Train start time: 2024-12-08_10:47:35 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: 1406856 Number of trainable weights: 1406856 ! Starting training ... validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 0 100 1.17e+03 1.04 1.15e+03 0.889 1.19 39.8 39.8 0.113 0.113 Initialization # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Initial Validation 0 7.167 0.005 1.07 1.43e+03 1.45e+03 0.904 1.21 42.6 44.4 0.121 0.126 Wall time: 7.167200142983347 ! Best model 0 1454.331 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 29.6 0.987 9.81 0.87 1.17 3.02 3.67 0.00859 0.0104 1 172 22.3 0.996 2.42 0.87 1.17 1.43 1.83 0.00407 0.00519 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 1 100 19.3 0.947 0.381 0.857 1.14 0.537 0.724 0.00152 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 1 110.593 0.005 0.995 5.37e+03 5.39e+03 0.871 1.17 32.3 86 0.0917 0.244 ! Validation 1 110.593 0.005 0.991 4.63 24.4 0.874 1.17 2.06 2.52 0.00586 0.00717 Wall time: 110.59405438276008 ! Best model 1 24.436 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 21.6 1.02 1.29 0.878 1.18 1.08 1.33 0.00307 0.00379 2 172 20.8 0.978 1.2 0.869 1.16 1.04 1.28 0.00295 0.00365 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 2 100 19.2 0.943 0.353 0.855 1.14 0.61 0.697 0.00173 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 2 209.950 0.005 0.986 1.8 21.5 0.868 1.16 1.18 1.57 0.00335 0.00447 ! Validation 2 209.950 0.005 0.987 5.18 24.9 0.872 1.17 2.17 2.67 0.00616 0.00758 Wall time: 209.95024680765346 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 21.8 0.989 2.04 0.866 1.17 1.12 1.67 0.00318 0.00476 3 172 20.4 0.958 1.21 0.858 1.15 1.02 1.29 0.00289 0.00367 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 3 100 19.2 0.938 0.48 0.853 1.14 0.659 0.813 0.00187 0.00231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 3 309.621 0.005 0.981 1.78 21.4 0.866 1.16 1.19 1.56 0.00337 0.00444 ! Validation 3 309.621 0.005 0.982 3.73 23.4 0.87 1.16 1.86 2.27 0.00528 0.00644 Wall time: 309.6226109559648 ! Best model 3 23.367 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 19.8 0.949 0.854 0.851 1.14 0.877 1.08 0.00249 0.00308 4 172 21 0.974 1.56 0.863 1.16 1.16 1.47 0.00331 0.00416 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 4 100 19 0.933 0.313 0.851 1.13 0.577 0.657 0.00164 0.00187 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 4 408.815 0.005 0.976 1.66 21.2 0.863 1.16 1.14 1.51 0.00325 0.0043 ! Validation 4 408.815 0.005 0.976 4.35 23.9 0.868 1.16 1.95 2.45 0.00553 0.00695 Wall time: 408.81552827870473 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 21.2 0.98 1.59 0.865 1.16 1.12 1.48 0.00319 0.0042 5 172 19.8 0.952 0.778 0.855 1.14 0.768 1.03 0.00218 0.00294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 5 100 18.8 0.927 0.29 0.849 1.13 0.49 0.631 0.00139 0.00179 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 5 508.011 0.005 0.97 1.66 21.1 0.861 1.16 1.15 1.51 0.00327 0.00429 ! Validation 5 508.011 0.005 0.97 3.54 23 0.865 1.16 1.79 2.21 0.0051 0.00627 Wall time: 508.01098964968696 ! Best model 5 22.950 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 19.7 0.952 0.637 0.853 1.14 0.689 0.936 0.00196 0.00266 6 172 23 0.977 3.47 0.865 1.16 1.42 2.19 0.00403 0.00621 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 6 100 18.7 0.921 0.274 0.846 1.13 0.548 0.614 0.00156 0.00174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 6 607.253 0.005 0.964 1.65 20.9 0.858 1.15 1.15 1.5 0.00326 0.00427 ! Validation 6 607.253 0.005 0.964 3.61 22.9 0.862 1.15 1.77 2.23 0.00504 0.00633 Wall time: 607.253901463002 ! Best model 6 22.883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 22.4 0.971 3.03 0.858 1.16 1.3 2.04 0.00368 0.0058 7 172 21.3 0.978 1.76 0.862 1.16 1.24 1.55 0.00351 0.00442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 7 100 18.5 0.914 0.257 0.843 1.12 0.534 0.594 0.00152 0.00169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 7 706.480 0.005 0.957 1.63 20.8 0.855 1.15 1.15 1.5 0.00326 0.00425 ! Validation 7 706.480 0.005 0.956 3.29 22.4 0.859 1.15 1.71 2.13 0.00485 0.00605 Wall time: 706.4805999617092 ! Best model 7 22.423 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 20.6 0.937 1.82 0.85 1.14 1.29 1.58 0.00366 0.00449 8 172 20.1 0.932 1.46 0.846 1.13 1.17 1.42 0.00333 0.00402 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 18.4 0.907 0.233 0.84 1.12 0.508 0.567 0.00144 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 8 805.718 0.005 0.95 1.58 20.6 0.852 1.14 1.13 1.47 0.00321 0.00419 ! Validation 8 805.718 0.005 0.949 3.03 22 0.856 1.14 1.64 2.04 0.00466 0.0058 Wall time: 805.7183519708924 ! Best model 8 22.008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 20.9 0.954 1.83 0.854 1.15 1.18 1.59 0.00335 0.00451 9 172 20.8 0.941 1.97 0.851 1.14 1.26 1.65 0.00357 0.00468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 9 100 18.5 0.899 0.487 0.837 1.11 0.705 0.818 0.002 0.00232 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 9 905.083 0.005 0.942 1.52 20.4 0.848 1.14 1.11 1.44 0.00316 0.0041 ! Validation 9 905.083 0.005 0.941 3.45 22.3 0.852 1.14 1.71 2.18 0.00485 0.00619 Wall time: 905.0831377785653 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 19.8 0.925 1.33 0.843 1.13 1.06 1.35 0.00302 0.00384 10 172 20.7 0.934 1.98 0.847 1.13 1.24 1.65 0.00351 0.00469 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 10 100 18 0.891 0.216 0.833 1.11 0.456 0.545 0.0013 0.00155 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 10 1004.288 0.005 0.933 1.53 20.2 0.844 1.13 1.12 1.45 0.00318 0.00412 ! Validation 10 1004.288 0.005 0.932 2.69 21.3 0.848 1.13 1.59 1.93 0.00452 0.00547 Wall time: 1004.2881284886971 ! Best model 10 21.334 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 19.9 0.92 1.51 0.838 1.13 1.17 1.44 0.00331 0.00409 11 172 19.7 0.9 1.75 0.83 1.11 1.31 1.55 0.00373 0.0044 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 11 100 21.1 0.882 3.42 0.829 1.1 2.1 2.17 0.00598 0.00616 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 1103.525 0.005 0.924 1.62 20.1 0.84 1.13 1.16 1.49 0.0033 0.00425 ! Validation 11 1103.525 0.005 0.923 6.68 25.1 0.844 1.13 2.49 3.03 0.00708 0.00861 Wall time: 1103.525265192613 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 19.5 0.901 1.53 0.829 1.11 1.19 1.45 0.00338 0.00413 12 172 19.7 0.921 1.27 0.838 1.13 1.05 1.32 0.00299 0.00375 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 17.9 0.873 0.411 0.824 1.1 0.646 0.752 0.00184 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 12 1202.733 0.005 0.915 1.53 19.8 0.835 1.12 1.12 1.45 0.00319 0.00413 ! Validation 12 1202.733 0.005 0.913 2.98 21.2 0.839 1.12 1.6 2.03 0.00454 0.00575 Wall time: 1202.7347644069232 ! Best model 12 21.233 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 19.1 0.9 1.05 0.829 1.11 1.01 1.2 0.00286 0.00341 13 172 19.5 0.892 1.62 0.824 1.11 1.21 1.49 0.00344 0.00424 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 21.7 0.863 4.4 0.819 1.09 2.41 2.46 0.00684 0.00699 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 1301.989 0.005 0.904 1.66 19.7 0.831 1.12 1.17 1.51 0.00333 0.00429 ! Validation 13 1301.989 0.005 0.902 7.36 25.4 0.834 1.11 2.69 3.18 0.00765 0.00904 Wall time: 1301.9895165967755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 19.1 0.883 1.46 0.82 1.1 1.08 1.42 0.00307 0.00402 14 172 19.7 0.887 1.96 0.824 1.1 1.25 1.64 0.00355 0.00466 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 14 100 17.3 0.852 0.3 0.814 1.08 0.549 0.642 0.00156 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 14 1401.272 0.005 0.893 1.79 19.6 0.825 1.11 1.23 1.57 0.00349 0.00445 ! Validation 14 1401.272 0.005 0.89 2.53 20.3 0.828 1.11 1.48 1.87 0.0042 0.0053 Wall time: 1401.271892610006 ! Best model 14 20.334 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 19.3 0.904 1.25 0.829 1.12 1.09 1.31 0.00309 0.00373 15 172 19.1 0.886 1.33 0.816 1.1 1.14 1.35 0.00324 0.00385 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 15 100 17.5 0.84 0.69 0.809 1.08 0.854 0.974 0.00243 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 1500.497 0.005 0.881 1.86 19.5 0.82 1.1 1.27 1.6 0.0036 0.00454 ! Validation 15 1500.497 0.005 0.878 2.87 20.4 0.823 1.1 1.57 1.99 0.00447 0.00565 Wall time: 1500.497506792657 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 19.8 0.882 2.17 0.819 1.1 1.1 1.73 0.00314 0.00491 16 172 19.2 0.869 1.83 0.812 1.09 1.35 1.59 0.00384 0.00451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 16 100 16.7 0.827 0.195 0.803 1.07 0.399 0.518 0.00113 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 16 1599.701 0.005 0.868 1.89 19.2 0.814 1.09 1.27 1.61 0.00362 0.00458 ! Validation 16 1599.701 0.005 0.864 2.09 19.4 0.816 1.09 1.37 1.7 0.00388 0.00482 Wall time: 1599.7013082616031 ! Best model 16 19.368 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 19.3 0.852 2.23 0.805 1.08 1.38 1.75 0.00391 0.00498 17 172 18.1 0.844 1.19 0.803 1.08 1.04 1.28 0.00295 0.00364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 17 100 16.9 0.813 0.65 0.796 1.06 0.835 0.946 0.00237 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 17 1698.913 0.005 0.853 1.96 19 0.807 1.08 1.29 1.64 0.00367 0.00466 ! Validation 17 1698.913 0.005 0.849 2.57 19.5 0.809 1.08 1.47 1.88 0.00418 0.00534 Wall time: 1698.9138078177348 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 18.4 0.845 1.45 0.803 1.08 1.2 1.41 0.00342 0.00401 18 172 18.5 0.848 1.53 0.806 1.08 1.15 1.45 0.00328 0.00412 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 18 100 22.1 0.796 6.14 0.787 1.05 2.87 2.91 0.00816 0.00826 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 1798.123 0.005 0.836 2.36 19.1 0.799 1.07 1.42 1.8 0.00403 0.00512 ! Validation 18 1798.123 0.005 0.831 7.27 23.9 0.8 1.07 2.59 3.16 0.00736 0.00899 Wall time: 1798.1237167678773 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 19.2 0.836 2.49 0.795 1.07 1.54 1.85 0.00438 0.00526 19 172 17.7 0.793 1.83 0.774 1.04 1.23 1.59 0.00349 0.00451 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 20.8 0.771 5.35 0.775 1.03 2.68 2.71 0.00761 0.00771 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 1897.333 0.005 0.816 1.98 18.3 0.789 1.06 1.3 1.65 0.0037 0.00469 ! Validation 19 1897.333 0.005 0.805 6.73 22.8 0.788 1.05 2.68 3.04 0.0076 0.00865 Wall time: 1897.3336674380116 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 18.3 0.797 2.31 0.777 1.05 1.41 1.78 0.00402 0.00507 20 172 17.5 0.765 2.22 0.762 1.03 1.42 1.75 0.00403 0.00496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 14.8 0.73 0.197 0.755 1 0.371 0.521 0.00106 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 20 1996.563 0.005 0.786 3.43 19.2 0.775 1.04 1.68 2.17 0.00478 0.00618 ! Validation 20 1996.563 0.005 0.763 1.71 17 0.767 1.02 1.21 1.53 0.00345 0.00436 Wall time: 1996.562934864778 ! Best model 20 16.978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 15.7 0.702 1.61 0.733 0.983 1.16 1.49 0.00329 0.00423 21 172 21.5 0.605 9.41 0.68 0.912 3.42 3.6 0.00972 0.0102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 13.5 0.579 1.92 0.675 0.893 1.57 1.63 0.00446 0.00462 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 2095.824 0.005 0.697 3.94 17.9 0.729 0.979 1.89 2.33 0.00537 0.00661 ! Validation 21 2095.824 0.005 0.609 2.51 14.7 0.685 0.916 1.54 1.86 0.00437 0.00528 Wall time: 2095.823926779907 ! Best model 21 14.700 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 13.5 0.446 4.6 0.586 0.784 2.23 2.52 0.00635 0.00715 22 172 12.2 0.379 4.65 0.537 0.722 2.3 2.53 0.00655 0.00718 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 10.6 0.361 3.33 0.532 0.705 2.08 2.14 0.0059 0.00609 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 22 2195.207 0.005 0.47 5.01 14.4 0.598 0.805 1.98 2.63 0.00562 0.00746 ! Validation 22 2195.207 0.005 0.39 3.76 11.6 0.541 0.732 2 2.27 0.00569 0.00646 Wall time: 2195.2072743065655 ! Best model 22 11.556 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 7.97 0.345 1.07 0.508 0.689 0.984 1.22 0.0028 0.00345 23 172 9.74 0.339 2.96 0.505 0.683 1.72 2.02 0.00489 0.00573 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 7.07 0.323 0.602 0.501 0.667 0.755 0.91 0.00214 0.00259 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 23 2294.404 0.005 0.355 5.13 12.2 0.517 0.699 2.1 2.66 0.00595 0.00755 ! Validation 23 2294.404 0.005 0.353 2.12 9.18 0.513 0.697 1.42 1.71 0.00402 0.00486 Wall time: 2294.4043608298525 ! Best model 23 9.183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 29.9 0.336 23.2 0.503 0.68 5.29 5.65 0.015 0.016 24 172 8.18 0.329 1.59 0.499 0.673 1.3 1.48 0.00371 0.00421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 8.13 0.311 1.92 0.491 0.654 1.54 1.62 0.00439 0.00461 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 2393.847 0.005 0.337 7.26 14 0.503 0.681 2.45 3.16 0.00695 0.00898 ! Validation 24 2393.847 0.005 0.339 2.71 9.48 0.503 0.683 1.55 1.93 0.0044 0.00548 Wall time: 2393.847176005598 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 7.09 0.31 0.892 0.482 0.653 0.843 1.11 0.0024 0.00315 25 172 8.09 0.302 2.05 0.476 0.645 1.37 1.68 0.00389 0.00477 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 6.01 0.282 0.368 0.468 0.623 0.591 0.712 0.00168 0.00202 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 25 2493.023 0.005 0.312 2.37 8.6 0.483 0.655 1.42 1.81 0.00404 0.00513 ! Validation 25 2493.023 0.005 0.307 2.83 8.97 0.479 0.65 1.64 1.97 0.00466 0.00561 Wall time: 2493.0229551037773 ! Best model 25 8.970 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 17 0.291 11.2 0.465 0.633 3.78 3.93 0.0107 0.0112 26 172 24.8 0.275 19.2 0.455 0.615 5.04 5.15 0.0143 0.0146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 6.29 0.266 0.967 0.456 0.605 1.06 1.15 0.00301 0.00328 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 26 2592.218 0.005 0.287 4.31 10.1 0.464 0.629 1.84 2.43 0.00523 0.00691 ! Validation 26 2592.218 0.005 0.288 1.91 7.67 0.466 0.63 1.35 1.62 0.00384 0.0046 Wall time: 2592.2183081009425 ! Best model 26 7.674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 22.9 0.273 17.4 0.454 0.612 4.69 4.9 0.0133 0.0139 27 172 8.33 0.278 2.77 0.457 0.618 1.64 1.95 0.00467 0.00555 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 16.7 0.26 11.5 0.452 0.598 3.95 3.97 0.0112 0.0113 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 2691.407 0.005 0.277 6.24 11.8 0.457 0.618 2.33 2.93 0.00662 0.00832 ! Validation 27 2691.407 0.005 0.281 11.3 16.9 0.46 0.621 3.81 3.94 0.0108 0.0112 Wall time: 2691.4077764656395 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 7.92 0.258 2.77 0.442 0.595 1.76 1.95 0.005 0.00554 28 172 11.5 0.242 6.62 0.428 0.577 2.87 3.02 0.00815 0.00857 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 6.99 0.241 2.17 0.435 0.576 1.68 1.73 0.00477 0.00491 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 28 2790.707 0.005 0.26 4.02 9.23 0.443 0.598 1.92 2.35 0.00545 0.00668 ! Validation 28 2790.707 0.005 0.257 2.27 7.42 0.442 0.595 1.5 1.77 0.00426 0.00502 Wall time: 2790.7074690288864 ! Best model 28 7.417 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 19.7 0.248 14.8 0.432 0.584 4.39 4.51 0.0125 0.0128 29 172 9.55 0.242 4.7 0.43 0.578 2.42 2.54 0.00687 0.00722 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 10.4 0.233 5.72 0.429 0.566 2.77 2.81 0.00788 0.00797 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 2889.976 0.005 0.245 5.8 10.7 0.431 0.581 2.28 2.82 0.00647 0.00802 ! Validation 29 2889.976 0.005 0.247 5.89 10.8 0.434 0.583 2.69 2.85 0.00763 0.00809 Wall time: 2889.976347334683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 5.35 0.224 0.861 0.414 0.555 0.935 1.09 0.00266 0.00309 30 172 4.81 0.222 0.376 0.412 0.552 0.589 0.719 0.00167 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 4.46 0.214 0.173 0.412 0.543 0.381 0.487 0.00108 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 30 2989.571 0.005 0.229 3.73 8.3 0.417 0.561 1.87 2.27 0.0053 0.00644 ! Validation 30 2989.571 0.005 0.226 0.673 5.2 0.416 0.558 0.773 0.962 0.0022 0.00273 Wall time: 2989.571747181006 ! Best model 30 5.200 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 8.35 0.203 4.28 0.394 0.529 2.28 2.43 0.00646 0.00689 31 172 7.78 0.199 3.8 0.392 0.523 2.19 2.29 0.00623 0.00649 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 4.97 0.196 1.06 0.395 0.519 1.16 1.21 0.00329 0.00343 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 31 3088.874 0.005 0.207 2.99 7.13 0.397 0.534 1.56 2.03 0.00443 0.00576 ! Validation 31 3088.874 0.005 0.205 2.73 6.84 0.397 0.531 1.7 1.94 0.00482 0.00551 Wall time: 3088.8742968728766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 13.6 0.199 9.6 0.39 0.523 3.53 3.63 0.01 0.0103 32 172 6.39 0.189 2.6 0.38 0.51 1.56 1.89 0.00444 0.00538 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 6.8 0.185 3.09 0.385 0.505 2.04 2.06 0.00579 0.00586 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 32 3188.207 0.005 0.195 4.05 7.96 0.387 0.518 1.89 2.36 0.00536 0.00671 ! Validation 32 3188.207 0.005 0.194 2.35 6.23 0.387 0.517 1.55 1.8 0.0044 0.00511 Wall time: 3188.2078669490293 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 12.1 0.184 8.45 0.376 0.504 3.36 3.41 0.00955 0.00969 33 172 4.15 0.173 0.698 0.366 0.487 0.806 0.98 0.00229 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 9.59 0.174 6.12 0.373 0.489 2.88 2.9 0.00819 0.00824 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 3288.092 0.005 0.182 4.05 7.69 0.375 0.501 1.92 2.36 0.00545 0.0067 ! Validation 33 3288.092 0.005 0.18 5.08 8.68 0.374 0.498 2.52 2.64 0.00716 0.00751 Wall time: 3288.0926908417605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 3.89 0.166 0.57 0.358 0.478 0.736 0.886 0.00209 0.00252 34 172 6.2 0.157 3.06 0.349 0.465 1.95 2.05 0.00553 0.00583 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 6.4 0.156 3.29 0.352 0.463 2.11 2.13 0.00598 0.00604 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 3387.208 0.005 0.167 2.72 6.06 0.36 0.48 1.51 1.93 0.00428 0.00549 ! Validation 34 3387.208 0.005 0.161 6.69 9.91 0.355 0.471 2.84 3.03 0.00806 0.00862 Wall time: 3387.208823295776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 9.9 0.152 6.86 0.344 0.457 2.94 3.07 0.00834 0.00873 35 172 3.73 0.151 0.704 0.344 0.456 0.725 0.984 0.00206 0.0028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 4.27 0.146 1.34 0.342 0.449 1.32 1.36 0.00376 0.00386 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 35 3486.423 0.005 0.154 3.54 6.63 0.347 0.461 1.7 2.21 0.00484 0.00628 ! Validation 35 3486.423 0.005 0.151 1.2 4.23 0.345 0.456 1.1 1.29 0.00311 0.00366 Wall time: 3486.4236030275933 ! Best model 35 4.227 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 4.51 0.141 1.69 0.333 0.441 1.38 1.53 0.00391 0.00433 36 172 13.4 0.135 10.7 0.326 0.431 3.77 3.84 0.0107 0.0109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 18.3 0.136 15.6 0.329 0.433 4.62 4.63 0.0131 0.0131 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 3585.632 0.005 0.142 3.03 5.86 0.333 0.442 1.62 2.04 0.00461 0.00579 ! Validation 36 3585.632 0.005 0.142 16.7 19.5 0.335 0.442 4.69 4.79 0.0133 0.0136 Wall time: 3585.6322093675844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 3.45 0.128 0.9 0.317 0.419 0.839 1.11 0.00238 0.00316 37 172 4.73 0.128 2.16 0.318 0.42 1.49 1.73 0.00424 0.0049 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 5.61 0.133 2.96 0.325 0.427 1.99 2.02 0.00566 0.00573 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 37 3684.843 0.005 0.136 3.88 6.61 0.327 0.433 1.82 2.31 0.00518 0.00657 ! Validation 37 3684.843 0.005 0.136 1.9 4.62 0.33 0.433 1.38 1.62 0.00391 0.00459 Wall time: 3684.843284035567 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 3.03 0.127 0.485 0.317 0.418 0.696 0.817 0.00198 0.00232 38 172 4.1 0.12 1.71 0.307 0.406 1.41 1.53 0.00401 0.00436 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 2.98 0.121 0.558 0.311 0.408 0.809 0.876 0.0023 0.00249 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 38 3784.080 0.005 0.129 2.77 5.36 0.32 0.422 1.43 1.95 0.00407 0.00555 ! Validation 38 3784.080 0.005 0.126 0.642 3.16 0.317 0.416 0.708 0.94 0.00201 0.00267 Wall time: 3784.0799398757517 ! Best model 38 3.157 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 3.39 0.121 0.961 0.309 0.409 0.964 1.15 0.00274 0.00327 39 172 2.83 0.116 0.514 0.303 0.399 0.697 0.841 0.00198 0.00239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 2.82 0.118 0.459 0.307 0.403 0.728 0.795 0.00207 0.00226 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 39 3883.367 0.005 0.121 2.87 5.29 0.31 0.408 1.63 1.99 0.00462 0.00565 ! Validation 39 3883.367 0.005 0.122 1.51 3.94 0.313 0.41 1.28 1.44 0.00362 0.00409 Wall time: 3883.3670983766206 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 2.74 0.114 0.461 0.3 0.396 0.696 0.796 0.00198 0.00226 40 172 4.79 0.119 2.41 0.308 0.405 1.64 1.82 0.00467 0.00517 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 3.7 0.121 1.28 0.311 0.408 1.29 1.33 0.00366 0.00377 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 3982.665 0.005 0.115 2.89 5.2 0.302 0.398 1.61 2 0.00457 0.00567 ! Validation 40 3982.665 0.005 0.124 0.8 3.28 0.315 0.413 0.886 1.05 0.00252 0.00298 Wall time: 3982.665332848672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 3.52 0.107 1.38 0.291 0.384 0.722 1.38 0.00205 0.00391 41 172 3.97 0.104 1.9 0.287 0.378 1.46 1.62 0.00414 0.00459 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 3.17 0.105 1.07 0.29 0.38 1.17 1.21 0.00333 0.00345 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 4081.844 0.005 0.108 1.29 3.44 0.293 0.385 1.04 1.33 0.00296 0.00378 ! Validation 41 4081.844 0.005 0.109 3.44 5.62 0.296 0.387 1.96 2.18 0.00557 0.00618 Wall time: 4081.8440004037693 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 3.22 0.104 1.13 0.287 0.379 1.17 1.25 0.00332 0.00355 42 172 2.89 0.108 0.734 0.294 0.385 0.846 1.01 0.0024 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 2.24 0.108 0.0825 0.294 0.386 0.288 0.337 0.000817 0.000957 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 42 4181.033 0.005 0.11 3.2 5.4 0.295 0.389 1.61 2.1 0.00458 0.00597 ! Validation 42 4181.033 0.005 0.112 0.362 2.59 0.299 0.392 0.545 0.705 0.00155 0.002 Wall time: 4181.033701509703 ! Best model 42 2.593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 4.86 0.111 2.65 0.296 0.39 1.83 1.91 0.00519 0.00542 43 172 3.4 0.098 1.44 0.28 0.367 1.28 1.41 0.00364 0.004 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 2.33 0.0999 0.334 0.283 0.371 0.608 0.678 0.00173 0.00193 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 43 4280.223 0.005 0.103 1.84 3.89 0.285 0.376 1.18 1.59 0.00336 0.00452 ! Validation 43 4280.223 0.005 0.104 1.45 3.53 0.289 0.378 1.23 1.41 0.00349 0.00401 Wall time: 4280.22345682187 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 3.98 0.0982 2.02 0.278 0.368 1.57 1.67 0.00445 0.00473 44 172 2.52 0.0907 0.704 0.268 0.353 0.883 0.984 0.00251 0.0028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 2.35 0.0957 0.437 0.277 0.363 0.72 0.776 0.00205 0.0022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 44 4379.403 0.005 0.0976 1.79 3.74 0.278 0.367 1.27 1.57 0.00361 0.00445 ! Validation 44 4379.403 0.005 0.1 0.479 2.48 0.283 0.371 0.666 0.812 0.00189 0.00231 Wall time: 4379.403298874851 ! Best model 44 2.478 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 4.07 0.0961 2.15 0.276 0.364 1.64 1.72 0.00465 0.00488 45 172 7.92 0.104 5.85 0.287 0.378 2.72 2.84 0.00772 0.00806 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 12 0.107 9.88 0.292 0.384 3.68 3.69 0.0104 0.0105 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 45 4478.592 0.005 0.0978 2.99 4.95 0.278 0.367 1.59 2.03 0.00452 0.00576 ! Validation 45 4478.592 0.005 0.109 10.7 12.9 0.295 0.388 3.76 3.84 0.0107 0.0109 Wall time: 4478.592810454778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 4.1 0.0924 2.25 0.271 0.357 1.63 1.76 0.00463 0.005 46 172 5.94 0.0925 4.09 0.271 0.357 2.23 2.37 0.00633 0.00674 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 6.54 0.0946 4.65 0.275 0.361 2.52 2.53 0.00715 0.00718 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 46 4578.020 0.005 0.0955 1.63 3.54 0.275 0.363 1.2 1.5 0.00342 0.00425 ! Validation 46 4578.020 0.005 0.0984 4.9 6.86 0.281 0.368 2.49 2.6 0.00708 0.00737 Wall time: 4578.020045607816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 2.97 0.0886 1.19 0.265 0.349 1.21 1.28 0.00343 0.00364 47 172 2.08 0.0871 0.338 0.263 0.346 0.597 0.682 0.0017 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 2.75 0.0915 0.919 0.271 0.355 1.09 1.12 0.00308 0.00319 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 47 4677.443 0.005 0.0911 1.72 3.54 0.268 0.354 1.22 1.54 0.00348 0.00437 ! Validation 47 4677.443 0.005 0.0949 0.695 2.59 0.275 0.361 0.818 0.978 0.00232 0.00278 Wall time: 4677.443589634728 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.44 0.0899 0.647 0.267 0.352 0.79 0.943 0.00225 0.00268 48 172 1.99 0.0889 0.215 0.264 0.35 0.456 0.543 0.00129 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.08 0.0926 0.225 0.272 0.357 0.498 0.557 0.00141 0.00158 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 48 4776.726 0.005 0.0887 1.83 3.6 0.265 0.349 1.28 1.59 0.00363 0.0045 ! Validation 48 4776.726 0.005 0.0954 1.37 3.27 0.275 0.362 1.09 1.37 0.00309 0.00389 Wall time: 4776.72616314888 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.37 0.0851 0.671 0.26 0.342 0.825 0.961 0.00234 0.00273 49 172 5.15 0.0962 3.23 0.275 0.364 2.04 2.11 0.00578 0.00599 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.9 0.0998 0.908 0.282 0.371 1.09 1.12 0.0031 0.00318 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 49 4876.102 0.005 0.0872 2.05 3.79 0.262 0.346 1.32 1.68 0.00374 0.00477 ! Validation 49 4876.102 0.005 0.102 0.685 2.73 0.285 0.375 0.803 0.971 0.00228 0.00276 Wall time: 4876.10235000262 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.19 0.0861 0.469 0.262 0.344 0.611 0.803 0.00174 0.00228 50 172 2.15 0.0854 0.444 0.26 0.343 0.673 0.782 0.00191 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.39 0.0891 0.61 0.267 0.35 0.879 0.916 0.0025 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 50 4975.364 0.005 0.0877 1.72 3.47 0.263 0.347 1.24 1.54 0.00353 0.00437 ! Validation 50 4975.364 0.005 0.0921 0.821 2.66 0.27 0.356 0.886 1.06 0.00252 0.00302 Wall time: 4975.364272894803 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 3.64 0.0798 2.04 0.251 0.331 1.58 1.68 0.0045 0.00476 51 172 1.76 0.0779 0.202 0.248 0.327 0.404 0.527 0.00115 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 1.63 0.0796 0.0412 0.253 0.331 0.217 0.238 0.000615 0.000676 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 51 5074.557 0.005 0.0804 0.895 2.5 0.252 0.333 0.892 1.11 0.00253 0.00315 ! Validation 51 5074.557 0.005 0.0839 0.728 2.41 0.259 0.34 0.766 1 0.00218 0.00284 Wall time: 5074.557348583825 ! Best model 51 2.405 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 4 0.0816 2.36 0.253 0.335 1.54 1.8 0.00436 0.00512 52 172 3.13 0.0731 1.66 0.24 0.317 1.43 1.51 0.00408 0.0043 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 1.56 0.0759 0.045 0.247 0.323 0.219 0.249 0.000622 0.000707 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 52 5173.883 0.005 0.0772 1.06 2.6 0.247 0.326 0.943 1.2 0.00268 0.00342 ! Validation 52 5173.883 0.005 0.08 0.276 1.88 0.252 0.332 0.476 0.617 0.00135 0.00175 Wall time: 5173.883842375595 ! Best model 52 1.877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 2.76 0.0749 1.26 0.243 0.321 1.17 1.32 0.00332 0.00375 53 172 3.29 0.0722 1.84 0.239 0.315 1.51 1.59 0.00428 0.00452 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 6.05 0.0776 4.5 0.25 0.327 2.48 2.49 0.00704 0.00707 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 5273.093 0.005 0.0762 1.4 2.92 0.245 0.324 1.11 1.39 0.00315 0.00394 ! Validation 53 5273.093 0.005 0.0817 3.83 5.46 0.255 0.335 2.19 2.29 0.00622 0.00652 Wall time: 5273.093750173692 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 1.94 0.0816 0.31 0.253 0.335 0.536 0.654 0.00152 0.00186 54 172 2.37 0.0743 0.884 0.242 0.32 0.961 1.1 0.00273 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 1.89 0.0741 0.403 0.245 0.319 0.713 0.744 0.00203 0.00212 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 54 5372.462 0.005 0.0779 1.5 3.06 0.248 0.327 1.09 1.44 0.0031 0.00408 ! Validation 54 5372.462 0.005 0.0787 1.03 2.6 0.25 0.329 1.04 1.19 0.00296 0.00338 Wall time: 5372.462512989994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 2.05 0.0757 0.533 0.244 0.323 0.706 0.856 0.00201 0.00243 55 172 4.08 0.0742 2.6 0.242 0.32 1.71 1.89 0.00485 0.00537 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 4.92 0.0767 3.38 0.248 0.325 2.15 2.16 0.0061 0.00613 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 5471.840 0.005 0.0741 1.34 2.83 0.241 0.319 1.1 1.36 0.00312 0.00386 ! Validation 55 5471.840 0.005 0.0796 3.14 4.74 0.251 0.331 1.99 2.08 0.00566 0.00591 Wall time: 5471.840503694024 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 3.05 0.0697 1.66 0.235 0.31 1.31 1.51 0.00373 0.00429 56 172 1.79 0.0762 0.265 0.246 0.324 0.469 0.604 0.00133 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 1.57 0.0771 0.0271 0.249 0.326 0.152 0.193 0.000433 0.000549 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 56 5571.122 0.005 0.0714 1.1 2.52 0.237 0.313 0.973 1.23 0.00276 0.00349 ! Validation 56 5571.122 0.005 0.0798 1.75 3.35 0.253 0.331 1.21 1.55 0.00343 0.00441 Wall time: 5571.12188292481 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 1.75 0.0694 0.363 0.234 0.309 0.593 0.707 0.00168 0.00201 57 172 5.17 0.0702 3.76 0.234 0.311 2.2 2.28 0.00625 0.00647 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 6.4 0.0707 4.98 0.238 0.312 2.61 2.62 0.00742 0.00744 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 5670.163 0.005 0.07 1.02 2.42 0.235 0.31 0.928 1.19 0.00264 0.00337 ! Validation 57 5670.163 0.005 0.0743 6.99 8.48 0.243 0.32 2.91 3.1 0.00827 0.00881 Wall time: 5670.162992400583 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 2.21 0.0654 0.899 0.227 0.3 0.999 1.11 0.00284 0.00316 58 172 2.77 0.0716 1.34 0.237 0.314 1.24 1.36 0.00353 0.00385 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 2.64 0.0739 1.16 0.243 0.319 1.25 1.26 0.00356 0.00359 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 5769.913 0.005 0.0684 1.34 2.7 0.232 0.307 1.07 1.36 0.00304 0.00385 ! Validation 58 5769.913 0.005 0.0768 4.39 5.92 0.246 0.325 2.01 2.46 0.00572 0.00698 Wall time: 5769.9130571857095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.43 0.0634 0.166 0.224 0.295 0.362 0.479 0.00103 0.00136 59 172 1.39 0.0622 0.15 0.222 0.293 0.386 0.454 0.0011 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.56 0.0647 0.261 0.229 0.298 0.561 0.599 0.00159 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 59 5869.099 0.005 0.067 0.851 2.19 0.229 0.304 0.842 1.08 0.00239 0.00308 ! Validation 59 5869.099 0.005 0.0689 0.585 1.96 0.234 0.308 0.707 0.897 0.00201 0.00255 Wall time: 5869.099507465027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 2.35 0.0753 0.841 0.242 0.322 0.839 1.08 0.00238 0.00306 60 172 2.86 0.0725 1.42 0.238 0.316 1.16 1.4 0.00331 0.00396 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 1.51 0.073 0.0501 0.241 0.317 0.217 0.262 0.000617 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 60 5968.321 0.005 0.0701 1.78 3.18 0.234 0.31 1.25 1.56 0.00354 0.00444 ! Validation 60 5968.321 0.005 0.0756 0.346 1.86 0.244 0.322 0.56 0.69 0.00159 0.00196 Wall time: 5968.321228379849 ! Best model 60 1.857 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 1.48 0.0638 0.206 0.223 0.296 0.4 0.533 0.00114 0.00151 61 172 5.08 0.0655 3.77 0.225 0.3 2.21 2.28 0.00629 0.00647 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 6.16 0.0687 4.78 0.235 0.307 2.56 2.57 0.00726 0.00729 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 61 6067.528 0.005 0.0657 0.964 2.28 0.227 0.301 0.928 1.15 0.00264 0.00327 ! Validation 61 6067.528 0.005 0.0716 7.21 8.64 0.237 0.314 2.98 3.15 0.00848 0.00895 Wall time: 6067.528626570944 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 1.84 0.0622 0.598 0.221 0.292 0.742 0.907 0.00211 0.00258 62 172 2.82 0.062 1.58 0.22 0.292 1.43 1.47 0.00407 0.00419 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 2.05 0.065 0.747 0.228 0.299 1 1.01 0.00284 0.00288 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 62 6166.729 0.005 0.0636 0.855 2.13 0.223 0.296 0.877 1.08 0.00249 0.00308 ! Validation 62 6166.729 0.005 0.0675 0.87 2.22 0.231 0.305 0.941 1.09 0.00267 0.00311 Wall time: 6166.729439999908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 10.6 0.0618 9.35 0.221 0.292 3.46 3.59 0.00982 0.0102 63 172 1.37 0.06 0.173 0.216 0.287 0.382 0.488 0.00108 0.00139 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 1.4 0.0602 0.197 0.22 0.288 0.492 0.52 0.0014 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 63 6265.982 0.005 0.0616 0.846 2.08 0.22 0.291 0.846 1.08 0.0024 0.00307 ! Validation 63 6265.982 0.005 0.0639 0.338 1.62 0.225 0.296 0.554 0.682 0.00157 0.00194 Wall time: 6265.982664298732 ! Best model 63 1.616 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 1.9 0.0635 0.632 0.224 0.296 0.8 0.933 0.00227 0.00265 64 172 3.36 0.0578 2.21 0.213 0.282 1.66 1.74 0.00472 0.00495 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 5.37 0.0616 4.14 0.221 0.291 