Train start time: 2024-12-11_09:10:52 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 7.157 0.005 1.07 1.43e+03 1.45e+03 0.904 1.21 42.6 44.4 0.121 0.126 Wall time: 7.15717696910724 ! 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 0.00858 0.0104 1 172 22.3 0.996 2.43 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 0.379 0.857 1.14 0.536 0.722 0.00152 0.00205 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 1 110.368 0.005 0.995 5.37e+03 5.39e+03 0.871 1.17 32.3 86 0.0917 0.244 ! Validation 1 110.368 0.005 0.991 4.63 24.4 0.874 1.17 2.07 2.53 0.00587 0.00717 Wall time: 110.36904512206092 ! Best model 1 24.445 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 0.00308 0.00379 2 172 20.8 0.978 1.2 0.869 1.16 1.04 1.29 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 0.354 0.855 1.14 0.61 0.698 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 2 209.575 0.005 0.986 1.8 21.5 0.868 1.16 1.18 1.57 0.00335 0.00447 ! Validation 2 209.575 0.005 0.987 5.18 24.9 0.872 1.17 2.17 2.67 0.00616 0.00758 Wall time: 209.57524254079908 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 1.22 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 0.481 0.853 1.14 0.66 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 3 308.831 0.005 0.981 1.78 21.4 0.866 1.16 1.19 1.56 0.00337 0.00444 ! Validation 3 308.831 0.005 0.982 3.73 23.4 0.87 1.16 1.86 2.27 0.00528 0.00644 Wall time: 308.8336060261354 ! Best model 3 23.366 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 0.876 1.08 0.00249 0.00308 4 172 21 0.974 1.56 0.863 1.16 1.16 1.47 0.00331 0.00417 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 0.315 0.851 1.13 0.578 0.658 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 4 408.084 0.005 0.976 1.66 21.2 0.863 1.16 1.14 1.51 0.00325 0.0043 ! Validation 4 408.084 0.005 0.976 4.36 23.9 0.868 1.16 1.95 2.45 0.00554 0.00696 Wall time: 408.0842995890416 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 0.00421 5 172 19.8 0.952 0.781 0.855 1.14 0.77 1.04 0.00219 0.00295 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 0.292 0.849 1.13 0.488 0.634 0.00139 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 5 507.315 0.005 0.97 1.66 21.1 0.861 1.16 1.15 1.51 0.00327 0.00429 ! Validation 5 507.315 0.005 0.97 3.54 22.9 0.865 1.16 1.79 2.21 0.0051 0.00627 Wall time: 507.3154271300882 ! Best model 5 22.943 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 0.636 0.853 1.14 0.688 0.935 0.00195 0.00266 6 172 23 0.977 3.47 0.865 1.16 1.42 2.18 0.00403 0.0062 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 0.272 0.846 1.13 0.546 0.612 0.00155 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 6 606.549 0.005 0.964 1.65 20.9 0.858 1.15 1.15 1.5 0.00325 0.00427 ! Validation 6 606.549 0.005 0.964 3.6 22.9 0.862 1.15 1.77 2.23 0.00503 0.00632 Wall time: 606.5492093269713 ! Best model 6 22.873 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 1.56 0.00352 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 0.258 0.843 1.12 0.535 0.595 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 7 705.765 0.005 0.957 1.63 20.8 0.855 1.15 1.15 1.5 0.00326 0.00425 ! Validation 7 705.765 0.005 0.956 3.3 22.4 0.859 1.15 1.71 2.13 0.00485 0.00605 Wall time: 705.7652482907288 ! Best model 7 22.428 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 8 100 20.5 0.937 1.8 0.85 1.14 1.29 1.58 0.00365 0.00448 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 0.566 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 8 805.014 0.005 0.95 1.58 20.6 0.852 1.14 1.13 1.47 0.00321 0.00418 ! Validation 8 805.014 0.005 0.949 3.03 22 0.856 1.14 1.64 2.04 0.00466 0.0058 Wall time: 805.0139237027615 ! Best model 8 22.006 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 1.85 0.854 1.15 1.18 1.6 0.00336 0.00453 9 172 20.8 0.941 1.98 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 0.479 0.837 1.11 0.7 0.812 0.00199 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 9 904.240 0.005 0.942 1.52 20.4 0.848 1.14 1.11 1.44 0.00316 0.0041 ! Validation 9 904.240 0.005 0.941 3.44 22.3 0.852 1.14 1.7 2.18 0.00484 0.00618 Wall time: 904.2403259389102 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 1.07 1.36 0.00303 0.00385 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 0.215 0.833 1.11 0.457 0.544 0.0013 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 10 1003.457 0.005 0.933 1.53 20.2 0.844 1.13 1.12 1.45 0.00318 0.00412 ! Validation 10 1003.457 0.005 0.932 2.69 21.3 0.848 1.13 1.59 1.93 0.00452 0.00547 Wall time: 1003.4577130246907 ! Best model 10 21.335 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 1.32 1.55 0.00374 0.00441 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 0.882 3.39 0.829 1.1 2.09 2.16 0.00594 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 11 1103.146 0.005 0.924 1.62 20.1 0.84 1.13 1.16 1.49 0.0033 0.00424 ! Validation 11 1103.146 0.005 0.923 6.64 25.1 0.844 1.13 2.48 3.02 0.00705 0.00859 Wall time: 1103.1463152989745 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 12 100 19.6 0.901 1.54 0.829 1.11 1.19 1.46 0.00339 0.00413 12 172 19.7 0.921 1.25 0.838 1.13 1.04 1.31 0.00297 0.00373 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 0.412 0.824 1.1 0.647 0.753 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 1202.330 0.005 0.915 1.53 19.8 0.835 1.12 1.12 1.45 0.00319 0.00413 ! Validation 12 1202.330 0.005 0.913 2.98 21.2 0.839 1.12 1.6 2.03 0.00454 0.00576 Wall time: 1202.3321713111363 ! Best model 12 21.235 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 19.4 0.892 1.59 0.824 1.11 1.2 1.48 0.00341 0.0042 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 13 100 21.6 0.863 4.35 0.819 1.09 2.39 2.45 0.0068 0.00695 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 1301.530 0.005 0.904 1.66 19.7 0.831 1.12 1.17 1.51 0.00333 0.00429 ! Validation 13 1301.530 0.005 0.902 7.31 25.3 0.834 1.11 2.68 3.17 0.00761 0.00901 Wall time: 1301.5299166478217 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 1.43 0.82 1.1 1.07 1.4 0.00304 0.00399 14 172 19.7 0.887 1.94 0.824 1.1 1.24 1.63 0.00353 0.00464 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 0.306 0.814 1.08 0.556 0.649 0.00158 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 14 1400.661 0.005 0.893 1.79 19.6 0.825 1.11 1.23 1.57 0.00349 0.00446 ! Validation 14 1400.661 0.005 0.89 2.54 20.3 0.828 1.11 1.48 1.87 0.00421 0.00531 Wall time: 1400.661627674941 ! Best model 14 20.344 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 0.689 0.809 1.08 0.853 0.974 0.00242 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 1499.801 0.005 0.881 1.86 19.5 0.82 1.1 1.27 1.6 0.0036 0.00454 ! Validation 15 1499.801 0.005 0.878 2.87 20.4 0.823 1.1 1.57 1.99 0.00447 0.00565 Wall time: 1499.8012794507667 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 2.18 0.819 1.1 1.1 1.73 0.00314 0.00492 16 172 19.2 0.869 1.82 0.812 1.09 1.35 1.58 0.00382 0.00449 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 0.192 0.803 1.07 0.397 0.514 0.00113 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 16 1598.928 0.005 0.868 1.89 19.2 0.814 1.09 1.27 1.61 0.00362 0.00458 ! Validation 16 1598.928 0.005 0.864 2.09 19.4 0.816 1.09 1.37 1.7 0.00388 0.00482 Wall time: 1598.9283730708994 ! Best model 16 19.366 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 2.28 0.805 1.08 1.39 1.77 0.00395 0.00503 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 0.674 0.796 1.06 0.854 0.963 0.00243 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 17 1698.090 0.005 0.853 1.96 19 0.807 1.08 1.29 1.64 0.00368 0.00467 ! Validation 17 1698.090 0.005 0.849 2.58 19.6 0.809 1.08 1.48 1.89 0.00419 0.00536 Wall time: 1698.090671802871 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 1.21 1.41 0.00342 0.00402 18 172 18.5 0.848 1.52 0.807 1.08 1.15 1.45 0.00328 0.00411 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 0.796 6.09 0.787 1.05 2.86 2.9 0.00813 0.00823 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 1797.247 0.005 0.836 2.36 19.1 0.799 1.07 1.42 1.8 0.00403 0.00512 ! Validation 18 1797.247 0.005 0.831 7.23 23.8 0.8 1.07 2.58 3.15 0.00734 0.00896 Wall time: 1797.2472156337462 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 2.45 0.795 1.07 1.53 1.84 0.00433 0.00521 19 172 17.7 0.793 1.84 0.774 1.04 1.23 1.59 0.00349 0.00452 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 19 100 20.9 0.771 5.48 0.775 1.03 2.71 2.74 0.0077 0.0078 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 1896.382 0.005 0.816 1.98 18.3 0.789 1.06 1.3 1.65 0.0037 0.00469 ! Validation 19 1896.382 0.005 0.805 6.88 23 0.788 1.05 2.71 3.08 0.00771 0.00874 Wall time: 1896.3821819210425 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 20 100 18.4 0.798 2.42 0.777 1.05 1.45 1.82 0.00413 0.00518 20 172 17.6 0.765 2.33 0.762 1.03 1.46 1.79 0.00416 0.00509 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 0.731 0.205 0.755 1 0.383 0.532 0.00109 0.00151 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 20 1995.567 0.005 0.787 3.45 19.2 0.775 1.04 1.69 2.18 0.00479 0.00619 ! Validation 20 1995.567 0.005 0.763 1.73 17 0.767 1.02 1.22 1.54 0.00347 0.00438 Wall time: 1995.5675058448687 ! Best model 20 16.999 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 21 100 15.5 0.703 1.47 0.734 0.983 1.1 1.42 0.00312 0.00405 21 172 21.1 0.605 9.03 0.68 0.912 3.35 3.52 0.00951 0.01 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 1.91 0.675 0.893 1.57 1.62 0.00445 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 21 2094.737 0.005 0.697 3.89 17.8 0.729 0.979 1.88 2.31 0.00533 0.00657 ! Validation 21 2094.737 0.005 0.609 2.49 14.7 0.685 0.916 1.52 1.85 0.00433 0.00525 Wall time: 2094.7375368489884 ! Best model 21 14.674 training # 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 0.449 1.6 0.587 0.786 1.24 1.48 0.00352 0.00421 22 172 11.9 0.38 4.3 0.538 0.723 2.21 2.43 0.00627 0.00691 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 22 100 9.73 0.362 2.5 0.532 0.706 1.78 1.85 0.00505 0.00527 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 2193.922 0.005 0.471 5.08 14.5 0.598 0.805 1.98 2.64 0.00562 0.00751 ! Validation 22 2193.922 0.005 0.39 3.05 10.9 0.541 0.733 1.77 2.05 0.00502 0.00582 Wall time: 2193.9226958728395 ! Best model 22 10.861 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 7.8 0.345 0.907 0.508 0.688 0.901 1.12 0.00256 0.00317 23 172 14.7 0.342 7.91 0.507 0.686 3.09 3.3 0.00878 0.00937 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 23 100 8.44 0.327 1.91 0.503 0.67 1.54 1.62 0.00436 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 23 2293.084 0.005 0.355 5.52 12.6 0.516 0.699 2.21 2.76 0.00629 0.00783 ! Validation 23 2293.084 0.005 0.356 4.45 11.6 0.515 0.7 2.11 2.47 0.006 0.00703 Wall time: 2293.0848326599225 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 17.6 0.332 11 0.499 0.676 3.52 3.88 0.01 0.011 24 172 7.93 0.331 1.31 0.5 0.675 1.17 1.34 0.00331 0.00382 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 24 100 8.44 0.312 2.2 0.492 0.655 1.67 1.74 0.00473 0.00494 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 2392.234 0.005 0.337 6.93 13.7 0.503 0.681 2.45 3.09 0.00695 0.00878 ! Validation 24 2392.234 0.005 0.34 2.97 9.77 0.504 0.684 1.64 2.02 0.00466 0.00574 Wall time: 2392.234491276089 ! Best model 24 9.769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 7.19 0.31 0.987 0.483 0.653 0.885 1.17 0.00251 0.00331 25 172 8.24 0.302 2.19 0.476 0.645 1.43 1.74 0.00407 0.00493 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 25 100 5.92 0.282 0.275 0.468 0.623 0.505 0.615 0.00143 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 25 2491.423 0.005 0.312 2.26 8.51 0.483 0.656 1.39 1.76 0.00395 0.00501 ! Validation 25 2491.423 0.005 0.307 2.58 8.72 0.48 0.65 1.57 1.88 0.00447 0.00535 Wall time: 2491.423011207953 ! Best model 25 8.724 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 12.9 0.29 7.07 0.464 0.631 2.92 3.12 0.0083 0.00886 26 172 14.8 0.276 9.25 0.456 0.616 3.44 3.57 0.00977 0.0101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 26 100 5.5 0.267 0.161 0.457 0.606 0.408 0.47 0.00116 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 26 2590.580 0.005 0.287 4.31 10.1 0.464 0.629 1.89 2.43 0.00538 0.00691 ! Validation 26 2590.580 0.005 0.289 0.836 6.62 0.466 0.631 0.852 1.07 0.00242 0.00305 Wall time: 2590.5803717658855 ! Best model 26 6.623 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 45.3 0.272 39.8 0.453 0.612 7.3 7.4 0.0207 0.021 27 172 12.4 0.282 6.74 0.46 0.623 2.83 3.04 0.00805 0.00865 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 27 100 13.1 0.264 7.8 0.455 0.603 3.25 3.28 0.00922 0.00931 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 2689.736 0.005 0.278 6.83 12.4 0.458 0.619 2.35 3.07 0.00669 0.00871 ! Validation 27 2689.736 0.005 0.284 7.88 13.6 0.463 0.625 3.13 3.29 0.00888 0.00935 Wall time: 2689.736094989814 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 6.88 0.256 1.75 0.441 0.594 1.32 1.55 0.00375 0.00441 28 172 11.5 0.242 6.69 0.427 0.577 2.89 3.03 0.0082 0.00862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 28 100 7.86 0.24 3.05 0.435 0.575 2.01 2.05 0.0057 0.00582 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 2788.883 0.005 0.261 3.4 8.62 0.444 0.599 1.77 2.16 0.00503 0.00615 ! Validation 28 2788.883 0.005 0.257 3.01 8.15 0.441 0.594 1.79 2.04 0.00507 0.00578 Wall time: 2788.8830666551366 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 13.2 0.247 8.22 0.431 0.583 3.18 3.36 0.00904 0.00955 29 172 8.09 0.24 3.3 0.427 0.574 1.98 2.13 0.00564 0.00605 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 29 100 11.6 0.23 6.98 0.427 0.563 3.07 3.1 0.00872 0.0088 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 2888.022 0.005 0.244 5.24 10.1 0.43 0.579 2.16 2.69 0.00613 0.00763 ! Validation 29 2888.022 0.005 0.244 7.15 12 0.431 0.58 2.99 3.14 0.0085 0.00891 Wall time: 2888.0225472711027 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 6.79 0.219 2.41 0.408 0.549 1.69 1.82 0.0048 0.00518 30 172 5.09 0.234 0.412 0.423 0.567 0.608 0.753 0.00173 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 30 100 6.43 0.225 1.93 0.423 0.557 1.59 1.63 0.00451 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 30 2987.587 0.005 0.229 5.17 9.75 0.417 0.561 2.02 2.67 0.00573 0.00758 ! Validation 30 2987.587 0.005 0.238 1.97 6.73 0.427 0.572 1.41 1.65 0.004 0.00468 Wall time: 2987.587835557759 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 6.38 0.212 2.15 0.402 0.54 1.34 1.72 0.00381 0.00488 31 172 5.57 0.201 1.54 0.394 0.526 1.33 1.46 0.00379 0.00414 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 31 100 4.46 0.199 0.479 0.398 0.523 0.726 0.812 0.00206 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 31 3086.775 0.005 0.214 3.26 7.53 0.404 0.542 1.62 2.12 0.0046 0.00601 ! Validation 31 3086.775 0.005 0.209 1.09 5.27 0.401 0.536 1.01 1.22 0.00286 0.00347 Wall time: 3086.775524634868 ! Best model 31 5.265 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 4.44 0.195 0.529 0.387 0.519 0.678 0.853 0.00193 0.00242 32 172 5.28 0.186 1.56 0.377 0.506 1.29 1.47 0.00365 0.00417 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 32 100 3.88 0.182 0.232 0.381 0.501 0.462 0.564 0.00131 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 32 3185.963 0.005 0.195 2.85 6.75 0.387 0.518 1.63 1.98 0.00462 0.00563 ! Validation 32 3185.963 0.005 0.191 0.623 4.44 0.384 0.512 0.737 0.926 0.00209 0.00263 Wall time: 3185.9638454900123 ! Best model 32 4.439 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 11.4 0.183 7.75 0.375 0.502 3.22 3.27 0.00914 0.00928 33 172 5.2 0.172 1.76 0.366 0.487 1.41 1.56 0.00401 0.00442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 33 100 7.35 0.173 3.89 0.371 0.488 2.29 2.31 0.00651 0.00658 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 3285.134 0.005 0.182 4.33 7.97 0.374 0.5 2.02 2.44 0.00573 0.00693 ! Validation 33 3285.134 0.005 0.179 2.8 6.38 0.373 0.497 1.78 1.96 0.00505 0.00557 Wall time: 3285.1344180507585 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 3.74 0.165 0.444 0.357 0.476 0.665 0.782 0.00189 0.00222 34 172 3.7 0.155 0.597 0.348 0.462 0.7 0.906 0.00199 0.00257 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 34 100 3.15 0.154 0.0679 0.35 0.46 0.279 0.306 0.000792 0.000868 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 34 3384.302 0.005 0.166 2.47 5.8 0.359 0.478 1.44 1.84 0.00409 0.00524 ! Validation 34 3384.302 0.005 0.16 0.638 3.84 0.354 0.469 0.751 0.937 0.00213 0.00266 Wall time: 3384.302093778737 ! Best model 34 3.840 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 3.66 0.155 0.556 0.347 0.462 0.717 0.875 0.00204 0.00248 35 172 3.66 0.154 0.582 0.347 0.46 0.638 0.895 0.00181 0.00254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 35 100 5.22 0.149 2.24 0.345 0.453 1.73 1.76 0.00492 0.00499 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 3483.482 0.005 0.154 3.99 7.07 0.346 0.46 1.84 2.34 0.00524 0.00666 ! Validation 35 3483.482 0.005 0.154 1.53 4.61 0.349 0.46 1.22 1.45 0.00347 0.00412 Wall time: 3483.482484943699 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 4.19 0.14 1.38 0.332 0.439 1.23 1.38 0.0035 0.00392 36 172 3.35 0.133 0.692 0.323 0.428 0.836 0.976 0.00238 0.00277 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 36 100 8.54 0.133 5.87 0.326 0.428 2.83 2.84 0.00803 0.00807 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 3582.615 0.005 0.141 2.2 5.01 0.332 0.44 1.38 1.74 0.00393 0.00494 ! Validation 36 3582.615 0.005 0.138 4.66 7.43 0.331 0.436 2.41 2.53 0.00685 0.00719 Wall time: 3582.6156194577925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 3.87 0.126 1.35 0.315 0.416 1.18 1.36 0.00336 0.00388 37 172 3.66 0.131 1.05 0.321 0.424 0.955 1.2 0.00271 0.00341 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 37 100 4.14 0.135 1.45 0.328 0.431 1.38 1.41 0.00391 0.00401 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 37 3681.791 0.005 0.135 4.19 6.9 0.326 0.432 1.77 2.4 0.00502 0.00683 ! Validation 37 3681.791 0.005 0.139 1.11 3.88 0.333 0.437 1.05 1.23 0.00297 0.00351 Wall time: 3681.7917679380625 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 3.2 0.128 0.645 0.317 0.419 0.792 0.942 0.00225 0.00268 38 172 4.76 0.119 2.38 0.307 0.405 1.71 1.81 0.00485 0.00514 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 38 100 2.62 0.121 0.195 0.311 0.408 0.398 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 38 3780.936 0.005 0.129 2.69 5.28 0.32 0.422 1.42 1.92 0.00402 0.00546 ! Validation 38 3780.936 0.005 0.126 0.398 2.91 0.317 0.416 0.556 0.74 0.00158 0.0021 Wall time: 3780.9361012941226 ! Best model 38 2.910 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 3.49 0.122 1.05 0.31 0.409 1.04 1.2 0.00296 0.00342 39 172 2.47 0.115 0.176 0.302 0.397 0.351 0.492 0.000998 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 39 100 2.45 0.117 0.112 0.306 0.401 0.339 0.392 0.000962 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 39 3880.082 0.005 0.121 2.78 5.21 0.31 0.409 1.57 1.96 0.00445 0.00556 ! Validation 39 3880.082 0.005 0.121 0.702 3.11 0.311 0.407 0.812 0.983 0.00231 0.00279 Wall time: 3880.0821703318506 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 2.72 0.114 0.444 0.3 0.395 0.679 0.782 0.00193 0.00222 40 172 4.52 0.115 2.22 0.302 0.398 1.6 1.75 0.00455 0.00496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 40 100 5.9 0.117 3.56 0.305 0.401 2.19 2.21 0.00622 0.00629 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 3979.231 0.005 0.115 2.82 5.12 0.302 0.398 1.59 1.97 0.00452 0.00559 ! Validation 40 3979.231 0.005 0.12 2.28 4.68 0.311 0.407 1.62 1.77 0.0046 0.00503 Wall time: 3979.2315068957396 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 4.34 0.112 2.1 0.297 0.392 1.21 1.7 0.00345 0.00483 41 172 3.58 0.103 1.51 0.287 0.377 1.3 1.44 0.00369 0.0041 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 41 100 2.44 0.105 0.337 0.29 0.38 0.609 0.681 0.00173 0.00194 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 41 4078.365 0.005 0.108 1.57 3.73 0.293 0.385 1.18 1.47 0.00335 0.00418 ! Validation 41 4078.365 0.005 0.109 1.88 4.06 0.296 0.388 1.36 1.61 0.00387 0.00457 Wall time: 4078.3658434809186 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 2.77 0.103 0.709 0.286 0.377 0.893 0.988 0.00254 0.00281 42 172 3.18 0.107 1.03 0.293 0.384 1.02 1.19 0.00289 0.00338 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 42 100 2.26 0.108 0.106 0.293 0.385 0.328 0.382 0.000932 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 42 4177.498 0.005 0.109 3.01 5.19 0.294 0.387 1.53 2.04 0.00433 0.00579 ! Validation 42 4177.498 0.005 0.111 0.522 2.74 0.299 0.391 0.695 0.848 0.00197 0.00241 Wall time: 4177.498285972979 ! Best model 42 2.743 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 3.84 0.109 1.66 0.293 0.387 1.41 1.51 0.004 0.0043 43 172 3.28 0.0973 1.34 0.279 0.366 1.23 1.36 0.0035 0.00385 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 43 100 2.49 0.0991 0.505 0.282 0.369 0.781 0.834 0.00222 0.00237 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 43 4276.653 0.005 0.102 1.84 3.88 0.285 0.375 1.19 1.59 0.00338 0.00452 ! Validation 43 4276.653 0.005 0.103 1.83 3.89 0.288 0.377 1.4 1.59 0.00398 0.00451 Wall time: 4276.65363668697 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 5.07 0.0992 3.09 0.279 0.369 1.95 2.06 0.00555 0.00586 44 172 2.17 0.0914 0.344 0.269 0.355 0.553 0.688 0.00157 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 44 100 3.89 0.0969 1.95 0.279 0.365 1.61 1.64 0.00458 0.00466 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 4375.797 0.005 0.0982 2.02 3.99 0.279 0.368 1.35 1.67 0.00383 0.00474 ! Validation 44 4375.797 0.005 0.101 1.2 3.23 0.285 0.373 1.12 1.29 0.00318 0.00366 Wall time: 4375.79727849504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 4.59 0.0999 2.6 0.281 0.371 1.79 1.89 0.00508 0.00537 45 172 3.58 0.0994 1.6 0.28 0.37 1.31 1.48 0.00372 0.00421 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 45 100 10.7 0.102 8.64 0.285 0.375 3.44 3.45 0.00976 0.00979 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 4474.942 0.005 0.098 2.88 4.84 0.278 0.367 1.57 1.99 0.00447 0.00566 ! Validation 45 4474.942 0.005 0.105 9.9 12 0.289 0.38 3.62 3.69 0.0103 0.0105 Wall time: 4474.942690178752 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 3.8 0.0915 1.97 0.27 0.355 1.51 1.65 0.00429 0.00468 46 172 6.09 0.0927 4.24 0.271 0.357 2.28 2.41 0.00646 0.00686 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 46 100 5.67 0.0949 3.78 0.275 0.361 2.26 2.28 0.00643 0.00647 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 4574.061 0.005 0.0948 1.67 3.56 0.274 0.361 1.2 1.51 0.00342 0.0043 ! Validation 46 4574.061 0.005 0.0986 3.77 5.75 0.281 0.368 2.17 2.28 0.00617 0.00647 Wall time: 4574.061486439779 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 3.16 0.0885 1.39 0.265 0.349 1.31 1.38 0.00373 0.00392 47 172 2.12 0.0872 0.372 0.263 0.346 0.622 0.715 0.00177 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 47 100 2.62 0.0916 0.792 0.271 0.355 1 1.04 0.00285 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 47 4673.204 0.005 0.0912 1.73 3.55 0.268 0.354 1.22 1.54 0.00348 0.00438 ! Validation 47 4673.204 0.005 0.095 0.607 2.51 0.276 0.362 0.76 0.914 0.00216 0.0026 Wall time: 4673.204568723682 ! Best model 47 2.507 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 48 100 2.57 0.0898 0.773 0.267 0.352 0.876 1.03 0.00249 0.00293 48 172 2 0.0892 0.214 0.264 0.35 0.453 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 48 100 2.07 0.0929 0.208 0.272 0.357 0.474 0.535 0.00135 0.00152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 48 4772.345 0.005 0.0886 1.82 3.59 0.265 0.349 1.28 1.58 0.00363 0.0045 ! Validation 48 4772.345 0.005 0.0956 1.34 3.25 0.275 0.363 1.07 1.36 0.00305 0.00385 Wall time: 4772.344966218807 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 2.72 0.0843 1.04 0.259 0.34 1.05 1.2 0.00297 0.0034 49 172 2.34 0.104 0.264 0.286 0.378 0.489 0.603 0.00139 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 49 100 3.06 0.106 0.937 0.291 0.382 1.11 1.14 0.00315 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 49 4871.491 0.005 0.0877 2.29 4.05 0.263 0.347 1.36 1.78 0.00387 0.00505 ! Validation 49 4871.491 0.005 0.108 3.22 5.38 0.292 0.385 1.81 2.11 0.00515 0.00598 Wall time: 4871.49185468303 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.08 0.0852 0.375 0.261 0.342 0.608 0.718 0.00173 0.00204 50 172 2 0.085 0.302 0.259 0.342 0.53 0.645 0.00151 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 50 100 2.58 0.0886 0.813 0.266 0.349 1.02 1.06 0.0029 0.003 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 50 4970.727 0.005 0.0888 1.59 3.37 0.265 0.349 1.19 1.48 0.00338 0.00421 ! Validation 50 4970.727 0.005 0.0917 0.945 2.78 0.27 0.355 0.97 1.14 0.00276 0.00324 Wall time: 4970.727562687825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 3.36 0.0796 1.77 0.251 0.331 1.46 1.56 0.00415 0.00443 51 172 1.79 0.0781 0.23 0.249 0.328 0.439 0.562 0.00125 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 51 100 1.64 0.0796 0.0484 0.253 0.331 0.23 0.258 0.000653 0.000733 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 5071.006 0.005 0.0801 0.866 2.47 0.252 0.332 0.87 1.09 0.00247 0.0031 ! Validation 51 5071.006 0.005 0.0839 0.846 2.52 0.259 0.34 0.829 1.08 0.00235 0.00307 Wall time: 5071.006171087734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 52 100 4.28 0.0812 2.66 0.253 0.334 1.66 1.91 0.00471 0.00543 52 172 2.95 0.073 1.49 0.24 0.317 1.35 1.43 0.00384 0.00407 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 0.0758 0.0479 0.247 0.323 0.226 0.257 0.000641 0.000729 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 52 5170.235 0.005 0.077 1.02 2.56 0.247 0.326 0.932 1.19 0.00265 0.00337 ! Validation 52 5170.235 0.005 0.0799 0.268 1.87 0.252 0.332 0.467 0.608 0.00133 0.00173 Wall time: 5170.235305855051 ! Best model 52 1.867 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 2.47 0.0751 0.971 0.243 0.321 0.988 1.16 0.00281 0.00328 53 172 1.86 0.0717 0.422 0.238 0.314 0.643 0.762 0.00183 0.00217 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 53 100 3.8 0.0767 2.27 0.248 0.325 1.75 1.77 0.00497 0.00502 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 5269.481 0.005 0.076 1.36 2.88 0.245 0.323 1.09 1.37 0.00311 0.00389 ! Validation 53 5269.481 0.005 0.0807 2.18 3.79 0.253 0.333 1.62 1.73 0.0046 0.00492 Wall time: 5269.4815447330475 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 2.74 0.089 0.963 0.264 0.35 1.05 1.15 0.00297 0.00327 54 172 2.01 0.0747 0.515 0.243 0.321 0.681 0.842 0.00193 0.00239 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 54 100 1.53 0.0746 0.0417 0.245 0.32 0.209 0.24 0.000594 0.000681 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 54 5368.709 0.005 0.0804 1.87 3.47 0.251 0.333 1.2 1.6 0.00341 0.00455 ! Validation 54 5368.709 0.005 0.079 0.3 1.88 0.251 0.33 0.499 0.642 0.00142 0.00182 Wall time: 5368.709273635875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 2.1 0.073 0.643 0.24 0.317 0.859 0.941 0.00244 0.00267 55 172 3.16 0.0768 1.62 0.246 0.325 1.29 1.49 0.00367 0.00424 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 55 100 4.7 0.0792 3.12 0.252 0.33 2.06 2.07 0.00585 0.00588 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 5467.930 0.005 0.0744 1.37 2.86 0.242 0.32 1.08 1.37 0.00306 0.0039 ! Validation 55 5467.930 0.005 0.0823 2.46 4.11 0.255 0.336 1.68 1.84 0.00476 0.00523 Wall time: 5467.930490877945 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 2.7 0.0682 1.34 0.232 0.306 1.23 1.36 0.0035 0.00385 56 172 2.41 0.0784 0.842 0.25 0.328 0.928 1.08 0.00264 0.00306 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 56 100 1.89 0.0792 0.301 0.253 0.33 0.616 0.644 0.00175 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 56 5567.143 0.005 0.0711 0.965 2.39 0.237 0.313 0.898 1.15 0.00255 0.00327 ! Validation 56 5567.143 0.005 0.0817 3.35 4.98 0.255 0.335 1.62 2.15 0.0046 0.0061 Wall time: 5567.143321761861 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 1.63 0.069 0.25 0.233 0.308 0.504 0.587 0.00143 0.00167 57 172 3.15 0.0696 1.76 0.233 0.309 1.45 1.56 0.00412 0.00442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 57 100 6.45 0.0695 5.06 0.237 0.309 2.63 2.64 0.00748 0.0075 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 5666.346 0.005 0.0699 0.937 2.33 0.234 0.31 0.899 1.14 0.00255 0.00323 ! Validation 57 5666.346 0.005 0.0735 6.21 7.68 0.242 0.318 2.79 2.92 0.00794 0.0083 Wall time: 5666.346710633952 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 2.83 0.067 1.49 0.23 0.304 1.33 1.43 0.00377 0.00406 58 172 1.9 0.067 0.562 0.23 0.304 0.645 0.879 0.00183 0.0025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 58 100 1.46 0.0694 0.077 0.236 0.309 0.277 0.326 0.000788 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 58 5769.851 0.005 0.0669 0.992 2.33 0.229 0.303 0.939 1.17 0.00267 0.00332 ! Validation 58 5769.851 0.005 0.0728 0.849 2.3 0.24 0.316 0.874 1.08 0.00248 0.00307 Wall time: 5769.850938248914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.47 0.0671 0.128 0.23 0.304 0.33 0.419 0.000938 0.00119 59 172 1.47 0.0626 0.217 0.223 0.294 0.461 0.546 0.00131 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 59 100 1.46 0.0652 0.154 0.229 0.3 0.413 0.46 0.00117 0.00131 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 59 5869.072 0.005 0.0664 1.04 2.36 0.229 0.302 0.946 1.19 0.00269 0.00339 ! Validation 59 5869.072 0.005 0.0692 0.448 1.83 0.234 0.309 0.605 0.785 0.00172 0.00223 Wall time: 5869.072261327878 ! Best model 59 1.832 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 2.01 0.0749 0.512 0.242 0.321 0.675 0.839 0.00192 0.00238 60 172 2.75 0.0678 1.39 0.231 0.306 1.27 1.39 0.00362 0.00394 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 60 100 3.84 0.0693 2.45 0.236 0.309 1.82 1.84 0.00518 0.00521 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 5968.306 0.005 0.0692 1.6 2.98 0.233 0.308 1.17 1.48 0.00331 0.00421 ! Validation 60 5968.306 0.005 0.0727 2.11 3.57 0.24 0.316 1.61 1.71 0.00456 0.00485 Wall time: 5968.306384562049 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 1.6 0.062 0.362 0.221 0.292 0.586 0.706 0.00166 0.00201 61 172 3.16 0.0645 1.87 0.224 0.298 1.5 1.6 0.00425 0.00455 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 61 100 5.36 0.0671 4.02 0.232 0.304 2.34 2.35 0.00665 0.00668 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 6067.537 0.005 0.0661 1.05 2.37 0.228 0.301 0.948 1.2 0.00269 0.00342 ! Validation 61 6067.537 0.005 0.0698 5.58 6.98 0.235 0.31 2.65 2.77 0.00753 0.00787 Wall time: 6067.53695712192 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 1.47 0.0591 0.284 0.215 0.285 0.538 0.626 0.00153 0.00178 62 172 2.42 0.0618 1.19 0.219 0.292 1.22 1.28 0.00348 0.00363 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 62 100 2.26 0.0645 0.967 0.227 0.298 1.14 1.15 0.00324 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 62 6167.917 0.005 0.0616 0.765 2 0.22 0.291 0.824 1.03 0.00234 0.00291 ! Validation 62 6167.917 0.005 0.0669 1.1 2.43 0.229 0.303 1.05 1.23 0.00298 0.00349 Wall time: 6167.916892725974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 63 100 8.72 0.0597 7.52 0.217 0.287 3.04 3.22 0.00865 0.00914 63 172 1.65 0.0588 0.478 0.213 0.284 0.608 0.811 0.00173 0.0023 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 0.0589 0.22 0.217 0.285 0.52 0.55 0.00148 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 63 6267.143 0.005 0.0608 0.836 2.05 0.218 0.289 0.84 1.07 0.00239 0.00305 ! Validation 63 6267.143 0.005 0.0625 0.418 1.67 0.222 0.293 0.597 0.758 0.0017 0.00215 Wall time: 6267.143151741009 ! Best model 63 1.668 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 2 0.0695 0.606 0.235 0.309 0.774 0.913 0.0022 0.00259 64 172 1.48 0.0559 0.358 0.21 0.277 0.61 0.702 0.00173 0.002 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 64 100 2.75 0.0594 1.56 0.218 0.286 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 64 6366.408 0.005 0.0623 1.14 2.38 0.221 0.293 0.949 1.25 0.0027 0.00355 ! Validation 64 6366.408 0.005 0.0626 1.3 2.55 0.222 0.293 1.23 1.34 0.00348 0.0038 Wall time: 6366.408780918922 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 1.45 0.0595 0.262 0.215 0.286 0.477 0.6 0.00136 0.00171 65 172 1.6 0.0533 0.532 0.204 0.271 0.725 0.856 0.00206 0.00243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 65 100 2.31 0.0577 1.15 0.215 0.282 1.25 1.26 0.00356 0.00358 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 6465.648 0.005 0.0576 0.767 1.92 0.212 0.281 0.815 1.03 0.00232 0.00292 ! Validation 65 6465.648 0.005 0.0615 1.04 2.27 0.22 0.291 1.06 1.2 0.003 0.0034 Wall time: 6465.648462120909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.66 0.0595 0.471 0.215 0.286 0.643 0.805 0.00183 0.00229 66 172 1.31 0.0547 0.217 0.207 0.274 0.437 0.546 0.00124 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 66 100 1.24 0.0562 0.12 0.212 0.278 0.383 0.407 0.00109 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 66 6564.885 0.005 0.0569 0.927 2.06 0.211 0.28 0.916 1.13 0.0026 0.00321 ! Validation 66 6564.885 0.005 0.0603 0.282 1.49 0.218 0.288 0.478 0.623 0.00136 0.00177 Wall time: 6564.885059399996 ! Best model 66 1.489 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.35 0.056 0.232 0.209 0.278 0.47 0.565 0.00134 0.00161 67 172 1.75 0.0571 0.607 0.211 0.28 0.771 0.914 0.00219 0.0026 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 67 100 1.15 0.0568 0.0146 0.213 0.28 0.116 0.142 0.000329 0.000402 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 67 6664.139 0.005 0.0552 0.867 1.97 0.208 0.276 0.869 1.09 0.00247 0.0031 ! Validation 67 6664.139 0.005 0.0603 0.28 1.49 0.218 0.288 0.475 0.621 0.00135 0.00176 Wall time: 6664.139083917718 ! Best model 67 1.487 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.47 0.0597 0.282 0.215 0.286 0.515 0.622 0.00146 0.00177 68 172 1.21 0.0538 0.129 0.204 0.272 0.348 0.422 0.000989 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 68 