Train start time: 2024-12-09_10:14:15 Torch device: cuda Processing dataset... Loaded data: Batch(atomic_numbers=[2112000, 1], batch=[2112000], cell=[6000, 3, 3], edge_cell_shift=[75876508, 3], edge_index=[2, 75876508], forces=[2112000, 3], pbc=[6000, 3], pos=[2112000, 3], ptr=[6001], total_energy=[6000, 1]) processed data size: ~3023.89 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 -348.07753442091644 Atomic outputs are scaled by: [H, C, N, O, Zn: None], shifted by [H, C, N, O, Zn: -348.077534]. Replace string dataset_forces_rms to 1.1729938929799306 Initially outputs are globally scaled by: 1.1729938929799306, 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 19.5 0.957 0.367 0.863 1.15 0.563 0.711 0.0016 0.00202 Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Initial Validation 0 6.197 0.005 1 11 31 0.881 1.17 2.96 3.89 0.0084 0.011 Wall time: 6.197441508062184 ! Best model 0 31.029 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 86 22 0.993 2.16 0.871 1.17 1.43 1.73 0.00406 0.0049 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 19.7 0.953 0.653 0.861 1.14 0.81 0.948 0.0023 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 1 121.317 0.005 0.998 2.76 22.7 0.874 1.17 1.41 1.95 0.004 0.00554 ! Validation 1 121.317 0.005 0.998 3.81 23.8 0.879 1.17 1.82 2.29 0.00516 0.0065 Wall time: 121.31774172279984 ! Best model 1 23.772 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 86 19.7 0.919 1.34 0.837 1.12 1.08 1.36 0.00308 0.00386 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 19.4 0.881 1.76 0.826 1.1 1.46 1.56 0.00415 0.00442 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 2 235.771 0.005 0.986 1.52 21.2 0.868 1.16 1.11 1.45 0.00316 0.00411 ! Validation 2 235.771 0.005 0.919 3.84 22.2 0.841 1.12 1.76 2.3 0.00501 0.00653 Wall time: 235.77185900090262 ! Best model 2 22.221 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 86 8.27 0.353 1.21 0.523 0.697 1.1 1.29 0.00311 0.00366 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 8.05 0.351 1.03 0.52 0.695 1.09 1.19 0.00309 0.00338 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 351.959 0.005 0.54 1.62 12.4 0.641 0.862 1.18 1.49 0.00336 0.00424 ! Validation 3 351.959 0.005 0.366 1.13 8.44 0.534 0.709 1.02 1.24 0.0029 0.00353 Wall time: 351.96142610674724 ! Best model 3 8.438 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 86 6.9 0.245 1.99 0.431 0.581 1.47 1.66 0.00417 0.00471 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 5.01 0.24 0.205 0.424 0.575 0.391 0.532 0.00111 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 4 466.668 0.005 0.284 1.22 6.9 0.465 0.625 1.03 1.3 0.00294 0.00368 ! Validation 4 466.668 0.005 0.251 0.977 6 0.439 0.588 0.912 1.16 0.00259 0.00329 Wall time: 466.6685953559354 ! Best model 4 5.998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 86 4.77 0.18 1.18 0.37 0.498 1.12 1.27 0.00319 0.00362 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 6.5 0.186 2.78 0.375 0.506 1.93 1.96 0.00548 0.00556 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 581.148 0.005 0.208 0.868 5.02 0.398 0.535 0.877 1.09 0.00249 0.0031 ! Validation 5 581.148 0.005 0.194 1.67 5.54 0.386 0.516 1.29 1.51 0.00368 0.0043 Wall time: 581.1489791930653 ! Best model 5 5.540 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 86 3.82 0.158 0.666 0.345 0.466 0.724 0.957 0.00206 0.00272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 6.79 0.159 3.61 0.347 0.468 2.21 2.23 0.00626 0.00633 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 6 695.739 0.005 0.167 1 4.34 0.356 0.479 0.949 1.17 0.0027 0.00334 ! Validation 6 695.739 0.005 0.165 2.2 5.49 0.354 0.476 1.54 1.74 0.00437 0.00494 Wall time: 695.7396738189273 ! Best model 6 5.491 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 86 3.2 0.135 0.497 0.32 0.431 0.705 0.827 0.002 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 2.86 0.137 0.115 0.323 0.435 0.314 0.398 0.000892 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 7 810.251 0.005 0.143 0.765 3.63 0.33 0.444 0.825 1.03 0.00234 0.00291 ! Validation 7 810.251 0.005 0.142 0.582 3.43 0.329 0.443 0.701 0.895 0.00199 0.00254 Wall time: 810.2519530029967 ! Best model 7 3.430 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 86 2.73 0.115 0.43 0.296 0.397 0.648 0.769 0.00184 0.00218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 5.57 0.119 3.2 0.302 0.404 2.08 2.1 0.00592 0.00596 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 8 925.004 0.005 0.125 0.472 2.97 0.308 0.414 0.634 0.806 0.0018 0.00229 ! Validation 8 925.004 0.005 0.125 1.65 4.14 0.308 0.414 1.36 1.51 0.00385 0.00428 Wall time: 925.0045287827961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 86 4.59 0.106 2.46 0.284 0.382 1.71 1.84 0.00486 0.00523 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 2.65 0.109 0.465 0.29 0.387 0.772 0.8 0.00219 0.00227 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 9 1039.485 0.005 0.112 0.736 2.97 0.292 0.392 0.805 1.01 0.00229 0.00286 ! Validation 9 1039.485 0.005 0.115 0.298 2.6 0.296 0.398 0.512 0.641 0.00145 0.00182 Wall time: 1039.4854728477076 ! Best model 9 2.603 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 86 2.28 0.101 0.267 0.278 0.373 0.491 0.606 0.0014 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 2.73 0.1 0.732 0.278 0.371 0.988 1 0.00281 0.00285 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 10 1154.714 0.005 0.105 0.721 2.82 0.283 0.38 0.787 0.996 0.00224 0.00283 ! Validation 10 1154.714 0.005 0.107 0.35 2.49 0.286 0.384 0.572 0.694 0.00163 0.00197 Wall time: 1154.7145884810016 ! Best model 10 2.489 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 86 2.3 0.0902 0.498 0.264 0.352 0.662 0.828 0.00188 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 2.63 0.0915 0.804 0.267 0.355 1.04 1.05 0.00296 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 11 1269.240 0.005 0.0952 0.466 2.37 0.271 0.362 0.635 0.801 0.0018 0.00227 ! Validation 11 1269.240 0.005 0.0985 0.331 2.3 0.275 0.368 0.557 0.675 0.00158 0.00192 Wall time: 1269.240075273905 ! Best model 11 2.302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 86 1.97 0.0867 0.239 0.258 0.345 0.457 0.573 0.0013 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 4.06 0.085 2.36 0.258 0.342 1.8 1.8 0.00511 0.00511 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 12 1383.813 0.005 0.0885 0.466 2.24 0.261 0.349 0.642 0.801 0.00182 0.00227 ! Validation 12 1383.813 0.005 0.0919 1.18 3.02 0.266 0.356 1.14 1.27 0.00324 0.00362 Wall time: 1383.8144327979535 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 86 1.87 0.0787 0.295 0.247 0.329 0.498 0.637 0.00141 0.00181 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 2.23 0.0803 0.622 0.25 0.332 0.922 0.925 0.00262 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 13 1498.311 0.005 0.0832 0.516 2.18 0.253 0.338 0.674 0.843 0.00192 0.00239 ! Validation 13 1498.311 0.005 0.0873 0.303 2.05 0.259 0.347 0.529 0.646 0.0015 0.00183 Wall time: 1498.311486416962 ! Best model 13 2.049 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 86 1.73 0.0757 0.211 0.243 0.323 0.421 0.539 0.0012 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 1.84 0.0776 0.286 0.247 0.327 0.622 0.628 0.00177 0.00178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 14 1613.383 0.005 0.0798 0.594 2.19 0.248 0.331 0.721 0.904 0.00205 0.00257 ! Validation 14 1613.383 0.005 0.0842 0.203 1.89 0.255 0.34 0.421 0.528 0.0012 0.0015 Wall time: 1613.3834931338206 ! Best model 14 1.887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 86 1.74 0.076 0.224 0.242 0.323 0.441 0.555 0.00125 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 2.12 0.0716 0.689 0.237 0.314 0.972 0.974 0.00276 0.00277 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 15 1727.865 0.005 0.0753 0.332 1.84 0.241 0.322 0.537 0.676 0.00152 0.00192 ! Validation 15 1727.865 0.005 0.0793 0.288 1.87 0.247 0.33 0.517 0.629 0.00147 0.00179 Wall time: 1727.8655843087472 ! Best model 15 1.873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 86 1.62 0.0696 0.231 0.232 0.31 0.444 0.564 0.00126 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 1.97 0.0698 0.574 0.234 0.31 0.887 0.889 0.00252 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 16 1842.347 0.005 0.0732 0.616 2.08 0.238 0.317 0.732 0.92 0.00208 0.00262 ! Validation 16 1842.347 0.005 0.0772 0.234 1.78 0.244 0.326 0.456 0.567 0.0013 0.00161 Wall time: 1842.3474059677683 ! Best model 16 1.777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 86 1.64 0.0695 0.255 0.232 0.309 0.485 0.592 0.00138 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 1.82 0.0677 0.464 0.231 0.305 0.797 0.799 0.00226 0.00227 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 17 1959.577 0.005 0.0697 0.485 1.88 0.233 0.31 0.663 0.817 0.00188 0.00232 ! Validation 17 1959.577 0.005 0.0746 0.267 1.76 0.24 0.32 0.49 0.606 0.00139 0.00172 Wall time: 1959.5772237670608 ! Best model 17 1.759 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 86 1.73 0.0652 0.425 0.226 0.299 0.671 0.764 0.00191 0.00217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 3.04 0.0646 1.75 0.226 0.298 1.55 1.55 0.0044 0.00441 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 2074.559 0.005 0.0678 0.44 1.8 0.229 0.305 0.615 0.778 0.00175 0.00221 ! Validation 18 2074.559 0.005 0.0718 0.72 2.16 0.236 0.314 0.885 0.995 0.00251 0.00283 Wall time: 2074.559196134098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 86 1.5 0.0617 0.265 0.219 0.291 0.49 0.604 0.00139 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 1.47 0.0632 0.204 0.224 0.295 0.526 0.529 0.00149 0.0015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 19 2189.087 0.005 0.0654 0.484 1.79 0.225 0.3 0.661 0.816 0.00188 0.00232 ! Validation 19 2189.087 0.005 0.0699 0.178 1.58 0.233 0.31 0.388 0.495 0.0011 0.00141 Wall time: 2189.087205308955 ! Best model 19 1.576 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 86 1.43 0.0624 0.18 0.22 0.293 0.385 0.498 0.00109 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 1.93 0.0629 0.675 0.223 0.294 0.962 0.964 0.00273 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 20 2303.537 0.005 0.0635 0.53 1.8 0.222 0.296 0.681 0.854 0.00193 0.00243 ! Validation 20 2303.537 0.005 0.0692 0.266 1.65 0.231 0.309 0.493 0.605 0.0014 0.00172 Wall time: 2303.5377535847947 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 86 1.71 0.0604 0.5 0.216 0.288 0.731 0.829 0.00208 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 1.4 0.0596 0.214 0.217 0.286 0.539 0.542 0.00153 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 21 2417.966 0.005 0.0619 0.429 1.67 0.219 0.292 0.614 0.768 0.00174 0.00218 ! Validation 21 2417.966 0.005 0.0667 0.271 1.61 0.228 0.303 0.475 0.611 0.00135 0.00174 Wall time: 2417.966158967931 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 86 2 0.0588 0.819 0.215 0.285 0.958 1.06 0.00272 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 1.15 0.0572 0.00406 0.213 0.281 0.0675 0.0747 0.000192 0.000212 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 22 2532.401 0.005 0.0596 0.298 1.49 0.215 0.286 0.513 0.64 0.00146 0.00182 ! Validation 22 2532.401 0.005 0.064 0.398 1.68 0.223 0.297 0.612 0.74 0.00174 0.0021 Wall time: 2532.401194536127 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 86 2.6 0.06 1.4 0.215 0.287 1.32 1.39 0.00376 0.00394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 2.04 0.0576 0.888 0.214 0.282 1.1 1.11 0.00314 0.00314 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 23 2646.838 0.005 0.0585 0.553 1.72 0.213 0.284 0.716 0.871 0.00203 0.00248 ! Validation 23 2646.838 0.005 0.0635 0.441 1.71 0.222 0.296 0.671 0.779 0.00191 0.00221 Wall time: 2646.8387903631665 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 86 1.26 0.0561 0.135 0.21 0.278 0.368 0.431 0.00105 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 1.42 0.0559 0.305 0.211 0.277 0.645 0.647 0.00183 0.00184 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 24 2762.913 0.005 0.0576 0.429 1.58 0.212 0.282 0.617 0.769 0.00175 0.00218 ! Validation 24 2762.913 0.005 0.0618 0.168 1.4 0.219 0.292 0.381 0.481 0.00108 0.00137 Wall time: 2762.913514182903 ! Best model 24 1.403 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 86 1.39 0.0538 0.311 0.204 0.272 0.555 0.655 0.00158 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 1.65 0.0541 0.571 0.208 0.273 0.884 0.886 0.00251 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 25 2877.560 0.005 0.0556 0.382 1.49 0.208 0.277 0.587 0.725 0.00167 0.00206 ! Validation 25 2877.560 0.005 0.0602 0.259 1.46 0.216 0.288 0.492 0.597 0.0014 0.0017 Wall time: 2877.5607912777923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 86 1.33 0.0533 0.266 0.204 0.271 0.503 0.604 0.00143 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 2.77 0.0524 1.72 0.205 0.268 1.54 1.54 0.00437 0.00437 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 26 2992.339 0.005 0.054 0.337 1.42 0.205 0.273 0.549 0.681 0.00156 0.00194 ! Validation 26 2992.339 0.005 0.0587 0.787 1.96 0.213 0.284 0.945 1.04 0.00269 0.00296 Wall time: 2992.340005619917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 86 1.32 0.053 0.263 0.202 0.27 0.514 0.602 0.00146 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 1.22 0.0508 0.202 0.202 0.264 0.524 0.528 0.00149 0.0015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 27 3106.792 0.005 0.0525 0.3 1.35 0.202 0.269 0.517 0.642 0.00147 0.00182 ! Validation 27 3106.792 0.005 0.0571 0.155 1.3 0.21 0.28 0.362 0.461 0.00103 0.00131 Wall time: 3106.79285723716 ! Best model 27 1.297 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 86 1.79 0.05 0.79 0.196 0.262 0.959 1.04 0.00272 0.00296 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 2.03 0.0499 1.04 0.2 0.262 1.19 1.19 0.00339 0.00339 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 28 3221.245 0.005 0.0514 0.372 1.4 0.2 0.266 0.587 0.716 0.00167 0.00203 ! Validation 28 3221.245 0.005 0.0559 0.464 1.58 0.208 0.277 0.681 0.799 0.00193 0.00227 Wall time: 3221.245424665045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 86 1.9 0.051 0.882 0.199 0.265 1.05 1.1 0.00297 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 2.63 0.0494 1.64 0.199 0.261 1.5 1.5 0.00426 0.00426 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 3336.013 0.005 0.0504 0.375 1.38 0.198 0.263 0.569 0.718 0.00162 0.00204 ! Validation 29 3336.013 0.005 0.0551 0.94 2.04 0.206 0.275 1.05 1.14 0.00299 0.00323 Wall time: 3336.0137137328275 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 86 1.23 0.0491 0.244 0.195 0.26 0.476 0.579 0.00135 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 0.966 0.0479 0.00814 0.196 0.257 0.089 0.106 0.000253 0.000301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 30 3450.470 0.005 0.05 0.408 1.41 0.197 0.262 0.594 0.749 0.00169 0.00213 ! Validation 30 3450.470 0.005 0.0538 0.334 1.41 0.204 0.272 0.543 0.677 0.00154 0.00192 Wall time: 3450.4701898740605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 86 1.78 0.0481 0.82 0.192 0.257 0.996 1.06 0.00283 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 1.12 0.0483 0.152 0.196 0.258 0.455 0.458 0.00129 0.0013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 31 3564.922 0.005 0.0485 0.434 1.4 0.194 0.258 0.623 0.772 0.00177 0.00219 ! Validation 31 3564.922 0.005 0.0535 0.145 1.21 0.203 0.271 0.352 0.447 0.001 0.00127 Wall time: 3564.9227597159334 ! Best model 31 1.215 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 86 1.08 0.0487 0.105 0.194 0.259 0.283 0.379 0.000804 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 1.28 0.0463 0.353 0.193 0.252 0.695 0.697 0.00197 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 32 3679.486 0.005 0.0483 0.365 1.33 0.193 0.258 0.563 0.709 0.0016 0.00201 ! Validation 32 3679.486 0.005 0.052 0.179 1.22 0.201 0.267 0.399 0.497 0.00113 0.00141 Wall time: 3679.4869756400585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 86 1.19 0.0456 0.278 0.188 0.251 0.533 0.618 0.00151 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 1.8 0.0448 0.903 0.19 0.248 1.11 1.11 0.00316 0.00317 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 33 3793.952 0.005 0.0468 0.306 1.24 0.191 0.254 0.506 0.649 0.00144 0.00184 ! Validation 33 3793.952 0.005 0.0508 0.448 1.46 0.199 0.264 0.687 0.785 0.00195 0.00223 Wall time: 3793.953048137948 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 86 1.11 0.0446 0.218 0.187 0.248 0.463 0.548 0.00131 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 1.65 0.0439 0.769 0.188 0.246 1.03 1.03 0.00292 0.00292 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 3908.416 0.005 0.0456 0.292 1.2 0.188 0.251 0.502 0.634 0.00143 0.0018 ! Validation 34 3908.416 0.005 0.0498 0.402 1.4 0.197 0.262 0.644 0.744 0.00183 0.00211 Wall time: 3908.41634042887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 86 1.93 0.0433 1.06 0.184 0.244 1.15 1.21 0.00328 0.00343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 1.73 0.0435 0.859 0.187 0.245 1.09 1.09 0.00308 0.00309 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 4024.526 0.005 0.0445 0.347 1.24 0.186 0.247 0.558 0.69 0.00159 0.00196 ! Validation 35 4024.526 0.005 0.049 0.47 1.45 0.195 0.26 0.701 0.804 0.00199 0.00228 Wall time: 4024.5265832580626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 86 0.972 0.0426 0.121 0.182 0.242 0.34 0.407 0.000966 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 1.42 0.0427 0.562 0.186 0.242 0.877 0.88 0.00249 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 36 4139.010 0.005 0.0446 0.41 1.3 0.186 0.248 0.601 0.751 0.00171 0.00213 ! Validation 36 4139.010 0.005 0.0481 0.281 1.24 0.194 0.257 0.521 0.621 0.00148 0.00177 Wall time: 4139.010640703142 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 86 1.06 0.0428 0.204 0.181 0.243 0.433 0.529 0.00123 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 2.66 0.0426 1.8 0.185 0.242 1.57 1.58 0.00447 0.00448 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 37 4255.008 0.005 0.043 0.335 1.2 0.183 0.243 0.557 0.679 0.00158 0.00193 ! Validation 37 4255.008 0.005 0.0478 1.1 2.05 0.192 0.256 1.15 1.23 0.00327 0.00349 Wall time: 4255.00827301573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 86 1.05 0.0414 0.225 0.18 0.239 0.471 0.556 0.00134 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 1.1 0.0406 0.284 0.182 0.236 0.621 0.625 0.00176 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 38 4369.777 0.005 0.0425 0.34 1.19 0.182 0.242 0.558 0.684 0.00158 0.00194 ! Validation 38 4369.777 0.005 0.0464 0.165 1.09 0.19 0.253 0.377 0.476 0.00107 0.00135 Wall time: 4369.777173079085 ! Best model 38 1.092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 86 1 0.0411 0.177 0.179 0.238 0.391 0.494 0.00111 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 0.892 0.0404 0.0842 0.181 0.236 0.334 0.34 0.000949 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 39 4484.240 0.005 0.0416 0.347 1.18 0.18 0.239 0.567 0.692 0.00161 0.00196 ! Validation 39 4484.240 0.005 0.0457 0.223 1.14 0.189 0.251 0.425 0.554 0.00121 0.00157 Wall time: 4484.240956788883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 86 1.1 0.0396 0.303 0.175 0.233 0.564 0.646 0.0016 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 1.77 0.0394 0.985 0.178 0.233 1.16 1.16 0.0033 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 40 4598.697 0.005 0.0407 0.299 1.11 0.178 0.237 0.515 0.641 0.00146 0.00182 ! Validation 40 4598.697 0.005 0.0447 0.492 1.39 0.187 0.248 0.714 0.823 0.00203 0.00234 Wall time: 4598.697280774824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 86 1.09 0.0403 0.282 0.177 0.236 0.493 0.622 0.0014 0.00177 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 1.13 0.0387 0.362 0.177 0.231 0.703 0.705 0.002 0.002 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 41 4713.129 0.005 0.0399 0.356 1.15 0.176 0.234 0.569 0.7 0.00162 0.00199 ! Validation 41 4713.129 0.005 0.0441 0.238 1.12 0.185 0.246 0.462 0.572 0.00131 0.00162 Wall time: 4713.129451646935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 86 1.36 0.0389 0.577 0.174 0.231 0.804 0.891 0.00229 0.00253 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 0.775 0.0377 0.0203 0.174 0.228 0.151 0.167 0.000429 0.000475 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 4827.570 0.005 0.039 0.296 1.08 0.174 0.232 0.515 0.638 0.00146 0.00181 ! Validation 42 4827.570 0.005 0.043 0.48 1.34 0.183 0.243 0.678 0.812 0.00193 0.00231 Wall time: 4827.570513345767 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 86 0.869 0.037 0.129 0.171 0.226 0.327 0.422 0.000929 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 1.16 0.0369 0.42 0.173 0.225 0.757 0.76 0.00215 0.00216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 43 4942.034 0.005 0.0384 0.305 1.07 0.173 0.23 0.521 0.648 0.00148 0.00184 ! Validation 43 4942.034 0.005 0.042 0.221 1.06 0.181 0.24 0.452 0.551 0.00128 0.00157 Wall time: 4942.034445376135 ! Best model 43 1.060 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 86 1.02 0.0367 0.287 0.169 0.225 0.535 0.628 0.00152 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 1.1 0.0366 0.364 0.172 0.224 0.704 0.707 0.002 0.00201 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 44 5056.483 0.005 0.0385 0.426 1.2 0.173 0.23 0.611 0.765 0.00174 0.00217 ! Validation 44 5056.483 0.005 0.0416 0.242 1.07 0.18 0.239 0.47 0.577 0.00134 0.00164 Wall time: 5056.483274004888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 86 0.971 0.0366 0.238 0.169 0.224 0.488 0.573 0.00139 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 1.62 0.0356 0.905 0.17 0.221 1.11 1.12 0.00316 0.00317 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 45 5171.022 0.005 0.0373 0.34 1.09 0.171 0.227 0.556 0.684 0.00158 0.00194 ! Validation 45 5171.022 0.005 0.0409 0.532 1.35 0.179 0.237 0.764 0.855 0.00217 0.00243 Wall time: 5171.022975393105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 86 1.23 0.0362 0.507 0.168 0.223 0.699 0.835 0.00199 0.00237 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 2.8 0.0354 2.09 0.17 0.221 1.7 1.7 0.00482 0.00482 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 46 5285.476 0.005 0.0361 0.285 1.01 0.168 0.223 0.509 0.626 0.00144 0.00178 ! Validation 46 5285.476 0.005 0.04 1.18 1.98 0.177 0.235 1.17 1.28 0.00334 0.00363 Wall time: 5285.477010047063 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 86 1.95 0.0359 1.24 0.167 0.222 1.25 1.3 0.00355 0.0037 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 0.891 0.0347 0.197 0.167 0.219 0.516 0.521 0.00147 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 47 5399.927 0.005 0.0354 0.299 1.01 0.166 0.221 0.51 0.64 0.00145 0.00182 ! Validation 47 5399.927 0.005 0.0397 0.628 1.42 0.176 0.234 0.844 0.93 0.0024 0.00264 Wall time: 5399.92802793812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 86 0.812 0.0343 0.126 0.164 0.217 0.346 0.417 0.000984 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.14 0.0344 1.45 0.167 0.218 1.41 1.41 0.004 0.00401 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 48 5514.367 0.005 0.0355 0.431 1.14 0.166 0.221 0.642 0.77 0.00182 0.00219 ! Validation 48 5514.367 0.005 0.0391 1.05 1.83 0.175 0.232 1.12 1.2 0.00319 0.00341 Wall time: 5514.367636820767 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 86 0.921 0.0334 0.254 0.162 0.214 0.505 0.591 0.00143 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 0.726 0.0331 0.0636 0.164 0.213 0.284 0.296 0.000807 0.000841 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 5628.788 0.005 0.0346 0.325 1.02 0.164 0.218 0.54 0.668 0.00153 0.0019 ! Validation 49 5628.788 0.005 0.0379 0.124 0.883 0.173 0.228 0.325 0.414 0.000923 0.00117 Wall time: 5628.788532138802 ! Best model 49 0.883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 86 0.819 0.0343 0.134 0.162 0.217 0.293 0.43 0.000833 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 0.665 0.0318 0.0281 0.161 0.209 0.176 0.197 0.000501 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 50 5743.242 0.005 0.0333 0.199 0.864 0.162 0.214 0.414 0.523 0.00118 0.00149 ! Validation 50 5743.242 0.005 0.0369 0.262 1 0.17 0.225 0.463 0.6 0.00131 0.0017 Wall time: 5743.24280113494 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 86 1.28 0.0344 0.588 0.163 0.218 0.833 0.899 0.00237 0.00256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 2.1 0.0334 1.43 0.165 0.214 1.4 1.4 0.00398 0.00398 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 51 5857.789 0.005 0.0325 0.283 0.933 0.16 0.211 0.497 0.623 0.00141 0.00177 ! Validation 51 5857.789 0.005 0.0378 0.91 1.67 0.171 0.228 1.04 1.12 0.00296 0.00318 Wall time: 5857.789742702153 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 86 1.06 0.0313 0.437 0.157 0.208 0.706 0.775 0.00201 0.0022 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 0.623 0.0308 0.00806 0.158 0.206 0.069 0.105 0.000196 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 52 5972.254 0.005 0.0324 0.357 1.01 0.159 0.211 0.575 0.701 0.00163 0.00199 ! Validation 52 5972.254 0.005 0.0358 0.199 0.915 0.168 0.222 0.414 0.523 0.00118 0.00149 Wall time: 5972.254543191753 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 86 1.56 0.0316 0.933 0.156 0.208 1.08 1.13 0.00306 0.00322 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 0.927 0.0299 0.329 0.157 0.203 0.666 0.673 0.00189 0.00191 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 53 6086.697 0.005 0.0312 0.189 0.813 0.157 0.207 0.401 0.509 0.00114 0.00145 ! Validation 53 6086.697 0.005 0.0348 1.07 1.77 0.165 0.219 1.12 1.22 0.00318 0.00345 Wall time: 6086.69752338808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 86 1.25 0.0306 0.639 0.155 0.205 0.875 0.937 0.00248 0.00266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 1.41 0.0299 0.81 0.156 0.203 1.05 1.06 0.00299 0.003 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 6201.145 0.005 0.0309 0.351 0.968 0.156 0.206 0.575 0.695 0.00163 0.00197 ! Validation 54 6201.145 0.005 0.0342 0.635 1.32 0.164 0.217 0.846 0.935 0.0024 0.00266 Wall time: 6201.145313520916 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 86 0.728 0.0304 0.119 0.154 0.205 0.326 0.404 0.000926 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 1.6 0.0291 1.02 0.155 0.2 1.18 1.18 0.00335 0.00336 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 6315.600 0.005 0.0304 0.293 0.902 0.155 0.205 0.503 0.635 0.00143 0.00181 ! Validation 55 6315.600 0.005 0.0337 0.951 1.63 0.162 0.215 1.06 1.14 0.00302 0.00325 Wall time: 6315.600346004125 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 86 0.741 0.0298 0.145 0.154 0.203 0.378 0.446 0.00107 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 0.617 0.0288 0.0418 0.154 0.199 0.213 0.24 0.000605 0.000681 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 56 6430.054 0.005 0.0296 0.284 0.876 0.153 0.202 0.493 0.625 0.0014 0.00177 ! Validation 56 6430.054 0.005 0.0332 0.254 0.918 0.162 0.214 0.455 0.592 0.00129 0.00168 Wall time: 6430.054305222817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 86 0.942 0.0291 0.36 0.152 0.2 0.643 0.704 0.00183 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 0.569 0.0273 0.0223 0.15 0.194 0.156 0.175 0.000442 0.000498 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 6544.616 0.005 0.0289 0.242 0.82 0.151 0.199 0.477 0.577 0.00135 0.00164 ! Validation 57 6544.616 0.005 0.0319 0.285 0.924 0.159 0.21 0.519 0.626 0.00148 0.00178 Wall time: 6544.6162818460725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 86 0.618 0.0275 0.0694 0.147 0.194 0.257 0.309 0.000731 0.000878 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 0.585 0.0274 0.0369 0.15 0.194 0.2 0.225 0.000569 0.00064 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 58 6659.063 0.005 0.0284 0.289 0.856 0.149 0.198 0.507 0.631 0.00144 0.00179 ! Validation 58 6659.063 0.005 0.0318 0.164 0.8 0.158 0.209 0.375 0.476 0.00107 0.00135 Wall time: 6659.0637996047735 ! Best model 58 0.800 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 86 0.673 0.0288 0.0964 0.151 0.199 0.307 0.364 0.000873 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 0.968 0.028 0.408 0.151 0.196 0.743 0.749 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 59 6773.513 0.005 0.0279 0.366 0.925 0.148 0.196 0.589 0.71 0.00167 0.00202 ! Validation 59 6773.513 0.005 0.0318 0.464 1.1 0.158 0.209 0.684 0.799 0.00194 0.00227 Wall time: 6773.513135524001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 86 0.733 0.0279 0.175 0.149 0.196 0.416 0.491 0.00118 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 0.982 0.0265 0.452 0.148 0.191 0.782 0.789 0.00222 0.00224 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 60 6887.963 0.005 0.0276 0.316 0.868 0.147 0.195 0.548 0.659 0.00156 0.00187 ! Validation 60 6887.963 0.005 0.0307 0.268 0.882 0.156 0.206 0.507 0.607 0.00144 0.00172 Wall time: 6887.963975216728 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 86 0.822 0.0272 0.278 0.146 0.194 0.538 0.618 0.00153 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 0.795 0.0257 0.28 0.145 0.188 0.612 0.62 0.00174 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 61 7002.419 0.005 0.0271 0.307 0.848 0.146 0.193 0.532 0.649 0.00151 0.00185 ! Validation 61 7002.419 0.005 0.03 0.201 0.802 0.154 0.203 0.426 0.526 0.00121 0.0015 Wall time: 7002.419252771884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 86 0.613 0.0264 0.0857 0.144 0.191 0.27 0.343 0.000767 0.000976 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 0.885 0.0252 0.381 0.144 0.186 0.716 0.724 0.00203 0.00206 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 62 7116.870 0.005 0.0263 0.215 0.741 0.144 0.19 0.442 0.545 0.00126 0.00155 ! Validation 62 7116.870 0.005 0.0294 0.308 0.896 0.152 0.201 0.546 0.651 0.00155 0.00185 Wall time: 7116.870621772949 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 86 0.596 0.0262 0.0732 0.143 0.19 0.249 0.317 0.000708 0.000902 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 0.752 0.0252 0.248 0.144 0.186 0.577 0.584 0.00164 0.00166 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 63 7231.312 0.005 0.026 0.285 0.804 0.143 0.189 0.51 0.626 0.00145 0.00178 ! Validation 63 7231.312 0.005 0.0292 0.224 0.807 0.151 0.2 0.445 0.555 0.00126 0.00158 Wall time: 7231.312845119741 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 86 0.604 0.0241 0.122 0.138 0.182 0.329 0.409 0.000935 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 0.793 0.0241 0.311 0.141 0.182 0.644 0.654 0.00183 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 64 7345.849 0.005 0.0252 0.193 0.698 0.141 0.186 0.421 0.516 0.00119 0.00147 ! Validation 64 7345.849 0.005 0.0282 0.183 0.746 0.149 0.197 0.408 0.502 0.00116 0.00142 Wall time: 7345.849995655939 ! Best model 64 0.746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 