2.38 2.39 0.00676 0.00678 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 6365.427 0.005 0.0604 0.99 2.2 0.218 0.288 0.919 1.17 0.00261 0.00331 ! Validation 64 6365.427 0.005 0.065 3.41 4.71 0.226 0.299 2.04 2.17 0.0058 0.00615 Wall time: 6365.427071207669 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.47 0.0619 0.232 0.219 0.292 0.44 0.565 0.00125 0.0016 65 172 1.69 0.0541 0.611 0.206 0.273 0.791 0.917 0.00225 0.00261 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 2.51 0.0592 1.33 0.218 0.285 1.35 1.35 0.00383 0.00384 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 6464.727 0.005 0.0581 0.814 1.98 0.213 0.283 0.818 1.06 0.00232 0.00301 ! Validation 65 6464.727 0.005 0.0627 1.21 2.46 0.222 0.294 1.15 1.29 0.00326 0.00367 Wall time: 6464.727210796904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.46 0.0587 0.283 0.213 0.284 0.493 0.624 0.0014 0.00177 66 172 1.43 0.0559 0.316 0.209 0.277 0.543 0.659 0.00154 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.17 0.0577 0.0121 0.215 0.282 0.105 0.129 0.000297 0.000366 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 66 6564.106 0.005 0.0565 0.862 1.99 0.21 0.279 0.882 1.09 0.0025 0.00309 ! Validation 66 6564.106 0.005 0.0613 0.227 1.45 0.22 0.291 0.428 0.559 0.00121 0.00159 Wall time: 6564.10622373689 ! Best model 66 1.454 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.38 0.056 0.264 0.21 0.277 0.459 0.602 0.0013 0.00171 67 172 1.75 0.0579 0.588 0.213 0.282 0.739 0.9 0.0021 0.00256 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.22 0.0577 0.0687 0.214 0.282 0.271 0.307 0.00077 0.000873 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 6663.320 0.005 0.0556 0.904 2.02 0.208 0.277 0.881 1.12 0.0025 0.00317 ! Validation 67 6663.320 0.005 0.0611 0.25 1.47 0.219 0.29 0.457 0.587 0.0013 0.00167 Wall time: 6663.320049967617 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.92 0.057 0.783 0.211 0.28 0.953 1.04 0.00271 0.00295 68 172 1.47 0.0537 0.393 0.204 0.272 0.631 0.735 0.00179 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.22 0.0536 0.152 0.206 0.272 0.427 0.457 0.00121 0.0013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 68 6762.501 0.005 0.0536 0.716 1.79 0.205 0.272 0.775 0.993 0.0022 0.00282 ! Validation 68 6762.501 0.005 0.0565 0.564 1.69 0.21 0.279 0.716 0.881 0.00203 0.0025 Wall time: 6762.501331060659 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 2.71 0.0525 1.66 0.202 0.269 1.41 1.51 0.00402 0.00429 69 172 1.37 0.053 0.313 0.204 0.27 0.509 0.656 0.00145 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 2.47 0.0554 1.36 0.209 0.276 1.36 1.37 0.00386 0.00388 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 6861.734 0.005 0.0521 0.909 1.95 0.202 0.268 0.882 1.12 0.0025 0.00318 ! Validation 69 6861.734 0.005 0.0579 1.09 2.25 0.213 0.282 1.08 1.23 0.00308 0.00348 Wall time: 6861.734424813651 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 2.05 0.0484 1.08 0.195 0.258 1.08 1.22 0.00308 0.00347 70 172 1.29 0.0474 0.346 0.192 0.255 0.592 0.69 0.00168 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 3.65 0.0503 2.65 0.199 0.263 1.91 1.91 0.00542 0.00542 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 6960.942 0.005 0.0495 0.588 1.58 0.197 0.261 0.715 0.9 0.00203 0.00256 ! Validation 70 6960.942 0.005 0.0533 2.29 3.36 0.204 0.271 1.66 1.77 0.00472 0.00504 Wall time: 6960.941960598808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 1.36 0.0489 0.385 0.195 0.259 0.533 0.728 0.00152 0.00207 71 172 2.62 0.0529 1.57 0.204 0.27 1.31 1.47 0.00372 0.00417 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 2.65 0.0565 1.52 0.214 0.279 1.44 1.45 0.0041 0.00411 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 71 7060.227 0.005 0.0488 0.814 1.79 0.195 0.259 0.846 1.06 0.0024 0.00301 ! Validation 71 7060.227 0.005 0.0586 0.961 2.13 0.216 0.284 1.01 1.15 0.00286 0.00327 Wall time: 7060.227285787929 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.56 0.0487 0.591 0.194 0.259 0.698 0.902 0.00198 0.00256 72 172 1.19 0.0462 0.261 0.19 0.252 0.448 0.6 0.00127 0.0017 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 0.948 0.0459 0.0298 0.19 0.251 0.181 0.202 0.000514 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 72 7159.423 0.005 0.048 0.677 1.64 0.193 0.257 0.764 0.965 0.00217 0.00274 ! Validation 72 7159.423 0.005 0.0494 0.219 1.21 0.197 0.261 0.433 0.55 0.00123 0.00156 Wall time: 7159.4229971775785 ! Best model 72 1.208 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 1.16 0.0449 0.264 0.187 0.249 0.494 0.603 0.0014 0.00171 73 172 2.97 0.0419 2.14 0.18 0.24 1.62 1.71 0.00461 0.00487 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 2.27 0.0454 1.36 0.189 0.25 1.36 1.37 0.00387 0.00388 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 7258.632 0.005 0.0449 0.609 1.51 0.187 0.249 0.732 0.914 0.00208 0.0026 ! Validation 73 7258.632 0.005 0.0478 1.67 2.63 0.193 0.257 1.39 1.52 0.00394 0.00431 Wall time: 7258.6321662236005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.64 0.0448 0.746 0.185 0.248 0.802 1.01 0.00228 0.00288 74 172 2.6 0.043 1.74 0.183 0.243 1.46 1.55 0.00414 0.00439 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 2.44 0.0429 1.59 0.184 0.243 1.48 1.48 0.00419 0.0042 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 7357.817 0.005 0.044 0.623 1.5 0.185 0.246 0.743 0.925 0.00211 0.00263 ! Validation 74 7357.817 0.005 0.0452 2.23 3.13 0.188 0.249 1.66 1.75 0.00473 0.00497 Wall time: 7357.817310115788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.04 0.0402 0.237 0.177 0.235 0.466 0.571 0.00132 0.00162 75 172 1.3 0.0427 0.444 0.182 0.242 0.713 0.781 0.00203 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 3.59 0.0437 2.72 0.186 0.245 1.93 1.93 0.00549 0.00549 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 75 7457.039 0.005 0.0426 0.643 1.5 0.182 0.242 0.757 0.941 0.00215 0.00267 ! Validation 75 7457.039 0.005 0.0471 2.14 3.08 0.192 0.255 1.59 1.72 0.00451 0.00487 Wall time: 7457.0395290507 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.65 0.0598 0.453 0.218 0.287 0.607 0.79 0.00172 0.00224 76 172 0.981 0.0426 0.128 0.182 0.242 0.347 0.419 0.000986 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 0.916 0.0443 0.0294 0.187 0.247 0.17 0.201 0.000482 0.000571 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 76 7556.241 0.005 0.0457 0.995 1.91 0.188 0.251 0.893 1.17 0.00254 0.00332 ! Validation 76 7556.241 0.005 0.0464 0.199 1.13 0.19 0.253 0.399 0.523 0.00113 0.00149 Wall time: 7556.241608914919 ! Best model 76 1.127 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.53 0.041 0.709 0.178 0.237 0.907 0.988 0.00258 0.00281 77 172 0.894 0.0391 0.112 0.175 0.232 0.333 0.392 0.000945 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 1.13 0.0412 0.303 0.179 0.238 0.642 0.646 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 77 7655.444 0.005 0.0406 0.478 1.29 0.177 0.236 0.651 0.812 0.00185 0.00231 ! Validation 77 7655.444 0.005 0.0431 0.38 1.24 0.183 0.243 0.607 0.723 0.00172 0.00205 Wall time: 7655.444204268977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 2.32 0.046 1.4 0.189 0.252 1.18 1.39 0.00336 0.00394 78 172 2.58 0.0403 1.77 0.177 0.235 1.42 1.56 0.00403 0.00444 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 0.854 0.0416 0.0228 0.18 0.239 0.153 0.177 0.000433 0.000503 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 78 7754.637 0.005 0.0395 0.599 1.39 0.175 0.233 0.718 0.907 0.00204 0.00258 ! Validation 78 7754.637 0.005 0.0431 0.705 1.57 0.183 0.244 0.758 0.985 0.00215 0.0028 Wall time: 7754.637716519646 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 1.48 0.036 0.763 0.166 0.223 0.908 1.02 0.00258 0.00291 79 172 1.33 0.0477 0.372 0.193 0.256 0.595 0.715 0.00169 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 2.62 0.0539 1.54 0.21 0.272 1.45 1.46 0.00413 0.00414 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 7853.835 0.005 0.0409 0.831 1.65 0.178 0.237 0.799 1.07 0.00227 0.00304 ! Validation 79 7853.835 0.005 0.0561 1.46 2.59 0.213 0.278 1.16 1.42 0.00329 0.00403 Wall time: 7853.83560272865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 1.13 0.0394 0.342 0.174 0.233 0.575 0.685 0.00163 0.00195 80 172 0.867 0.0365 0.137 0.167 0.224 0.362 0.435 0.00103 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 0.829 0.0378 0.0735 0.172 0.228 0.303 0.318 0.000861 0.000903 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 7953.237 0.005 0.0388 0.406 1.18 0.173 0.231 0.59 0.748 0.00168 0.00212 ! Validation 80 7953.237 0.005 0.0407 0.313 1.13 0.178 0.237 0.544 0.657 0.00155 0.00187 Wall time: 7953.237488963641 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 0.948 0.0332 0.283 0.16 0.214 0.554 0.624 0.00157 0.00177 81 172 1.34 0.0381 0.579 0.172 0.229 0.726 0.893 0.00206 0.00254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.3 0.0382 0.538 0.172 0.229 0.856 0.86 0.00243 0.00244 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 81 8052.434 0.005 0.0354 0.41 1.12 0.165 0.221 0.59 0.751 0.00168 0.00213 ! Validation 81 8052.434 0.005 0.0399 1.79 2.59 0.176 0.234 1.41 1.57 0.004 0.00446 Wall time: 8052.434710760601 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 0.874 0.0352 0.17 0.165 0.22 0.368 0.483 0.00105 0.00137 82 172 1.34 0.0323 0.697 0.158 0.211 0.901 0.979 0.00256 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 2.72 0.0361 2 0.168 0.223 1.66 1.66 0.00471 0.00471 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 8151.865 0.005 0.035 0.469 1.17 0.164 0.219 0.644 0.803 0.00183 0.00228 ! Validation 82 8151.865 0.005 0.0382 1.77 2.53 0.172 0.229 1.48 1.56 0.00421 0.00443 Wall time: 8151.8651736178435 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 0.805 0.033 0.145 0.16 0.213 0.364 0.446 0.00103 0.00127 83 172 1.65 0.0347 0.957 0.163 0.218 1.08 1.15 0.00306 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.11 0.0367 0.373 0.17 0.225 0.715 0.717 0.00203 0.00204 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 83 8250.996 0.005 0.0339 0.451 1.13 0.162 0.216 0.618 0.788 0.00175 0.00224 ! Validation 83 8250.996 0.005 0.0384 0.537 1.31 0.172 0.23 0.732 0.86 0.00208 0.00244 Wall time: 8250.995932019781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.04 0.0334 0.374 0.16 0.214 0.583 0.717 0.00165 0.00204 84 172 0.842 0.0323 0.196 0.158 0.211 0.437 0.519 0.00124 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 0.754 0.0338 0.0773 0.163 0.216 0.32 0.326 0.000908 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 84 8350.176 0.005 0.0348 0.61 1.31 0.164 0.219 0.73 0.917 0.00207 0.0026 ! Validation 84 8350.176 0.005 0.0363 0.183 0.909 0.167 0.224 0.387 0.502 0.0011 0.00143 Wall time: 8350.176331712864 ! Best model 84 0.909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 1.29 0.0334 0.627 0.161 0.214 0.844 0.929 0.0024 0.00264 85 172 0.689 0.0296 0.0968 0.152 0.202 0.291 0.365 0.000826 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 1.11 0.0324 0.459 0.159 0.211 0.792 0.795 0.00225 0.00226 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 85 8449.322 0.005 0.0321 0.382 1.02 0.157 0.21 0.583 0.726 0.00166 0.00206 ! Validation 85 8449.322 0.005 0.0345 0.6 1.29 0.163 0.218 0.751 0.909 0.00213 0.00258 Wall time: 8449.32205004897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.5 0.0341 0.813 0.164 0.217 1 1.06 0.00285 0.00301 86 172 0.767 0.0303 0.161 0.153 0.204 0.312 0.47 0.000886 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 0.644 0.0318 0.00703 0.157 0.209 0.0869 0.0983 0.000247 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 86 8548.540 0.005 0.0312 0.398 1.02 0.155 0.207 0.594 0.74 0.00169 0.0021 ! Validation 86 8548.540 0.005 0.0335 0.215 0.885 0.161 0.215 0.423 0.544 0.0012 0.00155 Wall time: 8548.540830655955 ! Best model 86 0.885 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.95 0.031 0.329 0.155 0.207 0.416 0.673 0.00118 0.00191 87 172 0.738 0.0278 0.182 0.147 0.196 0.427 0.5 0.00121 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.898 0.0299 0.301 0.153 0.203 0.641 0.643 0.00182 0.00183 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 87 8647.604 0.005 0.0307 0.407 1.02 0.154 0.205 0.595 0.748 0.00169 0.00213 ! Validation 87 8647.604 0.005 0.0324 0.471 1.12 0.158 0.211 0.636 0.805 0.00181 0.00229 Wall time: 8647.604526689742 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 0.714 0.0281 0.153 0.148 0.197 0.343 0.459 0.000973 0.0013 88 172 0.779 0.0278 0.222 0.147 0.196 0.465 0.553 0.00132 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 1.06 0.0297 0.471 0.152 0.202 0.801 0.805 0.00227 0.00229 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 88 8747.544 0.005 0.0293 0.349 0.934 0.151 0.201 0.549 0.693 0.00156 0.00197 ! Validation 88 8747.544 0.005 0.0316 0.542 1.18 0.157 0.209 0.762 0.864 0.00216 0.00245 Wall time: 8747.544640910812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 1.06 0.0301 0.455 0.153 0.204 0.692 0.791 0.00197 0.00225 89 172 1.2 0.0289 0.623 0.149 0.199 0.843 0.926 0.0024 0.00263 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 0.857 0.0297 0.264 0.154 0.202 0.599 0.602 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 89 8846.746 0.005 0.0303 0.496 1.1 0.153 0.204 0.643 0.826 0.00183 0.00235 ! Validation 89 8846.746 0.005 0.0319 0.792 1.43 0.158 0.209 0.952 1.04 0.0027 0.00296 Wall time: 8846.745984746609 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 2.2 0.0289 1.62 0.15 0.199 1.45 1.49 0.00413 0.00424 90 172 0.853 0.0272 0.309 0.145 0.193 0.545 0.652 0.00155 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 0.579 0.0288 0.00322 0.149 0.199 0.0568 0.0666 0.000161 0.000189 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 8945.944 0.005 0.0307 0.473 1.09 0.154 0.206 0.636 0.807 0.00181 0.00229 ! Validation 90 8945.944 0.005 0.0306 0.308 0.919 0.154 0.205 0.486 0.651 0.00138 0.00185 Wall time: 8945.944429768715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 0.799 0.0274 0.25 0.146 0.194 0.527 0.587 0.0015 0.00167 91 172 0.882 0.0282 0.318 0.148 0.197 0.546 0.661 0.00155 0.00188 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 1.06 0.0295 0.47 0.153 0.202 0.799 0.804 0.00227 0.00228 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 91 9045.145 0.005 0.0279 0.359 0.917 0.147 0.196 0.565 0.703 0.00161 0.002 ! Validation 91 9045.145 0.005 0.0321 0.319 0.961 0.159 0.21 0.554 0.662 0.00157 0.00188 Wall time: 9045.145664006937 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 2.34 0.0279 1.78 0.148 0.196 1.53 1.57 0.00435 0.00445 92 172 0.779 0.0266 0.247 0.143 0.191 0.506 0.583 0.00144 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 0.861 0.0272 0.316 0.145 0.194 0.655 0.659 0.00186 0.00187 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 92 9144.343 0.005 0.0288 0.419 0.996 0.149 0.199 0.592 0.76 0.00168 0.00216 ! Validation 92 9144.343 0.005 0.0295 0.231 0.822 0.151 0.202 0.475 0.564 0.00135 0.0016 Wall time: 9144.343748584855 ! Best model 92 0.822 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 0.721 0.0262 0.197 0.142 0.19 0.406 0.521 0.00115 0.00148 93 172 1.7 0.0269 1.17 0.146 0.192 1.22 1.27 0.00347 0.0036 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 3.98 0.0275 3.43 0.147 0.195 2.17 2.17 0.00617 0.00617 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 9243.558 0.005 0.0259 0.271 0.789 0.141 0.189 0.478 0.61 0.00136 0.00173 ! Validation 93 9243.558 0.005 0.0289 2.58 3.16 0.15 0.199 1.84 1.88 0.00522 0.00535 Wall time: 9243.55866326578 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 21 1 1.03 0.874 1.17 0.968 1.19 0.00275 0.00339 94 172 20.7 0.976 1.22 0.862 1.16 0.874 1.29 0.00248 0.00367 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 19.1 0.933 0.413 0.85 1.13 0.653 0.754 0.00186 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 94 9342.652 0.005 0.796 270 286 0.725 1.05 6.9 19.3 0.0196 0.0547 ! Validation 94 9342.652 0.005 0.976 2.99 22.5 0.867 1.16 1.58 2.03 0.00449 0.00576 Wall time: 9342.651987805963 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 20.4 0.927 1.84 0.839 1.13 1.29 1.59 0.00366 0.00452 95 172 19.7 0.922 1.22 0.838 1.13 1.1 1.29 0.00312 0.00368 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 17.8 0.875 0.294 0.824 1.1 0.544 0.636 0.00155 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 95 9441.790 0.005 0.941 1.4 20.2 0.846 1.14 1.07 1.39 0.00303 0.00394 ! Validation 95 9441.790 0.005 0.915 2.77 21.1 0.839 1.12 1.56 1.95 0.00444 0.00555 Wall time: 9441.790674739983 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 18.7 0.855 1.58 0.806 1.08 1.08 1.47 0.00306 0.00419 96 172 18.2 0.823 1.75 0.79 1.06 1.26 1.55 0.00357 0.00441 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 16.5 0.81 0.316 0.793 1.06 0.543 0.66 0.00154 0.00187 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 96 9540.992 0.005 0.879 1.36 18.9 0.818 1.1 1.05 1.37 0.00299 0.00389 ! Validation 96 9540.992 0.005 0.847 2.71 19.6 0.807 1.08 1.54 1.93 0.00437 0.00548 Wall time: 9540.992615857627 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 16.6 0.761 1.38 0.763 1.02 1.13 1.38 0.00322 0.00392 97 172 16.9 0.732 2.31 0.748 1 1.47 1.78 0.00419 0.00506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 14.7 0.694 0.766 0.737 0.977 0.976 1.03 0.00277 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 97 9640.371 0.005 0.79 1.31 17.1 0.776 1.04 1.03 1.34 0.00293 0.00382 ! Validation 97 9640.371 0.005 0.729 3.05 17.6 0.75 1 1.6 2.05 0.00454 0.00582 Wall time: 9640.371322836727 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 13.7 0.618 1.38 0.686 0.922 0.985 1.38 0.0028 0.00391 98 172 13.5 0.513 3.28 0.625 0.84 1.84 2.13 0.00522 0.00604 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 9.79 0.483 0.134 0.615 0.815 0.369 0.429 0.00105 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 98 9739.579 0.005 0.625 1.63 14.1 0.691 0.927 1.17 1.5 0.00331 0.00425 ! Validation 98 9739.579 0.005 0.516 2.04 12.4 0.631 0.843 1.39 1.67 0.00396 0.00476 Wall time: 9739.579698854592 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 14.9 0.4 6.91 0.55 0.741 2.9 3.08 0.00823 0.00876 99 172 7.99 0.365 0.68 0.527 0.709 0.794 0.967 0.00225 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 8.13 0.346 1.22 0.518 0.69 1.23 1.29 0.0035 0.00368 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 99 9838.904 0.005 0.423 2.1 10.6 0.567 0.763 1.35 1.7 0.00385 0.00483 ! Validation 99 9838.904 0.005 0.37 1.4 8.8 0.531 0.714 1.07 1.39 0.00303 0.00394 Wall time: 9838.904365289956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 9.25 0.313 2.99 0.489 0.657 1.76 2.03 0.00499 0.00576 100 172 12.4 0.281 6.79 0.462 0.621 2.94 3.06 0.00835 0.00869 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 6.07 0.273 0.62 0.46 0.612 0.857 0.924 0.00243 0.00262 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 9938.834 0.005 0.317 1.59 7.94 0.49 0.661 1.17 1.48 0.00333 0.0042 ! Validation 100 9938.834 0.005 0.286 0.731 6.45 0.467 0.627 0.786 1 0.00223 0.00285 Wall time: 9938.834167369641 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 5.56 0.244 0.68 0.432 0.58 0.781 0.967 0.00222 0.00275 101 172 4.91 0.221 0.477 0.412 0.552 0.688 0.811 0.00195 0.0023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 4.36 0.212 0.11 0.408 0.54 0.361 0.389 0.00103 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 101 10038.104 0.005 0.249 1.6 6.58 0.436 0.586 1.16 1.48 0.0033 0.00421 ! Validation 101 10038.104 0.005 0.224 2.52 7 0.416 0.555 1.54 1.86 0.00438 0.00529 Wall time: 10038.104114071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 8.33 0.181 4.7 0.376 0.5 2.35 2.54 0.00669 0.00722 102 172 3.73 0.166 0.415 0.36 0.478 0.607 0.755 0.00173 0.00215 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 4.12 0.164 0.844 0.359 0.475 1.07 1.08 0.00303 0.00306 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 102 10137.215 0.005 0.189 1.33 5.11 0.382 0.51 1.07 1.35 0.00303 0.00384 ! Validation 102 10137.215 0.005 0.176 0.823 4.35 0.372 0.492 0.862 1.06 0.00245 0.00302 Wall time: 10137.215139116626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 3.29 0.146 0.376 0.338 0.448 0.62 0.719 0.00176 0.00204 103 172 3.78 0.138 1.02 0.329 0.436 0.992 1.18 0.00282 0.00336 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 6.74 0.139 3.96 0.33 0.437 2.33 2.34 0.00663 0.00664 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 103 10236.584 0.005 0.149 1.41 4.39 0.342 0.453 1.1 1.39 0.00313 0.00395 ! Validation 103 10236.584 0.005 0.149 2.97 5.96 0.344 0.453 1.82 2.02 0.00517 0.00574 Wall time: 10236.584085545968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 5.19 0.134 2.51 0.324 0.429 1.73 1.86 0.0049 0.00528 104 172 3.05 0.124 0.568 0.311 0.413 0.744 0.884 0.00211 0.00251 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 8.83 0.125 6.33 0.314 0.415 2.95 2.95 0.00837 0.00838 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 10335.999 0.005 0.13 1.55 4.16 0.32 0.424 1.18 1.46 0.00337 0.00415 ! Validation 104 10335.999 0.005 0.135 3.94 6.65 0.328 0.432 2.15 2.33 0.00611 0.00661 Wall time: 10335.999523501843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 4.74 0.122 2.3 0.309 0.41 1.51 1.78 0.00429 0.00506 105 172 2.61 0.108 0.454 0.293 0.385 0.635 0.79 0.00181 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 5.69 0.111 3.46 0.296 0.392 2.17 2.18 0.00618 0.0062 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 10435.145 0.005 0.117 1.13 3.47 0.304 0.401 0.979 1.25 0.00278 0.00355 ! Validation 105 10435.145 0.005 0.121 2.18 4.6 0.31 0.408 1.57 1.73 0.00446 0.00492 Wall time: 10435.14588247193 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 2.88 0.105 0.792 0.289 0.379 0.9 1.04 0.00256 0.00297 106 172 2.6 0.0988 0.621 0.279 0.369 0.791 0.925 0.00225 0.00263 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 3.94 0.101 1.92 0.282 0.372 1.61 1.63 0.00458 0.00462 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 10534.726 0.005 0.104 0.858 2.94 0.287 0.379 0.879 1.09 0.0025 0.00309 ! Validation 106 10534.726 0.005 0.109 1.01 3.19 0.295 0.388 0.991 1.18 0.00281 0.00334 Wall time: 10534.726081540808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 2.74 0.0967 0.803 0.275 0.365 0.894 1.05 0.00254 0.00299 107 172 2.78 0.098 0.822 0.276 0.367 0.89 1.06 0.00253 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 4.78 0.0935 2.91 0.271 0.359 1.99 2 0.00566 0.00568 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 10633.914 0.005 0.0955 0.975 2.88 0.275 0.362 0.885 1.16 0.00251 0.00329 ! Validation 107 10633.914 0.005 0.102 1.76 3.8 0.284 0.375 1.35 1.56 0.00384 0.00442 Wall time: 10633.914130373858 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 2.07 0.0878 0.316 0.264 0.348 0.514 0.659 0.00146 0.00187 108 172 2.15 0.0893 0.365 0.264 0.351 0.609 0.709 0.00173 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 2.12 0.0876 0.363 0.263 0.347 0.68 0.707 0.00193 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 108 10733.114 0.005 0.0915 1.13 2.96 0.269 0.355 0.978 1.25 0.00278 0.00355 ! Validation 108 10733.114 0.005 0.0949 0.439 2.34 0.274 0.361 0.635 0.777 0.0018 0.00221 Wall time: 10733.114839817863 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 5.32 0.0808 3.71 0.252 0.333 2.18 2.26 0.00619 0.00642 109 172 1.87 0.0791 0.287 0.25 0.33 0.5 0.629 0.00142 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.69 0.0792 0.11 0.25 0.33 0.313 0.388 0.00089 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 109 10832.327 0.005 0.0827 0.666 2.32 0.256 0.337 0.742 0.958 0.00211 0.00272 ! Validation 109 10832.327 0.005 0.0872 0.389 2.13 0.263 0.346 0.578 0.732 0.00164 0.00208 Wall time: 10832.327117581852 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.92 0.0741 0.442 0.242 0.319 0.638 0.78 0.00181 0.00222 110 172 2.14 0.0717 0.704 0.238 0.314 0.849 0.984 0.00241 0.0028 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.57 0.0756 0.0614 0.244 0.322 0.275 0.291 0.000781 0.000826 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 10931.671 0.005 0.0762 0.835 2.36 0.245 0.324 0.85 1.07 0.00242 0.00305 ! Validation 110 10931.671 0.005 0.0821 1.2 2.84 0.255 0.336 1.06 1.28 0.00302 0.00364 Wall time: 10931.670961669646 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.59 0.0712 0.165 0.237 0.313 0.381 0.476 0.00108 0.00135 111 172 1.62 0.0664 0.287 0.229 0.302 0.516 0.629 0.00147 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.91 0.0686 0.536 0.233 0.307 0.824 0.859 0.00234 0.00244 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 111 11030.891 0.005 0.0723 0.702 2.15 0.239 0.316 0.769 0.983 0.00219 0.00279 ! Validation 111 11030.891 0.005 0.0757 0.304 1.82 0.245 0.323 0.506 0.646 0.00144 0.00184 Wall time: 11030.891060980968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 1.71 0.066 0.392 0.228 0.301 0.639 0.735 0.00181 0.00209 112 172 2.08 0.069 0.701 0.231 0.308 0.689 0.982 0.00196 0.00279 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 2.64 0.0652 1.33 0.228 0.3 1.34 1.35 0.0038 0.00385 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 112 11130.550 0.005 0.0664 0.726 2.05 0.229 0.302 0.796 1 0.00226 0.00284 ! Validation 112 11130.550 0.005 0.0714 0.68 2.11 0.238 0.313 0.802 0.968 0.00228 0.00275 Wall time: 11130.550777504686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 2.83 0.0614 1.61 0.22 0.291 1.44 1.49 0.0041 0.00422 113 172 1.62 0.063 0.361 0.223 0.294 0.578 0.705 0.00164 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 1.52 0.061 0.304 0.221 0.29 0.604 0.647 0.00172 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 113 11229.746 0.005 0.0628 0.708 1.96 0.222 0.294 0.801 0.987 0.00228 0.0028 ! Validation 113 11229.746 0.005 0.0672 0.33 1.67 0.231 0.304 0.523 0.673 0.00148 0.00191 Wall time: 11229.746532008983 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1.8 0.0551 0.699 0.209 0.275 0.809 0.981 0.0023 0.00279 114 172 1.75 0.0558 0.635 0.21 0.277 0.833 0.935 0.00237 0.00266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 3.22 0.0582 2.06 0.216 0.283 1.66 1.68 0.00472 0.00478 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 11329.070 0.005 0.0599 0.825 2.02 0.217 0.287 0.821 1.07 0.00233 0.00303 ! Validation 114 11329.070 0.005 0.0646 1.29 2.59 0.227 0.298 1.21 1.33 0.00343 0.00379 Wall time: 11329.070194219705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 1.53 0.0543 0.445 0.207 0.273 0.625 0.782 0.00178 0.00222 115 172 1.29 0.0534 0.226 0.205 0.271 0.406 0.557 0.00115 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 1.61 0.054 0.526 0.208 0.273 0.816 0.851 0.00232 0.00242 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 11428.219 0.005 0.056 0.635 1.75 0.21 0.278 0.762 0.935 0.00216 0.00266 ! Validation 115 11428.219 0.005 0.06 0.308 1.51 0.218 0.287 0.532 0.651 0.00151 0.00185 Wall time: 11428.219809177797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 1.2 0.0525 0.155 0.203 0.269 0.375 0.462 0.00107 0.00131 116 172 1.34 0.0513 0.311 0.202 0.266 0.537 0.654 0.00153 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 2.54 0.0507 1.52 0.201 0.264 1.43 1.45 0.00406 0.00412 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 116 11527.379 0.005 0.0523 0.552 1.6 0.203 0.268 0.681 0.871 0.00193 0.00248 ! Validation 116 11527.379 0.005 0.0562 0.855 1.98 0.211 0.278 0.943 1.08 0.00268 0.00308 Wall time: 11527.379533482715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 1.13 0.0488 0.151 0.196 0.259 0.375 0.456 0.00107 0.00129 117 172 2.59 0.0492 1.6 0.196 0.26 1.43 1.49 0.00406 0.00422 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 2.75 0.0472 1.8 0.194 0.255 1.55 1.57 0.00442 0.00447 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 117 11626.537 0.005 0.0495 0.561 1.55 0.197 0.261 0.707 0.878 0.00201 0.0025 ! Validation 117 11626.537 0.005 0.0529 1.28 2.34 0.205 0.27 1.21 1.33 0.00345 0.00377 Wall time: 11626.537768462673 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.65 0.0466 0.719 0.191 0.253 0.896 0.995 0.00255 0.00283 118 172 1.08 0.0454 0.167 0.189 0.25 0.406 0.479 0.00115 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 0.945 0.045 0.0439 0.19 0.249 0.205 0.246 0.000581 0.000698 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 11726.199 0.005 0.0463 0.502 1.43 0.191 0.252 0.667 0.832 0.00189 0.00236 ! Validation 118 11726.199 0.005 0.0501 0.254 1.26 0.199 0.263 0.472 0.591 0.00134 0.00168 Wall time: 11726.199726827908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 1.37 0.0459 0.455 0.189 0.251 0.737 0.791 0.00209 0.00225 119 172 2.64 0.0475 1.69 0.194 0.256 1.45 1.52 0.00411 0.00433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 1.17 0.0462 0.243 0.192 0.252 0.54 0.578 0.00154 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 119 11825.330 0.005 0.0451 0.724 1.63 0.188 0.249 0.795 0.997 0.00226 0.00283 ! Validation 119 11825.330 0.005 0.0507 0.888 1.9 0.201 0.264 0.979 1.11 0.00278 0.00314 Wall time: 11825.329865865875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 1.58 0.0417 0.748 0.181 0.24 0.937 1.01 0.00266 0.00288 120 172 1.04 0.0415 0.205 0.18 0.239 0.414 0.532 0.00118 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 1.5 0.0412 0.681 0.181 0.238 0.942 0.968 0.00268 0.00275 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 120 11924.491 0.005 0.0432 0.47 1.33 0.184 0.244 0.643 0.804 0.00183 0.00228 ! Validation 120 11924.491 0.005 0.0463 0.494 1.42 0.191 0.252 0.683 0.824 0.00194 0.00234 Wall time: 11924.491054954007 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 1.86 0.0414 1.03 0.18 0.239 1.12 1.19 0.00317 0.00338 121 172 1.23 0.0384 0.463 0.174 0.23 0.703 0.798 0.002 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.921 0.0396 0.13 0.178 0.233 0.361 0.423 0.00103 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 121 12023.636 0.005 0.0413 0.553 1.38 0.18 0.238 0.69 0.872 0.00196 0.00248 ! Validation 121 12023.636 0.005 0.0445 0.202 1.09 0.187 0.247 0.407 0.527 0.00116 0.0015 Wall time: 12023.636577416677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 1.3 0.0406 0.489 0.179 0.236 0.725 0.82 0.00206 0.00233 122 172 1.11 0.0377 0.358 0.171 0.228 0.629 0.702 0.00179 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 3.08 0.0387 2.31 0.176 0.231 1.77 1.78 0.00502 0.00506 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 12122.769 0.005 0.0403 0.618 1.42 0.178 0.235 0.727 0.922 0.00206 0.00262 ! Validation 122 12122.769 0.005 0.0435 1.31 2.18 0.185 0.245 1.25 1.34 0.00354 0.00381 Wall time: 12122.769216175657 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.827 0.0368 0.0912 0.17 0.225 0.267 0.354 0.000759 0.00101 123 172 1.14 0.0368 0.404 0.169 0.225 0.66 0.746 0.00187 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 1.46 0.0367 0.728 0.171 0.225 0.976 1 0.00277 0.00284 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 123 12221.895 0.005 0.0381 0.42 1.18 0.173 0.229 0.607 0.761 0.00173 0.00216 ! Validation 123 12221.895 0.005 0.0411 0.394 1.22 0.18 0.238 0.617 0.737 0.00175 0.00209 Wall time: 12221.895688586868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 1.47 0.0387 0.697 0.173 0.231 0.789 0.979 0.00224 0.00278 124 172 0.94 0.0368 0.204 0.17 0.225 0.447 0.53 0.00127 0.00151 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 0.766 0.0358 0.0511 0.169 0.222 0.25 0.265 0.000711 0.000753 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 124 12321.023 0.005 0.0371 0.536 1.28 0.17 0.226 0.697 0.859 0.00198 0.00244 ! Validation 124 12321.023 0.005 0.0403 0.61 1.42 0.178 0.235 0.76 0.916 0.00216 0.0026 Wall time: 12321.0230831597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 1.13 0.0364 0.405 0.169 0.224 0.641 0.747 0.00182 0.00212 125 172 0.938 0.0362 0.213 0.169 0.223 0.454 0.542 0.00129 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 0.794 0.0343 0.108 0.165 0.217 0.337 0.385 0.000958 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 125 12420.244 0.005 0.0362 0.498 1.22 0.168 0.223 0.65 0.828 0.00185 0.00235 ! Validation 125 12420.244 0.005 0.0389 0.233 1.01 0.175 0.231 0.455 0.566 0.00129 0.00161 Wall time: 12420.244166834746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 1.75 0.0394 0.963 0.176 0.233 1.05 1.15 0.00298 0.00327 126 172 1.03 0.0334 0.358 0.162 0.215 0.595 0.702 0.00169 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 2.15 0.034 1.47 0.165 0.216 1.41 1.42 0.00401 0.00404 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 126 12519.421 0.005 0.0362 0.633 1.36 0.169 0.223 0.753 0.933 0.00214 0.00265 ! Validation 126 12519.421 0.005 0.0384 0.967 1.74 0.174 0.23 1.06 1.15 0.00302 0.00328 Wall time: 12519.421729236841 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.84 0.0331 0.179 0.161 0.213 0.432 0.496 0.00123 0.00141 127 172 0.804 0.0341 0.121 0.164 0.217 0.314 0.408 0.000891 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 1.42 0.0334 0.752 0.163 0.214 0.996 1.02 0.00283 0.00289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 127 12618.607 0.005 0.0339 0.422 1.1 0.163 0.216 0.614 0.762 0.00174 0.00217 ! Validation 127 12618.607 0.005 0.0372 0.447 1.19 0.171 0.226 0.655 0.785 0.00186 0.00223 Wall time: 12618.60738998698 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.824 0.0347 0.131 0.164 0.218 0.352 0.424 0.000999 0.0012 128 172 0.82 0.0331 0.158 0.161 0.213 0.353 0.466 0.001 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 1.1 0.0332 0.433 0.162 0.214 0.759 0.772 0.00215 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 128 12717.785 0.005 0.0328 0.466 1.12 0.16 0.212 0.644 0.801 0.00183 0.00228 ! Validation 