0.00104 Wall time: 17659.852297800593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 24 0.299 0.0122 0.0548 0.1 0.132 0.251 0.28 0.000627 0.000699 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.23 0.0103 0.0234 0.0942 0.121 0.159 0.183 0.000399 0.000457 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 460 17697.793 0.005 0.0128 0.138 0.393 0.102 0.135 0.369 0.447 0.000923 0.00112 ! Validation 460 17697.793 0.005 0.0112 0.0562 0.279 0.0964 0.126 0.23 0.283 0.000576 0.000708 Wall time: 17697.7936875117 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 24 0.277 0.0125 0.0266 0.101 0.134 0.157 0.195 0.000391 0.000488 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.224 0.0102 0.0193 0.0939 0.121 0.148 0.166 0.000371 0.000415 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 461 17735.809 0.005 0.0126 0.09 0.341 0.101 0.134 0.291 0.362 0.000728 0.000904 ! Validation 461 17735.809 0.005 0.011 0.0504 0.271 0.0958 0.125 0.217 0.268 0.000543 0.000671 Wall time: 17735.80915417662 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 24 0.32 0.0133 0.0537 0.104 0.138 0.242 0.277 0.000604 0.000692 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.43 0.0105 0.22 0.0951 0.122 0.55 0.56 0.00137 0.0014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 462 17773.730 0.005 0.0127 0.141 0.395 0.102 0.135 0.359 0.452 0.000897 0.00113 ! Validation 462 17773.730 0.005 0.0113 0.275 0.501 0.097 0.127 0.575 0.626 0.00144 0.00157 Wall time: 17773.73013907997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 24 0.421 0.0133 0.155 0.105 0.138 0.448 0.47 0.00112 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.251 0.0104 0.0427 0.0949 0.122 0.223 0.247 0.000558 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 463 17811.659 0.005 0.0131 0.2 0.462 0.104 0.137 0.446 0.535 0.00111 0.00134 ! Validation 463 17811.659 0.005 0.0112 0.0881 0.312 0.0968 0.127 0.29 0.355 0.000725 0.000887 Wall time: 17811.65951627493 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 24 0.851 0.0129 0.594 0.103 0.135 0.907 0.921 0.00227 0.0023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.269 0.0122 0.025 0.102 0.132 0.164 0.189 0.000411 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 464 17849.581 0.005 0.0127 0.204 0.457 0.102 0.134 0.422 0.527 0.00106 0.00132 ! Validation 464 17849.581 0.005 0.0129 0.0818 0.339 0.103 0.135 0.275 0.342 0.000688 0.000855 Wall time: 17849.581873613875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 24 0.268 0.0124 0.0203 0.101 0.133 0.153 0.17 0.000381 0.000425 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.211 0.0102 0.00605 0.094 0.121 0.0703 0.093 0.000176 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 465 17889.039 0.005 0.0129 0.0991 0.358 0.103 0.136 0.308 0.38 0.000769 0.000949 ! Validation 465 17889.039 0.005 0.011 0.0454 0.266 0.0959 0.125 0.208 0.255 0.000519 0.000637 Wall time: 17889.039937766735 ! Best model 465 0.266 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 24 0.318 0.0121 0.0761 0.0995 0.131 0.278 0.33 0.000695 0.000824 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.399 0.01 0.198 0.0929 0.12 0.524 0.532 0.00131 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 466 17927.799 0.005 0.0124 0.0617 0.31 0.101 0.133 0.237 0.296 0.000592 0.00074 ! Validation 466 17927.799 0.005 0.0108 0.21 0.427 0.0949 0.124 0.489 0.548 0.00122 0.00137 Wall time: 17927.80018680077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 24 0.269 0.0128 0.0136 0.103 0.135 0.106 0.139 0.000264 0.000348 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.251 0.01 0.0498 0.093 0.12 0.251 0.267 0.000627 0.000667 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 467 17965.996 0.005 0.0124 0.0678 0.316 0.101 0.133 0.253 0.314 0.000633 0.000786 ! Validation 467 17965.996 0.005 0.0108 0.106 0.323 0.0951 0.124 0.323 0.39 0.000808 0.000975 Wall time: 17965.99762512464 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 24 0.326 0.0119 0.0892 0.0985 0.13 0.318 0.357 0.000794 0.000892 