86 0.551 0.0233 0.0846 0.136 0.179 0.283 0.341 0.000804 0.000969 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.14 0.0235 0.666 0.139 0.18 0.951 0.957 0.0027 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 65 7460.324 0.005 0.0247 0.177 0.671 0.139 0.184 0.394 0.493 0.00112 0.0014 ! Validation 65 7460.324 0.005 0.0275 0.453 1 0.147 0.195 0.708 0.79 0.00201 0.00224 Wall time: 7460.3245938248 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 86 0.612 0.0247 0.118 0.139 0.184 0.313 0.403 0.000888 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 0.502 0.0236 0.0297 0.14 0.18 0.17 0.202 0.000484 0.000575 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 7574.771 0.005 0.0248 0.314 0.81 0.14 0.185 0.523 0.658 0.00149 0.00187 ! Validation 66 7574.771 0.005 0.0276 0.155 0.707 0.148 0.195 0.361 0.462 0.00103 0.00131 Wall time: 7574.7713581817225 ! Best model 66 0.707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 86 0.802 0.0243 0.315 0.139 0.183 0.591 0.658 0.00168 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 0.47 0.023 0.00946 0.138 0.178 0.0964 0.114 0.000274 0.000324 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 7689.207 0.005 0.0242 0.25 0.734 0.138 0.182 0.483 0.587 0.00137 0.00167 ! Validation 67 7689.207 0.005 0.027 0.242 0.781 0.146 0.193 0.465 0.577 0.00132 0.00164 Wall time: 7689.207968058065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 86 0.556 0.0235 0.0859 0.136 0.18 0.266 0.344 0.000756 0.000977 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 0.476 0.0223 0.0304 0.136 0.175 0.173 0.204 0.000492 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 68 7803.638 0.005 0.0235 0.145 0.615 0.136 0.18 0.353 0.447 0.001 0.00127 ! Validation 68 7803.638 0.005 0.0263 0.109 0.634 0.144 0.19 0.306 0.387 0.00087 0.0011 Wall time: 7803.638761057984 ! Best model 68 0.634 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 86 0.577 0.023 0.117 0.134 0.178 0.322 0.401 0.000916 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 0.942 0.0223 0.496 0.136 0.175 0.819 0.826 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 69 7918.074 0.005 0.0233 0.247 0.713 0.135 0.179 0.472 0.583 0.00134 0.00165 ! Validation 69 7918.074 0.005 0.0262 0.338 0.861 0.143 0.19 0.594 0.682 0.00169 0.00194 Wall time: 7918.074242693838 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 86 0.541 0.0226 0.0897 0.133 0.176 0.275 0.351 0.000782 0.000998 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 0.599 0.0219 0.162 0.134 0.173 0.461 0.472 0.00131 0.00134 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 70 8032.596 0.005 0.023 0.242 0.703 0.135 0.178 0.464 0.578 0.00132 0.00164 ! Validation 70 8032.596 0.005 0.0256 0.144 0.657 0.142 0.188 0.354 0.445 0.001 0.00126 Wall time: 8032.596840946935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 86 0.702 0.0242 0.219 0.138 0.182 0.47 0.549 0.00134 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 0.579 0.0229 0.121 0.137 0.177 0.394 0.408 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 71 8147.038 0.005 0.0225 0.196 0.645 0.133 0.176 0.401 0.519 0.00114 0.00147 ! Validation 71 8147.038 0.005 0.0266 0.198 0.73 0.145 0.191 0.43 0.522 0.00122 0.00148 Wall time: 8147.038175308146 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 86 0.484 0.0215 0.0531 0.131 0.172 0.22 0.27 0.000626 0.000768 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 0.514 0.0213 0.0875 0.133 0.171 0.332 0.347 0.000943 0.000986 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 72 8261.479 0.005 0.0228 0.267 0.723 0.134 0.177 0.479 0.607 0.00136 0.00172 ! Validation 72 8261.479 0.005 0.025 0.108 0.607 0.14 0.185 0.309 0.385 0.000877 0.00109 Wall time: 8261.479704183992 ! Best model 72 0.607 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 86 0.484 0.0212 0.0605 0.129 0.171 0.229 0.289 0.00065 0.00082 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 0.548 0.0211 0.126 0.132 0.17 0.405 0.417 0.00115 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 73 8376.591 0.005 0.0218 0.172 0.608 0.131 0.173 0.387 0.487 0.0011 0.00138 ! Validation 73 8376.591 0.005 0.0245 0.125 0.615 0.139 0.184 0.333 0.415 0.000947 0.00118 Wall time: 8376.591729894746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 86 0.868 0.0217 0.435 0.131 0.173 0.711 0.773 0.00202 0.0022 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 0.835 0.0206 0.424 0.13 0.168 0.757 0.763 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 74 8491.065 0.005 0.0218 0.235 0.67 0.131 0.173 0.456 0.568 0.00129 0.00161 ! Validation 74 8491.065 0.005 0.0242 0.286 0.77 0.138 0.182 0.54 0.627 0.00153 0.00178 Wall time: 8491.065442526713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 86 0.503 0.0209 0.085 0.128 0.17 0.28 0.342 0.000795 0.000971 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 0.516 0.0203 0.109 0.13 0.167 0.372 0.388 0.00106 0.0011 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 75 8605.513 0.005 0.0212 0.165 0.589 0.129 0.171 0.387 0.476 0.0011 0.00135 ! Validation 75 8605.513 0.005 0.0239 0.138 0.616 0.137 0.181 0.348 0.436 0.000988 0.00124 Wall time: 8605.513142574113 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 86 0.505 0.0214 0.0759 0.13 0.172 0.274 0.323 0.000779 0.000918 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 0.592 0.0204 0.185 0.13 0.167 0.493 0.504 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 76 8719.952 0.005 0.0217 0.342 0.775 0.131 0.173 0.567 0.686 0.00161 0.00195 ! Validation 76 8719.952 0.005 0.0239 0.15 0.628 0.137 0.181 0.368 0.454 0.00105 0.00129 Wall time: 8719.952264676802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 86 0.509 0.0206 0.0963 0.128 0.168 0.271 0.364 0.000769 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 0.471 0.0199 0.0731 0.128 0.165 0.302 0.317 0.000859 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 77 8834.503 0.005 0.0207 0.147 0.562 0.128 0.169 0.363 0.449 0.00103 0.00128 ! Validation 77 8834.503 0.005 0.0234 0.0951 0.563 0.135 0.18 0.286 0.362 0.000812 0.00103 Wall time: 8834.503749962896 ! Best model 77 0.563 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 86 0.452 0.0206 0.0391 0.128 0.169 0.176 0.232 0.000501 0.000659 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 0.569 0.0195 0.179 0.127 0.164 0.484 0.497 0.00138 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 78 8949.035 0.005 0.0205 0.198 0.608 0.127 0.168 0.415 0.522 0.00118 0.00148 ! Validation 78 8949.035 0.005 0.0229 0.182 0.641 0.134 0.178 0.405 0.501 0.00115 0.00142 Wall time: 8949.035421770997 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 86 0.699 0.0195 0.308 0.124 0.164 0.591 0.651 0.00168 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 0.578 0.0196 0.186 0.128 0.164 0.497 0.505 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 79 9063.489 0.005 0.0206 0.282 0.693 0.127 0.168 0.509 0.623 0.00145 0.00177 ! Validation 79 9063.489 0.005 0.023 0.143 0.603 0.135 0.178 0.358 0.443 0.00102 0.00126 Wall time: 9063.48928467976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 86 0.854 0.0199 0.456 0.125 0.166 0.735 0.792 0.00209 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 1.73 0.0194 1.34 0.126 0.163 1.35 1.36 0.00385 0.00385 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 80 9177.935 0.005 0.0201 0.202 0.605 0.126 0.166 0.434 0.528 0.00123 0.0015 ! Validation 80 9177.935 0.005 0.0227 1.15 1.61 0.134 0.177 1.21 1.26 0.00343 0.00358 Wall time: 9177.935981338844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 86 0.468 0.02 0.0677 0.126 0.166 0.241 0.305 0.000684 0.000867 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.18 0.0197 0.79 0.128 0.165 1.04 1.04 0.00295 0.00296 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 81 9292.384 0.005 0.0201 0.284 0.685 0.126 0.166 0.512 0.625 0.00146 0.00178 ! Validation 81 9292.384 0.005 0.0231 0.562 1.02 0.135 0.178 0.799 0.88 0.00227 0.0025 Wall time: 9292.384936992079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 86 0.513 0.0198 0.118 0.124 0.165 0.333 0.402 0.000947 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 0.521 0.0186 0.15 0.124 0.16 0.441 0.454 0.00125 0.00129 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 82 9406.818 0.005 0.0199 0.2 0.599 0.125 0.166 0.399 0.525 0.00113 0.00149 ! Validation 82 9406.818 0.005 0.0219 0.134 0.572 0.131 0.174 0.342 0.43 0.000972 0.00122 Wall time: 9406.818854176905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 86 0.786 0.02 0.386 0.126 0.166 0.671 0.729 0.0019 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 0.839 0.0194 0.451 0.126 0.163 0.781 0.788 0.00222 0.00224 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 83 9521.349 0.005 0.0195 0.287 0.677 0.124 0.164 0.518 0.628 0.00147 0.00178 ! Validation 83 9521.349 0.005 0.0225 0.913 1.36 0.133 0.176 1.03 1.12 0.00293 0.00318 Wall time: 9521.349537142087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 86 0.408 0.0186 0.0365 0.121 0.16 0.175 0.224 0.000497 0.000636 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 0.459 0.0183 0.0923 0.123 0.159 0.341 0.356 0.00097 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 84 9635.779 0.005 0.0194 0.181 0.568 0.124 0.163 0.396 0.499 0.00113 0.00142 ! Validation 84 9635.779 0.005 0.0215 0.0941 0.525 0.13 0.172 0.285 0.36 0.00081 0.00102 Wall time: 9635.779297468718 ! Best model 84 0.525 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 86 0.598 0.0199 0.199 0.126 0.166 0.455 0.523 0.00129 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 1.16 0.0189 0.777 0.125 0.161 1.03 1.03 0.00292 0.00294 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 85 9750.216 0.005 0.0192 0.266 0.649 0.123 0.162 0.469 0.605 0.00133 0.00172 ! Validation 85 9750.216 0.005 0.0221 0.468 0.91 0.132 0.174 0.724 0.802 0.00206 0.00228 Wall time: 9750.216642797925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 86 0.742 0.0184 0.374 0.121 0.159 0.656 0.717 0.00186 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 0.379 0.0178 0.0232 0.121 0.157 0.16 0.179 0.000455 0.000508 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 86 9864.661 0.005 0.0187 0.134 0.508 0.122 0.16 0.339 0.428 0.000962 0.00122 ! Validation 86 9864.661 0.005 0.0211 0.16 0.582 0.129 0.17 0.382 0.469 0.00108 0.00133 Wall time: 9864.66174835898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 86 0.43 0.0178 0.073 0.119 0.157 0.252 0.317 0.000716 0.0009 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.36 0.0177 0.00702 0.121 0.156 0.068 0.0983 0.000193 0.000279 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 87 9979.080 0.005 0.0183 0.132 0.497 0.12 0.159 0.339 0.426 0.000962 0.00121 ! Validation 87 9979.080 0.005 0.0208 0.101 0.517 0.128 0.169 0.295 0.373 0.000839 0.00106 Wall time: 9979.080992132891 ! Best model 87 0.517 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 86 0.581 0.0177 0.228 0.118 0.156 0.494 0.56 0.0014 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 0.502 0.0174 0.154 0.12 0.155 0.45 0.46 0.00128 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 88 10093.537 0.005 0.0181 0.151 0.513 0.12 0.158 0.365 0.456 0.00104 0.00129 ! Validation 88 10093.537 0.005 0.0204 0.188 0.597 0.127 0.168 0.412 0.509 0.00117 0.00144 Wall time: 10093.537246466149 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 86 1.12 0.0187 0.745 0.122 0.161 0.984 1.01 0.00279 0.00288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 0.419 0.0172 0.0761 0.119 0.154 0.308 0.324 0.000876 0.00092 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 89 10207.964 0.005 0.0177 0.162 0.516 0.118 0.156 0.378 0.471 0.00107 0.00134 ! Validation 89 10207.964 0.005 0.0203 0.376 0.781 0.126 0.167 0.617 0.719 0.00175 0.00204 Wall time: 10207.964893002994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 86 1.12 0.017 0.785 0.116 0.153 0.999 1.04 0.00284 0.00295 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 0.967 0.0172 0.623 0.119 0.154 0.922 0.926 0.00262 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 90 10322.503 0.005 0.0179 0.239 0.598 0.119 0.157 0.461 0.573 0.00131 0.00163 ! Validation 90 10322.503 0.005 0.0202 0.44 0.844 0.126 0.167 0.667 0.778 0.0019 0.00221 Wall time: 10322.503673119936 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 86 0.409 0.0173 0.0633 0.117 0.154 0.236 0.295 0.000672 0.000838 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 0.413 0.0168 0.0775 0.118 0.152 0.311 0.326 0.000884 0.000927 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 91 10436.965 0.005 0.0178 0.21 0.566 0.119 0.157 0.428 0.537 0.00122 0.00153 ! Validation 91 10436.965 0.005 0.0199 0.0968 0.494 0.125 0.165 0.286 0.365 0.000811 0.00104 Wall time: 10436.965095231775 ! Best model 91 0.494 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 86 0.39 0.0167 0.0559 0.115 0.151 0.228 0.277 0.000649 0.000788 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 0.599 0.0167 0.266 0.118 0.151 0.599 0.605 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 92 10551.423 0.005 0.0173 0.17 0.515 0.117 0.154 0.391 0.484 0.00111 0.00137 ! Validation 92 10551.423 0.005 0.0197 0.263 0.658 0.125 0.165 0.512 0.602 0.00145 0.00171 Wall time: 10551.423875391018 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 86 0.41 0.0164 0.0812 0.114 0.15 0.283 0.334 0.000803 0.000949 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 0.328 0.0161 0.00612 0.116 0.149 0.0718 0.0918 0.000204 0.000261 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 93 10665.868 0.005 0.017 0.135 0.474 0.116 0.153 0.353 0.43 0.001 0.00122 ! Validation 93 10665.868 0.005 0.0192 0.0938 0.477 0.123 0.162 0.286 0.359 0.000814 0.00102 Wall time: 10665.869027679786 ! Best model 93 0.477 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 86 0.4 0.0164 0.0715 0.114 0.15 0.244 0.314 0.000693 0.000891 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 0.845 0.0163 0.519 0.116 0.15 0.84 0.845 0.00239 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 94 10780.329 0.005 0.0169 0.199 0.536 0.116 0.152 0.425 0.523 0.00121 0.00149 ! Validation 94 10780.329 0.005 0.0194 0.397 0.784 0.124 0.163 0.664 0.739 0.00189 0.0021 Wall time: 10780.32978547085 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 86 0.703 0.0167 0.37 0.115 0.151 0.664 0.713 0.00189 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 0.343 0.016 0.0225 0.115 0.148 0.159 0.176 0.000452 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 95 10894.779 0.005 0.0166 0.171 0.503 0.115 0.151 0.395 0.485 0.00112 0.00138 ! Validation 95 10894.779 0.005 0.0189 0.182 0.561 0.122 0.161 0.406 0.501 0.00115 0.00142 Wall time: 10894.779873111751 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 86 0.408 0.0158 0.0917 0.113 0.148 0.285 0.355 0.00081 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 0.724 0.0156 0.412 0.114 0.147 0.747 0.753 0.00212 0.00214 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 96 11009.336 0.005 0.0165 0.205 0.535 0.114 0.151 0.429 0.531 0.00122 0.00151 ! Validation 96 11009.336 0.005 0.0188 0.396 0.771 0.122 0.161 0.667 0.738 0.00189 0.0021 Wall time: 11009.33681402076 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 86 0.406 0.0177 0.0517 0.118 0.156 0.217 0.267 0.000617 0.000758 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 0.464 0.0164 0.136 0.117 0.15 0.423 0.433 0.0012 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 97 11123.775 0.005 0.0165 0.235 0.564 0.114 0.151 0.467 0.568 0.00133 0.00161 ! Validation 97 11123.775 0.005 0.0191 0.427 0.81 0.123 0.162 0.679 0.767 0.00193 0.00218 Wall time: 11123.775303318165 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 86 0.466 0.0158 0.149 0.112 0.148 0.376 0.453 0.00107 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 0.321 0.0154 0.0128 0.114 0.146 0.098 0.133 0.000278 0.000378 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 98 11238.232 0.005 0.0162 0.129 0.454 0.114 0.149 0.332 0.422 0.000943 0.0012 ! Validation 98 11238.232 0.005 0.0184 0.0814 0.449 0.121 0.159 0.263 0.335 0.000747 0.000951 Wall time: 11238.232779209036 ! Best model 98 0.449 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 86 0.376 0.0156 0.0638 0.112 0.146 0.228 0.296 0.000647 0.000842 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 0.739 0.0155 0.43 0.113 0.146 0.764 0.769 0.00217 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 99 11352.678 0.005 0.0161 0.215 0.537 0.113 0.149 0.447 0.544 0.00127 0.00154 ! Validation 99 11352.678 0.005 0.0184 0.399 0.767 0.121 0.159 0.665 0.741 0.00189 0.0021 Wall time: 11352.678199701943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 86 0.355 0.0146 0.0622 0.108 0.142 0.223 0.293 0.000633 0.000831 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 0.303 0.0147 0.00839 0.111 0.142 0.101 0.107 0.000286 0.000305 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 100 11467.123 0.005 0.0156 0.0857 0.398 0.111 0.147 0.276 0.343 0.000783 0.000976 ! Validation 100 11467.123 0.005 0.0178 0.112 0.467 0.119 0.156 0.313 0.392 0.000889 0.00111 Wall time: 11467.123501541093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 86 0.513 0.0152 0.209 0.11 0.145 0.486 0.536 0.00138 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 0.907 0.0149 0.609 0.111 0.143 0.912 0.916 0.00259 0.0026 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 101 11581.563 0.005 0.0154 0.139 0.447 0.111 0.146 0.352 0.437 0.000999 0.00124 ! Validation 101 11581.563 0.005 0.0177 0.447 0.802 0.118 0.156 0.705 0.785 0.002 0.00223 Wall time: 11581.56342635816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 86 0.522 0.0151 0.22 0.11 0.144 0.5 0.55 0.00142 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 0.318 0.0147 0.0231 0.111 0.142 0.157 0.178 0.000446 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 102 11695.989 0.005 0.0156 0.214 0.526 0.111 0.146 0.447 0.543 0.00127 0.00154 ! Validation 102 11695.989 0.005 0.0177 0.074 0.429 0.118 0.156 0.246 0.319 0.0007 0.000907 Wall time: 11695.989835633896 ! Best model 102 0.429 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 86 0.573 0.0158 0.257 0.112 0.148 0.541 0.594 0.00154 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 0.564 0.0151 0.263 0.112 0.144 0.593 0.602 0.00168 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 103 11810.543 0.005 0.0154 0.218 0.525 0.111 0.145 0.457 0.547 0.0013 0.00155 ! Validation 103 11810.543 0.005 0.0178 0.504 0.859 0.119 0.156 0.77 0.833 0.00219 0.00237 Wall time: 11810.543873142917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 86 0.347 0.0148 0.0505 0.108 0.143 0.213 0.264 0.000606 0.000749 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 0.511 0.0145 0.222 0.11 0.141 0.545 0.553 0.00155 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 104 11924.997 0.005 0.0151 0.158 0.46 0.11 0.144 0.377 0.466 0.00107 0.00132 ! Validation 104 11924.997 0.005 0.0173 0.153 0.5 0.117 0.154 0.377 0.459 0.00107 0.0013 Wall time: 11924.997189900838 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 86 1.08 0.0148 0.78 0.109 0.143 1.01 1.04 0.00287 0.00294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 0.727 0.0141 0.446 0.108 0.139 0.779 0.783 0.00221 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 12039.453 0.005 0.0149 0.152 0.451 0.109 0.143 0.367 0.457 0.00104 0.0013 ! Validation 105 12039.453 0.005 0.0171 0.642 0.984 0.116 0.153 0.89 0.94 0.00253 0.00267 Wall time: 12039.45396334678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 86 0.877 0.0145 0.587 0.108 0.141 0.87 0.899 0.00247 0.00255 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 0.387 0.0147 0.0929 0.111 0.142 0.344 0.358 0.000978 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 106 12153.904 0.005 0.0148 0.183 0.48 0.109 0.143 0.404 0.502 0.00115 0.00143 ! Validation 106 12153.904 0.005 0.0175 0.232 0.583 0.118 0.155 0.488 0.565 0.00139 0.00161 Wall time: 12153.904617092106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 86 0.37 0.0152 0.0662 0.109 0.145 0.23 0.302 0.000655 0.000858 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 0.303 0.014 0.0231 0.108 0.139 0.16 0.178 0.000454 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 107 12268.358 0.005 0.0147 0.152 0.446 0.108 0.142 0.364 0.458 0.00103 0.0013 ! Validation 107 12268.358 0.005 0.0167 0.129 0.463 0.115 0.152 0.343 0.421 0.000974 0.0012 Wall time: 12268.358577450737 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 86 0.497 0.015 0.197 0.108 0.144 0.467 0.52 0.00133 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 0.589 0.0138 0.313 0.108 0.138 0.648 0.656 0.00184 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 108 12382.780 0.005 0.0146 0.19 0.481 0.108 0.142 0.418 0.511 0.00119 0.00145 ! Validation 108 12382.780 0.005 0.0166 0.321 0.653 0.115 0.151 0.597 0.665 0.0017 0.00189 Wall time: 12382.780580607709 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 86 0.374 0.0142 0.0897 0.107 0.14 0.306 0.351 0.00087 0.000998 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 0.401 0.0138 0.124 0.108 0.138 0.4 0.413 0.00114 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 109 12497.291 0.005 0.0147 0.229 0.523 0.108 0.142 0.452 0.561 0.00128 0.00159 ! Validation 109 12497.291 0.005 0.0166 0.138 0.471 0.115 0.151 0.355 0.436 0.00101 0.00124 Wall time: 12497.291388845071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 86 0.615 0.0139 0.337 0.106 0.138 0.631 0.681 0.00179 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 0.301 0.0136 0.0284 0.107 0.137 0.175 0.198 0.000498 0.000562 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 110 12611.735 0.005 0.0141 0.113 0.396 0.106 0.139 0.317 0.394 0.0009 0.00112 ! Validation 110 12611.735 0.005 0.0163 0.234 0.561 0.114 0.15 0.464 0.567 0.00132 0.00161 Wall time: 12611.735167242121 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 86 0.423 0.013 0.163 0.102 0.134 0.429 0.474 0.00122 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 0.56 0.0132 0.296 0.106 0.135 0.632 0.638 0.00179 0.00181 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 111 12726.159 0.005 0.0139 0.107 0.385 0.105 0.138 0.309 0.383 0.000879 0.00109 ! Validation 111 12726.159 0.005 0.016 0.233 0.553 0.113 0.148 0.49 0.566 0.00139 0.00161 Wall time: 12726.159376129974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 86 0.443 0.0164 0.114 0.112 0.15 0.339 0.397 0.000962 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 0.293 0.0138 0.0178 0.108 0.138 0.129 0.156 0.000367 0.000444 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 12840.599 0.005 0.0139 0.16 0.438 0.105 0.138 0.382 0.469 0.00109 0.00133 ! Validation 112 12840.599 0.005 0.0163 0.066 0.393 0.114 0.15 0.236 0.301 0.000669 0.000856 Wall time: 12840.599813834764 ! Best model 112 0.393 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 86 0.417 0.0134 0.148 0.104 0.136 0.392 0.451 0.00111 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 0.282 0.013 0.0223 0.104 0.134 0.151 0.175 0.00043 0.000498 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 12955.068 0.005 0.0137 0.146 0.419 0.104 0.137 0.363 0.448 0.00103 0.00127 ! Validation 113 12955.068 0.005 0.0158 0.0691 0.384 0.112 0.147 0.239 0.308 0.00068 0.000876 Wall time: 12955.06821365701 ! Best model 113 0.384 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 86 0.335 0.0139 0.0562 0.106 0.138 0.233 0.278 0.000661 0.00079 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 0.343 0.014 0.0635 0.108 0.139 0.275 0.296 0.00078 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 114 13069.509 0.005 0.0134 0.138 0.406 0.103 0.136 0.34 0.437 0.000965 0.00124 ! Validation 114 13069.509 0.005 0.0167 0.215 0.548 0.115 0.151 0.459 0.544 0.0013 0.00155 Wall time: 13069.509214711841 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 86 0.406 0.0135 0.137 0.104 0.136 0.384 0.433 0.00109 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.284 0.013 0.0234 0.105 0.134 0.149 0.179 0.000423 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 115 13183.899 0.005 0.0138 0.225 0.501 0.105 0.138 0.459 0.556 0.0013 0.00158 ! Validation 115 13183.899 0.005 0.0156 0.068 0.381 0.111 0.147 0.238 0.306 0.000675 0.000869 Wall time: 13183.899520004168 ! Best model 115 0.381 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 86 0.325 0.0131 0.0627 0.103 0.134 0.236 0.294 0.000672 0.000834 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 0.259 0.0125 0.00846 0.103 0.131 0.0716 0.108 0.000203 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 116 13298.373 0.005 0.0132 0.0932 0.358 0.103 0.135 0.282 0.358 0.000802 0.00102 ! Validation 116 13298.373 0.005 0.0152 0.0678 0.371 0.11 0.145 0.241 0.305 0.000684 0.000868 Wall time: 13298.373762105126 ! Best model 116 0.371 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 86 0.308 0.0127 0.0544 0.101 0.132 0.21 0.274 0.000598 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 0.254 0.0123 0.00725 0.102 0.13 0.0935 0.0999 0.000266 0.000284 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 13412.772 0.005 0.0132 0.149 0.412 0.102 0.135 0.361 0.453 0.00103 0.00129 ! Validation 117 13412.772 0.005 0.0151 0.12 0.421 0.109 0.144 0.325 0.406 0.000923 0.00115 Wall time: 13412.772447479889 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 86 0.389 0.0125 0.138 0.1 0.131 0.359 0.436 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 118 100 0.522 0.0122 0.278 0.101 0.129 0.612 0.618 0.00174 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 118 13527.455 0.005 0.0129 0.14 0.398 0.101 0.133 0.361 0.439 0.00103 0.00125 ! Validation 118 13527.455 0.005 0.0149 0.264 0.563 0.109 0.143 0.524 0.603 0.00149 0.00171 Wall time: 13527.455648949835 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 86 0.301 0.0126 0.0483 0.101 0.132 0.204 0.258 0.000579 0.000732 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 0.258 0.0121 0.0155 0.101 0.129 0.128 0.146 0.000363 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 119 13641.897 0.005 0.0129 0.157 0.414 0.101 0.133 0.382 0.464 0.00108 0.00132 ! Validation 119 13641.897 0.005 0.0149 0.0653 0.364 0.109 0.143 0.236 0.3 0.00067 0.000851 Wall time: 13641.897139227949 ! Best model 119 0.364 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 86 0.413 0.0134 0.144 0.103 0.136 0.389 0.446 0.0011 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.351 0.0121 0.11 0.101 0.129 0.376 0.389 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 120 13756.336 0.005 0.0128 0.183 0.438 0.101 0.133 0.402 0.501 0.00114 0.00142 ! Validation 120 13756.336 0.005 0.0147 0.112 0.407 0.108 0.142 0.314 0.392 0.000892 0.00111 Wall time: 13756.33701589005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 86 0.417 0.0126 0.166 0.1 0.132 0.425 0.478 0.00121 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.308 0.0119 0.0707 0.1 0.128 0.296 0.312 0.000842 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 121 13870.740 0.005 0.0125 0.108 0.359 0.1 0.131 0.31 0.386 0.000879 0.0011 ! Validation 121 13870.740 0.005 0.0144 0.112 0.401 0.107 0.141 0.313 0.393 0.000888 0.00112 Wall time: 13870.740330745932 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 86 0.323 0.0134 0.0545 0.103 0.136 0.208 0.274 0.00059 0.000778 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 0.521 0.0126 0.269 0.103 0.132 0.6 0.608 0.0017 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 122 13985.249 0.005 0.0125 0.145 0.395 0.1 0.131 0.343 0.447 0.000973 0.00127 ! Validation 122 13985.249 0.005 0.0151 0.301 0.603 0.11 0.144 0.568 0.643 0.00161 0.00183 Wall time: 13985.249156539794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 86 0.321 0.0121 0.079 0.0986 0.129 0.279 0.33 0.000792 0.000937 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.466 0.0116 0.233 0.0991 0.127 0.559 0.567 0.00159 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 123 14099.648 0.005 0.0128 0.188 0.444 0.101 0.133 0.411 0.509 0.00117 0.00145 ! Validation 123 14099.648 0.005 0.0143 0.173 0.458 0.106 0.14 0.415 0.488 0.00118 0.00139 Wall time: 14099.648239607923 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 86 0.315 0.0122 0.0711 0.0985 0.13 0.262 0.313 0.000745 0.000889 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 0.317 0.0114 0.0879 0.0984 0.125 0.333 0.348 0.000946 0.000988 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 124 14214.038 0.005 0.012 0.0906 0.332 0.0981 0.129 0.287 0.353 0.000815 0.001 ! Validation 124 14214.038 0.005 0.0141 0.0857 0.367 0.106 0.139 0.269 0.343 0.000763 0.000976 Wall time: 14214.039010875858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 86 0.303 0.0122 0.0597 0.0988 0.129 0.248 0.287 0.000703 0.000814 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 0.324 0.0115 0.0944 0.0985 0.126 0.351 0.36 0.000996 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 125 14328.528 0.005 0.0119 0.121 0.359 0.0975 0.128 0.329 0.408 0.000934 0.00116 ! Validation 125 14328.528 0.005 0.014 0.123 0.403 0.106 0.139 0.337 0.411 0.000957 0.00117 Wall time: 14328.52870242903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 86 0.285 0.0119 0.0467 0.0978 0.128 0.2 0.253 0.000567 0.00072 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.248 0.0111 0.0252 0.097 0.124 0.165 0.186 0.000469 0.000529 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 14442.938 0.005 0.0118 0.107 0.343 0.0971 0.127 0.315 0.383 0.000894 0.00109 ! Validation 126 14442.938 0.005 0.0137 0.0591 0.334 0.104 0.137 0.221 0.285 0.000627 0.00081 Wall time: 14442.938850536942 ! Best model 126 0.334 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 86 0.303 0.0124 0.0544 0.1 0.131 0.222 0.274 0.00063 0.000777 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.462 0.0116 0.23 0.0993 0.126 0.556 0.563 0.00158 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 127 14557.441 0.005 0.0117 0.169 0.403 0.0968 0.127 0.375 0.482 0.00106 0.00137 ! Validation 127 14557.441 0.005 0.0145 0.202 0.491 0.108 0.141 0.439 0.527 0.00125 0.0015 Wall time: 14557.441795354709 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 86 0.3 0.0107 0.0857 0.0927 0.121 0.296 0.343 0.000841 0.000976 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.247 0.011 0.0283 0.0961 0.123 0.175 0.197 0.000496 0.00056 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 128 14672.000 0.005 0.0117 0.11 0.343 0.0965 0.127 0.313 0.389 0.00089 0.00111 ! Validation 128 14672.000 0.005 0.0135 0.166 0.437 0.103 0.136 0.397 0.478 0.00113 0.00136 Wall time: 14672.000444679987 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 86 0.296 0.0126 0.0436 0.101 0.132 0.2 0.245 0.000569 0.000696 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.254 0.0116 0.0233 0.099 0.126 0.16 0.179 0.000453 0.000509 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 14786.618 0.005 0.0116 0.16 0.392 0.0963 0.126 0.373 0.469 0.00106 0.00133 ! Validation 129 14786.618 0.005 0.014 0.158 0.438 0.105 0.139 0.377 0.466 0.00107 