128 12717.785 0.005 0.037 0.496 1.24 0.171 0.226 0.694 0.826 0.00197 0.00235 Wall time: 12717.785176514648 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.777 0.0316 0.145 0.157 0.208 0.345 0.447 0.000981 0.00127 129 172 0.727 0.0313 0.102 0.157 0.207 0.325 0.375 0.000922 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 2.46 0.0312 1.84 0.157 0.207 1.58 1.59 0.00449 0.00452 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 129 12816.970 0.005 0.033 0.54 1.2 0.161 0.213 0.683 0.862 0.00194 0.00245 ! Validation 129 12816.970 0.005 0.0351 1.12 1.82 0.166 0.22 1.13 1.24 0.0032 0.00353 Wall time: 12816.970785193611 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.894 0.033 0.234 0.16 0.213 0.476 0.567 0.00135 0.00161 130 172 0.758 0.0291 0.175 0.151 0.2 0.439 0.491 0.00125 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.65 0.0295 0.0608 0.153 0.201 0.258 0.289 0.000733 0.000821 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 130 12917.095 0.005 0.0313 0.422 1.05 0.156 0.207 0.608 0.762 0.00173 0.00217 ! Validation 130 12917.095 0.005 0.0336 0.363 1.04 0.162 0.215 0.601 0.707 0.00171 0.00201 Wall time: 12917.095458593685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 1.51 0.0314 0.878 0.157 0.208 1.02 1.1 0.0029 0.00312 131 172 0.75 0.0281 0.188 0.148 0.197 0.379 0.508 0.00108 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 0.62 0.0281 0.0571 0.149 0.197 0.246 0.28 0.0007 0.000797 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 131 13016.246 0.005 0.0294 0.322 0.911 0.152 0.201 0.536 0.666 0.00152 0.00189 ! Validation 131 13016.246 0.005 0.032 0.38 1.02 0.159 0.21 0.584 0.723 0.00166 0.00206 Wall time: 13016.24647185672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.736 0.0276 0.184 0.146 0.195 0.417 0.504 0.00118 0.00143 132 172 0.733 0.0285 0.164 0.149 0.198 0.38 0.474 0.00108 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.657 0.0276 0.105 0.148 0.195 0.357 0.38 0.00102 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 132 13115.491 0.005 0.0289 0.445 1.02 0.15 0.2 0.62 0.782 0.00176 0.00222 ! Validation 132 13115.491 0.005 0.0315 0.124 0.754 0.157 0.208 0.337 0.412 0.000958 0.00117 Wall time: 13115.490994169842 ! Best model 132 0.754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.66 0.0287 0.0849 0.15 0.199 0.267 0.342 0.000759 0.000971 133 172 1.27 0.0295 0.682 0.152 0.201 0.87 0.968 0.00247 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.587 0.0274 0.0385 0.147 0.194 0.197 0.23 0.000559 0.000654 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 13214.725 0.005 0.0284 0.475 1.04 0.149 0.198 0.649 0.808 0.00184 0.0023 ! Validation 133 13214.725 0.005 0.0311 0.154 0.776 0.156 0.207 0.365 0.461 0.00104 0.00131 Wall time: 13214.725680280942 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 1.02 0.026 0.501 0.143 0.189 0.775 0.83 0.0022 0.00236 134 172 0.659 0.0276 0.108 0.146 0.195 0.292 0.385 0.000829 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.749 0.0253 0.243 0.142 0.187 0.568 0.578 0.00161 0.00164 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 134 13314.090 0.005 0.0273 0.331 0.878 0.146 0.194 0.547 0.675 0.00155 0.00192 ! Validation 134 13314.090 0.005 0.0292 0.194 0.778 0.151 0.2 0.411 0.517 0.00117 0.00147 Wall time: 13314.090596259572 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.663 0.027 0.124 0.144 0.193 0.334 0.414 0.000948 0.00117 135 172 1.27 0.0286 0.695 0.149 0.198 0.748 0.978 0.00212 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 1.19 0.0285 0.617 0.15 0.198 0.914 0.921 0.0026 0.00262 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 13413.314 0.005 0.0263 0.453 0.979 0.143 0.19 0.62 0.789 0.00176 0.00224 ! Validation 135 13413.314 0.005 0.0313 0.652 1.28 0.157 0.208 0.834 0.947 0.00237 0.00269 Wall time: 13413.314550252631 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.726 0.0267 0.192 0.145 0.192 0.422 0.513 0.0012 0.00146 136 172 0.768 0.026 0.248 0.142 0.189 0.508 0.584 0.00144 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.557 0.0266 0.0249 0.145 0.191 0.139 0.185 0.000394 0.000526 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 136 13512.541 0.005 0.027 0.464 1 0.145 0.193 0.627 0.799 0.00178 0.00227 ! Validation 136 13512.541 0.005 0.0296 0.125 0.717 0.153 0.202 0.335 0.415 0.00095 0.00118 Wall time: 13512.54166425392 ! Best model 136 0.717 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.844 0.0272 0.3 0.145 0.193 0.556 0.642 0.00158 0.00183 137 172 0.549 0.0239 0.0719 0.136 0.181 0.252 0.314 0.000717 0.000893 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.528 0.0249 0.0306 0.139 0.185 0.178 0.205 0.000505 0.000583 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 137 13611.754 0.005 0.026 0.425 0.945 0.142 0.189 0.608 0.765 0.00173 0.00217 ! Validation 137 13611.754 0.005 0.028 0.284 0.844 0.148 0.196 0.53 0.625 0.0015 0.00177 Wall time: 13611.754002220929 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.777 0.0245 0.286 0.138 0.184 0.524 0.627 0.00149 0.00178 138 172 1.27 0.0235 0.803 0.135 0.18 0.96 1.05 0.00273 0.00299 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 2.5 0.0233 2.03 0.136 0.179 1.67 1.67 0.00475 0.00475 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 13710.972 0.005 0.0246 0.296 0.787 0.138 0.184 0.497 0.638 0.00141 0.00181 ! Validation 138 13710.972 0.005 0.0267 1.42 1.96 0.145 0.192 1.3 1.4 0.00368 0.00398 Wall time: 13710.972380169667 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.577 0.0232 0.113 0.135 0.179 0.299 0.394 0.00085 0.00112 139 172 0.62 0.0218 0.183 0.13 0.173 0.422 0.502 0.0012 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.503 0.0219 0.0638 0.132 0.174 0.277 0.296 0.000786 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 139 13810.430 0.005 0.0241 0.355 0.837 0.137 0.182 0.556 0.699 0.00158 0.00198 ! Validation 139 13810.430 0.005 0.0254 0.104 0.612 0.141 0.187 0.301 0.378 0.000855 0.00108 Wall time: 13810.430071696639 ! Best model 139 0.612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 1.13 0.0224 0.682 0.132 0.176 0.899 0.969 0.00255 0.00275 140 172 0.948 0.0226 0.495 0.133 0.177 0.739 0.826 0.0021 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.45 0.0218 0.0142 0.131 0.173 0.119 0.14 0.000337 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 140 13909.640 0.005 0.0227 0.334 0.788 0.133 0.177 0.54 0.678 0.00153 0.00192 ! Validation 140 13909.640 0.005 0.0248 0.397 0.893 0.139 0.185 0.593 0.739 0.00169 0.0021 Wall time: 13909.640708080027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 0.822 0.0225 0.373 0.133 0.176 0.595 0.716 0.00169 0.00203 141 172 0.524 0.0229 0.0668 0.134 0.177 0.254 0.303 0.000723 0.000861 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 1.57 0.0228 1.11 0.134 0.177 1.23 1.24 0.0035 0.00351 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 141 14008.837 0.005 0.0233 0.485 0.951 0.135 0.179 0.649 0.817 0.00184 0.00232 ! Validation 141 14008.837 0.005 0.0256 0.883 1.4 0.142 0.188 1.04 1.1 0.00295 0.00313 Wall time: 14008.837210956961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.654 0.0233 0.187 0.136 0.179 0.429 0.508 0.00122 0.00144 142 172 0.715 0.0209 0.298 0.128 0.169 0.526 0.64 0.0015 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.432 0.0204 0.0234 0.127 0.168 0.152 0.179 0.000431 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 142 14108.033 0.005 0.022 0.272 0.712 0.131 0.174 0.477 0.612 0.00135 0.00174 ! Validation 142 14108.033 0.005 0.0237 0.157 0.632 0.136 0.181 0.37 0.465 0.00105 0.00132 Wall time: 14108.033257637639 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 1 0.0226 0.553 0.133 0.176 0.768 0.872 0.00218 0.00248 143 172 0.634 0.0209 0.216 0.127 0.169 0.459 0.546 0.0013 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.933 0.0208 0.517 0.128 0.169 0.834 0.843 0.00237 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 143 14207.206 0.005 0.0227 0.47 0.924 0.133 0.177 0.61 0.804 0.00173 0.00228 ! Validation 143 14207.206 0.005 0.0238 0.541 1.02 0.136 0.181 0.763 0.863 0.00217 0.00245 Wall time: 14207.206005132757 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.728 0.0212 0.305 0.128 0.171 0.556 0.648 0.00158 0.00184 144 172 0.422 0.0184 0.0536 0.119 0.159 0.2 0.272 0.000568 0.000771 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.433 0.0188 0.0579 0.122 0.161 0.264 0.282 0.000749 0.000802 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 144 14306.453 0.005 0.0208 0.263 0.678 0.127 0.169 0.482 0.602 0.00137 0.00171 ! Validation 144 14306.453 0.005 0.0222 0.0757 0.519 0.132 0.175 0.261 0.323 0.000742 0.000917 Wall time: 14306.453088640701 ! Best model 144 0.519 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 1.92 0.0264 1.39 0.146 0.191 1.21 1.38 0.00344 0.00393 145 172 0.51 0.0189 0.133 0.121 0.161 0.319 0.428 0.000907 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 1.87 0.0184 1.51 0.121 0.159 1.44 1.44 0.00408 0.00409 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 14405.583 0.005 0.0198 0.287 0.684 0.125 0.165 0.475 0.628 0.00135 0.00179 ! Validation 145 14405.583 0.005 0.0215 1.12 1.55 0.13 0.172 1.04 1.24 0.00295 0.00352 Wall time: 14405.583310565911 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.564 0.0182 0.199 0.12 0.158 0.456 0.524 0.0013 0.00149 146 172 1.19 0.0213 0.767 0.13 0.171 0.932 1.03 0.00265 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.911 0.0208 0.496 0.128 0.169 0.811 0.826 0.0023 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 146 14504.821 0.005 0.0198 0.382 0.778 0.125 0.165 0.591 0.725 0.00168 0.00206 ! Validation 146 14504.821 0.005 0.0233 0.426 0.892 0.136 0.179 0.682 0.766 0.00194 0.00218 Wall time: 14504.821118787862 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.609 0.0185 0.238 0.122 0.16 0.509 0.572 0.00145 0.00163 147 172 0.514 0.0192 0.13 0.122 0.163 0.369 0.424 0.00105 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.364 0.0177 0.00971 0.119 0.156 0.0871 0.116 0.000247 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 147 14604.013 0.005 0.0191 0.258 0.641 0.122 0.162 0.469 0.596 0.00133 0.00169 ! Validation 147 14604.013 0.005 0.021 0.0858 0.506 0.129 0.17 0.278 0.344 0.00079 0.000976 Wall time: 14604.013345178682 ! Best model 147 0.506 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.611 0.018 0.252 0.119 0.157 0.497 0.588 0.00141 0.00167 148 172 0.545 0.0189 0.168 0.122 0.161 0.413 0.481 0.00117 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.445 0.0173 0.0985 0.117 0.154 0.356 0.368 0.00101 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 148 14703.657 0.005 0.0186 0.314 0.686 0.121 0.16 0.532 0.658 0.00151 0.00187 ! Validation 148 14703.657 0.005 0.0203 0.0764 0.482 0.126 0.167 0.263 0.324 0.000748 0.000921 Wall time: 14703.657456923742 ! Best model 148 0.482 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.406 0.0166 0.0733 0.115 0.151 0.271 0.317 0.000771 0.000902 149 172 0.479 0.0181 0.118 0.119 0.158 0.344 0.403 0.000978 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.564 0.0168 0.227 0.116 0.152 0.556 0.559 0.00158 0.00159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 149 14802.951 0.005 0.0176 0.265 0.617 0.117 0.156 0.479 0.604 0.00136 0.00172 ! Validation 149 14802.951 0.005 0.0196 0.221 0.614 0.125 0.164 0.468 0.551 0.00133 0.00157 Wall time: 14802.951351741794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.45 0.0174 0.101 0.117 0.155 0.304 0.373 0.000864 0.00106 150 172 0.537 0.0194 0.15 0.123 0.163 0.369 0.454 0.00105 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.389 0.0179 0.0306 0.119 0.157 0.185 0.205 0.000526 0.000583 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 150 14902.289 0.005 0.0184 0.4 0.769 0.12 0.159 0.575 0.742 0.00163 0.00211 ! Validation 150 14902.289 0.005 0.0209 0.108 0.526 0.128 0.169 0.312 0.386 0.000885 0.0011 Wall time: 14902.28915534867 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.435 0.0153 0.129 0.11 0.145 0.357 0.422 0.00101 0.0012 151 172 0.502 0.0179 0.144 0.118 0.157 0.373 0.445 0.00106 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.45 0.0159 0.132 0.112 0.148 0.407 0.426 0.00116 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 151 15001.492 0.005 0.0167 0.189 0.523 0.114 0.152 0.404 0.51 0.00115 0.00145 ! Validation 151 15001.492 0.005 0.0186 0.185 0.556 0.121 0.16 0.402 0.504 0.00114 0.00143 Wall time: 15001.492346567567 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.645 0.0174 0.297 0.117 0.155 0.585 0.64 0.00166 0.00182 152 172 0.386 0.0159 0.0683 0.111 0.148 0.23 0.307 0.000653 0.000871 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.32 0.0153 0.0138 0.11 0.145 0.119 0.138 0.000339 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 152 15100.701 0.005 0.0177 0.345 0.699 0.118 0.156 0.523 0.689 0.00148 0.00196 ! Validation 152 15100.701 0.005 0.0184 0.0796 0.448 0.12 0.159 0.268 0.331 0.000763 0.00094 Wall time: 15100.701246652752 ! Best model 152 0.448 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.797 0.0158 0.481 0.111 0.147 0.77 0.814 0.00219 0.00231 153 172 0.655 0.0154 0.347 0.11 0.145 0.627 0.691 0.00178 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 1.01 0.0141 0.727 0.106 0.139 0.991 1 0.00282 0.00284 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 153 15200.042 0.005 0.0154 0.191 0.5 0.11 0.146 0.408 0.513 0.00116 0.00146 ! Validation 153 15200.042 0.005 0.0171 0.366 0.707 0.116 0.153 0.636 0.709 0.00181 0.00201 Wall time: 15200.042637036648 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 1.44 0.0361 0.713 0.171 0.223 0.869 0.991 0.00247 0.00281 154 172 0.48 0.0164 0.152 0.112 0.15 0.385 0.457 0.00109 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.635 0.0152 0.331 0.109 0.145 0.666 0.675 0.00189 0.00192 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 154 15299.902 0.005 0.024 0.598 1.08 0.134 0.182 0.656 0.907 0.00186 0.00258 ! Validation 154 15299.902 0.005 0.0182 0.31 0.674 0.119 0.158 0.573 0.653 0.00163 0.00186 Wall time: 15299.902318107896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.451 0.0154 0.144 0.11 0.145 0.349 0.445 0.000991 0.00126 155 172 0.333 0.0145 0.044 0.107 0.141 0.19 0.246 0.00054 0.000699 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.648 0.0137 0.374 0.105 0.137 0.71 0.717 0.00202 0.00204 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 155 15399.046 0.005 0.0159 0.211 0.528 0.112 0.148 0.427 0.539 0.00121 0.00153 ! Validation 155 15399.046 0.005 0.0168 0.307 0.642 0.115 0.152 0.58 0.65 0.00165 0.00185 Wall time: 15399.046128375921 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.67 0.0136 0.399 0.103 0.137 0.707 0.741 0.00201 0.00211 156 172 0.34 0.0141 0.0581 0.105 0.139 0.218 0.283 0.000621 0.000803 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.741 0.0132 0.478 0.102 0.135 0.807 0.811 0.00229 0.0023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 156 15498.257 0.005 0.0141 0.19 0.472 0.105 0.14 0.415 0.511 0.00118 0.00145 ! Validation 156 15498.257 0.005 0.0161 0.475 0.798 0.113 0.149 0.714 0.808 0.00203 0.0023 Wall time: 15498.257129367907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.478 0.0129 0.219 0.101 0.133 0.482 0.549 0.00137 0.00156 157 172 0.533 0.0159 0.215 0.112 0.148 0.473 0.544 0.00134 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.449 0.0147 0.155 0.108 0.142 0.449 0.463 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 157 15597.483 0.005 0.0158 0.352 0.668 0.111 0.147 0.526 0.696 0.0015 0.00198 ! Validation 157 15597.483 0.005 0.0177 0.25 0.603 0.118 0.156 0.502 0.587 0.00143 0.00167 Wall time: 15597.483267741743 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 0.326 0.0128 0.0692 0.1 0.133 0.245 0.308 0.000695 0.000876 158 172 0.419 0.0135 0.15 0.103 0.136 0.376 0.454 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 158 100 0.277 0.0119 0.0399 0.0978 0.128 0.227 0.234 0.000645 0.000666 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 15696.710 0.005 0.0135 0.164 0.435 0.103 0.137 0.384 0.476 0.00109 0.00135 ! Validation 158 15696.710 0.005 0.015 0.125 0.425 0.109 0.144 0.354 0.415 0.00101 0.00118 Wall time: 15696.710119325668 ! Best model 158 0.425 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 0.572 0.0183 0.206 0.123 0.159 0.38 0.533 0.00108 0.00151 159 172 0.718 0.0137 0.444 0.104 0.137 0.711 0.781 0.00202 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 0.475 0.0127 0.22 0.1 0.132 0.54 0.55 0.00153 0.00156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 159 15795.924 0.005 0.0149 0.359 0.657 0.108 0.143 0.557 0.702 0.00158 0.002 ! Validation 159 15795.924 0.005 0.0156 0.171 0.482 0.11 0.146 0.411 0.484 0.00117 0.00138 Wall time: 15795.924833282828 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 0.329 0.0129 0.072 0.1 0.133 0.25 0.315 0.000712 0.000894 160 172 0.401 0.0154 0.0921 0.11 0.146 0.282 0.356 0.000801 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 1.28 0.0133 1.02 0.103 0.135 1.18 1.18 0.00336 0.00337 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 160 15895.755 0.005 0.0138 0.249 0.524 0.104 0.138 0.463 0.586 0.00132 0.00166 ! Validation 160 15895.755 0.005 0.0162 0.496 0.819 0.114 0.149 0.738 0.826 0.0021 0.00235 Wall time: 15895.755727910902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.295 0.0123 0.0485 0.0978 0.13 0.195 0.258 0.000554 0.000734 161 172 0.476 0.0109 0.258 0.0923 0.123 0.546 0.595 0.00155 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 0.499 0.0103 0.292 0.0911 0.119 0.632 0.634 0.0018 0.0018 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 161 15994.953 0.005 0.0122 0.124 0.368 0.098 0.13 0.332 0.413 0.000942 0.00117 ! Validation 161 15994.953 0.005 0.0131 0.323 0.586 0.102 0.134 0.598 0.667 0.0017 0.00189 Wall time: 15994.953510240652 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 0.331 0.0131 0.0683 0.102 0.134 0.249 0.306 0.000706 0.000871 162 172 0.607 0.0129 0.349 0.101 0.133 0.57 0.693 0.00162 0.00197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 0.273 0.0125 0.0225 0.1 0.131 0.158 0.176 0.000449 0.0005 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 16094.153 0.005 0.0123 0.244 0.491 0.0986 0.13 0.463 0.58 0.00132 0.00165 ! Validation 162 16094.153 0.005 0.0151 0.233 0.535 0.109 0.144 0.507 0.566 0.00144 0.00161 Wall time: 16094.153024145868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 0.348 0.0104 0.14 0.0907 0.119 0.382 0.439 0.00109 0.00125 163 172 0.34 0.0109 0.122 0.0917 0.122 0.34 0.41 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 163 100 0.206 0.00985 0.00872 0.0885 0.116 0.082 0.11 0.000233 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 163 16193.361 0.005 0.0115 0.147 0.377 0.095 0.126 0.36 0.45 0.00102 0.00128 ! Validation 163 16193.361 0.005 0.0126 0.166 0.417 0.0993 0.132 0.397 0.477 0.00113 0.00136 Wall time: 16193.36098803673 ! Best model 163 0.417 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 0.353 0.0123 0.108 0.0989 0.13 0.316 0.385 0.000897 0.00109 164 172 0.283 0.00963 0.0905 0.0868 0.115 0.291 0.353 0.000827 0.001 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 0.423 0.00935 0.237 0.0858 0.113 0.567 0.57 0.00161 0.00162 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 164 16292.541 0.005 0.0111 0.173 0.395 0.0933 0.123 0.389 0.488 0.00111 0.00139 ! Validation 164 16292.541 0.005 0.0119 0.145 0.384 0.0966 0.128 0.395 0.447 0.00112 0.00127 Wall time: 16292.541561258957 ! Best model 164 0.384 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 0.345 0.0114 0.117 0.0945 0.125 0.344 0.4 0.000977 0.00114 165 172 309 1.02 289 0.883 1.18 19.9 19.9 0.0564 0.0566 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 334 0.965 315 0.869 1.15 20.8 20.8 0.0591 0.0591 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 16391.705 0.005 0.198 353 357 0.221 0.522 6.03 22.1 0.0171 0.0627 ! Validation 165 16391.705 0.005 1.01 335 355 0.887 1.18 21.3 21.5 0.0604 0.061 Wall time: 16391.705220299773 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 20.4 0.95 1.41 0.853 1.14 1.1 1.39 0.00314 0.00396 166 172 18.7 0.879 1.08 0.814 1.1 0.996 1.22 0.00283 0.00346 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 17.5 0.858 0.328 0.816 1.09 0.574 0.671 0.00163 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 166 16490.881 0.005 0.947 12.4 31.3 0.851 1.14 2.26 4.13 0.00641 0.0117 ! Validation 166 16490.881 0.005 0.898 3.44 21.4 0.832 1.11 1.73 2.18 0.00491 0.00618 Wall time: 16490.881821196992 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 17.2 0.814 0.925 0.788 1.06 0.939 1.13 0.00267 0.00321 167 172 15.9 0.758 0.769 0.759 1.02 0.811 1.03 0.0023 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 14.8 0.727 0.26 0.753 1 0.496 0.598 0.00141 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 167 16589.964 0.005 0.83 1.36 18 0.795 1.07 1.04 1.37 0.00295 0.00388 ! Validation 167 16589.964 0.005 0.762 3.19 18.4 0.766 1.02 1.68 2.1 0.00477 0.00595 Wall time: 16589.964261463843 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 14 0.643 1.17 0.699 0.94 0.947 1.27 0.00269 0.0036 168 172 11.7 0.541 0.901 0.644 0.863 0.9 1.11 0.00256 0.00316 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 10.7 0.525 0.214 0.643 0.85 0.393 0.542 0.00112 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 168 16689.043 0.005 0.654 1.22 14.3 0.706 0.949 0.986 1.29 0.0028 0.00368 ! Validation 168 16689.043 0.005 0.551 2.75 13.8 0.652 0.87 1.6 1.95 0.00454 0.00553 Wall time: 16689.043012348004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 9.47 0.411 1.24 0.563 0.752 0.978 1.31 0.00278 0.00372 169 172 7.46 0.339 0.686 0.516 0.683 0.785 0.972 0.00223 0.00276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 6.95 0.343 0.0827 0.524 0.687 0.225 0.337 0.000639 0.000959 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 169 16788.136 0.005 0.429 1.02 9.61 0.574 0.769 0.916 1.18 0.0026 0.00336 ! Validation 169 16788.136 0.005 0.357 1.89 9.03 0.53 0.701 1.36 1.61 0.00386 0.00458 Wall time: 16788.13674594881 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 6.32 0.284 0.632 0.475 0.625 0.756 0.932 0.00215 0.00265 170 172 5.78 0.265 0.479 0.457 0.604 0.668 0.812 0.0019 0.00231 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 8.44 0.273 2.98 0.468 0.613 2 2.03 0.00568 0.00576 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 16887.225 0.005 0.297 0.968 6.91 0.484 0.639 0.911 1.15 0.00259 0.00328 ! Validation 170 16887.225 0.005 0.284 2.3 7.99 0.474 0.626 1.48 1.78 0.00421 0.00506 Wall time: 16887.22512210766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 5.89 0.24 1.1 0.43 0.574 0.994 1.23 0.00282 0.0035 171 172 5.35 0.229 0.769 0.421 0.561 0.911 1.03 0.00259 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 5.5 0.234 0.813 0.431 0.568 1.02 1.06 0.00289 0.00301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 171 16986.303 0.005 0.248 0.834 5.79 0.438 0.584 0.841 1.07 0.00239 0.00304 ! Validation 171 16986.303 0.005 0.246 0.963 5.89 0.436 0.582 0.93 1.15 0.00264 0.00327 Wall time: 16986.303817106877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 4.99 0.22 0.591 0.407 0.55 0.725 0.902 0.00206 0.00256 172 172 5.02 0.204 0.942 0.393 0.53 0.916 1.14 0.0026 0.00323 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 4.25 0.204 0.175 0.399 0.529 0.429 0.49 0.00122 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 172 17086.819 0.005 0.216 0.757 5.08 0.405 0.545 0.803 1.02 0.00228 0.0029 ! Validation 172 17086.819 0.005 0.216 0.807 5.12 0.405 0.545 0.847 1.05 0.00241 0.00299 Wall time: 17086.818921222817 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 5.05 0.191 1.22 0.376 0.513 1.14 1.3 0.00325 0.00369 173 172 4.45 0.179 0.865 0.364 0.497 0.858 1.09 0.00244 0.0031 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 3.89 0.179 0.305 0.373 0.497 0.602 0.648 0.00171 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 173 17186.043 0.005 0.191 0.932 4.75 0.377 0.513 0.904 1.13 0.00257 0.00322 ! Validation 173 17186.043 0.005 0.191 0.755 4.58 0.378 0.513 0.822 1.02 0.00234 0.0029 Wall time: 17186.043498147745 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 3.85 0.166 0.529 0.35 0.478 0.734 0.853 0.00209 0.00242 174 172 3.67 0.157 0.52 0.338 0.466 0.634 0.846 0.0018 0.0024 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 3.49 0.156 0.359 0.348 0.464 0.664 0.703 0.00189 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 174 17285.282 0.005 0.169 0.669 4.05 0.353 0.483 0.758 0.959 0.00215 0.00273 ! Validation 174 17285.282 0.005 0.169 0.725 4.11 0.353 0.482 0.807 0.999 0.00229 0.00284 Wall time: 17285.282811150886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 3.46 0.147 0.522 0.33 0.45 0.704 0.847 0.002 0.00241 175 172 3.51 0.142 0.659 0.324 0.443 0.792 0.952 0.00225 0.00271 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 2.91 0.141 0.0867 0.329 0.44 0.324 0.345 0.00092 0.000981 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 175 17384.509 0.005 0.151 0.892 3.91 0.332 0.456 0.871 1.11 0.00247 0.00315 ! Validation 175 17384.509 0.005 0.153 0.619 3.67 0.335 0.458 0.748 0.923 0.00212 0.00262 Wall time: 17384.50966481073 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 3.65 0.136 0.937 0.315 0.432 0.948 1.14 0.00269 0.00322 176 172 2.99 0.131 0.369 0.31 0.425 0.588 0.713 0.00167 0.00202 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 2.56 0.126 0.0436 0.311 0.416 0.176 0.245 0.000499 0.000696 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 176 17483.732 0.005 0.137 0.718 3.45 0.315 0.433 0.788 0.994 0.00224 0.00282 ! Validation 176 17483.732 0.005 0.138 0.911 3.66 0.318 0.435 0.906 1.12 0.00257 0.00318 Wall time: 17483.732310830615 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 3.04 0.119 0.663 0.295 0.404 0.796 0.955 0.00226 0.00271 177 172 2.8 0.119 0.411 0.295 0.405 0.6 0.752 0.0017 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 2.5 0.114 0.219 0.296 0.396 0.514 0.549 0.00146 0.00156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 177 17582.896 0.005 0.124 0.789 3.26 0.301 0.412 0.831 1.04 0.00236 0.00296 ! Validation 177 17582.896 0.005 0.125 0.598 3.1 0.304 0.415 0.735 0.907 0.00209 0.00258 Wall time: 17582.896377225872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 2.88 0.113 0.617 0.289 0.395 0.727 0.921 0.00207 0.00262 178 172 2.54 0.109 0.349 0.285 0.388 0.521 0.693 0.00148 0.00197 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 2.25 0.105 0.145 0.285 0.381 0.408 0.447 0.00116 0.00127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 178 17682.549 0.005 0.113 0.976 3.23 0.288 0.394 0.943 1.16 0.00268 0.00329 ! Validation 178 17682.549 0.005 0.115 1.21 3.52 0.293 0.398 1.06 1.29 0.00301 0.00367 Wall time: 17682.549567512702 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 2.32 0.0972 0.376 0.27 0.366 0.622 0.719 0.00177 0.00204 179 172 3 0.101 0.979 0.275 0.373 1.01 1.16 0.00287 0.0033 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 2.07 0.096 0.148 0.273 0.363 0.413 0.451 0.00117 0.00128 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 179 17781.757 0.005 0.103 0.882 2.95 0.277 0.377 0.884 1.1 0.00251 0.00313 ! Validation 179 17781.757 0.005 0.106 0.515 2.62 0.282 0.381 0.682 0.842 0.00194 0.00239 Wall time: 17781.757043811027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 2.06 0.0915 0.228 0.264 0.355 0.429 0.56 0.00122 0.00159 180 172 2.16 0.0888 0.388 0.26 0.35 0.546 0.731 0.00155 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 2.49 0.0871 0.748 0.261 0.346 1 1.01 0.00284 0.00288 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 180 17880.967 0.005 0.0941 0.745 2.63 0.267 0.36 0.815 1.01 0.00232 0.00288 ! Validation 180 17880.967 0.005 0.0961 0.655 2.58 0.271 0.364 0.778 0.95 0.00221 0.0027 Wall time: 17880.967454223894 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 2.26 0.0871 0.516 0.26 0.346 0.684 0.843 0.00194 0.00239 181 172 2.82 0.0817 1.19 0.251 0.335 1.15 1.28 0.00326 0.00364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 2.94 0.0809 1.32 0.252 0.334 1.34 1.35 0.0038 0.00383 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 181 17980.197 0.005 0.0862 0.98 2.7 0.257 0.344 0.93 1.16 0.00264 0.0033 ! Validation 181 17980.197 0.005 0.0892 0.84 2.62 0.263 0.35 0.897 1.07 0.00255 0.00305 Wall time: 17980.19740621792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.8 0.0778 0.246 0.246 0.327 0.477 0.582 0.00135 0.00165 182 172 1.64 0.0705 0.225 0.235 0.311 0.418 0.557 0.00119 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 3.51 0.0717 2.07 0.238 0.314 1.68 1.69 0.00478 0.0048 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 18079.441 0.005 0.0782 0.629 2.19 0.247 0.328 0.726 0.931 0.00206 0.00264 ! Validation 182 18079.441 0.005 0.0799 1.44 3.04 0.25 0.332 1.26 1.41 0.00358 0.004 Wall time: 18079.441065204795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 1.92 0.0714 0.493 0.237 0.314 0.689 0.823 0.00196 0.00234 183 172 5.7 0.0698 4.3 0.236 0.31 2.33 2.43 0.00661 0.00691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 2.15 0.0656 0.839 0.228 0.301 1.07 1.07 0.00303 0.00305 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 183 18178.704 0.005 0.0703 0.775 2.18 0.236 0.311 0.816 1.03 0.00232 0.00293 ! Validation 183 18178.704 0.005 0.0731 1.93 3.39 0.241 0.317 1.52 1.63 0.00431 0.00463 Wall time: 18178.70395442471 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 2.06 0.0673 0.714 0.23 0.304 0.9 0.991 0.00256 0.00282 184 172 2.88 0.0632 1.62 0.224 0.295 1.42 1.49 0.00403 0.00424 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 2.96 0.0605 1.75 0.22 0.289 1.55 1.55 0.00439 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 184 18277.989 0.005 0.0653 0.753 2.06 0.228 0.3 0.817 1.02 0.00232 0.00289 ! Validation 184 18277.989 0.005 0.0675 1.25 2.6 0.232 0.305 1.15 1.31 0.00328 0.00372 Wall time: 18277.98891823087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 2.93 0.0574 1.78 0.215 0.281 1.52 1.57 0.00432 0.00445 185 172 1.71 0.0576 0.562 0.216 0.282 0.778 0.879 0.00221 0.0025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 3.75 0.0555 2.64 0.211 0.276 1.9 1.91 0.00541 0.00542 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 18377.441 0.005 0.0603 0.66 1.86 0.22 0.288 0.757 0.953 0.00215 0.00271 ! Validation 185 18377.441 0.005 0.0624 1.53 2.78 0.224 0.293 1.35 1.45 0.00385 0.00413 Wall time: 18377.441387689672 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 3.26 0.0528 2.21 0.206 0.27 1.66 1.74 0.00472 0.00495 186 172 2.06 0.0519 1.03 0.204 0.267 1.11 1.19 0.00316 0.00338 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 1.04 0.0515 0.00994 0.204 0.266 0.0864 0.117 0.000246 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 186 18476.618 0.005 0.0549 0.614 1.71 0.21 0.275 0.73 0.919 0.00207 0.00261 ! Validation 186 18476.618 0.005 0.0579 0.532 1.69 0.216 0.282 0.709 0.856 0.00201 0.00243 Wall time: 18476.618735872675 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 1.78 0.0511 0.753 0.202 0.265 0.927 1.02 0.00263 0.00289 187 172 4.63 0.0504 3.62 0.202 0.263 2.2 2.23 0.00625 0.00634 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 3.96 0.05 2.96 0.201 0.262 2.01 2.02 0.00572 0.00573 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 187 18575.819 0.005 0.0518 0.703 1.74 0.204 0.267 0.793 0.982 0.00225 0.00279 ! Validation 187 18575.819 0.005 0.0553 2.11 3.21 0.211 0.276 1.6 1.7 0.00455 0.00484 Wall time: 18575.819384516682 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 1.29 0.0501 0.283 0.201 0.263 0.523 0.624 0.00149 0.00177 188 172 1.6 0.0491 0.616 0.199 0.26 0.788 0.921 0.00224 0.00262 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 1.76 0.0475 0.813 0.195 0.256 1.05 1.06 0.00299 0.00301 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 188 18675.020 0.005 0.0496 0.664 1.66 0.2 0.261 0.767 0.956 0.00218 0.00271 ! Validation 188 18675.020 0.005 0.0525 0.836 1.89 0.205 0.269 0.927 1.07 0.00263 0.00305 Wall time: 18675.02015458001 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 1.62 0.0489 0.644 0.197 0.259 0.854 0.941 0.00243 0.00267 189 172 1.07 0.048 0.112 0.196 0.257 0.332 0.393 0.000942 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 1.3 0.0453 0.395 0.191 0.25 0.726 0.737 0.00206 0.00209 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 189 18774.218 0.005 0.0486 0.777 1.75 0.198 0.259 0.829 1.03 0.00236 0.00294 ! Validation 189 18774.218 0.005 0.0503 0.329 1.33 0.201 0.263 0.526 0.672 0.00149 0.00191 Wall time: 18774.2183722239 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 1.14 0.0426 0.291 0.184 0.242 0.558 0.633 0.00159 0.0018 190 172 2.17 0.0417 1.34 0.183 0.24 1.27 1.36 0.00361 0.00385 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 2.77 0.0417 1.93 0.184 0.24 1.63 1.63 0.00462 0.00463 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 18873.400 0.005 0.0445 0.48 1.37 0.189 0.247 0.652 0.812 0.00185 0.00231 ! Validation 190 18873.400 0.005 0.0466 1.32 2.25 0.194 0.253 1.26 1.35 0.00358 0.00383 Wall time: 18873.400252586696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 1.92 0.0443 1.04 0.188 0.247 1.14 1.19 0.00323 0.00339 191 172 1.19 0.0428 0.338 0.186 0.243 0.597 0.682 0.0017 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 0.845 0.0415 0.0145 0.183 0.239 0.0957 0.141 0.000272 0.000401 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 18973.322 0.005 0.0439 0.769 1.65 0.188 0.246 0.82 1.03 0.00233 0.00292 ! Validation 191 18973.322 0.005 0.0463 0.324 1.25 0.193 0.252 0.533 0.668 0.00152 0.0019 Wall time: 18973.321909687947 