Starting scenario 4, validation against site 2
2022-03-01 13:17:48.842073: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:17:48.842103: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Cross validation site: 2
Training sites: [0, 1, 3, 4, 5, 6, 7, 8]
Number of files: 8
Number of east files: 4
Number of west files: 4
Source files: ['/projects/pxs/mlclouds/training_data/2016_east_v322/mlclouds_surfrad_east_2016.h5', '/projects/pxs/mlclouds/training_data/2016_west_v322/mlclouds_surfrad_west_2016.h5', '/projects/pxs/mlclouds/training_data/2017_east_v322/mlclouds_surfrad_east_2017.h5', '/projects/pxs/mlclouds/training_data/2017_west_v322/mlclouds_surfrad_west_2017.h5', '/projects/pxs/mlclouds/training_data/2018_east_v322/mlclouds_surfrad_east_2018.h5', '/projects/pxs/mlclouds/training_data/2018_west_v322/mlclouds_surfrad_west_2018.h5', '/projects/pxs/mlclouds/training_data/2019_east_v322/mlclouds_surfrad_east_2019.h5', '/projects/pxs/mlclouds/training_data/2019_west_v322/mlclouds_surfrad_west_2019.h5']
Full config: {'clean_training_data_kwargs': {'filter_clear': False, 'nan_option': 'interp'}, 'epochs_a': 100, 'epochs_b': 100, 'features': ['solar_zenith_angle', 'cloud_type', 'refl_0_65um_nom', 'refl_0_65um_nom_stddev_3x3', 'refl_3_75um_nom', 'temp_3_75um_nom', 'temp_11_0um_nom', 'temp_11_0um_nom_stddev_3x3', 'cloud_probability', 'cloud_fraction', 'air_temperature', 'dew_point', 'relative_humidity', 'total_precipitable_water', 'surface_albedo'], 'hidden_layers': [{'activation': 'relu', 'dropout': 0.1, 'units': 256}, {'activation': 'relu', 'dropout': 0.1, 'units': 256}, {'activation': 'relu', 'dropout': 0.1, 'units': 256}, {'activation': 'relu', 'dropout': 0.1, 'units': 256}, {'activation': 'relu', 'dropout': 0.1, 'units': 256}], 'learning_rate': 0.0005, 'loss_weights_a': [1, 0], 'loss_weights_b': [0.5, 0.5], 'metric': 'relative_mae', 'n_batch': 32, 'one_hot_categories': {'flag': ['clear', 'ice_cloud', 'water_cloud', 'bad_cloud']}, 'p_fun': 'p_fun_all_sky', 'p_kwargs': {'loss_terms': ['mae_ghi']}, 'phygnn_seed': 0, 'surfrad_window_minutes': 15, 'y_labels': ['cld_opd_dcomp', 'cld_reff_dcomp']}
INFO - 2022-03-01 13:17:55,792 [trainer.py:40] : Trainer: Training on sites [0, 1, 3, 4, 5, 6, 7, 8] from files ['/projects/pxs/mlclouds/training_data/2016_east_v322/mlclouds_surfrad_east_2016.h5', '/projects/pxs/mlclouds/training_data/2016_west_v322/mlclouds_surfrad_west_2016.h5', '/projects/pxs/mlclouds/training_data/2017_east_v322/mlclouds_surfrad_east_2017.h5', '/projects/pxs/mlclouds/training_data/2017_west_v322/mlclouds_surfrad_west_2017.h5', '/projects/pxs/mlclouds/training_data/2018_east_v322/mlclouds_surfrad_east_2018.h5', '/projects/pxs/mlclouds/training_data/2018_west_v322/mlclouds_surfrad_west_2018.h5', '/projects/pxs/mlclouds/training_data/2019_east_v322/mlclouds_surfrad_east_2019.h5', '/projects/pxs/mlclouds/training_data/2019_west_v322/mlclouds_surfrad_west_2019.h5']
INFO - 2022-03-01 13:17:55,792 [trainer.py:49] : Trainer: Training on sites [0, 1, 3, 4, 5, 6, 7, 8] from files ['/projects/pxs/mlclouds/training_data/2016_east_v322/mlclouds_surfrad_east_2016.h5', '/projects/pxs/mlclouds/training_data/2016_west_v322/mlclouds_surfrad_west_2016.h5', '/projects/pxs/mlclouds/training_data/2017_east_v322/mlclouds_surfrad_east_2017.h5', '/projects/pxs/mlclouds/training_data/2017_west_v322/mlclouds_surfrad_west_2017.h5', '/projects/pxs/mlclouds/training_data/2018_east_v322/mlclouds_surfrad_east_2018.h5', '/projects/pxs/mlclouds/training_data/2018_west_v322/mlclouds_surfrad_west_2018.h5', '/projects/pxs/mlclouds/training_data/2019_east_v322/mlclouds_surfrad_east_2019.h5', '/projects/pxs/mlclouds/training_data/2019_west_v322/mlclouds_surfrad_west_2019.h5']
INFO - 2022-03-01 13:17:55,792 [data_handlers.py:60] : Loading training data
DEBUG - 2022-03-01 13:17:55,792 [data_handlers.py:78] : Loading vars ['solar_zenith_angle', 'cloud_type', 'refl_0_65um_nom', 'refl_0_65um_nom_stddev_3x3', 'refl_3_75um_nom', 'temp_3_75um_nom', 'temp_11_0um_nom', 'temp_11_0um_nom_stddev_3x3', 'cloud_probability', 'cloud_fraction', 'air_temperature', 'dew_point', 'relative_humidity', 'total_precipitable_water', 'surface_albedo', 'cld_opd_dcomp', 'cld_reff_dcomp']
DEBUG - 2022-03-01 13:17:55,792 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2016_east_v322/mlclouds_surfrad_east_2016.h5
DEBUG - 2022-03-01 13:17:57,077 [data_handlers.py:103] : 	Shape temp_raw=(140544, 19), temp_all_sky=(140544, 14)
DEBUG - 2022-03-01 13:17:57,081 [data_handlers.py:106] : 	Time step is 30 minutes
DEBUG - 2022-03-01 13:17:57,081 [data_handlers.py:110] : 	Grabbing surface data for 2016 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:17:57,086 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2016.h5
DEBUG - 2022-03-01 13:17:57,780 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:17:57,783 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2016.h5
DEBUG - 2022-03-01 13:17:58,460 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:17:58,464 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2016.h5
DEBUG - 2022-03-01 13:17:59,167 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:17:59,171 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2016.h5
DEBUG - 2022-03-01 13:17:59,863 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:17:59,866 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2016.h5
DEBUG - 2022-03-01 13:18:00,559 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:00,563 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2016.h5