0.00132 Wall time: 14786.618748239707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 86 0.253 0.0103 0.0468 0.0914 0.119 0.211 0.254 0.000599 0.000721 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.254 0.0105 0.0431 0.0944 0.12 0.224 0.244 0.000637 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 130 14901.170 0.005 0.0115 0.141 0.371 0.096 0.126 0.354 0.44 0.00101 0.00125 ! Validation 130 14901.170 0.005 0.0132 0.0644 0.329 0.102 0.135 0.23 0.298 0.000654 0.000845 Wall time: 14901.170408147853 ! Best model 130 0.329 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 86 0.58 0.0108 0.365 0.0929 0.122 0.664 0.708 0.00189 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 0.743 0.0106 0.531 0.0947 0.121 0.849 0.855 0.00241 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 131 15015.687 0.005 0.0111 0.105 0.326 0.094 0.123 0.305 0.379 0.000866 0.00108 ! Validation 131 15015.687 0.005 0.0132 0.433 0.697 0.102 0.135 0.679 0.772 0.00193 0.00219 Wall time: 15015.68726996379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 86 0.26 0.011 0.0396 0.0939 0.123 0.186 0.233 0.000528 0.000663 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.212 0.0102 0.00762 0.0929 0.119 0.0959 0.102 0.000272 0.000291 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 15130.221 0.005 0.0111 0.131 0.354 0.0943 0.124 0.346 0.424 0.000983 0.00121 ! Validation 132 15130.221 0.005 0.0128 0.0786 0.335 0.101 0.133 0.26 0.329 0.000739 0.000934 Wall time: 15130.221872712951 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 86 0.278 0.0108 0.0624 0.0927 0.122 0.235 0.293 0.000669 0.000833 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.233 0.0101 0.03 0.0923 0.118 0.186 0.203 0.000529 0.000577 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 133 15244.729 0.005 0.0109 0.114 0.333 0.0934 0.123 0.322 0.396 0.000914 0.00113 ! Validation 133 15244.729 0.005 0.0128 0.0609 0.316 0.1 0.133 0.225 0.29 0.00064 0.000823 Wall time: 15244.730041989125 ! Best model 133 0.316 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 86 0.459 0.011 0.238 0.0932 0.123 0.536 0.573 0.00152 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.412 0.0102 0.209 0.0928 0.118 0.527 0.536 0.0015 0.00152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 134 15359.270 0.005 0.0117 0.203 0.437 0.0966 0.127 0.404 0.529 0.00115 0.0015 ! Validation 134 15359.270 0.005 0.0128 0.157 0.414 0.101 0.133 0.391 0.465 0.00111 0.00132 Wall time: 15359.270821379963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 86 0.799 0.0109 0.58 0.0937 0.123 0.864 0.893 0.00246 0.00254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.644 0.0112 0.421 0.0968 0.124 0.752 0.761 0.00214 0.00216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 135 15473.866 0.005 0.0108 0.118 0.334 0.0929 0.122 0.317 0.402 0.000901 0.00114 ! Validation 135 15473.866 0.005 0.0139 0.362 0.64 0.105 0.138 0.652 0.705 0.00185 0.002 Wall time: 15473.86687183706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 86 0.4 0.00973 0.205 0.0882 0.116 0.496 0.531 0.00141 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.461 0.00975 0.266 0.0907 0.116 0.599 0.605 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 136 15588.377 0.005 0.0109 0.144 0.362 0.0932 0.122 0.362 0.445 0.00103 0.00127 ! Validation 136 15588.377 0.005 0.0124 0.228 0.476 0.0989 0.131 0.482 0.56 0.00137 0.00159 Wall time: 15588.377351965755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 86 0.461 0.00962 0.268 0.0879 0.115 0.581 0.607 0.00165 0.00173 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.307 0.00974 0.112 0.0906 0.116 0.382 0.393 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 137 15702.878 0.005 0.0104 0.0931 0.302 0.0912 0.12 0.293 0.358 0.000833 0.00102 ! Validation 137 15702.878 0.005 0.0123 0.325 0.57 0.0983 0.13 0.602 0.668 0.00171 0.0019 Wall time: 15702.878372597042 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 86 0.376 0.0109 0.158 0.0932 0.123 0.422 0.467 0.0012 0.00133 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.208 0.00967 0.0148 0.0904 0.115 0.117 0.143 0.000333 0.000405 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 138 15817.359 0.005 0.0103 0.112 0.318 0.0907 0.119 0.325 0.392 0.000924 0.00111 ! Validation 138 15817.359 0.005 0.0122 0.054 0.298 0.0982 0.13 0.215 0.273 0.000611 0.000775 Wall time: 15817.359825631138 ! Best model 138 0.298 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 86 0.311 0.0104 0.104 0.0903 0.119 0.313 0.378 0.00089 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.217 0.00995 0.0179 0.0919 0.117 0.138 0.157 0.000393 0.000446 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 15931.871 0.005 0.0102 0.0895 0.294 0.0902 0.119 0.281 0.351 0.000799 0.000997 ! Validation 139 15931.871 0.005 0.0123 0.158 0.405 0.0987 0.13 0.38 0.466 0.00108 0.00132 Wall time: 15931.87159352284 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 86 0.308 0.0101 0.105 0.0904 0.118 0.34 0.38 0.000965 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.205 0.00951 0.0147 0.0898 0.114 0.115 0.142 0.000326 0.000404 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 140 16046.375 0.005 0.0103 0.153 0.36 0.0908 0.119 0.386 0.459 0.0011 0.0013 ! Validation 140 16046.375 0.005 0.012 0.0657 0.307 0.0975 0.129 0.232 0.301 0.000658 0.000854 Wall time: 16046.376000421122 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 86 0.363 0.00982 0.166 0.0886 0.116 0.434 0.478 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 141 100 0.243 0.00922 0.0587 0.0883 0.113 0.261 0.284 0.00074 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 141 16160.890 0.005 0.00994 0.0835 0.282 0.0889 0.117 0.272 0.339 0.000774 0.000963 ! Validation 141 16160.890 0.005 0.0116 0.138 0.371 0.0956 0.127 0.372 0.436 0.00106 0.00124 Wall time: 16160.890592372045 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 86 0.225 0.00936 0.0376 0.0869 0.114 0.184 0.228 0.000522 0.000646 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.271 0.00901 0.0907 0.0871 0.111 0.34 0.353 0.000966 0.001 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 142 16275.538 0.005 0.00985 0.0992 0.296 0.0884 0.116 0.291 0.37 0.000827 0.00105 ! Validation 142 16275.538 0.005 0.0116 0.0695 0.301 0.0954 0.126 0.249 0.309 0.000708 0.000879 Wall time: 16275.538137287833 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 86 0.21 0.00921 0.0256 0.0854 0.113 0.146 0.188 0.000416 0.000533 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.211 0.00879 0.0356 0.0861 0.11 0.197 0.221 0.000561 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 143 16390.051 0.005 0.00969 0.0927 0.287 0.0877 0.115 0.289 0.357 0.00082 0.00101 ! Validation 143 16390.051 0.005 0.0114 0.0637 0.292 0.0946 0.125 0.233 0.296 0.000663 0.000841 Wall time: 16390.051806929987 ! Best model 143 0.292 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 86 0.202 0.009 0.022 0.0844 0.111 0.137 0.174 0.000389 0.000494 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.237 0.00884 0.0601 0.0863 0.11 0.268 0.288 0.00076 0.000817 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 144 16504.584 0.005 0.00969 0.118 0.312 0.0877 0.115 0.321 0.404 0.000912 0.00115 ! Validation 144 16504.584 0.005 0.0114 0.0619 0.289 0.0944 0.125 0.229 0.292 0.000652 0.000829 Wall time: 16504.58436456509 ! Best model 144 0.289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 86 0.219 0.00902 0.0388 0.0844 0.111 0.184 0.231 0.000524 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.258 0.00872 0.0831 0.0857 0.11 0.321 0.338 0.000912 0.000961 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 145 16619.080 0.005 0.00961 0.113 0.305 0.0873 0.115 0.318 0.394 0.000905 0.00112 ! Validation 145 16619.080 0.005 0.0112 0.0783 0.302 0.0936 0.124 0.264 0.328 0.00075 0.000932 Wall time: 16619.080840572715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 86 0.602 0.0129 0.344 0.101 0.133 0.65 0.688 0.00185 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.262 0.0119 0.0234 0.0997 0.128 0.132 0.179 0.000375 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 146 16733.616 0.005 0.00947 0.121 0.31 0.0866 0.114 0.318 0.407 0.000903 0.00116 ! Validation 146 16733.616 0.005 0.0145 0.071 0.361 0.107 0.141 0.247 0.313 0.000702 0.000888 Wall time: 16733.616944375914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 86 0.229 0.00929 0.0433 0.0857 0.113 0.197 0.244 0.000558 0.000693 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.193 0.00871 0.0184 0.0856 0.109 0.129 0.159 0.000368 0.000451 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 16848.121 0.005 0.00973 0.151 0.346 0.0879 0.116 0.377 0.457 0.00107 0.0013 ! Validation 147 16848.121 0.005 0.0112 0.0603 0.284 0.0935 0.124 0.222 0.288 0.000632 0.000818 Wall time: 16848.121814733837 ! Best model 147 0.284 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 86 0.308 0.00919 0.124 0.0857 0.112 0.38 0.414 0.00108 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.311 0.00858 0.14 0.0848 0.109 0.423 0.438 0.0012 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 148 16962.744 0.005 0.00925 0.0936 0.279 0.0856 0.113 0.289 0.359 0.000821 0.00102 ! Validation 148 16962.744 0.005 0.011 0.151 0.372 0.0929 0.123 0.383 0.456 0.00109 0.0013 Wall time: 16962.744699593168 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 86 0.238 0.00948 0.0485 0.0863 0.114 0.223 0.258 0.000634 0.000734 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.27 0.00825 0.105 0.0834 0.107 0.367 0.379 0.00104 0.00108 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 149 17077.259 0.005 0.00906 0.0606 0.242 0.0847 0.112 0.231 0.289 0.000657 0.000821 ! Validation 149 17077.259 0.005 0.0108 0.0838 0.299 0.0917 0.122 0.276 0.34 0.000784 0.000965 Wall time: 17077.259298949968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 86 0.229 0.00903 0.0484 0.0845 0.111 0.213 0.258 0.000605 0.000733 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.232 0.00837 0.0646 0.0838 0.107 0.279 0.298 0.000792 0.000847 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 150 17191.768 0.005 0.00905 0.109 0.29 0.0846 0.112 0.314 0.388 0.000892 0.0011 ! Validation 150 17191.768 0.005 0.0107 0.0881 0.303 0.0916 0.122 0.276 0.348 0.000784 0.000989 Wall time: 17191.76873032702 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 86 0.224 0.01 0.024 0.0882 0.117 0.142 0.182 0.000404 0.000517 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.173 0.00841 0.00489 0.0842 0.108 0.0757 0.082 0.000215 0.000233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 151 17306.263 0.005 0.00921 0.124 0.309 0.0854 0.113 0.331 0.414 0.000942 0.00118 ! Validation 151 17306.263 0.005 0.0109 0.0563 0.275 0.0929 0.123 0.218 0.278 0.000618 0.00079 Wall time: 17306.263506521005 ! Best model 151 0.275 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 86 0.589 0.00917 0.405 0.0851 0.112 0.716 0.747 0.00203 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.737 0.00834 0.571 0.0836 0.107 0.881 0.886 0.0025 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 152 17420.902 0.005 0.00886 0.103 0.281 0.0837 0.11 0.301 0.377 0.000855 0.00107 ! Validation 152 17420.902 0.005 0.0108 0.475 0.691 0.092 0.122 0.762 0.808 0.00216 0.0023 Wall time: 17420.902688617818 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 86 0.306 0.00904 0.126 0.085 0.112 0.373 0.416 0.00106 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.183 0.00864 0.0101 0.0849 0.109 0.107 0.118 0.000303 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 153 17535.396 0.005 0.00892 0.113 0.292 0.084 0.111 0.314 0.395 0.000891 0.00112 ! Validation 153 17535.396 0.005 0.0111 0.0505 0.272 0.0932 0.123 0.204 0.264 0.000581 0.000749 Wall time: 17535.396265288815 ! Best model 153 0.272 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 86 0.208 0.00864 0.0352 0.0828 0.109 0.172 0.22 0.000488 0.000626 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.172 0.00791 0.0141 0.0814 0.104 0.115 0.139 0.000328 0.000396 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 154 17649.976 0.005 0.00889 0.115 0.293 0.0839 0.111 0.32 0.398 0.00091 0.00113 ! Validation 154 17649.976 0.005 0.0104 0.0484 0.256 0.09 0.12 0.199 0.258 0.000565 0.000733 Wall time: 17649.976121396758 ! Best model 154 0.256 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 86 0.188 0.00811 0.0258 0.0806 0.106 0.155 0.188 0.000441 0.000535 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.196 0.00772 0.042 0.0803 0.103 0.223 0.24 0.000632 0.000683 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 17764.461 0.005 0.00856 0.0757 0.247 0.0822 0.109 0.256 0.323 0.000727 0.000917 ! Validation 155 17764.461 0.005 0.0102 0.141 0.345 0.0892 0.118 0.38 0.44 0.00108 0.00125 Wall time: 17764.46180643188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 86 0.188 0.00838 0.0199 0.0811 0.107 0.129 0.166 0.000368 0.00047 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.203 0.00777 0.0478 0.0807 0.103 0.232 0.257 0.00066 0.000729 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 156 17878.972 0.005 0.00884 0.156 0.333 0.0836 0.11 0.37 0.463 0.00105 0.00132 ! Validation 156 17878.972 0.005 0.0102 0.0569 0.26 0.0889 0.118 0.215 0.28 0.00061 0.000795 Wall time: 17878.97234060103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 86 0.207 0.00855 0.036 0.0818 0.108 0.18 0.223 0.000512 0.000632 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.169 0.00761 0.0167 0.08 0.102 0.121 0.151 0.000344 0.00043 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 17993.452 0.005 0.00843 0.0656 0.234 0.0815 0.108 0.238 0.3 0.000677 0.000853 ! Validation 157 17993.452 0.005 0.00999 0.109 0.309 0.0882 0.117 0.321 0.387 0.000911 0.0011 Wall time: 17993.452166268136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 86 0.23 0.00848 0.0602 0.0818 0.108 0.241 0.288 0.000685 0.000818 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 0.261 0.00764 0.108 0.0798 0.103 0.369 0.385 0.00105 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 158 18107.954 0.005 0.00835 0.0704 0.237 0.0811 0.107 0.253 0.311 0.00072 0.000884 ! Validation 158 18107.954 0.005 0.00995 0.0866 0.286 0.0879 0.117 0.28 0.345 0.000794 0.00098 Wall time: 18107.9544797251 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 86 0.286 0.00793 0.127 0.0793 0.104 0.373 0.418 0.00106 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 0.199 0.00753 0.0481 0.0793 0.102 0.235 0.257 0.000668 0.000731 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 159 18222.427 0.005 0.0082 0.09 0.254 0.0803 0.106 0.291 0.352 0.000826 0.000999 ! Validation 159 18222.427 0.005 0.00977 0.166 0.361 0.087 0.116 0.418 0.478 0.00119 0.00136 Wall time: 18222.428043658845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 86 0.186 0.00789 0.0278 0.0788 0.104 0.168 0.196 0.000477 0.000556 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 0.152 0.00729 0.00644 0.0781 0.1 0.0861 0.0941 0.000245 0.000267 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 18336.891 0.005 0.00826 0.0874 0.253 0.0806 0.107 0.27 0.347 0.000768 0.000986 ! Validation 160 18336.891 0.005 0.00967 0.0639 0.257 0.0867 0.115 0.232 0.297 0.00066 0.000843 Wall time: 18336.89158429671 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 86 0.254 0.00774 0.0994 0.078 0.103 0.334 0.37 0.000949 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.523 0.00728 0.378 0.078 0.1 0.715 0.721 0.00203 0.00205 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 161 18451.478 0.005 0.00802 0.0865 0.247 0.0793 0.105 0.284 0.345 0.000808 0.00098 ! Validation 161 18451.478 0.005 0.00966 0.331 0.525 0.0866 0.115 0.624 0.675 0.00177 0.00192 Wall time: 18451.478197636083 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 86 0.178 0.0077 0.0244 0.0781 0.103 0.147 0.183 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 162 100 0.245 0.00725 0.101 0.0778 0.0999 0.363 0.372 0.00103 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 162 18565.948 0.005 0.00813 0.0886 0.251 0.08 0.106 0.278 0.349 0.00079 0.000992 ! Validation 162 18565.948 0.005 0.00956 0.0863 0.278 0.0862 0.115 0.279 0.345 0.000793 0.000979 Wall time: 18565.94864214491 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 86 0.241 0.00877 0.0658 0.0825 0.11 0.24 0.301 0.000681 0.000855 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 0.195 0.0074 0.0471 0.0784 0.101 0.235 0.255 0.000668 0.000723 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 163 18680.590 0.005 0.00822 0.146 0.311 0.0804 0.106 0.354 0.449 0.00101 0.00128 ! Validation 163 18680.590 0.005 0.00976 0.0653 0.26 0.0871 0.116 0.235 0.3 0.000668 0.000852 Wall time: 18680.59104084503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 86 0.22 0.00731 0.0738 0.0758 0.1 0.27 0.319 0.000766 0.000905 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 0.148 0.007 0.00772 0.0761 0.0981 0.0931 0.103 0.000265 0.000293 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 18795.114 0.005 0.00796 0.0589 0.218 0.0791 0.105 0.227 0.285 0.000646 0.000809 ! Validation 164 18795.114 0.005 0.00927 0.0444 0.23 0.0847 0.113 0.192 0.247 0.000545 0.000702 Wall time: 18795.114930113778 ! Best model 164 0.230 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 86 0.19 0.00832 0.0239 0.0809 0.107 0.136 0.181 0.000388 0.000515 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 0.172 0.0071 0.0303 0.0767 0.0989 0.185 0.204 0.000526 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 165 18909.656 0.005 0.00789 0.102 0.26 0.0787 0.104 0.295 0.374 0.000838 0.00106 ! Validation 165 18909.656 0.005 0.00937 0.0911 0.279 0.0852 0.114 0.298 0.354 0.000846 0.00101 Wall time: 18909.65698297508 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 86 0.331 0.00851 0.161 0.0811 0.108 0.439 0.471 0.00125 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 0.149 0.00712 0.00672 0.0769 0.099 0.0883 0.0962 0.000251 0.000273 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 166 19024.127 0.005 0.00773 0.0791 0.234 0.0778 0.103 0.267 0.33 0.000759 0.000937 ! Validation 166 19024.127 0.005 0.00933 0.0648 0.251 0.085 0.113 0.234 0.299 0.000665 0.000848 Wall time: 19024.128027931787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 86 0.172 0.00741 0.0243 0.0767 0.101 0.151 0.183 0.000428 0.000519 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 0.147 0.00705 0.00646 0.0765 0.0985 0.0793 0.0943 0.000225 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 167 19138.673 0.005 0.00773 0.0969 0.252 0.0779 0.103 0.293 0.365 0.000832 0.00104 ! Validation 167 19138.673 0.005 0.00938 0.0503 0.238 0.0853 0.114 0.203 0.263 0.000577 0.000748 Wall time: 19138.673334694002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 86 0.258 0.00754 0.107 0.0771 0.102 0.344 0.384 0.000977 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 0.148 0.00699 0.00794 0.0761 0.0981 0.0859 0.105 0.000244 0.000297 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 168 19253.159 0.005 0.00769 0.108 0.262 0.0776 0.103 0.311 0.385 0.000885 0.00109 ! Validation 168 19253.159 0.005 0.00913 0.0794 0.262 0.084 0.112 0.267 0.33 0.000758 0.000939 Wall time: 19253.16000499809 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 86 0.198 0.00731 0.0515 0.076 0.1 0.234 0.266 0.000664 0.000756 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 0.166 0.00699 0.0258 0.0762 0.0981 0.16 0.189 0.000455 0.000536 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 169 19367.619 0.005 0.0075 0.0724 0.222 0.0766 0.102 0.255 0.316 0.000725 0.000897 ! Validation 169 19367.619 0.005 0.00914 0.0581 0.241 0.0841 0.112 0.213 0.283 0.000604 0.000803 Wall time: 19367.61937226588 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 86 0.158 0.00687 0.0207 0.0735 0.0972 0.133 0.169 0.000377 0.000479 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 0.292 0.00686 0.154 0.0754 0.0971 0.451 0.461 0.00128 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 170 19482.060 0.005 0.00744 0.0729 0.222 0.0763 0.101 0.257 0.317 0.00073 0.0009 ! Validation 170 19482.060 0.005 0.00897 0.112 0.291 0.0832 0.111 0.333 0.392 0.000945 0.00111 Wall time: 19482.060477405787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 86 0.191 0.00741 0.0427 0.0761 0.101 0.203 0.242 0.000577 0.000689 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 0.16 0.00668 0.0263 0.0741 0.0959 0.169 0.19 0.000479 0.00054 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 171 19596.491 0.005 0.00743 0.0813 0.23 0.0762 0.101 0.267 0.334 0.000759 0.00095 ! Validation 171 19596.491 0.005 0.00896 0.0453 0.225 0.0832 0.111 0.186 0.25 0.000528 0.000709 Wall time: 19596.49150601402 ! Best model 171 0.225 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 86 0.176 0.00667 0.043 0.0727 0.0958 0.19 0.243 0.000539 0.000691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 0.139 0.0066 0.00715 0.0737 0.0953 0.0829 0.0992 0.000235 0.000282 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 172 19710.945 0.005 0.00725 0.0675 0.212 0.0752 0.0999 0.242 0.305 0.000689 0.000866 ! Validation 172 19710.945 0.005 0.00876 0.0503 0.226 0.0822 0.11 0.199 0.263 0.000566 0.000748 Wall time: 19710.945218524896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 86 0.169 0.00679 0.0329 0.0729 0.0966 0.166 0.213 0.000472 0.000605 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 0.216 0.0065 0.0857 0.0733 0.0946 0.33 0.343 0.000936 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 173 19825.413 0.005 0.00727 0.0788 0.224 0.0754 0.1 0.27 0.329 0.000768 0.000936 ! Validation 173 19825.413 0.005 0.00867 0.0791 0.252 0.0817 0.109 0.264 0.33 0.000751 0.000937 Wall time: 19825.41358888708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 86 0.371 0.00767 0.218 0.0781 0.103 0.526 0.548 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 174 100 0.238 0.00683 0.102 0.0751 0.097 0.361 0.374 0.00102 0.00106 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 174 19939.961 0.005 0.00736 0.103 0.25 0.0759 0.101 0.305 0.376 0.000867 0.00107 ! Validation 174 19939.961 0.005 0.0088 0.148 0.324 0.0824 0.11 0.399 0.451 0.00113 0.00128 Wall time: 19939.961428181734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 86 0.265 0.00748 0.115 0.0762 0.101 0.374 0.398 0.00106 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 0.141 0.00668 0.00693 0.074 0.0959 0.0801 0.0976 0.000227 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 175 20054.384 0.005 0.00729 0.0833 0.229 0.0756 0.1 0.279 0.338 0.000792 0.000962 ! Validation 175 20054.384 0.005 0.00876 0.0797 0.255 0.0821 0.11 0.266 0.331 0.000755 0.000941 Wall time: 20054.384446117096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 86 0.261 0.00702 0.12 0.0744 0.0983 0.379 0.407 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 176 100 0.14 0.00632 0.0134 0.0722 0.0932 0.109 0.136 0.00031 0.000386 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 20169.061 0.005 0.00717 0.0858 0.229 0.0749 0.0993 0.278 0.344 0.000789 0.000976 ! Validation 176 20169.061 0.005 0.00846 0.0691 0.238 0.0807 0.108 0.252 0.308 0.000717 0.000876 Wall time: 20169.061820309144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 86 0.353 0.00765 0.2 0.0772 0.103 0.498 0.524 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 177 100 0.297 0.00695 0.158 0.0757 0.0978 0.456 0.466 0.0013 0.00132 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 177 20284.053 0.005 0.00702 0.0836 0.224 0.074 0.0983 0.271 0.339 0.000771 0.000963 ! Validation 177 20284.053 0.005 0.00896 0.108 0.288 0.0836 0.111 0.326 0.386 0.000925 0.0011 Wall time: 20284.053232691716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 86 0.182 0.00689 0.0438 0.0738 0.0974 0.219 0.245 0.000622 0.000697 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 0.162 0.00623 0.0378 0.0715 0.0926 0.204 0.228 0.000581 0.000648 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 178 20398.484 0.005 0.007 0.0561 0.196 0.0739 0.0981 0.225 0.278 0.00064 0.00079 ! Validation 178 20398.484 0.005 0.00835 0.0486 0.216 0.0802 0.107 0.198 0.259 0.000563 0.000734 Wall time: 20398.484461484943 ! Best model 178 0.216 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 86 0.368 0.00706 0.227 0.0746 0.0986 0.53 0.559 0.0015 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 0.264 0.00643 0.135 0.0727 0.0941 0.423 0.431 0.0012 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 179 20512.901 0.005 0.00712 0.116 0.259 0.0746 0.099 0.331 0.4 0.00094 0.00114 ! Validation 179 20512.901 0.005 0.00847 0.133 0.302 0.0809 0.108 0.367 0.428 0.00104 0.00121 Wall time: 20512.901691298932 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 86 0.158 0.0068 0.0223 0.0726 0.0968 0.141 0.175 0.000401 0.000498 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 0.13 0.00611 0.00787 0.0707 0.0917 0.09 0.104 0.000256 0.000296 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 180 20627.403 0.005 0.00688 0.0622 0.2 0.0732 0.0973 0.23 0.293 0.000655 0.000832 ! Validation 180 20627.403 0.005 0.00817 0.0398 0.203 0.0793 0.106 0.177 0.234 0.000503 0.000665 Wall time: 20627.403239083942 ! Best model 180 0.203 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 86 0.172 0.00684 0.0354 0.0732 0.097 0.182 0.221 0.000518 0.000627 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 0.146 0.00627 0.0201 0.0716 0.0929 0.138 0.166 0.000393 0.000472 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 181 20741.820 0.005 0.00681 0.0634 0.2 0.0729 0.0968 0.239 0.295 0.000679 0.000839 ! Validation 181 20741.820 0.005 0.00836 0.04 0.207 0.08 0.107 0.174 0.235 0.000496 0.000666 Wall time: 20741.820555259008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 86 0.153 0.00636 0.026 0.0706 0.0936 0.153 0.189 0.000435 0.000537 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 0.245 0.00612 0.123 0.0707 0.0917 0.403 0.412 0.00115 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 182 20856.236 0.005 0.00683 0.0898 0.226 0.073 0.0969 0.281 0.352 0.000799 0.000999 ! Validation 182 20856.236 0.005 0.00821 0.108 0.272 0.0794 0.106 0.328 0.386 0.000933 0.0011 Wall time: 20856.236647409853 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 86 0.154 0.0064 0.0258 0.0706 0.0938 0.148 0.188 0.00042 0.000535 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 0.141 0.00583 0.024 0.069 0.0896 0.161 0.182 0.000458 0.000516 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 183 20970.624 0.005 0.00656 0.0423 0.173 0.0714 0.095 0.196 0.241 0.000556 0.000685 ! Validation 183 20970.624 0.005 0.0079 0.0386 0.197 0.0779 0.104 0.174 0.23 0.000495 0.000655 Wall time: 20970.624368416145 ! Best model 183 0.197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 86 0.255 0.00974 0.06 0.0873 0.116 0.238 0.287 0.000676 0.000816 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 0.208 0.0077 0.0538 0.0796 0.103 0.261 0.272 0.00074 0.000773 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 184 21085.075 0.005 0.00677 0.103 0.238 0.0726 0.0965 0.294 0.376 0.000835 0.00107 ! Validation 184 21085.075 0.005 0.00972 0.146 0.34 0.0873 0.116 0.39 0.448 0.00111 0.00127 Wall time: 21085.075434505008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 86 0.18 0.00705 0.0388 0.0738 0.0985 0.195 0.231 0.000554 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 0.123 0.00584 0.00651 0.069 0.0897 0.0764 0.0947 0.000217 0.000269 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 185 21199.590 0.005 0.00686 0.0829 0.22 0.0732 0.0971 0.267 0.338 0.000757 0.000959 ! Validation 185 21199.590 0.005 0.00788 0.0951 0.253 0.0777 0.104 0.293 0.362 0.000834 0.00103 Wall time: 21199.590476377867 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 86 0.469 0.00772 0.315 0.0781 0.103 0.623 0.658 0.00177 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 0.406 0.00705 0.265 0.0763 0.0985 0.599 0.604 0.0017 0.00171 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 186 21314.104 0.005 0.00675 0.126 0.261 0.0726 0.0964 0.334 0.416 0.000948 0.00118 ! Validation 186 21314.104 0.005 0.00884 0.391 0.568 0.083 0.11 0.692 0.734 0.00197 0.00208 Wall time: 21314.104956554715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 86 0.14 0.00644 0.0114 0.0711 0.0941 0.0992 0.125 0.000282 0.000355 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 0.127 0.00588 0.00967 0.0692 0.0899 0.0967 0.115 0.000275 0.000328 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 187 21428.618 0.005 0.00669 0.0464 0.18 0.0722 0.0959 0.195 0.253 0.000553 0.000718 ! Validation 187 21428.618 0.005 0.00784 0.0417 0.198 0.0775 0.104 0.179 0.24 0.00051 0.000681 Wall time: 21428.618458108976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 86 0.275 0.00617 0.152 0.0695 0.0922 0.427 0.457 0.00121 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.26 0.0063 0.134 0.0716 0.0931 0.416 0.429 0.00118 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 188 21543.024 0.005 0.00643 0.0552 0.184 0.0707 0.0941 0.22 0.275 0.000626 0.000782 ! Validation 188 21543.024 0.005 0.00823 0.0953 0.26 0.0797 0.106 0.302 0.362 0.000859 0.00103 Wall time: 21543.02450903505 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 86 0.191 0.00656 0.0598 0.0715 0.095 0.262 0.287 0.000744 0.000815 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.129 0.00578 0.013 0.0685 0.0892 0.101 0.134 0.000288 0.00038 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 21657.458 0.005 0.0064 0.0631 0.191 0.0706 0.0938 0.239 0.295 0.000679 0.000837 ! Validation 189 21657.458 0.005 0.00764 0.0686 0.221 0.0765 0.103 0.253 0.307 0.00072 0.000873 Wall time: 21657.458196769003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 86 0.193 0.00624 0.0686 0.0698 0.0926 0.269 0.307 0.000763 0.000873 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.118 0.00567 0.00504 0.0678 0.0883 0.0591 0.0833 0.000168 0.000237 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 190 21771.849 0.005 0.00642 0.0773 0.206 0.0707 0.094 0.254 0.326 0.000721 0.000927 ! Validation 190 21771.849 0.005 0.0076 0.0662 0.218 0.0764 0.102 0.24 0.302 0.00068 0.000857 Wall time: 21771.849803622812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 86 0.235 0.00605 0.114 0.0684 0.0913 0.368 0.395 0.00104 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 0.193 0.00574 0.0786 0.0685 0.0889 0.321 0.329 0.000913 0.000934 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 191 21886.249 0.005 0.00623 0.0421 0.167 0.0696 0.0926 0.19 0.24 0.000541 0.000683 ! Validation 191 21886.249 0.005 0.00757 0.161 0.313 0.0763 0.102 0.419 0.471 0.00119 0.00134 Wall time: 21886.24986256985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 86 0.621 0.00778 0.465 0.0784 0.103 0.778 0.8 0.00221 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.143 0.00696 0.00407 0.0753 0.0978 0.0664 0.0749 0.000189 0.000213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 192 22001.166 0.005 0.00626 0.0795 0.205 0.0697 0.0928 0.257 0.33 0.000731 