100 1.09 0.0531 0.026 0.206 0.27 0.144 0.189 0.00041 0.000537 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 68 6763.605 0.005 0.0548 0.886 1.98 0.207 0.275 0.865 1.1 0.00246 0.00314 ! Validation 68 6763.605 0.005 0.0566 0.223 1.36 0.211 0.279 0.428 0.554 0.00122 0.00157 Wall time: 6763.605134747922 ! Best model 68 1.355 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.7 0.0517 0.661 0.201 0.267 0.833 0.953 0.00237 0.00271 69 172 1.5 0.0562 0.375 0.21 0.278 0.55 0.718 0.00156 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 69 100 1.23 0.0583 0.0645 0.215 0.283 0.268 0.298 0.000763 0.000847 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 69 6862.824 0.005 0.0525 0.913 1.96 0.202 0.269 0.856 1.12 0.00243 0.00318 ! Validation 69 6862.824 0.005 0.0609 1.41 2.63 0.218 0.289 1.14 1.39 0.00322 0.00396 Wall time: 6862.824253462721 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 1.62 0.0479 0.666 0.194 0.257 0.801 0.958 0.00228 0.00272 70 172 3.91 0.0506 2.9 0.198 0.264 1.94 2 0.00551 0.00567 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 70 100 1.93 0.054 0.854 0.207 0.273 1.08 1.08 0.00306 0.00308 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 6962.016 0.005 0.0501 0.655 1.66 0.198 0.263 0.75 0.948 0.00213 0.00269 ! Validation 70 6962.016 0.005 0.057 1.12 2.26 0.211 0.28 1.13 1.24 0.0032 0.00353 Wall time: 6962.016252316069 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 2.2 0.05 1.2 0.197 0.262 1.13 1.29 0.0032 0.00366 71 172 3.94 0.0643 2.65 0.227 0.297 1.81 1.91 0.00514 0.00543 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 71 100 2.83 0.0676 1.48 0.236 0.305 1.42 1.43 0.00404 0.00405 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 7067.219 0.005 0.0502 0.825 1.83 0.198 0.263 0.825 1.06 0.00234 0.00302 ! Validation 71 7067.219 0.005 0.0694 0.971 2.36 0.237 0.309 1 1.16 0.00285 0.00328 Wall time: 7067.219436770771 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.21 0.048 0.253 0.192 0.257 0.384 0.59 0.00109 0.00168 72 172 1.24 0.0469 0.305 0.191 0.254 0.531 0.648 0.00151 0.00184 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 72 100 1.27 0.0467 0.337 0.192 0.253 0.673 0.681 0.00191 0.00194 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 72 7167.463 0.005 0.0482 0.586 1.55 0.194 0.257 0.733 0.898 0.00208 0.00255 ! Validation 72 7167.463 0.005 0.0499 0.522 1.52 0.198 0.262 0.722 0.848 0.00205 0.00241 Wall time: 7167.463452452794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 1.96 0.0488 0.982 0.195 0.259 1.04 1.16 0.00296 0.0033 73 172 0.999 0.0434 0.131 0.184 0.244 0.343 0.425 0.000973 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 73 100 0.917 0.0452 0.0133 0.189 0.249 0.104 0.135 0.000296 0.000384 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 73 7266.627 0.005 0.0457 0.623 1.54 0.189 0.251 0.742 0.926 0.00211 0.00263 ! Validation 73 7266.627 0.005 0.0482 0.219 1.18 0.194 0.257 0.429 0.549 0.00122 0.00156 Wall time: 7266.627151446883 ! Best model 73 1.183 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 1.17 0.0436 0.295 0.183 0.245 0.479 0.637 0.00136 0.00181 74 172 1.14 0.0437 0.268 0.184 0.245 0.461 0.607 0.00131 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 74 100 0.967 0.0437 0.0924 0.186 0.245 0.339 0.356 0.000962 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 74 7365.801 0.005 0.0441 0.593 1.48 0.185 0.246 0.713 0.903 0.00203 0.00257 ! Validation 74 7365.801 0.005 0.0461 0.335 1.26 0.19 0.252 0.563 0.679 0.0016 0.00193 Wall time: 7365.801838424988 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 1.7 0.04 0.899 0.176 0.235 1.04 1.11 0.00297 0.00316 75 172 2.13 0.0439 1.25 0.184 0.246 1.26 1.31 0.00358 0.00373 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 75 100 2.01 0.0449 1.11 0.188 0.249 1.24 1.24 0.00351 0.00352 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 7464.950 0.005 0.043 0.676 1.54 0.183 0.243 0.779 0.964 0.00221 0.00274 ! Validation 75 7464.950 0.005 0.0481 0.911 1.87 0.194 0.257 0.997 1.12 0.00283 0.00318 Wall time: 7464.950383770745 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 0.952 0.0425 0.101 0.181 0.242 0.286 0.373 0.000812 0.00106 76 172 3.06 0.043 2.2 0.183 0.243 1.61 1.74 0.00458 0.00495 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 76 100 1.13 0.046 0.213 0.19 0.252 0.537 0.541 0.00153 0.00154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 76 7564.115 0.005 0.0437 0.793 1.67 0.184 0.245 0.837 1.04 0.00238 0.00296 ! Validation 76 7564.115 0.005 0.0485 3.37 4.34 0.195 0.258 1.62 2.15 0.00459 0.00612 Wall time: 7564.115602356847 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 2 0.0424 1.15 0.182 0.241 1.18 1.26 0.00336 0.00357 77 172 0.891 0.0405 0.0815 0.179 0.236 0.275 0.335 0.000782 0.000951 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 77 100 0.901 0.0422 0.0568 0.183 0.241 0.269 0.279 0.000764 0.000794 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 77 7663.282 0.005 0.0419 0.649 1.49 0.18 0.24 0.762 0.945 0.00217 0.00268 ! Validation 77 7663.282 0.005 0.0444 0.343 1.23 0.187 0.247 0.547 0.687 0.00155 0.00195 Wall time: 7663.281979579944 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 3.07 0.0461 2.15 0.189 0.252 1.55 1.72 0.00441 0.00489 78 172 1.75 0.0395 0.959 0.175 0.233 1.03 1.15 0.00293 0.00326 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 78 100 0.818 0.0401 0.0159 0.177 0.235 0.123 0.148 0.000349 0.00042 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 78 7762.426 0.005 0.0405 0.604 1.41 0.177 0.236 0.727 0.911 0.00206 0.00259 ! Validation 78 7762.426 0.005 0.0422 0.243 1.09 0.181 0.241 0.428 0.578 0.00122 0.00164 Wall time: 7762.426311601885 ! Best model 78 1.086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 1.46 0.036 0.746 0.166 0.222 0.897 1.01 0.00255 0.00288 79 172 1.48 0.0484 0.508 0.194 0.258 0.683 0.836 0.00194 0.00237 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 79 100 2.8 0.0551 1.7 0.213 0.275 1.52 1.53 0.00433 0.00434 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 79 7861.601 0.005 0.041 0.842 1.66 0.178 0.238 0.8 1.08 0.00227 0.00306 ! Validation 79 7861.601 0.005 0.0569 1.52 2.66 0.215 0.28 1.19 1.45 0.00337 0.00411 Wall time: 7861.601318703964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 80 100 1.17 0.0398 0.377 0.175 0.234 0.607 0.72 0.00173 0.00205 80 172 0.868 0.0366 0.137 0.168 0.224 0.358 0.434 0.00102 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 0.775 0.0376 0.0232 0.172 0.227 0.162 0.179 0.000461 0.000507 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 80 7960.799 0.005 0.0391 0.402 1.18 0.174 0.232 0.586 0.744 0.00167 0.00211 ! Validation 80 7960.799 0.005 0.0407 0.238 1.05 0.178 0.237 0.462 0.572 0.00131 0.00162 Wall time: 7960.799543237779 ! Best model 80 1.052 training # 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 0.0363 0.576 0.167 0.223 0.815 0.89 0.00232 0.00253 81 172 1.48 0.0374 0.738 0.17 0.227 0.791 1.01 0.00225 0.00286 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 81 100 1.12 0.0371 0.374 0.171 0.226 0.711 0.718 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 81 8059.980 0.005 0.0364 0.488 1.22 0.168 0.224 0.64 0.819 0.00182 0.00233 ! Validation 81 8059.980 0.005 0.0394 1.3 2.09 0.175 0.233 1.17 1.34 0.00333 0.0038 Wall time: 8059.97993060993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 1.31 0.0368 0.571 0.169 0.225 0.731 0.886 0.00208 0.00252 82 172 1.02 0.0322 0.376 0.157 0.21 0.629 0.719 0.00179 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 82 100 2.65 0.0365 1.92 0.169 0.224 1.62 1.62 0.00461 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 82 8159.145 0.005 0.0352 0.472 1.18 0.165 0.22 0.643 0.806 0.00183 0.00229 ! Validation 82 8159.145 0.005 0.0386 1.69 2.46 0.173 0.23 1.45 1.53 0.00412 0.00433 Wall time: 8159.14547604695 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.25 0.034 0.567 0.162 0.216 0.789 0.883 0.00224 0.00251 83 172 0.86 0.0341 0.179 0.161 0.216 0.406 0.496 0.00115 0.00141 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 83 100 1.55 0.0354 0.846 0.166 0.221 1.08 1.08 0.00306 0.00307 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 83 8258.311 0.005 0.0342 0.461 1.15 0.162 0.217 0.633 0.797 0.0018 0.00226 ! Validation 83 8258.311 0.005 0.0376 0.942 1.69 0.17 0.228 1.04 1.14 0.00294 0.00323 Wall time: 8258.311232371721 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.24 0.0338 0.566 0.161 0.216 0.714 0.882 0.00203 0.00251 84 172 0.925 0.0381 0.163 0.172 0.229 0.382 0.473 0.00108 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 84 100 1.28 0.0393 0.496 0.176 0.232 0.823 0.826 0.00234 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 84 8357.449 0.005 0.0412 0.913 1.74 0.177 0.238 0.853 1.12 0.00242 0.00319 ! Validation 84 8357.449 0.005 0.0406 0.398 1.21 0.178 0.236 0.622 0.74 0.00177 0.0021 Wall time: 8357.449165069032 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 0.794 0.0333 0.127 0.16 0.214 0.325 0.418 0.000923 0.00119 85 172 1.72 0.0326 1.06 0.16 0.212 1.1 1.21 0.00313 0.00344 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 85 100 0.951 0.0376 0.2 0.174 0.227 0.507 0.525 0.00144 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 85 8456.608 0.005 0.0338 0.389 1.07 0.161 0.216 0.595 0.731 0.00169 0.00208 ! Validation 85 8456.608 0.005 0.0389 0.712 1.49 0.176 0.231 0.89 0.989 0.00253 0.00281 Wall time: 8456.608545959927 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 1.38 0.0356 0.669 0.166 0.221 0.889 0.96 0.00252 0.00273 86 172 0.772 0.0308 0.156 0.155 0.206 0.346 0.463 0.000984 0.00132 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 86 100 0.651 0.0321 0.00793 0.158 0.21 0.0714 0.104 0.000203 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 86 8555.788 0.005 0.0325 0.367 1.02 0.158 0.211 0.568 0.711 0.00161 0.00202 ! Validation 86 8555.788 0.005 0.034 0.176 0.856 0.162 0.216 0.382 0.492 0.00108 0.0014 Wall time: 8555.788130851928 ! Best model 86 0.856 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.995 0.0305 0.386 0.153 0.205 0.471 0.728 0.00134 0.00207 87 172 0.691 0.0278 0.135 0.147 0.196 0.348 0.431 0.00099 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 87 100 0.914 0.0299 0.316 0.153 0.203 0.656 0.66 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 87 8655.020 0.005 0.0304 0.325 0.932 0.153 0.204 0.527 0.669 0.0015 0.0019 ! Validation 87 8655.020 0.005 0.0324 0.292 0.94 0.158 0.211 0.531 0.634 0.00151 0.0018 Wall time: 8655.019893413875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 88 100 1.36 0.032 0.719 0.157 0.21 0.877 0.995 0.00249 0.00283 88 172 0.764 0.0298 0.168 0.152 0.202 0.41 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 88 100 2.4 0.032 1.76 0.158 0.21 1.56 1.56 0.00442 0.00443 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 8754.172 0.005 0.0318 0.503 1.14 0.157 0.209 0.638 0.832 0.00181 0.00236 ! Validation 88 8754.172 0.005 0.0334 1.84 2.51 0.161 0.215 1.52 1.59 0.00431 0.00452 Wall time: 8754.172228519805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 0.701 0.0293 0.114 0.149 0.201 0.331 0.396 0.000939 0.00113 89 172 1.47 0.0294 0.881 0.15 0.201 1.04 1.1 0.00295 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 89 100 0.97 0.0301 0.367 0.155 0.204 0.708 0.711 0.00201 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 89 8853.403 0.005 0.0298 0.371 0.968 0.151 0.203 0.562 0.714 0.0016 0.00203 ! Validation 89 8853.403 0.005 0.0323 0.855 1.5 0.159 0.211 0.998 1.08 0.00283 0.00308 Wall time: 8853.402904498857 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 90 100 1.99 0.0287 1.42 0.15 0.199 1.35 1.4 0.00385 0.00397 90 172 0.767 0.0294 0.18 0.151 0.201 0.398 0.497 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 90 100 0.668 0.0302 0.0642 0.153 0.204 0.294 0.297 0.000834 0.000844 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 8952.562 0.005 0.0298 0.467 1.06 0.152 0.202 0.64 0.801 0.00182 0.00228 ! Validation 90 8952.562 0.005 0.0319 0.405 1.04 0.157 0.209 0.551 0.746 0.00157 0.00212 Wall time: 8952.56211550394 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 0.881 0.0269 0.342 0.144 0.192 0.617 0.686 0.00175 0.00195 91 172 0.809 0.0276 0.257 0.145 0.195 0.472 0.594 0.00134 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 91 100 0.948 0.0281 0.386 0.148 0.197 0.725 0.729 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 91 9051.735 0.005 0.028 0.313 0.873 0.147 0.196 0.52 0.656 0.00148 0.00186 ! Validation 91 9051.735 0.005 0.0306 0.259 0.87 0.154 0.205 0.497 0.597 0.00141 0.0017 Wall time: 9051.734899946023 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 1.92 0.0269 1.38 0.145 0.193 1.34 1.38 0.00382 0.00392 92 172 0.766 0.0261 0.243 0.142 0.19 0.472 0.578 0.00134 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 92 100 1.08 0.0265 0.554 0.144 0.191 0.871 0.873 0.00247 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 92 9150.977 0.005 0.0271 0.323 0.865 0.145 0.193 0.524 0.666 0.00149 0.00189 ! Validation 92 9150.977 0.005 0.0289 0.372 0.951 0.15 0.2 0.63 0.715 0.00179 0.00203 Wall time: 9150.977616191842 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 0.584 0.0252 0.0795 0.139 0.186 0.275 0.331 0.000782 0.00094 93 172 2.19 0.0277 1.63 0.148 0.195 1.46 1.5 0.00414 0.00426 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 93 100 3.84 0.0278 3.29 0.148 0.196 2.13 2.13 0.00604 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 93 9250.149 0.005 0.0262 0.324 0.847 0.142 0.19 0.528 0.666 0.0015 0.00189 ! Validation 93 9250.149 0.005 0.0291 2.32 2.9 0.15 0.2 1.73 1.79 0.00492 0.00508 Wall time: 9250.14978603879 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 0.678 0.0266 0.146 0.143 0.191 0.356 0.449 0.00101 0.00127 94 172 0.598 0.0246 0.106 0.138 0.184 0.312 0.383 0.000885 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 94 100 0.528 0.0261 0.0059 0.142 0.19 0.0839 0.0901 0.000238 0.000256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 94 9349.314 0.005 0.0297 0.526 1.12 0.151 0.202 0.659 0.851 0.00187 0.00242 ! Validation 94 9349.314 0.005 0.0284 0.168 0.736 0.148 0.198 0.386 0.48 0.0011 0.00136 Wall time: 9349.314657618757 ! Best model 94 0.736 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 1.1 0.027 0.557 0.144 0.193 0.678 0.875 0.00193 0.00249 95 172 0.988 0.0255 0.477 0.141 0.187 0.697 0.81 0.00198 0.0023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 95 100 0.632 0.0264 0.105 0.143 0.19 0.375 0.38 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 95 9449.468 0.005 0.0262 0.383 0.908 0.142 0.19 0.591 0.726 0.00168 0.00206 ! Validation 95 9449.468 0.005 0.0279 0.416 0.973 0.147 0.196 0.561 0.756 0.00159 0.00215 Wall time: 9449.468749512918 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 0.635 0.0243 0.148 0.137 0.183 0.369 0.451 0.00105 0.00128 96 172 0.57 0.0245 0.0787 0.137 0.184 0.239 0.329 0.000678 0.000935 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 96 100 0.655 0.0261 0.133 0.142 0.189 0.427 0.428 0.00121 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 96 9548.686 0.005 0.0257 0.352 0.866 0.141 0.188 0.558 0.696 0.00158 0.00198 ! Validation 96 9548.686 0.005 0.0286 0.211 0.784 0.149 0.198 0.435 0.539 0.00124 0.00153 Wall time: 9548.68671449786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 0.688 0.0224 0.241 0.132 0.176 0.417 0.575 0.00119 0.00163 97 172 1.32 0.0533 0.254 0.207 0.271 0.448 0.591 0.00127 0.00168 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 97 100 1.55 0.0562 0.424 0.213 0.278 0.75 0.764 0.00213 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 97 9647.898 0.005 0.0257 0.363 0.877 0.141 0.188 0.567 0.706 0.00161 0.00201 ! Validation 97 9647.898 0.005 0.0569 0.316 1.45 0.214 0.28 0.553 0.659 0.00157 0.00187 Wall time: 9647.898024524096 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 0.722 0.0244 0.234 0.137 0.183 0.425 0.567 0.00121 0.00161 98 172 0.628 0.024 0.148 0.137 0.182 0.379 0.45 0.00108 0.00128 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 98 100 0.63 0.0251 0.128 0.139 0.186 0.411 0.419 0.00117 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 98 9747.467 0.005 0.0276 0.435 0.987 0.146 0.195 0.603 0.774 0.00171 0.0022 ! Validation 98 9747.467 0.005 0.0271 0.14 0.682 0.145 0.193 0.34 0.439 0.000966 0.00125 Wall time: 9747.467351772822 ! Best model 98 0.682 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 0.787 0.0276 0.235 0.147 0.195 0.491 0.569 0.0014 0.00162 99 172 0.82 0.0223 0.373 0.131 0.175 0.606 0.717 0.00172 0.00204 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 99 100 0.716 0.0231 0.254 0.134 0.178 0.581 0.592 0.00165 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 99 9846.788 0.005 0.026 0.387 0.907 0.142 0.189 0.542 0.729 0.00154 0.00207 ! Validation 99 9846.788 0.005 0.0252 0.484 0.988 0.14 0.186 0.626 0.816 0.00178 0.00232 Wall time: 9846.788214338943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 0.74 0.0243 0.253 0.137 0.183 0.492 0.59 0.0014 0.00168 100 172 0.585 0.0215 0.154 0.129 0.172 0.378 0.461 0.00107 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 100 100 0.547 0.0221 0.104 0.131 0.175 0.375 0.378 0.00107 0.00107 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 100 9946.002 0.005 0.0235 0.294 0.764 0.135 0.18 0.51 0.637 0.00145 0.00181 ! Validation 100 9946.002 0.005 0.0245 0.113 0.603 0.138 0.184 0.314 0.395 0.000892 0.00112 Wall time: 9946.002486272715 ! Best model 100 0.603 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 0.641 0.0203 0.234 0.127 0.167 0.494 0.568 0.0014 0.00161 101 172 0.621 0.0245 0.13 0.137 0.184 0.351 0.424 0.000996 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 101 100 0.56 0.0232 0.0957 0.134 0.179 0.349 0.363 0.000993 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 101 10045.229 0.005 0.0228 0.33 0.786 0.133 0.177 0.542 0.674 0.00154 0.00192 ! Validation 101 10045.229 0.005 0.0259 0.242 0.76 0.141 0.189 0.432 0.577 0.00123 0.00164 Wall time: 10045.229017916135 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 3.9e+03 0.703 3.89e+03 0.743 0.984 73.1 73.1 0.208 0.208 102 172 20.4 0.949 1.44 0.853 1.14 1.18 1.41 0.00335 0.004 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 102 100 18.3 0.905 0.205 0.838 1.12 0.456 0.532 0.0013 0.00151 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 102 10144.428 0.005 0.436 144 153 0.455 0.775 5.11 14.1 0.0145 0.04 ! Validation 102 10144.428 0.005 0.947 2.96 21.9 0.854 1.14 1.62 2.02 0.00459 0.00573 Wall time: 10144.42880064901 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 16.5 0.767 1.13 0.764 1.03 0.935 1.25 0.00266 0.00355 103 172 13.2 0.619 0.876 0.689 0.923 0.83 1.1 0.00236 0.00312 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 103 100 12.2 0.6 0.172 0.687 0.909 0.373 0.487 0.00106 0.00138 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 103 10244.109 0.005 0.792 1.26 17.1 0.776 1.04 1 1.31 0.00284 0.00374 ! Validation 103 10244.109 0.005 0.625 2.54 15 0.696 0.928 1.52 1.87 0.00431 0.00531 Wall time: 10244.10919845011 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 9.76 0.387 2.02 0.553 0.73 1.37 1.67 0.00389 0.00473 104 172 7.07 0.297 1.13 0.491 0.639 1.05 1.25 0.00299 0.00354 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 104 100 6.26 0.301 0.24 0.499 0.644 0.461 0.575 0.00131 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 104 10343.316 0.005 0.427 1.01 9.55 0.577 0.766 0.916 1.18 0.0026 0.00336 ! Validation 104 10343.316 0.005 0.313 0.834 7.09 0.505 0.656 0.863 1.07 0.00245 0.00304 Wall time: 10343.31606071908 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 5.37 0.235 0.671 0.436 0.569 0.823 0.961 0.00234 0.00273 105 172 4.45 0.196 0.527 0.396 0.519 0.721 0.851 0.00205 0.00242 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 105 100 4.99 0.196 1.07 0.4 0.52 1.19 1.21 0.00338 0.00344 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 105 10442.537 0.005 0.24 1.07 5.86 0.44 0.574 0.962 1.21 0.00273 0.00344 ! Validation 105 10442.537 0.005 0.207 1.08 5.22 0.41 0.534 0.995 1.22 0.00283 0.00346 Wall time: 10442.537355103064 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 106 100 3.61 0.16 0.402 0.36 0.47 0.559 0.744 0.00159 0.00211 106 172 3.17 0.141 0.358 0.334 0.44 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 106 100 2.94 0.14 0.126 0.337 0.44 0.378 0.417 0.00107 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 106 10542.526 0.005 0.165 0.855 4.16 0.364 0.477 0.855 1.08 0.00243 0.00308 ! Validation 106 10542.526 0.005 0.149 0.831 3.81 0.345 0.452 0.857 1.07 0.00243 0.00304 Wall time: 10542.526574341115 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 2.95 0.122 0.507 0.31 0.41 0.678 0.835 0.00193 0.00237 107 172 2.9 0.114 0.63 0.299 0.395 0.738 0.931 0.0021 0.00264 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 107 100 2.15 0.106 0.0251 0.293 0.382 0.156 0.186 0.000444 0.000528 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 10641.775 0.005 0.124 0.728 3.21 0.313 0.413 0.799 1 0.00227 0.00284 ! Validation 107 10641.775 0.005 0.115 0.523 2.82 0.302 0.398 0.686 0.848 0.00195 0.00241 Wall time: 10641.775559325702 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 2.12 0.0928 0.268 0.272 0.357 0.536 0.607 0.00152 0.00173 108 172 2.42 0.0884 0.654 0.265 0.349 0.83 0.949 0.00236 0.0027 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 108 100 1.69 0.0834 0.0263 0.259 0.339 0.146 0.19 0.000415 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 108 10740.983 0.005 0.096 0.769 2.69 0.276 0.363 0.817 1.03 0.00232 0.00292 ! Validation 108 10740.983 0.005 0.0916 0.587 2.42 0.27 0.355 0.752 0.899 0.00214 0.00255 Wall time: 10740.983145236969 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 5.77 0.074 4.29 0.243 0.319 2.37 2.43 0.00672 0.0069 109 172 1.93 0.0676 0.582 0.234 0.305 0.775 0.894 0.0022 0.00254 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 109 100 1.49 0.0665 0.159 0.233 0.303 0.435 0.468 0.00123 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 109 10840.901 0.005 0.0757 0.629 2.14 0.246 0.323 0.727 0.93 0.00207 0.00264 ! Validation 109 10840.901 0.005 0.0739 0.6 2.08 0.244 0.319 0.747 0.909 0.00212 0.00258 Wall time: 10840.9016009029 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.61 0.0638 0.333 0.226 0.296 0.537 0.677 0.00153 0.00192 110 172 1.67 0.056 0.546 0.213 0.277 0.698 0.867 0.00198 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 110 100 1.73 0.0583 0.567 0.218 0.283 0.872 0.883 0.00248 0.00251 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 110 10940.076 0.005 0.0627 0.776 2.03 0.225 0.294 0.824 1.03 0.00234 0.00294 ! Validation 110 10940.076 0.005 0.0638 0.627 1.9 0.227 0.296 0.758 0.929 0.00215 0.00264 Wall time: 10940.07660085475 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.63 0.0547 0.534 0.21 0.274 0.751 0.857 0.00213 0.00243 111 172 1.48 0.0524 0.428 0.205 0.269 0.678 0.767 0.00193 0.00218 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 111 100 1.25 0.0536 0.179 0.208 0.271 0.476 0.496 0.00135 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 111 11039.257 0.005 0.056 0.89 2.01 0.212 0.278 0.891 1.11 0.00253 0.00314 ! Validation 111 11039.257 0.005 0.0575 1.39 2.54 0.215 0.281 1.12 1.38 0.00317 0.00393 Wall time: 11039.257835091092 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 1.58 0.0507 0.566 0.202 0.264 0.784 0.883 0.00223 0.00251 112 172 1.91 0.0483 0.949 0.196 0.258 1.06 1.14 0.00302 0.00325 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 112 100 2.27 0.0476 1.32 0.196 0.256 1.34 1.35 0.00381 0.00382 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 11138.437 0.005 0.0508 0.648 1.66 0.202 0.264 0.74 0.944 0.0021 0.00268 ! Validation 112 11138.437 0.005 0.0507 0.893 1.91 0.202 0.264 0.959 1.11 0.00272 0.00315 Wall time: 11138.437191545032 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 1.63 0.0426 0.78 0.185 0.242 0.964 1.04 0.00274 0.00294 113 172 1.38 0.0424 0.531 0.183 0.242 0.688 0.855 0.00195 0.00243 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 113 100 1.36 0.0418 0.527 0.183 0.24 0.842 0.851 0.00239 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 113 11237.631 0.005 0.0435 0.411 1.28 0.186 0.245 0.598 0.752 0.0017 0.00214 ! Validation 113 11237.631 0.005 0.0441 0.865 1.75 0.188 0.246 0.99 1.09 0.00281 0.0031 Wall time: 11237.631340688094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1.14 0.0374 0.39 0.172 0.227 0.616 0.733 0.00175 0.00208 114 172 0.877 0.0375 0.128 0.172 0.227 0.355 0.419 0.00101 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 114 100 1.63 0.0399 0.834 0.179 0.234 1.06 1.07 0.00302 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 114 11336.876 0.005 0.0403 0.646 1.45 0.179 0.236 0.736 0.943 0.00209 0.00268 ! Validation 114 11336.876 0.005 0.0421 0.638 1.48 0.183 0.241 0.815 0.937 0.00232 0.00266 Wall time: 11336.876625792123 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.893 0.0357 0.178 0.167 0.222 0.396 0.496 0.00112 0.00141 115 172 0.817 0.0341 0.135 0.163 0.217 0.354 0.43 0.00101 0.00122 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 115 100 0.772 0.036 0.0518 0.169 0.223 0.241 0.267 0.000684 0.000758 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 115 11436.214 0.005 0.0363 0.35 1.07 0.169 0.223 0.555 0.694 0.00158 0.00197 ! Validation 115 11436.214 0.005 0.0378 0.142 0.898 0.172 0.228 0.356 0.443 0.00101 0.00126 Wall time: 11436.213884049095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 0.824 0.0337 0.15 0.162 0.215 0.377 0.454 0.00107 0.00129 116 172 1.53 0.0345 0.84 0.164 0.218 0.951 1.08 0.0027 0.00305 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 116 100 0.722 0.0359 0.00436 0.168 0.222 0.064 0.0774 0.000182 0.00022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 116 11535.324 0.005 0.034 0.56 1.24 0.163 0.216 0.689 0.878 0.00196 0.00249 ! Validation 116 11535.324 0.005 0.0371 0.167 0.91 0.171 0.226 0.39 0.48 0.00111 0.00136 Wall time: 11535.324196157977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 0.789 0.0326 0.138 0.158 0.212 0.358 0.435 0.00102 0.00124 117 172 1.06 0.0327 0.409 0.159 0.212 0.685 0.75 0.00195 0.00213 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 117 100 0.784 0.0329 0.125 0.161 0.213 0.405 0.415 0.00115 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 117 11634.609 0.005 0.0328 0.387 1.04 0.159 0.212 0.583 0.73 0.00166 0.00207 ! Validation 117 11634.609 0.005 0.0345 0.194 0.883 0.164 0.218 0.423 0.517 0.0012 0.00147 Wall time: 11634.609140445013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.37 0.0308 0.749 0.154 0.206 0.95 1.02 0.0027 0.00288 118 172 0.971 0.0301 0.369 0.152 0.203 0.635 0.713 0.0018 0.00202 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 118 100 1.22 0.0315 0.588 0.157 0.208 0.894 0.899 0.00254 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 118 11733.751 0.005 0.0303 0.33 0.935 0.153 0.204 0.526 0.673 0.00149 0.00191 ! Validation 118 11733.751 0.005 0.0325 0.722 1.37 0.159 0.211 0.872 0.997 0.00248 0.00283 Wall time: 11733.751847275067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 0.722 0.0303 0.115 0.152 0.204 0.311 0.398 0.000883 0.00113 119 172 0.98 0.0299 0.383 0.152 0.203 0.57 0.726 0.00162 0.00206 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 119 100 1.42 0.0311 0.803 0.156 0.207 1.05 1.05 0.00298 0.00299 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 119 11832.890 0.005 0.03 0.474 1.07 0.152 0.203 0.644 0.808 0.00183 0.00229 ! Validation 119 11832.890 0.005 0.0323 0.904 1.55 0.158 0.211 1.02 1.12 0.00289 0.00317 Wall time: 11832.890702614095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 1.02 0.0296 0.428 0.15 0.202 0.696 0.767 0.00198 0.00218 120 172 0.671 0.0285 0.1 0.148 0.198 0.314 0.371 0.000893 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 120 100 0.677 0.0296 0.0851 0.152 0.202 0.333 0.342 0.000945 0.000972 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 11932.006 0.005 0.0294 0.424 1.01 0.15 0.201 0.619 0.764 0.00176 0.00217 ! Validation 120 11932.006 0.005 0.0311 0.144 0.766 0.155 0.207 0.361 0.445 0.00103 0.00126 Wall time: 11932.006017109845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.63 0.0283 0.064 0.148 0.197 0.246 0.297 0.0007 0.000843 121 172 0.903 0.0279 0.346 0.146 0.196 0.609 0.69 0.00173 0.00196 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 121 100 0.848 0.0294 0.26 0.151 0.201 0.594 0.598 0.00169 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 121 12031.707 0.005 0.0285 0.453 1.02 0.148 0.198 0.622 0.79 0.00177 0.00224 ! Validation 121 12031.707 0.005 0.0307 0.34 0.955 0.154 0.206 0.569 0.684 0.00162 0.00194 Wall time: 12031.707559393719 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 0.609 0.0275 0.058 0.145 0.195 0.23 0.282 0.000654 0.000802 122 172 1.47 0.0253 0.964 0.139 0.186 1.11 1.15 0.00316 0.00327 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 122 100 2.2 0.0282 1.64 0.148 0.197 1.5 1.5 0.00427 0.00427 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 12130.958 0.005 0.0275 0.349 0.9 0.145 0.195 0.544 0.692 0.00154 0.00197 ! Validation 122 12130.958 0.005 0.0297 1.24 1.84 0.152 0.202 1.25 1.31 0.00354 0.00371 Wall time: 12130.957938927691 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 1.51 0.0253 1.01 0.139 0.186 1.14 1.18 0.00323 0.00335 123 172 0.598 0.0256 0.0867 0.14 0.188 0.31 0.345 0.00088 0.000981 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 123 100 0.703 0.0271 0.16 0.146 0.193 0.467 0.469 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 123 12230.228 0.005 0.0262 0.291 0.815 0.142 0.19 0.498 0.633 0.00141 0.0018 ! Validation 123 12230.228 0.005 0.0286 0.214 0.786 0.149 0.198 0.453 0.543 0.00129 0.00154 Wall time: 12230.228589823935 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 0.696 0.026 0.175 0.14 0.189 0.423 0.491 0.0012 0.00139 124 172 0.692 0.0276 0.139 0.145 0.195 0.351 0.438 0.000996 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 124 100 1.56 0.0287 0.985 0.149 0.199 1.16 1.16 0.0033 0.00331 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 124 12329.480 0.005 0.0258 0.447 0.964 0.141 0.188 0.633 0.785 0.0018 0.00223 ! Validation 124 12329.480 0.005 0.0299 0.807 1.4 0.152 0.203 0.981 1.05 0.00279 0.00299 Wall time: 12329.480136838742 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 0.632 0.0245 0.142 0.137 0.184 0.382 0.442 0.00108 0.00126 125 172 1.21 0.0256 0.695 0.14 0.188 0.933 0.978 0.00265 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 125 100 0.525 0.0257 0.0123 0.142 0.188 0.124 0.13 0.000353 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 125 12428.739 0.005 0.0256 0.276 0.787 0.14 0.188 0.496 0.615 0.00141 0.00175 ! Validation 125 12428.739 0.005 0.0275 0.28 0.829 0.146 0.194 0.488 0.621 0.00139 0.00176 Wall time: 12428.739619589876 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.575 0.0247 0.0807 0.138 0.184 0.242 0.333 0.000687 0.000946 126 172 0.646 0.0238 0.169 0.135 0.181 0.359 0.482 0.00102 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 126 100 0.59 0.0251 0.0874 0.14 0.186 0.345 0.347 0.000981 0.000985 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 126 12528.271 0.005 0.0245 0.291 0.782 0.137 0.184 0.507 0.633 0.00144 0.0018 ! Validation 126 12528.271 0.005 0.0268 0.162 0.698 0.144 0.192 0.36 0.472 0.00102 0.00134 Wall time: 12528.271563681774 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 0.803 0.0241 0.321 0.136 0.182 0.62 0.665 0.00176 0.00189 127 172 1.19 0.0246 0.7 0.137 0.184 0.92 0.982 0.00261 0.00279 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 127 100 1.04 0.026 0.521 0.142 0.189 0.846 0.847 0.0024 0.00241 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 127 12627.447 0.005 0.0242 0.379 0.863 0.136 0.182 0.584 0.721 0.00166 0.00205 ! Validation 127 12627.447 0.005 0.0273 0.449 0.995 0.145 0.194 0.7 0.786 0.00199 0.00223 Wall time: 12627.44778298214 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.899 0.0254 0.39 0.139 0.187 0.65 0.732 0.00185 0.00208 128 172 0.642 0.0229 0.184 0.133 0.177 0.432 0.504 0.00123 0.00143 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 128 100 0.551 0.0244 0.0644 0.138 0.183 0.296 0.298 0.00084 0.000845 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 12726.644 0.005 0.0238 0.261 0.736 0.135 0.181 0.471 0.6 0.00134 0.0017 ! Validation 128 12726.644 0.005 0.0261 0.426 0.949 0.142 0.19 0.635 0.765 0.00181 0.00217 Wall time: 12726.644501528703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.65 0.0214 0.221 0.129 0.172 0.47 0.552 0.00133 0.00157 129 172 0.524 0.0222 0.0789 0.131 0.175 0.28 0.33 0.000795 0.000936 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 129 100 0.691 0.023 0.231 0.134 0.178 0.563 0.564 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 129 12825.809 0.005 0.0226 0.218 0.67 0.132 0.176 0.435 0.548 0.00124 0.00156 ! Validation 129 12825.809 0.005 0.025 0.207 0.707 0.139 0.185 0.446 0.534 0.00127 0.00152 Wall time: 12825.809738405049 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.673 0.0235 0.204 0.134 0.18 0.448 0.53 0.00127 0.0015 130 172 0.551 0.0216 0.12 0.128 0.172 0.344 0.407 0.000979 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 130 100 0.542 0.0225 0.0917 0.133 0.176 0.353 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 130 12925.087 0.005 0.0228 0.336 0.792 0.132 0.177 0.534 0.681 0.00152 0.00193 ! Validation 130 12925.087 0.005 0.0246 0.267 0.759 0.138 0.184 0.527 0.606 0.0015 0.00172 Wall time: 12925.087715453003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 1.08 0.0234 0.614 0.134 0.179 0.857 0.919 0.00243 0.00261 131 172 0.581 0.021 0.161 0.128 0.17 0.362 0.471 0.00103 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 131 100 0.451 0.0223 0.00579 0.132 0.175 0.0864 0.0892 0.000245 0.000254 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 131 13024.348 0.005 0.0218 0.271 0.707 0.13 0.173 0.5 0.611 0.00142 0.00174 ! Validation 131 13024.348 0.005 0.0242 0.321 0.804 0.137 0.182 0.512 0.664 0.00145 0.00189 Wall time: 13024.348748942837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.992 0.0202 0.589 0.124 0.167 0.83 0.9 0.00236 0.00256 132 172 0.505 0.0213 