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.385 0.01 0.184 0.093 0.12 0.505 0.513 0.00126 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 468 18004.040 0.005 0.0123 0.102 0.347 0.1 0.132 0.316 0.382 0.000789 0.000955 ! Validation 468 18004.040 0.005 0.0108 0.196 0.412 0.0949 0.124 0.473 0.529 0.00118 0.00132 Wall time: 18004.040325297974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 24 0.511 0.0131 0.249 0.103 0.137 0.557 0.596 0.00139 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.606 0.0102 0.402 0.094 0.121 0.75 0.757 0.00187 0.00189 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 469 18042.025 0.005 0.0128 0.207 0.463 0.102 0.135 0.461 0.542 0.00115 0.00136 ! Validation 469 18042.025 0.005 0.0111 0.474 0.696 0.0963 0.126 0.783 0.823 0.00196 0.00206 Wall time: 18042.025302213617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 24 0.347 0.0126 0.0936 0.102 0.134 0.331 0.366 0.000828 0.000914 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.215 0.0103 0.00815 0.0944 0.122 0.0918 0.108 0.000229 0.00027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 470 18079.998 0.005 0.0127 0.206 0.461 0.102 0.135 0.476 0.546 0.00119 0.00136 ! Validation 470 18079.998 0.005 0.0112 0.0463 0.27 0.0966 0.126 0.21 0.257 0.000526 0.000643 Wall time: 18079.998635496013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 24 0.337 0.0132 0.0741 0.104 0.137 0.285 0.325 0.000714 0.000813 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.233 0.01 0.0322 0.0931 0.12 0.185 0.214 0.000463 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 471 18117.998 0.005 0.0125 0.0995 0.35 0.101 0.134 0.315 0.378 0.000787 0.000945 ! Validation 471 18117.998 0.005 0.0109 0.059 0.278 0.0955 0.125 0.235 0.29 0.000586 0.000726 Wall time: 18117.998746370897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 24 0.38 0.013 0.12 0.103 0.136 0.377 0.413 0.000943 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.359 0.0104 0.152 0.0944 0.122 0.454 0.465 0.00113 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 472 18155.989 0.005 0.0124 0.132 0.38 0.101 0.133 0.37 0.435 0.000926 0.00109 ! Validation 472 18155.989 0.005 0.0112 0.19 0.415 0.0967 0.127 0.462 0.521 0.00115 0.0013 Wall time: 18155.98965236591 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 24 0.444 0.0124 0.196 0.1 0.133 0.501 0.529 0.00125 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.373 0.00988 0.175 0.0924 0.119 0.489 0.5 0.00122 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 473 18193.976 0.005 0.0123 0.0768 0.323 0.1 0.133 0.266 0.325 0.000664 0.000812 ! Validation 473 18193.976 0.005 0.0107 0.226 0.44 0.0944 0.124 0.513 0.568 0.00128 0.00142 Wall time: 18193.976599022746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 24 0.643 0.013 0.384 0.103 0.136 0.729 0.74 0.00182 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.54 0.01 0.34 0.0928 0.12 0.689 0.697 0.00172 0.00174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 474 18231.946 0.005 0.0123 0.178 0.424 0.1 0.132 0.435 0.498 0.00109 0.00124 ! Validation 474 18231.946 0.005 0.0109 0.373 0.59 0.0949 0.124 0.684 0.73 0.00171 0.00182 Wall time: 18231.94683664292 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 24 0.278 0.0118 0.0418 0.0986 0.13 0.195 0.244 0.000488 0.000611 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.262 0.01 0.0616 0.093 0.12 0.282 0.297 0.000706 0.000741 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 18269.898 0.005 0.0124 0.0999 0.347 0.101 0.133 0.309 0.38 0.000772 0.000951 ! Validation 475 18269.898 0.005 0.0108 0.09 0.307 0.0952 0.124 0.297 0.359 0.000742 0.000896 Wall time: 18269.898739664815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 24 0.698 0.0123 0.452 0.101 0.133 0.782 0.803 0.00196 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.502 0.0105 0.293 0.0954 0.122 0.641 0.647 0.0016 0.00162 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 476 18307.861 0.005 0.0123 0.177 0.423 0.1 0.133 0.413 0.493 0.00103 0.00123 ! Validation 476 18307.861 0.005 0.011 0.313 0.534 0.0961 0.126 0.62 0.669 0.00155 0.00167 Wall time: 18307.861257874873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 24 0.297 0.0119 0.0584 0.0979 0.131 0.249 0.289 0.000624 0.000722 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.262 0.00985 0.0651 0.0921 0.119 0.288 0.305 0.000719 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 477 18345.826 0.005 0.0123 0.0758 0.323 0.101 0.133 0.271 0.33 0.000678 0.000825 ! Validation 477 18345.826 0.005 0.0107 0.106 0.32 0.0944 0.124 0.326 0.389 0.000815 0.000973 Wall time: 18345.826222762 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 24 0.54 0.0116 0.308 0.0984 0.129 0.63 0.664 0.00158 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.875 0.00978 0.679 0.092 0.118 0.98 0.985 0.00245 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 478 18383.820 0.005 0.0121 0.121 0.363 0.0996 0.132 0.346 0.408 0.000866 0.00102 ! Validation 478 18383.820 0.005 0.0106 0.687 0.899 0.094 0.123 0.954 0.991 0.00238 0.00248 Wall time: 18383.820573325735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 24 0.268 0.012 0.0281 0.0992 0.131 0.175 0.2 0.000438 0.000501 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.21 0.0098 0.0142 0.092 0.118 0.111 0.142 0.000277 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 479 18421.826 0.005 0.0128 0.158 0.414 0.103 0.135 0.358 0.479 0.000895 0.0012 ! Validation 479 18421.826 0.005 0.0107 0.0488 0.264 0.0945 0.124 0.212 0.264 0.000531 0.00066 Wall time: 18421.826497400645 ! Best model 479 0.264 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 24 0.281 0.0123 0.034 0.101 0.133 0.179 0.22 0.000448 0.000551 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.206 0.00979 0.00998 0.0921 0.118 0.0955 0.119 0.000239 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 480 18459.841 0.005 0.0121 0.0797 0.322 0.0996 0.132 0.278 0.34 0.000695 0.000849 ! Validation 480 18459.841 0.005 0.0107 0.0411 0.256 0.0948 0.124 0.199 0.242 0.000496 0.000606 Wall time: 18459.841749425977 ! Best model 480 0.256 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 24 0.577 0.0116 0.344 0.0986 0.129 0.68 0.701 0.0017 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.586 0.00987 0.388 0.0926 0.119 0.738 0.745 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 481 18497.825 0.005 0.0123 0.174 0.42 0.101 0.133 0.418 0.493 0.00104 0.00123 ! Validation 481 18497.825 0.005 0.0107 0.423 0.636 0.0944 0.123 0.735 0.777 0.00184 0.00194 Wall time: 18497.82603814872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 24 0.262 0.0118 0.0261 0.0981 0.13 0.143 0.193 0.000358 0.000482 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.221 0.00965 0.0279 0.0913 0.117 0.174 0.2 0.000436 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 482 18535.817 0.005 0.0121 0.128 0.37 0.0995 0.131 0.365 0.432 0.000913 0.00108 ! Validation 482 18535.817 0.005 0.0104 0.0701 0.279 0.0932 0.122 0.259 0.316 0.000649 0.000791 Wall time: 18535.81754028797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 24 0.387 0.0119 0.15 0.0984 0.13 0.427 0.462 0.00107 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.226 0.00985 0.0286 0.0922 0.119 0.174 0.202 0.000434 0.000506 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 483 18573.796 0.005 0.012 0.0932 0.332 0.099 0.131 0.306 0.362 0.000766 0.000905 ! Validation 483 18573.796 0.005 0.0106 0.0739 0.287 0.0942 0.123 0.264 0.325 0.000659 0.000812 Wall time: 18573.796715189703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 24 0.6 0.0124 0.353 0.0999 0.133 0.688 0.71 0.00172 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.619 0.01 0.419 0.093 0.12 0.768 0.773 0.00192 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 484 18611.996 0.005 0.0122 0.17 0.414 0.0999 0.132 0.412 0.487 0.00103 0.00122 ! Validation 484 18611.996 0.005 0.0107 0.458 0.673 0.0946 0.124 0.77 0.809 0.00193 0.00202 Wall time: 18611.997023202013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 24 0.282 0.0108 