0.000938 ! Validation 192 22001.166 0.005 0.00893 0.134 0.312 0.084 0.111 0.34 0.429 0.000965 0.00122 Wall time: 22001.167053562123 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 86 0.155 0.00664 0.0224 0.0711 0.0956 0.154 0.175 0.000437 0.000498 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.178 0.0055 0.0682 0.0668 0.087 0.299 0.306 0.000849 0.00087 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 193 22115.768 0.005 0.00648 0.076 0.206 0.0711 0.0944 0.25 0.324 0.000709 0.000919 ! Validation 193 22115.768 0.005 0.00747 0.0701 0.22 0.0757 0.101 0.248 0.31 0.000705 0.000882 Wall time: 22115.76810977608 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 86 0.164 0.00666 0.0304 0.0709 0.0957 0.165 0.204 0.000468 0.000581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.218 0.00581 0.102 0.0688 0.0894 0.359 0.374 0.00102 0.00106 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 194 22230.284 0.005 0.00613 0.0617 0.184 0.069 0.0918 0.242 0.292 0.000689 0.000828 ! Validation 194 22230.284 0.005 0.00754 0.0971 0.248 0.076 0.102 0.298 0.365 0.000845 0.00104 Wall time: 22230.28502114676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 86 0.152 0.00624 0.0271 0.0694 0.0926 0.157 0.193 0.000445 0.000549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.13 0.00615 0.00695 0.0707 0.092 0.076 0.0978 0.000216 0.000278 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 195 22344.785 0.005 0.00633 0.0977 0.224 0.0703 0.0933 0.309 0.367 0.000877 0.00104 ! Validation 195 22344.785 0.005 0.00799 0.0891 0.249 0.0783 0.105 0.281 0.35 0.000798 0.000995 Wall time: 22344.785860587843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 86 0.163 0.00582 0.0463 0.0672 0.0895 0.223 0.252 0.000632 0.000717 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.41 0.00553 0.3 0.0669 0.0872 0.637 0.642 0.00181 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 196 22459.434 0.005 0.0061 0.0563 0.178 0.0689 0.0916 0.226 0.278 0.000642 0.000791 ! Validation 196 22459.434 0.005 0.00739 0.212 0.36 0.0753 0.101 0.5 0.54 0.00142 0.00153 Wall time: 22459.434851088095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 86 0.141 0.00576 0.0255 0.0671 0.089 0.164 0.187 0.000467 0.000532 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.161 0.00538 0.0532 0.0658 0.0861 0.257 0.271 0.00073 0.000769 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 22573.948 0.005 0.00608 0.0673 0.189 0.0687 0.0914 0.248 0.304 0.000705 0.000865 ! Validation 197 22573.948 0.005 0.00722 0.0519 0.196 0.0743 0.0997 0.21 0.267 0.000597 0.000759 Wall time: 22573.949001215864 ! Best model 197 0.196 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 86 0.141 0.00578 0.0254 0.0677 0.0892 0.156 0.187 0.000443 0.000531 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.128 0.00545 0.0188 0.0664 0.0866 0.143 0.161 0.000407 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 198 22688.492 0.005 0.00613 0.0733 0.196 0.069 0.0919 0.251 0.318 0.000712 0.000903 ! Validation 198 22688.492 0.005 0.00725 0.0404 0.185 0.0745 0.0999 0.174 0.236 0.000494 0.00067 Wall time: 22688.492352377158 ! Best model 198 0.185 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 86 0.178 0.00598 0.0587 0.068 0.0907 0.239 0.284 0.000679 0.000807 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.146 0.00562 0.0335 0.0676 0.0879 0.203 0.215 0.000577 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 199 22803.020 0.005 0.00593 0.0523 0.171 0.0679 0.0903 0.21 0.268 0.000597 0.000762 ! Validation 199 22803.020 0.005 0.00748 0.0513 0.201 0.0757 0.101 0.199 0.266 0.000566 0.000755 Wall time: 22803.0205362821 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 86 0.196 0.00584 0.0796 0.0673 0.0897 0.306 0.331 0.000869 0.00094 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.169 0.00538 0.0618 0.0659 0.086 0.282 0.292 0.0008 0.000828 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 22917.651 0.005 0.0059 0.0459 0.164 0.0677 0.0901 0.205 0.251 0.000583 0.000713 ! Validation 200 22917.651 0.005 0.00711 0.125 0.267 0.0737 0.0989 0.371 0.415 0.00105 0.00118 Wall time: 22917.651178657077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 86 0.195 0.00655 0.0644 0.071 0.0949 0.251 0.298 0.000713 0.000845 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.621 0.00553 0.511 0.0672 0.0872 0.835 0.838 0.00237 0.00238 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 201 23032.327 0.005 0.00584 0.058 0.175 0.0673 0.0896 0.222 0.282 0.000631 0.000802 ! Validation 201 23032.327 0.005 0.00726 0.385 0.53 0.0747 0.1 0.674 0.728 0.00192 0.00207 Wall time: 23032.327141419984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 86 0.36 0.00612 0.238 0.0678 0.0917 0.542 0.572 0.00154 0.00162 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.198 0.00562 0.0855 0.0674 0.088 0.337 0.343 0.000957 0.000974 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 23146.850 0.005 0.00604 0.0714 0.192 0.0685 0.0912 0.242 0.313 0.000689 0.000889 ! Validation 202 23146.850 0.005 0.0074 0.146 0.294 0.0756 0.101 0.401 0.448 0.00114 0.00127 Wall time: 23146.85079492582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 86 0.139 0.00613 0.0159 0.0692 0.0919 0.115 0.148 0.000326 0.00042 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.188 0.00539 0.0806 0.0659 0.0861 0.326 0.333 0.000925 0.000946 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 23262.085 0.005 0.00602 0.0769 0.197 0.0685 0.091 0.267 0.325 0.000758 0.000924 ! Validation 203 23262.085 0.005 0.00713 0.0877 0.23 0.0741 0.0991 0.29 0.347 0.000824 0.000987 Wall time: 23262.085128142964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 86 0.145 0.00596 0.0254 0.0682 0.0906 0.154 0.187 0.000438 0.000531 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 0.139 0.00525 0.0343 0.065 0.085 0.206 0.217 0.000584 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 204 23376.757 0.005 0.00642 0.106 0.234 0.0708 0.094 0.302 0.381 0.000857 0.00108 ! Validation 204 23376.757 0.005 0.00703 0.0415 0.182 0.0733 0.0983 0.179 0.239 0.000508 0.000679 Wall time: 23376.757070100866 ! Best model 204 0.182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 86 0.189 0.00524 0.0844 0.064 0.0849 0.319 0.341 0.000905 0.000968 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.113 0.00524 0.00817 0.0649 0.0849 0.0787 0.106 0.000224 0.000301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 205 23491.327 0.005 0.00573 0.0452 0.16 0.0667 0.0888 0.201 0.249 0.000571 0.000709 ! Validation 205 23491.327 0.005 0.007 0.0432 0.183 0.0732 0.0982 0.194 0.244 0.000552 0.000693 Wall time: 23491.327523985878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 86 0.161 0.00577 0.0458 0.0668 0.0891 0.214 0.251 0.000607 0.000713 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.233 0.00515 0.13 0.0644 0.0842 0.417 0.423 0.00118 0.0012 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 206 23605.961 0.005 0.00568 0.0422 0.156 0.0664 0.0884 0.193 0.241 0.000549 0.000684 ! Validation 206 23605.961 0.005 0.00685 0.0885 0.226 0.0723 0.0971 0.297 0.349 0.000844 0.000991 Wall time: 23605.961085694842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 86 0.133 0.00509 0.0309 0.0628 0.0837 0.176 0.206 0.0005 0.000586 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.137 0.00511 0.035 0.0639 0.0839 0.204 0.22 0.000581 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 207 23721.270 0.005 0.00586 0.0942 0.211 0.0675 0.0898 0.288 0.36 0.00082 0.00102 ! Validation 207 23721.270 0.005 0.00681 0.047 0.183 0.0721 0.0968 0.194 0.254 0.000551 0.000722 Wall time: 23721.27086729696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 86 0.115 0.00506 0.0134 0.0625 0.0834 0.11 0.136 0.000313 0.000386 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.153 0.00494 0.0538 0.0629 0.0824 0.264 0.272 0.00075 0.000773 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 208 23835.722 0.005 0.00552 0.0296 0.14 0.0654 0.0872 0.162 0.202 0.00046 0.000574 ! Validation 208 23835.722 0.005 0.00667 0.0543 0.188 0.0713 0.0958 0.217 0.273 0.000617 0.000777 Wall time: 23835.72235611407 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 86 0.125 0.00545 0.0162 0.0651 0.0866 0.12 0.149 0.000341 0.000424 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.112 0.00512 0.00988 0.0642 0.0839 0.102 0.117 0.000291 0.000331 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 209 23950.223 0.005 0.00566 0.0775 0.191 0.0663 0.0883 0.259 0.327 0.000736 0.000928 ! Validation 209 23950.223 0.005 0.00676 0.0379 0.173 0.0719 0.0965 0.169 0.229 0.000481 0.000649 Wall time: 23950.22366891289 ! Best model 209 0.173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 86 0.125 0.00554 0.0145 0.0651 0.0873 0.121 0.141 0.000344 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.132 0.00508 0.0301 0.0639 0.0836 0.191 0.203 0.000544 0.000578 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 24064.716 0.005 0.00556 0.0531 0.164 0.0657 0.0875 0.214 0.27 0.000609 0.000768 ! Validation 210 24064.716 0.005 0.00675 0.0563 0.191 0.0719 0.0964 0.213 0.278 0.000606 0.000791 Wall time: 24064.716288825963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 86 0.13 0.0058 0.014 0.0661 0.0893 0.112 0.139 0.000318 0.000394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.121 0.005 0.0206 0.0632 0.0829 0.147 0.168 0.000417 0.000478 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 211 24179.749 0.005 0.00543 0.0431 0.152 0.0649 0.0865 0.195 0.244 0.000554 0.000692 ! Validation 211 24179.749 0.005 0.00665 0.0522 0.185 0.0711 0.0956 0.2 0.268 0.000569 0.000761 Wall time: 24179.749088430777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 86 0.171 0.00526 0.0653 0.0641 0.0851 0.274 0.3 0.000777 0.000852 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.186 0.00493 0.0872 0.0628 0.0823 0.339 0.346 0.000964 0.000984 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 212 24296.538 0.005 0.00564 0.0666 0.179 0.0662 0.0881 0.246 0.303 0.000699 0.00086 ! Validation 212 24296.538 0.005 0.00659 0.0923 0.224 0.0709 0.0952 0.298 0.356 0.000846 0.00101 Wall time: 24296.538109050132 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 86 0.108 0.00482 0.0114 0.0612 0.0814 0.0993 0.125 0.000282 0.000356 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.112 0.00493 0.0133 0.0628 0.0824 0.116 0.135 0.000331 0.000384 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 213 24412.916 0.005 0.00538 0.0417 0.149 0.0646 0.0861 0.191 0.24 0.000541 0.000681 ! Validation 213 24412.916 0.005 0.00657 0.0375 0.169 0.0709 0.0951 0.17 0.227 0.000484 0.000645 Wall time: 24412.916434788145 ! Best model 213 0.169 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 86 0.402 0.00583 0.286 0.0682 0.0896 0.605 0.627 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 214 100 0.214 0.00592 0.0957 0.0701 0.0903 0.355 0.363 0.00101 0.00103 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 214 24528.728 0.005 0.00556 0.0908 0.202 0.0658 0.0875 0.279 0.353 0.000794 0.001 ! Validation 214 24528.728 0.005 0.00744 0.204 0.353 0.0765 0.101 0.48 0.53 0.00136 0.00151 Wall time: 24528.728225661907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 86 0.243 0.00609 0.121 0.0682 0.0915 0.378 0.408 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 215 100 0.223 0.00498 0.123 0.0632 0.0828 0.404 0.411 0.00115 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 215 24643.186 0.005 0.00555 0.0579 0.169 0.0657 0.0874 0.227 0.282 0.000646 0.000802 ! Validation 215 24643.186 0.005 0.00663 0.118 0.251 0.0713 0.0955 0.342 0.404 0.000973 0.00115 Wall time: 24643.186669970863 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 86 0.172 0.00532 0.066 0.0642 0.0855 0.265 0.301 0.000753 0.000856 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.134 0.00491 0.0363 0.0625 0.0822 0.214 0.223 0.000609 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 216 24757.605 0.005 0.00542 0.0656 0.174 0.0649 0.0864 0.237 0.3 0.000674 0.000854 ! Validation 216 24757.605 0.005 0.00653 0.0587 0.189 0.0706 0.0948 0.217 0.284 0.000617 0.000807 Wall time: 24757.60581044806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 86 0.13 0.0058 0.0143 0.0668 0.0894 0.111 0.14 0.000316 0.000399 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.108 0.00496 0.00844 0.0631 0.0826 0.0925 0.108 0.000263 0.000306 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 24872.036 0.005 0.00555 0.0861 0.197 0.0657 0.0874 0.271 0.344 0.00077 0.000978 ! Validation 217 24872.036 0.005 0.00657 0.0311 0.163 0.0709 0.0951 0.157 0.207 0.000446 0.000588 Wall time: 24872.036900850013 ! Best model 217 0.163 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 86 0.138 0.00508 0.0367 0.063 0.0836 0.184 0.225 0.000522 0.000639 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.0978 0.0047 0.0037 0.0612 0.0804 0.0653 0.0713 0.000186 0.000203 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 24988.330 0.005 0.00527 0.0316 0.137 0.0639 0.0852 0.167 0.209 0.000475 0.000593 ! Validation 218 24988.330 0.005 0.00633 0.0447 0.171 0.0694 0.0933 0.195 0.248 0.000555 0.000705 Wall time: 24988.33059048513 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 86 0.198 0.00508 0.0963 0.0632 0.0836 0.33 0.364 0.000938 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 0.105 0.00499 0.00495 0.0637 0.0829 0.0797 0.0826 0.000226 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 219 25104.504 0.005 0.00515 0.0384 0.141 0.0631 0.0842 0.184 0.23 0.000521 0.000653 ! Validation 219 25104.504 0.005 0.00659 0.0387 0.171 0.071 0.0952 0.176 0.231 0.000501 0.000656 Wall time: 25104.504119612742 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 86 0.204 0.00497 0.104 0.0628 0.0827 0.361 0.379 0.00103 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.327 0.00486 0.23 0.0627 0.0818 0.559 0.563 0.00159 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 220 25218.907 0.005 0.00533 0.0575 0.164 0.0643 0.0856 0.229 0.281 0.00065 0.000799 ! Validation 220 25218.907 0.005 0.0065 0.191 0.321 0.0705 0.0946 0.474 0.512 0.00135 0.00145 Wall time: 25218.90785428416 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 86 0.111 0.00496 0.0118 0.0626 0.0826 0.103 0.127 0.000291 0.000361 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.182 0.00501 0.0817 0.0634 0.083 0.328 0.335 0.000932 0.000952 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 221 25333.292 0.005 0.00531 0.076 0.182 0.0642 0.0855 0.264 0.324 0.00075 0.000919 ! Validation 221 25333.292 0.005 0.00651 0.0765 0.207 0.0706 0.0947 0.269 0.324 0.000763 0.000922 Wall time: 25333.29236172 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 86 0.186 0.00521 0.0813 0.0639 0.0847 0.303 0.334 0.00086 0.00095 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.222 0.00526 0.117 0.0652 0.0851 0.394 0.401 0.00112 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 222 25448.154 0.005 0.00535 0.0636 0.171 0.0645 0.0858 0.24 0.296 0.00068 0.00084 ! Validation 222 25448.154 0.005 0.00677 0.107 0.242 0.0723 0.0965 0.333 0.384 0.000947 0.00109 Wall time: 25448.154511095956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 86 0.317 0.00562 0.205 0.0664 0.0879 0.511 0.53 0.00145 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.216 0.00541 0.108 0.0664 0.0862 0.378 0.385 0.00107 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 223 25562.603 0.005 0.00522 0.0407 0.145 0.0637 0.0847 0.188 0.236 0.000533 0.000672 ! Validation 223 25562.603 0.005 0.00703 0.145 0.286 0.0737 0.0983 0.392 0.447 0.00111 0.00127 Wall time: 25562.603180946782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 86 0.115 0.00512 0.0129 0.0629 0.0839 0.108 0.133 0.000308 0.000379 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.0957 0.00458 0.00404 0.0604 0.0794 0.0633 0.0746 0.00018 0.000212 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 224 25676.896 0.005 0.0052 0.0432 0.147 0.0635 0.0845 0.194 0.244 0.000551 0.000693 ! Validation 224 25676.896 0.005 0.00614 0.0351 0.158 0.0684 0.0919 0.175 0.22 0.000498 0.000625 Wall time: 25676.896539932117 ! Best model 224 0.158 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 86 0.121 0.00522 0.0161 0.0634 0.0848 0.116 0.149 0.00033 0.000423 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.129 0.0047 0.0351 0.0613 0.0804 0.211 0.22 0.0006 0.000625 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 225 25791.190 0.005 0.00534 0.088 0.195 0.0645 0.0857 0.288 0.348 0.000819 0.000989 ! Validation 225 25791.190 0.005 0.00631 0.035 0.161 0.0695 0.0932 0.168 0.219 0.000478 0.000624 Wall time: 25791.19068413414 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 86 0.122 0.00497 0.0221 0.0622 0.0827 0.142 0.175 0.000404 0.000496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.118 0.00459 0.0265 0.0606 0.0795 0.173 0.191 0.000492 0.000542 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 25905.986 0.005 0.00503 0.0424 0.143 0.0624 0.0832 0.194 0.242 0.000551 0.000686 ! Validation 226 25905.986 0.005 0.00615 0.0379 0.161 0.0684 0.092 0.172 0.228 0.000488 0.000648 Wall time: 25905.986975416075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 86 0.118 0.0046 0.0257 0.0597 0.0796 0.16 0.188 0.000455 0.000535 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.0973 0.00446 0.00805 0.0595 0.0783 0.0945 0.105 0.000268 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 227 26020.293 0.005 0.00495 0.0303 0.129 0.0618 0.0825 0.164 0.204 0.000466 0.00058 ! Validation 227 26020.293 0.005 0.00602 0.0366 0.157 0.0677 0.091 0.165 0.225 0.000468 0.000638 Wall time: 26020.29366719909 ! Best model 227 0.157 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 86 0.246 0.0054 0.138 0.0645 0.0862 0.415 0.436 0.00118 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.107 0.00477 0.0119 0.0617 0.081 0.111 0.128 0.000316 0.000363 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 228 26134.710 0.005 0.00549 0.0925 0.202 0.0653 0.0869 0.283 0.357 0.000805 0.00101 ! Validation 228 26134.710 0.005 0.00635 0.0552 0.182 0.0697 0.0934 0.203 0.276 0.000578 0.000783 Wall time: 26134.71071748808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 86 0.138 0.005 0.0384 0.0625 0.083 0.2 0.23 0.000567 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.182 0.00448 0.0924 0.0598 0.0785 0.351 0.357 0.000998 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 229 26249.001 0.005 0.00508 0.0511 0.153 0.0628 0.0836 0.216 0.265 0.000613 0.000753 ! Validation 229 26249.001 0.005 0.00605 0.099 0.22 0.0679 0.0912 0.317 0.369 0.000901 0.00105 Wall time: 26249.001915644854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 86 0.124 0.00492 0.0253 0.0618 0.0823 0.161 0.187 0.000456 0.00053 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.107 0.00445 0.0184 0.0595 0.0782 0.15 0.159 0.000425 0.000453 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 230 26363.553 0.005 0.00498 0.0417 0.141 0.0621 0.0828 0.194 0.239 0.00055 0.00068 ! Validation 230 26363.553 0.005 0.00599 0.0346 0.154 0.0675 0.0908 0.162 0.218 0.000461 0.00062 Wall time: 26363.55338321207 ! Best model 230 0.154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 86 0.18 0.0046 0.0879 0.0603 0.0796 0.311 0.348 0.000884 0.000988 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.0999 0.00449 0.0101 0.0601 0.0786 0.092 0.118 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 231 26477.853 0.005 0.00485 0.0321 0.129 0.0613 0.0817 0.166 0.21 0.000472 0.000596 ! Validation 231 26477.853 0.005 0.00599 0.0594 0.179 0.0676 0.0908 0.241 0.286 0.000684 0.000812 Wall time: 26477.853485642 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 86 0.124 0.00522 0.0196 0.0638 0.0847 0.137 0.164 0.00039 0.000467 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.298 0.00497 0.199 0.0634 0.0827 0.515 0.523 0.00146 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 232 26592.459 0.005 0.00501 0.0802 0.18 0.0624 0.083 0.265 0.332 0.000752 0.000944 ! Validation 232 26592.459 0.005 0.00629 0.123 0.249 0.0696 0.093 0.358 0.412 0.00102 0.00117 Wall time: 26592.45977270417 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 86 0.154 0.00485 0.0566 0.0617 0.0817 0.24 0.279 0.000682 0.000793 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.0978 0.00443 0.00928 0.0596 0.078 0.0923 0.113 0.000262 0.000321 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 26706.853 0.005 0.00487 0.0383 0.136 0.0614 0.0819 0.185 0.23 0.000526 0.000652 ! Validation 233 26706.853 0.005 0.00604 0.046 0.167 0.0679 0.0912 0.207 0.252 0.000589 0.000715 Wall time: 26706.853281299118 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 86 0.119 0.00483 0.0224 0.0613 0.0815 0.139 0.176 0.000395 0.000499 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.12 0.00427 0.0345 0.0583 0.0767 0.209 0.218 0.000592 0.000619 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 234 26827.970 0.005 0.00521 0.0659 0.17 0.0636 0.0847 0.233 0.301 0.000663 0.000856 ! Validation 234 26827.970 0.005 0.00581 0.0391 0.155 0.0664 0.0894 0.177 0.232 0.000504 0.000659 Wall time: 26827.970365707763 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 86 0.189 0.00509 0.0874 0.0627 0.0837 0.326 0.347 0.000927 0.000985 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.201 0.00458 0.11 0.0607 0.0793 0.38 0.388 0.00108 0.0011 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 235 26942.391 0.005 0.00482 0.0497 0.146 0.0611 0.0815 0.208 0.261 0.00059 0.000743 ! Validation 235 26942.391 0.005 0.00611 0.114 0.236 0.0683 0.0917 0.34 0.396 0.000965 0.00112 Wall time: 26942.391758581158 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 86 0.126 0.00475 0.0308 0.0611 0.0808 0.178 0.206 0.000506 0.000585 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.127 0.00448 0.0373 0.0599 0.0785 0.214 0.227 0.000607 0.000644 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 236 27057.161 0.005 0.00488 0.048 0.146 0.0615 0.0819 0.21 0.257 0.000597 0.00073 ! Validation 236 27057.161 0.005 0.00589 0.0526 0.17 0.067 0.09 0.208 0.269 0.000591 0.000765 Wall time: 27057.161301000044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 86 0.135 0.00446 0.0453 0.059 0.0784 0.221 0.25 0.000629 0.000709 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.146 0.00445 0.0567 0.0594 0.0782 0.27 0.279 0.000766 0.000794 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 237 27171.588 0.005 0.00482 0.0526 0.149 0.0611 0.0814 0.22 0.269 0.000625 0.000764 ! Validation 237 27171.588 0.005 0.00595 0.145 0.264 0.0673 0.0904 0.408 0.447 0.00116 0.00127 Wall time: 27171.58868222311 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 86 0.104 0.00457 0.0129 0.0599 0.0793 0.109 0.133 0.00031 0.000379 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.101 0.00423 0.0167 0.0579 0.0763 0.144 0.152 0.000408 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 238 27286.039 0.005 0.00473 0.0385 0.133 0.0605 0.0806 0.185 0.23 0.000525 0.000654 ! Validation 238 27286.039 0.005 0.00571 0.0328 0.147 0.0659 0.0887 0.158 0.213 0.00045 0.000604 Wall time: 27286.039363765158 ! Best model 238 0.147 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 86 0.17 0.00494 0.0709 0.0622 0.0824 0.288 0.312 0.000817 0.000887 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.234 0.00449 0.144 0.0599 0.0786 0.44 0.446 0.00125 0.00127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 239 27400.592 0.005 0.00469 0.0469 0.141 0.0602 0.0803 0.201 0.254 0.000571 0.000721 ! Validation 239 27400.592 0.005 0.00586 0.139 0.256 0.0669 0.0898 0.39 0.437 0.00111 0.00124 Wall time: 27400.592212554067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 86 0.163 0.00498 0.0639 0.0624 0.0828 0.271 0.296 0.000769 0.000842 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.0986 0.00457 0.00722 0.0604 0.0793 0.0762 0.0997 0.000217 0.000283 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 240 27515.200 0.005 0.00462 0.035 0.127 0.0598 0.0797 0.169 0.219 0.00048 0.000623 ! Validation 240 27515.200 0.005 0.00594 0.101 0.219 0.0674 0.0904 0.312 0.372 0.000886 0.00106 Wall time: 27515.20016250573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 86 0.106 0.00453 0.0154 0.0596 0.0789 0.114 0.146 0.000324 0.000413 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.13 0.00435 0.0428 0.0591 0.0773 0.233 0.243 0.000662 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 241 27629.823 0.005 0.0049 0.0749 0.173 0.0617 0.0821 0.263 0.321 0.000746 0.000912 ! Validation 241 27629.823 0.005 0.00573 0.0452 0.16 0.066 0.0888 0.2 0.249 0.000568 0.000708 Wall time: 27629.82309830794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 86 0.104 0.00459 0.0122 0.0595 0.0794 0.0971 0.13 0.000276 0.000369 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.143 0.00438 0.0558 0.0589 0.0777 0.27 0.277 0.000768 0.000787 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 27744.273 0.005 0.00484 0.0609 0.158 0.0613 0.0816 0.237 0.29 0.000673 0.000823 ! Validation 242 27744.273 0.005 0.00583 0.0429 0.159 0.0666 0.0895 0.191 0.243 0.000543 0.00069 Wall time: 27744.273904162925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 86 0.114 0.00486 0.0166 0.0613 0.0818 0.121 0.151 0.000345 0.00043 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.093 0.00409 0.0112 0.057 0.075 0.1 0.124 0.000284 0.000352 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 243 27858.741 0.005 0.00454 0.0279 0.119 0.0592 0.079 0.155 0.196 0.000441 0.000557 ! Validation 243 27858.741 0.005 0.00557 0.0495 0.161 0.065 0.0875 0.219 0.261 0.000621 0.000741 Wall time: 27858.741842087824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 86 0.156 0.0049 0.0581 0.0617 0.0821 0.257 0.283 0.00073 0.000804 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.0951 0.00445 0.00602 0.0597 0.0783 0.0793 0.091 0.000225 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 244 27973.194 0.005 0.00467 0.0612 0.155 0.0602 0.0802 0.243 0.29 0.00069 0.000824 ! Validation 244 27973.194 0.005 0.00588 0.0263 0.144 0.067 0.0899 0.146 0.19 0.000414 0.00054 Wall time: 27973.19470829796 ! Best model 244 0.144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 86 0.117 0.0046 0.0247 0.0592 0.0796 0.148 0.184 0.000422 0.000524 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.2 0.00407 0.119 0.0571 0.0749 0.402 0.404 0.00114 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 245 28087.842 0.005 0.00461 0.0465 0.139 0.0597 0.0796 0.208 0.253 0.00059 0.000718 ! Validation 245 28087.842 0.005 0.00553 0.105 0.216 0.0648 0.0872 0.339 0.381 0.000962 0.00108 Wall time: 28087.842874140013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 86 0.233 0.00442 0.144 0.0586 0.078 0.429 0.445 0.00122 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.204 0.00414 0.122 0.0574 0.0754 0.404 0.409 0.00115 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 246 28202.235 0.005 0.00456 0.0496 0.141 0.0594 0.0792 0.214 0.261 0.000609 0.000742 ! Validation 246 28202.235 0.005 0.00555 0.166 0.277 0.0651 0.0874 0.444 0.478 0.00126 0.00136 Wall time: 28202.23533857288 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 86 0.135 0.00466 0.0419 0.0595 0.0801 0.208 0.24 0.000591 0.000682 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.0883 0.00418 0.00476 0.0578 0.0758 0.0579 0.0809 0.000165 0.00023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 247 28316.789 0.005 0.00455 0.0544 0.145 0.0593 0.0791 0.226 0.274 0.000641 0.000777 ! Validation 247 28316.789 0.005 0.00561 0.0401 0.152 0.0655 0.0879 0.19 0.235 0.00054 0.000667 Wall time: 28316.789475332014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 86 0.0983 0.00421 0.0142 0.0572 0.0761 0.115 0.14 0.000325 0.000397 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.108 0.00423 0.0229 0.0581 0.0763 0.165 0.178 0.000468 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 248 28431.310 0.005 0.0046 0.0533 0.145 0.0597 0.0795 0.223 0.271 0.000632 0.00077 ! Validation 248 28431.310 0.005 0.00563 0.0249 0.137 0.0655 0.088 0.141 0.185 0.000402 0.000526 Wall time: 28431.31073016394 ! Best model 248 0.137 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 86 0.134 0.00459 0.0425 0.0597 0.0795 0.219 0.242 0.000622 0.000687 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 0.131 0.00406 0.0498 0.0569 0.0747 0.256 0.262 0.000727 0.000743 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 249 28547.453 0.005 0.00446 0.0335 0.123 0.0587 0.0783 0.174 0.215 0.000496 0.00061 ! Validation 249 28547.453 0.005 0.00555 0.0493 0.16 0.0651 0.0874 0.211 0.26 0.000599 0.00074 Wall time: 28547.453763423022 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 86 0.0991 0.0043 0.0131 0.0578 0.0769 0.108 0.135 0.000306 0.000382 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 0.0919 0.00398 0.0123 0.0563 0.074 0.118 0.13 0.000336 0.00037 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 250 28662.154 0.005 0.00441 0.0327 0.121 0.0583 0.0779 0.168 0.212 0.000478 0.000603 ! Validation 250 28662.154 0.005 0.00541 0.0283 0.136 0.0641 0.0863 0.147 0.197 0.000418 0.00056 Wall time: 28662.154640595894 ! Best model 250 0.136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 86 0.123 0.00493 0.0244 0.062 0.0824 0.158 0.183 0.00045 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 0.17 0.00447 0.0808 0.06 0.0784 0.329 0.333 0.000934 0.000947 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 251 28776.683 0.005 0.00461 0.0532 0.145 0.0598 0.0796 0.219 0.271 0.000622 0.000769 ! Validation 251 28776.683 0.005 0.00596 0.0847 0.204 0.0675 0.0906 0.291 0.341 0.000826 0.00097 Wall time: 28776.683734036982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 86 0.13 0.00526 0.0248 0.0632 0.085 0.158 0.185 0.000448 0.000525 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 0.108 0.005 0.00801 0.0636 0.0829 0.0748 0.105 0.000212 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 252 28891.376 0.005 0.00451 0.0448 0.135 0.059 0.0788 0.185 0.248 0.000526 0.000705 ! Validation 252 28891.376 0.005 0.00621 0.0482 0.173 0.0697 0.0925 0.209 0.258 0.000595 0.000732 Wall time: 28891.376407635864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 86 0.165 0.00577 0.0497 0.068 0.0891 0.233 0.261 0.000661 0.000743 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 0.104 0.00499 0.00367 0.0636 0.0829 0.0673 0.071 0.000191 0.000202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 253 29006.176 0.005 0.00499 0.0863 0.186 0.0623 0.0829 0.263 0.345 0.000748 0.000979 ! Validation 253 29006.176 0.005 0.00637 0.031 0.158 0.0706 0.0936 0.161 0.207 0.000456 0.000587 Wall time: 29006.176940295845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 86 0.106 0.00428 0.0205 0.0569 0.0767 0.144 0.168 0.000408 0.000477 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 0.085 0.00396 0.00591 0.056 0.0738 0.0645 0.0902 0.000183 0.000256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 254 29120.493 0.005 0.00454 0.0394 