0.0781 0.129 0.171 0.251 0.328 0.000712 0.000931 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 132 100 0.444 0.0219 0.00592 0.131 0.174 0.0882 0.0902 0.00025 0.000256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 132 13123.619 0.005 0.0214 0.261 0.69 0.129 0.172 0.479 0.6 0.00136 0.0017 ! Validation 132 13123.619 0.005 0.0238 0.156 0.632 0.136 0.181 0.367 0.464 0.00104 0.00132 Wall time: 13123.619208279066 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.556 0.0216 0.124 0.129 0.172 0.339 0.413 0.000962 0.00117 133 172 0.753 0.0237 0.278 0.136 0.181 0.498 0.618 0.00142 0.00176 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 133 100 0.486 0.0231 0.0232 0.134 0.178 0.164 0.179 0.000466 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 133 13222.935 0.005 0.0216 0.379 0.812 0.129 0.172 0.581 0.722 0.00165 0.00205 ! Validation 133 13222.935 0.005 0.0252 0.143 0.647 0.14 0.186 0.354 0.444 0.00101 0.00126 Wall time: 13222.935800862033 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 1.1 0.0199 0.707 0.124 0.165 0.955 0.986 0.00271 0.0028 134 172 0.805 0.0214 0.378 0.128 0.172 0.656 0.721 0.00186 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 134 100 0.43 0.0205 0.02 0.127 0.168 0.165 0.166 0.00047 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 134 13322.194 0.005 0.0208 0.209 0.625 0.127 0.169 0.431 0.536 0.00122 0.00152 ! Validation 134 13322.194 0.005 0.0226 0.326 0.779 0.133 0.177 0.544 0.67 0.00155 0.0019 Wall time: 13322.194110687822 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 0.615 0.0209 0.196 0.127 0.17 0.46 0.519 0.00131 0.00147 135 172 0.588 0.0191 0.207 0.121 0.162 0.475 0.534 0.00135 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 135 100 1.09 0.0198 0.694 0.124 0.165 0.976 0.977 0.00277 0.00278 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 135 13421.738 0.005 0.02 0.236 0.636 0.124 0.166 0.454 0.569 0.00129 0.00162 ! Validation 135 13421.738 0.005 0.0218 0.62 1.06 0.13 0.173 0.864 0.924 0.00246 0.00262 Wall time: 13421.738252806943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 0.509 0.0199 0.111 0.125 0.166 0.317 0.391 0.000901 0.00111 136 172 0.943 0.0193 0.558 0.122 0.163 0.833 0.876 0.00237 0.00249 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 136 100 1.16 0.0198 0.76 0.124 0.165 1.02 1.02 0.00291 0.00291 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 13520.997 0.005 0.0197 0.295 0.689 0.124 0.165 0.509 0.637 0.00144 0.00181 ! Validation 136 13520.997 0.005 0.0217 0.894 1.33 0.13 0.173 1.04 1.11 0.00296 0.00315 Wall time: 13520.997223794926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.661 0.0196 0.268 0.123 0.164 0.425 0.607 0.00121 0.00173 137 172 0.564 0.0177 0.21 0.117 0.156 0.478 0.538 0.00136 0.00153 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 137 100 0.419 0.0194 0.0311 0.123 0.163 0.194 0.207 0.00055 0.000588 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 137 13620.240 0.005 0.0192 0.238 0.621 0.122 0.162 0.452 0.572 0.00128 0.00162 ! Validation 137 13620.240 0.005 0.0212 0.119 0.543 0.129 0.171 0.305 0.405 0.000867 0.00115 Wall time: 13620.24076299183 ! Best model 137 0.543 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.472 0.0191 0.0895 0.121 0.162 0.27 0.351 0.000768 0.000997 138 172 0.44 0.0176 0.0884 0.117 0.156 0.278 0.349 0.000789 0.000991 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 138 100 0.506 0.0183 0.14 0.12 0.159 0.435 0.439 0.00123 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 138 13719.513 0.005 0.0192 0.28 0.664 0.122 0.162 0.476 0.621 0.00135 0.00176 ! Validation 138 13719.513 0.005 0.0204 0.153 0.561 0.126 0.168 0.366 0.459 0.00104 0.0013 Wall time: 13719.513229412958 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.398 0.0171 0.0549 0.116 0.154 0.241 0.275 0.000684 0.00078 139 172 0.395 0.0167 0.0609 0.114 0.152 0.228 0.29 0.000648 0.000823 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 139 100 0.469 0.0173 0.124 0.117 0.154 0.41 0.412 0.00116 0.00117 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 139 13818.773 0.005 0.0182 0.22 0.585 0.119 0.158 0.43 0.551 0.00122 0.00156 ! Validation 139 13818.773 0.005 0.0197 0.127 0.522 0.124 0.165 0.348 0.418 0.000988 0.00119 Wall time: 13818.773479344789 ! Best model 139 0.522 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 1.23 0.0173 0.888 0.116 0.154 1.07 1.11 0.00303 0.00314 140 172 0.398 0.0172 0.0548 0.116 0.154 0.233 0.275 0.000661 0.00078 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 140 100 0.344 0.017 0.0033 0.116 0.153 0.0566 0.0674 0.000161 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 140 13918.061 0.005 0.0176 0.233 0.585 0.117 0.156 0.445 0.567 0.00126 0.00161 ! Validation 140 13918.061 0.005 0.0193 0.129 0.514 0.123 0.163 0.315 0.422 0.000894 0.0012 Wall time: 13918.061692723073 ! Best model 140 0.514 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 1.02 0.0163 0.694 0.112 0.15 0.943 0.977 0.00268 0.00278 141 172 0.703 0.0193 0.318 0.123 0.163 0.494 0.661 0.0014 0.00188 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 141 100 3.86 0.0206 3.45 0.128 0.168 2.18 2.18 0.00618 0.00619 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 14017.336 0.005 0.0176 0.352 0.704 0.117 0.156 0.56 0.696 0.00159 0.00198 ! Validation 141 14017.336 0.005 0.0221 2.67 3.11 0.132 0.174 1.87 1.92 0.00532 0.00544 Wall time: 14017.336643776856 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.629 0.0173 0.284 0.116 0.154 0.536 0.625 0.00152 0.00177 142 172 0.401 0.0166 0.0679 0.114 0.151 0.24 0.306 0.000683 0.000868 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 142 100 0.945 0.0169 0.607 0.115 0.152 0.912 0.914 0.00259 0.0026 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 142 14116.605 0.005 0.0184 0.311 0.679 0.12 0.159 0.529 0.654 0.0015 0.00186 ! Validation 142 14116.605 0.005 0.0189 0.596 0.973 0.121 0.161 0.847 0.905 0.00241 0.00257 Wall time: 14116.605440590996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.571 0.0165 0.241 0.114 0.151 0.516 0.576 0.00147 0.00164 143 172 0.759 0.0163 0.433 0.113 0.15 0.68 0.772 0.00193 0.00219 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 143 100 0.405 0.0172 0.0609 0.116 0.154 0.259 0.29 0.000737 0.000823 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 143 14215.876 0.005 0.0165 0.196 0.525 0.113 0.15 0.41 0.519 0.00117 0.00147 ! Validation 143 14215.876 0.005 0.0187 0.562 0.935 0.121 0.16 0.641 0.879 0.00182 0.0025 Wall time: 14215.876511676703 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.401 0.0168 0.0649 0.114 0.152 0.233 0.299 0.000661 0.000849 144 172 0.38 0.0149 0.0814 0.108 0.143 0.273 0.335 0.000775 0.000951 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 144 100 0.793 0.0158 0.476 0.112 0.148 0.803 0.81 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 144 14315.143 0.005 0.0162 0.219 0.542 0.112 0.149 0.436 0.549 0.00124 0.00156 ! Validation 144 14315.143 0.005 0.018 0.594 0.954 0.119 0.157 0.855 0.904 0.00243 0.00257 Wall time: 14315.14303046884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.422 0.0149 0.125 0.108 0.143 0.371 0.414 0.00105 0.00118 145 172 0.472 0.0159 0.153 0.112 0.148 0.402 0.459 0.00114 0.00131 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 145 100 0.339 0.0165 0.00837 0.115 0.151 0.082 0.107 0.000233 0.000305 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 145 14414.393 0.005 0.0152 0.211 0.514 0.109 0.144 0.413 0.538 0.00117 0.00153 ! Validation 145 14414.393 0.005 0.0186 0.0763 0.449 0.121 0.16 0.27 0.324 0.000766 0.000921 Wall time: 14414.393551714718 ! Best model 145 0.449 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.452 0.0134 0.184 0.103 0.136 0.46 0.503 0.00131 0.00143 146 172 0.763 0.016 0.443 0.112 0.148 0.613 0.781 0.00174 0.00222 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 146 100 0.372 0.015 0.0729 0.11 0.144 0.312 0.317 0.000886 0.0009 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 14515.377 0.005 0.015 0.193 0.493 0.108 0.144 0.407 0.515 0.00116 0.00146 ! Validation 146 14515.377 0.005 0.0173 0.103 0.45 0.117 0.154 0.306 0.377 0.00087 0.00107 Wall time: 14515.377037862781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.645 0.0138 0.369 0.104 0.138 0.67 0.713 0.0019 0.00203 147 172 0.336 0.0145 0.0467 0.106 0.141 0.198 0.254 0.000562 0.00072 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 147 100 0.327 0.0137 0.0526 0.105 0.137 0.254 0.269 0.00072 0.000764 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 14615.230 0.005 0.0143 0.177 0.462 0.106 0.14 0.397 0.493 0.00113 0.0014 ! Validation 147 14615.230 0.005 0.0158 0.157 0.474 0.111 0.148 0.385 0.465 0.00109 0.00132 Wall time: 14615.230423382018 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.448 0.014 0.167 0.104 0.139 0.431 0.48 0.00123 0.00136 148 172 0.329 0.0144 0.0414 0.106 0.141 0.189 0.239 0.000537 0.000678 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 148 100 0.447 0.0128 0.191 0.101 0.133 0.51 0.512 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 148 14714.365 0.005 0.014 0.2 0.479 0.105 0.139 0.427 0.524 0.00121 0.00149 ! Validation 148 14714.365 0.005 0.0153 0.132 0.438 0.109 0.145 0.369 0.427 0.00105 0.00121 Wall time: 14714.365600502118 ! Best model 148 0.438 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.3 0.0125 0.0501 0.0993 0.131 0.208 0.263 0.000591 0.000746 149 172 0.31 0.0132 0.0461 0.103 0.135 0.205 0.252 0.000583 0.000716 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 149 100 0.534 0.0122 0.29 0.0995 0.13 0.63 0.631 0.00179 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 149 14813.501 0.005 0.0133 0.178 0.445 0.102 0.135 0.402 0.495 0.00114 0.00141 ! Validation 149 14813.501 0.005 0.0147 0.156 0.45 0.107 0.142 0.406 0.464 0.00115 0.00132 Wall time: 14813.50179499574 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.452 0.015 0.153 0.108 0.144 0.383 0.458 0.00109 0.0013 150 172 0.372 0.0135 0.102 0.102 0.136 0.315 0.375 0.000895 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 150 100 0.249 0.0123 0.00327 0.0996 0.13 0.0484 0.067 0.000137 0.00019 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 14913.158 0.005 0.0147 0.282 0.576 0.108 0.142 0.487 0.623 0.00138 0.00177 ! Validation 150 14913.158 0.005 0.0148 0.0557 0.352 0.107 0.143 0.22 0.277 0.000624 0.000787 Wall time: 14913.158161915839 ! Best model 150 0.352 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.643 0.0119 0.404 0.0968 0.128 0.695 0.746 0.00197 0.00212 151 172 0.405 0.0138 0.129 0.104 0.138 0.377 0.421 0.00107 0.0012 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 151 100 0.339 0.0127 0.0856 0.102 0.132 0.337 0.343 0.000957 0.000975 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 151 15012.323 0.005 0.0127 0.166 0.42 0.0999 0.132 0.38 0.478 0.00108 0.00136 ! Validation 151 15012.323 0.005 0.0153 0.121 0.427 0.11 0.145 0.331 0.407 0.000941 0.00116 Wall time: 15012.323235664051 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.289 0.0126 0.0377 0.099 0.131 0.179 0.228 0.000509 0.000647 152 172 0.428 0.0141 0.145 0.106 0.139 0.388 0.447 0.0011 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 152 100 0.285 0.0137 0.0121 0.106 0.137 0.102 0.129 0.000289 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 152 15111.469 0.005 0.0133 0.215 0.48 0.102 0.135 0.408 0.544 0.00116 0.00154 ! Validation 152 15111.469 0.005 0.0154 0.084 0.392 0.111 0.145 0.272 0.34 0.000774 0.000966 Wall time: 15111.469202772714 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 0.348 0.0128 0.0918 0.101 0.133 0.311 0.355 0.000883 0.00101 153 172 0.292 0.0119 0.0543 0.0967 0.128 0.223 0.273 0.000633 0.000776 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 153 100 1.32 0.0113 1.09 0.0958 0.125 1.22 1.23 0.00348 0.00349 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 15210.689 0.005 0.0126 0.16 0.412 0.0994 0.131 0.367 0.47 0.00104 0.00134 ! Validation 153 15210.689 0.005 0.0133 0.585 0.85 0.102 0.135 0.794 0.897 0.00226 0.00255 Wall time: 15210.688977936748 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.581 0.0139 0.303 0.106 0.138 0.555 0.646 0.00158 0.00184 154 172 0.325 0.0112 0.1 0.0932 0.124 0.328 0.372 0.000931 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 154 100 0.384 0.0105 0.175 0.0918 0.12 0.487 0.491 0.00138 0.00139 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 154 15311.916 0.005 0.012 0.181 0.421 0.0973 0.129 0.393 0.499 0.00112 0.00142 ! Validation 154 15311.916 0.005 0.0125 0.165 0.415 0.0985 0.131 0.424 0.476 0.0012 0.00135 Wall time: 15311.916090416722 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.443 0.011 0.222 0.0932 0.123 0.49 0.553 0.00139 0.00157 155 172 0.356 0.0121 0.113 0.0994 0.129 0.335 0.395 0.000952 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 155 100 0.705 0.0111 0.482 0.0957 0.124 0.811 0.814 0.0023 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 155 15411.223 0.005 0.0113 0.16 0.386 0.0943 0.125 0.374 0.469 0.00106 0.00133 ! Validation 155 15411.223 0.005 0.013 0.747 1.01 0.102 0.134 0.957 1.01 0.00272 0.00288 Wall time: 15411.222926496994 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.903 0.0106 0.692 0.0905 0.12 0.949 0.976 0.0027 0.00277 156 172 0.278 0.0104 0.0703 0.0897 0.12 0.225 0.311 0.000639 0.000883 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 156 100 0.258 0.00972 0.0635 0.089 0.116 0.289 0.296 0.000822 0.00084 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 156 15513.624 0.005 0.0111 0.145 0.367 0.0937 0.124 0.354 0.446 0.00101 0.00127 ! Validation 156 15513.624 0.005 0.0122 0.135 0.378 0.0971 0.129 0.358 0.431 0.00102 0.00122 Wall time: 15513.624763451982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 0.277 0.0106 0.0641 0.0917 0.121 0.23 0.297 0.000652 0.000844 157 172 26.7 0.955 7.6 0.852 1.15 3.08 3.23 0.00874 0.00919 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 157 100 33.6 0.9 15.6 0.835 1.11 4.6 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 157 15612.774 0.005 0.176 187 190 0.22 0.491 4.89 16 0.0139 0.0456 ! Validation 157 15612.774 0.005 0.944 16.3 35.1 0.853 1.14 4.21 4.73 0.0119 0.0134 Wall time: 15612.774665472098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 19.6 0.9 1.59 0.831 1.11 1.23 1.48 0.00348 0.00421 158 172 18 0.863 0.719 0.811 1.09 0.81 0.995 0.0023 0.00283 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 158 100 16.9 0.827 0.36 0.799 1.07 0.596 0.704 0.00169 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 158 15712.688 0.005 0.914 5.39 23.7 0.833 1.12 1.7 2.72 0.00483 0.00774 ! Validation 158 15712.688 0.005 0.866 3.53 20.9 0.814 1.09 1.74 2.2 0.00495 0.00626 Wall time: 15712.688853623811 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 17.3 0.798 1.35 0.781 1.05 1.09 1.36 0.00308 0.00387 159 172 16.4 0.753 1.31 0.755 1.02 1.15 1.34 0.00326 0.00381 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 159 100 14.3 0.712 0.0631 0.743 0.99 0.249 0.295 0.000707 0.000837 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 15812.167 0.005 0.808 1.32 17.5 0.783 1.05 1.03 1.35 0.00292 0.00382 ! Validation 159 15812.167 0.005 0.748 3.14 18.1 0.757 1.01 1.77 2.08 0.00503 0.00591 Wall time: 15812.167241037823 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 14.5 0.657 1.37 0.706 0.951 1.02 1.37 0.00291 0.0039 160 172 13.3 0.577 1.77 0.663 0.891 1.3 1.56 0.00369 0.00443 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 160 100 10.8 0.537 0.015 0.645 0.86 0.116 0.144 0.000328 0.000408 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 160 15911.452 0.005 0.66 1.3 14.5 0.707 0.953 1.02 1.34 0.0029 0.0038 ! Validation 160 15911.452 0.005 0.571 2.55 14 0.662 0.886 1.59 1.87 0.0045 0.00532 Wall time: 15911.452418562025 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 10.1 0.437 1.34 0.578 0.775 1.1 1.36 0.00312 0.00386 161 172 8.65 0.36 1.44 0.522 0.704 1.13 1.41 0.00322 0.004 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 161 100 6.99 0.332 0.348 0.51 0.676 0.676 0.692 0.00192 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 161 16010.748 0.005 0.458 1.12 10.3 0.588 0.794 0.964 1.24 0.00274 0.00353 ! Validation 161 16010.748 0.005 0.368 1.24 8.6 0.534 0.712 1.06 1.3 0.003 0.00371 Wall time: 16010.748608999886 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 6.56 0.279 0.99 0.459 0.619 0.921 1.17 0.00262 0.00332 162 172 5.37 0.238 0.613 0.426 0.572 0.707 0.919 0.00201 0.00261 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 162 100 4.99 0.217 0.648 0.415 0.546 0.935 0.944 0.00266 0.00268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 162 16112.429 0.005 0.288 1.05 6.81 0.468 0.629 0.94 1.2 0.00267 0.00342 ! Validation 162 16112.429 0.005 0.248 0.832 5.79 0.437 0.584 0.841 1.07 0.00239 0.00304 Wall time: 16112.429855627008 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 4.44 0.205 0.332 0.394 0.532 0.546 0.676 0.00155 0.00192 163 172 4.9 0.188 1.14 0.378 0.509 0.974 1.25 0.00277 0.00356 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 163 100 3.39 0.169 0.0187 0.367 0.482 0.143 0.16 0.000407 0.000456 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 163 16211.684 0.005 0.21 0.846 5.04 0.399 0.537 0.85 1.08 0.00241 0.00307 ! Validation 163 16211.684 0.005 0.194 0.964 4.84 0.386 0.516 0.926 1.15 0.00263 0.00327 Wall time: 16211.68424815312 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 3.9 0.169 0.517 0.361 0.482 0.681 0.843 0.00193 0.0024 164 172 3.68 0.157 0.543 0.344 0.464 0.693 0.864 0.00197 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 164 100 4.83 0.141 2.01 0.338 0.441 1.65 1.66 0.00469 0.00472 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 16310.979 0.005 0.17 0.965 4.36 0.36 0.483 0.901 1.15 0.00256 0.00327 ! Validation 164 16310.979 0.005 0.162 4.19 7.44 0.356 0.473 2.14 2.4 0.00609 0.00682 Wall time: 16310.979590232018 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 3.2 0.145 0.3 0.338 0.446 0.508 0.642 0.00144 0.00182 165 172 4.49 0.135 1.8 0.328 0.43 1.39 1.57 0.00394 0.00447 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 165 100 3.23 0.125 0.731 0.32 0.415 0.978 1 0.00278 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 165 16410.201 0.005 0.145 1.01 3.9 0.336 0.446 0.935 1.18 0.00266 0.00334 ! Validation 165 16410.201 0.005 0.142 0.702 3.54 0.336 0.442 0.797 0.982 0.00226 0.00279 Wall time: 16410.201478404924 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 2.73 0.123 0.28 0.315 0.411 0.494 0.621 0.0014 0.00176 166 172 4.77 0.123 2.3 0.315 0.412 1.66 1.78 0.00473 0.00506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 166 100 3.55 0.114 1.26 0.306 0.397 1.3 1.32 0.00369 0.00375 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 16509.437 0.005 0.127 0.962 3.5 0.318 0.418 0.908 1.15 0.00258 0.00327 ! Validation 166 16509.437 0.005 0.128 1.75 4.32 0.322 0.42 1.33 1.55 0.00379 0.00441 Wall time: 16509.43781369878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 2.43 0.112 0.199 0.301 0.392 0.425 0.523 0.00121 0.00149 167 172 3.97 0.111 1.75 0.299 0.391 1.42 1.55 0.00403 0.0044 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 167 100 3.05 0.107 0.918 0.295 0.383 1.1 1.12 0.00313 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 167 16609.316 0.005 0.115 0.899 3.2 0.305 0.398 0.889 1.11 0.00253 0.00316 ! Validation 167 16609.316 0.005 0.118 1.69 4.04 0.309 0.402 1.28 1.52 0.00362 0.00433 Wall time: 16609.316070901696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 3.38 0.104 1.29 0.291 0.379 1.12 1.33 0.00319 0.00378 168 172 2.64 0.099 0.66 0.284 0.369 0.758 0.953 0.00215 0.00271 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 168 100 2.02 0.0993 0.0352 0.285 0.37 0.186 0.22 0.000528 0.000625 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 168 16710.838 0.005 0.107 0.822 2.96 0.294 0.383 0.836 1.06 0.00237 0.00302 ! Validation 168 16710.838 0.005 0.11 0.69 2.9 0.3 0.39 0.776 0.974 0.0022 0.00277 Wall time: 16710.838702605106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 2.34 0.0972 0.4 0.281 0.366 0.594 0.742 0.00169 0.00211 169 172 2.27 0.0949 0.37 0.278 0.361 0.56 0.714 0.00159 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 169 100 2.03 0.0931 0.164 0.275 0.358 0.446 0.476 0.00127 0.00135 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 169 16828.041 0.005 0.099 0.702 2.68 0.283 0.369 0.783 0.983 0.00222 0.00279 ! Validation 169 16828.041 0.005 0.103 1.63 3.69 0.289 0.376 1.21 1.5 0.00344 0.00426 Wall time: 16828.041766988114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 2.04 0.0906 0.23 0.271 0.353 0.481 0.563 0.00137 0.0016 170 172 2.03 0.0864 0.299 0.265 0.345 0.55 0.641 0.00156 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 170 100 2.12 0.0884 0.347 0.268 0.349 0.676 0.691 0.00192 0.00196 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 16927.272 0.005 0.0925 0.74 2.59 0.274 0.357 0.806 1.01 0.00229 0.00287 ! Validation 170 16927.272 0.005 0.097 0.348 2.29 0.28 0.365 0.552 0.692 0.00157 0.00196 Wall time: 16927.272580138873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 2.24 0.0815 0.607 0.257 0.335 0.776 0.914 0.0022 0.0026 171 172 2.11 0.0867 0.378 0.264 0.345 0.557 0.721 0.00158 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 171 100 3.81 0.0841 2.13 0.261 0.34 1.71 1.71 0.00485 0.00487 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 17026.499 0.005 0.0871 0.856 2.6 0.265 0.346 0.875 1.09 0.00249 0.00308 ! Validation 171 17026.499 0.005 0.0921 2.07 3.91 0.273 0.356 1.53 1.69 0.00435 0.0048 Wall time: 17026.499237114098 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 0.0902 2.19 0.27 0.352 1.6 1.74 0.00455 0.00494 172 172 2.75 0.0816 1.12 0.257 0.335 1.14 1.24 0.00323 0.00352 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 172 100 2.09 0.0807 0.472 0.256 0.333 0.793 0.806 0.00225 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 172 17125.961 0.005 0.0847 1.05 2.74 0.261 0.341 0.941 1.2 0.00267 0.00341 ! Validation 172 17125.961 0.005 0.089 0.43 2.21 0.268 0.35 0.605 0.77 0.00172 0.00219 Wall time: 17125.961402252782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 1.77 0.0786 0.196 0.252 0.329 0.37 0.52 0.00105 0.00148 173 172 1.72 0.0737 0.245 0.243 0.318 0.452 0.58 0.00128 0.00165 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 173 100 2.15 0.075 0.648 0.247 0.321 0.934 0.944 0.00265 0.00268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 173 17225.187 0.005 0.0788 0.593 2.17 0.252 0.329 0.723 0.904 0.00205 0.00257 ! Validation 173 17225.187 0.005 0.0828 0.442 2.1 0.258 0.337 0.637 0.78 0.00181 0.00222 Wall time: 17225.18752154708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 1.72 0.0734 0.256 0.243 0.318 0.483 0.593 0.00137 0.00169 174 172 1.85 0.0735 0.377 0.242 0.318 0.615 0.721 0.00175 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 174 100 2.13 0.0754 0.62 0.246 0.322 0.915 0.924 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 174 17324.426 0.005 0.0728 0.767 2.22 0.242 0.317 0.782 1.03 0.00222 0.00292 ! Validation 174 17324.426 0.005 0.0812 0.962 2.59 0.255 0.334 1.02 1.15 0.00289 0.00327 Wall time: 17324.426308470778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 1.8 0.0736 0.327 0.242 0.318 0.523 0.671 0.00149 0.00191 175 172 2.11 0.0672 0.765 0.232 0.304 0.83 1.03 0.00236 0.00291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 175 100 1.38 0.0669 0.0413 0.233 0.303 0.186 0.238 0.000529 0.000677 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 17423.662 0.005 0.0706 0.625 2.04 0.238 0.312 0.746 0.927 0.00212 0.00263 ! Validation 175 17423.662 0.005 0.0741 0.276 1.76 0.244 0.319 0.48 0.616 0.00136 0.00175 Wall time: 17423.662343794014 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 3.01 0.0674 1.66 0.232 0.304 1.36 1.51 0.00387 0.00429 176 172 1.72 0.0653 0.411 0.229 0.3 0.618 0.752 0.00176 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 176 100 1.36 0.0637 0.0856 0.227 0.296 0.323 0.343 0.000917 0.000975 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 176 17522.879 0.005 0.0659 0.653 1.97 0.23 0.301 0.756 0.948 0.00215 0.00269 ! Validation 176 17522.879 0.005 0.0704 0.359 1.77 0.237 0.311 0.565 0.703 0.0016 0.002 Wall time: 17522.879330082797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 2.84 0.0616 1.61 0.222 0.291 1.41 1.49 0.00401 0.00422 177 172 2.1 0.064 0.823 0.226 0.297 0.913 1.06 0.00259 0.00302 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 177 100 1.6 0.0625 0.351 0.225 0.293 0.686 0.695 0.00195 0.00197 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 177 17622.135 0.005 0.0641 0.876 2.16 0.226 0.297 0.882 1.1 0.0025 0.00312 ! Validation 177 17622.135 0.005 0.0691 0.87 2.25 0.235 0.308 0.941 1.09 0.00267 0.00311 Wall time: 17622.13560336968 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 1.34 0.0599 0.145 0.219 0.287 0.36 0.447 0.00102 0.00127 178 172 1.39 0.0598 0.197 0.218 0.287 0.398 0.52 0.00113 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 178 100 1.29 0.0582 0.127 0.217 0.283 0.396 0.418 0.00113 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 178 17721.383 0.005 0.0606 0.581 1.79 0.22 0.289 0.695 0.894 0.00197 0.00254 ! Validation 178 17721.383 0.005 0.0645 0.213 1.5 0.227 0.298 0.427 0.541 0.00121 0.00154 Wall time: 17721.383616360836 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 1.79 0.0536 0.721 0.208 0.272 0.897 0.996 0.00255 0.00283 179 172 2.03 0.0605 0.824 0.219 0.289 0.993 1.06 0.00282 0.00303 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 179 100 1.21 0.0593 0.0289 0.219 0.286 0.163 0.199 0.000462 0.000566 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 17821.321 0.005 0.0573 0.741 1.89 0.214 0.281 0.792 1.01 0.00225 0.00287 ! Validation 179 17821.321 0.005 0.0639 0.392 1.67 0.225 0.296 0.569 0.734 0.00162 0.00209 Wall time: 17821.321225699037 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 1.32 0.0555 0.215 0.21 0.276 0.437 0.544 0.00124 0.00155 180 172 1.27 0.0537 0.191 0.207 0.272 0.407 0.513 0.00116 0.00146 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 180 100 1.86 0.0539 0.782 0.209 0.272 1.02 1.04 0.00291 0.00295 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 17920.737 0.005 0.0563 0.623 1.75 0.212 0.278 0.739 0.926 0.0021 0.00263 ! Validation 180 17920.737 0.005 0.0593 0.626 1.81 0.217 0.286 0.792 0.928 0.00225 0.00264 Wall time: 17920.737668925896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 4.66 0.0536 3.58 0.207 0.272 2.14 2.22 0.00608 0.00631 181 172 1.23 0.0548 0.133 0.208 0.275 0.339 0.428 0.000963 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 181 100 1.64 0.0542 0.558 0.209 0.273 0.864 0.876 0.00246 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 181 18020.047 0.005 0.0542 0.815 1.9 0.208 0.273 0.833 1.06 0.00237 0.00301 ! Validation 181 18020.047 0.005 0.0596 0.514 1.7 0.218 0.286 0.702 0.841 0.00199 0.00239 Wall time: 18020.047290422954 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.21 0.0525 0.158 0.204 0.269 0.352 0.467 0.001 0.00133 182 172 1.24 0.0499 0.242 0.199 0.262 0.501 0.577 0.00142 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 182 100 1.93 0.0524 0.878 0.205 0.269 1.09 1.1 0.00309 0.00312 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 18119.256 0.005 0.052 0.63 1.67 0.203 0.268 0.737 0.931 0.00209 0.00265 ! Validation 182 18119.256 0.005 0.0562 0.969 2.09 0.211 0.278 1.04 1.15 0.00295 0.00328 Wall time: 18119.25629404979 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 1.23 0.0488 0.254 0.197 0.259 0.488 0.591 0.00139 0.00168 183 172 1.8 0.0521 0.763 0.203 0.268 0.936 1.02 0.00266 0.00291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 183 100 2.84 0.0524 1.79 0.205 0.268 1.56 1.57 0.00444 0.00446 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 18218.471 0.005 0.0496 0.719 1.71 0.198 0.261 0.79 0.994 0.00224 0.00282 ! Validation 183 18218.471 0.005 0.0558 1 2.12 0.21 0.277 1.08 1.17 0.00306 0.00334 Wall time: 18218.471668263897 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 1.18 0.0485 0.21 0.195 0.258 0.472 0.538 0.00134 0.00153 184 172 1.13 0.0456 0.22 0.19 0.25 0.461 0.55 0.00131 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 184 100 2.33 0.0446 1.44 0.19 0.248 1.4 1.41 0.00397 0.00399 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 18317.678 0.005 0.0481 0.514 1.48 0.195 0.257 0.67 0.841 0.0019 0.00239 ! Validation 184 18317.678 0.005 0.0493 1.49 2.48 0.198 0.26 1.35 1.43 0.00383 0.00407 Wall time: 18317.678303736728 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 1.01 0.0452 0.106 0.189 0.249 0.307 0.382 0.000873 0.00109 185 172 1.72 0.0435 0.846 0.187 0.245 1.02 1.08 0.00288 0.00307 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 185 100 3.42 0.0442 2.54 0.189 0.247 1.86 1.87 0.00529 0.00531 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 18421.110 0.005 0.0459 0.659 1.58 0.191 0.251 0.768 0.952 0.00218 0.00271 ! Validation 185 18421.110 0.005 0.048 2.94 3.9 0.195 0.257 1.94 2.01 0.00552 0.00572 Wall time: 18421.1106161708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 2.24 0.0413 1.41 0.181 0.238 1.3 1.39 0.00369 0.00396 186 172 1.53 0.0416 0.694 0.181 0.239 0.888 0.977 0.00252 0.00278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 186 100 1.05 0.0418 0.215 0.184 0.24 0.53 0.544 0.00151 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 186 18520.317 0.005 0.0428 0.575 1.43 0.184 0.243 0.708 0.89 0.00201 0.00253 ! Validation 186 18520.317 0.005 0.0461 1.44 2.37 0.191 0.252 1.16 1.41 0.0033 0.004 Wall time: 18520.317038231995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 1.03 0.045 0.133 0.188 0.249 0.336 0.427 0.000955 0.00121 187 172 2.46 0.04 1.66 0.178 0.235 1.46 1.51 0.00415 0.0043 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 187 100 1.49 0.0416 0.663 0.183 0.239 0.943 0.955 0.00268 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 187 18619.537 0.005 0.0417 0.61 1.44 0.182 0.24 0.709 0.916 0.00201 0.0026 ! Validation 187 18619.537 0.005 0.0445 0.555 1.45 0.188 0.247 0.774 0.874 0.0022 0.00248 Wall time: 18619.5373643809 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.894 0.0392 0.109 0.176 0.232 0.314 0.388 0.000891 0.0011 188 172 1.16 0.041 0.341 0.181 0.238 0.588 0.685 0.00167 0.00195 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 188 100 0.954 0.0409 0.136 0.182 0.237 0.413 0.433 0.00117 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 188 18719.751 0.005 0.0413 0.682 1.51 0.18 0.238 0.738 0.969 0.0021 0.00275 ! Validation 188 18719.751 0.005 0.0446 0.466 1.36 0.188 0.248 0.671 0.801 0.00191 0.00228 Wall time: 18719.75156045705 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 1.82 0.037 1.08 0.171 0.226 1.17 1.22 0.00332 0.00347 189 172 0.998 0.0401 0.197 0.178 0.235 0.419 0.52 0.00119 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 189 100 0.788 0.039 0.00744 0.177 0.232 0.0746 0.101 0.000212 0.000287 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 189 18818.971 0.005 0.0389 0.548 1.33 0.175 0.231 0.698 0.869 0.00198 0.00247 ! Validation 189 18818.971 0.005 0.0419 0.393 1.23 0.182 0.24 0.605 0.735 0.00172 0.00209 Wall time: 18818.971349110827 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 0.883 0.0366 0.15 0.17 0.225 0.367 0.455 0.00104 0.00129 190 172 1.24 0.035 0.544 0.166 0.219 0.814 0.865 0.00231 0.00246 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 190 100 1.12 0.0362 0.393 0.171 0.223 0.725 0.735 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 190 18918.187 0.005 0.0369 0.542 1.28 0.171 0.225 0.696 0.864 0.00198 0.00245 ! Validation 190 18918.187 0.005 0.0395 0.518 1.31 0.177 0.233 0.74 0.844 0.0021 0.0024 Wall time: 18918.18749575084 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 1.28 0.0411 0.462 0.18 0.238 0.699 0.797 0.00199 0.00226 191 172 0.981 0.0357 0.268 0.167 0.221 0.496 0.607 0.00141 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 191 100 0.776 0.0353 0.0696 0.169 0.22 0.28 0.309 0.000795 0.000879 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 19017.395 0.005 0.0391 0.702 1.48 0.175 0.232 0.728 0.983 0.00207 0.00279 ! Validation 191 19017.395 0.005 0.0385 0.201 0.971 0.174 0.23 0.43 0.526 0.00122 0.0015 Wall time: 19017.395581814926 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 2.15 0.0353 1.45 0.166 0.22 1.38 1.41 0.00391 0.00401 192 172 1.13 0.0331 0.465 0.162 0.213 0.698 0.8 0.00198 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 192 100 1.43 0.0326 0.779 0.162 0.212 1.03 1.04 0.00293 0.00294 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 192 19116.611 0.005 0.0344 0.451 1.14 0.165 0.217 0.613 0.788 0.00174 0.00224 ! Validation 192 19116.611 0.005 0.0359 0.705 1.42 0.169 0.222 0.902 0.985 0.00256 0.0028 Wall time: 19116.611801058985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 1.11 0.0336 0.439 0.162 0.215 0.581 0.777 0.00165 0.00221 193 172 1.02 0.0353 0.312 0.166 0.22 0.543 0.655 0.00154 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 193 100 0.74 0.0339 0.0631 0.165 0.216 0.271 0.295 0.000771 0.000837 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 19216.250 0.005 0.0331 0.558 1.22 0.162 0.214 0.712 0.877 0.00202 0.00249 ! Validation 193 19216.250 0.005 0.0365 0.159 0.888 0.17 0.224 0.371 0.468 0.00106 0.00133 Wall time: 19216.25007096771 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 0.792 0.0322 0.148 0.159 0.211 0.366 0.451 0.00104 0.00128 194 172 1.43 0.0318 0.796 0.158 0.209 0.978 1.05 0.00278 0.00297 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 194 100 1.69 0.0302 1.09 0.156 0.204 1.22 1.22 0.00347 0.00348 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 19315.492 0.005 0.0318 0.463 1.1 0.158 0.209 0.65 0.798 0.00185 0.00227 ! Validation 194 19315.492 0.005 0.0332 1.14 1.8 0.162 0.214 1.19 1.25 0.00338 0.00355 Wall time: 19315.49259862583 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 0.957 0.0302 0.353 0.153 0.204 0.619 0.697 0.00176 0.00198 195 172 1.53 0.0369 0.791 0.17 0.225 0.833 1.04 0.00237 0.00296 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 195 100 3.68 0.0357 2.97 0.17 0.222 2.02 2.02 0.00573 0.00574 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 