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 1.06 0.0407 0.243 0.181 0.237 0.468 0.579 0.00133 0.00164 192 172 1.74 0.0459 0.822 0.192 0.251 0.971 1.06 0.00276 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 0.902 0.0438 0.0256 0.189 0.246 0.179 0.188 0.00051 0.000534 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 19072.551 0.005 0.0433 0.771 1.64 0.187 0.244 0.776 1.03 0.00221 0.00293 ! Validation 192 19072.551 0.005 0.0481 0.615 1.58 0.197 0.257 0.774 0.92 0.0022 0.00261 Wall time: 19072.551558391657 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 1.67 0.0407 0.857 0.18 0.237 0.97 1.09 0.00275 0.00308 193 172 0.873 0.0402 0.0688 0.179 0.235 0.25 0.308 0.00071 0.000874 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 1.44 0.0376 0.688 0.175 0.228 0.961 0.973 0.00273 0.00276 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 193 19171.841 0.005 0.0408 0.473 1.29 0.181 0.237 0.647 0.807 0.00184 0.00229 ! Validation 193 19171.841 0.005 0.042 0.516 1.36 0.184 0.24 0.722 0.842 0.00205 0.00239 Wall time: 19171.84091064986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.91 0.0382 0.147 0.175 0.229 0.316 0.449 0.000897 0.00128 194 172 0.987 0.038 0.228 0.174 0.229 0.353 0.56 0.001 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 1.19 0.0355 0.476 0.17 0.221 0.796 0.809 0.00226 0.0023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 194 19271.078 0.005 0.0384 0.521 1.29 0.176 0.23 0.678 0.847 0.00193 0.00241 ! Validation 194 19271.078 0.005 0.0395 0.323 1.11 0.178 0.233 0.562 0.667 0.0016 0.00189 Wall time: 19271.078224584926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 1.56 0.0383 0.79 0.175 0.23 0.96 1.04 0.00273 0.00296 195 172 0.87 0.0374 0.123 0.173 0.227 0.317 0.411 0.0009 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.717 0.0346 0.025 0.167 0.218 0.128 0.186 0.000365 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 195 19370.263 0.005 0.0373 0.606 1.35 0.173 0.226 0.726 0.913 0.00206 0.00259 ! Validation 195 19370.263 0.005 0.0384 0.161 0.929 0.176 0.23 0.364 0.471 0.00103 0.00134 Wall time: 19370.263111755718 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 1.35 0.0356 0.637 0.169 0.221 0.876 0.937 0.00249 0.00266 196 172 2.04 0.0369 1.3 0.171 0.225 1.27 1.34 0.00359 0.0038 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 3.75 0.0383 2.99 0.176 0.23 2.02 2.03 0.00575 0.00576 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 19469.474 0.005 0.0351 0.596 1.3 0.168 0.22 0.742 0.905 0.00211 0.00257 ! Validation 196 19469.474 0.005 0.0408 3.4 4.21 0.181 0.237 2.08 2.16 0.00591 0.00614 Wall time: 19469.474241463 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 1.35 0.0377 0.597 0.173 0.228 0.825 0.906 0.00234 0.00257 197 172 1.12 0.0327 0.468 0.162 0.212 0.73 0.803 0.00207 0.00228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 2.51 0.0323 1.86 0.162 0.211 1.59 1.6 0.00452 0.00455 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 19568.659 0.005 0.0362 0.557 1.28 0.17 0.223 0.685 0.875 0.00195 0.00249 ! Validation 197 19568.659 0.005 0.0357 1.23 1.95 0.169 0.222 1.22 1.3 0.00346 0.0037 Wall time: 19568.65954719484 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.836 0.0308 0.221 0.156 0.206 0.464 0.551 0.00132 0.00156 198 172 1.06 0.0339 0.38 0.164 0.216 0.646 0.723 0.00183 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.685 0.0326 0.0322 0.162 0.212 0.201 0.21 0.00057 0.000598 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 198 19667.848 0.005 0.0341 0.604 1.29 0.165 0.217 0.699 0.912 0.00199 0.00259 ! Validation 198 19667.848 0.005 0.0361 0.233 0.955 0.17 0.223 0.463 0.566 0.00131 0.00161 Wall time: 19667.84852519585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.862 0.0319 0.224 0.16 0.209 0.472 0.555 0.00134 0.00158 199 172 1.35 0.0299 0.75 0.155 0.203 0.962 1.02 0.00273 0.00289 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.885 0.0297 0.291 0.155 0.202 0.617 0.633 0.00175 0.0018 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 199 19767.083 0.005 0.032 0.451 1.09 0.16 0.21 0.624 0.788 0.00177 0.00224 ! Validation 199 19767.083 0.005 0.033 0.166 0.825 0.162 0.213 0.383 0.477 0.00109 0.00136 Wall time: 19767.083398428746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 1.14 0.0293 0.554 0.153 0.201 0.826 0.873 0.00235 0.00248 200 172 0.666 0.0282 0.101 0.151 0.197 0.317 0.373 0.000902 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 1.14 0.0283 0.579 0.151 0.197 0.882 0.893 0.0025 0.00254 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 200 19866.248 0.005 0.0312 0.513 1.14 0.157 0.207 0.676 0.84 0.00192 0.00239 ! Validation 200 19866.248 0.005 0.0315 0.354 0.985 0.159 0.208 0.613 0.698 0.00174 0.00198 Wall time: 19866.24820798263 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.892 0.0313 0.266 0.158 0.208 0.513 0.605 0.00146 0.00172 201 172 0.954 0.0285 0.384 0.15 0.198 0.628 0.727 0.00178 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.61 0.0267 0.0758 0.146 0.192 0.298 0.323 0.000845 0.000918 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 19965.331 0.005 0.0302 0.487 1.09 0.155 0.204 0.63 0.819 0.00179 0.00233 ! Validation 201 19965.331 0.005 0.03 0.287 0.888 0.154 0.203 0.527 0.629 0.0015 0.00179 Wall time: 19965.33132420294 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.991 0.0296 0.398 0.153 0.202 0.601 0.74 0.00171 0.0021 202 172 1.38 0.028 0.817 0.149 0.196 1.01 1.06 0.00288 0.00301 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.932 0.0274 0.384 0.148 0.194 0.714 0.726 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 202 20064.684 0.005 0.0299 0.601 1.2 0.154 0.203 0.718 0.91 0.00204 0.00258 ! Validation 202 20064.684 0.005 0.0305 0.253 0.862 0.155 0.205 0.499 0.59 0.00142 0.00168 Wall time: 20064.684869281016 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.668 0.0272 0.124 0.147 0.194 0.329 0.413 0.000934 0.00117 203 172 0.728 0.0268 0.193 0.145 0.192 0.384 0.515 0.00109 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.553 0.0264 0.0249 0.145 0.191 0.166 0.185 0.000472 0.000526 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 203 20164.048 0.005 0.027 0.383 0.924 0.146 0.193 0.584 0.726 0.00166 0.00206 ! Validation 203 20164.048 0.005 0.029 0.179 0.76 0.151 0.2 0.418 0.497 0.00119 0.00141 Wall time: 20164.048129001632 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 1.67 0.0263 1.15 0.144 0.19 1.23 1.26 0.00349 0.00357 204 172 0.726 0.0259 0.209 0.142 0.189 0.383 0.536 0.00109 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 3.14 0.026 2.62 0.144 0.189 1.89 1.9 0.00538 0.0054 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 204 20263.066 0.005 0.0263 0.462 0.988 0.144 0.19 0.643 0.797 0.00183 0.00226 ! Validation 204 20263.066 0.005 0.0287 1.77 2.34 0.151 0.199 1.41 1.56 0.00401 0.00443 Wall time: 20263.065918082837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.727 0.0249 0.229 0.14 0.185 0.491 0.561 0.00139 0.00159 205 172 0.613 0.025 0.113 0.14 0.185 0.306 0.395 0.000869 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 1.15 0.0243 0.661 0.139 0.183 0.947 0.954 0.00269 0.00271 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 205 20362.239 0.005 0.0264 0.49 1.02 0.144 0.191 0.662 0.821 0.00188 0.00233 ! Validation 205 20362.239 0.005 0.0278 0.356 0.913 0.148 0.196 0.619 0.7 0.00176 0.00199 Wall time: 20362.23952263361 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.653 0.0249 0.155 0.141 0.185 0.377 0.462 0.00107 0.00131 206 172 0.994 0.0239 0.517 0.137 0.181 0.786 0.843 0.00223 0.0024 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 3.02 0.0228 2.56 0.135 0.177 1.87 1.88 0.00532 0.00534 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 20461.400 0.005 0.0242 0.323 0.808 0.138 0.183 0.539 0.667 0.00153 0.00189 ! Validation 206 20461.400 0.005 0.0259 2.14 2.66 0.143 0.189 1.67 1.71 0.00475 0.00487 Wall time: 20461.39995254483 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 1.29 0.0233 0.828 0.135 0.179 0.985 1.07 0.0028 0.00303 207 172 0.548 0.0231 0.0861 0.134 0.178 0.272 0.344 0.000771 0.000978 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 1.19 0.0221 0.752 0.132 0.174 1.01 1.02 0.00287 0.00289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 207 20560.436 0.005 0.0238 0.392 0.868 0.136 0.181 0.593 0.735 0.00168 0.00209 ! Validation 207 20560.436 0.005 0.0254 0.511 1.02 0.141 0.187 0.754 0.839 0.00214 0.00238 Wall time: 20560.436681541614 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.685 0.0237 0.212 0.136 0.18 0.475 0.54 0.00135 0.00153 208 172 0.58 0.022 0.14 0.13 0.174 0.359 0.44 0.00102 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 1.1 0.0212 0.678 0.129 0.171 0.959 0.966 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 208 20659.502 0.005 0.0236 0.381 0.853 0.136 0.18 0.571 0.724 0.00162 0.00206 ! Validation 208 20659.502 0.005 0.0243 0.58 1.07 0.138 0.183 0.817 0.893 0.00232 0.00254 Wall time: 20659.502851539757 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.865 0.0219 0.428 0.131 0.173 0.689 0.767 0.00196 0.00218 209 172 0.601 0.0223 0.155 0.132 0.175 0.362 0.461 0.00103 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.691 0.0217 0.258 0.131 0.173 0.584 0.595 0.00166 0.00169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 209 20758.785 0.005 0.0229 0.415 0.873 0.134 0.177 0.592 0.756 0.00168 0.00215 ! Validation 209 20758.785 0.005 0.0248 0.158 0.655 0.14 0.185 0.383 0.466 0.00109 0.00132 Wall time: 20758.785539216828 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.509 0.0227 0.0542 0.133 0.177 0.227 0.273 0.000646 0.000776 210 172 0.812 0.0221 0.37 0.131 0.174 0.631 0.714 0.00179 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 1.12 0.0202 0.716 0.126 0.167 0.985 0.992 0.0028 0.00282 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 210 20857.961 0.005 0.0222 0.353 0.798 0.132 0.175 0.566 0.697 0.00161 0.00198 ! Validation 210 20857.961 0.005 0.0232 0.701 1.17 0.135 0.179 0.913 0.982 0.00259 0.00279 Wall time: 20857.961092982907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.887 0.0207 0.473 0.127 0.169 0.768 0.807 0.00218 0.00229 211 172 0.51 0.0202 0.107 0.125 0.167 0.251 0.384 0.000713 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.442 0.0196 0.0492 0.124 0.164 0.236 0.26 0.000671 0.000739 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 20957.055 0.005 0.0211 0.318 0.74 0.128 0.17 0.519 0.662 0.00147 0.00188 ! Validation 211 20957.055 0.005 0.0225 0.121 0.57 0.132 0.176 0.319 0.407 0.000906 0.00116 Wall time: 20957.055358870886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.506 0.0212 0.083 0.128 0.171 0.278 0.338 0.000791 0.00096 212 172 0.832 0.0208 0.415 0.128 0.169 0.685 0.756 0.00195 0.00215 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.698 0.0196 0.305 0.124 0.164 0.639 0.648 0.00181 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 212 21056.111 0.005 0.022 0.446 0.885 0.131 0.174 0.61 0.783 0.00173 0.00222 ! Validation 212 21056.111 0.005 0.023 0.205 0.664 0.134 0.178 0.451 0.531 0.00128 0.00151 Wall time: 21056.11114331195 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.909 0.0212 0.484 0.128 0.171 0.768 0.816 0.00218 0.00232 213 172 0.867 0.0206 0.455 0.126 0.168 0.749 0.791 0.00213 0.00225 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.525 0.021 0.105 0.128 0.17 0.354 0.381 0.00101 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 213 21155.182 0.005 0.0208 0.367 0.783 0.127 0.169 0.578 0.71 0.00164 0.00202 ! Validation 213 21155.182 0.005 0.0231 0.38 0.843 0.135 0.178 0.533 0.723 0.00151 0.00205 Wall time: 21155.182391807903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.493 0.0201 0.0913 0.125 0.166 0.288 0.354 0.000819 0.00101 214 172 1.23 0.0205 0.818 0.127 0.168 1.02 1.06 0.0029 0.00301 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 2.27 0.0212 1.85 0.129 0.171 1.59 1.59 0.00452 0.00453 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 21254.266 0.005 0.0203 0.342 0.747 0.126 0.167 0.522 0.686 0.00148 0.00195 ! Validation 214 21254.266 0.005 0.0236 1.25 1.72 0.136 0.18 1.17 1.31 0.00332 0.00373 Wall time: 21254.26607428398 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.862 0.0183 0.496 0.119 0.159 0.77 0.826 0.00219 0.00235 215 172 1.34 0.0211 0.921 0.129 0.17 1.03 1.13 0.00294 0.0032 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 1.06 0.0214 0.629 0.13 0.172 0.927 0.93 0.00263 0.00264 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 215 21353.433 0.005 0.0194 0.291 0.678 0.123 0.163 0.503 0.632 0.00143 0.0018 ! Validation 215 21353.433 0.005 0.0231 0.698 1.16 0.135 0.178 0.817 0.98 0.00232 0.00278 Wall time: 21353.43335029157 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.501 0.0187 0.126 0.121 0.161 0.373 0.416 0.00106 0.00118 216 172 0.539 0.0178 0.183 0.117 0.157 0.404 0.501 0.00115 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.448 0.0175 0.0983 0.117 0.155 0.352 0.368 0.000999 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 216 21452.543 0.005 0.0201 0.359 0.76 0.125 0.166 0.562 0.703 0.0016 0.002 ! Validation 216 21452.543 0.005 0.0204 0.107 0.515 0.126 0.168 0.302 0.383 0.000859 0.00109 Wall time: 21452.54351366777 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.442 0.0186 0.0712 0.12 0.16 0.244 0.313 0.000693 0.000889 217 172 0.763 0.0186 0.39 0.12 0.16 0.698 0.733 0.00198 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.372 0.018 0.0123 0.119 0.157 0.107 0.13 0.000305 0.000369 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 217 21551.653 0.005 0.0182 0.274 0.639 0.119 0.158 0.497 0.614 0.00141 0.00175 ! Validation 217 21551.653 0.005 0.0204 0.16 0.569 0.126 0.168 0.373 0.47 0.00106 0.00133 Wall time: 21551.653352086898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 2.78 0.018 2.42 0.119 0.157 1.75 1.82 0.00497 0.00518 218 172 0.444 0.0175 0.0946 0.116 0.155 0.273 0.361 0.000776 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.402 0.0163 0.076 0.113 0.15 0.308 0.323 0.000876 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 218 21650.767 0.005 0.0184 0.302 0.669 0.12 0.159 0.495 0.644 0.00141 0.00183 ! Validation 218 21650.767 0.005 0.0193 0.398 0.784 0.123 0.163 0.646 0.74 0.00183 0.0021 Wall time: 21650.767224215902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.802 0.0171 0.46 0.116 0.153 0.746 0.795 0.00212 0.00226 219 172 0.45 0.0176 0.0989 0.118 0.155 0.311 0.369 0.000884 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.365 0.0175 0.0145 0.118 0.155 0.118 0.141 0.000335 0.000401 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 21749.915 0.005 0.0175 0.29 0.641 0.117 0.155 0.511 0.632 0.00145 0.00179 ! Validation 219 21749.915 0.005 0.0199 0.186 0.584 0.125 0.165 0.427 0.506 0.00121 0.00144 Wall time: 21749.914910129737 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.444 0.0169 0.106 0.114 0.152 0.291 0.381 0.000828 0.00108 220 172 0.679 0.0148 0.382 0.108 0.143 0.662 0.725 0.00188 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 1.97 0.0155 1.66 0.11 0.146 1.51 1.51 0.00428 0.00429 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 21849.029 0.005 0.017 0.245 0.584 0.115 0.153 0.466 0.58 0.00132 0.00165 ! Validation 220 21849.029 0.005 0.0185 1.57 1.94 0.12 0.159 1.43 1.47 0.00406 0.00417 Wall time: 21849.029654779006 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.41 0.0156 0.0969 0.111 0.147 0.315 0.365 0.000895 0.00104 221 172 0.496 0.0162 0.173 0.113 0.149 0.42 0.487 0.00119 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.762 0.0162 0.437 0.113 0.15 0.769 0.776 0.00218 0.0022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 221 21948.184 0.005 0.0168 0.314 0.651 0.115 0.152 0.536 0.658 0.00152 0.00187 ! Validation 221 21948.184 0.005 0.0186 0.623 0.996 0.121 0.16 0.779 0.926 0.00221 0.00263 Wall time: 21948.184508234728 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 1.12 0.0168 0.785 0.115 0.152 0.815 1.04 0.00232 0.00295 222 172 0.477 0.015 0.176 0.109 0.144 0.407 0.492 0.00115 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.329 0.0148 0.0331 0.108 0.143 0.186 0.213 0.00053 0.000607 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 222 22047.299 0.005 0.0165 0.269 0.599 0.114 0.151 0.484 0.608 0.00138 0.00173 ! Validation 222 22047.299 0.005 0.0174 0.154 0.502 0.117 0.155 0.395 0.461 0.00112 0.00131 Wall time: 22047.299625552725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.425 0.0147 0.131 0.107 0.142 0.352 0.424 0.000999 0.0012 223 172 0.566 0.0158 0.25 0.112 0.147 0.52 0.587 0.00148 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.553 0.0149 0.255 0.108 0.143 0.578 0.592 0.00164 0.00168 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 223 22146.410 0.005 0.0158 0.264 0.58 0.111 0.148 0.478 0.602 0.00136 0.00171 ! Validation 223 22146.410 0.005 0.0175 0.395 0.745 0.117 0.155 0.603 0.738 0.00171 0.0021 Wall time: 22146.41042430559 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.898 0.0161 0.577 0.112 0.149 0.869 0.891 0.00247 0.00253 224 172 0.385 0.0146 0.0929 0.107 0.142 0.302 0.357 0.000859 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.555 0.014 0.274 0.105 0.139 0.606 0.614 0.00172 0.00175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 224 22245.507 0.005 0.0162 0.32 0.644 0.112 0.149 0.536 0.664 0.00152 0.00189 ! Validation 224 22245.507 0.005 0.0169 0.152 0.49 0.115 0.152 0.392 0.457 0.00111 0.0013 Wall time: 22245.506994643714 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.701 0.0155 0.391 0.11 0.146 0.689 0.733 0.00196 0.00208 225 172 0.464 0.0182 0.101 0.119 0.158 0.295 0.372 0.000838 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 1.87 0.0181 1.51 0.12 0.158 1.44 1.44 0.00409 0.0041 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 225 22344.605 0.005 0.0148 0.225 0.52 0.108 0.143 0.436 0.556 0.00124 0.00158 ! Validation 225 22344.605 0.005 0.0197 1.12 1.52 0.125 0.165 1.2 1.24 0.00341 0.00353 Wall time: 22344.605855665635 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.479 0.0149 0.182 0.108 0.143 0.393 0.5 0.00112 0.00142 226 172 0.99 0.0137 0.716 0.104 0.137 0.938 0.992 0.00266 0.00282 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.941 0.0139 0.663 0.105 0.138 0.952 0.955 0.0027 0.00271 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 226 22443.692 0.005 0.0146 0.234 0.527 0.107 0.142 0.455 0.567 0.00129 0.00161 ! Validation 226 22443.692 0.005 0.0161 0.51 0.832 0.112 0.149 0.785 0.838 0.00223 0.00238 Wall time: 22443.692698976956 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 1.3 0.0151 0.996 0.109 0.144 1.13 1.17 0.00321 0.00332 227 172 0.531 0.0142 0.246 0.106 0.14 0.475 0.581 0.00135 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.356 0.0144 0.0685 0.106 0.141 0.289 0.307 0.00082 0.000872 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 227 22544.088 0.005 0.0146 0.295 0.587 0.107 0.142 0.511 0.637 0.00145 0.00181 ! Validation 227 22544.088 0.005 0.0164 0.119 0.446 0.113 0.15 0.33 0.405 0.000938 0.00115 Wall time: 22544.088117673993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.316 0.0135 0.0457 0.102 0.136 0.192 0.251 0.000544 0.000713 228 172 1.3 0.0139 1.02 0.104 0.138 1.08 1.18 0.00307 0.00336 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 1.1 0.0133 0.835 0.102 0.135 1.07 1.07 0.00303 0.00304 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 22643.207 0.005 0.0139 0.218 0.496 0.104 0.138 0.433 0.547 0.00123 0.00155 ! Validation 228 22643.207 0.005 0.0154 0.6 0.908 0.11 0.145 0.789 0.909 0.00224 0.00258 Wall time: 22643.207458044868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.355 0.0136 0.0824 0.103 0.137 0.272 0.337 0.000772 0.000957 229 172 0.31 0.013 0.0497 0.102 0.134 0.215 0.262 0.000611 0.000743 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.433 0.0126 0.182 0.0998 0.131 0.492 0.501 0.0014 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 229 22742.259 0.005 0.0147 0.268 0.563 0.108 0.142 0.462 0.608 0.00131 0.00173 ! Validation 229 22742.259 0.005 0.0146 0.252 0.544 0.107 0.141 0.487 0.589 0.00138 0.00167 Wall time: 22742.25974423159 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.323 0.0117 0.0888 0.096 0.127 0.295 0.35 0.000837 0.000993 230 172 0.313 0.0115 0.0822 0.0959 0.126 0.292 0.336 0.00083 0.000956 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.394 0.0117 0.161 0.0963 0.127 0.464 0.47 0.00132 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 230 22841.394 0.005 0.0128 0.186 0.442 0.101 0.133 0.394 0.506 0.00112 0.00144 ! Validation 230 22841.394 0.005 0.0138 0.23 0.507 0.104 0.138 0.483 0.563 0.00137 0.0016 Wall time: 22841.394848833792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.365 0.0124 0.117 0.0995 0.131 0.345 0.402 0.000979 0.00114 231 172 0.287 0.011 0.0657 0.0936 0.123 0.26 0.301 0.00074 0.000854 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.954 0.0117 0.72 0.0968 0.127 0.991 0.995 0.00282 0.00283 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 231 22940.501 0.005 0.0124 0.194 0.441 0.0989 0.13 0.416 0.517 0.00118 0.00147 ! Validation 231 22940.501 0.005 0.0138 0.495 0.771 0.105 0.138 0.785 0.825 0.00223 0.00234 Wall time: 22940.50141254766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.378 0.0133 0.113 0.102 0.135 0.335 0.394 0.000953 0.00112 232 172 0.323 0.0126 0.0712 0.0991 0.132 0.247 0.313 0.000701 0.000889 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.8 0.0124 0.552 0.0986 0.13 0.866 0.872 0.00246 0.00248 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 232 23039.547 0.005 0.0138 0.333 0.609 0.104 0.138 0.558 0.678 0.00158 0.00192 ! Validation 232 23039.547 0.005 0.0146 0.57 0.861 0.107 0.142 0.818 0.885 0.00232 0.00251 Wall time: 23039.54730595462 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.503 0.0115 0.272 0.0959 0.126 0.568 0.612 0.00161 0.00174 233 172 0.304 0.0121 0.062 0.0988 0.129 0.243 0.292 0.000691 0.00083 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.25 0.011 0.0291 0.094 0.123 0.181 0.2 0.000514 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 233 23139.291 0.005 0.0122 0.178 0.422 0.0981 0.13 0.395 0.494 0.00112 0.0014 ! Validation 233 23139.291 0.005 0.0132 0.0801 0.344 0.102 0.135 0.252 0.332 0.000716 0.000943 Wall time: 23139.291755338665 ! Best model 233 0.344 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.354 0.0126 0.102 0.1 0.132 0.319 0.375 0.000906 0.00107 234 172 0.865 0.0134 0.598 0.104 0.136 0.88 0.907 0.0025 0.00258 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 1.08 0.0126 0.824 0.101 0.131 1.06 1.07 0.00302 0.00303 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 23238.396 0.005 0.0116 0.185 0.417 0.0958 0.126 0.394 0.504 0.00112 0.00143 ! Validation 234 23238.396 0.005 0.014 0.573 0.853 0.106 0.139 0.816 0.888 0.00232 0.00252 Wall time: 23238.39603045769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.371 0.0103 0.165 0.0905 0.119 0.425 0.476 0.00121 0.00135 235 172 0.344 0.0103 0.138 0.09 0.119 0.407 0.435 0.00116 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.311 0.0102 0.107 0.0896 0.118 0.377 0.384 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 235 23337.550 0.005 0.0109 0.149 0.367 0.0931 0.123 0.368 0.452 0.00104 0.00128 ! Validation 235 23337.550 0.005 0.012 0.0687 0.308 0.0969 0.128 0.248 0.307 0.000706 0.000874 Wall time: 23337.550049013924 ! Best model 235 0.308 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.42 0.0104 0.212 0.0904 0.12 0.476 0.54 0.00135 0.00153 236 172 0.451 0.0118 0.214 0.0959 0.128 0.451 0.542 0.00128 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.663 0.0112 0.438 0.094 0.124 0.774 0.777 0.0022 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 236 23436.672 0.005 0.0113 0.204 0.43 0.0945 0.125 0.404 0.53 0.00115 0.00151 ! Validation 236 23436.672 0.005 0.0129 0.407 0.666 0.101 0.133 0.666 0.748 0.00189 0.00213 Wall time: 23436.672858548816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.322 0.0121 0.0787 0.0988 0.129 0.264 0.329 0.00075 0.000935 237 172 0.605 0.00992 0.406 0.0884 0.117 0.652 0.748 0.00185 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.385 0.00966 0.192 0.0878 0.115 0.51 0.514 0.00145 0.00146 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 237 23535.804 0.005 0.0107 0.187 0.401 0.0922 0.121 0.405 0.507 0.00115 0.00144 ! Validation 237 23535.804 0.005 0.0115 0.234 0.465 0.0952 0.126 0.499 0.568 0.00142 0.00161 Wall time: 23535.804009064566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.671 0.0112 0.447 0.0949 0.124 0.745 0.784 0.00212 0.00223 238 172 0.269 0.0091 0.0866 0.0854 0.112 0.291 0.345 0.000828 0.000981 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.223 0.00853 0.0521 0.0824 0.108 0.261 0.268 0.000741 0.000761 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 238 23634.891 0.005 0.00982 0.131 0.327 0.0882 0.116 0.341 0.424 0.000968 0.0012 ! Validation 238 23634.891 0.005 0.0108 0.113 0.329 0.0918 0.122 0.344 0.395 0.000977 0.00112 Wall time: 23634.890947392676 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.772 0.0124 0.525 0.1 0.13 0.823 0.85 0.00234 0.00241 239 172 0.951 0.0105 0.74 0.0914 0.12 0.963 1.01 0.00274 0.00287 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.283 0.0104 0.0749 0.0912 0.12 0.315 0.321 0.000896 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 239 23733.987 0.005 0.0102 0.22 0.424 0.0899 0.119 0.436 0.549 0.00124 0.00156 ! Validation 239 23733.987 0.005 0.0121 0.0652 0.307 0.098 0.129 0.232 0.3 0.00066 0.000851 Wall time: 23733.987308635842 ! Best model 239 0.307 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.423 0.00913 0.24 0.0842 0.112 0.531 0.575 0.00151 0.00163 240 172 0.245 0.00965 0.0523 0.0874 0.115 0.213 0.268 0.000604 0.000762 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.254 0.00927 0.069 0.0867 0.113 0.302 0.308 0.000858 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 240 23833.187 0.005 0.00972 0.153 0.348 0.0877 0.116 0.372 0.46 0.00106 0.00131 ! Validation 240 23833.187 0.005 0.0109 0.102 0.32 0.0931 0.122 0.308 0.375 0.000875 0.00107 Wall time: 23833.18697689986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.198 0.00861 0.0258 0.0827 0.109 0.143 0.188 0.000405 0.000535 241 172 0.223 0.00887 0.0453 0.084 0.11 0.183 0.25 0.000519 0.000709 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.241 0.00835 0.0745 0.0818 0.107 0.313 0.32 0.000889 0.00091 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 241 23932.301 0.005 0.00995 0.176 0.375 0.0888 0.117 0.377 0.493 0.00107 0.0014 ! Validation 241 23932.301 0.005 0.0102 0.0848 0.289 0.0896 0.119 0.271 0.342 0.00077 0.000971 Wall time: 23932.301282670815 ! Best model 241 0.289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.229 0.00876 0.0537 0.0837 0.11 0.209 0.272 0.000594 0.000772 242 172 0.208 0.0092 0.0244 0.0854 0.113 0.131 0.183 0.000373 0.000521 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.82 0.00945 0.631 0.0868 0.114 0.929 0.932 0.00264 0.00265 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 242 24031.413 0.005 0.00922 0.172 0.357 0.0853 0.113 0.384 0.487 0.00109 0.00138 ! Validation 242 24031.413 0.005 0.0108 0.312 0.529 0.0923 0.122 0.618 0.655 0.00176 0.00186 Wall time: 24031.412964997813 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.659 0.00887 0.482 0.0835 0.11 0.779 0.814 0.00221 0.00231 243 172 0.31 0.0087 0.136 0.0833 0.109 0.405 0.433 0.00115 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.254 0.00927 0.0681 0.086 0.113 0.288 0.306 0.000817 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 243 24130.499 0.005 0.00884 0.126 0.302 0.0835 0.11 0.333 0.416 0.000946 0.00118 ! Validation 243 24130.499 0.005 0.0108 0.126 0.342 0.0922 0.122 0.321 0.416 0.000913 0.00118 Wall time: 24130.49934673868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.242 0.00887 0.0648 0.084 0.11 0.252 0.299 0.000715 0.000848 244 172 0.222 0.0087 0.0481 0.0831 0.109 0.215 0.257 0.000612 0.000731 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.21 0.00883 0.0333 0.0847 0.11 0.205 0.214 0.000582 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 244 24229.734 0.005 0.0089 0.147 0.325 0.0839 0.111 0.347 0.45 0.000984 0.00128 ! Validation 244 24229.734 0.005 0.0106 0.0844 0.296 0.0917 0.121 0.258 0.341 0.000733 0.000968 Wall time: 24229.73456612369 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.249 0.00869 0.0758 0.0833 0.109 0.279 0.323 0.000792 0.000917 245 172 0.561 0.00822 0.396 0.0803 0.106 0.697 0.739 0.00198 0.0021 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.201 0.00796 0.0418 0.0796 0.105 0.237 0.24 0.000672 0.000682 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 245 24329.841 0.005 0.0093 0.132 0.318 0.0852 0.113 0.324 0.426 0.00092 0.00121 ! Validation 245 24329.841 0.005 0.00963 0.085 0.278 0.0868 0.115 0.265 0.342 0.000753 0.000972 Wall time: 24329.841010999866 ! Best model 245 0.278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.201 0.00735 0.054 0.0762 0.101 0.225 0.273 0.00064 0.000775 246 172 0.204 0.00781 0.0482 0.0783 0.104 0.216 0.258 0.000613 0.000732 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.628 0.00696 0.488 0.0748 0.0979 0.819 0.82 0.00233 0.00233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 246 24428.934 0.005 0.008 0.111 0.271 0.0794 0.105 0.316 0.39 0.000897 0.00111 ! Validation 246 24428.934 0.005 0.00908 0.309 0.491 0.0842 0.112 0.592 0.652 0.00168 0.00185 Wall time: 24428.93412093399 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.65 0.00778 0.494 0.078 0.103 0.772 0.825 0.00219 0.00234 247 172 0.38 0.00839 0.212 0.0804 0.107 0.498 0.54 0.00141 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.303 0.00703 0.162 0.075 0.0984 0.471 0.473 0.00134 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 247 24528.000 0.005 0.00783 0.115 0.271 0.0785 0.104 0.32 0.397 0.000908 0.00113 ! Validation 247 24528.000 0.005 0.00915 0.367 0.55 0.0847 0.112 0.67 0.71 0.0019 0.00202 Wall time: 24528.0000699237 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.235 0.00832 0.0687 0.0808 0.107 0.253 0.307 0.000718 0.000873 248 172 0.181 0.00734 0.0339 0.0759 0.101 0.18 0.216 0.000511 0.000614 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.197 0.00672 0.0626 0.0732 0.0962 0.291 0.293 0.000827 0.000833 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 248 24627.111 0.005 0.00757 0.11 0.262 0.0771 0.102 0.314 0.39 0.000893 0.00111 ! Validation 248 24627.111 0.005 0.00881 0.0996 0.276 0.0828 0.11 0.317 0.37 0.0009 0.00105 Wall time: 24627.11174847558 ! Best model 248 0.276 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 19.4 0.891 1.56 0.821 1.11 1.07 1.47 0.00303 0.00416 249 172 13.3 0.605 1.21 0.678 0.912 1.03 1.29 0.00292 0.00367 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 12.4 0.584 0.733 0.674 0.896 0.933 1 0.00265 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 249 24726.224 0.005 0.79 23 38.8 0.746 1.04 2.66 5.62 0.00755 0.016 ! Validation 249 24726.224 0.005 0.61 2.96 15.2 0.683 0.916 1.58 2.02 0.00449 0.00574 Wall time: 24726.22405961668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 4.91 0.197 0.979 0.392 0.52 0.927 1.16 0.00263 0.0033 250 172 3.2 0.143 0.335 0.335 0.444 0.546 0.679 0.00155 0.00193 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 3.33 0.143 0.469 0.34 0.444 0.765 0.804 0.00217 0.00228 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 250 24825.282 0.005 0.259 1.51 6.68 0.438 0.597 1.12 1.44 0.00319 0.0041 ! Validation 250 24825.282 0.005 0.151 1.02 4.03 0.344 0.455 0.965 1.19 0.00274 0.00337 Wall time: 24825.28214884596 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 2.78 0.115 0.484 0.302 0.397 0.718 0.816 0.00204 0.00232 251 172 2.87 0.102 0.829 0.283 0.375 0.924 1.07 0.00262 0.00303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 2.04 0.1 0.0388 0.284 0.371 0.179 0.231 0.000509 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 251 24924.348 0.005 0.12 0.787 3.18 0.306 0.406 0.82 1.04 0.00233 0.00296 ! Validation 251 24924.348 0.005 0.108 0.401 2.56 0.291 0.386 0.59 0.743 0.00168 0.00211 Wall time: 24924.34842827497 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 3.92 0.0887 2.14 0.265 0.349 1.6 1.72 0.00454 0.00488 252 172 2.31 0.0824 0.665 0.254 0.337 0.775 0.957 0.0022 0.00272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 3.15 0.0798 1.56 0.254 0.331 1.45 1.46 0.00413 0.00416 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 25023.475 0.005 0.0905 0.748 2.56 0.267 0.353 0.813 1.01 0.00231 0.00288 ! Validation 252 25023.475 0.005 0.0863 1.3 3.03 0.261 0.345 1.13 1.34 0.00321 0.0038 Wall time: 25023.475807187613 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 1.85 0.0726 0.396 0.241 0.316 0.588 0.738 0.00167 0.0021 253 172 1.54 0.0666 0.212 0.229 0.303 0.432 0.54 0.00123 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 2.76 0.0647 1.47 0.229 0.298 1.41 1.42 0.004 0.00404 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 253 25122.523 0.005 0.0732 0.672 2.14 0.24 0.317 0.775 0.962 0.0022 0.00273 ! Validation 253 25122.523 0.005 0.0706 1.3 2.71 0.236 0.312 1.23 1.34 0.00349 0.0038 Wall time: 25122.523180502 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 1.58 0.057 0.438 0.212 0.28 0.648 0.777 0.00184 0.00221 254 172 1.36 0.0564 0.23 0.211 0.279 0.377 0.562 0.00107 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 1.19 0.0546 0.102 0.21 0.274 0.343 0.375 0.000973 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 254 25221.547 0.005 0.0601 0.601 1.8 0.218 0.288 0.725 0.909 0.00206 0.00258 ! Validation 