0.13 0.0593 0.079 0.184 0.233 0.000524 0.000661 ! Validation 254 29120.493 0.005 0.00535 0.0365 0.144 0.0638 0.0858 0.182 0.224 0.000518 0.000637 Wall time: 29120.493631978054 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 86 0.183 0.0041 0.101 0.0564 0.0751 0.353 0.373 0.001 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 0.137 0.00407 0.0552 0.057 0.0748 0.267 0.276 0.000759 0.000783 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 255 29234.793 0.005 0.0044 0.0335 0.121 0.0583 0.0778 0.174 0.214 0.000494 0.000609 ! Validation 255 29234.793 0.005 0.00544 0.137 0.246 0.0643 0.0865 0.353 0.434 0.001 0.00123 Wall time: 29234.793432983104 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 86 0.408 0.0068 0.272 0.0738 0.0967 0.591 0.612 0.00168 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 0.217 0.00547 0.107 0.0658 0.0868 0.366 0.384 0.00104 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 256 29349.130 0.005 0.00441 0.0594 0.148 0.0584 0.0779 0.217 0.285 0.000617 0.000811 ! Validation 256 29349.130 0.005 0.00684 0.134 0.271 0.0724 0.097 0.354 0.43 0.001 0.00122 Wall time: 29349.13012557896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 86 0.113 0.00445 0.0241 0.0584 0.0783 0.151 0.182 0.000429 0.000517 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 0.0951 0.00401 0.0149 0.0566 0.0743 0.135 0.143 0.000385 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 257 29463.583 0.005 0.00461 0.0453 0.138 0.0598 0.0797 0.202 0.25 0.000574 0.000709 ! Validation 257 29463.583 0.005 0.00542 0.033 0.141 0.0643 0.0863 0.161 0.213 0.000457 0.000605 Wall time: 29463.583094482776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 86 0.0954 0.0044 0.00745 0.0579 0.0778 0.0771 0.101 0.000219 0.000288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 0.11 0.00377 0.0344 0.0547 0.072 0.21 0.217 0.000596 0.000618 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 29578.206 0.005 0.00428 0.0334 0.119 0.0575 0.0767 0.177 0.214 0.000503 0.000609 ! Validation 258 29578.206 0.005 0.00518 0.0391 0.143 0.0628 0.0844 0.179 0.232 0.000509 0.000659 Wall time: 29578.20615377184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 86 0.12 0.00401 0.0401 0.0559 0.0743 0.214 0.235 0.000607 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 0.104 0.00387 0.0266 0.0554 0.073 0.181 0.191 0.000513 0.000543 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 259 29692.489 0.005 0.00421 0.0376 0.122 0.057 0.0761 0.182 0.227 0.000516 0.000646 ! Validation 259 29692.489 0.005 0.00519 0.0321 0.136 0.0629 0.0845 0.161 0.21 0.000456 0.000597 Wall time: 29692.489459804725 ! Best model 259 0.136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 86 0.291 0.00423 0.206 0.0575 0.0763 0.522 0.533 0.00148 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.154 0.00412 0.0718 0.0576 0.0753 0.308 0.314 0.000874 0.000893 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 29806.770 0.005 0.00414 0.0341 0.117 0.0565 0.0754 0.17 0.216 0.000484 0.000614 ! Validation 260 29806.770 0.005 0.0055 0.132 0.242 0.065 0.087 0.389 0.426 0.0011 0.00121 Wall time: 29806.77030129172 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 86 0.093 0.00411 0.0108 0.0569 0.0752 0.0981 0.122 0.000279 0.000346 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.0895 0.00377 0.0142 0.0547 0.072 0.131 0.14 0.000371 0.000397 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 261 29921.034 0.005 0.00433 0.0574 0.144 0.0579 0.0772 0.223 0.281 0.000633 0.000798 ! Validation 261 29921.034 0.005 0.00513 0.0264 0.129 0.0624 0.084 0.147 0.191 0.000416 0.000542 Wall time: 29921.03451887006 ! Best model 261 0.129 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 86 0.12 0.0043 0.0335 0.0573 0.0769 0.19 0.215 0.00054 0.000609 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 0.126 0.00384 0.0491 0.0552 0.0726 0.251 0.26 0.000713 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 262 30035.369 0.005 0.00412 0.0247 0.107 0.0564 0.0753 0.146 0.184 0.000414 0.000524 ! Validation 262 30035.369 0.005 0.00518 0.0962 0.2 0.0628 0.0844 0.326 0.364 0.000926 0.00103 Wall time: 30035.369052270893 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 86 0.0995 0.00412 0.0171 0.0564 0.0753 0.123 0.153 0.00035 0.000436 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.0939 0.0038 0.0179 0.0549 0.0723 0.143 0.157 0.000406 0.000446 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 30149.890 0.005 0.0042 0.0426 0.127 0.057 0.076 0.189 0.242 0.000537 0.000688 ! Validation 263 30149.890 0.005 0.00512 0.0258 0.128 0.0624 0.084 0.144 0.189 0.000409 0.000536 Wall time: 30149.890226833988 ! Best model 263 0.128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 86 0.104 0.00421 0.02 0.0571 0.0761 0.137 0.166 0.000391 0.000471 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.101 0.00374 0.0258 0.0546 0.0717 0.182 0.188 0.000518 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 264 30264.254 0.005 0.00411 0.0339 0.116 0.0564 0.0752 0.171 0.216 0.000487 0.000614 ! Validation 264 30264.254 0.005 0.00506 0.0602 0.161 0.062 0.0834 0.247 0.288 0.000701 0.000818 Wall time: 30264.254256844055 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 86 0.316 0.00473 0.221 0.0604 0.0807 0.538 0.551 0.00153 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.193 0.0048 0.0965 0.0624 0.0813 0.358 0.364 0.00102 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 265 30378.693 0.005 0.00421 0.0494 0.134 0.0571 0.0761 0.204 0.26 0.000578 0.00074 ! Validation 265 30378.693 0.005 0.00616 0.0806 0.204 0.069 0.0921 0.282 0.333 0.000802 0.000946 Wall time: 30378.693618462887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 86 0.173 0.00412 0.0904 0.0564 0.0753 0.324 0.353 0.00092 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 0.107 0.00384 0.0299 0.0553 0.0727 0.197 0.203 0.00056 0.000576 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 30493.059 0.005 0.00409 0.0376 0.119 0.0562 0.075 0.184 0.227 0.000523 0.000646 ! Validation 266 30493.059 0.005 0.00515 0.0642 0.167 0.0626 0.0842 0.257 0.297 0.00073 0.000844 Wall time: 30493.05929029407 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 86 0.116 0.0043 0.0297 0.0571 0.0769 0.17 0.202 0.000482 0.000575 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.0834 0.00373 0.00881 0.0545 0.0716 0.093 0.11 0.000264 0.000313 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 267 30607.883 0.005 0.00421 0.0521 0.136 0.0572 0.0761 0.219 0.268 0.000623 0.000761 ! Validation 267 30607.883 0.005 0.00502 0.0432 0.144 0.0618 0.0831 0.201 0.244 0.000572 0.000693 Wall time: 30607.88365345495 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 86 0.189 0.00453 0.0985 0.0601 0.079 0.351 0.368 0.000997 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.153 0.00389 0.0754 0.0558 0.0731 0.316 0.322 0.000899 0.000915 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 268 30722.163 0.005 0.00405 0.0427 0.124 0.056 0.0747 0.198 0.242 0.000561 0.000688 ! Validation 268 30722.163 0.005 0.00518 0.073 0.177 0.063 0.0844 0.264 0.317 0.00075 0.000901 Wall time: 30722.16322047077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 86 0.0986 0.00409 0.0169 0.0562 0.075 0.125 0.152 0.000355 0.000433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.122 0.00358 0.0501 0.0535 0.0702 0.257 0.262 0.000729 0.000746 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 30836.430 0.005 0.00411 0.0378 0.12 0.0564 0.0752 0.184 0.228 0.000523 0.000648 ! Validation 269 30836.430 0.005 0.00492 0.0358 0.134 0.0612 0.0823 0.182 0.222 0.000516 0.00063 Wall time: 30836.430049845017 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 86 0.115 0.0052 0.0108 0.064 0.0846 0.097 0.122 0.000276 0.000347 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.0975 0.0046 0.00549 0.0609 0.0796 0.0743 0.0869 0.000211 0.000247 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 30951.138 0.005 0.00411 0.0429 0.125 0.0564 0.0752 0.197 0.243 0.000559 0.000691 ! Validation 270 30951.138 0.005 0.00579 0.0394 0.155 0.067 0.0892 0.189 0.233 0.000536 0.000661 Wall time: 30951.138398382813 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 86 0.284 0.00421 0.2 0.0578 0.0761 0.504 0.524 0.00143 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.153 0.00436 0.0663 0.059 0.0774 0.298 0.302 0.000846 0.000858 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 31065.742 0.005 0.00418 0.0443 0.128 0.0568 0.0759 0.197 0.246 0.00056 0.0007 ! Validation 271 31065.742 0.005 0.00563 0.104 0.216 0.0659 0.088 0.296 0.378 0.000842 0.00107 Wall time: 31065.742614381015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 86 0.0845 0.00376 0.00929 0.0541 0.072 0.096 0.113 0.000273 0.000321 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.0803 0.00358 0.00867 0.0534 0.0702 0.1 0.109 0.000285 0.00031 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 272 31180.845 0.005 0.00413 0.0314 0.114 0.0566 0.0754 0.163 0.208 0.000464 0.000591 ! Validation 272 31180.845 0.005 0.00483 0.0236 0.12 0.0605 0.0815 0.138 0.18 0.000393 0.000512 Wall time: 31180.845705625135 ! Best model 272 0.120 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 86 0.0896 0.00394 0.0108 0.0552 0.0736 0.0985 0.122 0.00028 0.000346 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.076 0.00356 0.00479 0.0533 0.07 0.0678 0.0812 0.000193 0.000231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 273 31295.122 0.005 0.00392 0.0295 0.108 0.055 0.0734 0.161 0.201 0.000459 0.000572 ! Validation 273 31295.122 0.005 0.00483 0.0227 0.119 0.0605 0.0815 0.133 0.177 0.000378 0.000502 Wall time: 31295.123003272805 ! Best model 273 0.119 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 86 0.0793 0.00364 0.00646 0.0531 0.0708 0.0722 0.0943 0.000205 0.000268 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.0721 0.00351 0.00194 0.0529 0.0695 0.0404 0.0516 0.000115 0.000147 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 274 31409.414 0.005 0.00399 0.0438 0.124 0.0555 0.0741 0.192 0.246 0.000546 0.000698 ! Validation 274 31409.414 0.005 0.00482 0.0417 0.138 0.0605 0.0814 0.187 0.239 0.00053 0.00068 Wall time: 31409.414083572105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 86 0.0851 0.00384 0.00827 0.0547 0.0727 0.087 0.107 0.000247 0.000303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.0814 0.00354 0.0106 0.0531 0.0698 0.112 0.121 0.000318 0.000343 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 31523.875 0.005 0.00429 0.0508 0.137 0.0577 0.0768 0.203 0.264 0.000577 0.000751 ! Validation 275 31523.875 0.005 0.00482 0.0249 0.121 0.0605 0.0814 0.138 0.185 0.000391 0.000526 Wall time: 31523.875283833127 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 86 0.299 0.00407 0.217 0.0557 0.0749 0.535 0.547 0.00152 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.257 0.00426 0.171 0.0586 0.0766 0.485 0.486 0.00138 0.00138 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 276 31638.275 0.005 0.00386 0.0332 0.111 0.0546 0.0729 0.166 0.213 0.000472 0.000606 ! Validation 276 31638.275 0.005 0.00563 0.121 0.234 0.0662 0.088 0.343 0.409 0.000973 0.00116 Wall time: 31638.275200616103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 86 0.0976 0.0036 0.0255 0.0531 0.0704 0.162 0.187 0.000461 0.000533 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.0725 0.0035 0.0026 0.0528 0.0694 0.0462 0.0598 0.000131 0.00017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 277 31752.771 0.005 0.004 0.036 0.116 0.0557 0.0742 0.173 0.222 0.000491 0.000632 ! Validation 277 31752.771 0.005 0.00474 0.0269 0.122 0.0601 0.0808 0.159 0.193 0.000453 0.000547 Wall time: 31752.77184216911 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 86 0.0867 0.00389 0.00895 0.055 0.0732 0.0917 0.111 0.000261 0.000315 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.0755 0.00351 0.00533 0.0529 0.0695 0.0688 0.0857 0.000196 0.000243 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 278 31867.175 0.005 0.00385 0.0386 0.116 0.0545 0.0728 0.19 0.231 0.000541 0.000655 ! Validation 278 31867.175 0.005 0.00472 0.0215 0.116 0.0598 0.0806 0.134 0.172 0.000381 0.000489 Wall time: 31867.175318856724 ! Best model 278 0.116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 86 0.102 0.00353 0.031 0.0526 0.0697 0.179 0.206 0.000508 0.000587 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.0709 0.00338 0.00342 0.0518 0.0682 0.0529 0.0686 0.00015 0.000195 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 31981.722 0.005 0.00377 0.0239 0.0992 0.0539 0.072 0.145 0.181 0.000412 0.000515 ! Validation 279 31981.722 0.005 0.00462 0.0277 0.12 0.0592 0.0797 0.158 0.195 0.00045 0.000555 Wall time: 31981.72238655016 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 86 0.0895 0.00358 0.0179 0.0527 0.0702 0.124 0.157 0.000351 0.000445 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.133 0.00348 0.0638 0.0524 0.0691 0.289 0.296 0.000822 0.000842 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 32097.524 0.005 0.00456 0.0725 0.164 0.0594 0.0792 0.226 0.316 0.000641 0.000897 ! Validation 280 32097.524 0.005 0.0047 0.0458 0.14 0.0597 0.0804 0.209 0.251 0.000593 0.000713 Wall time: 32097.524100512732 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 86 0.0839 0.00375 0.00886 0.054 0.0718 0.0861 0.11 0.000245 0.000314 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.145 0.0034 0.0773 0.052 0.0684 0.323 0.326 0.000916 0.000926 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 32212.000 0.005 0.00381 0.0308 0.107 0.0542 0.0724 0.164 0.206 0.000467 0.000585 ! Validation 281 32212.000 0.005 0.00469 0.0726 0.166 0.0596 0.0803 0.273 0.316 0.000775 0.000898 Wall time: 32212.000850935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 86 0.0859 0.00356 0.0148 0.0527 0.0699 0.112 0.143 0.000318 0.000405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.225 0.00354 0.154 0.0531 0.0698 0.457 0.46 0.0013 0.00131 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 282 32326.371 0.005 0.00385 0.0456 0.123 0.0545 0.0728 0.203 0.251 0.000577 0.000712 ! Validation 282 32326.371 0.005 0.00483 0.134 0.231 0.0606 0.0815 0.388 0.43 0.0011 0.00122 Wall time: 32326.371409833897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 86 0.0872 0.00374 0.0124 0.0539 0.0717 0.104 0.13 0.000296 0.00037 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.112 0.00337 0.0451 0.0519 0.0681 0.245 0.249 0.000697 0.000708 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 283 32440.850 0.005 0.0038 0.0263 0.102 0.0542 0.0723 0.149 0.19 0.000424 0.000541 ! Validation 283 32440.850 0.005 0.00462 0.0493 0.142 0.0591 0.0797 0.215 0.261 0.00061 0.00074 Wall time: 32440.85068466002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 86 0.0877 0.00354 0.0169 0.0526 0.0698 0.119 0.152 0.000338 0.000433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.0922 0.00394 0.0133 0.057 0.0737 0.127 0.135 0.00036 0.000385 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 284 32555.602 0.005 0.00375 0.0383 0.113 0.0538 0.0719 0.171 0.23 0.000487 0.000653 ! Validation 284 32555.602 0.005 0.00514 0.0269 0.13 0.0634 0.0841 0.147 0.192 0.000418 0.000547 Wall time: 32555.602596976794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 86 0.0861 0.00345 0.0171 0.0521 0.0689 0.125 0.153 0.000355 0.000436 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.0897 0.00336 0.0224 0.0517 0.068 0.172 0.176 0.00049 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 285 32669.880 0.005 0.00379 0.0246 0.1 0.0541 0.0722 0.149 0.184 0.000422 0.000523 ! Validation 285 32669.880 0.005 0.00462 0.0265 0.119 0.0592 0.0797 0.142 0.191 0.000404 0.000543 Wall time: 32669.880228565075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 86 0.101 0.00417 0.0172 0.0565 0.0757 0.128 0.154 0.000364 0.000438 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.113 0.00356 0.0422 0.0536 0.07 0.235 0.241 0.000669 0.000684 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 286 32784.166 0.005 0.00378 0.0471 0.123 0.054 0.0721 0.208 0.255 0.000591 0.000723 ! Validation 286 32784.166 0.005 0.00473 0.0394 0.134 0.0602 0.0806 0.185 0.233 0.000526 0.000661 Wall time: 32784.16695027379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 86 0.0823 0.00363 0.00977 0.0528 0.0707 0.0871 0.116 0.000247 0.000329 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.0929 0.00328 0.0273 0.0511 0.0672 0.187 0.194 0.000533 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 287 32898.431 0.005 0.00376 0.0301 0.105 0.0539 0.0719 0.161 0.203 0.000457 0.000578 ! Validation 287 32898.431 0.005 0.00449 0.0272 0.117 0.0583 0.0786 0.149 0.193 0.000424 0.000549 Wall time: 32898.43167372607 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 86 0.15 0.00372 0.0756 0.0537 0.0715 0.304 0.323 0.000863 0.000917 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.169 0.00325 0.104 0.0507 0.0669 0.376 0.378 0.00107 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 288 33012.778 0.005 0.00366 0.0305 0.104 0.0532 0.071 0.166 0.205 0.00047 0.000582 ! Validation 288 33012.778 0.005 0.00453 0.158 0.249 0.0586 0.0789 0.434 0.466 0.00123 0.00133 Wall time: 33012.77811626578 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 86 0.137 0.0037 0.063 0.0535 0.0714 0.276 0.295 0.000784 0.000837 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.109 0.00338 0.0416 0.0519 0.0682 0.234 0.239 0.000665 0.00068 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 289 33127.059 0.005 0.00381 0.0506 0.127 0.0544 0.0724 0.223 0.264 0.000633 0.00075 ! Validation 289 33127.059 0.005 0.00466 0.12 0.214 0.0596 0.08 0.362 0.407 0.00103 0.00116 Wall time: 33127.059049382806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 86 0.0737 0.00342 0.00524 0.0512 0.0686 0.0681 0.0849 0.000194 0.000241 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.066 0.00322 0.00159 0.0506 0.0666 0.0405 0.0468 0.000115 0.000133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 290 33241.443 0.005 0.00388 0.0408 0.118 0.0549 0.0731 0.183 0.237 0.000519 0.000673 ! Validation 290 33241.443 0.005 0.00443 0.0247 0.113 0.0578 0.078 0.146 0.184 0.000416 0.000524 Wall time: 33241.44304487994 ! Best model 290 0.113 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 86 0.102 0.00367 0.0287 0.0531 0.0711 0.173 0.199 0.000491 0.000565 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.13 0.00324 0.065 0.0507 0.0668 0.294 0.299 0.000836 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 291 33355.717 0.005 0.00358 0.0199 0.0915 0.0525 0.0702 0.131 0.166 0.000373 0.00047 ! Validation 291 33355.717 0.005 0.00446 0.062 0.151 0.0581 0.0783 0.25 0.292 0.000709 0.00083 Wall time: 33355.71777871996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 86 0.074 0.00337 0.00656 0.0513 0.0681 0.076 0.095 0.000216 0.00027 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.0836 0.00315 0.0206 0.0501 0.0658 0.162 0.168 0.000459 0.000478 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 292 33470.090 0.005 0.00376 0.036 0.111 0.054 0.072 0.176 0.223 0.0005 0.000632 ! Validation 292 33470.090 0.005 0.00437 0.0244 0.112 0.0574 0.0775 0.14 0.183 0.000398 0.00052 Wall time: 33470.09007604094 ! Best model 292 0.112 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 86 0.0843 0.00346 0.0151 0.0517 0.069 0.112 0.144 0.000317 0.00041 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.132 0.00319 0.068 0.0505 0.0662 0.302 0.306 0.000859 0.000869 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 293 33584.508 0.005 0.00362 0.0327 0.105 0.0529 0.0706 0.167 0.212 0.000474 0.000602 ! Validation 293 33584.508 0.005 0.00438 0.0524 0.14 0.0575 0.0776 0.226 0.269 0.000643 0.000763 Wall time: 33584.50887597911 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 86 0.0773 0.00345 0.00822 0.0515 0.0689 0.0849 0.106 0.000241 0.000302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.0672 0.00323 0.00255 0.0509 0.0667 0.0484 0.0593 0.000138 0.000168 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 294 33698.925 0.005 0.00365 0.0427 0.116 0.0531 0.0709 0.198 0.242 0.000563 0.000689 ! Validation 294 33698.925 0.005 0.00442 0.0229 0.111 0.0579 0.078 0.14 0.178 0.000397 0.000505 Wall time: 33698.92533567082 ! Best model 294 0.111 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 86 0.159 0.0039 0.0808 0.055 0.0732 0.313 0.334 0.00089 0.000948 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.0829 0.00351 0.0127 0.0531 0.0695 0.129 0.132 0.000368 0.000375 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 33813.327 0.005 0.00357 0.0392 0.111 0.0525 0.0701 0.185 0.232 0.000526 0.00066 ! Validation 295 33813.327 0.005 0.00469 0.0774 0.171 0.06 0.0803 0.269 0.326 0.000765 0.000927 Wall time: 33813.32744115498 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 86 0.122 0.00357 0.0506 0.0522 0.0701 0.239 0.264 0.000678 0.00075 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.121 0.0033 0.0553 0.0514 0.0674 0.273 0.276 0.000775 0.000783 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 296 33927.736 0.005 0.00367 0.0329 0.106 0.0533 0.0711 0.171 0.213 0.000487 0.000604 ! Validation 296 33927.736 0.005 0.00443 0.0588 0.147 0.058 0.0781 0.238 0.285 0.000676 0.000808 Wall time: 33927.736489497125 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 86 0.0978 0.00405 0.0168 0.0562 0.0747 0.129 0.152 0.000366 0.000431 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.136 0.00344 0.0674 0.0526 0.0688 0.302 0.304 0.000857 0.000865 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 34042.594 0.005 0.00371 0.0479 0.122 0.0535 0.0714 0.197 0.257 0.000559 0.00073 ! Validation 297 34042.594 0.005 0.00462 0.0703 0.163 0.0594 0.0797 0.264 0.311 0.00075 0.000884 Wall time: 34042.5949473558 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 86 0.079 0.00363 0.00639 0.0534 0.0707 0.0749 0.0938 0.000213 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.142 0.00328 0.076 0.051 0.0671 0.318 0.323 0.000903 0.000919 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 298 34157.006 0.005 0.00366 0.0346 0.108 0.0533 0.071 0.179 0.218 0.000507 0.00062 ! Validation 298 34157.006 0.005 0.00444 0.0718 0.161 0.0581 0.0782 0.272 0.314 0.000773 0.000893 Wall time: 34157.00658455212 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 86 0.102 0.0034 0.0336 0.0514 0.0684 0.197 0.215 0.000561 0.000611 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.131 0.00339 0.0637 0.0522 0.0683 0.289 0.296 0.000821 0.000841 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 34271.393 0.005 0.00352 0.0293 0.0997 0.0521 0.0696 0.167 0.201 0.000475 0.00057 ! Validation 299 34271.393 0.005 0.00456 0.0674 0.159 0.059 0.0792 0.258 0.305 0.000734 0.000865 Wall time: 34271.39385189675 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 86 0.0917 0.00348 0.0221 0.0519 0.0692 0.152 0.174 0.000432 0.000496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.092 0.00325 0.0271 0.0509 0.0668 0.192 0.193 0.000544 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 300 34385.804 0.005 0.00346 0.0213 0.0904 0.0516 0.069 0.14 0.171 0.000396 0.000486 ! Validation 300 34385.804 0.005 0.00447 0.0315 0.121 0.0583 0.0784 0.166 0.208 0.000472 0.000591 Wall time: 34385.80430556601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 86 0.0803 0.00354 0.00948 0.0523 0.0698 0.0902 0.114 0.000256 0.000324 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.151 0.00363 0.0784 0.0538 0.0707 0.326 0.329 0.000928 0.000933 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 301 34500.224 0.005 0.00413 0.0564 0.139 0.0566 0.0754 0.214 0.279 0.000609 0.000792 ! Validation 301 34500.224 0.005 0.00476 0.0834 0.179 0.0603 0.081 0.3 0.339 0.000854 0.000962 Wall time: 34500.22470943909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 86 0.0849 0.0038 0.00896 0.0534 0.0723 0.0892 0.111 0.000254 0.000315 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.0637 0.00307 0.00235 0.0495 0.0649 0.0504 0.0568 0.000143 0.000161 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 34614.589 0.005 0.00348 0.0227 0.0924 0.0518 0.0692 0.143 0.177 0.000407 0.000502 ! Validation 302 34614.589 0.005 0.00421 0.0258 0.11 0.0564 0.0761 0.15 0.188 0.000426 0.000535 Wall time: 34614.589951876085 ! Best model 302 0.110 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 86 0.0773 0.00353 0.00671 0.0518 0.0697 0.0782 0.0961 0.000222 0.000273 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.0944 0.00304 0.0337 0.0492 0.0646 0.21 0.215 0.000595 0.000611 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 303 34729.038 0.005 0.00351 0.0258 0.0959 0.052 0.0695 0.145 0.188 0.000413 0.000535 ! Validation 303 34729.038 0.005 0.00418 0.0368 0.121 0.0562 0.0759 0.178 0.225 0.000506 0.000639 Wall time: 34729.03810866689 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 86 0.092 0.00381 0.0158 0.0548 0.0724 0.117 0.147 0.000332 0.000419 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.0696 0.00334 0.00276 0.052 0.0678 0.0537 0.0616 0.000153 0.000175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 304 34843.383 0.005 0.00353 0.0478 0.118 0.0523 0.0697 0.216 0.256 0.000613 0.000729 ! Validation 304 34843.383 0.005 0.00447 0.0281 0.118 0.0585 0.0784 0.157 0.196 0.000446 0.000558 Wall time: 34843.38385318499 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 86 0.0722 0.00317 0.00881 0.0497 0.0661 0.0866 0.11 0.000246 0.000313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.117 0.00304 0.0557 0.0492 0.0647 0.273 0.277 0.000776 0.000787 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 34957.816 0.005 0.00341 0.0235 0.0918 0.0513 0.0685 0.143 0.18 0.000407 0.000511 ! Validation 305 34957.816 0.005 0.00418 0.0531 0.137 0.0562 0.0758 0.225 0.27 0.000641 0.000768 Wall time: 34957.81693808595 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 86 0.0849 0.00348 0.0152 0.0513 0.0692 0.125 0.145 0.000355 0.000411 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.0784 0.00316 0.0151 0.0501 0.066 0.134 0.144 0.000382 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 306 35072.200 0.005 0.00353 0.0406 0.111 0.0523 0.0697 0.197 0.236 0.000561 0.000671 ! Validation 306 35072.200 0.005 0.00439 0.0683 0.156 0.0577 0.0777 0.26 0.307 0.000737 0.000871 Wall time: 35072.200207836926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 86 0.112 0.00335 0.0452 0.051 0.0679 0.231 0.249 0.000656 0.000708 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.071 0.0031 0.00896 0.0497 0.0653 0.0901 0.111 0.000256 0.000316 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 35186.569 0.005 0.00342 0.033 0.101 0.0514 0.0686 0.174 0.213 0.000494 0.000605 ! Validation 307 35186.569 0.005 0.00419 0.0355 0.119 0.0563 0.0759 0.185 0.221 0.000526 0.000627 Wall time: 35186.5695575471 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 86 0.105 0.00306 0.0435 0.0489 0.0649 0.228 0.245 0.000647 0.000695 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.0881 0.00319 0.0244 0.0504 0.0662 0.175 0.183 0.000498 0.00052 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 35300.948 0.005 0.0033 0.0194 0.0855 0.0504 0.0674 0.131 0.163 0.000372 0.000464 ! Validation 308 35300.948 0.005 0.0043 0.0559 0.142 0.0571 0.0769 0.24 0.277 0.000681 0.000788 Wall time: 35300.948487613816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 86 0.0817 0.00335 0.0148 0.0512 0.0679 0.112 0.143 0.000317 0.000405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.0669 0.00299 0.00704 0.0488 0.0642 0.0895 0.0984 0.000254 0.00028 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 309 35415.540 0.005 0.00358 0.044 0.116 0.0528 0.0702 0.2 0.246 0.000568 0.000699 ! Validation 309 35415.540 0.005 0.00414 0.048 0.131 0.056 0.0755 0.215 0.257 0.000612 0.00073 Wall time: 35415.54009464802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 86 0.0888 0.00333 0.0222 0.0508 0.0676 0.151 0.175 0.000428 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.072 0.00294 0.0133 0.0482 0.0636 0.128 0.135 0.000364 0.000384 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 310 35530.066 0.005 0.00334 0.0253 0.0921 0.0507 0.0678 0.15 0.187 0.000427 0.000531 ! Validation 310 35530.066 0.005 0.00412 0.0398 0.122 0.0558 0.0753 0.197 0.234 0.000559 0.000665 Wall time: 35530.066657879855 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 86 0.08 0.00336 0.0128 0.0508 0.068 0.109 0.133 0.00031 0.000378 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.114 0.00319 0.0507 0.0509 0.0662 0.258 0.264 0.000733 0.00075 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 35644.460 0.005 0.00339 0.0416 0.11 0.0512 0.0683 0.195 0.239 0.000553 0.00068 ! Validation 311 35644.460 0.005 0.00425 0.0739 0.159 0.057 0.0765 0.261 0.319 0.000742 0.000906 Wall time: 35644.460164856166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 86 0.0837 0.00359 0.0119 0.0522 0.0703 0.0994 0.128 0.000282 0.000364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.0823 0.00316 0.0192 0.0505 0.0659 0.156 0.162 0.000443 0.000461 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 35758.715 0.005 0.00372 0.061 0.135 0.0539 0.0715 0.242 0.29 0.000686 0.000823 ! Validation 312 35758.715 0.005 0.00433 0.0221 0.109 0.0575 0.0772 0.136 0.174 0.000386 0.000495 Wall time: 35758.71582361497 ! Best model 312 0.109 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 86 0.0815 0.00309 0.0197 0.0493 0.0652 0.147 0.165 0.000418 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.0696 0.00292 0.0112 0.0484 0.0634 0.115 0.124 0.000327 0.000352 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 313 35872.985 0.005 0.0033 0.0168 0.0829 0.0505 0.0674 0.122 0.152 0.000348 0.000432 ! Validation 313 35872.985 0.005 0.00405 0.0454 0.126 0.0553 0.0746 0.213 0.25 0.000605 0.00071 Wall time: 35872.98586694803 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 86 0.173 0.00363 0.101 0.0537 0.0707 0.349 0.372 0.000991 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.0872 0.00409 0.00525 0.058 0.0751 0.0704 0.085 0.0002 0.000242 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 35987.287 0.005 0.00335 0.0346 0.102 0.0509 0.0679 0.178 0.218 0.000505 0.00062 ! Validation 314 35987.287 0.005 0.00522 0.0243 0.129 0.0641 0.0847 0.144 0.183 0.000409 0.00052 Wall time: 35987.28766015172 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 86 0.0759 0.00326 0.0107 0.0503 0.067 0.0996 0.121 0.000283 0.000345 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.0602 0.00295 0.00123 0.0486 0.0637 0.0365 0.0412 0.000104 0.000117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 315 36101.575 0.005 0.00337 0.0225 0.0899 0.0511 0.0681 0.138 0.176 0.000393 0.0005 ! Validation 315 36101.575 0.005 0.00405 0.0306 0.112 0.0554 0.0746 0.157 0.205 0.000447 0.000583 Wall time: 36101.57592137577 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 86 0.0818 0.00355 0.0108 0.0524 0.0699 0.0987 0.122 0.00028 0.000346 