19414.720 0.005 0.0303 0.606 1.21 0.154 0.204 0.726 0.913 0.00206 0.00259 ! Validation 195 19414.720 0.005 0.0378 2.28 3.03 0.173 0.228 1.62 1.77 0.00459 0.00503 Wall time: 19414.72064040182 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 1.32 0.0291 0.736 0.151 0.2 0.908 1.01 0.00258 0.00286 196 172 0.737 0.0325 0.0868 0.159 0.211 0.272 0.346 0.000774 0.000982 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 196 100 0.97 0.0338 0.293 0.165 0.216 0.621 0.635 0.00176 0.0018 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 196 19513.937 0.005 0.0306 0.477 1.09 0.155 0.205 0.623 0.811 0.00177 0.0023 ! Validation 196 19513.937 0.005 0.0358 0.26 0.977 0.168 0.222 0.488 0.598 0.00139 0.0017 Wall time: 19513.936953532044 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 0.719 0.0298 0.122 0.153 0.203 0.322 0.41 0.000915 0.00116 197 172 0.803 0.0268 0.266 0.145 0.192 0.543 0.605 0.00154 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 197 100 1.04 0.0273 0.494 0.148 0.194 0.817 0.824 0.00232 0.00234 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 19613.145 0.005 0.0299 0.457 1.05 0.153 0.203 0.638 0.793 0.00181 0.00225 ! Validation 197 19613.145 0.005 0.0301 0.569 1.17 0.154 0.204 0.808 0.885 0.0023 0.00251 Wall time: 19613.14521943312 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 1.07 0.0292 0.481 0.15 0.201 0.735 0.814 0.00209 0.00231 198 172 0.683 0.029 0.102 0.151 0.2 0.3 0.375 0.000853 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 198 100 0.611 0.0295 0.0216 0.153 0.201 0.134 0.172 0.000381 0.00049 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 198 19712.387 0.005 0.0291 0.6 1.18 0.151 0.2 0.739 0.909 0.0021 0.00258 ! Validation 198 19712.387 0.005 0.032 0.165 0.805 0.159 0.21 0.384 0.476 0.00109 0.00135 Wall time: 19712.38724689884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 1.11 0.0268 0.573 0.145 0.192 0.793 0.888 0.00225 0.00252 199 172 1 0.0281 0.441 0.149 0.197 0.712 0.779 0.00202 0.00221 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 199 100 0.612 0.0288 0.0357 0.152 0.199 0.205 0.222 0.000581 0.00063 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 19811.959 0.005 0.0268 0.438 0.974 0.145 0.192 0.617 0.776 0.00175 0.00221 ! Validation 199 19811.959 0.005 0.0311 0.105 0.727 0.157 0.207 0.301 0.381 0.000856 0.00108 Wall time: 19811.959343359806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 1.94 0.0236 1.47 0.136 0.18 1.38 1.42 0.00393 0.00404 200 172 0.991 0.0249 0.493 0.14 0.185 0.738 0.823 0.0021 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 200 100 0.874 0.0265 0.345 0.146 0.191 0.685 0.689 0.00195 0.00196 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 19912.544 0.005 0.0262 0.397 0.92 0.143 0.19 0.588 0.739 0.00167 0.0021 ! Validation 200 19912.544 0.005 0.0291 0.602 1.18 0.151 0.2 0.804 0.91 0.00228 0.00259 Wall time: 19912.54408474872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 1.5 0.025 0.997 0.14 0.186 1.13 1.17 0.0032 0.00333 201 172 0.797 0.0249 0.298 0.14 0.185 0.521 0.64 0.00148 0.00182 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 201 100 0.539 0.0244 0.0507 0.14 0.183 0.25 0.264 0.000711 0.00075 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 201 20015.573 0.005 0.0247 0.354 0.849 0.139 0.185 0.563 0.698 0.0016 0.00198 ! Validation 201 20015.573 0.005 0.0267 0.138 0.672 0.145 0.192 0.361 0.435 0.00103 0.00124 Wall time: 20015.57359412592 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.813 0.0251 0.311 0.14 0.186 0.566 0.654 0.00161 0.00186 202 172 0.786 0.0242 0.302 0.138 0.182 0.595 0.645 0.00169 0.00183 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 202 100 0.573 0.0244 0.0861 0.14 0.183 0.336 0.344 0.000955 0.000978 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 202 20115.887 0.005 0.0245 0.449 0.94 0.139 0.184 0.615 0.786 0.00175 0.00223 ! Validation 202 20115.887 0.005 0.0266 0.101 0.633 0.145 0.191 0.301 0.372 0.000856 0.00106 Wall time: 20115.887482777704 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 1.13 0.0253 0.626 0.141 0.186 0.814 0.928 0.00231 0.00264 203 172 0.802 0.0245 0.312 0.139 0.183 0.588 0.656 0.00167 0.00186 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 203 100 0.788 0.0248 0.291 0.142 0.185 0.63 0.633 0.00179 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 203 20215.994 0.005 0.0233 0.404 0.87 0.135 0.179 0.592 0.746 0.00168 0.00212 ! Validation 203 20215.994 0.005 0.0268 0.239 0.775 0.145 0.192 0.489 0.573 0.00139 0.00163 Wall time: 20215.994861927815 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 1.19 0.0237 0.712 0.137 0.181 0.93 0.99 0.00264 0.00281 204 172 0.56 0.0223 0.114 0.132 0.175 0.327 0.397 0.000928 0.00113 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 204 100 1.06 0.0221 0.618 0.133 0.174 0.92 0.922 0.00261 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 204 20315.480 0.005 0.0228 0.365 0.821 0.134 0.177 0.561 0.709 0.00159 0.00201 ! Validation 204 20315.480 0.005 0.0244 0.432 0.92 0.138 0.183 0.655 0.771 0.00186 0.00219 Wall time: 20315.480222428683 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.798 0.0202 0.394 0.126 0.167 0.689 0.736 0.00196 0.00209 205 172 0.487 0.0207 0.0735 0.127 0.169 0.256 0.318 0.000726 0.000904 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 205 100 0.635 0.0207 0.221 0.129 0.169 0.548 0.551 0.00156 0.00157 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 205 20415.115 0.005 0.022 0.356 0.796 0.131 0.174 0.558 0.7 0.00159 0.00199 ! Validation 205 20415.115 0.005 0.0232 0.169 0.632 0.135 0.179 0.411 0.482 0.00117 0.00137 Wall time: 20415.115446727723 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.527 0.0213 0.102 0.129 0.171 0.309 0.374 0.000876 0.00106 206 172 0.838 0.0218 0.401 0.131 0.173 0.643 0.743 0.00183 0.00211 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 206 100 0.869 0.0212 0.445 0.13 0.171 0.779 0.782 0.00221 0.00222 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 206 20515.058 0.005 0.0205 0.33 0.741 0.127 0.168 0.538 0.674 0.00153 0.00191 ! Validation 206 20515.058 0.005 0.0233 1.14 1.6 0.135 0.179 1.09 1.25 0.00309 0.00355 Wall time: 20515.0581496628 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.481 0.0188 0.105 0.121 0.161 0.296 0.381 0.000842 0.00108 207 172 1.12 0.0237 0.643 0.137 0.181 0.804 0.94 0.00228 0.00267 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 207 100 0.757 0.0241 0.275 0.14 0.182 0.611 0.615 0.00173 0.00175 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 207 20614.555 0.005 0.021 0.448 0.867 0.128 0.17 0.628 0.785 0.00179 0.00223 ! Validation 207 20614.555 0.005 0.026 0.233 0.753 0.144 0.189 0.478 0.566 0.00136 0.00161 Wall time: 20614.55530199688 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.743 0.0202 0.339 0.126 0.167 0.481 0.683 0.00137 0.00194 208 172 0.455 0.0194 0.0658 0.123 0.164 0.242 0.301 0.000687 0.000855 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 208 100 0.607 0.0193 0.22 0.124 0.163 0.549 0.55 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 208 20716.569 0.005 0.0208 0.329 0.745 0.127 0.169 0.543 0.673 0.00154 0.00191 ! Validation 208 20716.569 0.005 0.0219 0.184 0.623 0.131 0.174 0.414 0.503 0.00118 0.00143 Wall time: 20716.569680999964 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 1.38 0.0192 0.994 0.123 0.163 1.13 1.17 0.0032 0.00332 209 172 0.424 0.0181 0.0617 0.119 0.158 0.248 0.291 0.000706 0.000828 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 209 100 0.498 0.0179 0.14 0.119 0.157 0.436 0.438 0.00124 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 209 20815.764 0.005 0.019 0.268 0.648 0.122 0.162 0.494 0.607 0.0014 0.00173 ! Validation 209 20815.764 0.005 0.0203 0.234 0.639 0.126 0.167 0.499 0.567 0.00142 0.00161 Wall time: 20815.76436902769 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.985 0.0196 0.593 0.123 0.164 0.796 0.903 0.00226 0.00257 210 172 0.522 0.0186 0.15 0.12 0.16 0.343 0.454 0.000973 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 210 100 0.833 0.0177 0.479 0.119 0.156 0.809 0.812 0.0023 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 210 20916.460 0.005 0.0186 0.318 0.689 0.121 0.16 0.542 0.661 0.00154 0.00188 ! Validation 210 20916.460 0.005 0.0198 0.424 0.821 0.125 0.165 0.697 0.764 0.00198 0.00217 Wall time: 20916.459933595732 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.409 0.0178 0.0543 0.118 0.156 0.212 0.273 0.000602 0.000776 211 172 0.405 0.0169 0.0669 0.115 0.152 0.24 0.303 0.000681 0.000862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 211 100 0.335 0.0163 0.00987 0.114 0.15 0.113 0.117 0.000321 0.000331 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 211 21015.759 0.005 0.0179 0.278 0.635 0.118 0.157 0.494 0.618 0.0014 0.00176 ! Validation 211 21015.759 0.005 0.0188 0.0878 0.465 0.122 0.161 0.272 0.348 0.000774 0.000987 Wall time: 21015.759493188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.497 0.0167 0.163 0.114 0.152 0.393 0.473 0.00112 0.00134 212 172 0.528 0.0165 0.199 0.114 0.151 0.455 0.523 0.00129 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 212 100 0.343 0.0163 0.0173 0.114 0.15 0.151 0.154 0.000429 0.000439 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 21115.025 0.005 0.0184 0.342 0.711 0.12 0.159 0.522 0.686 0.00148 0.00195 ! Validation 212 21115.025 0.005 0.0188 0.0863 0.462 0.121 0.161 0.265 0.345 0.000752 0.000979 Wall time: 21115.025696991943 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.369 0.0159 0.0517 0.111 0.148 0.212 0.267 0.000601 0.000758 213 172 0.46 0.0148 0.164 0.108 0.143 0.388 0.476 0.0011 0.00135 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 213 100 0.469 0.0155 0.159 0.112 0.146 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 213 21215.845 0.005 0.0168 0.253 0.59 0.115 0.152 0.459 0.591 0.0013 0.00168 ! Validation 213 21215.845 0.005 0.0177 0.149 0.502 0.118 0.156 0.375 0.452 0.00107 0.00128 Wall time: 21215.845589572098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.687 0.0164 0.359 0.113 0.15 0.647 0.703 0.00184 0.002 214 172 0.353 0.0153 0.0478 0.11 0.145 0.226 0.256 0.000643 0.000728 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 214 100 0.675 0.0158 0.359 0.113 0.147 0.702 0.703 0.002 0.002 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 214 21316.893 0.005 0.016 0.26 0.58 0.112 0.148 0.487 0.598 0.00138 0.0017 ! Validation 214 21316.893 0.005 0.0177 0.269 0.623 0.118 0.156 0.551 0.608 0.00157 0.00173 Wall time: 21316.893293920904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.565 0.0147 0.27 0.108 0.142 0.521 0.61 0.00148 0.00173 215 172 0.454 0.0168 0.119 0.115 0.152 0.311 0.404 0.000883 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 215 100 0.51 0.0165 0.18 0.115 0.151 0.496 0.498 0.00141 0.00142 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 215 21416.999 0.005 0.0156 0.284 0.597 0.111 0.147 0.5 0.625 0.00142 0.00178 ! Validation 215 21416.999 0.005 0.0186 0.553 0.925 0.122 0.16 0.748 0.872 0.00212 0.00248 Wall time: 21416.99917459674 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.462 0.0138 0.186 0.105 0.138 0.452 0.505 0.00129 0.00144 216 172 0.921 0.0137 0.647 0.104 0.137 0.922 0.944 0.00262 0.00268 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 216 100 0.626 0.0137 0.352 0.105 0.137 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 216 21516.985 0.005 0.0152 0.232 0.537 0.109 0.145 0.45 0.565 0.00128 0.00161 ! Validation 216 21516.985 0.005 0.016 0.44 0.761 0.113 0.149 0.722 0.778 0.00205 0.00221 Wall time: 21516.985674367752 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.438 0.0166 0.107 0.114 0.151 0.268 0.384 0.00076 0.00109 217 172 0.514 0.0137 0.24 0.104 0.137 0.54 0.575 0.00153 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 217 100 0.308 0.0133 0.0412 0.103 0.135 0.236 0.238 0.000669 0.000677 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 21617.313 0.005 0.015 0.264 0.565 0.109 0.144 0.473 0.603 0.00134 0.00171 ! Validation 217 21617.313 0.005 0.0156 0.114 0.426 0.111 0.147 0.34 0.396 0.000965 0.00112 Wall time: 21617.313357384875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.63 0.0144 0.342 0.107 0.141 0.632 0.686 0.00179 0.00195 218 172 0.966 0.0133 0.7 0.102 0.135 0.955 0.981 0.00271 0.00279 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 218 100 0.655 0.0127 0.4 0.102 0.132 0.741 0.742 0.00211 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 218 21718.279 0.005 0.0141 0.239 0.522 0.106 0.14 0.459 0.574 0.0013 0.00163 ! Validation 218 21718.279 0.005 0.0152 0.973 1.28 0.11 0.145 1.07 1.16 0.00305 0.00329 Wall time: 21718.279867100995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.413 0.0124 0.164 0.0993 0.131 0.397 0.475 0.00113 0.00135 219 172 0.319 0.0134 0.0513 0.104 0.136 0.214 0.266 0.000608 0.000755 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 219 100 0.285 0.0135 0.0157 0.105 0.136 0.142 0.147 0.000405 0.000418 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 219 21818.369 0.005 0.0134 0.222 0.491 0.103 0.136 0.446 0.553 0.00127 0.00157 ! Validation 219 21818.369 0.005 0.0157 0.106 0.42 0.112 0.147 0.324 0.381 0.000921 0.00108 Wall time: 21818.36911545787 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.507 0.0149 0.208 0.109 0.143 0.438 0.535 0.00124 0.00152 220 172 0.994 0.0115 0.763 0.0958 0.126 0.991 1.02 0.00282 0.00291 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 220 100 0.667 0.0127 0.414 0.101 0.132 0.753 0.755 0.00214 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 220 21917.757 0.005 0.0136 0.248 0.52 0.104 0.137 0.457 0.583 0.0013 0.00166 ! Validation 220 21917.757 0.005 0.0147 0.393 0.686 0.107 0.142 0.643 0.735 0.00183 0.00209 Wall time: 21917.758124636952 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.333 0.0113 0.107 0.0953 0.125 0.352 0.384 0.001 0.00109 221 172 0.288 0.0117 0.0533 0.0964 0.127 0.219 0.271 0.000621 0.000769 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 221 100 0.305 0.0113 0.0789 0.0952 0.125 0.329 0.33 0.000933 0.000936 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 22017.496 0.005 0.0126 0.192 0.444 0.1 0.132 0.407 0.515 0.00116 0.00146 ! Validation 221 22017.496 0.005 0.0136 0.0508 0.323 0.103 0.137 0.21 0.264 0.000597 0.000751 Wall time: 22017.496573579963 ! Best model 221 0.323 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.361 0.0122 0.117 0.0983 0.129 0.331 0.402 0.000939 0.00114 222 172 0.709 0.0116 0.478 0.096 0.126 0.761 0.811 0.00216 0.0023 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 222 100 0.228 0.0113 0.00144 0.0958 0.125 0.0274 0.0445 7.78e-05 0.000127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 222 22116.716 0.005 0.0123 0.211 0.457 0.0986 0.13 0.418 0.539 0.00119 0.00153 ! Validation 222 22116.716 0.005 0.0138 0.198 0.474 0.104 0.138 0.409 0.522 0.00116 0.00148 Wall time: 22116.716207324993 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.492 0.0121 0.251 0.098 0.129 0.474 0.587 0.00135 0.00167 223 172 0.811 0.0171 0.468 0.118 0.154 0.759 0.803 0.00216 0.00228 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 223 100 0.385 0.0187 0.0112 0.125 0.16 0.107 0.124 0.000304 0.000353 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 223 22215.922 0.005 0.0131 0.379 0.64 0.101 0.134 0.555 0.722 0.00158 0.00205 ! Validation 223 22215.922 0.005 0.02 0.407 0.807 0.128 0.166 0.614 0.748 0.00174 0.00212 Wall time: 22215.922607161105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.269 0.0119 0.0319 0.0971 0.128 0.162 0.21 0.00046 0.000595 224 172 0.297 0.0118 0.0601 0.0973 0.128 0.234 0.288 0.000666 0.000817 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 224 100 0.226 0.0113 0.00116 0.0954 0.124 0.0335 0.0399 9.51e-05 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 224 22315.128 0.005 0.0123 0.155 0.4 0.0983 0.13 0.372 0.462 0.00106 0.00131 ! Validation 224 22315.128 0.005 0.0132 0.0817 0.346 0.102 0.135 0.28 0.335 0.000796 0.000952 Wall time: 22315.128722218797 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.318 0.011 0.0977 0.0936 0.123 0.285 0.367 0.00081 0.00104 225 172 0.276 0.0104 0.0669 0.0904 0.12 0.224 0.303 0.000637 0.000862 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 225 100 0.495 0.00979 0.3 0.0888 0.116 0.641 0.642 0.00182 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 225 22414.299 0.005 0.0109 0.128 0.345 0.0926 0.122 0.336 0.42 0.000956 0.00119 ! Validation 225 22414.299 0.005 0.012 0.338 0.578 0.0969 0.128 0.583 0.682 0.00166 0.00194 Wall time: 22414.299613436684 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.528 0.011 0.308 0.0926 0.123 0.599 0.651 0.0017 0.00185 226 172 0.357 0.0104 0.15 0.0911 0.119 0.365 0.454 0.00104 0.00129 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 226 100 0.505 0.01 0.304 0.0901 0.118 0.646 0.647 0.00184 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 226 22513.501 0.005 0.0106 0.191 0.403 0.0914 0.121 0.406 0.513 0.00115 0.00146 ! Validation 226 22513.501 0.005 0.0121 0.191 0.433 0.0979 0.129 0.442 0.512 0.00126 0.00146 Wall time: 22513.501795507967 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.271 0.0115 0.04 0.0951 0.126 0.183 0.235 0.000519 0.000667 227 172 0.251 0.00981 0.0553 0.0884 0.116 0.22 0.276 0.000625 0.000784 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 227 100 0.235 0.00948 0.0453 0.0871 0.114 0.248 0.25 0.000705 0.000709 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 227 22612.717 0.005 0.0118 0.251 0.486 0.0961 0.127 0.436 0.587 0.00124 0.00167 ! Validation 227 22612.717 0.005 0.0114 0.0374 0.265 0.0945 0.125 0.181 0.227 0.000514 0.000645 Wall time: 22612.717472087126 ! Best model 227 0.265 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.241 0.00967 0.0478 0.0872 0.115 0.198 0.256 0.000561 0.000728 228 172 0.547 0.00977 0.351 0.0875 0.116 0.646 0.695 0.00183 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 228 100 0.3 0.0097 0.106 0.0885 0.116 0.381 0.383 0.00108 0.00109 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 228 22711.924 0.005 0.00973 0.127 0.322 0.0876 0.116 0.337 0.418 0.000958 0.00119 ! Validation 228 22711.924 0.005 0.0116 0.192 0.425 0.0957 0.126 0.447 0.514 0.00127 0.00146 Wall time: 22711.924712251872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.291 0.0092 0.107 0.0854 0.113 0.334 0.383 0.000948 0.00109 229 172 0.252 0.00885 0.0747 0.0836 0.11 0.262 0.321 0.000743 0.000911 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 229 100 0.195 0.00833 0.0288 0.0822 0.107 0.197 0.199 0.000558 0.000565 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 229 22811.125 0.005 0.0094 0.124 0.312 0.0861 0.114 0.323 0.414 0.000916 0.00118 ! Validation 229 22811.125 0.005 0.0104 0.0809 0.288 0.0901 0.119 0.263 0.334 0.000747 0.000948 Wall time: 22811.125607527792 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.237 0.00907 0.0555 0.0843 0.112 0.234 0.276 0.000665 0.000785 230 172 0.189 0.00826 0.0243 0.0808 0.107 0.15 0.183 0.000427 0.000519 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 230 100 0.211 0.00801 0.0506 0.0804 0.105 0.262 0.264 0.000744 0.00075 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 230 22911.085 0.005 0.00933 0.158 0.344 0.0858 0.113 0.371 0.466 0.00105 0.00132 ! Validation 230 22911.085 0.005 0.00999 0.0787 0.278 0.0884 0.117 0.263 0.329 0.000746 0.000935 Wall time: 22911.085663774982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.189 0.00841 0.0212 0.0815 0.108 0.131 0.171 0.000372 0.000485 231 172 0.343 0.00996 0.144 0.0887 0.117 0.388 0.445 0.0011 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 231 100 0.209 0.00994 0.0106 0.0899 0.117 0.104 0.121 0.000296 0.000344 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 231 23010.236 0.005 0.00893 0.168 0.347 0.0838 0.111 0.375 0.481 0.00107 0.00137 ! Validation 231 23010.236 0.005 0.0115 0.205 0.435 0.0955 0.126 0.457 0.531 0.0013 0.00151 Wall time: 23010.236516213976 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.388 0.0113 0.161 0.0955 0.125 0.413 0.471 0.00117 0.00134 232 172 0.192 0.00827 0.027 0.0801 0.107 0.156 0.193 0.000444 0.000548 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 232 100 0.352 0.00774 0.197 0.0792 0.103 0.52 0.521 0.00148 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 232 23109.384 0.005 0.01 0.21 0.411 0.0891 0.118 0.425 0.538 0.00121 0.00153 ! Validation 232 23109.384 0.005 0.00991 0.125 0.323 0.0879 0.117 0.366 0.414 0.00104 0.00118 Wall time: 23109.383906597737 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.739 0.00979 0.543 0.0886 0.116 0.776 0.864 0.0022 0.00245 233 172 0.207 0.00855 0.0362 0.0818 0.108 0.195 0.223 0.000553 0.000634 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 233 100 0.163 0.00775 0.00796 0.0788 0.103 0.101 0.105 0.000287 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 233 23208.537 0.005 0.00903 0.145 0.326 0.0842 0.111 0.343 0.447 0.000975 0.00127 ! Validation 233 23208.537 0.005 0.00953 0.0618 0.253 0.0861 0.115 0.247 0.292 0.000702 0.000829 Wall time: 23208.53742096806 ! Best model 233 0.253 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.305 0.0086 0.133 0.0823 0.109 0.392 0.428 0.00111 0.00122 234 172 0.298 0.00832 0.132 0.0811 0.107 0.399 0.426 0.00113 0.00121 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 234 100 0.465 0.00788 0.307 0.0797 0.104 0.648 0.65 0.00184 0.00185 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 234 23307.702 0.005 0.00835 0.134 0.301 0.0809 0.107 0.348 0.429 0.000988 0.00122 ! Validation 234 23307.702 0.005 0.00959 0.203 0.394 0.0866 0.115 0.466 0.528 0.00132 0.0015 Wall time: 23307.702184331138 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.222 0.00721 0.0776 0.076 0.0996 0.273 0.327 0.000777 0.000928 235 172 0.271 0.00787 0.114 0.0786 0.104 0.37 0.396 0.00105 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 235 100 0.169 0.00748 0.019 0.0779 0.101 0.157 0.162 0.000445 0.000459 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 235 23406.842 0.005 0.00814 0.123 0.286 0.0799 0.106 0.334 0.412 0.000948 0.00117 ! Validation 235 23406.842 0.005 0.00969 0.118 0.312 0.087 0.115 0.304 0.404 0.000864 0.00115 Wall time: 23406.84202185599 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.224 0.00942 0.0359 0.0866 0.114 0.183 0.222 0.000521 0.000631 236 172 0.191 0.00795 0.0324 0.0786 0.105 0.156 0.211 0.000444 0.000599 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 236 100 0.309 0.0069 0.171 0.075 0.0975 0.482 0.485 0.00137 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 236 23505.988 0.005 0.0084 0.155 0.323 0.0814 0.107 0.374 0.462 0.00106 0.00131 ! Validation 236 23505.988 0.005 0.00886 0.0735 0.251 0.083 0.11 0.271 0.318 0.00077 0.000903 Wall time: 23505.987879295833 ! Best model 236 0.251 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.353 0.00933 0.167 0.0861 0.113 0.422 0.479 0.0012 0.00136 237 172 0.222 0.00777 0.0667 0.078 0.103 0.235 0.303 0.000668 0.00086 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 237 100 0.374 0.00715 0.231 0.0765 0.0992 0.562 0.564 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 237 23605.151 0.005 0.00842 0.152 0.32 0.0815 0.108 0.374 0.457 0.00106 0.0013 ! Validation 237 23605.151 0.005 0.00891 0.153 0.331 0.0837 0.111 0.396 0.459 0.00112 0.0013 Wall time: 23605.151383154094 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.341 0.00762 0.189 0.077 0.102 0.48 0.51 0.00136 0.00145 238 172 0.173 0.00724 0.028 0.0755 0.0998 0.159 0.196 0.000453 0.000558 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 238 100 0.169 0.0065 0.0389 0.0728 0.0946 0.227 0.231 0.000645 0.000657 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 238 23704.324 0.005 0.00851 0.124 0.294 0.0814 0.108 0.33 0.413 0.000938 0.00117 ! Validation 238 23704.324 0.005 0.00832 0.143 0.309 0.0806 0.107 0.359 0.443 0.00102 0.00126 Wall time: 23704.32448319113 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.326 0.00818 0.163 0.0794 0.106 0.433 0.473 0.00123 0.00134 239 172 0.832 0.00829 0.667 0.081 0.107 0.916 0.958 0.0026 0.00272 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 239 100 0.249 0.00879 0.0736 0.0846 0.11 0.317 0.318 0.000899 0.000904 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 23803.413 0.005 0.00736 0.114 0.262 0.0759 0.101 0.308 0.396 0.000874 0.00113 ! Validation 239 23803.413 0.005 0.0102 0.08 0.285 0.0901 0.119 0.268 0.332 0.00076 0.000943 Wall time: 23803.412865833845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.873 0.01 0.673 0.0885 0.117 0.945 0.962 0.00269 0.00273 240 172 0.188 0.00727 0.0431 0.0756 0.1 0.208 0.244 0.000592 0.000692 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 240 100 0.133 0.00647 0.00409 0.0725 0.0943 0.0653 0.075 0.000186 0.000213 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 240 23903.108 0.005 0.00812 0.126 0.288 0.0795 0.106 0.319 0.416 0.000907 0.00118 ! Validation 240 23903.108 0.005 0.00824 0.0336 0.198 0.0801 0.106 0.172 0.215 0.000489 0.000611 Wall time: 23903.10831676703 ! Best model 240 0.198 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.165 0.00745 0.0163 0.0772 0.101 0.117 0.15 0.000332 0.000425 241 172 0.226 0.00693 0.0876 0.0736 0.0976 0.268 0.347 0.000762 0.000986 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 241 100 0.124 0.00616 0.000976 0.0703 0.092 0.0313 0.0366 8.89e-05 0.000104 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 24002.266 0.005 0.0072 0.102 0.246 0.0751 0.0995 0.303 0.374 0.00086 0.00106 ! Validation 241 24002.266 0.005 0.00799 0.0583 0.218 0.0787 0.105 0.232 0.283 0.00066 0.000804 Wall time: 24002.266632129904 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 0.198 0.00702 0.058 0.0744 0.0983 0.234 0.283 0.000664 0.000803 242 172 0.315 0.0067 0.181 0.0732 0.096 0.449 0.5 0.00128 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 242 100 1.03 0.00677 0.891 0.0745 0.0965 1.11 1.11 0.00314 0.00314 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 242 24104.515 0.005 0.00696 0.101 0.241 0.0738 0.0979 0.298 0.373 0.000846 0.00106 ! Validation 242 24104.515 0.005 0.00833 0.519 0.685 0.0812 0.107 0.82 0.845 0.00233 0.0024 Wall time: 24104.514976188075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.214 0.00784 0.0571 0.0796 0.104 0.246 0.28 0.000699 0.000796 243 172 0.195 0.00647 0.0657 0.0714 0.0943 0.261 0.301 0.000742 0.000854 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 243 100 0.146 0.00619 0.0226 0.071 0.0923 0.172 0.176 0.000488 0.000501 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 243 24203.674 0.005 0.0072 0.104 0.248 0.0753 0.0995 0.304 0.378 0.000863 0.00107 ! Validation 243 24203.674 0.005 0.00766 0.0434 0.197 0.0774 0.103 0.192 0.244 0.000544 0.000694 Wall time: 24203.674801639747 ! Best model 243 0.197 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.159 0.0064 0.0314 0.0702 0.0939 0.168 0.208 0.000477 0.00059 244 172 0.209 0.00618 0.085 0.0692 0.0922 0.309 0.342 0.000879 0.000972 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 244 100 0.171 0.00573 0.0569 0.068 0.0888 0.275 0.28 0.000782 0.000795 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 244 24302.853 0.005 0.00863 0.136 0.309 0.0806 0.109 0.325 0.433 0.000924 0.00123 ! Validation 244 24302.853 0.005 0.00759 0.179 0.331 0.0767 0.102 0.459 0.496 0.0013 0.00141 Wall time: 24302.853693644982 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.317 0.00717 0.174 0.0755 0.0993 0.393 0.489 0.00112 0.00139 245 172 0.826 0.0272 0.282 0.143 0.193 0.531 0.623 0.00151 0.00177 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 245 100 0.457 0.00957 0.266 0.0877 0.115 0.604 0.605 0.00171 0.00172 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 245 24402.006 0.005 0.00687 0.119 0.256 0.0727 0.0971 0.31 0.404 0.000882 0.00115 ! Validation 245 24402.006 0.005 0.0111 0.383 0.605 0.0936 0.124 0.634 0.726 0.0018 0.00206 Wall time: 24402.00669928873 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.15 0.00608 0.0287 0.069 0.0915 0.155 0.199 0.000441 0.000564 246 172 0.205 0.00712 0.0626 0.075 0.099 0.272 0.293 0.000773 0.000833 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 246 100 0.29 0.00676 0.154 0.0746 0.0964 0.457 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 246 24501.177 0.005 0.00782 0.111 0.268 0.0775 0.104 0.312 0.392 0.000887 0.00111 ! Validation 246 24501.177 0.005 0.00821 0.0769 0.241 0.0805 0.106 0.268 0.325 0.000762 0.000924 Wall time: 24501.17728252802 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.278 0.00648 0.149 0.071 0.0944 0.394 0.453 0.00112 0.00129 247 172 0.364 0.00669 0.23 0.0715 0.0959 0.508 0.563 0.00144 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 247 100 0.327 0.00563 0.214 0.0681 0.088 0.538 0.543 0.00153 0.00154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 247 24600.329 0.005 0.00644 0.0821 0.211 0.071 0.0941 0.269 0.336 0.000763 0.000954 ! Validation 247 24600.329 0.005 0.00735 0.381 0.528 0.0759 0.101 0.674 0.724 0.00192 0.00206 Wall time: 24600.328975963872 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.195 0.00601 0.0746 0.0685 0.091 0.281 0.32 0.000799 0.00091 248 172 0.162 0.00589 0.0439 0.0677 0.09 0.185 0.246 0.000526 0.000698 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 248 100 0.189 0.0053 0.0827 0.0657 0.0854 0.329 0.337 0.000934 0.000958 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 24699.486 0.005 0.00599 0.0688 0.189 0.0683 0.0908 0.245 0.308 0.000695 0.000874 ! Validation 248 24699.486 0.005 0.00696 0.0917 0.231 0.0735 0.0979 0.278 0.355 0.000789 0.00101 Wall time: 24699.486428562086 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 0.874 1.58 0.816 1.1 1.02 1.47 0.00289 0.00418 249 172 13.1 0.565 1.77 0.658 0.882 1.23 1.56 0.00349 0.00443 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 249 100 13.3 0.549 2.34 0.659 0.869 1.73 1.79 0.00492 0.0051 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 249 24798.643 0.005 0.8 26.9 42.9 0.759 1.05 2.67 6.08 0.00759 0.0173 ! Validation 249 24798.643 0.005 0.569 4.23 15.6 0.664 0.885 1.96 2.41 0.00558 0.00685 Wall time: 24798.643370438833 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 5.48 0.23 0.885 0.427 0.562 0.863 1.1 0.00245 0.00313 250 172 3.95 0.177 0.412 0.376 0.494 0.592 0.753 0.00168 0.00214 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 250 100 4.94 0.184 1.26 0.385 0.503 1.3 1.32 0.00368 0.00375 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 24897.798 0.005 0.289 1.72 7.51 0.472 0.631 1.2 1.54 0.0034 0.00437 ! Validation 250 24897.798 0.005 0.187 1.48 5.22 0.386 0.508 1.17 1.42 0.00333 0.00405 Wall time: 24897.797969439067 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 3.44 0.142 0.593 0.337 0.442 0.745 0.904 0.00212 0.00257 251 172 3.05 0.128 0.486 0.319 0.42 0.66 0.818 0.00188 0.00232 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 251 100 5.64 0.129 3.05 0.323 0.422 2.04 2.05 0.00579 0.00582 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 24996.942 0.005 0.145 1.26 4.17 0.339 0.447 1.04 1.32 0.00297 0.00374 ! Validation 251 24996.942 0.005 0.135 2.53 5.24 0.329 0.432 1.65 1.87 0.00469 0.0053 Wall time: 24996.942407481838 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 2.93 0.107 0.788 0.292 0.383 0.894 1.04 0.00254 0.00296 252 172 3.04 0.105 0.933 0.286 0.381 0.79 1.13 0.00225 0.00322 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 252 100 2.26 0.101 0.232 0.285 0.373 0.536 0.565 0.00152 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 252 25096.074 0.005 0.112 0.862 3.1 0.298 0.393 0.879 1.09 0.0025 0.00309 ! Validation 252 25096.074 0.005 0.109 0.984 3.16 0.294 0.387 0.968 1.16 0.00275 0.00331 Wall time: 25096.074252870865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 2.71 0.0967 0.772 0.278 0.365 0.873 1.03 0.00248 0.00293 253 172 2.18 0.0913 0.352 0.269 0.354 0.59 0.696 0.00168 0.00198 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 253 100 1.82 0.0891 0.0366 0.267 0.35 0.213 0.224 0.000605 0.000637 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 25195.237 0.005 0.0955 0.878 2.79 0.275 0.362 0.878 1.1 0.0025 0.00312 ! Validation 253 25195.237 0.005 0.0953 0.516 2.42 0.275 0.362 0.666 0.843 0.00189 0.00239 Wall time: 25195.236977016088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 3.47 0.0833 1.81 0.256 0.339 1.46 1.58 0.00414 0.00448 254 172 3.29 0.0799 1.69 0.25 0.331 1.46 1.52 0.00414 0.00433 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 254 100 5.14 0.079 3.56 0.251 0.33 2.21 2.21 0.00627 0.00629 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 25294.390 0.005 0.0843 0.752 2.44 0.258 0.341 0.811 1.02 0.0023 0.00289 ! Validation 254 25294.390 0.005 0.0851 2.84 4.54 0.259 0.342 1.86 1.98 0.00529 0.00562 Wall time: 25294.390120207798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 1.77 0.0748 0.271 0.243 0.321 0.486 0.61 0.00138 0.00173 255 172 1.67 0.0716 0.239 0.238 0.314 0.449 0.574 0.00128 0.00163 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 255 100 1.62 0.0735 0.153 0.243 0.318 0.426 0.459 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 255 25396.478 0.005 0.0766 0.777 2.31 0.245 0.325 0.818 1.03 0.00232 0.00294 ! Validation 255 25396.478 0.005 0.0778 0.559 2.12 0.248 0.327 0.668 0.877 0.0019 0.00249 Wall time: 25396.47803846188 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 2.19 0.0676 0.842 0.231 0.305 0.957 1.08 0.00272 0.00306 256 172 2.25 0.0703 0.843 0.235 0.311 0.882 1.08 0.00251 0.00306 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 256 100 1.47 0.0717 0.0319 0.239 0.314 0.13 0.209 0.000368 0.000595 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 256 25495.638 0.005 0.072 0.899 2.34 0.237 0.315 0.915 1.11 0.0026 0.00316 ! Validation 256 25495.638 0.005 0.0753 0.403 1.91 0.243 0.322 0.566 0.745 0.00161 0.00212 Wall time: 25495.638489835896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 1.72 0.0702 0.314 0.233 0.311 0.489 0.657 0.00139 0.00187 257 172 1.61 0.0677 0.262 0.23 0.305 0.479 0.6 0.00136 0.00171 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 257 100 2.18 0.069 0.803 0.234 0.308 1.04 1.05 0.00295 0.00299 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 257 25594.818 