0.0664 0.0946 0.124 0.258 0.308 0.000646 0.00077 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.253 0.00986 0.0556 0.0925 0.119 0.266 0.282 0.000665 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 485 18649.968 0.005 0.0122 0.0874 0.331 0.0999 0.132 0.272 0.354 0.00068 0.000886 ! Validation 485 18649.968 0.005 0.0106 0.0935 0.306 0.0941 0.123 0.303 0.365 0.000757 0.000913 Wall time: 18649.968484141864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 24 0.373 0.0118 0.136 0.0984 0.13 0.386 0.441 0.000965 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.218 0.00984 0.0209 0.0922 0.119 0.155 0.173 0.000388 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 486 18687.936 0.005 0.0118 0.116 0.352 0.0982 0.13 0.339 0.406 0.000847 0.00102 ! Validation 486 18687.936 0.005 0.0106 0.058 0.271 0.0943 0.123 0.234 0.288 0.000585 0.00072 Wall time: 18687.936712423805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 24 0.287 0.0123 0.0416 0.1 0.132 0.199 0.244 0.000497 0.000609 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.198 0.00951 0.00793 0.0908 0.117 0.0928 0.106 0.000232 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 487 18725.915 0.005 0.0119 0.0874 0.325 0.0986 0.13 0.278 0.355 0.000694 0.000889 ! Validation 487 18725.915 0.005 0.0103 0.044 0.25 0.0928 0.121 0.206 0.251 0.000514 0.000627 Wall time: 18725.915652507916 ! Best model 487 0.250 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 24 1.14 0.0139 0.863 0.106 0.141 1.1 1.11 0.00275 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.478 0.0115 0.249 0.0995 0.128 0.592 0.596 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 488 18763.906 0.005 0.0125 0.273 0.523 0.101 0.133 0.507 0.607 0.00127 0.00152 ! Validation 488 18763.906 0.005 0.0121 0.316 0.558 0.101 0.131 0.612 0.672 0.00153 0.00168 Wall time: 18763.90664487984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 24 0.335 0.0122 0.0911 0.0999 0.132 0.307 0.361 0.000767 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.311 0.00997 0.111 0.0929 0.119 0.388 0.399 0.00097 0.000997 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 18801.862 0.005 0.013 0.218 0.477 0.103 0.136 0.48 0.562 0.0012 0.0014 ! Validation 489 18801.862 0.005 0.0108 0.155 0.372 0.095 0.124 0.408 0.471 0.00102 0.00118 Wall time: 18801.862804368604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 24 0.267 0.0119 0.0279 0.0989 0.131 0.166 0.2 0.000416 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.273 0.00954 0.0817 0.0911 0.117 0.327 0.342 0.000818 0.000854 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 18839.846 0.005 0.012 0.0824 0.322 0.0992 0.131 0.293 0.346 0.000733 0.000864 ! Validation 490 18839.846 0.005 0.0104 0.116 0.323 0.093 0.122 0.347 0.407 0.000868 0.00102 Wall time: 18839.846874407027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 24 0.271 0.0114 0.0429 0.097 0.128 0.215 0.247 0.000537 0.000619 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.209 0.00945 0.0204 0.0904 0.116 0.151 0.171 0.000377 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 491 18877.840 0.005 0.0118 0.0973 0.332 0.0982 0.13 0.317 0.375 0.000793 0.000938 ! Validation 491 18877.840 0.005 0.0102 0.0543 0.259 0.0924 0.121 0.226 0.278 0.000566 0.000696 Wall time: 18877.840413300786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 24 0.319 0.0121 0.0769 0.0994 0.131 0.296 0.331 0.000741 0.000828 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.397 0.00949 0.207 0.091 0.116 0.537 0.543 0.00134 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 492 18915.823 0.005 0.0117 0.102 0.336 0.098 0.129 0.312 0.382 0.000779 0.000956 ! Validation 492 18915.823 0.005 0.0103 0.237 0.443 0.0928 0.121 0.53 0.582 0.00133 0.00146 Wall time: 18915.823438234627 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 24 0.442 0.0123 0.196 0.1 0.133 0.505 0.528 0.00126 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.524 0.00947 0.334 0.0904 0.116 0.682 0.691 0.00171 0.00173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 