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.0749 0.00307 0.0136 0.0493 0.065 0.124 0.137 0.000353 0.000388 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 36215.914 0.005 0.00326 0.0206 0.0857 0.0501 0.067 0.135 0.168 0.000383 0.000478 ! Validation 316 36215.914 0.005 0.00424 0.0318 0.117 0.0568 0.0764 0.181 0.209 0.000513 0.000594 Wall time: 36215.91493834788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 86 0.126 0.00367 0.0528 0.0534 0.0711 0.226 0.27 0.000643 0.000766 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 0.0964 0.00372 0.0219 0.0552 0.0716 0.167 0.174 0.000474 0.000493 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 317 36330.191 0.005 0.00344 0.0456 0.114 0.0516 0.0688 0.201 0.25 0.00057 0.000712 ! Validation 317 36330.191 0.005 0.00478 0.051 0.147 0.0608 0.0811 0.207 0.265 0.000588 0.000753 Wall time: 36330.19197968673 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 86 0.0817 0.00317 0.0183 0.0496 0.066 0.134 0.159 0.00038 0.000451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.0692 0.00288 0.0116 0.0481 0.063 0.12 0.126 0.000341 0.000359 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 36444.587 0.005 0.00364 0.0325 0.105 0.0532 0.0707 0.172 0.211 0.00049 0.0006 ! Validation 318 36444.587 0.005 0.00403 0.0262 0.107 0.0552 0.0744 0.147 0.19 0.000417 0.000539 Wall time: 36444.58757788874 ! Best model 318 0.107 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 86 0.0896 0.00303 0.0289 0.0483 0.0646 0.18 0.199 0.000512 0.000566 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.0676 0.00294 0.00876 0.0486 0.0636 0.1 0.11 0.000284 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 319 36558.831 0.005 0.00322 0.0241 0.0885 0.0498 0.0666 0.144 0.182 0.000409 0.000517 ! Validation 319 36558.831 0.005 0.00402 0.0314 0.112 0.0552 0.0744 0.176 0.208 0.000501 0.000591 Wall time: 36558.83110722294 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 86 0.0786 0.00307 0.0173 0.0491 0.065 0.132 0.154 0.000376 0.000438 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.139 0.00307 0.078 0.0496 0.065 0.324 0.328 0.000921 0.000931 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 36673.066 0.005 0.00327 0.0356 0.101 0.0502 0.067 0.173 0.221 0.000492 0.000629 ! Validation 320 36673.066 0.005 0.00417 0.0595 0.143 0.0564 0.0758 0.249 0.286 0.000708 0.000813 Wall time: 36673.066713503096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 86 0.107 0.00318 0.043 0.0489 0.0661 0.226 0.243 0.000642 0.000691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.0629 0.0029 0.00483 0.0483 0.0632 0.064 0.0815 0.000182 0.000231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 321 36787.328 0.005 0.00319 0.0243 0.088 0.0496 0.0662 0.148 0.183 0.00042 0.00052 ! Validation 321 36787.328 0.005 0.00398 0.0267 0.106 0.0549 0.074 0.162 0.192 0.000461 0.000545 Wall time: 36787.32896199776 ! Best model 321 0.106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 86 0.07 0.00304 0.00918 0.0488 0.0647 0.0859 0.112 0.000244 0.000319 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.0749 0.003 0.0149 0.049 0.0642 0.138 0.143 0.000392 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 322 36901.588 0.005 0.00323 0.0265 0.0911 0.0499 0.0667 0.153 0.191 0.000435 0.000542 ! Validation 322 36901.588 0.005 0.00406 0.0203 0.101 0.0554 0.0747 0.129 0.167 0.000368 0.000474 Wall time: 36901.58821007097 ! Best model 322 0.101 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 86 0.131 0.00321 0.067 0.05 0.0665 0.288 0.304 0.000818 0.000863 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.113 0.00299 0.0531 0.0494 0.0642 0.267 0.27 0.000759 0.000768 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 323 37015.979 0.005 0.00321 0.0279 0.092 0.0498 0.0664 0.159 0.196 0.000452 0.000556 ! Validation 323 37015.979 0.005 0.00404 0.0737 0.154 0.0557 0.0745 0.278 0.318 0.000791 0.000904 Wall time: 37015.979362798855 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 86 0.102 0.00367 0.0292 0.0527 0.071 0.179 0.2 0.000509 0.000569 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.0656 0.0028 0.00965 0.0473 0.062 0.109 0.115 0.000311 0.000327 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 324 37130.198 0.005 0.00387 0.046 0.123 0.0549 0.073 0.205 0.252 0.000582 0.000715 ! Validation 324 37130.198 0.005 0.00389 0.0255 0.103 0.0542 0.0732 0.144 0.187 0.000409 0.000532 Wall time: 37130.19844197808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 86 0.0656 0.00294 0.00686 0.0475 0.0636 0.0764 0.0972 0.000217 0.000276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.0823 0.00277 0.0269 0.0472 0.0618 0.189 0.192 0.000536 0.000546 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 325 37244.360 0.005 0.00315 0.0162 0.0793 0.0493 0.0659 0.118 0.15 0.000336 0.000425 ! Validation 325 37244.360 0.005 0.00386 0.0306 0.108 0.054 0.0729 0.165 0.205 0.000468 0.000583 Wall time: 37244.36048405012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 86 0.0923 0.00355 0.0212 0.0514 0.0699 0.157 0.171 0.000446 0.000485 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.0571 0.00283 0.000462 0.0476 0.0624 0.0192 0.0252 5.45e-05 7.16e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 326 37358.514 0.005 0.0031 0.0274 0.0895 0.0489 0.0653 0.162 0.194 0.000459 0.000552 ! Validation 326 37358.514 0.005 0.00398 0.0257 0.105 0.0549 0.074 0.15 0.188 0.000425 0.000534 Wall time: 37358.514976202976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 86 0.0657 0.00286 0.00849 0.0473 0.0627 0.0859 0.108 0.000244 0.000307 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.0651 0.00296 0.00586 0.0488 0.0638 0.071 0.0898 0.000202 0.000255 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 327 37472.757 0.005 0.00321 0.0392 0.103 0.0498 0.0665 0.186 0.232 0.000529 0.00066 ! Validation 327 37472.757 0.005 0.00401 0.0179 0.0982 0.0552 0.0743 0.122 0.157 0.000347 0.000446 Wall time: 37472.757768572774 ! Best model 327 0.098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 86 0.106 0.00419 0.022 0.0575 0.0759 0.149 0.174 0.000424 0.000494 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.0792 0.00392 0.000721 0.0564 0.0735 0.0246 0.0315 6.98e-05 8.95e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 328 37586.928 0.005 0.00356 0.0394 0.111 0.0524 0.07 0.18 0.233 0.000511 0.000662 ! Validation 328 37586.928 0.005 0.00507 0.0504 0.152 0.0629 0.0835 0.197 0.263 0.000561 0.000748 Wall time: 37586.92895086389 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 86 0.135 0.00357 0.0639 0.0531 0.0701 0.28 0.297 0.000794 0.000843 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.109 0.00305 0.0482 0.0494 0.0648 0.248 0.257 0.000706 0.000731 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 329 37701.178 0.005 0.00323 0.0331 0.0978 0.05 0.0667 0.173 0.213 0.000492 0.000606 ! Validation 329 37701.178 0.005 0.00412 0.0331 0.116 0.0559 0.0753 0.169 0.214 0.000481 0.000607 Wall time: 37701.17805614602 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 86 0.0694 0.00317 0.00596 0.0493 0.0661 0.0736 0.0905 0.000209 0.000257 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.0662 0.00279 0.0104 0.0471 0.062 0.109 0.12 0.00031 0.00034 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 37815.333 0.005 0.00311 0.0245 0.0868 0.049 0.0655 0.147 0.184 0.000417 0.000522 ! Validation 330 37815.333 0.005 0.00386 0.0337 0.111 0.0541 0.0729 0.182 0.215 0.000517 0.000612 Wall time: 37815.33333045291 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 86 0.092 0.00298 0.0323 0.0482 0.0641 0.192 0.211 0.000544 0.000599 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.0575 0.00277 0.00205 0.0472 0.0618 0.0471 0.0531 0.000134 0.000151 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 37929.657 0.005 0.00304 0.0171 0.078 0.0484 0.0647 0.118 0.153 0.000335 0.000436 ! Validation 331 37929.657 0.005 0.00381 0.0195 0.0957 0.0536 0.0724 0.13 0.164 0.00037 0.000465 Wall time: 37929.65752098709 ! Best model 331 0.096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 86 0.0809 0.00369 0.00721 0.0538 0.0712 0.0815 0.0996 0.000232 0.000283 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.102 0.00362 0.0297 0.0543 0.0706 0.198 0.202 0.000561 0.000575 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 38043.966 0.005 0.0032 0.0426 0.107 0.0497 0.0663 0.197 0.242 0.00056 0.000688 ! Validation 332 38043.966 0.005 0.00464 0.0296 0.122 0.0597 0.0799 0.163 0.202 0.000463 0.000573 Wall time: 38043.9662179579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 86 0.0857 0.00325 0.0208 0.0505 0.0668 0.142 0.169 0.000402 0.00048 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.0651 0.00288 0.00754 0.0482 0.0629 0.0825 0.102 0.000234 0.000289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 333 38158.266 0.005 0.0031 0.0246 0.0866 0.0489 0.0653 0.148 0.184 0.000422 0.000523 ! Validation 333 38158.266 0.005 0.00389 0.0185 0.0964 0.0546 0.0732 0.127 0.159 0.000361 0.000453 Wall time: 38158.26634589676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 86 0.0736 0.00305 0.0126 0.0488 0.0648 0.106 0.132 0.000301 0.000374 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.108 0.00275 0.053 0.047 0.0615 0.267 0.27 0.000759 0.000767 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 334 38272.557 0.005 0.00318 0.0347 0.0982 0.0496 0.0661 0.172 0.219 0.000488 0.000621 ! Validation 334 38272.557 0.005 0.0038 0.0648 0.141 0.0537 0.0723 0.261 0.299 0.000742 0.000848 Wall time: 38272.55731878709 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 86 0.0754 0.00307 0.0139 0.0487 0.065 0.116 0.138 0.000328 0.000393 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.0686 0.0027 0.0146 0.0465 0.0609 0.135 0.142 0.000384 0.000403 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 38386.866 0.005 0.00301 0.0209 0.0811 0.0481 0.0643 0.135 0.17 0.000384 0.000482 ! Validation 335 38386.866 0.005 0.00372 0.0222 0.0966 0.053 0.0716 0.135 0.175 0.000385 0.000497 Wall time: 38386.86606373079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 86 0.0737 0.00295 0.0148 0.0481 0.0637 0.114 0.143 0.000325 0.000405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.0635 0.00274 0.0087 0.0469 0.0614 0.0997 0.109 0.000283 0.000311 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 336 38501.302 0.005 0.00309 0.0316 0.0934 0.0489 0.0652 0.17 0.209 0.000482 0.000593 ! Validation 336 38501.302 0.005 0.00381 0.0235 0.0998 0.0538 0.0724 0.143 0.18 0.000406 0.000511 Wall time: 38501.30219032103 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 86 0.0813 0.00292 0.023 0.0476 0.0633 0.151 0.178 0.00043 0.000505 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.0676 0.00282 0.0111 0.0478 0.0623 0.118 0.123 0.000334 0.000351 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 337 38615.732 0.005 0.00302 0.0257 0.0862 0.0483 0.0645 0.152 0.188 0.000433 0.000535 ! Validation 337 38615.732 0.005 0.00386 0.03 0.107 0.054 0.0729 0.15 0.203 0.000427 0.000577 Wall time: 38615.73280162271 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 86 0.0633 0.00293 0.00479 0.0476 0.0635 0.0673 0.0812 0.000191 0.000231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.0612 0.00262 0.00879 0.046 0.0601 0.103 0.11 0.000294 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 338 38730.030 0.005 0.003 0.0246 0.0847 0.0481 0.0643 0.149 0.184 0.000423 0.000523 ! Validation 338 38730.030 0.005 0.00365 0.0168 0.0898 0.0525 0.0709 0.113 0.152 0.00032 0.000432 Wall time: 38730.03054728871 ! Best model 338 0.090 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 86 0.0675 0.003 0.00752 0.048 0.0642 0.0845 0.102 0.00024 0.000289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.0558 0.0026 0.00387 0.0457 0.0598 0.0587 0.073 0.000167 0.000207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 339 38844.329 0.005 0.00295 0.0202 0.0793 0.0477 0.0637 0.132 0.167 0.000375 0.000474 ! Validation 339 38844.329 0.005 0.00364 0.0222 0.095 0.0524 0.0708 0.147 0.175 0.000418 0.000497 Wall time: 38844.32965944102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 86 0.171 0.00342 0.102 0.0515 0.0686 0.359 0.375 0.00102 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.19 0.00325 0.125 0.0513 0.0669 0.41 0.414 0.00116 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 340 38958.743 0.005 0.00294 0.026 0.0848 0.0476 0.0636 0.154 0.189 0.000436 0.000536 ! Validation 340 38958.743 0.005 0.00431 0.127 0.213 0.0577 0.077 0.373 0.417 0.00106 0.00119 Wall time: 38958.74384630285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 86 0.0684 0.00303 0.00785 0.0486 0.0645 0.0857 0.104 0.000243 0.000295 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.0586 0.00276 0.00329 0.0473 0.0617 0.0519 0.0673 0.000147 0.000191 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 39073.039 0.005 0.0035 0.0515 0.121 0.0521 0.0694 0.212 0.266 0.000603 0.000757 ! Validation 341 39073.039 0.005 0.00377 0.0209 0.0964 0.0535 0.0721 0.14 0.169 0.000397 0.000481 Wall time: 39073.039155108854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 86 0.0716 0.00296 0.0124 0.0475 0.0638 0.107 0.131 0.000305 0.000371 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.0548 0.00265 0.00177 0.0462 0.0604 0.0425 0.0493 0.000121 0.00014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 342 39187.424 0.005 0.0029 0.0132 0.0712 0.0472 0.0632 0.107 0.135 0.000303 0.000383 ! Validation 342 39187.424 0.005 0.00368 0.0258 0.0994 0.0529 0.0712 0.146 0.188 0.000416 0.000535 Wall time: 39187.42484600516 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 86 0.0866 0.003 0.0267 0.0489 0.0642 0.159 0.192 0.000453 0.000544 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.062 0.00283 0.00535 0.0479 0.0624 0.074 0.0858 0.00021 0.000244 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 39301.706 0.005 0.00313 0.0465 0.109 0.0493 0.0656 0.206 0.253 0.000585 0.000718 ! Validation 343 39301.706 0.005 0.0038 0.0278 0.104 0.0538 0.0723 0.159 0.196 0.000451 0.000556 Wall time: 39301.70605668705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 86 0.159 0.00287 0.101 0.047 0.0629 0.362 0.374 0.00103 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.131 0.00282 0.0749 0.0475 0.0623 0.318 0.321 0.000904 0.000912 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 344 39416.109 0.005 0.00291 0.0166 0.0749 0.0474 0.0633 0.12 0.151 0.000342 0.000429 ! Validation 344 39416.109 0.005 0.00379 0.0536 0.129 0.0536 0.0722 0.235 0.272 0.000667 0.000772 Wall time: 39416.10938288504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 86 0.0762 0.00297 0.0167 0.0475 0.064 0.134 0.152 0.000381 0.000431 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.0768 0.00262 0.0245 0.0461 0.06 0.178 0.184 0.000507 0.000521 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 39531.332 0.005 0.00297 0.0249 0.0842 0.0479 0.0639 0.149 0.185 0.000422 0.000526 ! Validation 345 39531.332 0.005 0.00358 0.0321 0.104 0.0521 0.0702 0.166 0.21 0.00047 0.000597 Wall time: 39531.332548934966 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 86 0.0887 0.00371 0.0144 0.0551 0.0715 0.115 0.141 0.000328 0.000399 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.0684 0.00308 0.00673 0.0499 0.0651 0.0855 0.0962 0.000243 0.000273 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 346 39645.469 0.005 0.00297 0.0379 0.0972 0.0479 0.0639 0.177 0.228 0.000502 0.000649 ! Validation 346 39645.469 0.005 0.00411 0.0299 0.112 0.0563 0.0752 0.163 0.203 0.000463 0.000576 Wall time: 39645.46971714683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 86 0.0656 0.00275 0.0106 0.0462 0.0615 0.0986 0.121 0.00028 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.0797 0.00258 0.0281 0.0457 0.0596 0.194 0.197 0.00055 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 347 39759.623 0.005 0.00298 0.0215 0.081 0.048 0.064 0.14 0.172 0.000396 0.000488 ! Validation 347 39759.623 0.005 0.00362 0.0315 0.104 0.0524 0.0706 0.172 0.208 0.000488 0.000591 Wall time: 39759.62309840787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 86 0.0667 0.00302 0.00638 0.0476 0.0644 0.071 0.0937 0.000202 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.0524 0.00256 0.00122 0.0453 0.0593 0.037 0.041 0.000105 0.000116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 348 39873.844 0.005 0.00288 0.0182 0.0757 0.0471 0.0629 0.129 0.158 0.000367 0.00045 ! Validation 348 39873.844 0.005 0.00356 0.0277 0.0989 0.0518 0.07 0.156 0.195 0.000442 0.000555 Wall time: 39873.84447567398 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 86 0.0746 0.00288 0.017 0.0472 0.063 0.129 0.153 0.000366 0.000434 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.0631 0.00255 0.0122 0.0455 0.0592 0.122 0.13 0.000346 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 349 39988.054 0.005 0.00318 0.0374 0.101 0.0497 0.0661 0.18 0.227 0.000511 0.000644 ! Validation 349 39988.054 0.005 0.00354 0.0174 0.0882 0.0517 0.0698 0.12 0.155 0.00034 0.00044 Wall time: 39988.054876728915 ! Best model 349 0.088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 86 0.0755 0.00313 0.0128 0.0488 0.0657 0.106 0.133 0.0003 0.000378 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.0618 0.00291 0.00357 0.0486 0.0633 0.0633 0.0701 0.00018 0.000199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 350 40102.185 0.005 0.00286 0.0256 0.0828 0.0469 0.0627 0.147 0.188 0.000417 0.000534 ! Validation 350 40102.185 0.005 0.00391 0.0294 0.108 0.0547 0.0733 0.159 0.201 0.000451 0.000572 Wall time: 40102.18523543887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 86 0.0661 0.00251 0.016 0.0444 0.0587 0.132 0.148 0.000374 0.000421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.0788 0.00256 0.0275 0.0455 0.0594 0.191 0.195 0.000542 0.000553 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 40216.308 0.005 0.00291 0.0228 0.081 0.0474 0.0633 0.142 0.177 0.000402 0.000503 ! Validation 351 40216.308 0.005 0.00355 0.0342 0.105 0.0517 0.0699 0.173 0.217 0.000492 0.000617 Wall time: 40216.308924140874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 86 0.0745 0.0031 0.0125 0.0493 0.0653 0.107 0.131 0.000303 0.000373 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.3 0.0029 0.242 0.0484 0.0631 0.573 0.577 0.00163 0.00164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 352 40330.432 0.005 0.00291 0.0407 0.0988 0.0474 0.0632 0.187 0.237 0.000531 0.000673 ! Validation 352 40330.432 0.005 0.00396 0.153 0.232 0.0551 0.0738 0.42 0.458 0.00119 0.0013 Wall time: 40330.432651272975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 86 0.0739 0.00299 0.0141 0.0479 0.0641 0.116 0.139 0.000331 0.000396 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.0539 0.00248 0.00426 0.0446 0.0584 0.0658 0.0765 0.000187 0.000217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 353 40444.784 0.005 0.00292 0.0181 0.0765 0.0475 0.0634 0.122 0.158 0.000347 0.000448 ! Validation 353 40444.784 0.005 0.00351 0.0183 0.0885 0.0515 0.0695 0.123 0.159 0.000349 0.000451 Wall time: 40444.784245905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 86 0.0667 0.00305 0.00565 0.0485 0.0648 0.0658 0.0881 0.000187 0.00025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.053 0.00253 0.0024 0.0451 0.059 0.0439 0.0575 0.000125 0.000163 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 40559.311 0.005 0.00282 0.0172 0.0737 0.0466 0.0623 0.12 0.154 0.000341 0.000437 ! Validation 354 40559.311 0.005 0.00349 0.015 0.0847 0.0513 0.0693 0.11 0.143 0.000313 0.000408 Wall time: 40559.31125431275 ! Best model 354 0.085 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 86 0.229 0.00324 0.164 0.0503 0.0668 0.458 0.475 0.0013 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.0875 0.00322 0.0231 0.0513 0.0666 0.165 0.178 0.000468 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 355 40673.675 0.005 0.00307 0.047 0.109 0.0488 0.065 0.208 0.254 0.000592 0.000722 ! Validation 355 40673.675 0.005 0.00414 0.0407 0.124 0.0567 0.0755 0.194 0.237 0.000551 0.000672 Wall time: 40673.67575074779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 86 0.0885 0.00268 0.0348 0.0456 0.0607 0.204 0.219 0.00058 0.000622 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.0981 0.00245 0.049 0.0444 0.0581 0.257 0.26 0.00073 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 356 40788.967 0.005 0.00285 0.0186 0.0756 0.0469 0.0626 0.123 0.16 0.000349 0.000454 ! Validation 356 40788.967 0.005 0.00342 0.0614 0.13 0.0507 0.0686 0.25 0.291 0.000711 0.000826 Wall time: 40788.96755926311 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 86 0.0902 0.00288 0.0325 0.0476 0.063 0.191 0.211 0.000542 0.000601 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.061 0.00264 0.00826 0.0465 0.0602 0.0971 0.107 0.000276 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 357 40903.319 0.005 0.00285 0.0259 0.0828 0.0469 0.0626 0.154 0.189 0.000437 0.000536 ! Validation 357 40903.319 0.005 0.00357 0.0378 0.109 0.0521 0.0701 0.191 0.228 0.000543 0.000648 Wall time: 40903.319649167825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 86 0.0642 0.003 0.00425 0.0478 0.0642 0.0542 0.0765 0.000154 0.000217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.068 0.00244 0.0191 0.0445 0.058 0.159 0.162 0.000453 0.000461 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 41021.100 0.005 0.00281 0.014 0.0703 0.0466 0.0622 0.113 0.139 0.00032 0.000394 ! Validation 358 41021.100 0.005 0.00341 0.0329 0.101 0.0507 0.0685 0.171 0.213 0.000485 0.000605 Wall time: 41021.100666001905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 86 0.0767 0.00303 0.0161 0.0484 0.0646 0.136 0.149 0.000387 0.000422 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.0978 0.00255 0.0468 0.0454 0.0592 0.249 0.254 0.000707 0.000721 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 41135.400 0.005 0.0028 0.0265 0.0826 0.0465 0.0621 0.154 0.191 0.000437 0.000543 ! Validation 359 41135.400 0.005 0.00354 0.0405 0.111 0.0518 0.0697 0.202 0.236 0.000575 0.000671 Wall time: 41135.40045774588 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 86 0.2 0.00657 0.0684 0.0719 0.0951 0.267 0.307 0.000759 0.000872 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.123 0.00377 0.0476 0.0546 0.072 0.255 0.256 0.000724 0.000727 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 360 41249.697 0.005 0.0029 0.0368 0.0948 0.0472 0.0631 0.168 0.225 0.000478 0.000639 ! Validation 360 41249.697 0.005 0.00471 0.0559 0.15 0.06 0.0805 0.233 0.277 0.000661 0.000788 Wall time: 41249.697630397044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 86 0.0819 0.00274 0.027 0.0463 0.0614 0.171 0.193 0.000486 0.000548 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.0535 0.00256 0.00243 0.0453 0.0593 0.0473 0.0578 0.000134 0.000164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 361 41364.121 0.005 0.0032 0.021 0.0849 0.0495 0.0663 0.138 0.17 0.000391 0.000482 ! Validation 361 41364.121 0.005 0.0035 0.016 0.0861 0.0515 0.0694 0.112 0.149 0.000319 0.000422 Wall time: 41364.1210724921 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 86 0.0591 0.00266 0.00592 0.0452 0.0605 0.0694 0.0902 0.000197 0.000256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.0493 0.00241 0.00108 0.0441 0.0576 0.035 0.0386 9.94e-05 0.00011 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 41478.443 0.005 0.00275 0.0179 0.073 0.0461 0.0616 0.124 0.157 0.000351 0.000446 ! Validation 362 41478.443 0.005 0.00338 0.0143 0.0819 0.0505 0.0682 0.111 0.14 0.000316 0.000398 Wall time: 41478.443272565026 ! Best model 362 0.082 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 86 0.0735 0.00274 0.0186 0.0462 0.0614 0.14 0.16 0.000397 0.000455 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.0951 0.00251 0.0449 0.0452 0.0587 0.245 0.249 0.000697 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 363 41592.728 0.005 0.00277 0.0283 0.0836 0.0462 0.0617 0.161 0.197 0.000458 0.00056 ! Validation 363 41592.728 0.005 0.00342 0.0335 0.102 0.0508 0.0686 0.178 0.215 0.000506 0.00061 Wall time: 41592.72817003215 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 86 0.274 0.00413 0.192 0.0574 0.0753 0.503 0.513 0.00143 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.152 0.003 0.0919 0.0494 0.0642 0.35 0.356 0.000993 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 364 41707.021 0.005 0.00282 0.0361 0.0924 0.0466 0.0622 0.171 0.223 0.000485 0.000632 ! Validation 364 41707.021 0.005 0.00384 0.0963 0.173 0.0542 0.0727 0.321 0.364 0.000911 0.00103 Wall time: 41707.02199621778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 86 0.0536 0.00245 0.00459 0.0436 0.058 0.0637 0.0795 0.000181 0.000226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.0747 0.00242 0.0263 0.0443 0.0577 0.188 0.19 0.000534 0.000541 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 365 41821.294 0.005 0.00285 0.0267 0.0837 0.0469 0.0626 0.152 0.192 0.000432 0.000545 ! Validation 365 41821.294 0.005 0.00336 0.0287 0.0959 0.0503 0.068 0.161 0.199 0.000457 0.000565 Wall time: 41821.29409625195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 86 0.0582 0.0026 0.00618 0.0446 0.0598 0.0756 0.0922 0.000215 0.000262 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.0508 0.0024 0.00286 0.044 0.0575 0.0569 0.0627 0.000162 0.000178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 366 41936.284 0.005 0.0027 0.015 0.0691 0.0457 0.061 0.116 0.144 0.000329 0.000408 ! Validation 366 41936.284 0.005 0.00334 0.0192 0.086 0.0503 0.0678 0.128 0.162 0.000363 0.000461 Wall time: 41936.285013584886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 86 0.0658 0.00289 0.00792 0.0479 0.0631 0.0836 0.104 0.000237 0.000297 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.064 0.00261 0.0118 0.0459 0.0599 0.112 0.127 0.000318 0.000362 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 367 42050.566 0.005 0.00271 0.0218 0.076 0.0458 0.0611 0.139 0.173 0.000395 0.000492 ! Validation 367 42050.566 0.005 0.00355 0.0344 0.105 0.052 0.0699 0.187 0.218 0.00053 0.000618 Wall time: 42050.5660529579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 86 0.0614 0.00271 0.00718 0.046 0.0611 0.0711 0.0994 0.000202 0.000282 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.0538 0.00261 0.00153 0.0464 0.0599 0.0412 0.0458 0.000117 0.00013 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 42165.566 0.005 0.00293 0.0354 0.0939 0.0478 0.0635 0.184 0.221 0.000521 0.000627 ! Validation 368 42165.566 0.005 0.00352 0.0189 0.0894 0.0519 0.0696 0.13 0.161 0.00037 0.000458 Wall time: 42165.566823801026 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 86 0.0594 0.00249 0.00956 0.044 0.0586 0.0969 0.115 0.000275 0.000326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.0482 0.00234 0.00146 0.0434 0.0567 0.0415 0.0448 0.000118 0.000127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 369 42279.853 0.005 0.00268 0.0131 0.0667 0.0454 0.0607 0.107 0.134 0.000303 0.000381 ! Validation 369 42279.853 0.005 0.00327 0.017 0.0825 0.0497 0.0671 0.12 0.153 0.00034 0.000434 Wall time: 42279.853926835116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 86 0.0642 0.0027 0.0103 0.0461 0.0609 0.0946 0.119 0.000269 0.000338 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.0557 0.0026 0.00366 0.0459 0.0598 0.0581 0.071 0.000165 0.000202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 370 42394.295 0.005 0.0027 0.0207 0.0747 0.0456 0.0609 0.137 0.169 0.000388 0.00048 ! Validation 370 42394.295 0.005 0.00356 0.0262 0.0973 0.052 0.0699 0.156 0.19 0.000444 0.000539 Wall time: 42394.29510666197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 86 7.39 0.333 0.726 0.514 0.677 0.724 0.999 0.00206 0.00284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 6.61 0.309 0.434 0.498 0.652 0.638 0.773 0.00181 0.00219 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 371 42508.550 0.005 0.459 5.93 15.1 0.509 0.795 1.6 2.86 0.00455 0.00812 ! Validation 371 42508.550 0.005 0.327 0.985 7.52 0.51 0.67 0.895 1.16 0.00254 0.00331 Wall time: 42508.55077509489 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 86 1.79 0.0761 0.272 0.245 0.323 0.53 0.612 0.0015 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 1.49 0.0717 0.0593 0.241 0.314 0.256 0.286 0.000727 0.000811 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 372 42622.816 0.005 0.132 0.716 3.36 0.318 0.427 0.781 0.993 0.00222 0.00282 ! Validation 372 42622.816 0.005 0.0783 0.179 1.74 0.248 0.328 0.403 0.496 0.00114 0.00141 Wall time: 42622.81696711993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 86 0.927 0.042 0.0876 0.181 0.24 0.273 0.347 0.000776 0.000986 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.966 0.0413 0.141 0.182 0.238 0.436 0.44 0.00124 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 373 42737.090 0.005 0.055 0.445 1.55 0.207 0.275 0.633 0.783 0.0018 0.00222 ! Validation 373 42737.090 0.005 0.0462 0.289 1.21 0.191 0.252 0.523 0.631 0.00149 0.00179 Wall time: 42737.09072922496 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 86 0.683 0.0291 0.102 0.151 0.2 0.28 0.374 0.000796 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.627 0.0279 0.0691 0.15 0.196 0.306 0.308 0.000868 0.000876 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 42851.480 0.005 0.0355 0.304 1.01 0.166 0.221 0.492 0.646 0.0014 0.00184 ! Validation 374 42851.480 0.005 0.0319 0.1 0.739 0.159 0.21 0.302 0.372 0.000857 0.00106 Wall time: 42851.48040233413 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 86 0.865 0.0213 0.438 0.13 0.171 0.721 0.776 0.00205 0.00221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.511 0.0199 0.112 0.127 0.166 0.39 0.393 0.00111 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 375 42965.864 0.005 0.0248 0.233 0.729 0.139 0.185 0.456 0.566 0.00129 0.00161 ! Validation 375 42965.864 0.005 0.0234 0.257 0.725 0.136 0.179 0.517 0.595 0.00147 0.00169 Wall time: 42965.8646219261 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 86 0.398 0.0164 0.0696 0.114 0.15 0.262 0.31 0.000745 0.000879 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.495 0.0151 0.193 0.111 0.144 0.514 0.516 0.00146 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 376 43080.160 0.005 0.0184 0.22 0.588 0.12 0.159 0.448 0.551 0.00127 0.00156 ! Validation 376 43080.160 0.005 0.018 0.297 0.658 0.12 0.158 0.586 0.639 0.00166 0.00182 Wall time: 43080.16077427706 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 86 0.348 0.0127 0.0935 0.101 0.132 0.321 0.359 0.000913 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.411 0.0118 0.174 0.0991 0.128 0.487 0.489 0.00138 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 377 43194.437 0.005 0.0145 0.171 0.461 0.107 0.141 0.388 0.486 0.0011 0.00138 ! Validation 377 43194.437 0.005 0.0148 0.178 0.475 0.109 0.143 0.439 0.496 0.00125 0.00141 Wall time: 43194.43791111093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 86 0.247 0.0107 0.0342 0.0931 