0.005 0.0691 0.847 2.23 0.232 0.308 0.859 1.08 0.00244 0.00307 ! Validation 257 25594.818 0.005 0.0726 0.679 2.13 0.238 0.316 0.814 0.967 0.00231 0.00275 Wall time: 25594.818521712907 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 1.44 0.0619 0.199 0.221 0.292 0.387 0.523 0.0011 0.00149 258 172 2.53 0.0683 1.16 0.23 0.307 1.14 1.27 0.00323 0.0036 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 258 100 1.79 0.0666 0.461 0.23 0.303 0.786 0.796 0.00223 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 258 25693.987 0.005 0.0625 0.727 1.98 0.22 0.293 0.793 1 0.00225 0.00284 ! Validation 258 25693.987 0.005 0.0695 0.737 2.13 0.233 0.309 0.862 1.01 0.00245 0.00286 Wall time: 25693.987128399778 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 1.25 0.0585 0.0859 0.213 0.284 0.286 0.344 0.000812 0.000977 259 172 1.54 0.0561 0.421 0.208 0.278 0.635 0.762 0.0018 0.00216 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 259 100 1.34 0.0579 0.179 0.214 0.282 0.469 0.497 0.00133 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 259 25793.148 0.005 0.0596 0.64 1.83 0.215 0.286 0.74 0.939 0.0021 0.00267 ! Validation 259 25793.148 0.005 0.0616 0.747 1.98 0.219 0.291 0.841 1.01 0.00239 0.00288 Wall time: 25793.148298642132 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 260 100 1.24 0.0551 0.134 0.206 0.275 0.291 0.43 0.000828 0.00122 260 172 1.42 0.0516 0.386 0.2 0.267 0.627 0.729 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 260 100 1.37 0.0529 0.311 0.205 0.27 0.638 0.655 0.00181 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 260 25892.314 0.005 0.0544 0.595 1.68 0.205 0.274 0.732 0.905 0.00208 0.00257 ! Validation 260 25892.314 0.005 0.0563 0.491 1.62 0.209 0.278 0.672 0.822 0.00191 0.00234 Wall time: 25892.314457305707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 1.83 0.0536 0.762 0.203 0.272 0.933 1.02 0.00265 0.00291 261 172 1.48 0.0483 0.509 0.193 0.258 0.706 0.837 0.002 0.00238 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 261 100 1.46 0.0505 0.446 0.199 0.264 0.765 0.783 0.00217 0.00223 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 261 25991.601 0.005 0.0529 0.688 1.74 0.202 0.27 0.754 0.973 0.00214 0.00276 ! Validation 261 25991.601 0.005 0.0535 0.827 1.9 0.203 0.271 0.946 1.07 0.00269 0.00303 Wall time: 25991.601298999973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 1.28 0.0488 0.299 0.194 0.259 0.549 0.641 0.00156 0.00182 262 172 1.14 0.0484 0.177 0.193 0.258 0.374 0.494 0.00106 0.0014 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 262 100 1.54 0.0484 0.576 0.196 0.258 0.883 0.89 0.00251 0.00253 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 262 26090.792 0.005 0.0493 0.655 1.64 0.195 0.26 0.77 0.95 0.00219 0.0027 ! Validation 262 26090.792 0.005 0.0517 0.459 1.49 0.2 0.267 0.672 0.795 0.00191 0.00226 Wall time: 26090.792576585896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 1.02 0.0436 0.147 0.183 0.245 0.357 0.45 0.00102 0.00128 263 172 1.5 0.0529 0.438 0.202 0.27 0.616 0.776 0.00175 0.0022 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 263 100 3.13 0.0553 2.02 0.209 0.276 1.67 1.67 0.00473 0.00474 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 26190.042 0.005 0.046 0.677 1.6 0.188 0.251 0.747 0.966 0.00212 0.00274 ! Validation 263 26190.042 0.005 0.0574 1.8 2.94 0.211 0.281 1.31 1.57 0.00371 0.00446 Wall time: 26190.042802839074 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 264 100 1.08 0.0434 0.214 0.183 0.244 0.455 0.542 0.00129 0.00154 264 172 1.07 0.0416 0.238 0.179 0.239 0.452 0.572 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 264 100 0.889 0.042 0.0487 0.182 0.24 0.237 0.259 0.000674 0.000735 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 264 26289.287 0.005 0.0452 0.494 1.4 0.186 0.249 0.663 0.825 0.00188 0.00234 ! Validation 264 26289.287 0.005 0.0452 0.334 1.24 0.187 0.249 0.542 0.678 0.00154 0.00193 Wall time: 26289.287654215004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 1.18 0.0418 0.343 0.179 0.24 0.591 0.687 0.00168 0.00195 265 172 1.09 0.0434 0.22 0.183 0.244 0.432 0.551 0.00123 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 265 100 1.03 0.0427 0.172 0.183 0.242 0.48 0.487 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 265 26388.539 0.005 0.0408 0.589 1.41 0.177 0.237 0.727 0.901 0.00207 0.00256 ! Validation 265 26388.539 0.005 0.0447 0.614 1.51 0.186 0.248 0.774 0.919 0.0022 0.00261 Wall time: 26388.53938273294 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 266 100 2.26 0.0391 1.48 0.173 0.232 1.39 1.43 0.00396 0.00405 266 172 1.03 0.0375 0.281 0.17 0.227 0.536 0.622 0.00152 0.00177 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 0.038 0.612 0.173 0.229 0.911 0.918 0.00259 0.00261 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 26487.812 0.005 0.0396 0.531 1.32 0.174 0.233 0.695 0.855 0.00197 0.00243 ! Validation 266 26487.812 0.005 0.0406 0.672 1.48 0.177 0.236 0.863 0.962 0.00245 0.00273 Wall time: 26487.812118500937 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 1.47 0.0365 0.744 0.168 0.224 0.913 1.01 0.00259 0.00287 267 172 1.08 0.0364 0.355 0.167 0.224 0.612 0.699 0.00174 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 267 100 0.956 0.0369 0.218 0.17 0.225 0.538 0.548 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 267 26587.014 0.005 0.0387 0.617 1.39 0.172 0.231 0.733 0.922 0.00208 0.00262 ! Validation 267 26587.014 0.005 0.0394 0.385 1.17 0.175 0.233 0.6 0.728 0.0017 0.00207 Wall time: 26587.014460640028 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 1.18 0.0344 0.498 0.162 0.217 0.774 0.827 0.0022 0.00235 268 172 0.827 0.0355 0.117 0.165 0.221 0.364 0.401 0.00103 0.00114 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 268 100 1.09 0.0366 0.355 0.169 0.224 0.686 0.699 0.00195 0.00199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 268 26686.503 0.005 0.0364 0.617 1.34 0.167 0.224 0.732 0.922 0.00208 0.00262 ! Validation 268 26686.503 0.005 0.0388 0.653 1.43 0.173 0.231 0.792 0.948 0.00225 0.00269 Wall time: 26686.503501731902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 1.26 0.0328 0.607 0.158 0.212 0.837 0.914 0.00238 0.0026 269 172 1.13 0.032 0.495 0.157 0.21 0.789 0.825 0.00224 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 269 100 1.33 0.0337 0.654 0.162 0.215 0.943 0.949 0.00268 0.0027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 269 26785.739 0.005 0.0338 0.501 1.18 0.161 0.216 0.67 0.83 0.0019 0.00236 ! Validation 269 26785.739 0.005 0.0359 0.626 1.34 0.166 0.222 0.829 0.928 0.00236 0.00264 Wall time: 26785.739663162734 training # 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 0.0331 0.607 0.159 0.214 0.87 0.914 0.00247 0.0026 270 172 1.01 0.0323 0.362 0.156 0.211 0.582 0.706 0.00165 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 270 100 0.755 0.0334 0.0858 0.16 0.215 0.33 0.344 0.000936 0.000976 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 26885.005 0.005 0.032 0.494 1.13 0.156 0.21 0.669 0.824 0.0019 0.00234 ! Validation 270 26885.005 0.005 0.0351 0.878 1.58 0.164 0.22 0.814 1.1 0.00231 0.00312 Wall time: 26885.005261918064 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 0.593 0.0269 0.0559 0.143 0.192 0.216 0.277 0.000613 0.000788 271 172 1.92 0.0279 1.36 0.146 0.196 1.34 1.37 0.0038 0.00389 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 271 100 2.08 0.0293 1.5 0.151 0.201 1.43 1.44 0.00407 0.00408 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 26984.335 0.005 0.0292 0.42 1 0.149 0.201 0.616 0.759 0.00175 0.00216 ! Validation 271 26984.335 0.005 0.0313 1.14 1.77 0.155 0.208 1.18 1.25 0.00336 0.00356 Wall time: 26984.335812299047 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 1.16 0.0254 0.654 0.14 0.187 0.898 0.948 0.00255 0.00269 272 172 1.09 0.0264 0.563 0.142 0.191 0.841 0.88 0.00239 0.0025 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 272 100 1 0.0266 0.473 0.144 0.191 0.804 0.806 0.00228 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 272 27083.643 0.005 0.0278 0.419 0.974 0.146 0.196 0.619 0.759 0.00176 0.00216 ! Validation 272 27083.643 0.005 0.0286 0.431 1 0.148 0.198 0.694 0.77 0.00197 0.00219 Wall time: 27083.64355977485 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.774 0.0281 0.213 0.146 0.196 0.458 0.541 0.0013 0.00154 273 172 0.907 0.0269 0.37 0.143 0.192 0.654 0.713 0.00186 0.00203 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 273 100 0.677 0.0268 0.141 0.144 0.192 0.437 0.44 0.00124 0.00125 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 273 27183.013 0.005 0.0312 0.591 1.21 0.153 0.207 0.656 0.902 0.00186 0.00256 ! Validation 273 27183.013 0.005 0.0288 0.259 0.835 0.149 0.199 0.5 0.597 0.00142 0.0017 Wall time: 27183.01378800301 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.549 0.0252 0.0452 0.139 0.186 0.2 0.249 0.000567 0.000709 274 172 0.694 0.0266 0.163 0.142 0.191 0.403 0.473 0.00115 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 274 100 0.577 0.0266 0.0449 0.144 0.191 0.239 0.248 0.000678 0.000706 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 274 27282.277 0.005 0.0264 0.452 0.98 0.142 0.191 0.627 0.789 0.00178 0.00224 ! Validation 274 27282.277 0.005 0.0286 0.127 0.699 0.149 0.198 0.332 0.417 0.000943 0.00119 Wall time: 27282.277173631825 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 1.03 0.0232 0.563 0.134 0.179 0.83 0.88 0.00236 0.0025 275 172 1.12 0.0223 0.674 0.132 0.175 0.847 0.963 0.00241 0.00274 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 275 100 0.551 0.0227 0.0964 0.133 0.177 0.362 0.364 0.00103 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 275 27381.518 0.005 0.0237 0.319 0.793 0.135 0.181 0.544 0.662 0.00155 0.00188 ! Validation 275 27381.518 0.005 0.0245 0.151 0.642 0.138 0.184 0.376 0.456 0.00107 0.00129 Wall time: 27381.51789854793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 1.21 0.0232 0.745 0.133 0.179 0.97 1.01 0.00275 0.00288 276 172 0.459 0.0205 0.0492 0.126 0.168 0.22 0.26 0.000626 0.000739 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 276 100 0.441 0.0218 0.00387 0.131 0.173 0.0632 0.0729 0.00018 0.000207 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 276 27480.810 0.005 0.0234 0.413 0.881 0.134 0.18 0.611 0.754 0.00174 0.00214 ! Validation 276 27480.810 0.005 0.0237 0.122 0.596 0.136 0.181 0.321 0.409 0.000912 0.00116 Wall time: 27480.81085507013 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 1.59 0.0232 1.13 0.133 0.179 1.21 1.25 0.00345 0.00354 277 172 0.548 0.0224 0.0989 0.132 0.176 0.296 0.369 0.00084 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 277 100 0.674 0.0223 0.229 0.132 0.175 0.56 0.561 0.00159 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 277 27581.461 0.005 0.0233 0.473 0.939 0.134 0.179 0.65 0.807 0.00185 0.00229 ! Validation 277 27581.461 0.005 0.0242 0.257 0.741 0.137 0.183 0.51 0.595 0.00145 0.00169 Wall time: 27581.460967167746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 1.32 0.0204 0.915 0.126 0.167 1.1 1.12 0.00312 0.00319 278 172 1.18 0.0215 0.755 0.128 0.172 0.991 1.02 0.00281 0.0029 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 278 100 0.643 0.0223 0.197 0.133 0.175 0.519 0.52 0.00147 0.00148 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 278 27680.695 0.005 0.0208 0.343 0.76 0.127 0.169 0.529 0.686 0.0015 0.00195 ! Validation 278 27680.695 0.005 0.0239 0.147 0.625 0.136 0.181 0.377 0.45 0.00107 0.00128 Wall time: 27680.69563226169 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 0.529 0.02 0.129 0.125 0.166 0.336 0.421 0.000954 0.0012 279 172 0.562 0.0212 0.138 0.128 0.171 0.353 0.435 0.001 0.00124 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 279 100 1.36 0.0221 0.916 0.132 0.174 1.12 1.12 0.00319 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 279 27780.079 0.005 0.0202 0.373 0.777 0.125 0.167 0.576 0.717 0.00164 0.00204 ! Validation 279 27780.079 0.005 0.0237 0.901 1.38 0.136 0.181 1.07 1.11 0.00304 0.00316 Wall time: 27780.079714438878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.866 0.0182 0.502 0.119 0.158 0.775 0.831 0.0022 0.00236 280 172 0.502 0.0175 0.153 0.116 0.155 0.381 0.458 0.00108 0.0013 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 280 100 0.493 0.0171 0.15 0.117 0.154 0.454 0.455 0.00129 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 280 27879.542 0.005 0.0189 0.28 0.657 0.121 0.161 0.502 0.621 0.00143 0.00176 ! Validation 280 27879.542 0.005 0.0193 0.159 0.544 0.123 0.163 0.397 0.467 0.00113 0.00133 Wall time: 27879.542836804874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 0.42 0.018 0.0591 0.119 0.157 0.243 0.285 0.000689 0.00081 281 172 1.25 0.0185 0.884 0.12 0.159 1.01 1.1 0.00287 0.00313 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 281 100 1.38 0.0188 1 0.122 0.161 1.17 1.17 0.00333 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 281 27980.607 0.005 0.0194 0.442 0.83 0.123 0.163 0.636 0.78 0.00181 0.00222 ! Validation 281 27980.607 0.005 0.0204 0.852 1.26 0.126 0.168 1.02 1.08 0.00289 0.00308 Wall time: 27980.607495476957 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.477 0.0187 0.103 0.12 0.16 0.304 0.376 0.000862 0.00107 282 172 0.509 0.0202 0.104 0.125 0.167 0.289 0.379 0.000821 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 282 100 0.488 0.0203 0.0823 0.127 0.167 0.328 0.336 0.000932 0.000956 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 28080.214 0.005 0.0182 0.377 0.741 0.119 0.158 0.585 0.721 0.00166 0.00205 ! Validation 282 28080.214 0.005 0.0218 0.315 0.75 0.131 0.173 0.599 0.658 0.0017 0.00187 Wall time: 28080.214078834746 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.987 0.0185 0.616 0.12 0.16 0.868 0.92 0.00247 0.00261 283 172 0.456 0.016 0.137 0.112 0.148 0.371 0.435 0.00105 0.00123 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 283 100 0.346 0.0155 0.0352 0.112 0.146 0.218 0.22 0.00062 0.000625 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 283 28179.491 0.005 0.0176 0.294 0.646 0.117 0.156 0.492 0.636 0.0014 0.00181 ! Validation 283 28179.491 0.005 0.0179 0.146 0.504 0.119 0.157 0.37 0.449 0.00105 0.00127 Wall time: 28179.49090572074 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.385 0.0158 0.0697 0.112 0.147 0.26 0.31 0.000738 0.00088 284 172 0.915 0.0178 0.559 0.118 0.157 0.818 0.877 0.00232 0.00249 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 284 100 0.668 0.0181 0.306 0.12 0.158 0.646 0.649 0.00184 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 284 28278.759 0.005 0.0153 0.283 0.588 0.11 0.145 0.497 0.624 0.00141 0.00177 ! Validation 284 28278.759 0.005 0.0201 0.518 0.919 0.126 0.166 0.773 0.844 0.0022 0.0024 Wall time: 28278.75922420295 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.338 0.0153 0.0314 0.109 0.145 0.173 0.208 0.000491 0.00059 285 172 0.33 0.0142 0.0452 0.106 0.14 0.215 0.249 0.00061 0.000708 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 285 100 0.382 0.0144 0.094 0.108 0.141 0.358 0.36 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 285 28378.586 0.005 0.0152 0.27 0.575 0.11 0.145 0.493 0.61 0.0014 0.00173 ! Validation 285 28378.586 0.005 0.0162 0.109 0.432 0.113 0.149 0.319 0.387 0.000906 0.0011 Wall time: 28378.58639467368 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.648 0.0136 0.376 0.103 0.137 0.655 0.719 0.00186 0.00204 286 172 0.753 0.0147 0.458 0.108 0.142 0.751 0.794 0.00213 0.00226 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 286 100 0.377 0.0146 0.0844 0.108 0.142 0.338 0.341 0.000961 0.000968 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 286 28477.814 0.005 0.014 0.257 0.537 0.105 0.139 0.491 0.594 0.0014 0.00169 ! Validation 286 28477.814 0.005 0.0162 0.361 0.685 0.113 0.149 0.572 0.705 0.00162 0.002 Wall time: 28477.81481027603 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.744 0.013 0.484 0.101 0.134 0.781 0.816 0.00222 0.00232 287 172 0.301 0.0129 0.0434 0.101 0.133 0.189 0.244 0.000537 0.000694 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 287 100 0.248 0.0123 0.00089 0.0997 0.13 0.0302 0.035 8.57e-05 9.94e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 287 28577.141 0.005 0.0135 0.23 0.501 0.104 0.137 0.463 0.563 0.00132 0.0016 ! Validation 287 28577.141 0.005 0.0144 0.177 0.465 0.107 0.141 0.378 0.493 0.00107 0.0014 Wall time: 28577.141314255074 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.33 0.012 0.0902 0.0981 0.128 0.289 0.352 0.000822 0.001 288 172 0.408 0.0144 0.12 0.107 0.141 0.313 0.407 0.00089 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 288 100 0.711 0.0147 0.417 0.109 0.142 0.755 0.758 0.00215 0.00215 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 288 28676.332 0.005 0.0137 0.328 0.602 0.104 0.137 0.531 0.672 0.00151 0.00191 ! Validation 288 28676.332 0.005 0.0163 0.43 0.757 0.114 0.15 0.693 0.769 0.00197 0.00219 Wall time: 28676.332629305776 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.507 0.0129 0.248 0.101 0.133 0.533 0.584 0.00151 0.00166 289 172 0.314 0.0117 0.0806 0.0958 0.127 0.289 0.333 0.000822 0.000946 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 289 100 0.349 0.011 0.128 0.0948 0.123 0.418 0.419 0.00119 0.00119 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 289 28775.514 0.005 0.0126 0.182 0.434 0.0998 0.132 0.403 0.501 0.00115 0.00142 ! Validation 289 28775.514 0.005 0.0133 0.16 0.426 0.103 0.135 0.41 0.469 0.00116 0.00133 Wall time: 28775.51413883269 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.364 0.0111 0.141 0.0941 0.124 0.38 0.441 0.00108 0.00125 290 172 0.442 0.0111 0.22 0.0938 0.123 0.509 0.551 0.00145 0.00156 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 290 100 0.758 0.0113 0.532 0.0955 0.125 0.855 0.856 0.00243 0.00243 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 290 28874.691 0.005 0.0125 0.256 0.506 0.0995 0.131 0.465 0.594 0.00132 0.00169 ! Validation 290 28874.691 0.005 0.0134 0.42 0.687 0.103 0.136 0.727 0.76 0.00206 0.00216 Wall time: 28874.691502331756 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.324 0.0122 0.0806 0.0976 0.129 0.278 0.333 0.000789 0.000946 291 172 0.301 0.0133 0.0352 0.103 0.135 0.175 0.22 0.000498 0.000625 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 291 100 0.293 0.0129 0.035 0.103 0.133 0.218 0.219 0.000619 0.000624 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 291 28973.897 0.005 0.0118 0.234 0.471 0.0968 0.128 0.46 0.568 0.00131 0.00161 ! Validation 291 28973.897 0.005 0.015 0.109 0.409 0.11 0.144 0.333 0.387 0.000947 0.0011 Wall time: 28973.897211275995 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 1 0.0162 0.679 0.114 0.149 0.897 0.967 0.00255 0.00275 292 172 0.354 0.0114 0.125 0.0957 0.125 0.36 0.415 0.00102 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 292 100 0.342 0.0113 0.116 0.0957 0.125 0.399 0.4 0.00113 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 292 29073.625 0.005 0.0129 0.308 0.566 0.101 0.133 0.506 0.651 0.00144 0.00185 ! Validation 292 29073.625 0.005 0.0133 0.164 0.429 0.103 0.135 0.408 0.475 0.00116 0.00135 Wall time: 29073.625462302007 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.38 0.0118 0.145 0.0969 0.127 0.374 0.446 0.00106 0.00127 293 172 0.226 0.01 0.0252 0.089 0.117 0.156 0.186 0.000442 0.000529 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 293 100 0.19 0.00949 0.000585 0.0876 0.114 0.0215 0.0284 6.12e-05 8.06e-05 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 293 29173.337 0.005 0.0111 0.17 0.391 0.0938 0.123 0.383 0.483 0.00109 0.00137 ! Validation 293 29173.337 0.005 0.0118 0.0548 0.291 0.0966 0.128 0.218 0.275 0.00062 0.00078 Wall time: 29173.33695950685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.272 0.01 0.0715 0.0897 0.117 0.275 0.314 0.000781 0.000891 294 172 0.448 0.0108 0.232 0.0924 0.122 0.482 0.565 0.00137 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 294 100 0.227 0.0103 0.0213 0.0917 0.119 0.169 0.171 0.00048 0.000487 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 29273.916 0.005 0.0103 0.186 0.392 0.0905 0.119 0.404 0.505 0.00115 0.00144 ! Validation 294 29273.916 0.005 0.0122 0.0769 0.32 0.0986 0.129 0.277 0.325 0.000786 0.000924 Wall time: 29273.916528809816 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.565 0.0118 0.328 0.0977 0.128 0.622 0.672 0.00177 0.00191 295 172 0.268 0.00972 0.0735 0.0876 0.116 0.269 0.318 0.000764 0.000903 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 295 100 0.611 0.00929 0.425 0.0869 0.113 0.764 0.765 0.00217 0.00217 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 295 29376.543 0.005 0.0103 0.187 0.393 0.0904 0.119 0.393 0.508 0.00112 0.00144 ! Validation 295 29376.543 0.005 0.0113 0.627 0.854 0.0948 0.125 0.858 0.929 0.00244 0.00264 Wall time: 29376.543537419755 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.256 0.00985 0.0595 0.0883 0.116 0.248 0.286 0.000705 0.000813 296 172 0.465 0.00866 0.292 0.0822 0.109 0.573 0.633 0.00163 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 296 100 0.821 0.00819 0.658 0.0813 0.106 0.95 0.951 0.0027 0.0027 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 296 29477.300 0.005 0.00952 0.148 0.339 0.0869 0.114 0.358 0.451 0.00102 0.00128 ! Validation 296 29477.300 0.005 0.0105 0.467 0.677 0.0906 0.12 0.715 0.802 0.00203 0.00228 Wall time: 29477.300834078807 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.332 0.00896 0.153 0.0847 0.111 0.387 0.458 0.0011 0.0013 297 172 0.254 0.00924 0.0686 0.0856 0.113 0.259 0.307 0.000736 0.000873 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 297 100 0.183 0.00902 0.00213 0.0857 0.111 0.0391 0.0541 0.000111 0.000154 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 297 29577.968 0.005 0.00943 0.189 0.377 0.0865 0.114 0.419 0.51 0.00119 0.00145 ! Validation 297 29577.968 0.005 0.0109 0.0579 0.276 0.0929 0.122 0.231 0.282 0.000657 0.000802 Wall time: 29577.96814883314 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.433 0.00908 0.251 0.0854 0.112 0.55 0.588 0.00156 0.00167 298 172 0.703 0.0103 0.497 0.0901 0.119 0.814 0.827 0.00231 0.00235 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 298 100 0.245 0.00972 0.051 0.0889 0.116 0.26 0.265 0.000739 0.000752 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 298 29678.909 0.005 0.00989 0.252 0.45 0.0885 0.117 0.452 0.589 0.00128 0.00167 ! Validation 298 29678.909 0.005 0.0115 0.102 0.332 0.0953 0.126 0.312 0.375 0.000888 0.00107 Wall time: 29678.909815339837 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.267 0.00853 0.096 0.0819 0.108 0.304 0.363 0.000862 0.00103 299 172 0.252 0.00829 0.0863 0.0814 0.107 0.249 0.345 0.000706 0.000979 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 299 100 0.648 0.00776 0.493 0.0792 0.103 0.821 0.823 0.00233 0.00234 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 29779.642 0.005 0.00882 0.105 0.281 0.0834 0.11 0.306 0.38 0.00087 0.00108 ! Validation 299 29779.642 0.005 0.00969 0.291 0.485 0.0873 0.115 0.537 0.633 0.00152 0.0018 Wall time: 29779.642451779917 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.361 0.00956 0.17 0.0873 0.115 0.378 0.483 0.00107 0.00137 300 172 0.175 0.00823 0.0104 0.0805 0.106 0.0944 0.119 0.000268 0.000339 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 300 100 0.204 0.00765 0.0513 0.0781 0.103 0.263 0.266 0.000747 0.000754 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 300 29879.953 0.005 0.00868 0.157 0.33 0.0828 0.109 0.373 0.464 0.00106 0.00132 ! Validation 300 29879.953 0.005 0.00951 0.0568 0.247 0.0863 0.114 0.218 0.28 0.000619 0.000794 Wall time: 29879.95309729781 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.397 0.00873 0.222 0.0845 0.11 0.517 0.553 0.00147 0.00157 301 172 0.192 0.00742 0.044 0.077 0.101 0.208 0.246 0.000591 0.000699 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 301 100 0.155 0.00749 0.00566 0.0781 0.101 0.0764 0.0882 0.000217 0.000251 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 29981.516 0.005 0.00823 0.128 0.293 0.0806 0.106 0.334 0.42 0.000948 0.00119 ! Validation 301 29981.516 0.005 0.00933 0.156 0.343 0.086 0.113 0.354 0.464 0.00101 0.00132 Wall time: 29981.516731077805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.178 0.00709 0.0361 0.0752 0.0988 0.187 0.223 0.000532 0.000634 302 172 0.19 0.00787 0.0332 0.0784 0.104 0.162 0.214 0.000459 0.000607 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 302 100 0.273 0.00694 0.134 0.0744 0.0977 0.426 0.429 0.00121 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 302 30081.328 0.005 0.00793 0.112 0.27 0.079 0.104 0.313 0.392 0.00089 0.00111 ! Validation 302 30081.328 0.005 0.00878 0.14 0.315 0.0828 0.11 0.398 0.439 0.00113 0.00125 Wall time: 30081.328623842914 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.328 0.00682 0.191 0.074 0.0969 0.485 0.513 0.00138 0.00146 303 172 0.191 0.00789 0.0333 0.0782 0.104 0.171 0.214 0.000485 0.000608 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 303 100 0.143 0.00703 0.00226 0.0754 0.0983 0.0454 0.0558 0.000129 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 303 30181.988 0.005 0.00777 0.13 0.286 0.0783 0.103 0.346 0.423 0.000984 0.0012 ! Validation 303 30181.988 0.005 0.00869 0.028 0.202 0.0825 0.109 0.158 0.196 0.000449 0.000557 Wall time: 30181.98851043079 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.225 0.0083 0.0594 0.0789 0.107 0.228 0.286 0.000648 0.000812 304 172 0.236 0.00818 0.0722 0.0808 0.106 0.278 0.315 0.000791 0.000896 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 304 100 0.153 0.0074 0.00521 0.0772 0.101 0.0706 0.0846 0.0002 0.00024 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 30284.031 0.005 0.00834 0.201 0.367 0.0811 0.107 0.408 0.525 0.00116 0.00149 ! Validation 304 30284.031 0.005 0.00915 0.0302 0.213 0.0847 0.112 0.167 0.204 0.000475 0.00058 Wall time: 30284.03091074573 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.232 0.00879 0.0557 0.0848 0.11 0.226 0.277 0.000643 0.000786 305 172 0.173 0.0074 0.0254 0.0762 0.101 0.143 0.187 0.000407 0.000531 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 305 100 0.183 0.0066 0.0508 0.0725 0.0953 0.26 0.264 0.000739 0.000751 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 30383.732 0.005 0.00796 0.145 0.304 0.0792 0.105 0.34 0.447 0.000966 0.00127 ! Validation 305 30383.732 0.005 0.00849 0.0505 0.22 0.0812 0.108 0.208 0.264 0.00059 0.000749 Wall time: 30383.73209020309 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.452 0.00986 0.254 0.0882 0.116 0.558 0.592 0.00159 0.00168 306 172 0.165 0.00744 0.0158 0.0736 0.101 0.107 0.147 0.000303 0.000419 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 306 100 0.133 0.0064 0.00454 0.0713 0.0938 0.064 0.079 0.000182 0.000224 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 306 30483.015 0.005 0.00746 0.112 0.261 0.0765 0.101 0.283 0.392 0.000803 0.00111 ! Validation 306 30483.015 0.005 0.00809 0.0234 0.185 0.0792 0.106 0.145 0.18 0.000413 0.00051 Wall time: 30483.015810682904 ! Best model 306 0.185 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.274 0.00648 0.144 0.0717 0.0944 0.419 0.446 0.00119 0.00127 307 172 0.524 0.00684 0.387 0.0734 0.097 0.705 0.73 0.002 0.00207 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 307 100 0.162 0.00676 0.0264 0.0733 0.0964 0.187 0.191 0.00053 0.000542 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 307 30582.483 0.005 0.00712 0.116 0.258 0.0748 0.099 0.307 0.399 0.000873 0.00113 ! Validation 307 30582.483 0.005 0.00824 0.0585 0.223 0.0802 0.106 0.23 0.284 0.000653 0.000806 Wall time: 30582.483409829903 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.155 0.00644 0.0265 0.0708 0.0941 0.153 0.191 0.000434 0.000543 308 172 0.18 0.00695 0.0408 0.0742 0.0978 0.182 0.237 0.000517 0.000673 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 308 100 0.267 0.0077 0.113 0.0797 0.103 0.391 0.395 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 308 30681.785 0.005 0.00705 0.106 0.247 0.0743 0.0985 0.288 0.382 0.000818 0.00108 ! Validation 308 30681.785 0.005 0.00928 0.108 0.293 0.0862 0.113 0.32 0.385 0.000908 0.00109 Wall time: 30681.78530120896 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.252 0.00638 0.124 0.0705 0.0937 0.386 0.413 0.0011 0.00117 309 172 0.229 0.00703 0.088 0.0747 0.0983 0.262 0.348 0.000745 0.000988 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 309 100 0.162 0.00718 0.0185 0.0764 0.0994 0.15 0.16 0.000427 0.000454 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 309 30781.057 0.005 0.00707 0.14 0.282 0.0744 0.0986 0.333 0.44 0.000946 0.00125 ! Validation 309 30781.057 0.005 0.00857 0.4 0.572 0.082 0.109 0.596 0.742 0.00169 0.00211 Wall time: 30781.057707316708 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.242 0.00659 0.11 0.0721 0.0952 0.364 0.389 0.00103 0.0011 310 172 0.475 0.00827 0.31 0.0809 0.107 0.492 0.653 0.0014 0.00185 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 310 100 0.168 0.00705 0.0272 0.0748 0.0985 0.192 0.194 0.000546 0.00055 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 310 30880.465 0.005 0.00726 0.14 0.285 0.0754 0.0999 0.346 0.438 0.000984 0.00124 ! Validation 310 30880.465 0.005 0.00835 0.0607 0.228 0.0809 0.107 0.235 0.289 0.000666 0.000821 Wall time: 30880.465388544835 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.166 0.00718 0.0228 0.0748 0.0994 0.139 0.177 0.000394 0.000504 311 172 0.226 0.00627 0.1 0.0696 0.0929 0.332 0.372 0.000944 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 311 100 0.132 0.00622 0.00738 0.0704 0.0925 0.0973 0.101 0.000276 0.000286 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 311 30979.843 0.005 0.00665 0.0853 0.218 0.0721 0.0957 0.276 0.343 0.000785 0.000973 ! Validation 311 30979.843 0.005 0.00763 0.0213 0.174 0.0772 0.102 0.135 0.171 0.000384 0.000487 Wall time: 30979.84372470202 ! Best model 311 0.174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.242 0.00675 0.107 0.0725 0.0964 0.346 0.384 0.000984 0.00109 312 172 0.206 0.00692 0.0682 0.0736 0.0975 0.259 0.306 0.000736 0.00087 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 312 100 0.159 0.00671 0.0253 0.073 0.0961 0.161 0.187 0.000459 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 312 31079.116 0.005 0.00644 0.109 0.238 0.071 0.0941 0.316 0.387 0.000898 0.0011 ! Validation 312 31079.116 0.005 0.00827 0.176 0.341 0.0802 0.107 0.427 0.492 0.00121 0.0014 Wall time: 31079.116181476973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.164 0.0062 0.0399 0.0699 0.0923 0.18 0.234 0.000512 0.000665 313 172 0.141 0.00593 0.0221 0.0681 0.0903 0.145 0.175 0.000412 0.000496 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 313 100 0.12 0.00584 0.00358 0.0674 0.0896 0.0569 0.0701 0.000162 0.000199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 313 31178.360 0.005 0.00661 0.0977 0.23 0.0719 0.0954 0.295 0.367 0.000837 0.00104 ! Validation 313 31178.360 0.005 0.00726 0.023 0.168 0.0749 0.1 0.144 0.178 0.000408 0.000505 Wall time: 31178.36013646284 ! Best model 313 0.168 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.202 0.00718 0.0583 0.0757 0.0994 0.235 0.283 0.000668 0.000805 314 172 0.136 0.00552 0.026 0.0655 0.0872 0.159 0.189 0.00045 0.000537 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 314 100 0.113 0.0056 0.000969 0.0661 0.0878 0.0289 0.0365 8.21e-05 0.000104 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 31277.631 0.005 0.00668 0.11 0.244 0.0724 0.0959 0.304 0.389 0.000863 0.00111 ! Validation 314 31277.631 0.005 0.00694 0.0255 0.164 0.0731 0.0977 0.154 0.187 0.000438 0.000532 Wall time: 31277.631589702796 ! Best model 314 0.164 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.331 0.00583 0.214 0.0676 0.0896 0.503 0.543 0.00143 0.00154 315 172 0.178 0.00575 0.0633 0.0671 0.089 0.248 0.295 0.000705 0.000838 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 315 100 0.132 0.00596 0.0131 0.0687 0.0905 0.12 0.134 0.000341 0.000381 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 315 31376.888 0.005 0.00598 0.0756 0.195 0.0682 0.0907 0.256 0.323 0.000729 0.000916 ! Validation 315 31376.888 0.005 0.00728 0.0541 0.2 0.0753 0.1 0.222 0.273 0.000632 0.000775 Wall time: 31376.88884732593 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.131 0.00578 0.0156 0.0676 0.0892 0.122 0.146 0.000346 0.000416 316 172 0.131 0.00569 0.0168 0.0664 0.0885 0.135 0.152 0.000385 0.000432 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 316 100 0.111 0.00525 0.00589 0.0645 0.085 0.0764 0.09 0.000217 0.000256 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 316 31476.133 0.005 0.00623 0.0959 0.22 0.0698 0.0926 0.289 0.363 0.000821 0.00103 ! Validation 316 31476.133 0.005 0.00659 0.0437 0.175 0.0713 0.0952 0.209 0.245 0.000594 0.000697 Wall time: 31476.13304886408 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 0.26 0.00635 0.133 0.0704 0.0935 0.364 0.428 0.00103 0.00122 317 172 0.168 0.00543 0.0595 0.0645 0.0864 0.261 0.286 0.000741 0.000813 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 317 100 0.153 0.00551 0.0427 0.0664 0.0871 0.239 0.242 0.00068 0.000688 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 317 31575.375 0.005 0.00574 0.0843 0.199 0.0668 0.0888 0.268 0.341 0.000762 0.000968 ! Validation 317 31575.375 0.005 0.00675 0.0367 0.172 0.0725 0.0964 0.172 0.225 0.000488 0.000638 Wall time: 31575.37564951973 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.312 0.00661 0.18 0.0721 0.0954 0.444 0.497 0.00126 0.00141 318 172 0.391 0.00622 0.267 0.07 0.0925 0.572 0.606 0.00163 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 318 100 0.506 0.00691 0.368 0.0759 0.0975 0.708 0.711 0.00201 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 318 31674.611 0.005 0.00567 0.0828 0.196 0.0664 0.0883 0.265 0.337 0.000754 0.000958 ! Validation 318 31674.611 0.005 0.00799 0.395 0.555 0.0799 0.105 0.695 0.737 0.00197 0.00209 Wall time: 31674.611805174965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.18 0.00597 0.0603 0.0668 0.0907 0.256 0.288 0.000729 0.000818 319 172 0.164 0.00482 0.0672 0.061 0.0814 0.268 0.304 0.000762 0.000864 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 319 100 0.159 0.0049 0.0608 0.0624 