493 18954.899 0.005 0.0118 0.117 0.354 0.0984 0.13 0.339 0.406 0.000848 0.00101 ! Validation 493 18954.899 0.005 0.0103 0.409 0.616 0.0928 0.122 0.717 0.765 0.00179 0.00191 Wall time: 18954.90016983496 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 24 0.271 0.0122 0.0271 0.1 0.132 0.17 0.197 0.000425 0.000492 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.216 0.00944 0.0268 0.0907 0.116 0.17 0.196 0.000424 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 494 18992.879 0.005 0.012 0.14 0.38 0.099 0.131 0.364 0.452 0.00091 0.00113 ! Validation 494 18992.879 0.005 0.0102 0.0591 0.264 0.0925 0.121 0.236 0.291 0.000591 0.000727 Wall time: 18992.87979663303 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 24 0.327 0.0117 0.0926 0.0982 0.129 0.301 0.364 0.000753 0.000909 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.531 0.00972 0.336 0.0919 0.118 0.687 0.693 0.00172 0.00173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 495 19030.882 0.005 0.0117 0.121 0.355 0.0981 0.129 0.338 0.416 0.000846 0.00104 ! Validation 495 19030.882 0.005 0.0106 0.415 0.627 0.0942 0.123 0.728 0.77 0.00182 0.00192 Wall time: 19030.882511147764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 24 0.54 0.0135 0.271 0.105 0.139 0.588 0.622 0.00147 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.224 0.0109 0.00621 0.0974 0.125 0.0709 0.0942 0.000177 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 496 19068.873 0.005 0.0149 0.517 0.815 0.111 0.146 0.706 0.864 0.00177 0.00216 ! Validation 496 19068.873 0.005 0.0117 0.053 0.286 0.0989 0.129 0.225 0.275 0.000563 0.000688 Wall time: 19068.87349237781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 24 0.303 0.0126 0.0506 0.101 0.134 0.217 0.269 0.000542 0.000672 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.35 0.00996 0.15 0.0928 0.119 0.452 0.463 0.00113 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 497 19106.880 0.005 0.0124 0.125 0.372 0.101 0.133 0.362 0.425 0.000906 0.00106 ! Validation 497 19106.880 0.005 0.0108 0.168 0.383 0.0947 0.124 0.428 0.49 0.00107 0.00122 Wall time: 19106.880248491652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 24 0.287 0.013 0.0276 0.102 0.136 0.158 0.199 0.000395 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.209 0.0095 0.0192 0.0909 0.116 0.143 0.166 0.000356 0.000414 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 498 19144.922 0.005 0.0119 0.0735 0.311 0.0985 0.13 0.27 0.326 0.000675 0.000816 ! Validation 498 19144.922 0.005 0.0103 0.0578 0.264 0.0928 0.121 0.233 0.287 0.000583 0.000718 Wall time: 19144.922393105924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 24 0.236 0.0108 0.0198 0.0945 0.124 0.133 0.168 0.000331 0.000421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.262 0.00935 0.075 0.0901 0.116 0.315 0.327 0.000787 0.000818 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 499 19182.969 0.005 0.0116 0.0928 0.325 0.0977 0.129 0.3 0.367 0.000751 0.000919 ! Validation 499 19182.969 0.005 0.0101 0.12 0.322 0.092 0.12 0.344 0.414 0.00086 0.00103 Wall time: 19182.969868290704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 24 0.259 0.0112 0.0344 0.0964 0.127 0.183 0.222 0.000458 0.000554 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.219 0.00932 0.0322 0.0901 0.115 0.198 0.214 0.000495 0.000536 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 500 19221.010 0.005 0.0116 0.0541 0.285 0.0974 0.129 0.216 0.279 0.000539 0.000698 ! Validation 500 19221.010 0.005 0.01 0.0648 0.266 0.0916 0.12 0.247 0.304 0.000619 0.000761 Wall time: 19221.0101998318 ! Stop training: max epochs Wall time: 19221.03568739863 Cumulative wall time: 19221.03568739863 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 = 0.100449 f_rmse = 0.132221 e_mae = 0.377318 e_rmse = 0.456406 e/N_mae = 0.000943 e/N_rmse = 0.001141 f_mae = 0.100449 f_rmse = 0.132221 e_mae = 0.377318 e_rmse = 0.456406 e/N_mae = 0.000943 e/N_rmse = 0.001141 Train end time: 2024-12-07_13:21:39 Training duration: 5h 24m 12s