0.121 0.18 0.217 0.00051 0.000616 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.206 0.00999 0.00645 0.0914 0.117 0.0831 0.0942 0.000236 0.000268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 378 43308.703 0.005 0.012 0.149 0.388 0.0981 0.128 0.371 0.452 0.00105 0.00129 ! Validation 378 43308.703 0.005 0.0127 0.0601 0.315 0.101 0.132 0.222 0.288 0.000631 0.000817 Wall time: 43308.70333909895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 86 0.38 0.00958 0.188 0.0885 0.115 0.477 0.509 0.00136 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.352 0.00858 0.181 0.085 0.109 0.496 0.499 0.00141 0.00142 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 379 43423.078 0.005 0.0103 0.0572 0.263 0.0912 0.119 0.225 0.28 0.000638 0.000796 ! Validation 379 43423.078 0.005 0.0112 0.294 0.518 0.0951 0.124 0.595 0.636 0.00169 0.00181 Wall time: 43423.078054857906 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 86 0.282 0.00862 0.11 0.0834 0.109 0.357 0.389 0.00102 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.507 0.0077 0.353 0.0807 0.103 0.695 0.697 0.00198 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 380 43537.331 0.005 0.00919 0.0679 0.252 0.0862 0.112 0.245 0.306 0.000697 0.000868 ! Validation 380 43537.331 0.005 0.0102 0.325 0.529 0.0905 0.118 0.631 0.669 0.00179 0.0019 Wall time: 43537.33180978475 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 86 0.202 0.00799 0.042 0.0805 0.105 0.202 0.24 0.000574 0.000683 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.272 0.00697 0.133 0.0768 0.098 0.424 0.427 0.00121 0.00121 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 381 43651.683 0.005 0.00839 0.0736 0.241 0.0823 0.107 0.252 0.318 0.000716 0.000904 ! Validation 381 43651.683 0.005 0.00937 0.11 0.297 0.0867 0.114 0.34 0.389 0.000966 0.00111 Wall time: 43651.68358783517 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 86 0.193 0.00777 0.0372 0.0788 0.103 0.194 0.226 0.000552 0.000643 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.291 0.00649 0.161 0.0741 0.0945 0.467 0.47 0.00133 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 382 43765.962 0.005 0.00775 0.0912 0.246 0.0791 0.103 0.28 0.354 0.000796 0.00101 ! Validation 382 43765.962 0.005 0.00874 0.15 0.325 0.0837 0.11 0.406 0.454 0.00115 0.00129 Wall time: 43765.962944898754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 86 0.219 0.00672 0.0844 0.0736 0.0961 0.307 0.341 0.000871 0.000968 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.132 0.00609 0.0107 0.0718 0.0915 0.111 0.121 0.000317 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 383 43880.363 0.005 0.00725 0.0823 0.227 0.0765 0.0999 0.269 0.336 0.000765 0.000956 ! Validation 383 43880.363 0.005 0.00822 0.0688 0.233 0.081 0.106 0.247 0.308 0.000701 0.000874 Wall time: 43880.363567700144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 86 0.156 0.00649 0.0265 0.0724 0.0945 0.15 0.191 0.000425 0.000543 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.244 0.00575 0.129 0.0698 0.0889 0.419 0.422 0.00119 0.0012 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 384 43994.621 0.005 0.00677 0.067 0.202 0.0738 0.0965 0.247 0.304 0.000703 0.000863 ! Validation 384 43994.621 0.005 0.00775 0.102 0.257 0.0786 0.103 0.329 0.374 0.000933 0.00106 Wall time: 43994.62126441579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 86 0.142 0.00596 0.0231 0.0692 0.0906 0.152 0.178 0.000431 0.000507 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.129 0.00536 0.0217 0.0674 0.0858 0.164 0.173 0.000467 0.000491 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 44108.872 0.005 0.00637 0.044 0.171 0.0716 0.0936 0.198 0.246 0.000561 0.000699 ! Validation 385 44108.872 0.005 0.00732 0.032 0.178 0.0763 0.1 0.169 0.21 0.000479 0.000596 Wall time: 44108.87270804215 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 86 0.316 0.00597 0.196 0.0694 0.0907 0.491 0.52 0.0014 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.116 0.00531 0.00939 0.0671 0.0855 0.0966 0.114 0.000274 0.000323 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 44223.129 0.005 0.00607 0.102 0.223 0.0698 0.0914 0.314 0.374 0.000892 0.00106 ! Validation 386 44223.129 0.005 0.00714 0.0439 0.187 0.0753 0.0991 0.203 0.246 0.000576 0.000698 Wall time: 44223.12917190371 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 86 0.183 0.00584 0.0666 0.0681 0.0897 0.262 0.303 0.000744 0.00086 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.239 0.00487 0.142 0.0642 0.0819 0.438 0.441 0.00125 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 387 44339.001 0.005 0.00578 0.0399 0.156 0.0681 0.0892 0.186 0.234 0.000527 0.000666 ! Validation 387 44339.001 0.005 0.00672 0.128 0.262 0.073 0.0962 0.378 0.419 0.00107 0.00119 Wall time: 44339.0018069651 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 86 0.126 0.00554 0.0147 0.0667 0.0873 0.113 0.142 0.000321 0.000405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.13 0.00469 0.0367 0.0629 0.0803 0.219 0.225 0.000622 0.000638 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 388 44453.171 0.005 0.00553 0.0683 0.179 0.0665 0.0872 0.25 0.307 0.000709 0.000871 ! Validation 388 44453.171 0.005 0.00647 0.0415 0.171 0.0715 0.0944 0.194 0.239 0.000552 0.000679 Wall time: 44453.17130634701 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 86 0.213 0.00527 0.107 0.0651 0.0852 0.357 0.384 0.00101 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.16 0.00452 0.0698 0.0616 0.0789 0.306 0.31 0.000869 0.00088 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 44567.301 0.005 0.00529 0.0581 0.164 0.065 0.0853 0.228 0.283 0.000648 0.000803 ! Validation 389 44567.301 0.005 0.00623 0.109 0.234 0.0701 0.0926 0.343 0.387 0.000975 0.0011 Wall time: 44567.30170685984 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 86 0.264 0.00584 0.147 0.0671 0.0897 0.415 0.45 0.00118 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.17 0.00442 0.0821 0.0608 0.078 0.331 0.336 0.00094 0.000955 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 390 44681.435 0.005 0.00511 0.076 0.178 0.0639 0.0839 0.267 0.323 0.000759 0.000918 ! Validation 390 44681.435 0.005 0.00606 0.136 0.257 0.069 0.0913 0.393 0.432 0.00112 0.00123 Wall time: 44681.435250713956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 86 0.0977 0.00447 0.00823 0.0602 0.0785 0.088 0.106 0.00025 0.000302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.12 0.00419 0.0363 0.0592 0.0759 0.216 0.223 0.000614 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 391 44795.563 0.005 0.00491 0.0311 0.129 0.0625 0.0822 0.164 0.207 0.000466 0.000588 ! Validation 391 44795.563 0.005 0.00582 0.0549 0.171 0.0676 0.0895 0.223 0.275 0.000634 0.000781 Wall time: 44795.56316492986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 86 0.109 0.00497 0.00922 0.0622 0.0827 0.0916 0.113 0.00026 0.00032 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.0917 0.00408 0.0101 0.0582 0.0749 0.106 0.118 0.000302 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 44909.768 0.005 0.00474 0.0442 0.139 0.0614 0.0807 0.194 0.247 0.000551 0.000701 ! Validation 392 44909.768 0.005 0.00565 0.0352 0.148 0.0665 0.0882 0.179 0.22 0.000508 0.000625 Wall time: 44909.76809462579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 86 0.1 0.00449 0.0106 0.0598 0.0786 0.0969 0.121 0.000275 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.0986 0.00399 0.0189 0.0576 0.0741 0.153 0.161 0.000436 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 393 45023.893 0.005 0.00465 0.0774 0.17 0.0607 0.08 0.258 0.327 0.000733 0.000928 ! Validation 393 45023.893 0.005 0.00553 0.0456 0.156 0.0658 0.0872 0.207 0.25 0.000588 0.000711 Wall time: 45023.89318213193 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 86 0.199 0.00425 0.114 0.0583 0.0765 0.379 0.396 0.00108 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.169 0.00394 0.0906 0.0571 0.0737 0.35 0.353 0.000994 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 394 45138.087 0.005 0.00452 0.0854 0.176 0.0599 0.0788 0.277 0.343 0.000787 0.000974 ! Validation 394 45138.087 0.005 0.00541 0.0905 0.199 0.0651 0.0863 0.312 0.353 0.000888 0.001 Wall time: 45138.08779705409 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 86 0.114 0.0045 0.0244 0.0597 0.0787 0.163 0.183 0.000464 0.00052 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.0899 0.0038 0.0139 0.056 0.0723 0.128 0.138 0.000365 0.000393 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 395 45252.333 0.005 0.00437 0.0354 0.123 0.0588 0.0775 0.181 0.221 0.000513 0.000627 ! Validation 395 45252.333 0.005 0.00525 0.0303 0.135 0.064 0.085 0.162 0.204 0.000459 0.00058 Wall time: 45252.333297449164 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 86 0.098 0.00388 0.0203 0.0558 0.0731 0.13 0.167 0.000371 0.000475 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.163 0.00374 0.0879 0.0555 0.0717 0.344 0.348 0.000976 0.000988 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 396 45366.603 0.005 0.00426 0.0511 0.136 0.058 0.0766 0.211 0.265 0.000598 0.000754 ! Validation 396 45366.603 0.005 0.00514 0.0961 0.199 0.0633 0.0841 0.324 0.364 0.000921 0.00103 Wall time: 45366.603830327746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 86 0.324 0.004 0.244 0.0563 0.0742 0.568 0.58 0.00161 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.262 0.00365 0.189 0.0548 0.0709 0.507 0.51 0.00144 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 397 45480.740 0.005 0.00414 0.0335 0.116 0.0572 0.0755 0.164 0.214 0.000467 0.000608 ! Validation 397 45480.740 0.005 0.00505 0.21 0.311 0.0627 0.0833 0.507 0.538 0.00144 0.00153 Wall time: 45480.740740690846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 86 0.102 0.0042 0.0183 0.0573 0.076 0.136 0.159 0.000387 0.00045 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.089 0.00353 0.0184 0.0539 0.0697 0.148 0.159 0.000421 0.000451 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 45594.873 0.005 0.00409 0.0577 0.139 0.0568 0.075 0.218 0.282 0.00062 0.000801 ! Validation 398 45594.873 0.005 0.00492 0.0311 0.13 0.0618 0.0823 0.16 0.207 0.000455 0.000587 Wall time: 45594.87362185074 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 86 0.0898 0.00397 0.0103 0.056 0.0739 0.101 0.119 0.000287 0.000339 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.0817 0.00361 0.00957 0.0544 0.0704 0.1 0.115 0.000285 0.000326 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 45709.010 0.005 0.00399 0.0635 0.143 0.0561 0.0741 0.248 0.296 0.000705 0.00084 ! Validation 399 45709.010 0.005 0.00492 0.0289 0.127 0.0618 0.0822 0.166 0.199 0.000472 0.000566 Wall time: 45709.01055036485 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 86 0.0987 0.00369 0.0248 0.0541 0.0713 0.159 0.185 0.000453 0.000525 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.0986 0.00349 0.0287 0.0534 0.0693 0.191 0.199 0.000542 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 400 45823.241 0.005 0.00393 0.0446 0.123 0.0556 0.0735 0.201 0.248 0.00057 0.000704 ! Validation 400 45823.241 0.005 0.00479 0.0382 0.134 0.0609 0.0812 0.187 0.229 0.000531 0.000652 Wall time: 45823.24102286203 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 86 0.146 0.00395 0.0666 0.0556 0.0738 0.284 0.303 0.000806 0.00086 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.196 0.00344 0.127 0.0531 0.0688 0.414 0.419 0.00118 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 401 45937.410 0.005 0.00383 0.042 0.119 0.0549 0.0726 0.194 0.24 0.000552 0.000683 ! Validation 401 45937.410 0.005 0.0047 0.122 0.216 0.0603 0.0804 0.376 0.409 0.00107 0.00116 Wall time: 45937.410524085164 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 86 0.202 0.00367 0.129 0.0536 0.071 0.408 0.421 0.00116 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.199 0.00333 0.133 0.0522 0.0676 0.423 0.427 0.0012 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 402 46051.540 0.005 0.00377 0.0414 0.117 0.0544 0.072 0.199 0.238 0.000564 0.000677 ! Validation 402 46051.540 0.005 0.00463 0.154 0.247 0.0599 0.0798 0.425 0.46 0.00121 0.00131 Wall time: 46051.53995925002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 86 0.137 0.00373 0.0628 0.0541 0.0717 0.276 0.294 0.000783 0.000835 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.0708 0.00337 0.00337 0.0526 0.0681 0.0542 0.0681 0.000154 0.000193 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 403 46165.648 0.005 0.00374 0.0646 0.139 0.0542 0.0717 0.237 0.298 0.000674 0.000847 ! Validation 403 46165.648 0.005 0.00466 0.0296 0.123 0.0601 0.0801 0.163 0.202 0.000462 0.000573 Wall time: 46165.648371268064 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 86 0.162 0.00375 0.0868 0.0541 0.0718 0.325 0.346 0.000923 0.000982 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.178 0.00332 0.112 0.052 0.0676 0.39 0.393 0.00111 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 404 46279.781 0.005 0.0037 0.0605 0.134 0.0538 0.0713 0.233 0.288 0.000661 0.000819 ! Validation 404 46279.781 0.005 0.00455 0.108 0.199 0.0594 0.0792 0.348 0.386 0.00099 0.0011 Wall time: 46279.781586060766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 86 0.108 0.00356 0.0369 0.053 0.07 0.193 0.225 0.00055 0.000641 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.0665 0.00319 0.00261 0.0511 0.0663 0.0531 0.0599 0.000151 0.00017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 405 46393.978 0.005 0.00363 0.0357 0.108 0.0534 0.0707 0.183 0.222 0.00052 0.00063 ! Validation 405 46393.978 0.005 0.00444 0.0226 0.111 0.0585 0.0782 0.138 0.176 0.000393 0.000501 Wall time: 46393.97804561816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 86 0.0826 0.00351 0.0125 0.0526 0.0694 0.101 0.131 0.000286 0.000372 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.0653 0.00316 0.00212 0.0508 0.066 0.0434 0.054 0.000123 0.000154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 406 46508.095 0.005 0.00357 0.0375 0.109 0.0528 0.0701 0.184 0.227 0.000524 0.000645 ! Validation 406 46508.095 0.005 0.00438 0.0213 0.109 0.0581 0.0776 0.138 0.171 0.000393 0.000486 Wall time: 46508.09515272081 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 86 0.0773 0.00351 0.00708 0.0522 0.0695 0.0823 0.0987 0.000234 0.00028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.0637 0.00309 0.00194 0.0502 0.0652 0.0446 0.0517 0.000127 0.000147 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 407 46622.301 0.005 0.0035 0.029 0.099 0.0523 0.0694 0.152 0.2 0.000432 0.000568 ! Validation 407 46622.301 0.005 0.00431 0.02 0.106 0.0577 0.0771 0.133 0.166 0.000378 0.000472 Wall time: 46622.30131404707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 86 0.0834 0.0034 0.0155 0.0519 0.0684 0.121 0.146 0.000344 0.000414 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.0658 0.0031 0.00385 0.0502 0.0653 0.0646 0.0728 0.000184 0.000207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 408 46736.446 0.005 0.00349 0.0597 0.129 0.0523 0.0693 0.223 0.287 0.000634 0.000814 ! Validation 408 46736.446 0.005 0.00428 0.0204 0.106 0.0575 0.0768 0.129 0.167 0.000366 0.000475 Wall time: 46736.44633851107 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 86 0.0811 0.00339 0.0132 0.0517 0.0683 0.112 0.135 0.000318 0.000383 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.0708 0.00306 0.00954 0.0499 0.0649 0.102 0.115 0.000291 0.000326 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 46850.655 0.005 0.00344 0.0403 0.109 0.0518 0.0687 0.189 0.235 0.000538 0.000669 ! Validation 409 46850.655 0.005 0.00423 0.0308 0.115 0.0571 0.0763 0.173 0.206 0.000492 0.000585 Wall time: 46850.65586894285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 86 0.0817 0.00342 0.0132 0.0518 0.0686 0.109 0.135 0.000308 0.000383 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.141 0.00314 0.0782 0.0504 0.0657 0.322 0.328 0.000915 0.000932 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 410 46964.836 0.005 0.0034 0.0528 0.121 0.0516 0.0684 0.223 0.27 0.000635 0.000766 ! Validation 410 46964.836 0.005 0.0043 0.114 0.2 0.0576 0.0769 0.346 0.395 0.000982 0.00112 Wall time: 46964.836438943166 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 86 0.0976 0.00326 0.0323 0.0501 0.067 0.185 0.211 0.000525 0.000599 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.131 0.00298 0.0713 0.0493 0.0641 0.309 0.313 0.000878 0.00089 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 411 47078.975 0.005 0.00336 0.0284 0.0956 0.0512 0.068 0.157 0.198 0.000446 0.000561 ! Validation 411 47078.975 0.005 0.00415 0.0795 0.163 0.0565 0.0756 0.292 0.331 0.00083 0.00094 Wall time: 47078.97563790716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 86 0.0779 0.00352 0.00757 0.052 0.0696 0.09 0.102 0.000256 0.00029 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.0664 0.00295 0.0075 0.0489 0.0637 0.0878 0.102 0.000249 0.000289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 412 47193.138 0.005 0.00332 0.0428 0.109 0.0509 0.0676 0.201 0.243 0.000571 0.00069 ! Validation 412 47193.138 0.005 0.00409 0.02 0.102 0.0561 0.075 0.123 0.166 0.00035 0.000471 Wall time: 47193.13826203579 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 86 0.135 0.00334 0.0681 0.0508 0.0678 0.291 0.306 0.000828 0.00087 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.0942 0.00291 0.0361 0.0486 0.0632 0.218 0.223 0.000619 0.000633 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 413 47309.106 0.005 0.00328 0.0264 0.0919 0.0505 0.0671 0.156 0.19 0.000442 0.000541 ! Validation 413 47309.106 0.005 0.00403 0.0722 0.153 0.0556 0.0745 0.278 0.315 0.00079 0.000896 Wall time: 47309.10650933487 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 86 0.0752 0.00312 0.0128 0.0495 0.0656 0.113 0.133 0.000322 0.000376 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.0822 0.00286 0.025 0.0482 0.0627 0.181 0.186 0.000514 0.000527 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 47423.375 0.005 0.00323 0.0241 0.0887 0.0502 0.0667 0.145 0.182 0.000411 0.000517 ! Validation 414 47423.375 0.005 0.00399 0.0304 0.11 0.0553 0.0741 0.165 0.205 0.00047 0.000581 Wall time: 47423.37528995285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 86 0.0917 0.00352 0.0213 0.0521 0.0696 0.147 0.171 0.000417 0.000486 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.0973 0.00293 0.0387 0.0488 0.0635 0.226 0.231 0.000642 0.000656 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 415 47537.643 0.005 0.00327 0.0656 0.131 0.0505 0.0671 0.236 0.3 0.00067 0.000854 ! Validation 415 47537.643 0.005 0.00407 0.0525 0.134 0.0559 0.0748 0.218 0.269 0.00062 0.000763 Wall time: 47537.64371362887 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 86 0.103 0.00362 0.031 0.0528 0.0705 0.187 0.207 0.000532 0.000587 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.0694 0.00288 0.0119 0.0483 0.0629 0.118 0.128 0.000334 0.000363 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 416 47651.914 0.005 0.00323 0.0483 0.113 0.0502 0.0667 0.217 0.258 0.000617 0.000733 ! Validation 416 47651.914 0.005 0.00399 0.0208 0.101 0.0553 0.0741 0.13 0.169 0.000368 0.000481 Wall time: 47651.91428771475 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 86 0.0724 0.00297 0.0131 0.0482 0.0639 0.114 0.134 0.000325 0.000381 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.058 0.00282 0.00155 0.0479 0.0623 0.0425 0.0461 0.000121 0.000131 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 47766.177 0.005 0.00317 0.0233 0.0866 0.0496 0.066 0.14 0.179 0.000399 0.000508 ! Validation 417 47766.177 0.005 0.00391 0.0173 0.0956 0.0548 0.0734 0.124 0.154 0.000352 0.000439 Wall time: 47766.17756087612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 86 0.079 0.0028 0.0229 0.047 0.0621 0.157 0.177 0.000445 0.000504 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.0952 0.00277 0.0398 0.0474 0.0617 0.227 0.234 0.000646 0.000664 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 418 47880.512 0.005 0.00312 0.0209 0.0833 0.0493 0.0655 0.134 0.169 0.00038 0.000481 ! Validation 418 47880.512 0.005 0.00387 0.0358 0.113 0.0544 0.0729 0.183 0.222 0.000521 0.000631 Wall time: 47880.5122607369 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 86 0.11 0.00315 0.0465 0.0498 0.0658 0.237 0.253 0.000674 0.000719 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.0997 0.00282 0.0432 0.0479 0.0623 0.241 0.244 0.000684 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 419 47994.790 0.005 0.0031 0.0402 0.102 0.0491 0.0654 0.194 0.235 0.000551 0.000668 ! Validation 419 47994.790 0.005 0.00392 0.0621 0.141 0.0549 0.0735 0.251 0.292 0.000712 0.00083 Wall time: 47994.79022677196 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 86 0.136 0.00296 0.0765 0.0482 0.0638 0.31 0.324 0.000882 0.000922 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.0781 0.0029 0.0202 0.0485 0.0631 0.161 0.167 0.000457 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 420 48109.140 0.005 0.00308 0.0316 0.0933 0.049 0.0651 0.157 0.208 0.000446 0.000592 ! Validation 420 48109.140 0.005 0.00397 0.0316 0.111 0.0553 0.0739 0.164 0.209 0.000465 0.000593 Wall time: 48109.14046415081 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 86 0.175 0.00308 0.113 0.0487 0.0651 0.386 0.394 0.0011 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.0912 0.00272 0.0367 0.0469 0.0612 0.218 0.225 0.00062 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 421 48223.467 0.005 0.00309 0.0368 0.0985 0.049 0.0652 0.184 0.225 0.000522 0.000639 ! Validation 421 48223.467 0.005 0.00382 0.0743 0.151 0.0541 0.0725 0.287 0.32 0.000815 0.000908 Wall time: 48223.46791426698 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 86 0.0728 0.00308 0.0113 0.0487 0.065 0.104 0.125 0.000295 0.000354 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.0689 0.00274 0.0141 0.0471 0.0614 0.128 0.139 0.000364 0.000395 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 48337.894 0.005 0.00311 0.0589 0.121 0.0492 0.0655 0.234 0.285 0.000664 0.000809 ! Validation 422 48337.894 0.005 0.00382 0.0217 0.0982 0.0541 0.0725 0.132 0.173 0.000376 0.000491 Wall time: 48337.89498958597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 86 0.0673 0.00305 0.00627 0.0489 0.0648 0.0664 0.0929 0.000189 0.000264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.0578 0.00277 0.00241 0.0474 0.0617 0.0487 0.0576 0.000138 0.000164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 48452.188 0.005 0.00304 0.034 0.0948 0.0486 0.0647 0.179 0.216 0.000508 0.000615 ! Validation 423 48452.188 0.005 0.00381 0.0218 0.098 0.054 0.0724 0.13 0.173 0.00037 0.000492 Wall time: 48452.188954012 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 86 0.0648 0.00302 0.00438 0.0481 0.0645 0.0605 0.0777 0.000172 0.000221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.0589 0.00266 0.00561 0.0465 0.0606 0.0755 0.0878 0.000214 0.00025 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 48566.479 0.005 0.00302 0.0275 0.0879 0.0484 0.0644 0.159 0.195 0.000453 0.000553 ! Validation 424 48566.479 0.005 0.00373 0.0188 0.0935 0.0534 0.0717 0.122 0.161 0.000348 0.000457 Wall time: 48566.47905223677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 86 0.0742 0.00314 0.0113 0.0489 0.0658 0.0863 0.125 0.000245 0.000354 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.0564 0.00269 0.00267 0.0468 0.0608 0.0471 0.0606 0.000134 0.000172 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 48680.757 0.005 0.00298 0.0272 0.0869 0.0481 0.0641 0.158 0.194 0.00045 0.00055 ! Validation 425 48680.757 0.005 0.00374 0.0303 0.105 0.0535 0.0718 0.165 0.204 0.000468 0.00058 Wall time: 48680.75787201198 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 86 0.0713 0.0029 0.0133 0.0478 0.0632 0.113 0.135 0.000321 0.000384 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.0548 0.00266 0.0016 0.0465 0.0605 0.0427 0.0469 0.000121 0.000133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 426 48795.243 0.005 0.00296 0.0249 0.084 0.0479 0.0638 0.15 0.185 0.000426 0.000526 ! Validation 426 48795.243 0.005 0.0037 0.0176 0.0916 0.0532 0.0714 0.125 0.156 0.000356 0.000442 Wall time: 48795.24363063183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 86 0.0659 0.00276 0.0107 0.0466 0.0616 0.1 0.121 0.000284 0.000345 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.0938 0.00261 0.0416 0.046 0.0599 0.236 0.239 0.000671 0.00068 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 427 48909.896 0.005 0.00293 0.0176 0.0762 0.0476 0.0635 0.124 0.156 0.000352 0.000443 ! Validation 427 48909.896 0.005 0.00367 0.0378 0.111 0.0529 0.071 0.191 0.228 0.000544 0.000648 Wall time: 48909.896527667996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 86 0.0979 0.00288 0.0402 0.0472 0.063 0.213 0.235 0.000604 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.0537 0.00261 0.00152 0.046 0.0599 0.0409 0.0457 0.000116 0.00013 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 49024.162 0.005 0.00291 0.0404 0.0986 0.0475 0.0633 0.191 0.236 0.000543 0.00067 ! Validation 428 49024.162 0.005 0.00366 0.0251 0.0984 0.0529 0.071 0.143 0.186 0.000407 0.000528 Wall time: 49024.16274564015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 86 0.0977 0.00287 0.0402 0.0475 0.0629 0.22 0.235 0.000625 0.000668 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.0558 0.00258 0.0042 0.0458 0.0596 0.0623 0.0761 0.000177 0.000216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 429 49138.398 0.005 0.00292 0.0295 0.0879 0.0476 0.0634 0.159 0.202 0.000452 0.000572 ! Validation 429 49138.398 0.005 0.00363 0.0358 0.108 0.0527 0.0707 0.186 0.222 0.000528 0.00063 Wall time: 49138.39808538277 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 86 0.302 0.00272 0.248 0.0461 0.0612 0.576 0.584 0.00164 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.499 0.00267 0.446 0.0466 0.0607 0.781 0.783 0.00222 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 430 49252.670 0.005 0.00289 0.039 0.0968 0.0473 0.0631 0.181 0.231 0.000514 0.000657 ! Validation 430 49252.670 0.005 0.0037 0.36 0.434 0.0532 0.0713 0.686 0.703 0.00195 0.002 Wall time: 49252.670208251104 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 86 0.116 0.00303 0.0555 0.0482 0.0646 0.262 0.276 0.000745 0.000785 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.174 0.00257 0.123 0.0456 0.0594 0.409 0.411 0.00116 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 431 49367.247 0.005 0.00293 0.027 0.0856 0.0477 0.0635 0.148 0.193 0.000421 0.000547 ! Validation 431 49367.247 0.005 0.00358 0.1 0.172 0.0522 0.0702 0.343 0.372 0.000976 0.00106 Wall time: 49367.24749396276 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 86 0.061 0.00275 0.00613 0.0463 0.0615 0.0727 0.0918 0.000207 0.000261 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.069 0.0026 0.017 0.046 0.0598 0.147 0.153 0.000417 0.000434 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 49481.397 0.005 0.00285 0.0254 0.0823 0.047 0.0626 0.153 0.187 0.000435 0.000531 ! Validation 432 49481.397 0.005 0.0036 0.0451 0.117 0.0524 0.0704 0.196 0.249 0.000558 0.000707 Wall time: 49481.39758547116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 86 0.0687 0.00306 0.00754 0.0487 0.0649 0.0824 0.102 0.000234 0.000289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.054 0.00261 0.0019 0.046 0.0599 0.0487 0.0512 0.000138 0.000145 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 433 49595.605 0.005 0.00284 0.0311 0.0879 0.0469 0.0625 0.167 0.207 0.000475 0.000588 ! Validation 433 49595.605 0.005 0.00363 0.0178 0.0903 0.0526 0.0706 0.127 0.156 0.00036 0.000444 Wall time: 49595.605240305886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 86 0.107 0.00294 0.0482 0.0479 0.0636 0.243 0.258 0.00069 0.000732 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.0612 0.00276 0.00606 0.0474 0.0616 0.0764 0.0913 0.000217 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 434 49709.738 0.005 0.00282 0.0338 0.0901 0.0467 0.0623 0.166 0.215 0.000472 0.000612 ! Validation 434 49709.738 0.005 0.00375 0.0174 0.0925 0.0536 0.0719 0.122 0.155 0.000346 0.00044 Wall time: 49709.73898242973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 86 0.0835 0.00304 0.0227 0.0482 0.0647 0.158 0.177 0.000449 0.000502 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.054 0.00253 0.00346 0.0453 0.059 0.0587 0.069 0.000167 0.000196 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 435 49823.882 0.005 0.00283 0.0229 0.0795 0.0469 0.0624 0.142 0.177 0.000403 0.000504 ! Validation 435 49823.882 0.005 0.00351 0.0158 0.0859 0.0516 0.0695 0.114 0.147 0.000325 0.000418 Wall time: 49823.88201422198 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 86 0.0985 0.00315 0.0355 0.0497 0.0658 0.198 0.221 0.000562 0.000628 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.0595 0.00268 0.00581 0.0466 0.0608 0.0764 0.0894 0.000217 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 436 49938.011 0.005 0.00282 0.0352 0.0916 0.0468 0.0623 0.176 0.22 0.0005 0.000625 ! Validation 436 49938.011 0.005 0.00372 0.0321 0.107 0.0534 0.0716 0.181 0.21 0.000514 0.000597 Wall time: 49938.01195998676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 86 0.0687 0.00292 0.0102 0.0474 0.0634 0.0976 0.119 0.000277 0.000337 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.0879 0.00245 0.0388 0.0447 0.0581 0.227 0.231 0.000646 0.000656 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 437 50052.141 0.005 0.00281 0.0194 0.0755 0.0466 0.0621 0.132 0.163 0.000374 0.000464 ! Validation 437 50052.141 0.005 0.00346 0.0375 0.107 0.0513 0.069 0.188 0.227 0.000533 0.000646 Wall time: 50052.141461382154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 86 0.073 0.00253 0.0223 0.0447 0.0591 0.157 0.175 0.000447 0.000497 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.081 0.00249 0.0312 0.045 0.0585 0.201 0.207 0.000572 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 438 50166.267 0.005 0.00286 0.0415 0.0988 0.0471 0.0628 0.194 0.239 0.000551 0.000679 ! Validation 438 50166.267 0.005 0.00347 0.0294 0.0988 0.0513 0.0691 0.161 0.201 0.000458 0.000572 Wall time: 50166.26739494689 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 86 0.0753 0.0027 0.0213 0.0457 0.061 0.15 0.171 0.000425 0.000486 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.0848 0.00246 0.0357 0.0446 0.0581 0.217 0.221 0.000615 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 439 50280.694 0.005 0.00277 0.0311 0.0865 0.0463 0.0618 0.167 0.207 0.000473 0.000588 ! Validation 439 50280.694 0.005 0.00344 0.0279 0.0968 0.0512 0.0688 0.158 0.196 0.000449 0.000556 Wall time: 50280.694693211 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 86 0.0676 0.00295 0.00868 0.0474 0.0637 0.0868 0.109 0.000247 0.00031 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.0992 0.0025 0.0493 0.045 0.0586 0.256 0.26 0.000728 0.00074 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 