0.0821 0.284 0.289 0.000807 0.000822 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 319 31773.843 0.005 0.00572 0.0715 0.186 0.0667 0.0887 0.253 0.314 0.000718 0.000891 ! Validation 319 31773.843 0.005 0.00616 0.0595 0.183 0.0689 0.092 0.232 0.286 0.00066 0.000813 Wall time: 31773.843258054927 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.207 0.00848 0.0372 0.0821 0.108 0.181 0.226 0.000514 0.000643 320 172 0.214 0.00536 0.107 0.0651 0.0858 0.353 0.383 0.001 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 320 100 0.133 0.00558 0.0212 0.0668 0.0876 0.168 0.171 0.000476 0.000485 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 320 31873.083 0.005 0.00698 0.122 0.262 0.0734 0.098 0.334 0.41 0.000948 0.00117 ! Validation 320 31873.083 0.005 0.00695 0.191 0.33 0.0736 0.0978 0.452 0.513 0.00128 0.00146 Wall time: 31873.08292365214 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.208 0.00531 0.102 0.0641 0.0854 0.316 0.375 0.000898 0.00107 321 172 0.14 0.00505 0.0386 0.0621 0.0834 0.182 0.231 0.000518 0.000655 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 321 100 0.248 0.00486 0.151 0.0617 0.0818 0.455 0.456 0.00129 0.0013 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 321 31972.392 0.005 0.00548 0.0661 0.176 0.0653 0.0869 0.232 0.302 0.000659 0.000857 ! Validation 321 31972.392 0.005 0.00601 0.0973 0.217 0.068 0.0909 0.323 0.366 0.000919 0.00104 Wall time: 31972.39193573268 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 0.158 0.00483 0.0611 0.0616 0.0815 0.252 0.29 0.000715 0.000824 322 172 13.8 0.0581 12.6 0.212 0.283 4.08 4.17 0.0116 0.0118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 322 100 9.51 0.103 7.44 0.291 0.377 3.19 3.2 0.00908 0.00909 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 32071.619 0.005 0.00742 0.228 0.377 0.0725 0.101 0.338 0.549 0.00096 0.00156 ! Validation 322 32071.619 0.005 0.107 17.7 19.8 0.294 0.383 4.54 4.93 0.0129 0.014 Wall time: 32071.619659977034 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 7.33 0.335 0.624 0.512 0.679 0.68 0.927 0.00193 0.00263 323 172 3.05 0.129 0.466 0.318 0.421 0.654 0.801 0.00186 0.00227 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 323 100 2.69 0.123 0.229 0.314 0.411 0.549 0.562 0.00156 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 323 32170.940 0.005 0.492 9.5 19.3 0.58 0.823 1.96 3.62 0.00556 0.0103 ! Validation 323 32170.940 0.005 0.131 2.14 4.76 0.322 0.424 1.45 1.72 0.00411 0.00488 Wall time: 32170.940060133114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 1.77 0.0801 0.169 0.25 0.332 0.369 0.482 0.00105 0.00137 324 172 1.83 0.0736 0.357 0.238 0.318 0.574 0.701 0.00163 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 324 100 1.36 0.0679 0.00746 0.234 0.306 0.093 0.101 0.000264 0.000288 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 324 32270.192 0.005 0.0849 0.839 2.54 0.257 0.342 0.856 1.07 0.00243 0.00305 ! Validation 324 32270.192 0.005 0.0734 0.317 1.79 0.241 0.318 0.534 0.66 0.00152 0.00187 Wall time: 32270.192817111965 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 2.05 0.0633 0.786 0.221 0.295 0.899 1.04 0.00255 0.00295 325 172 1.21 0.0496 0.221 0.196 0.261 0.469 0.552 0.00133 0.00157 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 325 100 1.17 0.048 0.209 0.196 0.257 0.509 0.536 0.00145 0.00152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 325 32369.434 0.005 0.0601 0.62 1.82 0.216 0.288 0.722 0.924 0.00205 0.00262 ! Validation 325 32369.434 0.005 0.0541 0.274 1.36 0.206 0.273 0.475 0.614 0.00135 0.00174 Wall time: 32369.43447923381 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 1.21 0.0468 0.276 0.191 0.254 0.49 0.616 0.00139 0.00175 326 172 1.59 0.0424 0.745 0.18 0.242 0.93 1.01 0.00264 0.00288 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 326 100 1.68 0.0393 0.896 0.177 0.232 1.1 1.11 0.00312 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 326 32468.690 0.005 0.0524 0.629 1.68 0.201 0.269 0.671 0.93 0.00191 0.00264 ! Validation 326 32468.690 0.005 0.0444 0.641 1.53 0.186 0.247 0.837 0.939 0.00238 0.00267 Wall time: 32468.690225448925 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 1.53 0.0394 0.743 0.175 0.233 0.923 1.01 0.00262 0.00287 327 172 1.19 0.0348 0.491 0.163 0.219 0.75 0.822 0.00213 0.00233 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 327 100 0.769 0.0349 0.0707 0.166 0.219 0.265 0.312 0.000752 0.000886 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 327 32567.918 0.005 0.0385 0.51 1.28 0.173 0.23 0.681 0.838 0.00194 0.00238 ! Validation 327 32567.918 0.005 0.0384 0.303 1.07 0.173 0.23 0.529 0.646 0.0015 0.00183 Wall time: 32567.9188079047 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.716 0.0324 0.067 0.159 0.211 0.229 0.304 0.000651 0.000862 328 172 0.699 0.0319 0.0614 0.156 0.209 0.246 0.291 0.000698 0.000826 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 328 100 0.636 0.0305 0.0254 0.156 0.205 0.147 0.187 0.000419 0.000531 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 328 32667.334 0.005 0.033 0.364 1.02 0.16 0.213 0.534 0.708 0.00152 0.00201 ! Validation 328 32667.334 0.005 0.0333 0.164 0.83 0.161 0.214 0.384 0.474 0.00109 0.00135 Wall time: 32667.334280868992 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 0.68 0.0287 0.107 0.148 0.199 0.302 0.383 0.000858 0.00109 329 172 0.797 0.027 0.257 0.144 0.193 0.503 0.595 0.00143 0.00169 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 329 100 2.18 0.0279 1.62 0.148 0.196 1.48 1.49 0.00421 0.00424 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 329 32766.475 0.005 0.029 0.381 0.962 0.149 0.2 0.584 0.724 0.00166 0.00206 ! Validation 329 32766.475 0.005 0.0303 1.24 1.85 0.153 0.204 1.23 1.31 0.00348 0.00371 Wall time: 32766.47537271073 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.706 0.026 0.186 0.141 0.189 0.437 0.506 0.00124 0.00144 330 172 0.686 0.0257 0.172 0.14 0.188 0.406 0.486 0.00115 0.00138 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 330 100 0.553 0.0269 0.0143 0.144 0.193 0.12 0.14 0.000342 0.000398 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 330 32865.627 0.005 0.0273 0.419 0.965 0.145 0.194 0.613 0.759 0.00174 0.00216 ! Validation 330 32865.627 0.005 0.029 0.11 0.69 0.15 0.2 0.312 0.39 0.000887 0.00111 Wall time: 32865.6275042831 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 1.18 0.0237 0.709 0.135 0.181 0.943 0.988 0.00268 0.00281 331 172 0.56 0.0232 0.0962 0.133 0.179 0.301 0.364 0.000854 0.00103 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 331 100 0.653 0.0239 0.175 0.136 0.181 0.47 0.491 0.00134 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 331 32965.868 0.005 0.0249 0.306 0.804 0.138 0.185 0.518 0.649 0.00147 0.00184 ! Validation 331 32965.868 0.005 0.026 0.17 0.69 0.142 0.189 0.39 0.484 0.00111 0.00137 Wall time: 32965.86844993569 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 0.718 0.0203 0.311 0.125 0.167 0.573 0.655 0.00163 0.00186 332 172 0.529 0.0233 0.0632 0.134 0.179 0.239 0.295 0.00068 0.000838 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 332 100 1.77 0.0237 1.3 0.136 0.181 1.33 1.34 0.00378 0.0038 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 33065.086 0.005 0.0221 0.276 0.718 0.13 0.174 0.491 0.616 0.00139 0.00175 ! Validation 332 33065.086 0.005 0.0253 1.75 2.25 0.14 0.187 1.49 1.55 0.00422 0.0044 Wall time: 33065.086433404125 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.783 0.0203 0.377 0.125 0.167 0.672 0.72 0.00191 0.00205 333 172 0.472 0.0212 0.0483 0.128 0.171 0.222 0.258 0.000629 0.000732 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 333 100 0.441 0.0204 0.0331 0.125 0.168 0.186 0.213 0.000529 0.000606 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 333 33164.306 0.005 0.0216 0.292 0.724 0.129 0.172 0.518 0.634 0.00147 0.0018 ! Validation 333 33164.306 0.005 0.0225 0.187 0.637 0.132 0.176 0.417 0.508 0.00118 0.00144 Wall time: 33164.30677458504 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.504 0.0197 0.111 0.123 0.165 0.311 0.39 0.000885 0.00111 334 172 0.601 0.0186 0.229 0.12 0.16 0.513 0.561 0.00146 0.00159 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 334 100 0.388 0.0188 0.0126 0.12 0.161 0.11 0.132 0.000313 0.000375 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 334 33263.626 0.005 0.0194 0.231 0.618 0.122 0.163 0.457 0.564 0.0013 0.0016 ! Validation 334 33263.626 0.005 0.0207 0.0939 0.508 0.127 0.169 0.298 0.359 0.000848 0.00102 Wall time: 33263.625913750846 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.759 0.0182 0.394 0.119 0.158 0.626 0.737 0.00178 0.00209 335 172 0.521 0.0177 0.168 0.117 0.156 0.414 0.48 0.00118 0.00136 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 335 100 0.375 0.0179 0.0172 0.117 0.157 0.13 0.154 0.000371 0.000437 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 33362.901 0.005 0.0183 0.255 0.621 0.119 0.159 0.481 0.593 0.00137 0.00168 ! Validation 335 33362.901 0.005 0.0199 0.196 0.594 0.124 0.165 0.393 0.519 0.00112 0.00148 Wall time: 33362.901857806835 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.521 0.0162 0.198 0.112 0.149 0.472 0.521 0.00134 0.00148 336 172 0.486 0.0182 0.122 0.118 0.158 0.331 0.41 0.000941 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 336 100 0.399 0.0181 0.0374 0.118 0.158 0.21 0.227 0.000595 0.000644 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 336 33462.209 0.005 0.0175 0.275 0.624 0.116 0.155 0.497 0.615 0.00141 0.00175 ! Validation 336 33462.209 0.005 0.0199 0.143 0.542 0.125 0.166 0.371 0.444 0.00105 0.00126 Wall time: 33462.209711777046 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 1 0.0156 0.693 0.11 0.146 0.932 0.976 0.00265 0.00277 337 172 0.615 0.0148 0.319 0.108 0.143 0.593 0.662 0.00168 0.00188 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 337 100 0.324 0.0158 0.00791 0.111 0.147 0.0781 0.104 0.000222 0.000296 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 337 33561.443 0.005 0.0166 0.202 0.535 0.114 0.151 0.416 0.527 0.00118 0.0015 ! Validation 337 33561.443 0.005 0.0177 0.0841 0.438 0.118 0.156 0.284 0.34 0.000807 0.000966 Wall time: 33561.44310050784 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.388 0.0164 0.0589 0.113 0.15 0.226 0.285 0.000642 0.000809 338 172 0.385 0.0143 0.0993 0.106 0.14 0.332 0.37 0.000943 0.00105 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 338 100 0.294 0.0145 0.00502 0.106 0.141 0.073 0.0831 0.000207 0.000236 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 33660.692 0.005 0.0155 0.183 0.492 0.11 0.146 0.388 0.501 0.0011 0.00142 ! Validation 338 33660.692 0.005 0.0165 0.0734 0.404 0.114 0.151 0.262 0.318 0.000744 0.000903 Wall time: 33660.6923828898 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.404 0.0134 0.136 0.103 0.136 0.357 0.433 0.00101 0.00123 339 172 0.357 0.0152 0.053 0.109 0.145 0.224 0.27 0.000637 0.000767 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 339 100 0.321 0.0146 0.0304 0.106 0.141 0.193 0.204 0.000549 0.000581 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 339 33759.936 0.005 0.015 0.248 0.548 0.108 0.144 0.455 0.585 0.00129 0.00166 ! Validation 339 33759.936 0.005 0.0164 0.0744 0.403 0.113 0.15 0.248 0.32 0.000704 0.000909 Wall time: 33759.935919686686 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 0.338 0.0132 0.0733 0.102 0.135 0.253 0.318 0.000718 0.000902 340 172 0.667 0.0137 0.393 0.104 0.137 0.71 0.735 0.00202 0.00209 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 340 100 1.3 0.0139 1.02 0.104 0.138 1.18 1.19 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 340 33859.140 0.005 0.0139 0.171 0.449 0.104 0.138 0.387 0.485 0.0011 0.00138 ! Validation 340 33859.140 0.005 0.0157 0.819 1.13 0.111 0.147 1.03 1.06 0.00291 0.00302 Wall time: 33859.14070365485 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.666 0.0151 0.364 0.109 0.144 0.651 0.708 0.00185 0.00201 341 172 0.347 0.0143 0.0605 0.106 0.14 0.244 0.289 0.000692 0.00082 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 341 100 0.278 0.0137 0.00436 0.104 0.137 0.068 0.0775 0.000193 0.00022 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 341 33958.332 0.005 0.0138 0.231 0.507 0.104 0.138 0.461 0.564 0.00131 0.0016 ! Validation 341 33958.332 0.005 0.0156 0.117 0.428 0.111 0.146 0.318 0.401 0.000903 0.00114 Wall time: 33958.33280013176 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.392 0.0136 0.121 0.104 0.137 0.336 0.408 0.000954 0.00116 342 172 0.795 0.0131 0.534 0.102 0.134 0.804 0.857 0.00228 0.00244 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 342 100 0.276 0.0133 0.0105 0.103 0.135 0.11 0.12 0.000311 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 342 34057.517 0.005 0.0131 0.188 0.45 0.101 0.134 0.404 0.509 0.00115 0.00144 ! Validation 342 34057.517 0.005 0.015 0.0922 0.393 0.109 0.144 0.293 0.356 0.000834 0.00101 Wall time: 34057.5176285631 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 0.653 0.012 0.413 0.0978 0.129 0.686 0.753 0.00195 0.00214 343 172 1.31 0.0117 1.07 0.0961 0.127 1.2 1.21 0.0034 0.00345 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 343 100 1.01 0.0116 0.778 0.0972 0.127 1.03 1.03 0.00293 0.00294 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 343 34156.690 0.005 0.0123 0.147 0.393 0.0984 0.13 0.36 0.449 0.00102 0.00127 ! Validation 343 34156.690 0.005 0.0137 1.27 1.54 0.105 0.137 1.27 1.32 0.00362 0.00375 Wall time: 34156.69026110275 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.344 0.0114 0.116 0.0944 0.125 0.339 0.4 0.000964 0.00114 344 172 0.322 0.012 0.0818 0.0969 0.129 0.263 0.335 0.000747 0.000953 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 344 100 0.402 0.0119 0.164 0.0978 0.128 0.471 0.474 0.00134 0.00135 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 344 34255.863 0.005 0.0121 0.201 0.443 0.0978 0.129 0.41 0.526 0.00116 0.00149 ! Validation 344 34255.863 0.005 0.0137 0.164 0.438 0.104 0.137 0.393 0.474 0.00112 0.00135 Wall time: 34255.8629900231 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.258 0.0112 0.0334 0.0939 0.124 0.171 0.214 0.000485 0.000609 345 172 0.467 0.0125 0.217 0.0998 0.131 0.504 0.546 0.00143 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 345 100 0.246 0.0115 0.0159 0.0958 0.126 0.141 0.148 0.0004 0.00042 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 345 34355.032 0.005 0.0114 0.159 0.386 0.0948 0.125 0.376 0.468 0.00107 0.00133 ! Validation 345 34355.032 0.005 0.0132 0.117 0.381 0.102 0.135 0.324 0.402 0.00092 0.00114 Wall time: 34355.032443919685 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.341 0.0124 0.0925 0.099 0.131 0.298 0.357 0.000846 0.00101 346 172 0.314 0.0105 0.105 0.0911 0.12 0.308 0.379 0.000874 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 346 100 0.223 0.0104 0.0151 0.091 0.12 0.134 0.144 0.00038 0.00041 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 346 34454.227 0.005 0.0112 0.177 0.4 0.094 0.124 0.398 0.493 0.00113 0.0014 ! Validation 346 34454.227 0.005 0.0121 0.108 0.35 0.0977 0.129 0.322 0.385 0.000915 0.00109 Wall time: 34454.22747228807 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.302 0.0117 0.069 0.0971 0.127 0.252 0.308 0.000716 0.000875 347 172 0.444 0.00961 0.252 0.0876 0.115 0.56 0.589 0.00159 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 347 100 0.5 0.00973 0.305 0.0886 0.116 0.647 0.648 0.00184 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 347 34553.370 0.005 0.0105 0.143 0.352 0.0912 0.12 0.35 0.443 0.000995 0.00126 ! Validation 347 34553.370 0.005 0.0114 0.351 0.579 0.0952 0.125 0.644 0.695 0.00183 0.00197 Wall time: 34553.370681103785 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.341 0.01 0.14 0.0896 0.117 0.379 0.44 0.00108 0.00125 348 172 0.225 0.00896 0.0458 0.0851 0.111 0.205 0.251 0.000583 0.000713 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 348 100 0.341 0.00983 0.144 0.089 0.116 0.443 0.446 0.00126 0.00127 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 348 34652.529 0.005 0.0104 0.181 0.389 0.0908 0.12 0.398 0.499 0.00113 0.00142 ! Validation 348 34652.529 0.005 0.0113 0.378 0.603 0.0945 0.124 0.581 0.721 0.00165 0.00205 Wall time: 34652.52950935578 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.299 0.0107 0.0846 0.0922 0.122 0.29 0.341 0.000824 0.000969 349 172 0.267 0.00932 0.0807 0.0859 0.113 0.27 0.333 0.000767 0.000947 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 349 100 0.445 0.00908 0.263 0.0858 0.112 0.6 0.601 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 349 34752.300 0.005 0.00969 0.144 0.337 0.0878 0.115 0.358 0.444 0.00102 0.00126 ! Validation 349 34752.300 0.005 0.0106 0.265 0.478 0.0918 0.121 0.551 0.604 0.00157 0.00172 Wall time: 34752.300681513734 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.22 0.00874 0.0449 0.0833 0.11 0.213 0.249 0.000604 0.000706 350 172 0.254 0.0102 0.05 0.0898 0.118 0.225 0.262 0.000639 0.000745 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 350 100 0.178 0.00863 0.00519 0.0839 0.109 0.07 0.0845 0.000199 0.00024 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 34851.447 0.005 0.00937 0.149 0.336 0.0864 0.114 0.351 0.453 0.000998 0.00129 ! Validation 350 34851.447 0.005 0.0103 0.0561 0.262 0.0903 0.119 0.221 0.278 0.000627 0.000789 Wall time: 34851.44718130492 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.196 0.00869 0.0225 0.0834 0.109 0.135 0.176 0.000385 0.0005 351 172 0.293 0.0084 0.125 0.0826 0.107 0.36 0.415 0.00102 0.00118 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 351 100 0.35 0.00887 0.173 0.085 0.11 0.485 0.488 0.00138 0.00139 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 351 34950.593 0.005 0.00883 0.14 0.316 0.0839 0.11 0.354 0.439 0.00101 0.00125 ! Validation 351 34950.593 0.005 0.0104 0.138 0.346 0.091 0.12 0.357 0.436 0.00101 0.00124 Wall time: 34950.59292689478 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.24 0.00904 0.0588 0.0844 0.112 0.247 0.284 0.000702 0.000808 352 172 0.526 0.00842 0.358 0.0819 0.108 0.648 0.702 0.00184 0.00199 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 352 100 0.278 0.00886 0.1 0.0846 0.11 0.368 0.371 0.00104 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 352 35049.762 0.005 0.0085 0.126 0.296 0.0824 0.108 0.335 0.415 0.000952 0.00118 ! Validation 352 35049.762 0.005 0.0102 0.295 0.499 0.0899 0.118 0.49 0.637 0.00139 0.00181 Wall time: 35049.76237504184 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.713 0.00908 0.531 0.0852 0.112 0.764 0.855 0.00217 0.00243 353 172 0.187 0.00794 0.0279 0.0798 0.105 0.164 0.196 0.000467 0.000557 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 353 100 0.27 0.00781 0.114 0.0794 0.104 0.393 0.396 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 353 35148.901 0.005 0.00862 0.155 0.327 0.083 0.109 0.376 0.462 0.00107 0.00131 ! Validation 353 35148.901 0.005 0.00926 0.16 0.345 0.0857 0.113 0.415 0.469 0.00118 0.00133 Wall time: 35148.90171428397 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.226 0.00794 0.0677 0.08 0.104 0.247 0.305 0.000701 0.000867 354 172 0.195 0.00858 0.0239 0.083 0.109 0.143 0.181 0.000407 0.000515 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 354 100 0.162 0.00764 0.00887 0.0792 0.103 0.0967 0.11 0.000275 0.000314 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 354 35248.033 0.005 0.00791 0.12 0.278 0.0795 0.104 0.332 0.407 0.000944 0.00116 ! Validation 354 35248.033 0.005 0.00905 0.0719 0.253 0.0847 0.112 0.262 0.315 0.000744 0.000894 Wall time: 35248.03314050101 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.168 0.00697 0.0282 0.0752 0.0979 0.154 0.197 0.000437 0.000559 355 172 0.515 0.00948 0.325 0.0872 0.114 0.636 0.669 0.00181 0.0019 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 355 100 0.424 0.00771 0.27 0.0793 0.103 0.607 0.61 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 355 35347.168 0.005 0.00754 0.112 0.263 0.0775 0.102 0.309 0.393 0.000879 0.00112 ! Validation 355 35347.168 0.005 0.00909 0.201 0.383 0.0851 0.112 0.478 0.526 0.00136 0.00149 Wall time: 35347.16876124684 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.241 0.00746 0.0918 0.0764 0.101 0.291 0.355 0.000825 0.00101 356 172 0.188 0.00697 0.0483 0.0748 0.0979 0.201 0.258 0.000572 0.000732 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 356 100 0.14 0.00667 0.00608 0.0739 0.0958 0.083 0.0914 0.000236 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 356 35446.308 0.005 0.00733 0.0957 0.242 0.0765 0.1 0.292 0.363 0.000831 0.00103 ! Validation 356 35446.308 0.005 0.00845 0.0474 0.216 0.0817 0.108 0.201 0.255 0.000571 0.000725 Wall time: 35446.308437582105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.399 0.00787 0.241 0.0789 0.104 0.44 0.576 0.00125 0.00164 357 172 0.172 0.00662 0.0393 0.0726 0.0955 0.186 0.233 0.000529 0.000661 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 357 100 0.179 0.00633 0.0527 0.072 0.0933 0.264 0.269 0.000751 0.000765 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 357 35545.467 0.005 0.00711 0.119 0.261 0.0752 0.0989 0.318 0.405 0.000903 0.00115 ! Validation 357 35545.467 0.005 0.00791 0.0494 0.208 0.0789 0.104 0.21 0.261 0.000597 0.000741 Wall time: 35545.46776398085 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.186 0.00688 0.048 0.0738 0.0973 0.213 0.257 0.000604 0.00073 358 172 0.215 0.00668 0.0818 0.0721 0.0959 0.284 0.335 0.000808 0.000953 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 358 100 0.36 0.00628 0.235 0.0716 0.0929 0.566 0.568 0.00161 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 358 35644.603 0.005 0.00674 0.105 0.239 0.0732 0.0963 0.31 0.379 0.000881 0.00108 ! Validation 358 35644.603 0.005 0.00761 0.148 0.3 0.0772 0.102 0.396 0.451 0.00113 0.00128 Wall time: 35644.60293134395 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.207 0.00673 0.0729 0.0732 0.0962 0.274 0.317 0.000778 0.0009 359 172 0.187 0.00685 0.0503 0.0734 0.0971 0.202 0.263 0.000575 0.000747 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 359 100 0.158 0.00584 0.041 0.0689 0.0896 0.232 0.237 0.000659 0.000675 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 359 35744.840 0.005 0.00674 0.104 0.239 0.0732 0.0963 0.299 0.378 0.000848 0.00107 ! Validation 359 35744.840 0.005 0.00726 0.0387 0.184 0.0754 0.0999 0.182 0.231 0.000516 0.000656 Wall time: 35744.84017899213 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.159 0.00657 0.0273 0.0722 0.0951 0.158 0.194 0.000449 0.000551 360 172 0.189 0.00598 0.0699 0.0692 0.0907 0.261 0.31 0.00074 0.000881 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 360 100 0.153 0.0061 0.0309 0.0706 0.0917 0.202 0.206 0.000573 0.000586 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 360 35843.910 0.005 0.00638 0.091 0.219 0.0711 0.0937 0.282 0.354 0.000801 0.00101 ! Validation 360 35843.910 0.005 0.00746 0.0349 0.184 0.0765 0.101 0.172 0.219 0.000489 0.000622 Wall time: 35843.91009361809 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.417 0.00583 0.3 0.0683 0.0896 0.634 0.643 0.0018 0.00183 361 172 0.195 0.00772 0.0411 0.0786 0.103 0.207 0.238 0.000587 0.000675 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 361 100 0.489 0.0067 0.355 0.0744 0.096 0.696 0.699 0.00198 0.00199 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 361 35943.054 0.005 0.00639 0.127 0.255 0.071 0.0937 0.324 0.418 0.00092 0.00119 ! Validation 361 35943.054 0.005 0.00806 0.385 0.546 0.0801 0.105 0.683 0.727 0.00194 0.00207 Wall time: 35943.0546308998 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.266 0.0065 0.136 0.0713 0.0946 0.396 0.433 0.00113 0.00123 362 172 0.15 0.00604 0.0292 0.0688 0.0912 0.162 0.2 0.00046 0.000569 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 362 100 0.124 0.00558 0.0125 0.068 0.0876 0.12 0.131 0.000341 0.000372 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 362 36042.133 0.005 0.0064 0.0976 0.226 0.0712 0.0938 0.3 0.366 0.000853 0.00104 ! Validation 362 36042.133 0.005 0.00696 0.0272 0.166 0.0741 0.0978 0.156 0.193 0.000443 0.00055 Wall time: 36042.13335689902 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.126 0.00512 0.0239 0.0639 0.0839 0.142 0.181 0.000402 0.000515 363 172 0.129 0.0054 0.0205 0.0652 0.0862 0.134 0.168 0.00038 0.000477 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 363 100 0.143 0.00522 0.0387 0.0652 0.0848 0.226 0.231 0.000642 0.000656 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 363 36141.227 0.005 0.00574 0.0681 0.183 0.0673 0.0889 0.248 0.306 0.000705 0.00087 ! Validation 363 36141.227 0.005 0.00661 0.0241 0.156 0.0718 0.0954 0.144 0.182 0.000409 0.000518 Wall time: 36141.22704916587 ! Best model 363 0.156 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.17 0.00554 0.0595 0.0668 0.0873 0.25 0.286 0.000709 0.000813 364 172 0.134 0.00516 0.0311 0.064 0.0843 0.175 0.207 0.000498 0.000588 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 364 100 0.12 0.00521 0.0163 0.0652 0.0847 0.136 0.15 0.000386 0.000425 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 364 36240.573 0.005 0.00555 0.0677 0.179 0.0661 0.0874 0.247 0.305 0.000702 0.000867 ! Validation 364 36240.573 0.005 0.0064 0.06 0.188 0.0707 0.0938 0.225 0.287 0.000639 0.000816 Wall time: 36240.573757648934 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.191 0.00587 0.0738 0.0681 0.0898 0.27 0.319 0.000767 0.000905 365 172 0.223 0.00556 0.112 0.0662 0.0875 0.334 0.392 0.000949 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 365 100 0.487 0.0056 0.376 0.0682 0.0878 0.716 0.719 0.00204 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 365 36339.660 0.005 0.0056 0.092 0.204 0.0663 0.0877 0.285 0.356 0.000809 0.00101 ! Validation 365 36339.660 0.005 0.00668 0.316 0.45 0.0728 0.0959 0.578 0.66 0.00164 0.00187 Wall time: 36339.660277350806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.193 0.00517 0.0898 0.0638 0.0843 0.317 0.352 0.000901 0.000999 366 172 0.131 0.0053 0.0247 0.064 0.0854 0.149 0.184 0.000423 0.000523 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 366 100 0.207 0.00474 0.112 0.062 0.0808 0.389 0.392 0.00111 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 366 36438.757 0.005 0.00541 0.0682 0.176 0.0652 0.0863 0.246 0.306 0.0007 0.00087 ! Validation 366 36438.757 0.005 0.00603 0.0873 0.208 0.0683 0.0911 0.28 0.347 0.000796 0.000984 Wall time: 36438.75743989693 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.149 0.0056 0.0369 0.0665 0.0878 0.167 0.225 0.000475 0.000641 367 172 0.148 0.00611 0.0262 0.0698 0.0917 0.148 0.19 0.000421 0.00054 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 367 100 0.194 0.00597 0.075 0.07 0.0906 0.316 0.321 0.000898 0.000913 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 367 36537.833 0.005 0.00762 0.159 0.311 0.075 0.102 0.364 0.467 0.00103 0.00133 ! Validation 367 36537.833 0.005 0.00733 0.0672 0.214 0.0759 0.1 0.251 0.304 0.000712 0.000864 Wall time: 36537.83361016307 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.108 0.0044 0.0202 0.0592 0.0778 0.135 0.167 0.000384 0.000474 368 172 0.119 0.0048 0.0235 0.061 0.0812 0.157 0.18 0.000445 0.000511 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 368 100 0.38 0.00464 0.287 0.0612 0.0799 0.627 0.628 0.00178 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 368 36636.922 0.005 0.00522 0.0493 0.154 0.0639 0.0847 0.207 0.26 0.000587 0.00074 ! Validation 368 36636.922 0.005 0.00587 0.244 0.361 0.0674 0.0899 0.55 0.579 0.00156 0.00165 Wall time: 36636.92192039406 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.161 0.00534 0.0542 0.0649 0.0857 0.229 0.273 0.00065 0.000776 369 172 0.172 0.00457 0.0811 0.06 0.0793 0.316 0.334 0.000899 0.000949 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 369 100 0.293 0.00445 0.204 0.0602 0.0783 0.527 0.529 0.0015 0.0015 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 369 36736.024 0.005 0.00505 0.0592 0.16 0.0628 0.0834 0.227 0.285 0.000645 0.000811 ! Validation 369 36736.024 0.005 0.00572 0.114 0.228 0.0666 0.0887 0.352 0.396 0.001 0.00113 Wall time: 36736.02397155808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.176 0.00499 0.0762 0.0624 0.0828 0.3 0.324 0.000852 0.00092 370 172 0.254 0.00479 0.158 0.0611 0.0812 0.436 0.467 0.00124 0.00133 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 370 100 0.137 0.00521 0.0323 0.0651 0.0847 0.203 0.211 0.000576 0.000599 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 370 36835.114 0.005 0.00518 0.0933 0.197 0.0636 0.0844 0.292 0.358 0.000828 0.00102 ! Validation 370 36835.114 0.005 0.00642 0.0976 0.226 0.0709 0.094 0.306 0.366 0.00087 0.00104 Wall time: 36835.11409716681 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 1.22 0.0108 1 0.0953 0.122 1.16 1.17 0.00329 0.00333 371 172 0.108 0.00462 0.0153 0.0604 0.0798 0.124 0.145 0.000351 0.000412 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 371 100 0.0976 0.00466 0.00432 0.0613 0.0801 0.0581 0.0771 0.000165 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 371 36934.194 0.005 0.00706 0.119 0.261 0.0724 0.0986 0.3 0.406 0.000853 0.00115 ! Validation 371 36934.194 0.005 0.00589 0.0343 0.152 0.0676 0.0901 0.178 0.217 0.000507 0.000617 Wall time: 36934.19403334614 ! Best model 371 0.152 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.238 0.00499 0.138 0.0624 0.0828 0.409 0.436 0.00116 0.00124 372 172 0.115 0.00486 0.0177 0.0611 0.0817 0.117 0.156 0.000332 0.000443 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 372 100 0.111 0.0042 0.0268 0.0582 0.076 0.187 0.192 0.00053 0.000546 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 372 37033.289 0.005 0.0048 0.0404 0.136 0.061 0.0812 0.186 0.236 0.000528 0.00067 ! Validation 372 37033.289 0.005 0.00533 0.0292 0.136 0.064 0.0856 0.153 0.2 0.000434 0.000569 Wall time: 37033.28947161976 ! Best model 372 0.136 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.167 0.00432 0.0808 0.0584 0.0771 0.316 0.333 0.000899 0.000947 373 172 0.145 0.00447 0.0557 0.0586 0.0785 0.239 0.277 0.000678 0.000786 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 373 100 0.258 0.00419 0.175 0.058 0.0759 0.488 0.49 0.00139 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 373 37132.396 0.005 0.00463 0.0531 0.146 0.06 0.0798 0.218 0.27 0.000618 0.000768 ! Validation 373 37132.396 0.005 0.00526 0.1 0.205 0.0636 0.0851 0.336 0.371 0.000955 0.00105 Wall time: 37132.39630415477 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.109 0.00478 0.0132 0.0608 0.0811 0.115 0.135 0.000328 0.000383 374 172 0.198 0.00579 0.0822 0.0661 0.0892 0.274 0.336 0.000777 0.000955 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 374 100 0.145 0.00455 0.0537 0.0611 0.0791 0.265 0.272 0.000752 0.000772 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 37231.459 0.005 0.00468 0.0596 0.153 0.0603 0.0802 0.226 0.286 0.000643 0.000814 ! Validation 374 37231.459 0.005 0.00562 0.149 0.261 0.0667 0.0879 0.384 0.452 0.00109 0.00129 Wall time: 37231.45969038177 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.116 0.00461 0.0238 0.0599 0.0797 0.126 0.181 0.000358 0.000514 375 172 0.161 0.00577 0.0456 0.0675 0.0891 0.172 0.25 0.000489 0.000711 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 375 100 0.18 0.0048 0.0838 0.0624 0.0813 0.334 0.34 0.000948 0.000965 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 375 37330.555 0.005 0.00577 0.0917 0.207 0.0665 0.0891 0.269 0.355 0.000765 0.00101 ! Validation 375 37330.555 0.005 0.00582 0.0443 0.161 0.0675 0.0895 0.202 0.247 0.000573 0.000701 Wall time: 37330.555069148075 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.116 0.00498 0.0166 0.0613 0.0828 0.122 0.151 0.000348 0.000429 376 172 0.135 0.00454 0.0438 0.0594 0.079 0.188 0.245 0.000534 0.000697 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 376 100 0.182 0.00405 0.101 0.0567 0.0747 0.369 0.374 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 376 37429.632 0.005 0.00453 0.0479 0.139 0.0593 0.0789 0.203 0.257 0.000576 0.00073 ! Validation 376 37429.632 0.005 0.00509 0.0662 0.168 0.0624 0.0837 0.259 0.302 0.000735 0.000857 Wall time: 37429.63233057503 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.125 0.00459 0.0337 0.0597 0.0795 0.191 0.215 0.000543 0.000612 377 172 0.101 0.00415 0.0179 0.057 0.0756 0.128 0.157 0.000364 0.000446 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 377 100 0.12 0.0039 0.0417 0.0559 0.0733 0.235 0.24 0.000668 0.000681 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 377 37528.732 0.005 0.00488 0.0727 0.17 0.0618 0.0819 0.255 0.316 0.000724 0.000898 ! Validation 377 37528.732 0.005 0.00495 0.0241 0.123 0.0617 0.0825 0.142 0.182 0.000404 0.000518 Wall time: 37528.73219972802 ! Best model 377 0.123 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.168 0.00525 0.0632 0.0638 0.085 0.259 0.295 0.000735 0.000838 378 172 0.0945 0.00385 0.0175 0.0549 0.0728 0.129 0.155 0.000365 0.000441 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 378 100 0.0958 0.00391 0.0176 0.0555 0.0734 0.146 0.155 0.000414 0.000442 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 378 37629.585 0.005 0.00454 0.0587 0.149 0.0594 0.079 0.232 0.284 0.00066 0.000808 ! Validation 378 37629.585 0.005 0.0049 0.031 0.129 0.0613 0.0821 0.162 0.207 0.00046 0.000587 Wall time: 37629.58509454969 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.149 0.00617 0.0261 0.07 0.0921 0.152 0.189 0.000431 0.000538 379 172 0.1 0.00426 0.0152 0.0576 0.0766 0.113 0.145 0.000321 0.000411 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 379 100 0.0821 0.00396 0.00296 0.0564 0.0738 0.0545 0.0638 0.000155 0.000181 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 37728.654 0.005 0.00513 0.0782 0.181 0.0628 0.084 0.253 0.328 0.00072 0.000932 ! Validation 379 37728.654 0.005 0.00499 0.0466 0.146 0.0619 0.0829 0.212 0.253 0.000602 0.000719 Wall time: 37728.65427752584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.162 0.00415 0.0794 0.0564 0.0756 0.297 0.331 0.000844 0.000939 380 172 0.347 0.00572 0.233 0.0672 0.0887 0.468 0.566 0.00133 0.00161 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 380 100 0.607 0.00583 0.49 0.0692 0.0895 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 380 