254 25221.547 0.005 0.06 0.241 1.44 0.218 0.287 0.449 0.576 0.00127 0.00164 Wall time: 25221.54746695375 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 1.24 0.0519 0.202 0.203 0.267 0.434 0.527 0.00123 0.0015 255 172 1.78 0.0479 0.819 0.194 0.257 0.972 1.06 0.00276 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 1.35 0.0477 0.4 0.196 0.256 0.725 0.742 0.00206 0.00211 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 255 25320.711 0.005 0.0526 0.626 1.68 0.203 0.269 0.722 0.928 0.00205 0.00264 ! Validation 255 25320.711 0.005 0.0527 0.363 1.42 0.204 0.269 0.595 0.707 0.00169 0.00201 Wall time: 25320.71146480972 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 1.09 0.0436 0.219 0.187 0.245 0.47 0.549 0.00133 0.00156 256 172 0.953 0.0411 0.13 0.181 0.238 0.368 0.423 0.00105 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 1.81 0.0403 1.01 0.181 0.236 1.16 1.18 0.00331 0.00334 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 256 25419.793 0.005 0.0454 0.519 1.43 0.189 0.25 0.637 0.845 0.00181 0.0024 ! Validation 256 25419.793 0.005 0.0447 0.718 1.61 0.189 0.248 0.908 0.994 0.00258 0.00282 Wall time: 25419.793485241942 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 0.888 0.0383 0.122 0.174 0.229 0.356 0.41 0.00101 0.00117 257 172 0.789 0.0351 0.0878 0.167 0.22 0.283 0.347 0.000804 0.000987 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 2.07 0.0356 1.36 0.169 0.221 1.36 1.37 0.00386 0.00388 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 25519.452 0.005 0.0383 0.497 1.26 0.174 0.23 0.669 0.827 0.0019 0.00235 ! Validation 257 25519.452 0.005 0.0391 1.01 1.79 0.176 0.232 1.11 1.18 0.00316 0.00335 Wall time: 25519.452604205813 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 1.22 0.0359 0.502 0.169 0.222 0.776 0.831 0.0022 0.00236 258 172 0.849 0.0333 0.184 0.163 0.214 0.396 0.503 0.00113 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 1.65 0.0321 1.01 0.161 0.21 1.17 1.18 0.00333 0.00335 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 258 25618.538 0.005 0.0346 0.509 1.2 0.166 0.218 0.691 0.837 0.00196 0.00238 ! Validation 258 25618.538 0.005 0.0351 0.937 1.64 0.167 0.22 1.06 1.14 0.003 0.00322 Wall time: 25618.538251427002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 1.76 0.0294 1.17 0.153 0.201 1.22 1.27 0.00346 0.0036 259 172 0.697 0.0295 0.108 0.153 0.201 0.321 0.385 0.000911 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 1.08 0.0303 0.478 0.156 0.204 0.804 0.811 0.00228 0.0023 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 259 25717.525 0.005 0.0301 0.469 1.07 0.155 0.204 0.634 0.803 0.0018 0.00228 ! Validation 259 25717.525 0.005 0.0332 0.611 1.27 0.163 0.214 0.795 0.917 0.00226 0.00261 Wall time: 25717.524913260713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 0.645 0.0271 0.102 0.146 0.193 0.309 0.374 0.000878 0.00106 260 172 0.97 0.0248 0.473 0.14 0.185 0.714 0.807 0.00203 0.00229 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 1.97 0.0251 1.47 0.142 0.186 1.42 1.42 0.00402 0.00403 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 260 25816.527 0.005 0.0267 0.362 0.897 0.146 0.192 0.569 0.706 0.00162 0.002 ! Validation 260 25816.527 0.005 0.0281 0.867 1.43 0.149 0.196 1.02 1.09 0.0029 0.0031 Wall time: 25816.52770473063 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.745 0.026 0.225 0.143 0.189 0.481 0.556 0.00137 0.00158 261 172 0.751 0.0216 0.32 0.131 0.172 0.579 0.664 0.00164 0.00189 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 0.462 0.0224 0.0128 0.135 0.176 0.113 0.133 0.000321 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 261 25915.539 0.005 0.0253 0.421 0.927 0.141 0.186 0.603 0.762 0.00171 0.00216 ! Validation 261 25915.539 0.005 0.0253 0.132 0.638 0.142 0.187 0.337 0.426 0.000957 0.00121 Wall time: 25915.539699574 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 3.16 0.0233 2.7 0.135 0.179 1.88 1.93 0.00534 0.00547 262 172 0.919 0.022 0.478 0.131 0.174 0.762 0.811 0.00217 0.0023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 0.436 0.0212 0.0115 0.131 0.171 0.119 0.126 0.000338 0.000357 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 262 26014.540 0.005 0.0229 0.375 0.833 0.134 0.177 0.563 0.719 0.0016 0.00204 ! Validation 262 26014.540 0.005 0.0239 0.112 0.591 0.138 0.181 0.304 0.392 0.000864 0.00111 Wall time: 26014.54076685384 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.524 0.0201 0.122 0.126 0.166 0.333 0.409 0.000946 0.00116 263 172 0.522 0.0203 0.116 0.126 0.167 0.326 0.4 0.000927 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 0.422 0.02 0.0225 0.127 0.166 0.146 0.176 0.000414 0.0005 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 26114.299 0.005 0.021 0.309 0.73 0.129 0.17 0.527 0.652 0.0015 0.00185 ! Validation 263 26114.299 0.005 0.0226 0.0837 0.535 0.133 0.176 0.266 0.339 0.000757 0.000964 Wall time: 26114.299315748736 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.649 0.0195 0.259 0.123 0.164 0.531 0.597 0.00151 0.00169 264 172 0.505 0.0191 0.123 0.122 0.162 0.371 0.411 0.00105 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 0.444 0.019 0.0632 0.123 0.162 0.281 0.295 0.000797 0.000838 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 264 26213.407 0.005 0.0199 0.301 0.699 0.125 0.165 0.518 0.644 0.00147 0.00183 ! Validation 264 26213.407 0.005 0.0214 0.0877 0.515 0.129 0.171 0.276 0.347 0.000785 0.000987 Wall time: 26213.40728895692 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.505 0.0191 0.122 0.122 0.162 0.336 0.41 0.000953 0.00117 265 172 0.427 0.0189 0.0496 0.122 0.161 0.201 0.261 0.00057 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 0.435 0.0176 0.0831 0.119 0.156 0.318 0.338 0.000905 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 265 26312.503 0.005 0.0186 0.24 0.612 0.121 0.16 0.458 0.575 0.0013 0.00163 ! Validation 265 26312.503 0.005 0.0201 0.0893 0.491 0.126 0.166 0.285 0.351 0.000809 0.000996 Wall time: 26312.503401177935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 2.28 0.0174 1.94 0.116 0.155 1.6 1.63 0.00456 0.00464 266 172 0.599 0.0176 0.247 0.117 0.156 0.5 0.583 0.00142 0.00166 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 1.37 0.0175 1.02 0.118 0.155 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 266 26411.618 0.005 0.0178 0.279 0.635 0.118 0.156 0.485 0.62 0.00138 0.00176 ! Validation 266 26411.618 0.005 0.0197 1.02 1.42 0.124 0.165 1.13 1.19 0.0032 0.00337 Wall time: 26411.61828486994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.854 0.0198 0.458 0.125 0.165 0.661 0.794 0.00188 0.00225 267 172 0.44 0.0182 0.076 0.119 0.158 0.263 0.323 0.000747 0.000919 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.35 0.0172 0.00687 0.117 0.154 0.0841 0.0973 0.000239 0.000276 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 26510.735 0.005 0.019 0.398 0.779 0.122 0.162 0.561 0.741 0.00159 0.0021 ! Validation 267 26510.735 0.005 0.0194 0.0894 0.478 0.123 0.164 0.276 0.351 0.000785 0.000996 Wall time: 26510.734989081975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.407 0.0173 0.0596 0.116 0.155 0.228 0.286 0.000646 0.000814 268 172 0.839 0.0168 0.503 0.115 0.152 0.781 0.832 0.00222 0.00236 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 0.715 0.0176 0.363 0.118 0.156 0.703 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 268 26609.853 0.005 0.0171 0.286 0.628 0.115 0.153 0.516 0.627 0.00147 0.00178 ! Validation 268 26609.853 0.005 0.0197 0.375 0.77 0.124 0.165 0.611 0.718 0.00173 0.00204 Wall time: 26609.853651812766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 0.43 0.0161 0.108 0.112 0.149 0.309 0.385 0.000879 0.00109 269 172 0.582 0.0152 0.277 0.109 0.145 0.547 0.617 0.00156 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 1.56 0.0151 1.25 0.11 0.144 1.31 1.31 0.00372 0.00373 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 269 26709.081 0.005 0.0162 0.171 0.494 0.112 0.149 0.386 0.485 0.0011 0.00138 ! Validation 269 26709.081 0.005 0.0175 0.736 1.09 0.117 0.155 0.898 1.01 0.00255 0.00286 Wall time: 26709.081462401897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.392 0.0153 0.0864 0.109 0.145 0.28 0.345 0.000797 0.00098 270 172 0.492 0.016 0.172 0.112 0.148 0.387 0.487 0.0011 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 1.27 0.0162 0.942 0.113 0.149 1.14 1.14 0.00323 0.00323 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 270 26808.182 0.005 0.0154 0.252 0.56 0.11 0.146 0.473 0.589 0.00134 0.00167 ! Validation 270 26808.182 0.005 0.0179 1.12 1.48 0.119 0.157 1.2 1.24 0.00341 0.00352 Wall time: 26808.182707258966 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.431 0.0135 0.161 0.103 0.136 0.428 0.471 0.00122 0.00134 271 172 0.393 0.0145 0.102 0.106 0.141 0.313 0.375 0.00089 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.374 0.015 0.0731 0.109 0.144 0.305 0.317 0.000867 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 271 26907.269 0.005 0.0157 0.271 0.585 0.111 0.147 0.485 0.611 0.00138 0.00173 ! Validation 271 26907.269 0.005 0.0173 0.141 0.486 0.116 0.154 0.356 0.44 0.00101 0.00125 Wall time: 26907.269056948833 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.354 0.0134 0.0856 0.102 0.136 0.264 0.343 0.000749 0.000975 272 172 0.337 0.0147 0.0424 0.108 0.142 0.19 0.242 0.000541 0.000686 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 0.312 0.0146 0.0204 0.108 0.142 0.149 0.167 0.000422 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 272 27006.464 0.005 0.0144 0.194 0.482 0.106 0.141 0.412 0.516 0.00117 0.00147 ! Validation 272 27006.464 0.005 0.0164 0.134 0.462 0.114 0.15 0.334 0.43 0.000948 0.00122 Wall time: 27006.464647867717 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.366 0.0142 0.0818 0.105 0.14 0.266 0.336 0.000756 0.000953 273 172 0.589 0.0149 0.29 0.108 0.143 0.592 0.632 0.00168 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 1.4 0.0145 1.11 0.108 0.141 1.23 1.23 0.0035 0.0035 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 273 27105.522 0.005 0.0138 0.183 0.459 0.104 0.138 0.395 0.501 0.00112 0.00142 ! Validation 273 27105.522 0.005 0.0162 1.19 1.52 0.113 0.149 1.22 1.28 0.00348 0.00364 Wall time: 27105.521943276748 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.397 0.0152 0.093 0.109 0.145 0.277 0.358 0.000788 0.00102 274 172 0.382 0.0138 0.107 0.104 0.138 0.343 0.383 0.000975 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.267 0.013 0.00691 0.102 0.134 0.0831 0.0975 0.000236 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 274 27204.601 0.005 0.0139 0.234 0.512 0.104 0.138 0.453 0.568 0.00129 0.00161 ! Validation 274 27204.601 0.005 0.0153 0.0954 0.401 0.11 0.145 0.286 0.362 0.000813 0.00103 Wall time: 27204.601435412653 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.328 0.0137 0.0537 0.104 0.137 0.214 0.272 0.000607 0.000772 275 172 0.322 0.0129 0.0629 0.101 0.133 0.234 0.294 0.000665 0.000836 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.304 0.0128 0.0475 0.101 0.133 0.243 0.256 0.000691 0.000726 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 275 27304.260 0.005 0.0134 0.23 0.499 0.103 0.136 0.456 0.563 0.00129 0.0016 ! Validation 275 27304.260 0.005 0.0149 0.0868 0.386 0.108 0.143 0.268 0.346 0.000763 0.000982 Wall time: 27304.259903523605 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.335 0.0124 0.0877 0.0989 0.13 0.285 0.347 0.00081 0.000987 276 172 0.331 0.0117 0.0978 0.0959 0.127 0.305 0.367 0.000867 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.428 0.0119 0.191 0.0976 0.128 0.506 0.512 0.00144 0.00146 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 276 27403.891 0.005 0.0127 0.172 0.425 0.0997 0.132 0.39 0.486 0.00111 0.00138 ! Validation 276 27403.891 0.005 0.0141 0.176 0.458 0.105 0.139 0.416 0.492 0.00118 0.0014 Wall time: 27403.891480580904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.345 0.0128 0.089 0.1 0.133 0.297 0.35 0.000842 0.000994 277 172 0.32 0.0121 0.0781 0.0972 0.129 0.277 0.328 0.000786 0.000931 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.24 0.0117 0.00634 0.0967 0.127 0.0861 0.0934 0.000244 0.000265 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 27503.008 0.005 0.0122 0.179 0.423 0.098 0.13 0.394 0.496 0.00112 0.00141 ! Validation 277 27503.008 0.005 0.0137 0.0569 0.331 0.104 0.137 0.223 0.28 0.000633 0.000795 Wall time: 27503.00810627779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.387 0.0111 0.165 0.0942 0.124 0.39 0.476 0.00111 0.00135 278 172 0.246 0.0107 0.0326 0.092 0.121 0.165 0.212 0.000469 0.000602 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.515 0.0105 0.304 0.092 0.12 0.643 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 278 27602.111 0.005 0.0115 0.131 0.361 0.095 0.126 0.344 0.425 0.000977 0.00121 ! Validation 278 27602.111 0.005 0.0128 0.164 0.42 0.1 0.133 0.404 0.475 0.00115 0.00135 Wall time: 27602.111706404015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.285 0.0124 0.0371 0.0983 0.131 0.184 0.226 0.000524 0.000642 279 172 0.533 0.0109 0.315 0.093 0.122 0.619 0.658 0.00176 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.24 0.0105 0.0287 0.0923 0.12 0.175 0.199 0.000496 0.000564 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 279 27701.230 0.005 0.0116 0.209 0.441 0.0956 0.126 0.423 0.536 0.0012 0.00152 ! Validation 279 27701.230 0.005 0.0128 0.0882 0.344 0.1 0.133 0.279 0.348 0.000791 0.00099 Wall time: 27701.22999404883 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.353 0.0104 0.145 0.0908 0.12 0.404 0.447 0.00115 0.00127 280 172 0.442 0.0107 0.229 0.092 0.121 0.523 0.561 0.00148 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.483 0.00997 0.284 0.0892 0.117 0.621 0.625 0.00177 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 280 27800.330 0.005 0.011 0.163 0.384 0.0933 0.123 0.378 0.473 0.00108 0.00134 ! Validation 280 27800.330 0.005 0.0122 0.27 0.513 0.0976 0.129 0.528 0.609 0.0015 0.00173 Wall time: 27800.330658715684 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.898 0.0111 0.675 0.0944 0.124 0.935 0.964 0.00266 0.00274 281 172 0.592 0.0115 0.362 0.0953 0.126 0.597 0.706 0.0017 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.227 0.0104 0.02 0.0911 0.119 0.153 0.166 0.000435 0.000471 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 281 27900.673 0.005 0.0108 0.197 0.413 0.0924 0.122 0.414 0.52 0.00118 0.00148 ! Validation 281 27900.673 0.005 0.0125 0.477 0.727 0.0993 0.131 0.716 0.81 0.00203 0.0023 Wall time: 27900.673602459952 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.227 0.00935 0.0403 0.0862 0.113 0.189 0.236 0.000536 0.000669 282 172 0.252 0.0094 0.0638 0.0858 0.114 0.239 0.296 0.00068 0.000842 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.386 0.0093 0.2 0.0866 0.113 0.519 0.525 0.00147 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 282 27999.748 0.005 0.0102 0.152 0.357 0.0899 0.119 0.366 0.457 0.00104 0.0013 ! Validation 282 27999.748 0.005 0.0117 0.116 0.351 0.096 0.127 0.341 0.4 0.000968 0.00114 Wall time: 27999.748411096632 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.669 0.0106 0.457 0.092 0.121 0.75 0.793 0.00213 0.00225 283 172 0.303 0.00924 0.118 0.0856 0.113 0.357 0.403 0.00102 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.278 0.00885 0.101 0.0847 0.11 0.369 0.373 0.00105 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 283 28098.873 0.005 0.00991 0.159 0.357 0.0885 0.117 0.376 0.468 0.00107 0.00133 ! Validation 283 28098.873 0.005 0.0111 0.0643 0.286 0.0933 0.124 0.246 0.297 0.0007 0.000845 Wall time: 28098.87314686086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.24 0.00969 0.0467 0.0873 0.115 0.204 0.253 0.000579 0.00072 284 172 0.371 0.00887 0.194 0.0834 0.11 0.458 0.516 0.0013 0.00147 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.218 0.00862 0.0456 0.0832 0.109 0.242 0.25 0.000687 0.000711 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 284 28197.967 0.005 0.00931 0.127 0.313 0.0858 0.113 0.331 0.417 0.00094 0.00119 ! Validation 284 28197.967 0.005 0.0109 0.125 0.343 0.0925 0.122 0.325 0.415 0.000924 0.00118 Wall time: 28197.96701757703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.251 0.0086 0.0792 0.0824 0.109 0.295 0.33 0.000837 0.000938 285 172 0.217 0.00852 0.0468 0.0827 0.108 0.207 0.254 0.000588 0.000721 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.341 0.00851 0.171 0.0831 0.108 0.48 0.485 0.00136 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 285 28297.049 0.005 0.00914 0.154 0.336 0.085 0.112 0.369 0.46 0.00105 0.00131 ! Validation 285 28297.049 0.005 0.0106 0.121 0.333 0.0912 0.121 0.344 0.409 0.000979 0.00116 Wall time: 28297.049760202877 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.237 0.00968 0.0434 0.088 0.115 0.205 0.244 0.000582 0.000695 286 172 0.325 0.00965 0.132 0.0865 0.115 0.329 0.426 0.000935 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.18 0.00798 0.0206 0.08 0.105 0.158 0.168 0.00045 0.000479 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 286 28396.140 0.005 0.00908 0.161 0.343 0.0847 0.112 0.38 0.471 0.00108 0.00134 ! Validation 286 28396.140 0.005 0.0102 0.0894 0.293 0.0891 0.118 0.298 0.351 0.000846 0.000997 Wall time: 28396.140011897776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.179 0.00787 0.0212 0.0788 0.104 0.138 0.171 0.000391 0.000485 287 172 0.247 0.00945 0.0578 0.0858 0.114 0.236 0.282 0.000671 0.000801 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.293 0.00873 0.118 0.084 0.11 0.396 0.403 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 287 28496.067 0.005 0.00863 0.145 0.318 0.0826 0.109 0.359 0.447 0.00102 0.00127 ! Validation 287 28496.067 0.005 0.0106 0.178 0.39 0.0913 0.121 0.436 0.495 0.00124 0.00141 Wall time: 28496.067083501723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.224 0.00831 0.0577 0.0815 0.107 0.252 0.282 0.000716 0.000801 288 172 0.469 0.00864 0.296 0.0838 0.109 0.601 0.638 0.00171 0.00181 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 1.07 0.00908 0.885 0.0861 0.112 1.1 1.1 0.00313 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 288 28595.126 0.005 0.00875 0.204 0.379 0.0832 0.11 0.431 0.529 0.00122 0.0015 ! Validation 288 28595.126 0.005 0.0107 1.23 1.44 0.0924 0.122 1.27 1.3 0.00361 0.00369 Wall time: 28595.12673362065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.214 0.00842 0.0456 0.0817 0.108 0.207 0.25 0.000587 0.000712 289 172 0.27 0.00821 0.105 0.0801 0.106 0.314 0.381 0.000892 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.311 0.0074 0.163 0.0772 0.101 0.47 0.473 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 289 28694.249 0.005 0.00872 0.141 0.315 0.083 0.11 0.338 0.44 0.000961 0.00125 ! Validation 289 28694.249 0.005 0.00945 0.0997 0.289 0.086 0.114 0.312 0.37 0.000885 0.00105 Wall time: 28694.249276167713 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.178 0.00727 0.0326 0.0757 0.1 0.158 0.212 0.000448 0.000601 290 172 0.246 0.00704 0.105 0.0749 0.0984 0.33 0.381 0.000939 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.988 0.00731 0.842 0.0774 0.1 1.07 1.08 0.00305 0.00306 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 290 28793.272 0.005 0.00771 0.0782 0.232 0.078 0.103 0.262 0.328 0.000744 0.000932 ! Validation 290 28793.272 0.005 0.00918 1.03 1.22 0.0849 0.112 1.15 1.19 0.00325 0.00339 Wall time: 28793.27197480295 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.2 0.00752 0.0492 0.077 0.102 0.213 0.26 0.000605 0.000739 291 172 0.175 0.0077 0.0209 0.0777 0.103 0.132 0.17 0.000374 0.000482 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.209 0.00668 0.0748 0.0738 0.0959 0.316 0.321 0.000899 0.000911 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 291 28892.325 0.005 0.00764 0.104 0.257 0.0776 0.103 0.289 0.378 0.000821 0.00107 ! Validation 291 28892.325 0.005 0.00878 0.0431 0.219 0.083 0.11 0.201 0.243 0.000571 0.000691 Wall time: 28892.325242852792 ! Best model 291 0.219 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.593 0.0175 0.243 0.121 0.155 0.502 0.578 0.00142 0.00164 292 172 0.186 0.00784 0.0288 0.079 0.104 0.145 0.199 0.000413 0.000566 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.37 0.00715 0.227 0.0763 0.0992 0.554 0.558 0.00157 0.00159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 292 28991.378 0.005 0.00952 0.218 0.409 0.0861 0.114 0.403 0.548 0.00115 0.00156 ! Validation 292 28991.378 0.005 0.00915 0.317 0.5 0.0847 0.112 0.581 0.66 0.00165 0.00187 Wall time: 28991.378369856626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.258 0.00749 0.108 0.0766 0.102 0.332 0.386 0.000945 0.0011 293 172 0.182 0.00702 0.0418 0.0742 0.0983 0.181 0.24 0.000513 0.000681 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.145 0.00675 0.0105 0.0738 0.0964 0.1 0.12 0.000285 0.000342 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 29090.642 0.005 0.00749 0.108 0.258 0.0769 0.102 0.309 0.385 0.000877 0.00109 ! Validation 293 29090.642 0.005 0.00873 0.101 0.276 0.0825 0.11 0.322 0.373 0.000915 0.00106 Wall time: 29090.64225100074 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.16 0.00659 0.0277 0.0723 0.0953 0.151 0.195 0.00043 0.000554 294 172 0.202 0.00674 0.0669 0.0724 0.0963 0.274 0.303 0.000779 0.000862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.166 0.00627 0.0401 0.0711 0.0929 0.228 0.235 0.000649 0.000668 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 29189.681 0.005 0.00692 0.0611 0.199 0.0737 0.0975 0.23 0.29 0.000653 0.000823 ! Validation 294 29189.681 0.005 0.0082 0.0573 0.221 0.0798 0.106 0.219 0.281 0.000621 0.000798 Wall time: 29189.681148660835 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.241 0.00746 0.0913 0.076 0.101 0.298 0.354 0.000847 0.00101 295 172 0.188 0.00692 0.0496 0.0732 0.0976 0.222 0.261 0.00063 0.000742 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.474 0.00616 0.35 0.0703 0.0921 0.692 0.694 0.00197 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 295 29288.726 0.005 0.00691 0.106 0.245 0.0737 0.0975 0.31 0.383 0.00088 0.00109 ! Validation 295 29288.726 0.005 0.00802 0.206 0.367 0.0788 0.105 0.495 0.533 0.00141 0.00151 Wall time: 29288.726613782812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.134 0.00594 0.0155 0.0686 0.0904 0.113 0.146 0.00032 0.000414 296 172 0.173 0.00675 0.0384 0.0725 0.0964 0.197 0.23 0.000559 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.246 0.00635 0.119 0.0717 0.0935 0.401 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 296 29387.808 0.005 0.00711 0.124 0.266 0.0748 0.0989 0.324 0.413 0.000922 0.00117 ! Validation 296 29387.808 0.005 0.00821 0.0625 0.227 0.0799 0.106 0.25 0.293 0.000709 0.000833 Wall time: 29387.808745532762 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.141 0.00628 0.0156 0.0705 0.0929 0.124 0.147 0.000352 0.000416 297 172 0.161 0.00649 0.0315 0.0712 0.0945 0.168 0.208 0.000477 0.000591 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.18 0.00569 0.0664 0.0676 0.0885 0.298 0.302 0.000847 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 297 29486.860 0.005 0.00651 0.0779 0.208 0.0714 0.0946 0.257 0.328 0.00073 0.000931 ! Validation 297 29486.860 0.005 0.00764 0.0499 0.203 0.077 0.102 0.208 0.262 0.00059 0.000744 Wall time: 29486.86029793974 ! Best model 297 0.203 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.221 0.00561 0.109 0.0667 0.0879 0.324 0.387 0.00092 0.0011 298 172 0.49 0.00749 0.34 0.077 0.102 0.667 0.684 0.00189 0.00194 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.188 0.00831 0.0222 0.0835 0.107 0.164 0.175 0.000467 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 298 29585.892 0.005 0.00703 0.126 0.267 0.0742 0.0984 0.321 0.417 0.000912 0.00118 ! Validation 298 29585.892 0.005 0.0101 0.121 0.322 0.0907 0.118 0.344 0.408 0.000978 0.00116 Wall time: 29585.89205616899 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.142 0.00598 0.0226 0.0679 0.0907 0.132 0.176 0.000376 0.000501 299 172 0.155 0.00596 0.0359 0.0687 0.0905 0.192 0.222 0.000546 0.000631 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.116 0.00557 0.00474 0.0668 0.0875 0.0717 0.0808 0.000204 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 299 29685.200 0.005 0.00632 0.0693 0.196 0.0703 0.0933 0.247 0.309 0.000701 0.000877 ! Validation 299 29685.200 0.005 0.00725 0.116 0.261 0.0747 0.0999 0.314 0.4 0.000891 0.00114 Wall time: 29685.200472522993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.257 0.00735 0.11 0.0759 0.101 0.346 0.389 0.000982 0.00111 300 172 0.454 0.00625 0.329 0.0705 0.0928 0.657 0.672 0.00187 0.00191 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.231 0.00678 0.0955 0.0745 0.0966 0.358 0.362 0.00102 0.00103 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 300 29784.247 0.005 0.00663 0.111 0.243 0.0721 0.0955 0.315 0.39 0.000894 0.00111 ! Validation 300 29784.247 0.005 0.00845 0.236 0.405 0.0813 0.108 0.459 0.569 0.0013 0.00162 Wall time: 29784.24685668759 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.253 0.00633 0.126 0.0714 0.0933 0.393 0.417 0.00112 0.00118 301 172 0.211 0.00617 0.0875 0.0705 0.0921 0.285 0.347 0.00081 0.000986 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.148 0.00622 0.0232 0.0718 0.0925 0.169 0.179 0.000481 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 301 29883.272 0.005 0.00624 0.0704 0.195 0.07 0.0927 0.251 0.311 0.000714 0.000884 ! Validation 301 29883.272 0.005 0.00784 0.225 0.382 0.0788 0.104 0.472 0.556 0.00134 0.00158 Wall time: 29883.27202681685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.133 0.00523 0.0284 0.0638 0.0848 0.159 0.198 0.000451 0.000561 302 172 0.204 0.00612 0.0811 0.069 0.0918 0.302 0.334 0.000859 0.000949 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.202 0.005 0.102 0.0634 0.0829 0.372 0.375 0.00106 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 302 29982.357 0.005 0.00616 0.0711 0.194 0.0694 0.092 0.246 0.313 0.000699 0.000888 ! Validation 302 29982.357 0.005 0.00674 0.0867 0.222 0.072 0.0963 0.297 0.345 0.000842 0.000981 Wall time: 29982.357225182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.179 0.00519 0.0748 0.0644 0.0845 0.292 0.321 0.00083 0.000911 303 172 0.185 0.00601 0.0647 0.0679 0.0909 0.221 0.298 0.000629 0.000848 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.135 0.00483 0.0382 0.0623 0.0815 0.224 0.229 0.000636 0.000651 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 303 30081.373 0.005 0.00559 0.0575 0.169 0.066 0.0877 0.226 0.281 0.000642 0.000799 ! Validation 303 30081.373 0.005 0.00658 0.127 0.259 0.0711 0.0952 0.362 0.418 0.00103 0.00119 Wall time: 30081.373662311584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.167 0.00707 0.0257 0.0732 0.0986 0.162 0.188 0.00046 0.000534 304 172 0.139 0.00551 0.0292 0.0659 0.0871 0.16 0.2 0.000454 0.000569 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.113 0.00487 0.0156 0.0627 0.0819 0.138 0.147 0.000393 0.000416 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 304 30180.454 0.005 0.00691 0.0957 0.234 0.073 0.0975 0.281 0.363 0.000798 0.00103 ! Validation 304 30180.454 0.005 0.00652 0.057 0.187 0.0708 0.0947 0.228 0.28 0.000647 0.000796 Wall time: 30180.454783455934 ! Best model 304 0.187 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.155 0.00661 0.0226 0.072 0.0954 0.14 0.176 0.000398 0.0005 305 172 0.168 0.0065 0.0384 0.0712 0.0946 0.196 0.23 0.000558 0.000653 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.139 0.00564 0.0261 0.0673 0.0881 0.184 0.19 0.000523 0.000539 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 30279.489 0.005 0.00652 0.119 0.25 0.0714 0.0947 0.313 0.405 0.000888 0.00115 ! Validation 305 30279.489 0.005 0.00741 0.0351 0.183 0.0761 0.101 0.174 0.22 0.000493 0.000625 Wall time: 30279.489616738632 ! Best model 305 0.183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.146 0.00621 0.0221 0.0696 0.0924 0.137 0.174 0.000389 0.000496 306 172 0.184 0.00552 0.0735 0.0631 0.0872 0.291 0.318 0.000826 0.000903 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.109 0.00526 0.0033 0.0651 0.0851 0.0581 0.0674 0.000165 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 306 30378.844 0.005 0.00569 0.0776 0.191 0.0667 0.0885 0.263 0.327 0.000748 0.000928 ! Validation 306 30378.844 0.005 0.00694 0.0308 0.17 0.0733 0.0977 0.171 0.206 0.000485 0.000585 Wall time: 30378.84406326199 ! Best model 306 0.170 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.349 0.00515 0.246 0.0638 0.0842 0.562 0.582 0.0016 0.00165 307 172 0.143 0.00551 0.0324 0.0658 0.087 0.176 0.211 0.0005 0.0006 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.116 0.00471 0.0216 0.0613 0.0805 0.163 0.172 0.000464 0.000489 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 307 30477.946 0.005 0.00569 0.0649 0.179 0.0666 0.0885 0.23 0.299 0.000653 0.000849 ! Validation 307 30477.946 0.005 0.00625 0.0472 0.172 0.0692 0.0927 0.195 0.255 0.000553 0.000724 Wall time: 30477.94666928891 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.324 0.00517 0.221 0.0633 0.0843 0.537 0.551 0.00153 0.00157 308 172 0.118 0.0051 0.0159 0.0631 0.0838 0.108 0.148 0.000306 0.00042 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.124 0.00451 0.0341 0.0603 0.0788 0.214 0.217 0.000607 0.000615 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 308 30577.028 0.005 0.00519 0.052 0.156 0.0634 0.0845 0.209 0.268 0.000593 0.00076 ! Validation 308 30577.028 0.005 0.00615 0.0307 0.154 0.0688 0.092 0.16 0.205 0.000455 0.000584 Wall time: 30577.028397592716 ! Best model 308 0.154 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.224 0.00838 0.0559 0.083 0.107 0.206 0.277 0.000585 0.000788 309 172 0.195 0.00811 0.0332 0.0802 0.106 0.165 0.214 0.000468 0.000607 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.276 0.00758 0.124 0.0786 0.102 0.407 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 309 30676.125 0.005 0.00552 0.0781 0.189 0.0654 0.0871 0.255 0.328 0.000724 0.000932 ! Validation 309 30676.125 0.005 0.009 0.19 0.369 0.0842 0.111 0.415 0.511 0.00118 0.00145 Wall time: 30676.124999444 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.118 0.00478 0.0228 0.0616 0.0811 0.14 0.177 0.000399 0.000503 310 172 5.28 0.0103 5.08 0.0911 0.119 2.62 2.64 0.00744 0.00751 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 5.1 0.0404 4.29 0.186 0.236 2.43 2.43 0.00689 0.0069 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 30775.224 0.005 0.00552 0.126 0.236 0.0653 0.0871 0.292 0.41 0.00083 0.00116 ! Validation 310 30775.224 0.005 0.0437 5.88 6.75 0.192 0.245 2.81 2.84 0.00798 0.00808 Wall time: 30775.22418876365 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 19.7 0.905 1.63 0.826 1.12 1.18 1.5 0.00335 0.00426 311 172 15.2 0.688 1.39 0.718 0.973 1.15 1.38 0.00328 0.00393 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 14.9 0.678 1.39 0.724 0.966 1.31 1.38 0.00373 0.00393 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 30874.294 0.005 0.888 21 38.8 0.816 1.11 2.41 5.38 0.00684 0.0153 ! Validation 311 30874.294 0.005 0.699 2 16 0.73 0.98 1.28 1.66 0.00363 0.00471 Wall time: 30874.29389970191 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 9.55 0.369 2.18 0.533 0.712 1.3 1.73 0.00368 0.00492 312 172 16.3 0.251 11.3 0.448 0.587 3.86 3.93 0.011 0.0112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 8.48 0.239 3.7 0.441 0.574 2.24 2.26 0.00636 0.00641 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 30974.312 0.005 0.399 1.83 9.81 0.55 0.741 1.24 1.58 0.00351 0.0045 ! Validation 312 30974.312 0.005 0.26 2.66 7.87 0.456 0.599 1.7 1.91 0.00483 0.00543 Wall time: 30974.312719282694 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 3.78 0.164 0.501 0.362 0.475 0.694 0.83 0.00197 0.00236 313 172 5.22 0.137 2.47 0.332 0.435 1.71 1.84 0.00486 0.00524 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 5.18 0.133 2.51 0.331 0.428 1.85 1.86 0.00525 0.00528 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 313 31073.348 0.005 0.178 1.26 4.83 0.376 0.496 1.05 1.32 0.00298 0.00374 ! Validation 313 31073.348 0.005 0.15 2.1 5.11 0.348 0.455 1.53 1.7 0.00434 0.00483 Wall time: 31073.348223065957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 2.65 0.119 0.274 0.31 0.404 0.489 0.613 0.00139 0.00174 314 172 2.43 0.103 0.361 0.289 0.377 0.552 0.705 0.00157 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 2.2 0.0997 0.21 0.287 0.37 0.504 0.537 0.00143 0.00153 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 314 31172.478 0.005 0.119 0.842 3.23 0.31 0.405 0.858 1.08 0.00244 0.00306 ! Validation 314 31172.478 0.005 0.112 0.357 2.59 0.301 0.392 0.563 0.701 0.0016 0.00199 Wall time: 31172.478337006643 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 2.96 0.0918 1.12 0.273 0.355 1.12 1.24 0.00318 0.00353 315 172 2.08 0.0824 0.429 0.259 0.337 0.623 0.768 0.00177 0.00218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 3.45 0.0797 1.86 0.256 0.331 1.59 1.6 0.00451 0.00454 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 315 31271.525 0.005 0.0934 0.766 2.63 0.275 0.358 0.816 1.03 0.00232 0.00292 ! Validation 315 31271.525 0.005 0.09 1.05 2.85 0.269 0.352 1.07 1.2 0.00304 0.00342 Wall time: 31271.525067706592 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 2.19 0.085 0.488 0.26 0.342 0.686 0.82 0.00195 0.00233 316 172 2.33 0.0752 0.823 0.245 0.322 0.954 1.06 0.00271 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 2.39 0.0693 0.999 0.239 0.309 1.16 1.17 0.0033 0.00333 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 31370.576 0.005 0.079 0.856 2.44 0.252 0.33 0.858 1.09 0.00244 0.00308 ! Validation 316 