50394.836 0.005 0.00274 0.0262 0.081 0.046 0.0614 0.15 0.19 0.000427 0.000539 ! Validation 440 50394.836 0.005 0.0035 0.0479 0.118 0.0517 0.0694 0.219 0.257 0.000623 0.000729 Wall time: 50394.836092824116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 86 0.0823 0.00271 0.028 0.0458 0.0611 0.18 0.196 0.00051 0.000558 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.155 0.00241 0.107 0.0442 0.0576 0.381 0.383 0.00108 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 441 50508.961 0.005 0.00274 0.02 0.0748 0.046 0.0614 0.131 0.166 0.000373 0.000471 ! Validation 441 50508.961 0.005 0.00339 0.0894 0.157 0.0507 0.0683 0.323 0.351 0.000917 0.000996 Wall time: 50508.96155092586 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 86 0.0682 0.00282 0.0118 0.047 0.0623 0.104 0.127 0.000295 0.000362 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0514 0.00246 0.00218 0.0447 0.0582 0.0468 0.0548 0.000133 0.000156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 442 50623.389 0.005 0.00275 0.0433 0.0984 0.0462 0.0616 0.203 0.244 0.000577 0.000694 ! Validation 442 50623.389 0.005 0.00346 0.0509 0.12 0.0514 0.069 0.213 0.265 0.000604 0.000752 Wall time: 50623.38976940699 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 86 0.0668 0.00275 0.0118 0.046 0.0615 0.108 0.128 0.000306 0.000363 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.0502 0.00242 0.00173 0.0444 0.0578 0.0417 0.0488 0.000119 0.000139 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 50737.609 0.005 0.00273 0.0236 0.0782 0.0459 0.0613 0.146 0.18 0.000415 0.000513 ! Validation 443 50737.609 0.005 0.0034 0.0186 0.0866 0.0508 0.0684 0.127 0.16 0.000362 0.000455 Wall time: 50737.609967150725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 86 0.0636 0.00289 0.0058 0.0469 0.0631 0.0709 0.0893 0.000201 0.000254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.0611 0.00242 0.0127 0.0444 0.0578 0.124 0.132 0.000353 0.000375 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 50852.056 0.005 0.00272 0.025 0.0793 0.0458 0.0611 0.144 0.185 0.00041 0.000527 ! Validation 444 50852.056 0.005 0.00337 0.0194 0.0868 0.0506 0.0681 0.126 0.163 0.000357 0.000464 Wall time: 50852.056731555145 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 86 0.0556 0.00247 0.00611 0.0441 0.0584 0.0744 0.0917 0.000211 0.000261 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.0496 0.0024 0.00155 0.0441 0.0575 0.0438 0.0462 0.000124 0.000131 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 50966.409 0.005 0.00267 0.0175 0.0709 0.0454 0.0606 0.122 0.155 0.000347 0.000441 ! Validation 445 50966.409 0.005 0.00338 0.0188 0.0865 0.0506 0.0682 0.13 0.161 0.000368 0.000457 Wall time: 50966.40969097614 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 86 0.0611 0.00276 0.0058 0.0463 0.0617 0.0669 0.0893 0.00019 0.000254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.0819 0.00243 0.0333 0.0445 0.0578 0.208 0.214 0.00059 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 446 51080.841 0.005 0.0027 0.029 0.083 0.0457 0.061 0.166 0.2 0.00047 0.000567 ! Validation 446 51080.841 0.005 0.00342 0.0355 0.104 0.051 0.0686 0.178 0.221 0.000505 0.000627 Wall time: 51080.8411450251 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 86 0.0687 0.00267 0.0153 0.0457 0.0606 0.124 0.145 0.000352 0.000412 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.0528 0.00239 0.00504 0.044 0.0573 0.0725 0.0833 0.000206 0.000237 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 447 51195.105 0.005 0.00269 0.0309 0.0847 0.0456 0.0608 0.17 0.206 0.000484 0.000586 ! Validation 447 51195.105 0.005 0.00334 0.0178 0.0846 0.0503 0.0678 0.125 0.157 0.000355 0.000445 Wall time: 51195.10575478105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 86 0.183 0.00268 0.13 0.0456 0.0608 0.412 0.422 0.00117 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.237 0.00245 0.188 0.0445 0.0581 0.506 0.508 0.00144 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 51309.364 0.005 0.0027 0.0289 0.0829 0.0457 0.0609 0.155 0.199 0.000441 0.000566 ! Validation 448 51309.364 0.005 0.00339 0.15 0.217 0.0507 0.0683 0.431 0.454 0.00123 0.00129 Wall time: 51309.364166480955 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 86 0.144 0.00277 0.0884 0.0466 0.0618 0.334 0.349 0.000948 0.000991 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.201 0.00247 0.152 0.0449 0.0583 0.454 0.457 0.00129 0.0013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 449 51423.626 0.005 0.00267 0.0247 0.078 0.0454 0.0606 0.146 0.184 0.000414 0.000523 ! Validation 449 51423.626 0.005 0.00338 0.116 0.183 0.0506 0.0682 0.368 0.399 0.00104 0.00113 Wall time: 51423.6263132859 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 86 0.0634 0.00268 0.00975 0.0455 0.0608 0.0922 0.116 0.000262 0.000329 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.0635 0.00249 0.0138 0.0448 0.0585 0.128 0.138 0.000364 0.000391 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 51537.894 0.005 0.00271 0.0302 0.0843 0.0458 0.061 0.167 0.204 0.000476 0.000579 ! Validation 450 51537.894 0.005 0.00341 0.0306 0.0988 0.0509 0.0685 0.172 0.205 0.000488 0.000582 Wall time: 51537.894764585886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 86 0.0838 0.00258 0.0322 0.0447 0.0596 0.192 0.211 0.000546 0.000598 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.078 0.00231 0.0318 0.0432 0.0564 0.204 0.209 0.00058 0.000595 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 51652.156 0.005 0.00262 0.017 0.0694 0.045 0.0601 0.116 0.153 0.000328 0.000434 ! Validation 451 51652.156 0.005 0.00326 0.0349 0.1 0.0497 0.067 0.175 0.219 0.000496 0.000623 Wall time: 51652.156668778975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 86 0.0718 0.00279 0.016 0.0459 0.062 0.122 0.148 0.000348 0.000421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.0822 0.00234 0.0355 0.0435 0.0567 0.215 0.221 0.000612 0.000628 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 452 51766.506 0.005 0.00262 0.0256 0.0781 0.045 0.0601 0.151 0.188 0.00043 0.000534 ! Validation 452 51766.506 0.005 0.00327 0.0519 0.117 0.0497 0.0671 0.219 0.267 0.000623 0.000759 Wall time: 51766.506643447094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 86 0.0719 0.00267 0.0185 0.0452 0.0606 0.141 0.159 0.000402 0.000453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.0476 0.00233 0.00107 0.0435 0.0566 0.0375 0.0383 0.000107 0.000109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 453 51880.783 0.005 0.00259 0.0198 0.0716 0.0447 0.0597 0.135 0.165 0.000384 0.000469 ! Validation 453 51880.783 0.005 0.00323 0.0174 0.0821 0.0494 0.0667 0.122 0.155 0.000346 0.00044 Wall time: 51880.783108546864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 86 0.099 0.00246 0.0497 0.0435 0.0582 0.247 0.262 0.000702 0.000743 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.0815 0.00232 0.035 0.0434 0.0565 0.215 0.22 0.000611 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 454 51995.152 0.005 0.00255 0.0148 0.0659 0.0444 0.0593 0.116 0.143 0.000329 0.000406 ! Validation 454 51995.152 0.005 0.00327 0.0569 0.122 0.0498 0.0671 0.248 0.28 0.000704 0.000795 Wall time: 51995.152514670976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 86 0.0728 0.0026 0.0208 0.0443 0.0598 0.146 0.169 0.000416 0.00048 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.0538 0.0023 0.00785 0.0432 0.0562 0.0916 0.104 0.00026 0.000295 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 455 52109.418 0.005 0.00271 0.0463 0.1 0.0457 0.061 0.195 0.253 0.000554 0.000718 ! Validation 455 52109.418 0.005 0.00322 0.015 0.0795 0.0494 0.0666 0.108 0.144 0.000306 0.000408 Wall time: 52109.41806633584 ! Best model 455 0.079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 86 0.0632 0.00262 0.0108 0.0451 0.06 0.102 0.122 0.00029 0.000347 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.0611 0.00235 0.014 0.0437 0.0569 0.13 0.139 0.00037 0.000394 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 52223.694 0.005 0.0026 0.0293 0.0813 0.0448 0.0598 0.16 0.201 0.000455 0.00057 ! Validation 456 52223.694 0.005 0.00329 0.0227 0.0884 0.0499 0.0673 0.135 0.177 0.000384 0.000502 Wall time: 52223.69396506995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 86 0.0593 0.00238 0.0118 0.0428 0.0572 0.109 0.127 0.000309 0.000362 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.0631 0.00226 0.018 0.0428 0.0557 0.151 0.157 0.00043 0.000447 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 52337.980 0.005 0.00255 0.0108 0.0618 0.0443 0.0592 0.0997 0.122 0.000283 0.000347 ! Validation 457 52337.980 0.005 0.00318 0.0269 0.0905 0.049 0.0661 0.147 0.192 0.000418 0.000546 Wall time: 52337.98079257272 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 86 0.0596 0.00265 0.00651 0.0452 0.0604 0.0756 0.0947 0.000215 0.000269 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.0549 0.0023 0.00891 0.0432 0.0562 0.103 0.111 0.000293 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 458 52452.352 0.005 0.00259 0.0283 0.08 0.0447 0.0596 0.155 0.197 0.00044 0.00056 ! Validation 458 52452.352 0.005 0.00323 0.0162 0.0807 0.0494 0.0666 0.115 0.149 0.000327 0.000424 Wall time: 52452.3526321128 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 86 0.107 0.00297 0.0473 0.0471 0.064 0.243 0.255 0.000689 0.000725 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.109 0.00226 0.0633 0.043 0.0558 0.293 0.295 0.000831 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 459 52566.631 0.005 0.00252 0.0151 0.0656 0.0441 0.0589 0.118 0.144 0.000334 0.00041 ! Validation 459 52566.631 0.005 0.00319 0.0767 0.14 0.0491 0.0662 0.289 0.325 0.000822 0.000923 Wall time: 52566.63136034878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 86 0.0628 0.00275 0.0078 0.0455 0.0615 0.0861 0.104 0.000245 0.000294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.0496 0.0023 0.00366 0.0432 0.0562 0.0563 0.071 0.00016 0.000202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 460 52680.917 0.005 0.00256 0.0301 0.0814 0.0445 0.0594 0.167 0.204 0.000476 0.000578 ! Validation 460 52680.917 0.005 0.00319 0.0147 0.0785 0.0491 0.0662 0.11 0.142 0.000313 0.000404 Wall time: 52680.9177249209 ! Best model 460 0.078 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 86 0.0532 0.00239 0.00552 0.0434 0.0573 0.066 0.0871 0.000188 0.000248 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.0483 0.00231 0.00206 0.0434 0.0564 0.0459 0.0533 0.00013 0.000151 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 52795.338 0.005 0.00254 0.0188 0.0697 0.0443 0.0591 0.129 0.161 0.000367 0.000457 ! Validation 461 52795.338 0.005 0.00316 0.0171 0.0803 0.0489 0.0659 0.126 0.154 0.000357 0.000436 Wall time: 52795.33861444285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 86 0.0545 0.00246 0.00529 0.0437 0.0582 0.0696 0.0853 0.000198 0.000242 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.0676 0.00227 0.0222 0.0431 0.0559 0.169 0.175 0.000481 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 462 52909.636 0.005 0.00267 0.039 0.0923 0.0454 0.0606 0.176 0.232 0.000499 0.000658 ! Validation 462 52909.636 0.005 0.00316 0.0219 0.0852 0.0489 0.0659 0.139 0.174 0.000394 0.000494 Wall time: 52909.63638178399 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 86 0.0656 0.00276 0.0104 0.0457 0.0617 0.1 0.119 0.000284 0.000339 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.0554 0.00225 0.0103 0.0426 0.0557 0.111 0.119 0.000316 0.000339 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 463 53023.939 0.005 0.00252 0.019 0.0693 0.0441 0.0589 0.133 0.162 0.000379 0.000459 ! Validation 463 53023.939 0.005 0.00314 0.0171 0.08 0.0487 0.0657 0.119 0.154 0.000339 0.000436 Wall time: 53023.939825337846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 86 0.0835 0.0023 0.0374 0.0425 0.0563 0.213 0.227 0.000604 0.000645 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.0472 0.00226 0.0021 0.0428 0.0557 0.037 0.0537 0.000105 0.000153 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 464 53138.230 0.005 0.00252 0.023 0.0733 0.044 0.0588 0.146 0.178 0.000414 0.000505 ! Validation 464 53138.230 0.005 0.00315 0.0187 0.0816 0.0488 0.0658 0.136 0.16 0.000387 0.000455 Wall time: 53138.23080101609 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 86 0.0539 0.00225 0.00894 0.042 0.0556 0.096 0.111 0.000273 0.000315 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.144 0.00228 0.0984 0.0432 0.056 0.365 0.368 0.00104 0.00105 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 465 53252.744 0.005 0.0025 0.024 0.074 0.0439 0.0586 0.149 0.182 0.000422 0.000517 ! Validation 465 53252.744 0.005 0.00317 0.0802 0.144 0.049 0.0661 0.304 0.332 0.000865 0.000944 Wall time: 53252.744081216864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 86 0.118 0.00248 0.0684 0.0439 0.0585 0.294 0.307 0.000836 0.000871 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.0503 0.00235 0.00317 0.0438 0.0569 0.0463 0.066 0.000132 0.000188 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 53367.023 0.005 0.00256 0.0362 0.0873 0.0444 0.0593 0.181 0.223 0.000515 0.000634 ! Validation 466 53367.023 0.005 0.00326 0.0179 0.083 0.0497 0.0669 0.129 0.157 0.000368 0.000445 Wall time: 53367.0236078538 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 86 0.0701 0.00247 0.0208 0.0436 0.0583 0.153 0.169 0.000436 0.00048 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.0616 0.00223 0.0171 0.0425 0.0554 0.147 0.153 0.000418 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 467 53481.284 0.005 0.00249 0.0143 0.064 0.0438 0.0585 0.113 0.14 0.00032 0.000399 ! Validation 467 53481.284 0.005 0.00312 0.0283 0.0908 0.0486 0.0656 0.149 0.197 0.000423 0.000561 Wall time: 53481.284950646106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 86 0.0731 0.0026 0.0211 0.0446 0.0598 0.157 0.17 0.000445 0.000484 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.0466 0.00223 0.00197 0.0427 0.0554 0.05 0.052 0.000142 0.000148 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 468 53595.540 0.005 0.00248 0.0194 0.0691 0.0438 0.0585 0.131 0.164 0.000373 0.000465 ! Validation 468 53595.540 0.005 0.0031 0.0204 0.0823 0.0484 0.0653 0.126 0.167 0.000358 0.000475 Wall time: 53595.540941603016 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 86 0.0594 0.00247 0.00998 0.0439 0.0583 0.1 0.117 0.000285 0.000333 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.0505 0.00231 0.00439 0.0433 0.0563 0.0677 0.0778 0.000192 0.000221 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 53709.812 0.005 0.00248 0.0204 0.0701 0.0438 0.0585 0.134 0.168 0.000381 0.000477 ! Validation 469 53709.812 0.005 0.00317 0.0293 0.0927 0.0491 0.066 0.165 0.201 0.000468 0.000571 Wall time: 53709.8125356161 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 86 0.0477 0.00221 0.00342 0.0416 0.0552 0.058 0.0686 0.000165 0.000195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.0478 0.00222 0.00333 0.0426 0.0553 0.056 0.0677 0.000159 0.000192 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 53824.069 0.005 0.00246 0.0209 0.0701 0.0435 0.0581 0.137 0.17 0.00039 0.000482 ! Validation 470 53824.069 0.005 0.00308 0.0135 0.0751 0.0482 0.0651 0.109 0.136 0.000309 0.000387 Wall time: 53824.06942611607 ! Best model 470 0.075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 86 0.0504 0.00228 0.0048 0.0424 0.056 0.0663 0.0813 0.000188 0.000231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.0468 0.00221 0.00271 0.0423 0.0551 0.0494 0.061 0.00014 0.000173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 471 53938.424 0.005 0.00246 0.023 0.0722 0.0435 0.0582 0.148 0.178 0.00042 0.000505 ! Validation 471 53938.424 0.005 0.00306 0.0138 0.075 0.0481 0.0649 0.109 0.138 0.000309 0.000391 Wall time: 53938.42501786584 ! Best model 471 0.075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 86 0.119 0.0027 0.0653 0.0456 0.0609 0.28 0.3 0.000797 0.000851 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.108 0.00234 0.0608 0.0437 0.0568 0.287 0.289 0.000815 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 472 54052.692 0.005 0.00246 0.0275 0.0768 0.0436 0.0582 0.161 0.195 0.000458 0.000553 ! Validation 472 54052.692 0.005 0.00319 0.0757 0.14 0.0492 0.0663 0.285 0.323 0.00081 0.000917 Wall time: 54052.69208817184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 86 0.0761 0.00229 0.0303 0.0422 0.0561 0.186 0.204 0.00053 0.00058 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.067 0.00218 0.0234 0.042 0.0548 0.175 0.179 0.000498 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 473 54166.967 0.005 0.00243 0.0131 0.0617 0.0433 0.0579 0.11 0.134 0.000311 0.000381 ! Validation 473 54166.967 0.005 0.00304 0.0348 0.0956 0.0479 0.0647 0.189 0.219 0.000538 0.000622 Wall time: 54166.96748686908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 86 0.0505 0.00223 0.00589 0.0419 0.0554 0.0721 0.09 0.000205 0.000256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.0477 0.00224 0.00287 0.0428 0.0555 0.0531 0.0629 0.000151 0.000179 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 54281.231 0.005 0.00247 0.0287 0.0781 0.0437 0.0583 0.162 0.199 0.00046 0.000564 ! Validation 474 54281.231 0.005 0.00306 0.0213 0.0824 0.048 0.0648 0.142 0.171 0.000404 0.000486 Wall time: 54281.231872987 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 86 0.0599 0.00246 0.0108 0.0435 0.0581 0.0994 0.122 0.000282 0.000346 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.0516 0.00221 0.00734 0.0425 0.0552 0.0873 0.101 0.000248 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 475 54395.509 0.005 0.00244 0.0273 0.076 0.0434 0.0579 0.159 0.194 0.000451 0.00055 ! Validation 475 54395.509 0.005 0.00308 0.014 0.0755 0.0483 0.0651 0.108 0.139 0.000307 0.000394 Wall time: 54395.50931491703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 86 0.0557 0.00216 0.0125 0.0411 0.0545 0.116 0.131 0.000329 0.000373 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.0454 0.00219 0.00169 0.0421 0.0548 0.0475 0.0482 0.000135 0.000137 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 54509.766 0.005 0.00246 0.0208 0.07 0.0436 0.0582 0.138 0.169 0.000393 0.00048 ! Validation 476 54509.766 0.005 0.00304 0.0165 0.0772 0.0479 0.0646 0.119 0.151 0.000339 0.000428 Wall time: 54509.76677816594 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 86 0.0506 0.00229 0.00478 0.0421 0.0562 0.0644 0.0811 0.000183 0.00023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.0535 0.00218 0.00994 0.0421 0.0547 0.107 0.117 0.000303 0.000332 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 477 54624.039 0.005 0.00239 0.0138 0.0616 0.0429 0.0573 0.11 0.138 0.000313 0.000392 ! Validation 477 54624.039 0.005 0.003 0.015 0.0749 0.0476 0.0642 0.111 0.144 0.000315 0.000408 Wall time: 54624.039677796885 ! Best model 477 0.075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 86 0.0592 0.00243 0.0106 0.0433 0.0578 0.0979 0.121 0.000278 0.000343 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.063 0.00232 0.0167 0.0436 0.0565 0.146 0.151 0.000414 0.00043 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 54738.483 0.005 0.00246 0.0341 0.0833 0.0436 0.0582 0.179 0.217 0.000509 0.000616 ! Validation 478 54738.483 0.005 0.00314 0.0185 0.0814 0.0489 0.0658 0.125 0.16 0.000355 0.000454 Wall time: 54738.48400998302 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 86 0.079 0.0027 0.0249 0.0454 0.061 0.151 0.185 0.000428 0.000526 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.0523 0.00224 0.00739 0.0427 0.0556 0.0959 0.101 0.000272 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 479 54852.760 0.005 0.0025 0.0269 0.0769 0.044 0.0587 0.156 0.192 0.000442 0.000546 ! Validation 479 54852.760 0.005 0.00311 0.0234 0.0855 0.0485 0.0654 0.14 0.179 0.000398 0.000509 Wall time: 54852.76032523392 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 86 0.0735 0.0023 0.0275 0.0424 0.0562 0.173 0.195 0.000491 0.000553 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.0478 0.00227 0.00242 0.0431 0.0559 0.0538 0.0577 0.000153 0.000164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 480 54967.036 0.005 0.00241 0.0193 0.0676 0.0431 0.0576 0.129 0.163 0.000367 0.000463 ! Validation 480 54967.036 0.005 0.00309 0.0164 0.0782 0.0484 0.0652 0.119 0.15 0.000337 0.000426 Wall time: 54967.036380012054 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 86 0.0626 0.00212 0.0201 0.0405 0.054 0.152 0.166 0.000431 0.000473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.0938 0.00213 0.0513 0.0416 0.0541 0.262 0.266 0.000745 0.000755 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 481 55081.318 0.005 0.00236 0.0128 0.06 0.0426 0.057 0.108 0.133 0.000306 0.000377 ! Validation 481 55081.318 0.005 0.00294 0.0368 0.0956 0.0471 0.0636 0.194 0.225 0.000551 0.000639 Wall time: 55081.31835538801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 86 0.0532 0.00236 0.00603 0.0426 0.057 0.0698 0.0911 0.000198 0.000259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.0451 0.00215 0.00203 0.0418 0.0544 0.0514 0.0529 0.000146 0.00015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 482 55195.621 0.005 0.00246 0.0306 0.0798 0.0436 0.0582 0.165 0.205 0.000468 0.000583 ! Validation 482 55195.621 0.005 0.00298 0.0133 0.073 0.0475 0.0641 0.108 0.135 0.000306 0.000384 Wall time: 55195.62171491189 ! Best model 482 0.073 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 86 0.0663 0.0022 0.0223 0.0414 0.055 0.15 0.175 0.000425 0.000498 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.0435 0.00212 0.00107 0.0416 0.054 0.0358 0.0384 0.000102 0.000109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 483 55309.919 0.005 0.00232 0.00985 0.0563 0.0423 0.0565 0.0928 0.116 0.000264 0.000331 ! Validation 483 55309.919 0.005 0.00296 0.0221 0.0812 0.0473 0.0638 0.141 0.174 0.000401 0.000495 Wall time: 55309.919538210146 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 86 0.0502 0.00221 0.00595 0.0413 0.0552 0.0725 0.0905 0.000206 0.000257 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.0465 0.00209 0.00473 0.0413 0.0536 0.0709 0.0807 0.000201 0.000229 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 484 55424.314 0.005 0.00235 0.0195 0.0665 0.0426 0.0569 0.132 0.164 0.000376 0.000465 ! Validation 484 55424.314 0.005 0.0029 0.0135 0.0715 0.0467 0.0632 0.105 0.136 0.000299 0.000387 Wall time: 55424.31410572585 ! Best model 484 0.072 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 86 0.0808 0.00231 0.0345 0.0423 0.0564 0.205 0.218 0.000583 0.000619 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.13 0.00214 0.0872 0.0419 0.0543 0.344 0.346 0.000978 0.000984 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 485 55538.598 0.005 0.00232 0.0186 0.0649 0.0422 0.0565 0.126 0.16 0.000359 0.000454 ! Validation 485 55538.598 0.005 0.00298 0.0789 0.139 0.0475 0.0641 0.305 0.33 0.000866 0.000936 Wall time: 55538.598894905765 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 86 0.0685 0.00255 0.0176 0.0442 0.0592 0.133 0.155 0.000379 0.000442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.0731 0.0022 0.0292 0.0425 0.055 0.195 0.2 0.000554 0.000569 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 486 55652.898 0.005 0.00262 0.0466 0.099 0.0451 0.0601 0.19 0.253 0.000539 0.00072 ! Validation 486 55652.898 0.005 0.00298 0.0396 0.0991 0.0475 0.064 0.194 0.233 0.00055 0.000663 Wall time: 55652.898430887144 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 86 0.0526 0.00236 0.00533 0.0426 0.057 0.0723 0.0857 0.000205 0.000243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.0443 0.0021 0.00231 0.0413 0.0538 0.0413 0.0563 0.000117 0.00016 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 55767.181 0.005 0.00232 0.013 0.0594 0.0423 0.0565 0.11 0.134 0.000311 0.00038 ! Validation 487 55767.181 0.005 0.0029 0.0159 0.074 0.0468 0.0632 0.124 0.148 0.000353 0.00042 Wall time: 55767.18128540879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 86 0.0688 0.00215 0.0259 0.0409 0.0544 0.173 0.189 0.000491 0.000536 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.114 0.00218 0.0701 0.0421 0.0547 0.309 0.311 0.000878 0.000882 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 55881.465 0.005 0.00228 0.00758 0.0533 0.0419 0.0561 0.0803 0.102 0.000228 0.00029 ! Validation 488 55881.465 0.005 0.00296 0.0619 0.121 0.0473 0.0639 0.261 0.292 0.000741 0.000829 Wall time: 55881.465157181025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 86 0.0553 0.00246 0.00619 0.0437 0.0582 0.0706 0.0923 0.000201 0.000262 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0526 0.00216 0.00947 0.0421 0.0545 0.106 0.114 0.000302 0.000324 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 55995.755 0.005 0.00241 0.0338 0.0821 0.0432 0.0576 0.177 0.216 0.000502 0.000613 ! Validation 489 55995.755 0.005 0.00298 0.0164 0.076 0.0475 0.064 0.114 0.15 0.000323 0.000427 Wall time: 55995.75603948999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 86 0.0842 0.00255 0.0333 0.0445 0.0592 0.187 0.214 0.000531 0.000608 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.0508 0.00214 0.00793 0.0418 0.0543 0.0943 0.104 0.000268 0.000297 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 490 56110.046 0.005 0.00231 0.0161 0.0623 0.0422 0.0564 0.118 0.149 0.000334 0.000422 ! Validation 490 56110.046 0.005 0.00296 0.0253 0.0845 0.0473 0.0638 0.137 0.187 0.000389 0.00053 Wall time: 56110.046732101124 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 86 0.0688 0.00252 0.0185 0.0443 0.0589 0.14 0.159 0.000399 0.000453 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.0499 0.00217 0.00644 0.0421 0.0547 0.0776 0.0942 0.000221 0.000267 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 56224.426 0.005 0.00234 0.0201 0.0668 0.0425 0.0567 0.135 0.166 0.000384 0.000473 ! Validation 491 56224.426 0.005 0.003 0.0235 0.0836 0.0478 0.0643 0.155 0.18 0.000441 0.000511 Wall time: 56224.42639362393 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 86 0.0527 0.00225 0.00773 0.0416 0.0556 0.0818 0.103 0.000232 0.000293 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.0446 0.00207 0.00331 0.041 0.0533 0.0501 0.0674 0.000142 0.000192 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 56338.712 0.005 0.00234 0.0183 0.0652 0.0425 0.0568 0.124 0.159 0.000353 0.000451 ! Validation 492 56338.712 0.005 0.00288 0.0306 0.0882 0.0466 0.0629 0.17 0.205 0.000483 0.000583 Wall time: 56338.712942223065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 86 0.0524 0.00233 0.00579 0.0421 0.0566 0.0712 0.0893 0.000202 0.000254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.059 0.00212 0.0165 0.0416 0.054 0.143 0.151 0.000406 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 493 56452.989 0.005 0.00231 0.0187 0.065 0.0423 0.0564 0.127 0.161 0.000361 0.000456 ! Validation 493 56452.989 0.005 0.00289 0.0173 0.0751 0.0467 0.0631 0.121 0.155 0.000345 0.000439 Wall time: 56452.989995297976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 86 0.0645 0.00224 0.0197 0.042 0.0555 0.142 0.165 0.000404 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.0448 0.00219 0.000955 0.0423 0.0549 0.035 0.0363 9.94e-05 0.000103 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 56567.270 0.005 0.0023 0.0204 0.0664 0.0422 0.0563 0.134 0.167 0.000381 0.000476 ! Validation 494 56567.270 0.005 0.00293 0.0205 0.0791 0.0472 0.0635 0.137 0.168 0.000389 0.000477 Wall time: 56567.27010961808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 86 0.0597 0.00233 0.013 0.0427 0.0566 0.118 0.134 0.000334 0.000381 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.0525 0.00222 0.00821 0.0425 0.0552 0.0962 0.106 0.000273 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 495 56681.551 0.005 0.0023 0.0144 0.0605 0.0422 0.0563 0.114 0.141 0.000324 0.0004 ! Validation 495 56681.551 0.005 0.003 0.0214 0.0815 0.0478 0.0643 0.135 0.172 0.000385 0.000488 Wall time: 56681.55142406374 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 86 0.0499 0.00211 0.00764 0.0402 0.0539 0.0863 0.103 0.000245 0.000291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.0426 0.00207 0.00133 0.0411 0.0533 0.0423 0.0427 0.00012 0.000121 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 56795.845 0.005 0.00232 0.0215 0.0679 0.0424 0.0565 0.137 0.172 0.000389 0.000488 ! Validation 496 56795.845 0.005 0.00284 0.0126 0.0693 0.0463 0.0625 0.105 0.132 0.000298 0.000374 Wall time: 56795.845371644944 ! Best model 496 0.069 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 86 0.083 0.00239 0.0352 0.0428 0.0573 0.206 0.22 0.000584 0.000625 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.0489 0.00205 0.00796 0.0407 0.0531 0.099 0.105 0.000281 0.000297 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 497 56910.209 0.005 0.00227 0.0166 0.062 0.0418 0.0559 0.119 0.151 0.000338 0.000429 ! Validation 497 56910.209 0.005 0.00284 0.029 0.0858 0.0463 0.0625 0.163 0.2 0.000464 0.000568 Wall time: 56910.209449869115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 86 0.0577 0.00229 0.0119 0.042 0.0561 0.11 0.128 0.000313 0.000364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.0578 0.00206 0.0167 0.041 0.0532 0.143 0.152 0.000406 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 498 57024.496 0.005 0.00238 0.0278 0.0755 0.043 0.0573 0.166 0.196 0.000472 0.000556 ! Validation 498 57024.496 0.005 0.00284 0.0167 0.0736 0.0463 0.0625 0.121 0.152 0.000343 0.000431 Wall time: 57024.49682582589 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 86 0.0678 0.00243 0.0192 0.0437 0.0578 0.146 0.163 0.000415 0.000462 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.0488 0.00235 0.00186 0.0437 0.0568 0.0409 0.0506 0.000116 0.000144 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 57138.757 0.005 0.00232 0.0233 0.0698 0.0424 0.0566 0.141 0.179 0.000401 0.000509 ! Validation 499 57138.757 0.005 0.00304 0.0159 0.0767 0.0482 0.0647 0.122 0.148 0.000348 0.00042 Wall time: 57138.757547360845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 86 0.0475 0.00211 0.00516 0.0407 0.0539 0.0622 0.0843 0.000177 0.000239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.0431 0.00208 0.00153 0.0412 0.0535 0.0456 0.0459 0.00013 0.00013 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 57253.039 0.005 0.00224 0.0135 0.0583 0.0416 0.0555 0.11 0.136 0.000313 0.000387 ! Validation 500 57253.039 0.005 0.00285 0.0164 0.0733 0.0465 0.0626 0.12 0.15 0.000342 0.000427 Wall time: 57253.039194075856 ! Stop training: max epochs Wall time: 57253.05954145314 Cumulative wall time: 57253.05954145314 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 = 0.053028 f_rmse = 0.073340 e_mae = 0.123127 e_rmse = 0.157577 e/N_mae = 0.000350 e/N_rmse = 0.000448 f_mae = 0.053028 f_rmse = 0.073340 e_mae = 0.123127 e_rmse = 0.157577 e/N_mae = 0.000350 e/N_rmse = 0.000448 Train end time: 2024-12-10_02:11:56 Training duration: 15h 57m 41s