37827.752 0.005 0.00472 0.0663 0.161 0.06 0.0806 0.23 0.302 0.000652 0.000857 ! Validation 380 37827.752 0.005 0.00708 0.386 0.528 0.075 0.0987 0.707 0.729 0.00201 0.00207 Wall time: 37827.75259894878 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.152 0.00419 0.0683 0.0568 0.0759 0.28 0.306 0.000794 0.000871 381 172 0.106 0.00421 0.0221 0.057 0.0761 0.138 0.174 0.000392 0.000495 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 381 100 0.135 0.00377 0.0597 0.0547 0.072 0.283 0.287 0.000804 0.000814 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 37926.896 0.005 0.00521 0.059 0.163 0.0628 0.0846 0.22 0.285 0.000625 0.00081 ! Validation 381 37926.896 0.005 0.00463 0.0695 0.162 0.0595 0.0798 0.257 0.309 0.000729 0.000879 Wall time: 37926.89620273793 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.1 0.00439 0.0124 0.0581 0.0777 0.111 0.131 0.000314 0.000372 382 172 0.104 0.00419 0.0197 0.0569 0.0759 0.136 0.165 0.000385 0.000468 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 382 100 0.0916 0.0038 0.0156 0.0552 0.0723 0.138 0.147 0.000392 0.000417 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 382 38025.988 0.005 0.00414 0.0402 0.123 0.0566 0.0754 0.188 0.235 0.000535 0.000669 ! Validation 382 38025.988 0.005 0.00466 0.0205 0.114 0.0596 0.08 0.133 0.168 0.000379 0.000477 Wall time: 38025.988786384 ! Best model 382 0.114 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.1 0.00419 0.0164 0.0569 0.0759 0.117 0.15 0.000333 0.000427 383 172 0.105 0.00371 0.0312 0.0537 0.0714 0.181 0.207 0.000514 0.000589 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 383 100 0.0799 0.00374 0.00511 0.0548 0.0717 0.0718 0.0839 0.000204 0.000238 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 383 38125.075 0.005 0.00413 0.0495 0.132 0.0566 0.0754 0.209 0.261 0.000593 0.000742 ! Validation 383 38125.075 0.005 0.0048 0.0232 0.119 0.0609 0.0813 0.141 0.179 0.0004 0.000507 Wall time: 38125.075753015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.108 0.00405 0.0271 0.0559 0.0747 0.154 0.193 0.000438 0.000548 384 172 0.09 0.00383 0.0135 0.0547 0.0726 0.114 0.136 0.000323 0.000387 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 384 100 0.073 0.00359 0.00118 0.0537 0.0703 0.0354 0.0402 0.0001 0.000114 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 38224.153 0.005 0.00414 0.045 0.128 0.0567 0.0755 0.203 0.249 0.000577 0.000707 ! Validation 384 38224.153 0.005 0.00458 0.0239 0.116 0.0596 0.0794 0.15 0.182 0.000426 0.000516 Wall time: 38224.153684647754 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.118 0.00496 0.019 0.0629 0.0826 0.132 0.162 0.000375 0.000459 385 172 0.0777 0.00354 0.00697 0.0523 0.0698 0.0808 0.098 0.000229 0.000278 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 385 100 0.145 0.00343 0.0759 0.0523 0.0687 0.321 0.323 0.000912 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 385 38323.217 0.005 0.00477 0.0624 0.158 0.0601 0.081 0.23 0.293 0.000652 0.000833 ! Validation 385 38323.217 0.005 0.00439 0.0713 0.159 0.0579 0.0777 0.271 0.313 0.000771 0.00089 Wall time: 38323.217162009794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.106 0.00387 0.0288 0.0544 0.073 0.165 0.199 0.000469 0.000565 386 172 0.181 0.00526 0.0759 0.0635 0.0851 0.284 0.323 0.000807 0.000918 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 386 100 0.0794 0.00374 0.0047 0.055 0.0717 0.0724 0.0804 0.000206 0.000228 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 386 38423.516 0.005 0.0042 0.0574 0.141 0.0568 0.076 0.222 0.281 0.00063 0.000799 ! Validation 386 38423.516 0.005 0.00462 0.0277 0.12 0.0601 0.0797 0.162 0.195 0.00046 0.000555 Wall time: 38423.51609157678 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.117 0.00407 0.0359 0.0559 0.0748 0.191 0.222 0.000543 0.000631 387 172 0.101 0.00402 0.0211 0.0549 0.0743 0.139 0.17 0.000395 0.000484 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 387 100 0.0921 0.00347 0.0228 0.0523 0.0691 0.174 0.177 0.000493 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 387 38522.591 0.005 0.00448 0.0622 0.152 0.0585 0.0785 0.226 0.293 0.000642 0.000831 ! Validation 387 38522.591 0.005 0.00442 0.0328 0.121 0.0582 0.078 0.168 0.212 0.000477 0.000603 Wall time: 38522.590920678806 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.0922 0.00342 0.0239 0.0515 0.0686 0.143 0.181 0.000407 0.000515 388 172 0.107 0.0042 0.023 0.0566 0.076 0.148 0.178 0.000421 0.000506 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 388 100 0.137 0.00535 0.0298 0.0652 0.0858 0.195 0.202 0.000555 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 388 38621.659 0.005 0.00392 0.0494 0.128 0.0551 0.0735 0.206 0.261 0.000585 0.000741 ! Validation 388 38621.659 0.005 0.00593 0.0648 0.183 0.0679 0.0903 0.235 0.299 0.000667 0.000848 Wall time: 38621.65969283786 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 389 100 0.253 0.00378 0.178 0.054 0.0721 0.468 0.495 0.00133 0.00141 389 172 0.0988 0.00363 0.0261 0.0534 0.0707 0.164 0.189 0.000467 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 0.0858 0.00341 0.0176 0.0526 0.0685 0.15 0.156 0.000427 0.000442 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 389 38720.725 0.005 0.00374 0.0342 0.109 0.0538 0.0718 0.173 0.217 0.000492 0.000616 ! Validation 389 38720.725 0.005 0.00418 0.0149 0.0984 0.0568 0.0758 0.111 0.143 0.000314 0.000406 Wall time: 38720.72569893906 ! Best model 389 0.098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.115 0.00436 0.0283 0.0585 0.0774 0.149 0.197 0.000422 0.000561 390 172 0.356 0.00442 0.267 0.0572 0.0779 0.58 0.607 0.00165 0.00172 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 390 100 0.117 0.00356 0.0458 0.0529 0.07 0.251 0.251 0.000712 0.000713 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 390 38819.810 0.005 0.00374 0.0414 0.116 0.0538 0.0717 0.192 0.238 0.000544 0.000676 ! Validation 390 38819.810 0.005 0.0045 0.338 0.428 0.0588 0.0787 0.574 0.682 0.00163 0.00194 Wall time: 38819.809860400856 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 9.14 0.328 2.58 0.511 0.672 1.56 1.88 0.00443 0.00535 391 172 3.5 0.117 1.15 0.304 0.402 1.06 1.26 0.00302 0.00358 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 391 100 2.22 0.111 0.00405 0.299 0.39 0.0733 0.0746 0.000208 0.000212 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 391 38919.133 0.005 0.355 8.52 15.6 0.452 0.699 1.68 3.43 0.00476 0.00973 ! Validation 391 38919.133 0.005 0.121 0.345 2.76 0.309 0.408 0.545 0.689 0.00155 0.00196 Wall time: 38919.13342347974 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 2.47 0.0901 0.67 0.265 0.352 0.842 0.96 0.00239 0.00273 392 172 2.16 0.0805 0.554 0.25 0.333 0.715 0.873 0.00203 0.00248 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 392 100 3.52 0.0777 1.97 0.25 0.327 1.64 1.65 0.00467 0.00468 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 39018.389 0.005 0.0973 0.958 2.9 0.276 0.366 0.91 1.15 0.00258 0.00326 ! Validation 392 39018.389 0.005 0.0833 1.86 3.52 0.256 0.339 1.49 1.6 0.00424 0.00454 Wall time: 39018.389745828696 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 2.19 0.0697 0.793 0.235 0.31 0.942 1.04 0.00268 0.00297 393 172 3.03 0.0624 1.78 0.219 0.293 1.49 1.56 0.00424 0.00444 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 393 100 1.77 0.0618 0.533 0.221 0.292 0.852 0.856 0.00242 0.00243 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 393 39117.448 0.005 0.069 0.688 2.07 0.232 0.308 0.779 0.973 0.00221 0.00276 ! Validation 393 39117.448 0.005 0.0652 1.76 3.06 0.225 0.3 1.4 1.56 0.00398 0.00442 Wall time: 39117.44789461093 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 1.83 0.0532 0.761 0.203 0.271 0.91 1.02 0.00258 0.00291 394 172 1.72 0.05 0.718 0.196 0.262 0.949 0.994 0.0027 0.00282 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 394 100 2.69 0.0505 1.68 0.199 0.264 1.51 1.52 0.0043 0.00432 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 39216.517 0.005 0.0567 0.551 1.68 0.209 0.279 0.702 0.871 0.00199 0.00247 ! Validation 394 39216.517 0.005 0.0541 1.34 2.42 0.205 0.273 1.28 1.36 0.00364 0.00385 Wall time: 39216.51707758196 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 1.12 0.0478 0.163 0.193 0.256 0.409 0.474 0.00116 0.00135 395 172 2.33 0.0519 1.29 0.2 0.267 1.29 1.33 0.00368 0.00379 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 395 100 1.23 0.0501 0.229 0.197 0.263 0.556 0.562 0.00158 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 395 39315.704 0.005 0.0467 0.543 1.48 0.19 0.253 0.69 0.864 0.00196 0.00245 ! Validation 395 39315.704 0.005 0.0532 1.08 2.14 0.203 0.27 1.05 1.22 0.00298 0.00346 Wall time: 39315.70412020199 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 1.97 0.0401 1.17 0.177 0.235 1.2 1.27 0.00341 0.00361 396 172 2.78 0.0366 2.05 0.168 0.224 1.63 1.68 0.00463 0.00477 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 396 100 4.05 0.0388 3.27 0.174 0.231 2.12 2.12 0.00602 0.00603 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 39414.767 0.005 0.0423 0.448 1.29 0.181 0.241 0.617 0.784 0.00175 0.00223 ! Validation 396 39414.767 0.005 0.0413 3.16 3.99 0.179 0.238 2.05 2.09 0.00581 0.00593 Wall time: 39414.767763813026 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 2.83 0.0416 2 0.179 0.239 1.59 1.66 0.00453 0.00471 397 172 2.56 0.0375 1.81 0.17 0.227 1.54 1.58 0.00437 0.00449 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 397 100 2.01 0.0391 1.23 0.174 0.232 1.3 1.3 0.00369 0.0037 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 397 39513.837 0.005 0.0378 0.57 1.33 0.171 0.228 0.73 0.885 0.00207 0.00251 ! Validation 397 39513.837 0.005 0.0412 0.954 1.78 0.179 0.238 1.08 1.15 0.00308 0.00326 Wall time: 39513.837092359085 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.771 0.0342 0.0857 0.164 0.217 0.259 0.343 0.000737 0.000976 398 172 0.854 0.0286 0.281 0.15 0.199 0.523 0.622 0.00149 0.00177 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 398 100 0.861 0.0288 0.284 0.151 0.199 0.614 0.625 0.00175 0.00178 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 398 39613.120 0.005 0.0337 0.398 1.07 0.161 0.215 0.603 0.74 0.00171 0.0021 ! Validation 398 39613.120 0.005 0.0312 0.167 0.79 0.157 0.207 0.405 0.48 0.00115 0.00136 Wall time: 39613.12056401977 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 0.6 0.0272 0.0552 0.146 0.194 0.211 0.276 0.0006 0.000783 399 172 1.23 0.0233 0.769 0.136 0.179 0.995 1.03 0.00283 0.00292 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 399 100 1.39 0.0248 0.891 0.14 0.185 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 399 39712.237 0.005 0.0275 0.382 0.933 0.147 0.195 0.581 0.725 0.00165 0.00206 ! Validation 399 39712.237 0.005 0.0269 0.768 1.31 0.146 0.192 0.975 1.03 0.00277 0.00292 Wall time: 39712.23772554286 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.776 0.0235 0.307 0.136 0.18 0.564 0.65 0.0016 0.00185 400 172 1.1 0.0211 0.68 0.129 0.17 0.93 0.967 0.00264 0.00275 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 400 100 0.657 0.0213 0.231 0.13 0.171 0.556 0.563 0.00158 0.0016 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 400 39812.191 0.005 0.0234 0.369 0.837 0.136 0.18 0.575 0.712 0.00163 0.00202 ! Validation 400 39812.191 0.005 0.0233 0.363 0.828 0.136 0.179 0.626 0.707 0.00178 0.00201 Wall time: 39812.19128274312 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 0.572 0.0217 0.138 0.132 0.173 0.378 0.436 0.00107 0.00124 401 172 0.978 0.0187 0.605 0.121 0.16 0.887 0.912 0.00252 0.00259 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 401 100 1.3 0.0176 0.952 0.118 0.156 1.14 1.14 0.00324 0.00325 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 39912.753 0.005 0.02 0.305 0.705 0.126 0.166 0.508 0.648 0.00144 0.00184 ! Validation 401 39912.753 0.005 0.02 1.54 1.94 0.126 0.166 1.41 1.46 0.004 0.00413 Wall time: 39912.75356761087 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.464 0.0169 0.125 0.116 0.153 0.299 0.414 0.000849 0.00118 402 172 0.429 0.0163 0.102 0.113 0.15 0.31 0.375 0.000881 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 402 100 0.327 0.016 0.00724 0.113 0.148 0.0858 0.0998 0.000244 0.000284 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 402 40011.854 0.005 0.0186 0.316 0.688 0.121 0.16 0.501 0.659 0.00142 0.00187 ! Validation 402 40011.854 0.005 0.0182 0.101 0.466 0.12 0.158 0.301 0.373 0.000856 0.00106 Wall time: 40011.854742655065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.503 0.017 0.162 0.116 0.153 0.395 0.472 0.00112 0.00134 403 172 0.506 0.0148 0.209 0.108 0.143 0.467 0.536 0.00133 0.00152 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 403 100 0.565 0.015 0.265 0.109 0.144 0.596 0.604 0.00169 0.00172 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 403 40110.950 0.005 0.016 0.262 0.581 0.112 0.148 0.488 0.601 0.00139 0.00171 ! Validation 403 40110.950 0.005 0.0172 0.263 0.607 0.117 0.154 0.513 0.602 0.00146 0.00171 Wall time: 40110.95041480893 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.378 0.0152 0.0736 0.11 0.145 0.268 0.318 0.000761 0.000904 404 172 0.468 0.0134 0.201 0.103 0.136 0.481 0.526 0.00137 0.00149 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 404 100 0.27 0.0131 0.00783 0.102 0.134 0.0945 0.104 0.000268 0.000295 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 404 40210.040 0.005 0.0146 0.213 0.504 0.107 0.142 0.435 0.541 0.00124 0.00154 ! Validation 404 40210.040 0.005 0.0155 0.121 0.431 0.111 0.146 0.33 0.408 0.000937 0.00116 Wall time: 40210.04045453388 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.407 0.0136 0.136 0.104 0.137 0.355 0.433 0.00101 0.00123 405 172 0.321 0.0133 0.0549 0.103 0.135 0.221 0.275 0.000626 0.000781 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 405 100 0.348 0.0127 0.0945 0.1 0.132 0.353 0.361 0.001 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 405 40309.279 0.005 0.0134 0.217 0.486 0.103 0.136 0.435 0.547 0.00124 0.00155 ! Validation 405 40309.279 0.005 0.0147 0.0851 0.38 0.108 0.142 0.27 0.342 0.000768 0.000972 Wall time: 40309.27902002772 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.334 0.0114 0.107 0.0956 0.125 0.353 0.383 0.001 0.00109 406 172 0.299 0.0114 0.0705 0.0949 0.125 0.257 0.311 0.000731 0.000885 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 406 100 0.617 0.0111 0.396 0.0941 0.123 0.732 0.738 0.00208 0.0021 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 406 40408.370 0.005 0.0123 0.147 0.393 0.0989 0.13 0.351 0.45 0.000997 0.00128 ! Validation 406 40408.370 0.005 0.0134 0.234 0.501 0.103 0.136 0.509 0.567 0.00144 0.00161 Wall time: 40408.36986656068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.306 0.0109 0.088 0.0933 0.122 0.298 0.348 0.000846 0.000989 407 172 0.313 0.0118 0.0764 0.0969 0.128 0.253 0.324 0.000719 0.000921 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 407 100 0.321 0.0114 0.094 0.0952 0.125 0.352 0.36 0.001 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 407 40507.465 0.005 0.0118 0.194 0.43 0.0968 0.127 0.405 0.517 0.00115 0.00147 ! Validation 407 40507.465 0.005 0.0131 0.113 0.376 0.102 0.134 0.323 0.395 0.000916 0.00112 Wall time: 40507.46514093969 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 0.517 0.0111 0.294 0.0941 0.124 0.576 0.636 0.00164 0.00181 408 172 0.569 0.0102 0.365 0.0908 0.118 0.674 0.709 0.00191 0.00201 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 408 100 1.06 0.0103 0.851 0.0906 0.119 1.08 1.08 0.00307 0.00307 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 408 40606.575 0.005 0.0108 0.127 0.342 0.0927 0.122 0.334 0.417 0.00095 0.00118 ! Validation 408 40606.575 0.005 0.0123 0.702 0.948 0.0991 0.13 0.945 0.983 0.00268 0.00279 Wall time: 40606.57493520109 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.548 0.0106 0.337 0.092 0.121 0.648 0.681 0.00184 0.00193 409 172 0.301 0.0103 0.0938 0.0906 0.119 0.301 0.359 0.000854 0.00102 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 409 100 0.194 0.00951 0.00375 0.0878 0.114 0.054 0.0718 0.000153 0.000204 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 409 40705.693 0.005 0.0105 0.164 0.375 0.0918 0.12 0.381 0.475 0.00108 0.00135 ! Validation 409 40705.693 0.005 0.0115 0.0466 0.277 0.0957 0.126 0.206 0.253 0.000586 0.000719 Wall time: 40705.69313446805 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.346 0.0106 0.133 0.0921 0.121 0.396 0.428 0.00112 0.00122 410 172 0.197 0.00843 0.028 0.0821 0.108 0.159 0.196 0.000452 0.000557 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 410 100 0.4 0.00876 0.225 0.0843 0.11 0.55 0.556 0.00156 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 410 40804.791 0.005 0.00984 0.118 0.315 0.0888 0.116 0.316 0.404 0.000898 0.00115 ! Validation 410 40804.791 0.005 0.0108 0.18 0.396 0.0927 0.122 0.451 0.498 0.00128 0.00141 Wall time: 40804.79167195782 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.363 0.0103 0.157 0.0908 0.119 0.416 0.465 0.00118 0.00132 411 172 0.446 0.00978 0.25 0.0885 0.116 0.542 0.587 0.00154 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 411 100 0.199 0.00955 0.00849 0.088 0.115 0.0896 0.108 0.000254 0.000307 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 411 40903.891 0.005 0.00975 0.209 0.404 0.0885 0.116 0.439 0.537 0.00125 0.00153 ! Validation 411 40903.891 0.005 0.0112 0.0689 0.293 0.0947 0.124 0.255 0.308 0.000724 0.000875 Wall time: 40903.89142251108 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.228 0.00922 0.0441 0.0861 0.113 0.199 0.246 0.000565 0.000699 412 172 0.25 0.00909 0.0684 0.0853 0.112 0.241 0.307 0.000684 0.000871 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 412 100 0.174 0.00827 0.00901 0.0818 0.107 0.0966 0.111 0.000274 0.000316 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 412 41003.006 0.005 0.00933 0.118 0.305 0.0865 0.113 0.321 0.403 0.000912 0.00114 ! Validation 412 41003.006 0.005 0.0102 0.0647 0.269 0.0901 0.118 0.252 0.298 0.000715 0.000848 Wall time: 41003.00631391909 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.301 0.00836 0.134 0.082 0.107 0.39 0.429 0.00111 0.00122 413 172 0.249 0.00906 0.0679 0.0853 0.112 0.244 0.306 0.000692 0.000868 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 413 100 0.224 0.00928 0.038 0.0866 0.113 0.223 0.229 0.000634 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 413 41105.166 0.005 0.00882 0.148 0.324 0.0842 0.11 0.359 0.451 0.00102 0.00128 ! Validation 413 41105.166 0.005 0.011 0.0659 0.287 0.0943 0.123 0.244 0.301 0.000693 0.000856 Wall time: 41105.16608838597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.199 0.00843 0.0302 0.0828 0.108 0.171 0.204 0.000485 0.000579 414 172 0.273 0.00852 0.102 0.0828 0.108 0.338 0.375 0.000961 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 414 100 0.208 0.00781 0.0513 0.0799 0.104 0.257 0.266 0.000729 0.000755 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 414 41204.282 0.005 0.00875 0.139 0.314 0.0839 0.11 0.357 0.437 0.00101 0.00124 ! Validation 414 41204.282 0.005 0.0096 0.131 0.323 0.0875 0.115 0.381 0.425 0.00108 0.00121 Wall time: 41204.28218707582 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.189 0.00749 0.0389 0.0783 0.102 0.19 0.231 0.000539 0.000658 415 172 0.425 0.00872 0.25 0.0831 0.11 0.551 0.587 0.00156 0.00167 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 415 100 0.313 0.00759 0.161 0.0787 0.102 0.467 0.471 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 415 41304.347 0.005 0.00806 0.092 0.253 0.0805 0.105 0.286 0.355 0.000812 0.00101 ! Validation 415 41304.347 0.005 0.00923 0.121 0.306 0.086 0.113 0.355 0.408 0.00101 0.00116 Wall time: 41304.34700274095 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.257 0.00765 0.104 0.0783 0.103 0.344 0.379 0.000977 0.00108 416 172 0.26 0.00796 0.101 0.08 0.105 0.324 0.372 0.00092 0.00106 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 416 100 0.326 0.00674 0.191 0.0745 0.0963 0.508 0.513 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 416 41403.508 0.005 0.00786 0.0992 0.256 0.0795 0.104 0.298 0.369 0.000846 0.00105 ! Validation 416 41403.508 0.005 0.00851 0.116 0.286 0.0824 0.108 0.344 0.399 0.000976 0.00113 Wall time: 41403.50884542707 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.211 0.00726 0.0656 0.0763 0.0999 0.265 0.3 0.000752 0.000853 417 172 0.263 0.00761 0.111 0.0782 0.102 0.369 0.391 0.00105 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 417 100 0.162 0.00708 0.0205 0.0761 0.0987 0.158 0.168 0.00045 0.000477 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 417 41502.647 0.005 0.00771 0.13 0.284 0.0788 0.103 0.332 0.423 0.000944 0.0012 ! Validation 417 41502.647 0.005 0.00887 0.0517 0.229 0.0843 0.11 0.217 0.267 0.000616 0.000757 Wall time: 41502.64702105196 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.203 0.00656 0.0717 0.0726 0.095 0.246 0.314 0.000699 0.000892 418 172 0.548 0.00703 0.408 0.0751 0.0983 0.72 0.749 0.00204 0.00213 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 418 100 0.387 0.00678 0.252 0.0749 0.0966 0.584 0.588 0.00166 0.00167 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 418 41601.945 0.005 0.00718 0.0823 0.226 0.076 0.0994 0.272 0.336 0.000773 0.000954 ! Validation 418 41601.945 0.005 0.00848 0.379 0.549 0.0825 0.108 0.668 0.722 0.0019 0.00205 Wall time: 41601.94534032978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.183 0.00717 0.0396 0.0751 0.0993 0.181 0.233 0.000514 0.000663 419 172 0.154 0.0063 0.0276 0.0715 0.0931 0.153 0.195 0.000435 0.000554 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 419 100 0.152 0.00594 0.0337 0.0701 0.0904 0.202 0.215 0.000575 0.000612 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 419 41701.117 0.005 0.00691 0.087 0.225 0.0746 0.0975 0.278 0.346 0.000789 0.000983 ! Validation 419 41701.117 0.005 0.00765 0.0278 0.181 0.0781 0.103 0.156 0.196 0.000442 0.000556 Wall time: 41701.11766520608 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.169 0.00629 0.0429 0.072 0.0931 0.199 0.243 0.000566 0.00069 420 172 0.244 0.00609 0.122 0.0701 0.0915 0.388 0.409 0.0011 0.00116 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 420 100 0.422 0.00593 0.303 0.0702 0.0903 0.642 0.646 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 420 41800.251 0.005 0.00663 0.0997 0.232 0.073 0.0955 0.296 0.37 0.000841 0.00105 ! Validation 420 41800.251 0.005 0.00747 0.137 0.286 0.0773 0.101 0.393 0.434 0.00112 0.00123 Wall time: 41800.2511010007 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.156 0.00707 0.0147 0.0756 0.0986 0.117 0.142 0.000332 0.000404 421 172 0.145 0.00586 0.0274 0.0693 0.0898 0.161 0.194 0.000459 0.000552 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 421 100 0.159 0.00562 0.0465 0.0682 0.0879 0.24 0.253 0.000682 0.000719 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 41899.793 0.005 0.00645 0.0937 0.223 0.072 0.0942 0.29 0.359 0.000825 0.00102 ! Validation 421 41899.793 0.005 0.00724 0.0384 0.183 0.0759 0.0998 0.188 0.23 0.000534 0.000653 Wall time: 41899.79357567895 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.166 0.00637 0.0389 0.0718 0.0936 0.195 0.231 0.000554 0.000657 422 172 0.149 0.00638 0.0212 0.0711 0.0937 0.138 0.171 0.000392 0.000486 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 422 100 0.236 0.00529 0.13 0.0661 0.0853 0.416 0.424 0.00118 0.0012 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 422 42000.623 0.005 0.00614 0.0865 0.209 0.0702 0.0919 0.27 0.345 0.000768 0.00098 ! Validation 422 42000.623 0.005 0.00685 0.0772 0.214 0.0738 0.0971 0.275 0.326 0.000782 0.000926 Wall time: 42000.62311507808 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.284 0.00587 0.166 0.0689 0.0899 0.441 0.478 0.00125 0.00136 423 172 0.155 0.00556 0.0434 0.0669 0.0875 0.218 0.244 0.000619 0.000694 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 423 100 0.107 0.00512 0.00481 0.0648 0.0839 0.0554 0.0814 0.000157 0.000231 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 423 42099.977 0.005 0.00583 0.085 0.202 0.0683 0.0895 0.277 0.342 0.000788 0.000972 ! Validation 423 42099.977 0.005 0.00661 0.0272 0.159 0.0724 0.0954 0.156 0.193 0.000443 0.000549 Wall time: 42099.97751955874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.14 0.00547 0.0307 0.0661 0.0867 0.165 0.206 0.000469 0.000584 424 172 0.271 0.0063 0.145 0.0713 0.0931 0.391 0.446 0.00111 0.00127 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 424 100 0.159 0.00545 0.0496 0.0667 0.0866 0.255 0.261 0.000724 0.000742 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 424 42200.162 0.005 0.00568 0.098 0.212 0.0673 0.0884 0.287 0.367 0.000816 0.00104 ! Validation 424 42200.162 0.005 0.00685 0.116 0.253 0.0738 0.0971 0.306 0.399 0.00087 0.00113 Wall time: 42200.162753027864 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.137 0.0056 0.0249 0.0671 0.0878 0.166 0.185 0.000473 0.000525 425 172 0.27 0.00534 0.163 0.0657 0.0857 0.438 0.473 0.00124 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 425 100 0.2 0.00518 0.0964 0.0654 0.0844 0.362 0.364 0.00103 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 425 42299.339 0.005 0.00569 0.0925 0.206 0.0675 0.0885 0.279 0.357 0.000793 0.00101 ! Validation 425 42299.339 0.005 0.00655 0.225 0.356 0.0721 0.0949 0.528 0.556 0.0015 0.00158 Wall time: 42299.33932728972 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.126 0.00517 0.0224 0.0641 0.0844 0.149 0.175 0.000424 0.000498 426 172 0.246 0.00524 0.141 0.0646 0.0849 0.412 0.441 0.00117 0.00125 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 426 100 0.103 0.00459 0.0107 0.0613 0.0795 0.11 0.121 0.000312 0.000345 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 42398.712 0.005 0.00542 0.0714 0.18 0.0657 0.0863 0.255 0.313 0.000725 0.00089 ! Validation 426 42398.712 0.005 0.00603 0.0382 0.159 0.0688 0.0911 0.183 0.229 0.00052 0.000651 Wall time: 42398.712744325865 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.109 0.00477 0.0135 0.0619 0.081 0.114 0.137 0.000324 0.000388 427 172 0.165 0.00505 0.0638 0.0633 0.0834 0.245 0.296 0.000695 0.000842 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 427 100 0.127 0.00456 0.0355 0.0611 0.0792 0.209 0.221 0.000593 0.000628 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 427 42497.886 0.005 0.00521 0.0706 0.175 0.0644 0.0846 0.244 0.312 0.000694 0.000885 ! Validation 427 42497.886 0.005 0.00588 0.025 0.143 0.068 0.0899 0.144 0.185 0.000409 0.000527 Wall time: 42497.88621415105 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.155 0.00459 0.0637 0.0604 0.0795 0.265 0.296 0.000753 0.000841 428 172 0.389 0.00507 0.287 0.0629 0.0835 0.583 0.629 0.00166 0.00179 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 428 100 0.291 0.00661 0.159 0.0733 0.0954 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 428 42597.174 0.005 0.00497 0.0632 0.163 0.0627 0.0827 0.233 0.295 0.000662 0.000837 ! Validation 428 42597.174 0.005 0.00774 0.0845 0.239 0.0785 0.103 0.291 0.341 0.000826 0.000969 Wall time: 42597.17477588402 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.125 0.00492 0.027 0.0623 0.0823 0.168 0.193 0.000477 0.000547 429 172 0.115 0.00458 0.0238 0.0604 0.0794 0.152 0.181 0.000431 0.000514 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 429 100 0.0874 0.00426 0.00209 0.0588 0.0766 0.0469 0.0537 0.000133 0.000152 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 429 42696.338 0.005 0.00503 0.0754 0.176 0.0631 0.0832 0.251 0.322 0.000713 0.000915 ! Validation 429 42696.338 0.005 0.00555 0.0354 0.146 0.0658 0.0874 0.188 0.221 0.000534 0.000627 Wall time: 42696.33824804798 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.158 0.00489 0.0604 0.0615 0.0821 0.26 0.288 0.000739 0.000819 430 172 0.111 0.0041 0.0286 0.0571 0.0751 0.166 0.198 0.000471 0.000564 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 430 100 0.165 0.0042 0.0807 0.0583 0.076 0.33 0.333 0.000938 0.000946 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 430 42795.523 0.005 0.00473 0.0488 0.143 0.061 0.0807 0.208 0.259 0.00059 0.000736 ! Validation 430 42795.523 0.005 0.00546 0.0294 0.139 0.0653 0.0866 0.167 0.201 0.000474 0.000571 Wall time: 42795.52360943612 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.127 0.00463 0.0339 0.061 0.0798 0.198 0.216 0.000564 0.000614 431 172 0.113 0.00471 0.0184 0.0605 0.0805 0.119 0.159 0.000338 0.000452 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 431 100 0.222 0.00428 0.136 0.0592 0.0768 0.427 0.432 0.00121 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 431 42894.744 0.005 0.00476 0.0746 0.17 0.0612 0.0809 0.248 0.32 0.000703 0.00091 ! Validation 431 42894.744 0.005 0.00545 0.0608 0.17 0.0653 0.0866 0.24 0.289 0.000682 0.000822 Wall time: 42894.74473732989 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.14 0.00426 0.0544 0.0577 0.0766 0.244 0.274 0.000692 0.000777 432 172 0.133 0.00454 0.0424 0.0599 0.079 0.221 0.242 0.000628 0.000687 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 432 100 0.211 0.00412 0.129 0.0579 0.0753 0.418 0.421 0.00119 0.0012 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 432 42993.898 0.005 0.00454 0.0678 0.159 0.0597 0.079 0.231 0.305 0.000655 0.000868 ! Validation 432 42993.898 0.005 0.00532 0.0362 0.143 0.0645 0.0856 0.186 0.223 0.000529 0.000634 Wall time: 42993.89877800597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.107 0.00432 0.0202 0.0583 0.0771 0.131 0.167 0.000374 0.000474 433 172 0.159 0.00475 0.0635 0.0609 0.0808 0.259 0.296 0.000737 0.00084 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 433 100 0.14 0.0042 0.0565 0.0589 0.076 0.275 0.279 0.00078 0.000792 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 433 43093.086 0.005 0.00447 0.057 0.146 0.0593 0.0784 0.222 0.28 0.000631 0.000796 ! Validation 433 43093.086 0.005 0.00544 0.0282 0.137 0.0657 0.0865 0.16 0.197 0.000455 0.00056 Wall time: 43093.085945581086 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.102 0.00414 0.0192 0.0573 0.0755 0.134 0.163 0.000381 0.000462 434 172 0.21 0.00496 0.111 0.0623 0.0826 0.372 0.391 0.00106 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 434 100 0.173 0.00427 0.0879 0.0591 0.0766 0.341 0.348 0.000969 0.000988 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 434 43192.283 0.005 0.00433 0.0527 0.139 0.0583 0.0772 0.214 0.269 0.000608 0.000765 ! Validation 434 43192.283 0.005 0.00525 0.0743 0.179 0.0641 0.085 0.269 0.32 0.000766 0.000908 Wall time: 43192.283692758065 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.376 0.00461 0.284 0.0603 0.0796 0.605 0.625 0.00172 0.00178 435 172 0.104 0.00433 0.0177 0.0578 0.0772 0.129 0.156 0.000368 0.000443 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 435 100 0.159 0.00378 0.0832 0.0556 0.0721 0.335 0.338 0.000952 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 435 43291.496 0.005 0.00461 0.0696 0.162 0.0598 0.0797 0.246 0.31 0.000699 0.000879 ! Validation 435 43291.496 0.005 0.00486 0.092 0.189 0.0614 0.0818 0.289 0.356 0.000821 0.00101 Wall time: 43291.496397128794 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.0801 0.00366 0.00686 0.0536 0.071 0.0746 0.0972 0.000212 0.000276 436 172 0.14 0.00403 0.0591 0.0564 0.0745 0.262 0.285 0.000744 0.00081 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 436 100 0.284 0.00366 0.211 0.0543 0.0709 0.537 0.539 0.00153 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 436 43390.722 0.005 0.00407 0.0464 0.128 0.0564 0.0748 0.196 0.253 0.000558 0.000718 ! Validation 436 43390.722 0.005 0.00474 0.157 0.252 0.0607 0.0808 0.437 0.465 0.00124 0.00132 Wall time: 43390.722431523725 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.131 0.00485 0.0344 0.0612 0.0817 0.184 0.218 0.000523 0.000618 437 172 0.113 0.00414 0.0304 0.0566 0.0755 0.172 0.205 0.000489 0.000581 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 437 100 0.228 0.00361 0.156 0.0541 0.0705 0.46 0.463 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 437 43491.610 0.005 0.00425 0.0462 0.131 0.0578 0.0765 0.2 0.252 0.00057 0.000716 ! Validation 437 43491.610 0.005 0.0047 0.0658 0.16 0.0604 0.0804 0.259 0.301 0.000736 0.000855 Wall time: 43491.61060962174 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.145 0.00373 0.0709 0.0543 0.0716 0.278 0.312 0.000791 0.000887 438 172 0.0788 0.00334 0.0119 0.0514 0.0678 0.114 0.128 0.000322 0.000364 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 438 100 0.106 0.00357 0.0342 0.0537 0.0701 0.212 0.217 0.000601 0.000616 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 43590.972 0.005 0.00468 0.0796 0.173 0.0597 0.0803 0.256 0.331 0.000727 0.000941 ! Validation 438 43590.972 0.005 0.00455 0.0148 0.106 0.0593 0.0791 0.113 0.143 0.000321 0.000406 Wall time: 43590.972236400004 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.132 0.00371 0.0577 0.0541 0.0715 0.258 0.282 0.000733 0.0008 439 172 0.171 0.00531 0.0644 0.065 0.0855 0.261 0.298 0.000741 0.000846 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 439 100 0.346 0.00461 0.254 0.0607 0.0796 0.588 0.591 0.00167 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 439 43690.345 0.005 0.0041 0.05 0.132 0.0566 0.0751 0.208 0.262 0.000592 0.000745 ! Validation 439 43690.345 0.005 0.00574 0.145 0.26 0.0672 0.0889 0.421 0.446 0.00119 0.00127 Wall time: 43690.3457357171 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.104 0.00403 0.0236 0.056 0.0745 0.156 0.18 0.000444 0.000512 440 172 0.119 0.0044 0.0305 0.0579 0.0778 0.175 0.205 0.000497 0.000582 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 440 100 0.102 0.0033 0.0356 0.0516 0.0674 0.215 0.221 0.000612 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 440 43789.857 0.005 0.00417 0.0594 0.143 0.057 0.0757 0.22 0.286 0.000624 0.000812 ! Validation 440 43789.857 0.005 0.00433 0.0151 0.102 0.0578 0.0772 0.114 0.144 0.000323 0.00041 Wall time: 43789.85763926385 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.0793 0.00351 0.00914 0.0522 0.0695 0.0936 0.112 0.000266 0.000319 441 172 0.089 0.00365 0.0161 0.0531 0.0708 0.114 0.149 0.000323 0.000422 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 441 100 0.0944 0.0034 0.0264 0.0524 0.0684 0.185 0.191 0.000524 0.000542 