31370.576 0.005 0.0791 0.536 2.12 0.252 0.33 0.722 0.859 0.00205 0.00244 Wall time: 31370.576037482824 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 1.72 0.0677 0.364 0.232 0.305 0.574 0.708 0.00163 0.00201 317 172 1.39 0.061 0.167 0.219 0.29 0.392 0.479 0.00111 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 1.32 0.0571 0.175 0.217 0.28 0.467 0.49 0.00133 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 317 31469.612 0.005 0.0665 0.607 1.94 0.23 0.303 0.727 0.914 0.00207 0.0026 ! Validation 317 31469.612 0.005 0.0663 0.221 1.55 0.23 0.302 0.436 0.552 0.00124 0.00157 Wall time: 31469.612187071703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 1.48 0.0633 0.212 0.224 0.295 0.398 0.541 0.00113 0.00154 318 172 2.59 0.0607 1.37 0.218 0.289 1.2 1.37 0.0034 0.0039 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 1.2 0.0549 0.103 0.212 0.275 0.344 0.377 0.000977 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 318 31568.736 0.005 0.0607 0.821 2.03 0.219 0.289 0.856 1.06 0.00243 0.00302 ! Validation 318 31568.736 0.005 0.0626 0.282 1.53 0.223 0.293 0.51 0.623 0.00145 0.00177 Wall time: 31568.736715805717 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 1.22 0.0507 0.202 0.199 0.264 0.402 0.527 0.00114 0.0015 319 172 1.13 0.0476 0.18 0.194 0.256 0.397 0.498 0.00113 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 1.02 0.0459 0.101 0.193 0.251 0.333 0.373 0.000945 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 319 31667.821 0.005 0.0522 0.4 1.44 0.203 0.268 0.578 0.742 0.00164 0.00211 ! Validation 319 31667.821 0.005 0.0537 0.2 1.27 0.206 0.272 0.405 0.524 0.00115 0.00149 Wall time: 31667.8218687349 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 1.51 0.0472 0.562 0.192 0.255 0.798 0.88 0.00227 0.0025 320 172 1.34 0.0439 0.467 0.186 0.246 0.704 0.802 0.002 0.00228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 1.11 0.0425 0.265 0.185 0.242 0.588 0.604 0.00167 0.00171 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 31766.899 0.005 0.0466 0.534 1.47 0.191 0.253 0.695 0.857 0.00197 0.00244 ! Validation 320 31766.899 0.005 0.0499 0.579 1.58 0.198 0.262 0.784 0.892 0.00223 0.00253 Wall time: 31766.899773606565 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 1.03 0.0424 0.186 0.181 0.242 0.385 0.507 0.00109 0.00144 321 172 1.03 0.0423 0.187 0.181 0.241 0.417 0.507 0.00118 0.00144 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.803 0.0394 0.0142 0.178 0.233 0.115 0.14 0.000327 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 321 31865.978 0.005 0.0441 0.518 1.4 0.185 0.246 0.674 0.845 0.00191 0.0024 ! Validation 321 31865.978 0.005 0.0467 0.132 1.07 0.191 0.254 0.328 0.425 0.000932 0.00121 Wall time: 31865.978710304014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.87 0.0368 0.133 0.17 0.225 0.35 0.428 0.000996 0.00121 322 172 2.11 0.0412 1.28 0.178 0.238 1.27 1.33 0.00362 0.00378 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.925 0.0377 0.171 0.173 0.228 0.469 0.485 0.00133 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 322 31965.054 0.005 0.0399 0.476 1.27 0.176 0.234 0.646 0.809 0.00183 0.0023 ! Validation 322 31965.054 0.005 0.0444 0.395 1.28 0.186 0.247 0.633 0.737 0.0018 0.00209 Wall time: 31965.054289296735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 1.34 0.0362 0.616 0.168 0.223 0.851 0.92 0.00242 0.00261 323 172 1.52 0.0361 0.802 0.167 0.223 1 1.05 0.00284 0.00298 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 0.947 0.0337 0.273 0.164 0.215 0.597 0.613 0.0017 0.00174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 323 32064.085 0.005 0.0382 0.424 1.19 0.172 0.229 0.606 0.764 0.00172 0.00217 ! Validation 323 32064.085 0.005 0.0403 0.381 1.19 0.177 0.235 0.624 0.724 0.00177 0.00206 Wall time: 32064.08543839678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.877 0.037 0.136 0.168 0.226 0.343 0.433 0.000973 0.00123 324 172 0.864 0.0379 0.107 0.171 0.228 0.299 0.383 0.000848 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 0.846 0.0321 0.203 0.16 0.21 0.513 0.529 0.00146 0.0015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 324 32164.476 0.005 0.0363 0.494 1.22 0.167 0.223 0.658 0.825 0.00187 0.00234 ! Validation 324 32164.476 0.005 0.0383 0.335 1.1 0.172 0.23 0.594 0.679 0.00169 0.00193 Wall time: 32164.476322642993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.772 0.0321 0.13 0.157 0.21 0.326 0.423 0.000926 0.0012 325 172 0.848 0.0328 0.192 0.158 0.212 0.43 0.514 0.00122 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 0.71 0.0304 0.102 0.155 0.205 0.358 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 325 32263.595 0.005 0.0333 0.4 1.07 0.16 0.214 0.605 0.742 0.00172 0.00211 ! Validation 325 32263.595 0.005 0.0364 0.147 0.875 0.168 0.224 0.364 0.449 0.00103 0.00128 Wall time: 32263.595764354803 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.762 0.0322 0.118 0.157 0.211 0.328 0.403 0.00093 0.00115 326 172 0.747 0.032 0.108 0.155 0.21 0.276 0.385 0.000785 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 0.573 0.028 0.0131 0.149 0.196 0.104 0.134 0.000296 0.000382 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 32362.689 0.005 0.0318 0.356 0.993 0.156 0.209 0.558 0.7 0.00159 0.00199 ! Validation 326 32362.689 0.005 0.0336 0.116 0.789 0.161 0.215 0.324 0.4 0.00092 0.00114 Wall time: 32362.689870101865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 1.17 0.0299 0.572 0.151 0.203 0.747 0.887 0.00212 0.00252 327 172 0.656 0.0272 0.112 0.143 0.194 0.263 0.392 0.000747 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 1.01 0.0261 0.49 0.143 0.19 0.815 0.821 0.00232 0.00233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 327 32461.820 0.005 0.0289 0.301 0.879 0.149 0.2 0.523 0.644 0.00149 0.00183 ! Validation 327 32461.820 0.005 0.0314 0.356 0.983 0.155 0.208 0.605 0.7 0.00172 0.00199 Wall time: 32461.82061140798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.583 0.0263 0.0573 0.142 0.19 0.221 0.281 0.000627 0.000798 328 172 0.949 0.0281 0.388 0.146 0.197 0.684 0.73 0.00194 0.00208 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.554 0.0244 0.0664 0.138 0.183 0.288 0.302 0.000818 0.000859 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 328 32560.898 0.005 0.0273 0.292 0.838 0.144 0.194 0.5 0.634 0.00142 0.0018 ! Validation 328 32560.898 0.005 0.0296 0.4 0.992 0.151 0.202 0.62 0.742 0.00176 0.00211 Wall time: 32560.898842049763 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 1.4 0.0253 0.895 0.138 0.186 1.03 1.11 0.00292 0.00315 329 172 0.696 0.0254 0.189 0.139 0.187 0.421 0.51 0.0012 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.819 0.0244 0.33 0.138 0.183 0.67 0.674 0.0019 0.00191 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 329 32659.990 0.005 0.0257 0.323 0.837 0.14 0.188 0.533 0.667 0.00151 0.00189 ! Validation 329 32659.990 0.005 0.0289 0.954 1.53 0.149 0.2 1.04 1.15 0.00296 0.00325 Wall time: 32659.99047651561 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.836 0.0249 0.338 0.137 0.185 0.626 0.682 0.00178 0.00194 330 172 0.922 0.0249 0.424 0.136 0.185 0.714 0.764 0.00203 0.00217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.535 0.0239 0.0572 0.136 0.181 0.268 0.28 0.00076 0.000797 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 330 32759.219 0.005 0.0261 0.461 0.984 0.14 0.19 0.638 0.797 0.00181 0.00226 ! Validation 330 32759.219 0.005 0.0285 0.129 0.7 0.147 0.198 0.344 0.422 0.000978 0.0012 Wall time: 32759.219394738786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.685 0.0237 0.211 0.134 0.181 0.442 0.539 0.00125 0.00153 331 172 0.535 0.0233 0.0688 0.132 0.179 0.259 0.308 0.000737 0.000874 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.575 0.0221 0.133 0.131 0.174 0.422 0.428 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 331 32858.302 0.005 0.0247 0.31 0.805 0.137 0.184 0.529 0.654 0.0015 0.00186 ! Validation 331 32858.302 0.005 0.0265 0.312 0.842 0.142 0.191 0.564 0.655 0.0016 0.00186 Wall time: 32858.302034102846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.463 0.0212 0.0401 0.126 0.171 0.21 0.235 0.000598 0.000667 332 172 0.573 0.0232 0.109 0.132 0.179 0.303 0.387 0.00086 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.547 0.0216 0.114 0.13 0.173 0.393 0.396 0.00112 0.00112 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 332 32957.386 0.005 0.0226 0.255 0.708 0.131 0.176 0.474 0.593 0.00135 0.00168 ! Validation 332 32957.386 0.005 0.0256 0.792 1.31 0.14 0.188 0.876 1.04 0.00249 0.00297 Wall time: 32957.3861013758 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.462 0.0196 0.0691 0.122 0.164 0.25 0.308 0.000711 0.000876 333 172 0.563 0.0239 0.0849 0.135 0.181 0.273 0.342 0.000774 0.000971 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.68 0.0214 0.252 0.129 0.172 0.587 0.589 0.00167 0.00167 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 333 33056.527 0.005 0.0218 0.314 0.749 0.128 0.173 0.522 0.657 0.00148 0.00187 ! Validation 333 33056.527 0.005 0.0254 0.244 0.752 0.139 0.187 0.492 0.58 0.0014 0.00165 Wall time: 33056.52784507768 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.497 0.0218 0.0622 0.128 0.173 0.239 0.292 0.00068 0.000831 334 172 0.677 0.0202 0.274 0.123 0.167 0.558 0.614 0.00158 0.00174 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.519 0.0188 0.144 0.121 0.161 0.44 0.444 0.00125 0.00126 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 334 33155.693 0.005 0.0219 0.291 0.729 0.128 0.174 0.506 0.633 0.00144 0.0018 ! Validation 334 33155.693 0.005 0.0227 0.181 0.636 0.132 0.177 0.421 0.5 0.0012 0.00142 Wall time: 33155.693777696695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.734 0.0195 0.343 0.122 0.164 0.606 0.687 0.00172 0.00195 335 172 0.587 0.0194 0.198 0.122 0.164 0.47 0.522 0.00134 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.58 0.0179 0.223 0.117 0.157 0.551 0.553 0.00157 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 335 33254.796 0.005 0.0198 0.247 0.644 0.122 0.165 0.477 0.583 0.00135 0.00166 ! Validation 335 33254.796 0.005 0.0217 0.474 0.907 0.128 0.173 0.721 0.807 0.00205 0.00229 Wall time: 33254.79632542282 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.407 0.0176 0.0546 0.116 0.156 0.231 0.274 0.000656 0.000779 336 172 0.501 0.0194 0.113 0.121 0.163 0.319 0.394 0.000907 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.407 0.0179 0.0488 0.117 0.157 0.257 0.259 0.000729 0.000737 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 33353.955 0.005 0.019 0.268 0.648 0.12 0.162 0.475 0.608 0.00135 0.00173 ! Validation 336 33353.955 0.005 0.0215 0.107 0.537 0.128 0.172 0.31 0.383 0.00088 0.00109 Wall time: 33353.95530401077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.648 0.0174 0.301 0.115 0.155 0.549 0.643 0.00156 0.00183 337 172 0.465 0.0172 0.121 0.114 0.154 0.341 0.407 0.000968 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.857 0.0169 0.518 0.115 0.153 0.844 0.845 0.0024 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 337 33453.017 0.005 0.0183 0.24 0.606 0.118 0.159 0.463 0.574 0.00132 0.00163 ! Validation 337 33453.017 0.005 0.0204 0.444 0.852 0.125 0.168 0.71 0.781 0.00202 0.00222 Wall time: 33453.01732687373 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.428 0.0174 0.0799 0.115 0.155 0.251 0.332 0.000714 0.000942 338 172 0.378 0.0162 0.0538 0.111 0.149 0.231 0.272 0.000657 0.000773 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.306 0.015 0.0069 0.108 0.143 0.0929 0.0974 0.000264 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 338 33552.225 0.005 0.0174 0.202 0.55 0.115 0.155 0.414 0.528 0.00118 0.0015 ! Validation 338 33552.225 0.005 0.0184 0.113 0.481 0.119 0.159 0.299 0.394 0.000848 0.00112 Wall time: 33552.22581290966 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.463 0.015 0.162 0.107 0.144 0.428 0.473 0.00122 0.00134 339 172 0.433 0.019 0.0529 0.121 0.162 0.216 0.27 0.000613 0.000766 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.375 0.0173 0.0282 0.115 0.155 0.193 0.197 0.000549 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 339 33651.309 0.005 0.0177 0.377 0.732 0.116 0.156 0.574 0.72 0.00163 0.00205 ! Validation 339 33651.309 0.005 0.0203 0.133 0.54 0.124 0.167 0.343 0.427 0.000974 0.00121 Wall time: 33651.309865977615 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.583 0.0153 0.276 0.108 0.145 0.583 0.617 0.00166 0.00175 340 172 0.345 0.0156 0.032 0.109 0.147 0.176 0.21 0.000501 0.000596 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.352 0.0144 0.0648 0.106 0.141 0.298 0.299 0.000845 0.000848 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 33750.383 0.005 0.0163 0.16 0.486 0.111 0.15 0.375 0.469 0.00106 0.00133 ! Validation 340 33750.383 0.005 0.0177 0.152 0.506 0.116 0.156 0.37 0.458 0.00105 0.0013 Wall time: 33750.38366360171 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.436 0.0161 0.113 0.112 0.149 0.328 0.395 0.000932 0.00112 341 172 0.434 0.0156 0.123 0.109 0.146 0.333 0.411 0.000947 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.319 0.014 0.0379 0.105 0.139 0.225 0.228 0.000639 0.000649 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 341 33849.464 0.005 0.0155 0.234 0.545 0.109 0.146 0.44 0.568 0.00125 0.00161 ! Validation 341 33849.464 0.005 0.017 0.0881 0.429 0.114 0.153 0.289 0.348 0.000822 0.000989 Wall time: 33849.464821796864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 1.47 0.016 1.15 0.111 0.148 1.21 1.26 0.00344 0.00357 342 172 0.379 0.0138 0.102 0.103 0.138 0.337 0.374 0.000956 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.294 0.0133 0.0283 0.102 0.135 0.196 0.197 0.000557 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 342 33949.260 0.005 0.015 0.227 0.527 0.107 0.144 0.444 0.559 0.00126 0.00159 ! Validation 342 33949.260 0.005 0.0163 0.0691 0.394 0.112 0.15 0.25 0.308 0.000711 0.000876 Wall time: 33949.25999599695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.421 0.0136 0.149 0.102 0.137 0.398 0.453 0.00113 0.00129 343 172 0.548 0.0136 0.277 0.102 0.137 0.534 0.617 0.00152 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.296 0.0122 0.0515 0.0983 0.13 0.264 0.266 0.000749 0.000756 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 343 34048.449 0.005 0.0139 0.173 0.45 0.103 0.138 0.391 0.487 0.00111 0.00138 ! Validation 343 34048.449 0.005 0.0152 0.182 0.487 0.108 0.145 0.402 0.501 0.00114 0.00142 Wall time: 34048.44894336397 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.305 0.013 0.0439 0.0997 0.134 0.207 0.246 0.000587 0.000698 344 172 1.14 0.0129 0.879 0.0987 0.133 1.07 1.1 0.00303 0.00312 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.665 0.0113 0.439 0.0949 0.125 0.777 0.777 0.00221 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 344 34147.757 0.005 0.0132 0.186 0.45 0.101 0.135 0.405 0.505 0.00115 0.00144 ! Validation 344 34147.757 0.005 0.0143 0.659 0.944 0.105 0.14 0.897 0.952 0.00255 0.00271 Wall time: 34147.75690756366 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.416 0.0121 0.175 0.0966 0.129 0.419 0.491 0.00119 0.0014 345 172 0.347 0.0131 0.0852 0.1 0.134 0.289 0.342 0.00082 0.000973 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.235 0.0108 0.0197 0.0926 0.122 0.163 0.165 0.000463 0.000468 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 34246.943 0.005 0.0124 0.172 0.421 0.0981 0.131 0.392 0.486 0.00111 0.00138 ! Validation 345 34246.943 0.005 0.0136 0.0583 0.33 0.102 0.137 0.229 0.283 0.000652 0.000805 Wall time: 34246.94335338473 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.401 0.0147 0.108 0.106 0.142 0.329 0.385 0.000934 0.00109 346 172 0.37 0.0113 0.143 0.0935 0.125 0.392 0.443 0.00111 0.00126 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.26 0.0105 0.0501 0.0915 0.12 0.262 0.262 0.000745 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 346 34346.023 0.005 0.0131 0.26 0.522 0.1 0.134 0.452 0.599 0.00128 0.0017 ! Validation 346 34346.023 0.005 0.0134 0.0694 0.337 0.102 0.136 0.258 0.309 0.000733 0.000878 Wall time: 34346.02312878566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 1.03 0.0122 0.787 0.0981 0.13 1.01 1.04 0.00288 0.00296 347 172 0.525 0.0105 0.315 0.0906 0.12 0.631 0.658 0.00179 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.351 0.00976 0.156 0.0888 0.116 0.463 0.463 0.00131 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 347 34445.082 0.005 0.0115 0.155 0.385 0.0944 0.126 0.357 0.462 0.00101 0.00131 ! Validation 347 34445.082 0.005 0.0126 0.132 0.383 0.0986 0.131 0.373 0.426 0.00106 0.00121 Wall time: 34445.08236807585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.339 0.0114 0.112 0.094 0.125 0.332 0.393 0.000942 0.00112 348 172 0.26 0.00949 0.07 0.0863 0.114 0.256 0.31 0.000729 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.297 0.00954 0.106 0.0873 0.115 0.382 0.383 0.00109 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 348 34544.803 0.005 0.0113 0.188 0.414 0.0937 0.125 0.404 0.509 0.00115 0.00145 ! Validation 348 34544.803 0.005 0.0121 0.441 0.683 0.0966 0.129 0.615 0.779 0.00175 0.00221 Wall time: 34544.803150235675 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.335 0.0111 0.114 0.0926 0.123 0.295 0.396 0.000837 0.00112 349 172 0.268 0.00998 0.0681 0.0885 0.117 0.262 0.306 0.000745 0.00087 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.381 0.00905 0.2 0.0856 0.112 0.524 0.525 0.00149 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 349 34643.769 0.005 0.0104 0.167 0.376 0.0902 0.12 0.376 0.48 0.00107 0.00136 ! Validation 349 34643.769 0.005 0.0116 0.197 0.43 0.095 0.127 0.463 0.521 0.00131 0.00148 Wall time: 34643.76949831098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.254 0.00914 0.0709 0.0844 0.112 0.261 0.312 0.000741 0.000888 350 172 0.37 0.0107 0.157 0.0904 0.121 0.417 0.464 0.00118 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.188 0.00923 0.00333 0.0864 0.113 0.0561 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 350 34742.757 0.005 0.00977 0.132 0.327 0.0873 0.116 0.334 0.426 0.00095 0.00121 ! Validation 350 34742.757 0.005 0.0116 0.0437 0.275 0.0947 0.126 0.188 0.245 0.000535 0.000697 Wall time: 34742.75749848876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.214 0.00924 0.0296 0.0853 0.113 0.166 0.202 0.000472 0.000573 351 172 0.474 0.0099 0.276 0.0888 0.117 0.569 0.617 0.00162 0.00175 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.536 0.00918 0.352 0.086 0.112 0.695 0.696 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 351 34841.824 0.005 0.0095 0.156 0.346 0.0861 0.114 0.37 0.463 0.00105 0.00132 ! Validation 351 34841.824 0.005 0.0113 0.403 0.629 0.094 0.125 0.697 0.744 0.00198 0.00211 Wall time: 34841.824247639626 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.231 0.00956 0.0397 0.0861 0.115 0.185 0.234 0.000526 0.000664 352 172 0.456 0.00957 0.265 0.0869 0.115 0.536 0.604 0.00152 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.417 0.00896 0.238 0.0848 0.111 0.567 0.572 0.00161 0.00162 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 352 34940.898 0.005 0.00917 0.145 0.328 0.0846 0.112 0.357 0.446 0.00101 0.00127 ! Validation 352 34940.898 0.005 0.0111 0.343 0.565 0.0929 0.124 0.583 0.687 0.00166 0.00195 Wall time: 34940.89823625097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.403 0.00857 0.232 0.0811 0.109 0.537 0.565 0.00153 0.0016 353 172 0.484 0.00804 0.323 0.0798 0.105 0.641 0.667 0.00182 0.00189 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.309 0.00762 0.156 0.0782 0.102 0.462 0.464 0.00131 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 353 35040.078 0.005 0.0086 0.112 0.284 0.0819 0.109 0.321 0.392 0.000913 0.00111 ! Validation 353 35040.078 0.005 0.00985 0.138 0.335 0.0873 0.116 0.386 0.436 0.0011 0.00124 Wall time: 35040.07856230065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.21 0.0089 0.0321 0.0837 0.111 0.171 0.21 0.000485 0.000597 354 172 0.223 0.00934 0.0366 0.0857 0.113 0.191 0.225 0.000542 0.000638 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.186 0.00795 0.0269 0.0799 0.105 0.186 0.193 0.000527 0.000547 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 354 35139.196 0.005 0.00884 0.178 0.355 0.0831 0.11 0.39 0.495 0.00111 0.00141 ! Validation 354 35139.196 0.005 0.0101 0.134 0.335 0.0884 0.118 0.367 0.429 0.00104 0.00122 Wall time: 35139.196521143895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.183 0.00762 0.0307 0.0776 0.102 0.162 0.206 0.000459 0.000584 355 172 0.233 0.00898 0.0532 0.0832 0.111 0.197 0.271 0.000559 0.000769 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.189 0.00775 0.0339 0.0785 0.103 0.214 0.216 0.000608 0.000614 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 355 35238.310 0.005 0.00846 0.139 0.309 0.0813 0.108 0.349 0.438 0.00099 0.00124 ! Validation 355 35238.310 0.005 0.00993 0.0618 0.26 0.0876 0.117 0.247 0.292 0.000702 0.000828 Wall time: 35238.310825075954 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.313 0.0082 0.149 0.0796 0.106 0.372 0.453 0.00106 0.00129 356 172 0.273 0.00758 0.121 0.0769 0.102 0.343 0.408 0.000975 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.146 0.00667 0.0126 0.0737 0.0958 0.13 0.132 0.000369 0.000374 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 356 35337.371 0.005 0.00789 0.0981 0.256 0.0784 0.104 0.3 0.367 0.000852 0.00104 ! Validation 356 35337.371 0.005 0.00894 0.0428 0.222 0.0832 0.111 0.205 0.243 0.000581 0.000689 Wall time: 35337.371738955844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.456 0.00795 0.297 0.0786 0.105 0.575 0.64 0.00163 0.00182 357 172 0.201 0.00754 0.0503 0.0766 0.102 0.22 0.263 0.000626 0.000748 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.234 0.007 0.0939 0.0755 0.0981 0.359 0.359 0.00102 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 357 35436.676 0.005 0.00758 0.116 0.267 0.077 0.102 0.321 0.399 0.000911 0.00113 ! Validation 357 35436.676 0.005 0.00917 0.147 0.33 0.0844 0.112 0.401 0.449 0.00114 0.00128 Wall time: 35436.67642557761 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.333 0.00739 0.185 0.0761 0.101 0.449 0.505 0.00128 0.00143 358 172 0.249 0.0075 0.0985 0.0757 0.102 0.314 0.368 0.000892 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.139 0.00641 0.0105 0.072 0.0939 0.118 0.12 0.000335 0.000341 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 35535.753 0.005 0.00733 0.0941 0.241 0.0756 0.1 0.294 0.36 0.000837 0.00102 ! Validation 358 35535.753 0.005 0.00847 0.142 0.311 0.0809 0.108 0.373 0.442 0.00106 0.00126 Wall time: 35535.753380711656 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.185 0.00721 0.0405 0.0752 0.0996 0.196 0.236 0.000555 0.00067 359 172 0.167 0.00756 0.0158 0.0763 0.102 0.122 0.147 0.000347 0.000419 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.232 0.00598 0.112 0.0696 0.0907 0.391 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 359 35634.888 0.005 0.00728 0.11 0.256 0.0754 0.1 0.314 0.389 0.000893 0.00111 ! Validation 359 35634.888 0.005 0.00795 0.132 0.291 0.0784 0.105 0.384 0.426 0.00109 0.00121 Wall time: 35634.88872693665 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.407 0.0085 0.237 0.0818 0.108 0.535 0.571 0.00152 0.00162 360 172 0.197 0.0068 0.0608 0.0733 0.0967 0.247 0.289 0.000701 0.000821 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.126 0.00601 0.00611 0.0698 0.091 0.0871 0.0917 0.000247 0.00026 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 360 35734.338 0.005 0.00723 0.115 0.259 0.0751 0.0997 0.316 0.397 0.000897 0.00113 ! Validation 360 35734.338 0.005 0.008 0.0896 0.25 0.0787 0.105 0.257 0.351 0.000731 0.000998 Wall time: 35734.33838810865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.253 0.0065 0.124 0.0717 0.0945 0.387 0.412 0.0011 0.00117 361 172 0.147 0.00649 0.0172 0.0716 0.0945 0.123 0.154 0.000348 0.000437 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.145 0.00592 0.0262 0.0694 0.0902 0.189 0.19 0.000537 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 361 35833.586 0.005 0.00694 0.1 0.239 0.0736 0.0977 0.297 0.371 0.000843 0.00105 ! Validation 361 35833.586 0.005 0.0078 0.0312 0.187 0.0781 0.104 0.161 0.207 0.000459 0.000589 Wall time: 35833.586550053675 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.194 0.007 0.0543 0.0735 0.0982 0.217 0.273 0.000617 0.000776 362 172 0.403 0.00907 0.221 0.0844 0.112 0.491 0.552 0.00139 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.616 0.00632 0.49 0.0715 0.0932 0.819 0.821 0.00233 0.00233 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 362 35932.666 0.005 0.00699 0.114 0.254 0.0738 0.0981 0.314 0.396 0.000893 0.00112 ! Validation 362 35932.666 0.005 0.00815 0.399 0.562 0.0798 0.106 0.712 0.741 0.00202 0.0021 Wall time: 35932.666437181644 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.184 0.00653 0.0537 0.0724 0.0948 0.235 0.272 0.000668 0.000773 363 172 0.139 0.0061 0.0171 0.0691 0.0916 0.118 0.154 0.000336 0.000436 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.115 0.0056 0.00343 0.0672 0.0878 0.0617 0.0687 0.000175 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 363 36031.734 0.005 0.00669 0.0927 0.226 0.0723 0.0959 0.29 0.357 0.000824 0.00101 ! Validation 363 36031.734 0.005 0.00748 0.0449 0.195 0.0762 0.101 0.207 0.249 0.000587 0.000706 Wall time: 36031.73393648071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.348 0.00603 0.227 0.0686 0.0911 0.535 0.559 0.00152 0.00159 364 172 0.182 0.0061 0.0599 0.0693 0.0916 0.246 0.287 0.0007 0.000816 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.128 0.00556 0.0169 0.0669 0.0874 0.15 0.152 0.000426 0.000433 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 364 36130.970 0.005 0.00642 0.0877 0.216 0.0708 0.094 0.28 0.347 0.000794 0.000987 ! Validation 364 36130.970 0.005 0.0074 0.218 0.366 0.0757 0.101 0.468 0.548 0.00133 0.00156 Wall time: 36130.97005000571 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.298 0.00625 0.173 0.07 0.0928 0.458 0.488 0.0013 0.00139 365 172 0.167 0.00626 0.0419 0.0698 0.0928 0.206 0.24 0.000585 0.000682 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.212 0.00588 0.0948 0.0688 0.09 0.359 0.361 0.00102 0.00103 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 365 36230.048 0.005 0.00628 0.0959 0.222 0.07 0.093 0.291 0.363 0.000826 0.00103 ! Validation 365 36230.048 0.005 0.00745 0.0723 0.221 0.0759 0.101 0.258 0.315 0.000732 0.000896 Wall time: 36230.04846654786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.186 0.00608 0.064 0.0687 0.0914 0.267 0.297 0.000759 0.000843 366 172 0.164 0.00591 0.0455 0.0676 0.0902 0.221 0.25 0.000628 0.000711 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.103 0.00504 0.00276 0.0638 0.0832 0.0562 0.0617 0.00016 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 366 36329.548 0.005 0.0061 0.0722 0.194 0.069 0.0916 0.247 0.315 0.000702 0.000896 ! Validation 366 36329.548 0.005 0.00678 0.0461 0.182 0.0724 0.0966 0.195 0.252 0.000553 0.000715 Wall time: 36329.54878682364 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.567 0.00707 0.426 0.0749 0.0987 0.723 0.766 0.00205 0.00218 367 172 0.232 0.00628 0.106 0.0703 0.093 0.331 0.382 0.000939 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.106 0.00519 0.00257 0.0648 0.0845 0.0465 0.0594 0.000132 0.000169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 367 36428.650 0.005 0.00611 0.108 0.23 0.069 0.0917 0.303 0.386 0.000862 0.0011 ! Validation 367 36428.650 0.005 0.00699 0.0993 0.239 0.0733 0.0981 0.326 0.37 0.000927 0.00105 Wall time: 36428.65041461401 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.204 0.00513 0.101 0.0635 0.084 0.31 0.373 0.00088 0.00106 368 172 0.167 0.00541 0.0586 0.0649 0.0863 0.257 0.284 0.000729 0.000807 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.419 0.00528 0.313 0.065 0.0852 0.655 0.656 0.00186 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 368 36527.671 0.005 0.00604 0.0936 0.214 0.0686 0.0912 0.292 0.359 0.00083 0.00102 ! Validation 368 36527.671 0.005 0.00687 0.24 0.378 0.0727 0.0972 0.522 0.575 0.00148 0.00163 Wall time: 36527.67151879566 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.176 0.00626 0.0504 0.0698 0.0928 0.219 0.263 0.000623 0.000748 369 172 0.126 0.00496 0.0268 0.0626 0.0826 0.162 0.192 0.00046 0.000545 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.204 0.00476 0.109 0.0615 0.0809 0.385 0.388 0.00109 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 369 36626.746 0.005 0.00583 0.0764 0.193 0.0674 0.0896 0.256 0.324 0.000728 0.000921 ! Validation 369 36626.746 0.005 0.00633 0.061 0.188 0.0697 0.0933 0.246 0.29 0.0007 0.000823 Wall time: 36626.746594614815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.15 0.00604 0.029 0.0692 0.0912 0.155 0.2 0.000442 0.000568 370 172 0.354 0.00668 0.22 0.0726 0.0958 0.529 0.55 0.0015 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.164 0.00676 0.0284 0.0743 0.0964 0.187 0.198 0.000533 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 370 36725.786 0.005 0.0063 0.112 0.238 0.0701 0.0931 0.314 0.392 0.000893 0.00111 ! Validation 370 36725.786 0.005 0.00819 0.0725 0.236 0.0808 0.106 0.266 0.316 0.000756 0.000897 Wall time: 36725.78662191657 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.946 0.0087 0.772 0.0832 0.109 1.01 1.03 0.00288 0.00293 371 172 0.132 0.00515 0.0291 0.0635 0.0842 0.148 0.2 0.000419 0.000568 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.114 0.0052 0.00991 0.0644 0.0846 0.107 0.117 0.000304 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 371 36825.108 0.005 0.00762 0.12 0.273 0.0756 0.102 0.307 0.407 0.000873 0.00116 ! Validation 371 36825.108 0.005 0.00678 0.0456 0.181 0.0723 0.0966 0.216 0.251 0.000612 0.000712 Wall time: 36825.1082525556 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.409 0.00563 0.297 0.0664 0.088 0.62 0.639 0.00176 0.00181 372 172 0.139 0.00602 0.0186 0.0678 0.091 0.125 0.16 0.000355 0.000454 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.151 0.00517 0.0477 0.064 0.0843 0.249 0.256 0.000709 0.000728 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 372 36924.835 0.005 0.00557 0.0811 0.193 0.0658 0.0876 0.26 0.334 0.000739 0.000949 ! Validation 372 36924.835 0.005 0.00661 0.0352 0.167 0.0712 0.0953 0.171 0.22 0.000485 0.000625 Wall time: 36924.83502261201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.145 0.00497 0.0452 0.0626 0.0827 0.219 0.249 0.000623 0.000708 373 172 0.178 0.00507 0.0762 0.0629 0.0836 0.277 0.324 0.000787 0.00092 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.271 0.00455 0.18 0.0602 0.0791 0.496 0.497 0.00141 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 373 37024.093 0.005 0.00523 0.0432 0.148 0.0637 0.0848 0.194 0.244 0.000551 0.000692 ! Validation 373 37024.093 0.005 0.00601 0.0792 0.199 0.068 0.0909 0.285 0.33 0.00081 0.000938 Wall time: 37024.092977492604 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.266 0.00578 0.151 0.0671 0.0892 0.41 0.456 0.00116 0.00129 374 172 0.323 0.00673 0.189 0.0733 0.0962 0.435 0.51 0.00123 0.00145 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.16 0.00517 0.0571 0.0648 0.0843 0.269 0.28 0.000765 0.000796 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 374 37123.167 0.005 0.00518 0.0586 0.162 0.0634 0.0844 0.221 0.284 0.000626 0.000806 ! Validation 374 37123.167 0.005 0.00653 0.194 0.324 0.0718 0.0948 0.464 0.516 0.00132 0.00147 Wall time: 37123.16744639678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.241 0.00477 0.146 0.0607 0.081 0.425 0.448 0.00121 0.00127 375 172 0.127 0.00515 0.0244 0.0631 0.0842 0.125 0.183 0.000355 0.00052 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.199 0.00427 0.113 0.0582 0.0767 0.393 0.395 0.00112 0.00112 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 375 37222.268 0.005 0.00642 0.0804 0.209 0.0701 0.094 0.252 0.333 0.000716 0.000945 ! Validation 375 37222.268 0.005 0.00569 0.0433 0.157 0.0659 0.0885 0.209 0.244 0.000593 0.000693 Wall time: 37222.268333851825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.137 0.00596 0.018 0.0668 0.0906 0.112 0.157 0.000317 0.000447 376 172 0.145 0.00501 0.0444 0.0619 0.083 0.194 0.247 0.00055 0.000703 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.0987 0.00442 0.0103 0.0595 0.078 0.113 0.119 0.000322 0.000338 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 376 37321.338 0.005 0.00494 0.064 0.163 0.0618 0.0825 0.241 0.297 0.000685 0.000843 ! Validation 376 37321.338 0.005 0.00581 0.0351 0.151 0.0669 0.0894 0.171 0.22 0.000486 0.000624 Wall time: 37321.33879244374 ! Best model 376 0.151 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.137 0.00519 0.033 0.0646 0.0845 0.192 0.213 0.000545 0.000605 377 172 0.114 0.00474 0.0192 0.0605 0.0808 0.132 0.162 0.000374 0.000461 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.158 0.00427 0.0727 0.0578 0.0766 0.313 0.316 0.00089 0.000898 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 37420.445 0.005 0.00543 0.0823 0.191 0.065 0.0864 0.269 0.337 0.000765 0.000956 ! Validation 377 37420.445 0.005 0.00563 0.0386 0.151 0.0656 0.088 0.188 0.23 0.000535 0.000655 Wall time: 37420.44562253589 ! Best model 377 0.151 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.178 0.00521 0.0742 0.0635 0.0846 0.282 0.32 0.0008 0.000908 378 172 0.129 0.00421 0.0447 0.0572 0.0761 0.213 0.248 0.000606 0.000705 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.0869 0.00415 0.00389 0.0572 0.0756 0.0651 0.0731 0.000185 0.000208 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 378 37519.873 0.005 0.00494 0.0599 0.159 0.0619 0.0825 0.226 0.287 0.000643 0.000816 ! Validation 378 37519.873 0.005 0.00542 0.0277 0.136 0.0643 0.0863 0.148 0.195 0.00042 0.000555 Wall time: 37519.873545574956 ! Best model 378 0.136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.201 0.00845 0.0321 0.0821 0.108 0.169 0.21 0.000481 0.000597 379 172 0.12 0.00462 0.0281 0.0598 0.0797 0.163 0.197 0.000464 0.000559 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.121 0.00416 0.0382 0.0572 0.0756 0.224 0.229 0.000636 0.000651 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 379 37618.979 0.005 0.00559 0.0791 0.191 0.0657 0.0877 0.256 0.33 0.000726 0.000937 ! Validation 379 37618.979 0.005 0.00543 0.0289 0.138 0.0644 0.0865 0.149 0.199 0.000422 0.000566 Wall time: 37618.97929262975 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.165 0.00536 0.0578 0.0656 0.0859 0.244 0.282 0.000693 0.000801 380 172 0.338 0.00534 0.231 0.0649 0.0857 0.437 0.564 0.00124 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.586 0.00499 0.487 0.0633 0.0829 0.817 0.818 0.00232 0.00232 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 380 37718.243 0.005 0.005 0.0709 0.171 0.0623 0.0829 0.239 0.312 0.000679 0.000887 ! Validation 380 37718.243 0.005 0.00621 0.287 0.411 0.0694 0.0925 0.593 0.628 0.00168 0.00179 Wall time: 37718.2429964398 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.113 0.00467 0.0198 0.0601 0.0801 0.129 0.165 0.000366 0.000469 381 172 0.153 0.0048 0.0575 0.0606 0.0813 0.235 0.281 0.000667 0.000799 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.198 0.00418 0.115 0.0573 0.0758 0.394 0.397 0.00112 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 381 37817.573 0.005 0.0048 0.0636 0.16 0.061 0.0813 0.24 0.296 0.000681 0.00084 ! Validation 381 37817.573 0.005 0.00537 0.132 0.239 0.0639 0.0859 0.357 0.426 0.00102 0.00121 Wall time: 37817.57343235379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.108 0.00476 0.0127 0.0603 0.0809 0.101 0.132 0.000288 0.000375 382 172 0.13 0.00514 0.0269 0.0634 0.0841 0.168 0.192 0.000477 0.000547 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.0917 0.0044 0.00363 0.0594 0.0778 0.0619 0.0707 0.000176 0.000201 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 382 37916.836 0.005 0.00457 0.0449 0.136 0.0595 0.0793 0.198 0.249 0.000563 0.000706 ! Validation 382 37916.836 0.005 0.00554 0.0304 0.141 0.0654 0.0873 0.171 0.205 0.000486 0.000581 Wall time: 37916.8363126358 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.118 0.00453 0.0278 0.0589 0.0789 0.157 0.195 0.000445 0.000555 383 172 0.166 0.00434 0.0788 0.0579 0.0773 0.309 0.329 0.000877 0.000935 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.0919 0.00437 0.00457 0.0587 0.0775 0.0701 0.0793 0.000199 0.000225 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 38016.249 0.005 0.00462 0.0587 0.151 0.0598 0.0798 0.228 0.284 0.000647 0.000807 ! Validation 383 38016.249 0.005 0.00552 0.0648 0.175 0.0651 0.0871 0.235 0.299 0.000669 0.000848 Wall time: 38016.24896558188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.107 0.00435 0.02 0.0579 0.0773 0.139 0.166 0.000396 0.000472 384 172 0.207 0.00434 0.12 0.058 0.0773 0.36 0.406 0.00102 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.213 0.00384 0.137 0.0551 0.0727 0.431 0.433 0.00123 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 384 38116.081 0.005 0.00508 0.0508 0.152 0.0623 0.0836 0.204 0.264 0.00058 0.000751 ! Validation 384 38116.081 0.005 0.00494 0.213 0.312 0.0616 0.0824 0.446 0.542 0.00127 0.00154 Wall time: 38116.080908278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.138 0.00445 0.0487 0.0586 0.0783 0.212 0.259 0.000604 0.000736 385 172 0.0929 0.0038 0.0168 0.0542 0.0723 0.12 0.152 0.000341 0.000432 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.0941 0.00397 0.0148 0.0557 0.0739 0.137 0.143 0.00039 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 385 38215.126 0.005 0.0044 0.0532 0.141 0.0583 0.0778 0.214 0.271 0.000608 0.000769 ! Validation 385 38215.126 0.005 0.00507 0.0283 0.13 0.0624 0.0835 0.148 0.197 0.000421 0.00056 Wall time: 38215.1261427477 ! Best model 385 0.130 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.13 0.00449 0.0402 0.0586 0.0786 0.192 0.235 0.000545 0.000668 386 172 0.156 0.00668 0.022 0.0726 0.0959 0.115 0.174 0.000326 0.000494 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.103 0.00431 0.0168 0.0584 0.077 0.146 0.152 0.000414 0.000432 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 386 38314.127 0.005 0.00497 0.099 0.198 0.0621 0.0827 0.287 0.369 0.000817 0.00105 ! Validation 386 38314.127 0.005 0.00533 0.0608 0.167 0.0639 0.0857 0.233 0.289 0.000663 0.000822 Wall time: 38314.127380163874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.0928 0.00393 0.0143 0.0551 0.0735 0.11 0.14 0.000313 0.000398 387 172 0.113 0.0045 0.0234 0.0584 0.0787 0.15 0.18 0.000426 0.00051 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.0829 0.00375 0.00792 0.0546 0.0718 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 387 38413.123 0.005 0.0045 0.0402 0.13 0.0588 0.0787 0.189 0.235 0.000537 0.000669 ! Validation 387 38413.123 0.005 0.00476 0.0168 0.112 0.0604 0.081 0.123 0.152 0.000349 0.000432 Wall time: 38413.12310817465 ! Best model 387 0.112 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.139 0.00469 0.0453 0.0605 0.0803 0.204 0.25 0.00058 0.000709 388 172 0.191 0.00736 0.0439 0.0758 0.101 0.188 0.246 0.000533 0.000698 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.171 0.00433 0.0848 0.0586 0.0772 0.339 0.342 0.000963 0.000971 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 388 38512.188 0.005 0.00483 0.0785 0.175 0.0612 0.0815 0.265 0.329 0.000753 0.000934 ! Validation 388 38512.188 0.005 0.00532 0.0556 0.162 0.0638 0.0856 0.221 0.277 0.000627 0.000786 Wall time: 38512.18856991362 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.146 0.0044 0.0583 0.0584 0.0778 0.254 0.283 0.000723 0.000805 389 172 0.105 0.00397 0.026 0.0556 0.0739 0.148 0.189 0.000421 0.000538 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.232 0.00352 0.161 0.0522 0.0696 0.47 0.471 0.00134 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 389 38611.248 0.005 0.00443 0.0475 0.136 0.0585 0.078 0.195 0.256 0.000553 0.000726 ! Validation 389 38611.248 0.005 0.00458 0.122 0.214 0.0589 0.0794 0.37 0.41 0.00105 0.00116 Wall time: 38611.24798953161 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.139 0.00421 0.0547 0.0572 0.0761 0.196 0.274 0.000558 0.000779 390 172 0.21 0.00481 0.113 0.0593 0.0813 0.358 0.395 0.00102 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.0971 0.00362 0.0246 0.0532 0.0706 0.182 0.184 0.000516 0.000523 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 38710.853 0.005 0.00429 0.051 0.137 0.0575 0.0768 0.21 0.265 0.000597 0.000753 ! Validation 390 38710.853 0.005 0.00469 0.101 0.195 0.0599 0.0804 0.291 0.373 0.000828 0.00106 Wall time: 38710.853132712655 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.258 0.00438 0.171 0.0581 0.0776 0.463 0.485 0.00132 0.00138 391 172 0.154 0.00387 0.0769 0.0557 0.073 0.292 0.325 0.000831 0.000924 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 0.1 0.00369 0.0263 0.0539 0.0713 0.186 0.19 0.00053 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 391 38810.131 0.005 0.00416 0.047 0.13 0.0567 0.0756 0.205 0.254 0.000582 0.000722 ! Validation 391 38810.131 0.005 0.00465 0.0312 0.124 0.0594 0.08 0.166 0.207 0.000473 0.000589 Wall time: 38810.13094396889 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.129 0.00377 0.0538 0.0541 0.072 0.253 0.272 0.00072 0.000773 392 172 0.147 0.00386 0.0698 0.0543 0.0729 0.293 0.31 0.000833 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 0.1 0.00372 0.0256 0.054 0.0715 0.184 0.188 0.000522 0.000533 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 392 38909.197 0.005 0.00506 0.0701 0.171 0.0621 0.0834 0.243 0.311 0.000691 0.000882 ! Validation 392 38909.197 0.005 0.00466 0.0476 0.141 0.0598 0.0801 0.195 0.256 0.000554 0.000727 Wall time: 38909.19730852684 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.133 0.0041 0.0508 0.0562 0.0751 0.225 0.264 0.000638 0.000751 393 172 0.096 0.00385 0.0189 0.0548 0.0728 0.138 0.161 0.000393 0.000458 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 0.275 0.00364 0.203 0.0534 0.0707 0.526 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 393 39008.276 0.005 0.00411 0.0467 0.129 0.0562 0.0752 0.203 0.253 0.000576 0.00072 ! Validation 393 39008.276 0.005 0.00449 0.128 0.218 0.0585 0.0786 0.39 0.419 0.00111 0.00119 Wall time: 39008.27652410278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.175 0.00601 0.0543 0.0697 0.0909 0.228 0.273 0.000647 0.000776 394 172 0.102 0.00368 0.0284 0.0533 0.0711 0.156 0.198 0.000444 0.000562 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 0.0702 0.00344 0.00144 0.0518 0.0688 0.0385 0.0445 0.00011 0.000126 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 394 39107.768 0.005 0.00417 0.0646 0.148 0.0568 0.0757 0.237 0.298 0.000673 0.000847 ! Validation 394 39107.768 0.005 0.00439 0.0288 0.117 0.0578 0.0777 0.169 0.199 0.000479 0.000565 Wall time: 39107.76868397277 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.114 0.00383 0.0378 0.0538 0.0726 0.197 0.228 0.00056 0.000648 395 172 0.147 0.00458 0.0551 0.0592 0.0794 0.242 0.275 0.000688 0.000782 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 0.0937 0.00376 0.0184 0.0545 0.0719 0.151 0.159 0.000429 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 395 39206.868 0.005 0.00433 0.0551 0.142 0.0575 0.0772 0.208 0.275 0.000591 0.000783 ! Validation 395 39206.868 0.005 0.00464 0.0283 0.121 0.0594 0.0799 0.16 0.197 0.000454 0.00056 Wall time: 39206.868006678764 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.0947 0.00366 0.0215 0.0531 0.071 0.138 0.172 0.000391 0.000489 396 172 0.143 0.00341 0.0747 0.0514 0.0685 0.297 0.321 0.000843 0.000911 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 0.308 0.00344 0.239 0.0518 0.0688 0.572 0.574 0.00163 0.00163 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 396 39306.180 0.005 0.00397 0.0444 0.124 0.0554 0.0739 0.193 0.247 0.000549 0.000702 ! Validation 396 39306.180 0.005 0.00432 0.156 0.243 0.0573 0.0771 0.442 0.464 0.00125 0.00132 Wall time: 39306.18032305781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.126 0.00378 0.0508 0.0538 0.0721 0.244 0.264 0.000692 0.000751 397 172 0.111 0.00399 0.0315 0.0555 0.0741 0.177 0.208 0.000504 0.000591 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 0.297 0.00391 0.219 0.056 0.0733 0.548 0.549 0.00156 0.00156 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 397 39405.395 0.005 0.00381 0.04 0.116 0.0542 0.0724 0.186 0.235 0.000528 0.000667 ! Validation 397 39405.395 0.005 0.0048 0.11 0.206 0.0607 0.0812 0.329 0.389 0.000934 0.00111 Wall time: 39405.39561076788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.132 0.0034 0.0638 0.0513 0.0684 0.274 0.296 0.000778 0.000842 398 172 0.143 0.00375 0.0677 0.054 0.0718 0.244 0.305 0.000694 0.000867 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.103 0.00396 0.0233 0.0558 0.0738 0.176 0.179 0.000499 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 398 39504.475 0.005 0.00447 0.0698 0.159 0.0585 0.0784 0.243 0.31 0.000691 0.000881 ! Validation 398 39504.475 0.005 0.00487 0.0508 0.148 0.061 0.0819 0.216 0.264 0.000613 0.000751 Wall time: 39504.47512879176 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.199 0.00453 0.109 0.0603 0.0789 0.369 0.387 0.00105 0.0011 399 172 0.102 0.00404 0.0216 0.057 0.0746 0.137 0.172 0.000389 0.000489 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.0785 0.00381 0.00227 0.0554 0.0724 0.0433 0.0559 0.000123 0.000159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 399 39603.602 0.005 0.00408 0.0494 0.131 0.0562 0.0749 0.199 0.261 0.000565 0.000741 ! Validation 399 39603.602 0.005 0.00465 0.0292 0.122 0.0605 0.08 0.169 0.2 0.00048 0.00057 Wall time: 39603.602822146844 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.166 0.004 0.0859 0.0552 0.0742 0.303 0.344 0.00086 0.000976 400 172 0.0931 0.00364 0.0203 0.053 0.0708 0.14 0.167 0.000397 0.000475 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.118 0.00339 0.0505 0.0515 0.0683 0.262 0.264 0.000744 0.000749 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 400 39702.659 0.005 0.00386 0.0442 0.121 0.0546 0.0729 0.2 0.247 0.000567 0.000701 ! Validation 400 39702.659 0.005 0.00424 0.021 0.106 0.0567 0.0764 0.13 0.17 0.00037 0.000483 Wall time: 39702.659037556965 ! Best model 400 0.106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.124 0.00316 0.0607 0.0497 0.0659 0.247 0.289 0.000702 0.000821 401 172 0.104 0.00457 0.0126 0.059 0.0793 0.104 0.131 0.000295 0.000373 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.11 0.00416 0.0268 0.0569 0.0757 0.183 0.192 0.00052 0.000545 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 39801.889 0.005 0.00386 0.0493 0.126 0.0546 0.0728 0.202 0.26 0.000574 0.00074 ! Validation 401 39801.889 0.005 0.00492 0.0221 0.12 0.0613 0.0822 0.128 0.174 0.000365 0.000495 Wall time: 39801.88933490403 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.0802 0.0035 0.0103 0.0519 0.0694 0.0967 0.119 0.000275 0.000338 402 172 0.105 0.00349 0.0349 0.0519 0.0693 0.186 0.219 0.000528 0.000622 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.379 0.00328 0.314 0.0508 0.0672 0.656 0.657 0.00186 0.00187 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 402 39901.327 0.005 0.00365 0.0383 0.111 0.0531 0.0709 0.185 0.23 0.000526 0.000652 ! Validation 402 39901.327 0.005 0.00407 0.106 0.187 0.0555 0.0748 0.344 0.381 0.000977 0.00108 Wall time: 39901.32729095267 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.0815 0.00354 0.0106 0.052 0.0698 0.0953 0.121 0.000271 0.000343 403 172 0.117 0.00424 0.0322 0.0574 0.0763 0.19 0.21 0.000539 0.000598 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.196 0.00467 0.102 0.0609 0.0802 0.373 0.375 0.00106 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 403 40000.550 0.005 0.00421 0.0503 0.135 0.0568 0.0761 0.203 0.263 0.000577 0.000748 ! Validation 403 40000.550 0.005 0.00542 0.117 0.226 0.065 0.0864 0.319 0.402 0.000907 0.00114 Wall time: 40000.55040254677 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.0908 0.00354 0.02 0.0527 0.0698 0.143 0.166 0.000406 0.000471 404 172 0.155 0.0045 0.0653 0.0598 0.0786 0.274 0.3 0.000778 0.000852 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.166 0.00372 0.0917 0.054 0.0716 0.352 0.355 0.001 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 404 40099.635 0.005 0.00377 0.053 0.128 0.054 0.072 0.211 0.27 0.0006 0.000767 ! Validation 404 40099.635 0.005 0.00462 0.0273 0.12 0.0597 0.0797 0.156 0.194 0.000444 0.00055 Wall time: 40099.63547313586 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.0841 0.0034 0.0161 0.0513 0.0684 0.123 0.149 0.000349 0.000423 405 172 0.116 0.00355 0.0452 0.0522 0.0699 0.219 0.249 0.000621 0.000709 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.0667 0.00328 0.00116 0.0506 0.0672 0.0379 0.0399 0.000108 0.000113 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 40198.703 0.005 0.00346 0.0337 0.103 0.0517 0.069 0.169 0.215 0.000481 0.000612 ! Validation 405 40198.703 0.005 0.00403 0.0265 0.107 0.0551 0.0745 0.158 0.191 0.00045 0.000543 Wall time: 40198.70338606462 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.0956 0.00324 0.0308 0.0502 0.0668 0.175 0.206 0.000496 0.000585 406 172 0.203 0.0037 0.129 0.0538 0.0714 0.383 0.421 0.00109 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.292 0.00308 0.231 0.0488 0.0651 0.562 0.563 0.0016 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 406 40297.764 0.005 0.00348 0.0455 0.115 0.0517 0.0692 0.196 0.25 0.000556 0.000711 ! Validation 406 40297.764 0.005 0.00394 0.0725 0.151 0.0546 0.0736 0.274 0.316 0.000779 0.000897 Wall time: 40297.76488826098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.0744 0.00298 0.0148 0.0481 0.064 0.111 0.143 0.000314 0.000405 407 172 0.0897 0.00375 0.0147 0.054 0.0718 0.105 0.142 0.000298 0.000405 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.0926 0.00312 0.0302 0.0489 0.0655 0.2 0.204 0.000568 0.000579 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 40396.811 0.005 0.0036 0.0495 0.122 0.0528 0.0704 0.203 0.261 0.000576 0.000742 ! Validation 407 40396.811 0.005 0.00383 0.02 0.0965 0.0538 0.0726 0.13 0.166 0.000371 0.000471 Wall time: 40396.81089370698 ! Best model 407 0.097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.0819 0.0036 0.00991 0.0529 0.0703 0.0907 0.117 0.000258 0.000332 408 172 0.0862 0.00347 0.0169 0.0518 0.0691 0.128 0.152 0.000365 0.000433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.0862 0.00304 0.0253 0.0486 0.0647 0.184 0.186 0.000522 0.00053 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 408 40496.333 0.005 0.00356 0.041 0.112 0.0524 0.07 0.191 0.238 0.000541 0.000675 ! Validation 408 40496.333 0.005 0.00396 0.0216 0.101 0.0547 0.0738 0.133 0.172 0.000379 0.00049 Wall time: 40496.33356868988 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.0932 0.00335 0.0262 0.0511 0.0679 0.163 0.19 0.000462 0.00054 409 172 0.0785 0.00321 0.0142 0.0497 0.0665 0.119 0.14 0.000338 0.000398 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.143 0.00294 0.0845 0.0477 0.0636 0.338 0.341 0.000961 0.000969 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 40595.414 0.005 0.00328 0.0223 0.0879 0.0502 0.0672 0.14 0.175 0.000396 0.000498 ! Validation 409 40595.414 0.005 0.00364 0.0658 0.139 0.0525 0.0708 0.264 0.301 0.000751 0.000855 Wall time: 40595.41400292097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.149 0.00337 0.0814 0.0507 0.0681 0.32 0.335 0.000909 0.000951 410 172 0.0986 0.00317 0.0353 0.0491 0.066 0.173 0.22 0.000491 0.000626 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.415 0.00325 0.35 0.0507 0.0668 0.692 0.694 0.00197 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 410 40694.490 0.005 0.00348 0.0447 0.114 0.0518 0.0692 0.2 0.248 0.000569 0.000705 ! Validation 410 40694.490 0.005 0.00397 0.404 0.483 0.0552 0.0739 0.67 0.745 0.0019 0.00212 Wall time: 40694.49038894661 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 20 0.854 2.93 0.808 1.08 1.77 2.01 0.00502 0.00571 411 172 4.34 0.188 0.577 0.389 0.509 0.671 0.891 0.00191 0.00253 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 4.02 0.19 0.212 0.394 0.512 0.451 0.541 0.00128 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 411 40793.575 0.005 0.594 7.48 19.4 0.646 0.904 2.01 3.21 0.00571 0.00911 ! Validation 411 40793.575 0.005 0.196 0.705 4.62 0.398 0.519 0.757 0.985 0.00215 0.0028 Wall time: 40793.574881444685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 2.66 0.11 0.465 0.298 0.389 0.667 0.8 0.0019 0.00227 412 172 2.94 0.0986 0.972 0.283 0.368 1.03 1.16 0.00293 0.00329 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 2.04 0.0963 0.117 0.28 0.364 0.33 0.401 0.000939 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 412 40892.662 0.005 0.124 0.956 3.43 0.314 0.413 0.906 1.15 0.00257 0.00326 ! Validation 412 40892.662 0.005 0.106 1.38 3.51 0.293 0.382 1.12 1.38 0.00319 0.00392 Wall time: 40892.66226890078 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 1.95 0.0822 0.308 0.256 0.336 0.562 0.651 0.0016 0.00185 413 172 2.06 0.076 0.543 0.246 0.323 0.718 0.865 0.00204 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 3.21 0.0746 1.72 0.246 0.32 1.52 1.54 0.00432 0.00436 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 413 40991.746 0.005 0.0875 0.824 2.57 0.264 0.347 0.859 1.07 0.00244 0.00303 ! Validation 413 40991.746 0.005 0.0834 1.09 2.76 0.258 0.339 1.07 1.23 0.00303 0.00348 Wall time: 40991.745969554875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 2.93 0.0703 1.52 0.235 0.311 1.27 1.45 0.0036 0.00411 414 172 2.77 0.0764 1.24 0.247 0.324 1.13 1.3 0.00322 0.00371 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 1.44 0.0707 0.0238 0.239 0.312 0.18 0.181 0.00051 0.000514 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 41091.754 0.005 0.0722 0.718 2.16 0.239 0.315 0.784 0.994 0.00223 0.00282 ! Validation 414 41091.754 0.005 0.0782 0.359 1.92 0.25 0.328 0.567 0.703 0.00161 0.002 Wall time: 41091.75398411788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 1.73 0.0578 0.571 0.213 0.282 0.738 0.886 0.0021 0.00252 415 172 1.56 0.0571 0.415 0.213 0.28 0.659 0.755 0.00187 0.00215 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 1.19 0.0539 0.112 0.209 0.272 0.364 0.392 0.00103 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 415 41190.842 0.005 0.0606 0.547 1.76 0.219 0.289 0.691 0.868 0.00196 0.00247 ! Validation 415 41190.842 0.005 0.0598 0.55 1.75 0.217 0.287 0.723 0.87 0.00206 0.00247 Wall time: 41190.84235384269 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 2.37 0.0506 1.36 0.2 0.264 1.28 1.37 0.00365 0.00388 416 172 1.79 0.0528 0.739 0.204 0.269 0.897 1.01 0.00255 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 1.57 0.0493 0.587 0.2 0.26 0.889 0.899 0.00252 0.00255 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 416 41289.913 0.005 0.0538 0.658 1.73 0.205 0.272 0.771 0.952 0.00219 0.0027 ! Validation 416 41289.913 0.005 0.0555 0.459 1.57 0.209 0.276 0.663 0.794 0.00188 0.00226 Wall time: 41289.91299563879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 1.25 0.0473 0.303 0.192 0.255 0.493 0.646 0.0014 0.00183 417 172 2.87 0.0424 2.02 0.182 0.242 1.63 1.67 0.00463 0.00473 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 1.46 0.041 0.644 0.182 0.237 0.932 0.941 0.00265 0.00267 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 417 41389.006 0.005 0.048 0.545 1.51 0.194 0.257 0.682 0.865 0.00194 0.00246 ! Validation 417 41389.006 0.005 0.0459 1.02 1.94 0.191 0.251 1.06 1.18 0.00302 0.00336 Wall time: 41389.006227174774 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 1.92 0.038 1.16 0.173 0.229 1.14 1.26 0.00325 0.00359 418 172 1.27 0.0368 0.534 0.17 0.225 0.75 0.857 0.00213 0.00243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 1.46 0.0356 0.75 0.17 0.221 1.01 1.02 0.00286 0.00289 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 418 41488.078 0.005 0.0407 0.467 1.28 0.179 0.237 0.635 0.802 0.0018 0.00228 ! Validation 418 41488.078 0.005 0.0399 0.674 1.47 0.178 0.234 0.868 0.963 0.00246 0.00274 Wall time: 41488.07852254575 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.92 0.0349 0.221 0.165 0.219 0.461 0.551 0.00131 0.00157 419 172 2.27 0.0364 1.54 0.169 0.224 1.36 1.46 0.00386 0.00414 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.955 0.0364 0.227 0.172 0.224 0.545 0.559 0.00155 0.00159 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 419 41587.166 0.005 0.0382 0.627 1.39 0.173 0.229 0.739 0.929 0.0021 0.00264 ! Validation 419 41587.166 0.005 0.0406 1.35 2.16 0.179 0.236 1.09 1.36 0.00309 0.00387 Wall time: 41587.16605317779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.683 0.0304 0.0756 0.155 0.204 0.243 0.323 0.000691 0.000916 420 172 0.928 0.0288 0.352 0.151 0.199 0.628 0.696 0.00178 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.954 0.0301 0.353 0.156 0.203 0.68 0.697 0.00193 0.00198 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 420 41687.197 0.005 0.0344 0.456 1.14 0.165 0.218 0.619 0.792 0.00176 0.00225 ! Validation 420 41687.197 0.005 0.0334 0.27 0.938 0.163 0.214 0.519 0.61 0.00147 0.00173 Wall time: 41687.19772635959 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.948 0.0298 0.351 0.153 0.203 0.626 0.695 0.00178 0.00198 421 172 1.46 0.0294 0.873 0.152 0.201 1 1.1 0.00285 0.00311 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.602 0.0286 0.0306 0.152 0.198 0.186 0.205 0.000528 0.000583 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 421 41786.234 0.005 0.029 0.461 1.04 0.151 0.2 0.643 0.796 0.00183 0.00226 ! Validation 421 41786.234 0.005 0.0314 0.186 0.815 0.158 0.208 0.422 0.506 0.0012 0.00144 Wall time: 41786.23457440175 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.551 0.0248 0.0553 0.14 0.185 0.231 0.276 0.000657 0.000783 422 172 0.762 0.0279 0.204 0.149 0.196 0.447 0.529 0.00127 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.554 0.0272 0.00982 0.149 0.193 0.0997 0.116 0.000283 0.00033 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 41885.252 0.005 0.0253 0.365 0.871 0.141 0.187 0.559 0.709 0.00159 0.00201 ! Validation 422 41885.252 0.005 0.03 0.265 0.864 0.154 0.203 0.462 0.604 0.00131 0.00172 Wall time: 41885.25262810569 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.672 0.0215 0.241 0.13 0.172 0.485 0.576 0.00138 0.00164 423 172 0.516 0.0212 0.0925 0.129 0.171 0.292 0.357 0.000828 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.57 0.0209 0.151 0.13 0.17 0.443 0.455 0.00126 0.00129 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 41984.261 0.005 0.0227 0.286 0.74 0.134 0.177 0.505 0.627 0.00143 0.00178 ! Validation 423 41984.261 0.005 0.0239 0.127 0.605 0.137 0.181 0.347 0.418 0.000985 0.00119 Wall time: 41984.261483580805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.672 0.0202 0.269 0.125 0.167 0.529 0.608 0.0015 0.00173 424 172 0.563 0.0193 0.177 0.124 0.163 0.448 0.493 0.00127 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.46 0.0193 0.0744 0.124 0.163 0.297 0.32 0.000844 0.000909 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 424 42083.292 0.005 0.0209 0.311 0.729 0.128 0.17 0.524 0.654 0.00149 0.00186 ! Validation 424 42083.292 0.005 0.0221 0.111 0.553 0.132 0.174 0.315 0.391 0.000896 0.00111 Wall time: 42083.29263414396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.44 0.0185 0.0698 0.121 0.16 0.247 0.31 0.000702 0.00088 425 172 0.416 0.0178 0.0609 0.119 0.156 0.226 0.29 0.000643 0.000823 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.37 0.0178 0.0136 0.12 0.157 0.126 0.137 0.000357 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 425 42182.332 0.005 0.0187 0.221 0.596 0.121 0.16 0.436 0.552 0.00124 0.00157 ! Validation 425 42182.332 0.005 0.0206 0.5 0.911 0.127 0.168 0.601 0.829 0.00171 0.00236 Wall time: 42182.33229473792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.874 0.0178 0.517 0.119 0.157 0.79 0.844 0.00224 0.0024 426 172 1.04 0.018 0.684 0.119 0.157 0.933 0.97 0.00265 0.00276 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.535 0.0178 0.179 0.119 0.156 0.489 0.496 0.00139 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 426 42281.385 0.005 0.0187 0.348 0.722 0.121 0.161 0.548 0.692 0.00156 0.00196 ! Validation 426 42281.385 0.005 0.0205 0.27 0.679 0.127 0.168 0.495 0.609 0.00141 0.00173 Wall time: 42281.385095664766 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.397 0.0163 0.0716 0.114 0.15 0.254 0.314 0.000722 0.000891 427 172 0.807 0.0156 0.496 0.111 0.146 0.788 0.826 0.00224 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.654 0.0152 0.351 0.111 0.144 0.688 0.695 0.00196 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 427 42381.470 0.005 0.0167 0.221 0.556 0.115 0.152 0.448 0.552 0.00127 0.00157 ! Validation 427 42381.470 0.005 0.018 0.631 0.991 0.119 0.157 0.882 0.932 0.00251 0.00265 Wall time: 42381.47071440192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.628 0.0146 0.337 0.107 0.142 0.627 0.681 0.00178 0.00193 428 172 0.627 0.0156 0.314 0.111 0.147 0.551 0.658 0.00156 0.00187 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.561 0.0152 0.257 0.111 0.145 0.589 0.595 0.00167 0.00169 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 428 42480.567 0.005 0.0155 0.231 0.54 0.11 0.146 0.457 0.563 0.0013 0.0016 ! Validation 428 42480.567 0.005 0.0177 0.188 0.542 0.118 0.156 0.45 0.508 0.00128 0.00144 Wall time: 42480.567725720815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.404 0.0154 0.0961 0.11 0.146 0.295 0.364 0.000838 0.00103 429 172 0.507 0.0139 0.229 0.105 0.138 0.522 0.562 0.00148 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.361 0.0136 0.0889 0.105 0.137 0.341 0.35 0.000968 0.000993 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 42579.588 0.005 0.0151 0.239 0.54 0.109 0.144 0.45 0.573 0.00128 0.00163 ! Validation 429 42579.588 0.005 0.0163 0.116 0.442 0.113 0.15 0.346 0.4 0.000982 0.00114 Wall time: 42579.58787144581 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.418 0.0146 0.127 0.107 0.142 0.354 0.418 0.00101 0.00119 430 172 0.482 0.0128 0.226 0.102 0.133 0.431 0.557 0.00122 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.699 0.0125 0.45 0.101 0.131 0.785 0.787 0.00223 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 430 42678.631 0.005 0.0135 0.173 0.444 0.103 0.137 0.392 0.488 0.00111 0.00139 ! Validation 430 42678.631 0.005 0.0153 0.25 0.556 0.11 0.145 0.516 0.586 0.00147 0.00167 Wall time: 42678.6314611109 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.557 0.0147 0.264 0.108 0.142 0.567 0.602 0.00161 0.00171 431 172 0.32 0.0131 0.058 0.102 0.134 0.204 0.283 0.00058 0.000803 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.29 0.0122 0.0465 0.0998 0.13 0.238 0.253 0.000676 0.000718 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 431 42777.679 0.005 0.0141 0.281 0.563 0.106 0.139 0.494 0.621 0.0014 0.00177 ! Validation 431 42777.679 0.005 0.015 0.106 0.406 0.109 0.144 0.298 0.382 0.000848 0.00108 Wall time: 42777.67972488282 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.614 0.0126 0.362 0.1 0.132 0.657 0.705 0.00187 0.002 432 172 0.3 0.0117 0.0666 0.0958 0.127 0.225 0.303 0.000639 0.00086 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.23 0.0113 0.00431 0.0954 0.125 0.0689 0.077 0.000196 0.000219 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 432 42876.732 0.005 0.0126 0.179 0.432 0.0998 0.132 0.392 0.496 0.00111 0.00141 ! Validation 432 42876.732 0.005 0.0139 0.048 0.325 0.104 0.138 0.204 0.257 0.000579 0.00073 Wall time: 42876.73230440263 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.568 0.012 0.328 0.0975 0.129 0.637 0.672 0.00181 0.00191 433 172 0.67 0.0133 0.403 0.102 0.135 0.687 0.745 0.00195 0.00212 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.264 0.0124 0.0168 0.101 0.13 0.136 0.152 0.000387 0.000432 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 433 42975.954 0.005 0.0118 0.195 0.43 0.0963 0.127 0.405 0.518 0.00115 0.00147 ! Validation 433 42975.954 0.005 0.0151 0.134 0.436 0.11 0.144 0.333 0.429 0.000945 0.00122 Wall time: 42975.9543998898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.386 0.0108 0.17 0.0927 0.122 0.417 0.484 0.00119 0.00137 434 172 0.363 0.012 0.124 0.0982 0.128 0.339 0.413 0.000962 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.405 0.0114 0.178 0.0971 0.125 0.487 0.494 0.00138 0.0014 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 434 43075.196 0.005 0.0116 0.197 0.429 0.0957 0.126 0.413 0.52 0.00117 0.00148 ! Validation 434 43075.196 0.005 0.0134 0.185 0.454 0.103 0.136 0.449 0.505 0.00128 0.00143 Wall time: 43075.19665994868 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.332 0.011 0.112 0.0929 0.123 0.351 0.393 0.000996 0.00112 435 172 0.447 0.0104 0.238 0.09 0.12 0.541 0.573 0.00154 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.432 0.00947 0.242 0.0879 0.114 0.575 0.578 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 435 43174.244 0.005 0.0108 0.148 0.365 0.0924 0.122 0.36 0.452 0.00102 0.00128 ! Validation 435 43174.244 0.005 0.0121 0.285 0.527 0.0972 0.129 0.579 0.626 0.00164 0.00178 Wall time: 43174.24413316278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.265 0.00968 0.0715 0.0877 0.115 0.264 0.314 0.000751 0.000891 436 172 0.235 0.0101 0.0333 0.0896 0.118 0.173 0.214 0.000492 0.000608 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.224 0.00941 0.0354 0.0873 0.114 0.21 0.221 0.000597 0.000627 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 436 43273.343 0.005 0.0103 0.181 0.387 0.0899 0.119 0.392 0.499 0.00111 0.00142 ! Validation 436 43273.343 0.005 0.0117 0.0617 0.296 0.0956 0.127 0.234 0.291 0.000664 0.000828 Wall time: 43273.34350942401 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.258 0.0101 0.0555 0.0884 0.118 0.218 0.276 0.000618 0.000785 437 172 0.239 0.00986 0.0419 0.0879 0.116 0.194 0.24 0.000552 0.000682 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.24 0.00879 0.0639 0.0845 0.11 0.289 0.296 0.00082 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 437 43372.359 0.005 0.00977 0.136 0.331 0.0875 0.116 0.343 0.432 0.000976 0.00123 ! Validation 437 43372.359 0.005 0.0113 0.096 0.323 0.0938 0.125 0.303 0.363 0.000861 0.00103 Wall time: 43372.35965156974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.443 0.00859 0.271 0.0827 0.109 0.588 0.61 0.00167 0.00173 438 172 0.205 0.00809 0.0432 0.0804 0.105 0.2 0.244 0.000569 0.000693 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.171 0.00804 0.0105 0.0806 0.105 0.105 0.12 0.000298 0.000342 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 43471.400 0.005 0.0095 0.152 0.342 0.0863 0.114 0.364 0.457 0.00103 0.0013 ! Validation 438 43471.400 0.005 0.0104 0.083 0.292 0.0898 0.12 0.279 0.338 0.000792 0.00096 Wall time: 43471.400246463716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.294 0.00881 0.118 0.0834 0.11 0.36 0.402 0.00102 0.00114 439 172 0.311 0.00994 0.113 0.0885 0.117 0.308 0.394 0.000875 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.357 0.0084 0.189 0.0823 0.107 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 439 43571.768 0.005 0.00948 0.18 0.37 0.0861 0.114 0.389 0.498 0.0011 0.00141 ! Validation 439 43571.768 0.005 0.0105 0.223 0.434 0.0907 0.12 0.516 0.554 0.00147 0.00157 Wall time: 