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 441 43889.072 0.005 0.00376 0.0374 0.113 0.0541 0.0719 0.178 0.227 0.000506 0.000644 ! Validation 441 43889.072 0.005 0.00431 0.0198 0.106 0.0577 0.077 0.126 0.165 0.000357 0.000469 Wall time: 43889.07252209587 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0851 0.00369 0.0113 0.0537 0.0713 0.102 0.124 0.000289 0.000353 442 172 0.151 0.00353 0.0802 0.0524 0.0697 0.312 0.332 0.000886 0.000944 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 442 100 0.0758 0.00344 0.00705 0.0523 0.0688 0.0947 0.0985 0.000269 0.00028 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 442 43988.281 0.005 0.00373 0.0448 0.119 0.0538 0.0716 0.197 0.248 0.000559 0.000705 ! Validation 442 43988.281 0.005 0.00437 0.0293 0.117 0.0581 0.0775 0.16 0.201 0.000453 0.000571 Wall time: 43988.2818187559 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.116 0.0044 0.0279 0.0588 0.0778 0.166 0.196 0.000471 0.000557 443 172 0.126 0.00348 0.0564 0.0524 0.0692 0.256 0.278 0.000727 0.000791 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 443 100 0.152 0.00322 0.0874 0.0507 0.0666 0.345 0.347 0.000981 0.000985 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 443 44087.503 0.005 0.00412 0.0522 0.135 0.0566 0.0753 0.211 0.268 0.0006 0.000761 ! Validation 443 44087.503 0.005 0.00413 0.0475 0.13 0.0563 0.0754 0.219 0.256 0.000621 0.000726 Wall time: 44087.5030376208 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.0778 0.00347 0.00842 0.0523 0.0691 0.0967 0.108 0.000275 0.000306 444 172 0.186 0.00373 0.111 0.0534 0.0716 0.367 0.391 0.00104 0.00111 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 444 100 0.0704 0.00321 0.00629 0.0512 0.0664 0.0806 0.093 0.000229 0.000264 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 44186.710 0.005 0.00368 0.0468 0.12 0.0535 0.0711 0.207 0.254 0.000588 0.000721 ! Validation 444 44186.710 0.005 0.00409 0.0327 0.114 0.0562 0.075 0.18 0.212 0.000513 0.000602 Wall time: 44186.710497228894 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.105 0.00432 0.0186 0.0581 0.0771 0.137 0.16 0.000388 0.000454 445 172 0.11 0.0037 0.0356 0.0538 0.0714 0.197 0.221 0.00056 0.000628 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 445 100 0.183 0.00307 0.121 0.0496 0.065 0.406 0.409 0.00115 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 445 44285.957 0.005 0.00375 0.0382 0.113 0.0542 0.0719 0.182 0.229 0.000518 0.000651 ! Validation 445 44285.957 0.005 0.00396 0.103 0.183 0.0552 0.0738 0.346 0.377 0.000983 0.00107 Wall time: 44285.95733941905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.227 0.00379 0.152 0.0544 0.0722 0.427 0.457 0.00121 0.0013 446 172 0.0887 0.00356 0.0176 0.0525 0.0699 0.131 0.156 0.000372 0.000442 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 446 100 0.227 0.00388 0.149 0.0559 0.073 0.452 0.453 0.00128 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 446 44385.539 0.005 0.00417 0.0638 0.147 0.0569 0.0758 0.238 0.296 0.000675 0.000842 ! Validation 446 44385.539 0.005 0.00496 0.0624 0.162 0.0625 0.0826 0.264 0.293 0.000749 0.000833 Wall time: 44385.53974602278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.0924 0.00351 0.0221 0.0524 0.0695 0.154 0.174 0.000438 0.000496 447 172 0.113 0.0037 0.0387 0.0539 0.0714 0.19 0.231 0.000541 0.000656 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 447 100 0.0688 0.00332 0.00236 0.0514 0.0676 0.0492 0.057 0.00014 0.000162 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 44485.788 0.005 0.00356 0.042 0.113 0.0526 0.07 0.184 0.24 0.000524 0.000683 ! Validation 447 44485.788 0.005 0.00429 0.0441 0.13 0.0575 0.0769 0.217 0.246 0.000617 0.0007 Wall time: 44485.78880511597 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.109 0.00453 0.018 0.0619 0.0789 0.115 0.157 0.000328 0.000447 448 172 0.0913 0.00326 0.026 0.0501 0.067 0.148 0.189 0.000421 0.000537 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 448 100 0.11 0.00302 0.0497 0.0492 0.0645 0.256 0.262 0.000728 0.000743 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 448 44586.061 0.005 0.00422 0.0503 0.135 0.057 0.0762 0.204 0.263 0.000579 0.000747 ! Validation 448 44586.061 0.005 0.00381 0.0384 0.115 0.054 0.0724 0.183 0.23 0.000519 0.000653 Wall time: 44586.061520401854 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.144 0.00365 0.0715 0.0535 0.0708 0.296 0.314 0.000842 0.000891 449 172 0.0959 0.00355 0.0249 0.0531 0.0699 0.123 0.185 0.00035 0.000526 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 449 100 0.0846 0.00327 0.0192 0.0512 0.0671 0.16 0.162 0.000455 0.000462 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 449 44685.286 0.005 0.00386 0.0565 0.134 0.0548 0.0729 0.223 0.279 0.000633 0.000792 ! Validation 449 44685.286 0.005 0.00407 0.0174 0.0987 0.0561 0.0748 0.121 0.155 0.000344 0.000439 Wall time: 44685.28630817402 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.118 0.00386 0.0412 0.0556 0.0729 0.216 0.238 0.000614 0.000676 450 172 0.0833 0.00367 0.00988 0.0529 0.0711 0.0995 0.117 0.000283 0.000331 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 450 100 0.138 0.00335 0.0707 0.0517 0.0679 0.311 0.312 0.000882 0.000886 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 450 44784.517 0.005 0.0034 0.0312 0.0993 0.0513 0.0684 0.161 0.207 0.000458 0.000588 ! Validation 450 44784.517 0.005 0.00402 0.0469 0.127 0.0557 0.0744 0.21 0.254 0.000597 0.000722 Wall time: 44784.51702154614 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.0797 0.003 0.0197 0.0481 0.0642 0.126 0.165 0.000359 0.000468 451 172 0.0692 0.00314 0.00636 0.0497 0.0657 0.0751 0.0935 0.000213 0.000266 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 451 100 0.0864 0.003 0.0264 0.0491 0.0642 0.184 0.191 0.000522 0.000541 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 451 44883.752 0.005 0.00363 0.0445 0.117 0.0531 0.0707 0.194 0.247 0.000551 0.000703 ! Validation 451 44883.752 0.005 0.00385 0.0206 0.0977 0.0544 0.0728 0.132 0.169 0.000375 0.000479 Wall time: 44883.752509461716 ! Best model 451 0.098 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.0922 0.00335 0.0252 0.051 0.0679 0.168 0.186 0.000477 0.000529 452 172 0.094 0.00341 0.0257 0.0512 0.0685 0.161 0.188 0.000459 0.000534 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 452 100 0.135 0.00308 0.0733 0.0493 0.0651 0.316 0.318 0.000898 0.000902 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 44983.013 0.005 0.00352 0.0466 0.117 0.0523 0.0696 0.201 0.253 0.000572 0.00072 ! Validation 452 44983.013 0.005 0.00388 0.04 0.118 0.0544 0.0731 0.202 0.235 0.000573 0.000667 Wall time: 44983.013703547884 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 0.143 0.00313 0.0805 0.0489 0.0656 0.308 0.333 0.000876 0.000946 453 172 19.5 0.873 2.03 0.821 1.1 1.24 1.67 0.00352 0.00474 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 453 100 20.8 0.939 2.01 0.859 1.14 1.54 1.66 0.00437 0.00472 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 45082.259 0.005 0.0157 1.59 1.91 0.0683 0.144 0.397 1.48 0.00113 0.0042 ! Validation 453 45082.259 0.005 0.984 5.08 24.8 0.875 1.16 2.2 2.64 0.00624 0.00751 Wall time: 45082.25963498512 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 4.49 0.204 0.407 0.405 0.53 0.582 0.749 0.00165 0.00213 454 172 2.41 0.109 0.241 0.294 0.387 0.488 0.576 0.00139 0.00164 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 454 100 2.29 0.102 0.259 0.286 0.374 0.553 0.597 0.00157 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 454 45181.521 0.005 0.406 8.58 16.7 0.524 0.748 1.75 3.44 0.00496 0.00976 ! Validation 454 45181.521 0.005 0.115 0.586 2.88 0.301 0.397 0.722 0.898 0.00205 0.00255 Wall time: 45181.52183894068 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 2.03 0.0721 0.585 0.237 0.315 0.765 0.897 0.00217 0.00255 455 172 1.4 0.0623 0.159 0.22 0.293 0.37 0.467 0.00105 0.00133 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 455 100 1.6 0.0603 0.39 0.217 0.288 0.689 0.732 0.00196 0.00208 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 455 45280.950 0.005 0.077 0.623 2.16 0.245 0.326 0.749 0.926 0.00213 0.00263 ! Validation 455 45280.950 0.005 0.0672 0.283 1.63 0.228 0.304 0.508 0.624 0.00144 0.00177 Wall time: 45280.950020472985 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 1.17 0.052 0.13 0.198 0.268 0.352 0.423 0.001 0.0012 456 172 1.11 0.0497 0.113 0.194 0.262 0.309 0.395 0.000877 0.00112 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 456 100 1.1 0.0488 0.122 0.194 0.259 0.344 0.41 0.000977 0.00116 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 456 45380.246 0.005 0.0541 0.588 1.67 0.204 0.273 0.717 0.9 0.00204 0.00256 ! Validation 456 45380.246 0.005 0.054 0.602 1.68 0.203 0.273 0.798 0.91 0.00227 0.00259 Wall time: 45380.24684267584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 1.34 0.0409 0.52 0.176 0.237 0.788 0.846 0.00224 0.0024 457 172 1.71 0.0489 0.733 0.193 0.259 0.935 1 0.00266 0.00285 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 457 100 1.42 0.0489 0.438 0.194 0.259 0.767 0.776 0.00218 0.00221 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 457 45479.506 0.005 0.0438 0.502 1.38 0.182 0.245 0.634 0.831 0.0018 0.00236 ! Validation 457 45479.506 0.005 0.0531 0.318 1.38 0.201 0.27 0.534 0.662 0.00152 0.00188 Wall time: 45479.50595975574 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 1.37 0.0417 0.533 0.177 0.24 0.797 0.856 0.00226 0.00243 458 172 1.37 0.0322 0.728 0.156 0.21 0.947 1 0.00269 0.00284 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 458 100 1.74 0.0328 1.08 0.158 0.212 1.21 1.22 0.00344 0.00347 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 45578.753 0.005 0.0386 0.355 1.13 0.17 0.23 0.55 0.699 0.00156 0.00199 ! Validation 458 45578.753 0.005 0.037 0.995 1.74 0.168 0.226 1.1 1.17 0.00313 0.00332 Wall time: 45578.75377674913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 0.816 0.0304 0.209 0.152 0.204 0.465 0.536 0.00132 0.00152 459 172 1.1 0.0302 0.491 0.151 0.204 0.784 0.822 0.00223 0.00234 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 459 100 1.31 0.0278 0.755 0.146 0.196 1.01 1.02 0.00286 0.0029 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 459 45678.011 0.005 0.0313 0.362 0.989 0.154 0.208 0.567 0.706 0.00161 0.002 ! Validation 459 45678.011 0.005 0.0315 0.553 1.18 0.156 0.208 0.811 0.872 0.0023 0.00248 Wall time: 45678.01156442799 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 0.592 0.0274 0.0447 0.145 0.194 0.19 0.248 0.00054 0.000705 460 172 0.942 0.0353 0.235 0.163 0.22 0.464 0.569 0.00132 0.00162 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 460 100 1.45 0.0342 0.767 0.162 0.217 1.02 1.03 0.0029 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 460 45777.416 0.005 0.03 0.508 1.11 0.151 0.203 0.617 0.836 0.00175 0.00237 ! Validation 460 45777.416 0.005 0.0364 0.549 1.28 0.167 0.224 0.729 0.869 0.00207 0.00247 Wall time: 45777.4164768788 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 2.16 0.0235 1.69 0.134 0.18 1.49 1.52 0.00422 0.00433 461 172 0.587 0.0203 0.181 0.126 0.167 0.432 0.499 0.00123 0.00142 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 461 100 0.513 0.0203 0.108 0.125 0.167 0.36 0.385 0.00102 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 461 45876.680 0.005 0.0245 0.244 0.733 0.137 0.184 0.454 0.579 0.00129 0.00165 ! Validation 461 45876.680 0.005 0.0232 0.336 0.8 0.135 0.179 0.63 0.68 0.00179 0.00193 Wall time: 45876.68004205171 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.484 0.0192 0.0999 0.123 0.163 0.323 0.371 0.000918 0.00105 462 172 0.453 0.0173 0.107 0.116 0.154 0.327 0.383 0.000929 0.00109 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 462 100 0.637 0.0179 0.28 0.118 0.157 0.611 0.62 0.00174 0.00176 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 462 45975.922 0.005 0.0203 0.336 0.742 0.125 0.167 0.544 0.68 0.00155 0.00193 ! Validation 462 45975.922 0.005 0.02 0.343 0.743 0.125 0.166 0.608 0.687 0.00173 0.00195 Wall time: 45975.922517356 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.355 0.0157 0.0421 0.112 0.147 0.199 0.241 0.000566 0.000684 463 172 0.423 0.0148 0.128 0.109 0.143 0.356 0.419 0.00101 0.00119 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 463 100 0.594 0.0134 0.326 0.104 0.136 0.656 0.67 0.00186 0.0019 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 463 46075.167 0.005 0.0164 0.273 0.602 0.114 0.15 0.487 0.613 0.00138 0.00174 ! Validation 463 46075.167 0.005 0.0156 0.196 0.509 0.112 0.147 0.461 0.52 0.00131 0.00148 Wall time: 46075.16758517083 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.561 0.013 0.302 0.102 0.134 0.592 0.645 0.00168 0.00183 464 172 0.403 0.0117 0.17 0.0974 0.127 0.424 0.483 0.0012 0.00137 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 464 100 0.381 0.0118 0.145 0.0985 0.128 0.432 0.446 0.00123 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 464 46175.234 0.005 0.0133 0.286 0.553 0.104 0.135 0.497 0.628 0.00141 0.00178 ! Validation 464 46175.234 0.005 0.0137 0.0996 0.373 0.105 0.137 0.306 0.37 0.000871 0.00105 Wall time: 46175.23483109102 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.265 0.0101 0.0624 0.0909 0.118 0.247 0.293 0.000703 0.000833 465 172 0.474 0.00914 0.292 0.0866 0.112 0.594 0.633 0.00169 0.0018 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 465 100 0.303 0.00893 0.125 0.0861 0.111 0.398 0.414 0.00113 0.00118 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 465 46274.454 0.005 0.0106 0.182 0.394 0.0928 0.121 0.402 0.5 0.00114 0.00142 ! Validation 465 46274.454 0.005 0.011 0.125 0.346 0.0945 0.123 0.357 0.415 0.00102 0.00118 Wall time: 46274.45425525913 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.354 0.00908 0.172 0.0863 0.112 0.437 0.487 0.00124 0.00138 466 172 0.206 0.00867 0.0326 0.0833 0.109 0.169 0.212 0.00048 0.000602 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 466 100 0.17 0.00809 0.00791 0.0819 0.105 0.0973 0.104 0.000276 0.000296 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 466 46374.638 0.005 0.00917 0.185 0.368 0.0862 0.112 0.406 0.505 0.00115 0.00143 ! Validation 466 46374.638 0.005 0.01 0.0557 0.256 0.0898 0.117 0.231 0.277 0.000656 0.000787 Wall time: 46374.638366717845 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.29 0.00901 0.11 0.0845 0.111 0.341 0.389 0.00097 0.00111 467 172 0.194 0.00791 0.0355 0.0806 0.104 0.191 0.221 0.000542 0.000628 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 467 100 0.364 0.00746 0.214 0.0789 0.101 0.534 0.543 0.00152 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 467 46473.844 0.005 0.00832 0.187 0.354 0.082 0.107 0.419 0.508 0.00119 0.00144 ! Validation 467 46473.844 0.005 0.00936 0.139 0.326 0.0868 0.113 0.388 0.437 0.0011 0.00124 Wall time: 46473.84396525007 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.297 0.00732 0.151 0.0768 0.1 0.423 0.455 0.0012 0.00129 468 172 0.555 0.00887 0.378 0.0845 0.11 0.586 0.721 0.00167 0.00205 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 468 100 0.162 0.00775 0.00673 0.0807 0.103 0.0864 0.0962 0.000245 0.000273 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 468 46573.146 0.005 0.00749 0.126 0.276 0.0776 0.102 0.321 0.417 0.000913 0.00118 ! Validation 468 46573.146 0.005 0.00944 0.0727 0.262 0.0875 0.114 0.268 0.316 0.000761 0.000899 Wall time: 46573.14641161589 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.189 0.00722 0.0446 0.0755 0.0996 0.214 0.248 0.000608 0.000704 469 172 0.189 0.0067 0.0554 0.0735 0.096 0.227 0.276 0.000645 0.000784 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 469 100 0.164 0.00634 0.037 0.0724 0.0934 0.207 0.226 0.000587 0.000641 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 46672.565 0.005 0.00725 0.157 0.302 0.0763 0.0999 0.38 0.464 0.00108 0.00132 ! Validation 469 46672.565 0.005 0.00807 0.0454 0.207 0.0802 0.105 0.2 0.25 0.000567 0.00071 Wall time: 46672.564973474015 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.261 0.00686 0.124 0.0733 0.0972 0.384 0.413 0.00109 0.00117 470 172 0.22 0.0064 0.092 0.0719 0.0939 0.313 0.356 0.00089 0.00101 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 470 100 0.296 0.00581 0.179 0.0693 0.0894 0.487 0.497 0.00138 0.00141 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 470 46771.980 0.005 0.00665 0.124 0.257 0.0729 0.0956 0.322 0.413 0.000915 0.00117 ! Validation 470 46771.980 0.005 0.00767 0.104 0.258 0.078 0.103 0.314 0.378 0.000893 0.00108 Wall time: 46771.980327386875 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.167 0.00599 0.047 0.0689 0.0908 0.229 0.254 0.00065 0.000723 471 172 0.146 0.0056 0.0345 0.0669 0.0877 0.18 0.218 0.00051 0.000619 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 471 100 0.201 0.00542 0.0922 0.0666 0.0864 0.348 0.356 0.000988 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 471 46871.371 0.005 0.00632 0.101 0.227 0.0709 0.0932 0.286 0.372 0.000813 0.00106 ! Validation 471 46871.371 0.005 0.00711 0.0531 0.195 0.0747 0.0989 0.222 0.27 0.00063 0.000768 Wall time: 46871.37135564303 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.14 0.00569 0.0265 0.067 0.0885 0.163 0.191 0.000464 0.000543 472 172 0.133 0.00579 0.0172 0.0677 0.0893 0.13 0.154 0.000371 0.000437 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 472 100 0.149 0.00502 0.049 0.064 0.0831 0.248 0.26 0.000704 0.000738 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 472 46970.773 0.005 0.00583 0.079 0.196 0.0679 0.0896 0.267 0.33 0.000759 0.000937 ! Validation 472 46970.773 0.005 0.0067 0.0322 0.166 0.0724 0.096 0.168 0.21 0.000476 0.000598 Wall time: 46970.77321800077 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.157 0.00593 0.0382 0.0672 0.0903 0.188 0.229 0.000534 0.000652 473 172 0.224 0.0064 0.0965 0.0718 0.0938 0.317 0.364 0.000899 0.00104 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 473 100 0.514 0.00612 0.391 0.071 0.0917 0.731 0.734 0.00208 0.00208 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 473 47070.175 0.005 0.00576 0.144 0.259 0.0674 0.089 0.352 0.445 0.001 0.00126 ! Validation 473 47070.175 0.005 0.00745 0.37 0.519 0.0771 0.101 0.68 0.714 0.00193 0.00203 Wall time: 47070.17490906874 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.157 0.00562 0.0446 0.0668 0.0879 0.215 0.248 0.00061 0.000703 474 172 0.509 0.00548 0.399 0.0657 0.0869 0.705 0.741 0.002 0.00211 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 474 100 0.112 0.0054 0.0042 0.0661 0.0862 0.0607 0.076 0.000172 0.000216 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 474 47169.566 0.005 0.00556 0.0961 0.207 0.0663 0.0875 0.291 0.363 0.000826 0.00103 ! Validation 474 47169.566 0.005 0.00682 0.159 0.296 0.0732 0.0969 0.36 0.468 0.00102 0.00133 Wall time: 47169.566434423905 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.125 0.00532 0.0187 0.0652 0.0855 0.139 0.16 0.000395 0.000456 475 172 0.335 0.00517 0.232 0.0643 0.0843 0.506 0.565 0.00144 0.0016 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 475 100 0.106 0.00503 0.00527 0.0639 0.0832 0.0606 0.0851 0.000172 0.000242 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 475 47269.039 0.005 0.00535 0.0946 0.202 0.0649 0.0858 0.281 0.361 0.000799 0.00102 ! Validation 475 47269.039 0.005 0.00638 0.0757 0.203 0.0707 0.0937 0.264 0.323 0.000751 0.000917 Wall time: 47269.0393852517 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.173 0.00572 0.0586 0.0674 0.0887 0.246 0.284 0.000698 0.000807 476 172 0.106 0.00468 0.0121 0.0608 0.0802 0.103 0.129 0.000292 0.000366 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 476 100 0.11 0.00458 0.0182 0.0611 0.0794 0.138 0.158 0.000391 0.000449 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 47368.388 0.005 0.0053 0.114 0.221 0.0646 0.0854 0.329 0.397 0.000936 0.00113 ! Validation 476 47368.388 0.005 0.00599 0.117 0.237 0.0683 0.0908 0.362 0.401 0.00103 0.00114 Wall time: 47368.388119976036 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.25 0.0052 0.146 0.0624 0.0846 0.428 0.448 0.00122 0.00127 477 172 0.129 0.0047 0.0349 0.0608 0.0804 0.188 0.219 0.000533 0.000622 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 477 100 0.395 0.0046 0.303 0.0612 0.0796 0.643 0.646 0.00183 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 477 47467.825 0.005 0.00495 0.0661 0.165 0.0623 0.0825 0.24 0.302 0.000682 0.000857 ! Validation 477 47467.825 0.005 0.00588 0.413 0.531 0.0676 0.0899 0.709 0.754 0.00201 0.00214 Wall time: 47467.82503301278 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.164 0.00489 0.0664 0.0617 0.082 0.273 0.302 0.000776 0.000859 478 172 0.118 0.00458 0.0264 0.0601 0.0794 0.158 0.191 0.00045 0.000541 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 478 100 0.0984 0.00425 0.0134 0.0588 0.0765 0.116 0.136 0.000331 0.000386 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 478 47567.428 0.005 0.00474 0.0628 0.158 0.0609 0.0808 0.232 0.294 0.000658 0.000835 ! Validation 478 47567.428 0.005 0.00563 0.0869 0.199 0.0663 0.088 0.298 0.346 0.000847 0.000982 Wall time: 47567.42819278268 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.112 0.00472 0.0178 0.0611 0.0806 0.129 0.157 0.000366 0.000445 479 172 0.194 0.00455 0.103 0.06 0.0791 0.353 0.376 0.001 0.00107 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 479 100 0.149 0.00434 0.0622 0.0591 0.0773 0.288 0.293 0.000818 0.000831 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 47666.769 0.005 0.00472 0.0954 0.19 0.0608 0.0806 0.297 0.362 0.000843 0.00103 ! Validation 479 47666.769 0.005 0.00553 0.07 0.181 0.0655 0.0873 0.256 0.31 0.000728 0.000882 Wall time: 47666.76933458587 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.177 0.00453 0.0862 0.0596 0.079 0.281 0.344 0.000799 0.000979 480 172 0.109 0.00468 0.0151 0.0609 0.0803 0.115 0.144 0.000328 0.000409 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 480 100 0.144 0.00425 0.0588 0.0588 0.0765 0.276 0.285 0.000784 0.000808 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 480 47766.152 0.005 0.00465 0.0856 0.179 0.0603 0.08 0.26 0.343 0.000737 0.000976 ! Validation 480 47766.152 0.005 0.0056 0.0268 0.139 0.066 0.0878 0.152 0.192 0.000431 0.000545 Wall time: 47766.15232719714 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.1 0.00434 0.0134 0.0583 0.0772 0.11 0.136 0.000313 0.000386 481 172 0.097 0.00439 0.00929 0.0585 0.0777 0.094 0.113 0.000267 0.000321 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 481 100 0.108 0.00403 0.0276 0.057 0.0745 0.186 0.195 0.000529 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 481 47865.505 0.005 0.00442 0.0474 0.136 0.0587 0.078 0.2 0.255 0.000569 0.000726 ! Validation 481 47865.505 0.005 0.00519 0.0329 0.137 0.0633 0.0845 0.168 0.213 0.000478 0.000605 Wall time: 47865.505682929885 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.192 0.0041 0.11 0.0562 0.0751 0.366 0.389 0.00104 0.0011 482 172 0.101 0.00429 0.0156 0.0577 0.0768 0.123 0.146 0.00035 0.000416 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 482 100 0.11 0.00402 0.0294 0.0568 0.0744 0.196 0.201 0.000556 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 482 47964.870 0.005 0.0042 0.0427 0.127 0.0571 0.076 0.194 0.242 0.000552 0.000689 ! Validation 482 47964.870 0.005 0.00512 0.0443 0.147 0.0629 0.0839 0.195 0.247 0.000555 0.000701 Wall time: 47964.87059345003 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.103 0.00402 0.0224 0.0559 0.0744 0.146 0.176 0.000415 0.000499 483 172 0.101 0.00434 0.0145 0.0585 0.0772 0.112 0.141 0.000317 0.000401 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 483 100 0.159 0.00388 0.0818 0.0559 0.0731 0.332 0.335 0.000945 0.000953 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 483 48064.283 0.005 0.00422 0.0624 0.147 0.0573 0.0762 0.228 0.293 0.000647 0.000832 ! Validation 483 48064.283 0.005 0.00492 0.0348 0.133 0.0616 0.0823 0.182 0.219 0.000518 0.000621 Wall time: 48064.28356881486 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.236 0.00428 0.15 0.0581 0.0768 0.442 0.455 0.00126 0.00129 484 172 0.233 0.00355 0.162 0.0526 0.0699 0.46 0.472 0.00131 0.00134 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 484 100 0.132 0.00382 0.0554 0.0553 0.0725 0.273 0.276 0.000775 0.000785 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 484 48163.693 0.005 0.00412 0.0572 0.14 0.0566 0.0753 0.226 0.28 0.000641 0.000797 ! Validation 484 48163.693 0.005 0.00488 0.12 0.218 0.0613 0.082 0.378 0.406 0.00107 0.00115 Wall time: 48163.69365112996 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.0905 0.0039 0.0124 0.0551 0.0733 0.108 0.131 0.000307 0.000372 485 172 0.0966 0.00347 0.0272 0.052 0.0691 0.148 0.193 0.000422 0.000549 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 485 100 0.074 0.0035 0.00391 0.053 0.0694 0.063 0.0733 0.000179 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 485 48263.091 0.005 0.00407 0.0523 0.134 0.0563 0.0749 0.204 0.268 0.000578 0.000762 ! Validation 485 48263.091 0.005 0.00455 0.0261 0.117 0.0591 0.0791 0.156 0.19 0.000445 0.000539 Wall time: 48263.091649987735 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.0995 0.00374 0.0247 0.0546 0.0717 0.157 0.184 0.000447 0.000524 486 172 0.298 0.00502 0.198 0.0627 0.0831 0.482 0.522 0.00137 0.00148 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 486 100 0.763 0.00389 0.685 0.0557 0.0731 0.97 0.971 0.00275 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 486 48362.592 0.005 0.00412 0.0858 0.168 0.0566 0.0753 0.274 0.343 0.000777 0.000975 ! Validation 486 48362.592 0.005 0.0049 0.491 0.589 0.0617 0.0821 0.809 0.822 0.0023 0.00233 Wall time: 48362.592037464026 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.0863 0.00387 0.00889 0.0549 0.073 0.0917 0.111 0.00026 0.000314 487 172 0.297 0.00403 0.217 0.0555 0.0745 0.534 0.546 0.00152 0.00155 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 487 100 0.231 0.00373 0.156 0.0545 0.0717 0.464 0.464 0.00132 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 487 48461.967 0.005 0.00388 0.0448 0.122 0.0549 0.0731 0.194 0.248 0.000551 0.000704 ! Validation 487 48461.967 0.005 0.00464 0.226 0.319 0.0599 0.0799 0.48 0.558 0.00136 0.00158 Wall time: 48461.96738791978 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.101 0.004 0.0206 0.0552 0.0742 0.149 0.168 0.000425 0.000478 488 172 0.0839 0.0036 0.0119 0.0528 0.0704 0.102 0.128 0.00029 0.000363 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 488 100 0.333 0.00356 0.262 0.0531 0.0699 0.598 0.6 0.0017 0.0017 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 488 48561.353 0.005 0.00401 0.0774 0.158 0.0558 0.0743 0.247 0.326 0.000701 0.000927 ! Validation 488 48561.353 0.005 0.00466 0.184 0.278 0.0598 0.0801 0.481 0.504 0.00137 0.00143 Wall time: 48561.3529014159 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0938 0.00371 0.0196 0.0534 0.0714 0.135 0.164 0.000385 0.000466 489 172 0.117 0.00359 0.045 0.0533 0.0703 0.227 0.249 0.000646 0.000707 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 489 100 0.0675 0.00333 0.000964 0.0513 0.0676 0.0353 0.0364 0.0001 0.000103 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 489 48660.789 0.005 0.00376 0.0468 0.122 0.054 0.072 0.201 0.254 0.000572 0.000721 ! Validation 489 48660.789 0.005 0.00433 0.0209 0.108 0.0575 0.0772 0.143 0.17 0.000408 0.000482 Wall time: 48660.78957576584 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.0878 0.00359 0.0159 0.0529 0.0703 0.128 0.148 0.000363 0.00042 490 172 0.101 0.00328 0.0351 0.0506 0.0672 0.192 0.22 0.000544 0.000624 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 490 100 0.072 0.00354 0.00127 0.0526 0.0698 0.0349 0.0418 9.92e-05 0.000119 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 48760.106 0.005 0.00365 0.047 0.12 0.0532 0.0709 0.202 0.254 0.000574 0.000722 ! Validation 490 48760.106 0.005 0.00456 0.061 0.152 0.0591 0.0792 0.246 0.29 0.000698 0.000823 Wall time: 48760.1061433088 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.112 0.00489 0.0143 0.0622 0.0821 0.104 0.14 0.000294 0.000399 491 172 0.185 0.00399 0.105 0.0558 0.0741 0.363 0.381 0.00103 0.00108 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 491 100 0.0711 0.0034 0.00306 0.052 0.0684 0.0615 0.0649 0.000175 0.000184 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 491 48859.361 0.005 0.00375 0.0528 0.128 0.0539 0.0718 0.222 0.27 0.000632 0.000766 ! Validation 491 48859.361 0.005 0.00427 0.0941 0.18 0.0573 0.0767 0.311 0.36 0.000884 0.00102 Wall time: 48859.36092009209 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.0988 0.00365 0.0259 0.0533 0.0708 0.162 0.189 0.000459 0.000536 492 172 0.196 0.00385 0.119 0.055 0.0727 0.388 0.405 0.0011 0.00115 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 492 100 0.279 0.00462 0.186 0.0605 0.0798 0.505 0.506 0.00143 0.00144 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 492 48958.842 0.005 0.00361 0.047 0.119 0.0528 0.0704 0.203 0.254 0.000576 0.000722 ! Validation 492 48958.842 0.005 0.00541 0.161 0.269 0.0649 0.0863 0.425 0.47 0.00121 0.00134 Wall time: 48958.84262259072 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.0841 0.00337 0.0168 0.0509 0.0681 0.124 0.152 0.000351 0.000432 493 172 0.106 0.00381 0.0296 0.0543 0.0724 0.174 0.202 0.000494 0.000574 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 493 100 0.0723 0.00335 0.00524 0.0517 0.0679 0.0793 0.0849 0.000225 0.000241 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 493 49058.076 0.005 0.00397 0.0759 0.155 0.0555 0.0739 0.256 0.323 0.000727 0.000919 ! Validation 493 49058.076 0.005 0.00439 0.0329 0.121 0.058 0.0777 0.178 0.213 0.000507 0.000604 Wall time: 49058.07629441796 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.0799 0.00312 0.0175 0.0492 0.0655 0.124 0.155 0.000353 0.000441 494 172 0.119 0.00336 0.0521 0.0511 0.068 0.249 0.268 0.000708 0.00076 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 494 100 0.0656 0.00312 0.0031 0.0495 0.0655 0.0579 0.0653 0.000165 0.000186 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 49157.281 0.005 0.00342 0.0266 0.095 0.0514 0.0686 0.149 0.191 0.000423 0.000543 ! Validation 494 49157.281 0.005 0.00398 0.0442 0.124 0.0551 0.074 0.203 0.247 0.000578 0.000701 Wall time: 49157.28108585812 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.127 0.00343 0.0588 0.0517 0.0687 0.262 0.284 0.000745 0.000808 495 172 0.129 0.00347 0.0599 0.0522 0.0691 0.256 0.287 0.000728 0.000816 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 495 100 0.0683 0.00336 0.00116 0.0518 0.0679 0.0288 0.04 8.17e-05 0.000114 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 49257.055 0.005 0.00346 0.043 0.112 0.0517 0.069 0.191 0.243 0.000544 0.000691 ! Validation 495 49257.055 0.005 0.00434 0.0422 0.129 0.0577 0.0773 0.211 0.241 0.000599 0.000684 Wall time: 49257.05534891691 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.0858 0.00307 0.0244 0.0492 0.065 0.145 0.183 0.000411 0.00052 496 172 0.372 0.00742 0.224 0.0772 0.101 0.543 0.555 0.00154 0.00158 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 496 100 0.729 0.00404 0.648 0.0567 0.0745 0.943 0.944 0.00268 0.00268 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 496 49356.316 0.005 0.00353 0.0606 0.131 0.052 0.0697 0.22 0.288 0.000625 0.000819 ! Validation 496 49356.316 0.005 0.0048 0.587 0.683 0.0614 0.0813 0.862 0.899 0.00245 0.00255 Wall time: 49356.31649160106 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.289 0.00454 0.198 0.0601 0.079 0.509 0.522 0.00145 0.00148 497 172 0.0766 0.00344 0.0078 0.0508 0.0688 0.0848 0.104 0.000241 0.000294 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 497 100 0.109 0.00311 0.0467 0.0496 0.0654 0.252 0.254 0.000715 0.00072 Train # Epoch wal LR loss_f loss_e loss f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse ! Train 497 49455.578 0.005 0.00348 0.0469 0.117 0.0519 0.0692 0.195 0.254 0.000555 0.000722 ! Validation 497 49455.578 0.005 0.00392 0.0234 0.102 0.0547 0.0734 0.144 0.179 0.000409 0.000509 Wall time: 49455.57823927002 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.0907 0.00297 0.0312 0.0481 0.0639 0.177 0.207 0.000502 0.000589 498 172 0.0993 0.00319 0.0356 0.05 0.0662 0.197 0.221 0.000559 0.000629 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 498 100 0.122 0.00323 0.0571 0.0504 0.0667 0.278 0.28 0.000789 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 498 49554.839 0.005 0.00332 0.0476 0.114 0.0506 0.0676 0.202 0.256 0.000575 0.000727 ! Validation 498 49554.839 0.005 0.00412 0.0194 0.102 0.0562 0.0753 0.131 0.163 0.000372 0.000464 Wall time: 49554.839762617834 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.0713 0.00292 0.013 0.0475 0.0634 0.102 0.134 0.00029 0.00038 499 172 0.146 0.00317 0.0823 0.0494 0.0661 0.315 0.336 0.000895 0.000956 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 499 100 0.347 0.00294 0.288 0.0481 0.0636 0.629 0.63 0.00179 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 499 49654.104 0.005 0.00327 0.0286 0.0941 0.0502 0.0671 0.157 0.198 0.000447 0.000564 ! Validation 499 49654.104 0.005 0.00374 0.16 0.235 0.0534 0.0718 0.446 0.469 0.00127 0.00133 Wall time: 49654.104273080826 training # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.15 0.00326 0.085 0.0503 0.067 0.315 0.342 0.000896 0.000972 500 172 0.071 0.0028 0.015 0.0464 0.0621 0.11 0.144 0.000311 0.000409 validation # Epoch batch loss loss_f loss_e f_mae f_rmse e_mae e_rmse e/N_mae e/N_rmse 500 100 0.12 0.0029 0.062 0.0477 0.0631 0.291 0.292 0.000826 0.00083 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 49753.362 0.005 0.00333 0.0454 0.112 0.0507 0.0677 0.198 0.25 0.000562 0.00071 ! Validation 500 49753.362 0.005 0.00362 0.0405 0.113 0.0524 0.0705 0.197 0.236 0.000561 0.00067 Wall time: 49753.36243097391 ! Stop training: max epochs Wall time: 49753.40694246907 Cumulative wall time: 49753.40694246907 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 = 0.060269 f_rmse = 0.082274 e_mae = 0.132707 e_rmse = 0.175397 e/N_mae = 0.000377 e/N_rmse = 0.000498 f_mae = 0.060269 f_rmse = 0.082274 e_mae = 0.132707 e_rmse = 0.175397 e/N_mae = 0.000377 e/N_rmse = 0.000498 Train end time: 2024-12-11_23:03:32 Training duration: 13h 52m 40s