43571.768683251925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.261 0.00913 0.078 0.0854 0.112 0.279 0.327 0.000792 0.00093 440 172 0.303 0.0114 0.0756 0.0948 0.125 0.258 0.322 0.000732 0.000916 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.169 0.00813 0.00597 0.0808 0.106 0.0735 0.0907 0.000209 0.000258 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 43671.008 0.005 0.00892 0.141 0.32 0.0835 0.111 0.342 0.441 0.000972 0.00125 ! Validation 440 43671.008 0.005 0.0103 0.0529 0.258 0.0894 0.119 0.211 0.27 0.0006 0.000767 Wall time: 43671.00805983879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.379 0.00808 0.218 0.0795 0.105 0.514 0.547 0.00146 0.00155 441 172 0.209 0.00815 0.0457 0.079 0.106 0.206 0.251 0.000586 0.000712 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.149 0.00694 0.0105 0.0747 0.0977 0.114 0.12 0.000324 0.000342 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 43770.028 0.005 0.00837 0.116 0.283 0.0808 0.107 0.315 0.399 0.000895 0.00113 ! Validation 441 43770.028 0.005 0.00931 0.0238 0.21 0.0846 0.113 0.148 0.181 0.000419 0.000514 Wall time: 43770.02871948993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.258 0.00868 0.0842 0.0819 0.109 0.29 0.34 0.000823 0.000967 442 172 0.178 0.00736 0.031 0.076 0.101 0.171 0.207 0.000485 0.000587 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.143 0.00669 0.00938 0.0732 0.096 0.11 0.114 0.000314 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 442 43869.151 0.005 0.00793 0.0997 0.258 0.0786 0.104 0.301 0.37 0.000854 0.00105 ! Validation 442 43869.151 0.005 0.00892 0.0421 0.22 0.0829 0.111 0.188 0.241 0.000535 0.000683 Wall time: 43869.15182204964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.168 0.00703 0.0278 0.0746 0.0984 0.162 0.196 0.00046 0.000556 443 172 0.239 0.00733 0.0927 0.0757 0.1 0.308 0.357 0.000876 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.372 0.00635 0.245 0.0714 0.0935 0.58 0.58 0.00165 0.00165 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 443 43968.230 0.005 0.00783 0.125 0.282 0.0782 0.104 0.332 0.415 0.000943 0.00118 ! Validation 443 43968.230 0.005 0.00865 0.235 0.408 0.0815 0.109 0.535 0.568 0.00152 0.00161 Wall time: 43968.23032552097 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.214 0.00795 0.0548 0.0799 0.105 0.225 0.274 0.00064 0.00078 444 172 0.176 0.00753 0.025 0.0764 0.102 0.137 0.186 0.000389 0.000527 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.137 0.00671 0.00321 0.0738 0.0961 0.0615 0.0665 0.000175 0.000189 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 44067.301 0.005 0.00761 0.129 0.281 0.077 0.102 0.334 0.421 0.000949 0.0012 ! Validation 444 44067.301 0.005 0.00891 0.0358 0.214 0.0834 0.111 0.171 0.222 0.000485 0.00063 Wall time: 44067.30120523181 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.181 0.00716 0.0376 0.0741 0.0992 0.177 0.228 0.000503 0.000646 445 172 0.285 0.00882 0.108 0.0838 0.11 0.296 0.386 0.00084 0.0011 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.148 0.00728 0.00263 0.0769 0.1 0.0527 0.0601 0.00015 0.000171 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 44167.102 0.005 0.0072 0.113 0.257 0.0749 0.0996 0.322 0.395 0.000914 0.00112 ! Validation 445 44167.102 0.005 0.00933 0.304 0.49 0.0855 0.113 0.518 0.647 0.00147 0.00184 Wall time: 44167.10214920668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.572 0.0078 0.416 0.078 0.104 0.738 0.756 0.0021 0.00215 446 172 0.396 0.00662 0.264 0.072 0.0955 0.586 0.603 0.00167 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.232 0.00638 0.104 0.0715 0.0937 0.377 0.379 0.00107 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 446 44266.190 0.005 0.00731 0.106 0.253 0.0754 0.1 0.307 0.382 0.000874 0.00109 ! Validation 446 44266.190 0.005 0.00838 0.11 0.278 0.0804 0.107 0.336 0.389 0.000953 0.00111 Wall time: 44266.190440468024 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.151 0.00673 0.0168 0.0724 0.0962 0.13 0.152 0.000369 0.000432 447 172 0.191 0.00681 0.0548 0.073 0.0968 0.219 0.275 0.000621 0.00078 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.124 0.00605 0.00266 0.0698 0.0913 0.0582 0.0606 0.000165 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 447 44365.419 0.005 0.00724 0.132 0.276 0.0751 0.0998 0.338 0.426 0.000961 0.00121 ! Validation 447 44365.419 0.005 0.008 0.0403 0.2 0.0786 0.105 0.189 0.236 0.000538 0.000669 Wall time: 44365.41895963857 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.605 0.00717 0.461 0.0756 0.0993 0.754 0.797 0.00214 0.00226 448 172 0.238 0.0063 0.112 0.0702 0.0931 0.332 0.392 0.000944 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.132 0.00627 0.0069 0.071 0.0929 0.0962 0.0974 0.000273 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 448 44464.628 0.005 0.00713 0.139 0.281 0.0746 0.0991 0.337 0.437 0.000957 0.00124 ! Validation 448 44464.628 0.005 0.0082 0.0629 0.227 0.0796 0.106 0.24 0.294 0.000681 0.000836 Wall time: 44464.62841332378 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.304 0.00642 0.175 0.0709 0.094 0.458 0.491 0.0013 0.0014 449 172 0.162 0.00684 0.0255 0.0727 0.097 0.131 0.187 0.000371 0.000532 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.114 0.0056 0.00241 0.0668 0.0878 0.0537 0.0576 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 449 44563.710 0.005 0.00786 0.141 0.298 0.0776 0.104 0.333 0.441 0.000946 0.00125 ! Validation 449 44563.710 0.005 0.00753 0.0188 0.169 0.0761 0.102 0.13 0.161 0.00037 0.000457 Wall time: 44563.710337301716 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.154 0.00627 0.0284 0.0697 0.0929 0.153 0.198 0.000436 0.000562 450 172 0.168 0.00699 0.028 0.0734 0.0981 0.153 0.196 0.000435 0.000558 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.132 0.00552 0.0213 0.0667 0.0871 0.169 0.171 0.00048 0.000486 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 44662.888 0.005 0.00612 0.0681 0.19 0.0688 0.0917 0.243 0.306 0.000691 0.000869 ! Validation 450 44662.888 0.005 0.00732 0.0495 0.196 0.0752 0.1 0.219 0.261 0.000622 0.000741 Wall time: 44662.88864534674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.185 0.00592 0.0662 0.0685 0.0903 0.256 0.302 0.000727 0.000857 451 172 0.163 0.0057 0.0488 0.0669 0.0886 0.218 0.259 0.000619 0.000736 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.38 0.00539 0.273 0.0661 0.0861 0.611 0.612 0.00174 0.00174 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 451 44763.631 0.005 0.00655 0.102 0.233 0.0713 0.0949 0.294 0.375 0.000836 0.00107 ! Validation 451 44763.631 0.005 0.00725 0.28 0.425 0.075 0.0999 0.585 0.621 0.00166 0.00176 Wall time: 44763.63111284189 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.139 0.00644 0.0104 0.0713 0.0942 0.0923 0.12 0.000262 0.00034 452 172 0.248 0.00627 0.122 0.0705 0.0929 0.396 0.411 0.00112 0.00117 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.38 0.00512 0.277 0.0643 0.0839 0.617 0.618 0.00175 0.00175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 452 44862.694 0.005 0.00653 0.119 0.249 0.0713 0.0948 0.312 0.404 0.000885 0.00115 ! Validation 452 44862.694 0.005 0.00693 0.184 0.323 0.0733 0.0977 0.452 0.503 0.00128 0.00143 Wall time: 44862.69447084982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.131 0.00543 0.0221 0.0647 0.0864 0.138 0.174 0.000391 0.000495 453 172 0.186 0.00591 0.0679 0.0684 0.0902 0.273 0.306 0.000775 0.000868 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.54 0.00517 0.437 0.0643 0.0843 0.775 0.775 0.0022 0.0022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 453 44961.749 0.005 0.00583 0.0758 0.192 0.0673 0.0896 0.265 0.323 0.000753 0.000918 ! Validation 453 44961.749 0.005 0.00687 0.301 0.439 0.0728 0.0972 0.625 0.644 0.00178 0.00183 Wall time: 44961.74957965594 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.188 0.00599 0.0684 0.0679 0.0908 0.259 0.307 0.000737 0.000871 454 172 0.211 0.00568 0.0975 0.0669 0.0884 0.337 0.366 0.000956 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 0.133 0.00528 0.0276 0.0654 0.0852 0.193 0.195 0.000549 0.000554 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 454 45060.822 0.005 0.006 0.113 0.233 0.0683 0.0909 0.31 0.394 0.00088 0.00112 ! Validation 454 45060.822 0.005 0.00695 0.0559 0.195 0.0735 0.0978 0.24 0.277 0.000681 0.000788 Wall time: 45060.82253036974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.16 0.00614 0.0377 0.0687 0.0919 0.179 0.228 0.000508 0.000647 455 172 0.344 0.00712 0.202 0.0755 0.099 0.423 0.527 0.0012 0.0015 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 0.176 0.00786 0.0188 0.0807 0.104 0.156 0.161 0.000444 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 455 45159.865 0.005 0.00591 0.0884 0.207 0.0678 0.0902 0.283 0.349 0.000804 0.00099 ! Validation 455 45159.865 0.005 0.00937 0.186 0.374 0.0869 0.114 0.433 0.506 0.00123 0.00144 Wall time: 45159.86586904293 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.142 0.00525 0.0366 0.0641 0.085 0.178 0.224 0.000506 0.000638 456 172 0.125 0.0048 0.0294 0.0614 0.0813 0.162 0.201 0.000461 0.000572 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 0.179 0.00462 0.0866 0.0611 0.0797 0.344 0.345 0.000978 0.000981 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 456 45258.985 0.005 0.00589 0.0863 0.204 0.0677 0.0901 0.283 0.345 0.000805 0.000979 ! Validation 456 45258.985 0.005 0.00624 0.0408 0.166 0.0693 0.0926 0.202 0.237 0.000573 0.000673 Wall time: 45258.985791224986 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.28 0.00675 0.145 0.0733 0.0964 0.313 0.446 0.00089 0.00127 457 172 0.283 0.00516 0.18 0.0634 0.0842 0.478 0.497 0.00136 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 0.324 0.00488 0.226 0.0627 0.0819 0.557 0.558 0.00158 0.00158 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 45358.341 0.005 0.00663 0.133 0.266 0.0718 0.0955 0.339 0.428 0.000964 0.00122 ! Validation 457 45358.341 0.005 0.0065 0.183 0.313 0.0708 0.0946 0.477 0.502 0.00135 0.00143 Wall time: 45358.341274973005 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.117 0.00472 0.0228 0.061 0.0805 0.152 0.177 0.000433 0.000503 458 172 0.133 0.00576 0.0174 0.0667 0.089 0.125 0.155 0.000356 0.000439 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 0.108 0.00528 0.00259 0.0656 0.0852 0.0589 0.0597 0.000167 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 458 45457.427 0.005 0.00529 0.0522 0.158 0.064 0.0853 0.211 0.268 0.0006 0.000762 ! Validation 458 45457.427 0.005 0.00672 0.0215 0.156 0.0729 0.0962 0.136 0.172 0.000388 0.000489 Wall time: 45457.42748853285 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.163 0.00583 0.0468 0.0672 0.0896 0.191 0.254 0.000542 0.000721 459 172 0.131 0.00561 0.0192 0.0642 0.0878 0.131 0.163 0.000371 0.000462 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.0934 0.00432 0.00714 0.0591 0.0771 0.0976 0.0991 0.000277 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 459 45556.781 0.005 0.00525 0.066 0.171 0.0639 0.085 0.245 0.301 0.000697 0.000856 ! Validation 459 45556.781 0.005 0.00584 0.0605 0.177 0.0669 0.0896 0.227 0.289 0.000646 0.00082 Wall time: 45556.78094804194 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.162 0.00578 0.0463 0.0667 0.0891 0.207 0.252 0.000587 0.000717 460 172 0.737 0.00583 0.621 0.0635 0.0896 0.894 0.924 0.00254 0.00263 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.187 0.007 0.0475 0.0758 0.0981 0.248 0.256 0.000705 0.000726 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 460 45655.867 0.005 0.00562 0.0924 0.205 0.0659 0.0879 0.27 0.356 0.000768 0.00101 ! Validation 460 45655.867 0.005 0.00853 0.0887 0.259 0.0826 0.108 0.295 0.349 0.000837 0.000992 Wall time: 45655.867054016795 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.132 0.00484 0.0355 0.061 0.0816 0.185 0.221 0.000526 0.000628 461 172 0.108 0.00455 0.0165 0.0596 0.0792 0.127 0.151 0.00036 0.000428 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.12 0.00412 0.0377 0.0576 0.0752 0.224 0.228 0.000638 0.000647 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 45754.947 0.005 0.00525 0.0688 0.174 0.0638 0.085 0.243 0.308 0.000691 0.000875 ! Validation 461 45754.947 0.005 0.00555 0.0197 0.131 0.0651 0.0874 0.135 0.165 0.000385 0.000468 Wall time: 45754.94728724379 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.214 0.00494 0.115 0.0617 0.0824 0.351 0.398 0.000999 0.00113 462 172 0.119 0.00427 0.0338 0.0583 0.0766 0.191 0.216 0.000543 0.000612 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.0921 0.0045 0.00212 0.06 0.0787 0.0481 0.054 0.000137 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 462 45854.023 0.005 0.00534 0.0944 0.201 0.0645 0.0858 0.291 0.361 0.000826 0.00102 ! Validation 462 45854.023 0.005 0.00588 0.0247 0.142 0.0675 0.09 0.149 0.184 0.000424 0.000524 Wall time: 45854.02297613071 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.197 0.00642 0.0681 0.0712 0.094 0.265 0.306 0.000752 0.00087 463 172 0.235 0.00547 0.126 0.0643 0.0868 0.379 0.416 0.00108 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.106 0.00449 0.016 0.0609 0.0786 0.14 0.148 0.000399 0.000422 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 463 45953.432 0.005 0.0052 0.0741 0.178 0.0637 0.0846 0.262 0.319 0.000743 0.000907 ! Validation 463 45953.432 0.005 0.00578 0.0499 0.166 0.0672 0.0892 0.206 0.262 0.000585 0.000744 Wall time: 45953.43278004974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.105 0.00457 0.0136 0.0597 0.0793 0.0992 0.137 0.000282 0.000388 464 172 0.195 0.0083 0.0293 0.0829 0.107 0.159 0.201 0.000452 0.000571 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.249 0.0105 0.0384 0.0901 0.12 0.223 0.23 0.000634 0.000653 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 46052.526 0.005 0.00555 0.102 0.213 0.0652 0.0873 0.298 0.374 0.000847 0.00106 ! Validation 464 46052.526 0.005 0.0122 0.283 0.527 0.0985 0.13 0.537 0.624 0.00153 0.00177 Wall time: 46052.52613657201 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.119 0.00471 0.0251 0.0607 0.0805 0.153 0.186 0.000434 0.000527 465 172 0.0961 0.00422 0.0116 0.0577 0.0762 0.102 0.126 0.00029 0.000359 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.151 0.00403 0.0701 0.0573 0.0744 0.31 0.311 0.000881 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 465 46151.611 0.005 0.00537 0.0695 0.177 0.0643 0.086 0.239 0.309 0.00068 0.000879 ! Validation 465 46151.611 0.005 0.00538 0.0397 0.147 0.0644 0.086 0.198 0.234 0.000563 0.000664 Wall time: 46151.61171402596 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.16 0.00441 0.0721 0.0585 0.0779 0.292 0.315 0.000829 0.000895 466 172 0.238 0.00498 0.139 0.062 0.0828 0.398 0.437 0.00113 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.103 0.00449 0.0131 0.06 0.0786 0.126 0.134 0.000359 0.000382 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 46250.717 0.005 0.00513 0.0682 0.171 0.0629 0.084 0.236 0.306 0.000671 0.00087 ! Validation 466 46250.717 0.005 0.00578 0.0683 0.184 0.0669 0.0892 0.267 0.307 0.000759 0.000871 Wall time: 46250.71777864173 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.112 0.00495 0.0127 0.0612 0.0825 0.112 0.132 0.000319 0.000376 467 172 0.107 0.00475 0.0116 0.0607 0.0809 0.105 0.126 0.000298 0.000359 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.114 0.00436 0.0267 0.0598 0.0774 0.189 0.191 0.000536 0.000544 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 46349.843 0.005 0.00525 0.0909 0.196 0.0638 0.085 0.275 0.354 0.000781 0.00101 ! Validation 467 46349.843 0.005 0.00569 0.0755 0.189 0.0667 0.0885 0.246 0.322 0.0007 0.000915 Wall time: 46349.843483191915 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.157 0.00449 0.0674 0.0589 0.0786 0.274 0.305 0.000778 0.000865 468 172 0.109 0.00486 0.0118 0.0613 0.0818 0.109 0.127 0.00031 0.000362 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.111 0.00397 0.0317 0.0569 0.0739 0.206 0.209 0.000586 0.000593 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 46448.958 0.005 0.00479 0.0635 0.159 0.0611 0.0812 0.238 0.296 0.000675 0.00084 ! Validation 468 46448.958 0.005 0.00534 0.0238 0.131 0.0644 0.0857 0.147 0.181 0.000417 0.000514 Wall time: 46448.95837347396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.109 0.00445 0.02 0.0585 0.0782 0.132 0.166 0.000374 0.000472 469 172 0.123 0.00443 0.0348 0.0584 0.0781 0.168 0.219 0.000479 0.000621 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.0841 0.0039 0.00619 0.0562 0.0732 0.0894 0.0923 0.000254 0.000262 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 469 46548.412 0.005 0.00434 0.0393 0.126 0.058 0.0773 0.186 0.232 0.00053 0.00066 ! Validation 469 46548.412 0.005 0.00509 0.0598 0.162 0.0625 0.0837 0.229 0.287 0.00065 0.000815 Wall time: 46548.412723633926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 2.08e+03 0.99 2.06e+03 0.872 1.17 53.2 53.2 0.151 0.151 470 172 13.4 0.47 3.96 0.604 0.805 2.11 2.33 0.00599 0.00663 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 13.8 0.452 4.8 0.603 0.788 2.53 2.57 0.00718 0.0073 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 46647.518 0.005 0.357 16.5 23.6 0.38 0.7 1.62 4.76 0.00461 0.0135 ! Validation 470 46647.518 0.005 0.483 8.23 17.9 0.617 0.815 3 3.36 0.00852 0.00956 Wall time: 46647.518053326756 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 7.99 0.206 3.87 0.399 0.532 2.17 2.31 0.00616 0.00656 471 172 3.82 0.132 1.19 0.32 0.426 1.12 1.28 0.00318 0.00363 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 3.36 0.128 0.787 0.32 0.42 1.03 1.04 0.00293 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 471 46746.628 0.005 0.221 1.91 6.33 0.409 0.551 1.28 1.62 0.00365 0.00461 ! Validation 471 46746.628 0.005 0.139 1.17 3.95 0.33 0.438 1.05 1.27 0.00299 0.0036 Wall time: 46746.62865875289 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 3.06 0.11 0.85 0.292 0.39 0.95 1.08 0.0027 0.00307 472 172 2.07 0.0915 0.237 0.267 0.355 0.454 0.571 0.00129 0.00162 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 1.76 0.0869 0.0237 0.262 0.346 0.177 0.181 0.000504 0.000513 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 472 46845.717 0.005 0.108 0.77 2.93 0.291 0.386 0.804 1.03 0.00228 0.00293 ! Validation 472 46845.717 0.005 0.0981 0.33 2.29 0.277 0.367 0.512 0.673 0.00145 0.00191 Wall time: 46845.717063678894 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 1.97 0.0824 0.321 0.252 0.337 0.522 0.665 0.00148 0.00189 473 172 2 0.0777 0.443 0.245 0.327 0.668 0.781 0.0019 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 1.49 0.0737 0.0124 0.24 0.318 0.107 0.13 0.000303 0.000371 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 473 46944.789 0.005 0.0826 0.784 2.44 0.253 0.337 0.834 1.04 0.00237 0.00295 ! Validation 473 46944.789 0.005 0.0822 0.423 2.07 0.252 0.336 0.59 0.763 0.00168 0.00217 Wall time: 46944.789820218924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 1.51 0.0683 0.144 0.229 0.306 0.356 0.445 0.00101 0.00127 474 172 2.46 0.0604 1.25 0.214 0.288 1.24 1.31 0.00353 0.00372 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 1.24 0.061 0.0191 0.217 0.29 0.152 0.162 0.000431 0.00046 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 47043.869 0.005 0.0699 0.63 2.03 0.231 0.31 0.752 0.931 0.00214 0.00264 ! Validation 474 47043.869 0.005 0.068 0.226 1.59 0.228 0.306 0.431 0.558 0.00122 0.00158 Wall time: 47043.86945348559 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 1.52 0.0572 0.378 0.208 0.281 0.674 0.722 0.00191 0.00205 475 172 1.22 0.0554 0.111 0.204 0.276 0.318 0.39 0.000903 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 1.09 0.0527 0.0348 0.202 0.269 0.192 0.219 0.000546 0.000622 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 475 47143.073 0.005 0.0585 0.555 1.73 0.211 0.284 0.706 0.874 0.00201 0.00248 ! Validation 475 47143.073 0.005 0.0592 0.202 1.39 0.213 0.285 0.41 0.527 0.00117 0.0015 Wall time: 47143.07301388262 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 2.59 0.0559 1.47 0.205 0.277 1.38 1.42 0.00392 0.00404 476 172 1.7 0.048 0.741 0.192 0.257 0.935 1.01 0.00266 0.00287 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 1.2 0.0471 0.26 0.191 0.255 0.587 0.599 0.00167 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 476 47242.143 0.005 0.0536 0.647 1.72 0.202 0.272 0.759 0.943 0.00216 0.00268 ! Validation 476 47242.143 0.005 0.0531 0.289 1.35 0.202 0.27 0.53 0.63 0.00151 0.00179 Wall time: 47242.143388900906 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 1.39 0.0461 0.465 0.186 0.252 0.679 0.8 0.00193 0.00227 477 172 1.22 0.0429 0.359 0.18 0.243 0.623 0.702 0.00177 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 1.74 0.0423 0.893 0.181 0.241 1.1 1.11 0.00313 0.00315 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 47341.214 0.005 0.0467 0.492 1.43 0.188 0.254 0.658 0.823 0.00187 0.00234 ! Validation 477 47341.214 0.005 0.0476 0.797 1.75 0.191 0.256 0.972 1.05 0.00276 0.00298 Wall time: 47341.214519649744 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 1.36 0.0414 0.534 0.178 0.239 0.782 0.857 0.00222 0.00244 478 172 2.32 0.0377 1.56 0.169 0.228 1.45 1.47 0.00412 0.00417 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 1.14 0.0388 0.363 0.172 0.231 0.7 0.707 0.00199 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 478 47440.283 0.005 0.0433 0.528 1.39 0.181 0.244 0.654 0.852 0.00186 0.00242 ! Validation 478 47440.283 0.005 0.043 0.542 1.4 0.181 0.243 0.763 0.863 0.00217 0.00245 Wall time: 47440.2834072127 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 1.02 0.037 0.28 0.167 0.226 0.521 0.62 0.00148 0.00176 479 172 0.993 0.0379 0.236 0.17 0.228 0.451 0.569 0.00128 0.00162 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.824 0.0373 0.0771 0.168 0.227 0.32 0.326 0.000908 0.000925 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 479 47539.350 0.005 0.0382 0.502 1.27 0.17 0.229 0.679 0.831 0.00193 0.00236 ! Validation 479 47539.350 0.005 0.0412 0.238 1.06 0.177 0.238 0.454 0.572 0.00129 0.00162 Wall time: 47539.35076577077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 1.33 0.0344 0.644 0.161 0.218 0.83 0.941 0.00236 0.00267 480 172 0.75 0.0319 0.113 0.155 0.209 0.346 0.395 0.000984 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 1.76 0.0314 1.13 0.154 0.208 1.25 1.25 0.00354 0.00355 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 480 47638.510 0.005 0.0346 0.415 1.11 0.162 0.218 0.609 0.755 0.00173 0.00215 ! Validation 480 47638.510 0.005 0.0356 1.41 2.12 0.165 0.221 1.32 1.39 0.00375 0.00395 Wall time: 47638.50994864758 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.915 0.0308 0.298 0.153 0.206 0.589 0.64 0.00167 0.00182 481 172 0.858 0.0286 0.287 0.147 0.198 0.56 0.628 0.00159 0.00178 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.577 0.0279 0.0201 0.146 0.196 0.151 0.166 0.000428 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 481 47737.937 0.005 0.0312 0.398 1.02 0.154 0.207 0.595 0.74 0.00169 0.0021 ! Validation 481 47737.937 0.005 0.0317 0.127 0.76 0.156 0.209 0.331 0.418 0.000939 0.00119 Wall time: 47737.93757801177 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.712 0.0309 0.0942 0.153 0.206 0.29 0.36 0.000823 0.00102 482 172 2.15 0.027 1.61 0.143 0.193 1.46 1.49 0.00414 0.00422 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 1.49 0.027 0.955 0.143 0.193 1.14 1.15 0.00325 0.00326 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 482 47837.127 0.005 0.0289 0.439 1.02 0.148 0.199 0.61 0.777 0.00173 0.00221 ! Validation 482 47837.127 0.005 0.0299 1.5 2.1 0.151 0.203 1.32 1.44 0.00374 0.00409 Wall time: 47837.126908497885 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 1.08 0.026 0.561 0.14 0.189 0.848 0.879 0.00241 0.0025 483 172 1.67 0.0257 1.16 0.139 0.188 1.22 1.26 0.00348 0.00359 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 1.4 0.0258 0.884 0.139 0.188 1.1 1.1 0.00313 0.00313 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 483 47936.389 0.005 0.027 0.455 0.996 0.143 0.193 0.626 0.791 0.00178 0.00225 ! Validation 483 47936.389 0.005 0.0288 0.812 1.39 0.148 0.199 1 1.06 0.00285 0.003 Wall time: 47936.38908212772 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.71 0.023 0.25 0.133 0.178 0.525 0.587 0.00149 0.00167 484 172 0.935 0.0214 0.507 0.128 0.172 0.79 0.835 0.00224 0.00237 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.832 0.0218 0.395 0.128 0.173 0.735 0.737 0.00209 0.00209 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 484 48035.806 0.005 0.0245 0.321 0.81 0.136 0.184 0.519 0.664 0.00147 0.00189 ! Validation 484 48035.806 0.005 0.0249 0.692 1.19 0.138 0.185 0.9 0.976 0.00256 0.00277 Wall time: 48035.80626890063 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 1 0.0231 0.54 0.132 0.178 0.838 0.862 0.00238 0.00245 485 172 0.67 0.0208 0.255 0.126 0.169 0.53 0.592 0.00151 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.568 0.0204 0.16 0.124 0.168 0.465 0.469 0.00132 0.00133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 485 48135.075 0.005 0.0224 0.345 0.793 0.131 0.176 0.562 0.689 0.0016 0.00196 ! Validation 485 48135.075 0.005 0.0232 0.24 0.704 0.134 0.179 0.501 0.574 0.00142 0.00163 Wall time: 48135.07517444203 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.708 0.0216 0.275 0.129 0.172 0.572 0.616 0.00162 0.00175 486 172 0.619 0.0203 0.214 0.125 0.167 0.494 0.542 0.0014 0.00154 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.648 0.019 0.269 0.12 0.161 0.605 0.608 0.00172 0.00173 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 486 48234.155 0.005 0.0219 0.382 0.82 0.129 0.174 0.579 0.725 0.00164 0.00206 ! Validation 486 48234.155 0.005 0.0218 0.41 0.847 0.13 0.173 0.671 0.751 0.00191 0.00213 Wall time: 48234.15499668801 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.707 0.0206 0.295 0.125 0.168 0.578 0.637 0.00164 0.00181 487 172 0.52 0.0195 0.13 0.122 0.164 0.358 0.423 0.00102 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.939 0.0183 0.573 0.118 0.159 0.886 0.888 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 487 48333.285 0.005 0.021 0.383 0.803 0.126 0.17 0.563 0.726 0.0016 0.00206 ! Validation 487 48333.285 0.005 0.0212 0.767 1.19 0.128 0.171 0.969 1.03 0.00275 0.00292 Wall time: 48333.28510484472 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.52 0.019 0.141 0.12 0.162 0.378 0.44 0.00107 0.00125 488 172 1.16 0.0185 0.786 0.118 0.159 1.02 1.04 0.00291 0.00295 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.742 0.0179 0.384 0.116 0.157 0.726 0.727 0.00206 0.00207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 488 48432.418 0.005 0.0206 0.4 0.812 0.125 0.168 0.553 0.742 0.00157 0.00211 ! Validation 488 48432.418 0.005 0.0207 0.519 0.933 0.126 0.169 0.773 0.845 0.0022 0.0024 Wall time: 48432.417900831904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.567 0.0187 0.193 0.119 0.16 0.453 0.516 0.00129 0.00146 489 172 1.26 0.0317 0.626 0.157 0.209 0.728 0.928 0.00207 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.98 0.0309 0.362 0.155 0.206 0.693 0.706 0.00197 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 489 48531.518 0.005 0.0195 0.454 0.845 0.121 0.164 0.572 0.79 0.00163 0.00225 ! Validation 489 48531.518 0.005 0.033 0.761 1.42 0.16 0.213 0.967 1.02 0.00275 0.00291 Wall time: 48531.51881339401 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.401 0.0172 0.0569 0.115 0.154 0.237 0.28 0.000673 0.000795 490 172 0.429 0.0167 0.0941 0.112 0.152 0.313 0.36 0.000889 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.638 0.0155 0.327 0.109 0.146 0.669 0.671 0.0019 0.00191 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 490 48630.580 0.005 0.0189 0.213 0.59 0.119 0.161 0.439 0.541 0.00125 0.00154 ! Validation 490 48630.580 0.005 0.0181 0.498 0.86 0.118 0.158 0.76 0.828 0.00216 0.00235 Wall time: 48630.58026764961 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.537 0.0156 0.225 0.109 0.147 0.497 0.556 0.00141 0.00158 491 172 0.406 0.0159 0.087 0.112 0.148 0.292 0.346 0.00083 0.000983 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.316 0.0156 0.00369 0.109 0.147 0.0561 0.0712 0.000159 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 491 48729.652 0.005 0.0167 0.302 0.636 0.113 0.152 0.514 0.645 0.00146 0.00183 ! Validation 491 48729.652 0.005 0.0181 0.106 0.467 0.118 0.158 0.297 0.381 0.000843 0.00108 Wall time: 48729.65282322373 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.561 0.0173 0.215 0.115 0.154 0.506 0.543 0.00144 0.00154 492 172 0.327 0.0146 0.0358 0.106 0.142 0.184 0.222 0.000522 0.00063 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.528 0.0141 0.246 0.104 0.139 0.58 0.581 0.00165 0.00165 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 492 48828.781 0.005 0.0157 0.274 0.588 0.109 0.147 0.492 0.614 0.0014 0.00174 ! Validation 492 48828.781 0.005 0.0167 0.24 0.573 0.114 0.151 0.516 0.574 0.00147 0.00163 Wall time: 48828.78096831497 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.336 0.0142 0.0518 0.104 0.14 0.206 0.267 0.000586 0.000759 493 172 0.371 0.0169 0.0315 0.114 0.153 0.159 0.208 0.000452 0.000592 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.473 0.0159 0.154 0.111 0.148 0.459 0.461 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 493 48928.035 0.005 0.0151 0.314 0.617 0.108 0.144 0.524 0.657 0.00149 0.00187 ! Validation 493 48928.035 0.005 0.018 0.327 0.686 0.118 0.157 0.598 0.67 0.0017 0.0019 Wall time: 48928.035783819854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.339 0.0137 0.0653 0.102 0.137 0.237 0.3 0.000673 0.000852 494 172 0.378 0.0132 0.113 0.101 0.135 0.342 0.395 0.000971 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.29 0.0127 0.0356 0.0992 0.132 0.218 0.221 0.000619 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 494 49027.061 0.005 0.0143 0.245 0.53 0.105 0.14 0.47 0.58 0.00134 0.00165 ! Validation 494 49027.061 0.005 0.0151 0.0849 0.386 0.108 0.144 0.278 0.342 0.000788 0.000971 Wall time: 49027.06117773382 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.42 0.0127 0.166 0.0988 0.132 0.432 0.477 0.00123 0.00136 495 172 0.297 0.0127 0.0436 0.0987 0.132 0.211 0.245 0.0006 0.000696 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.399 0.012 0.159 0.0966 0.128 0.467 0.468 0.00133 0.00133 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 495 49126.095 0.005 0.0133 0.236 0.501 0.101 0.135 0.46 0.57 0.00131 0.00162 ! Validation 495 49126.095 0.005 0.0141 0.185 0.467 0.104 0.139 0.446 0.505 0.00127 0.00143 Wall time: 49126.095131180715 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.408 0.0142 0.125 0.105 0.14 0.35 0.414 0.000996 0.00118 496 172 0.47 0.0209 0.0518 0.128 0.17 0.221 0.267 0.000627 0.000758 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 1.41 0.0199 1.02 0.126 0.165 1.18 1.18 0.00336 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 496 49225.116 0.005 0.0128 0.278 0.533 0.0992 0.132 0.453 0.619 0.00129 0.00176 ! Validation 496 49225.116 0.005 0.0211 0.574 0.997 0.129 0.17 0.837 0.889 0.00238 0.00253 Wall time: 49225.116727026645 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.546 0.0126 0.293 0.0982 0.132 0.519 0.635 0.00147 0.0018 497 172 0.356 0.0142 0.0717 0.104 0.14 0.242 0.314 0.000686 0.000892 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.317 0.0134 0.0492 0.102 0.136 0.258 0.26 0.000734 0.000739 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 49324.183 0.005 0.0127 0.231 0.485 0.0989 0.132 0.445 0.564 0.00127 0.0016 ! Validation 497 49324.183 0.005 0.0154 0.354 0.661 0.109 0.146 0.573 0.698 0.00163 0.00198 Wall time: 49324.18321443768 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.481 0.0139 0.204 0.103 0.138 0.446 0.53 0.00127 0.00151 498 172 0.286 0.0111 0.0636 0.0929 0.124 0.247 0.296 0.000703 0.00084 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.259 0.0115 0.0285 0.0943 0.126 0.197 0.198 0.000559 0.000563 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 498 49423.260 0.005 0.0123 0.265 0.512 0.0974 0.13 0.481 0.604 0.00137 0.00172 ! Validation 498 49423.260 0.005 0.0134 0.0842 0.352 0.102 0.136 0.26 0.34 0.000739 0.000967 Wall time: 49423.26021330897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.577 0.0107 0.363 0.0912 0.121 0.674 0.706 0.00191 0.00201 499 172 0.502 0.0106 0.29 0.0911 0.121 0.592 0.632 0.00168 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.218 0.0103 0.0106 0.0909 0.119 0.114 0.121 0.000325 0.000342 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 49523.782 0.005 0.0106 0.15 0.362 0.0907 0.121 0.36 0.454 0.00102 0.00129 ! Validation 499 49523.782 0.005 0.012 0.159 0.399 0.097 0.129 0.35 0.467 0.000995 0.00133 Wall time: 49523.78217855189 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.99 0.0104 0.782 0.0898 0.12 1.01 1.04 0.00287 0.00295 500 172 0.342 0.00917 0.159 0.0847 0.112 0.43 0.468 0.00122 0.00133 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.301 0.00944 0.112 0.0865 0.114 0.392 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 500 49622.844 0.005 0.0105 0.211 0.42 0.0903 0.12 0.434 0.539 0.00123 0.00153 ! Validation 500 49622.844 0.005 0.0114 0.215 0.443 0.0945 0.125 0.458 0.544 0.0013 0.00155 Wall time: 49622.84460850386 ! Stop training: max epochs Wall time: 49622.886416003574 Cumulative wall time: 49622.886416003574 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 = 0.059706 f_rmse = 0.081823 e_mae = 0.214943 e_rmse = 0.283073 e/N_mae = 0.000611 e/N_rmse = 0.000804 f_mae = 0.059706 f_rmse = 0.081823 e_mae = 0.214943 e_rmse = 0.283073 e/N_mae = 0.000611 e/N_rmse = 0.000804 Train end time: 2024-12-09_00:37:55 Training duration: 13h 50m 20s