DEBUG - 2022-03-01 13:18:01,256 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:01,260 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2016.h5
DEBUG - 2022-03-01 13:18:01,944 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:01,947 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2016.h5
DEBUG - 2022-03-01 13:18:02,755 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:02,755 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2016_west_v322/mlclouds_surfrad_west_2016.h5
DEBUG - 2022-03-01 13:18:03,791 [data_handlers.py:103] : 	Shape temp_raw=(140544, 19), temp_all_sky=(140544, 14)
DEBUG - 2022-03-01 13:18:03,796 [data_handlers.py:106] : 	Time step is 30 minutes
DEBUG - 2022-03-01 13:18:03,796 [data_handlers.py:110] : 	Grabbing surface data for 2016 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:03,799 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2016.h5
DEBUG - 2022-03-01 13:18:04,462 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:04,465 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2016.h5
DEBUG - 2022-03-01 13:18:05,126 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:05,130 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2016.h5
DEBUG - 2022-03-01 13:18:05,797 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:05,801 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2016.h5
DEBUG - 2022-03-01 13:18:06,463 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:06,466 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2016.h5
DEBUG - 2022-03-01 13:18:07,133 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:07,137 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2016.h5
DEBUG - 2022-03-01 13:18:07,799 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:07,803 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2016.h5
DEBUG - 2022-03-01 13:18:08,457 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:08,460 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2016.h5
DEBUG - 2022-03-01 13:18:09,125 [data_handlers.py:134] : 	Shape: temp_surf=(17568, 5)
DEBUG - 2022-03-01 13:18:09,125 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2017_east_v322/mlclouds_surfrad_east_2017.h5
DEBUG - 2022-03-01 13:18:10,171 [data_handlers.py:103] : 	Shape temp_raw=(140160, 19), temp_all_sky=(140160, 14)
DEBUG - 2022-03-01 13:18:10,175 [data_handlers.py:106] : 	Time step is 30 minutes
DEBUG - 2022-03-01 13:18:10,175 [data_handlers.py:110] : 	Grabbing surface data for 2017 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:10,179 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2017.h5
DEBUG - 2022-03-01 13:18:10,854 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:10,858 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2017.h5
DEBUG - 2022-03-01 13:18:11,531 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:11,535 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2017.h5
DEBUG - 2022-03-01 13:18:12,263 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:12,267 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2017.h5
DEBUG - 2022-03-01 13:18:12,933 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:12,936 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2017.h5
DEBUG - 2022-03-01 13:18:13,612 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:13,615 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2017.h5
DEBUG - 2022-03-01 13:18:14,284 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:14,287 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2017.h5
DEBUG - 2022-03-01 13:18:14,957 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:14,960 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2017.h5
DEBUG - 2022-03-01 13:18:15,634 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:15,635 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2017_west_v322/mlclouds_surfrad_west_2017.h5
DEBUG - 2022-03-01 13:18:16,691 [data_handlers.py:103] : 	Shape temp_raw=(140160, 19), temp_all_sky=(140160, 14)
DEBUG - 2022-03-01 13:18:16,695 [data_handlers.py:106] : 	Time step is 30 minutes
DEBUG - 2022-03-01 13:18:16,696 [data_handlers.py:110] : 	Grabbing surface data for 2017 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:16,699 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2017.h5
DEBUG - 2022-03-01 13:18:17,354 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:17,357 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2017.h5
DEBUG - 2022-03-01 13:18:18,006 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:18,009 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2017.h5
DEBUG - 2022-03-01 13:18:18,670 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:18,673 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2017.h5
DEBUG - 2022-03-01 13:18:19,328 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:19,331 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2017.h5
DEBUG - 2022-03-01 13:18:20,010 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:20,013 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2017.h5
DEBUG - 2022-03-01 13:18:20,674 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:20,678 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2017.h5
DEBUG - 2022-03-01 13:18:21,346 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:21,350 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2017.h5
DEBUG - 2022-03-01 13:18:22,017 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:22,018 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2018_east_v322/mlclouds_surfrad_east_2018.h5
DEBUG - 2022-03-01 13:18:28,299 [data_handlers.py:103] : 	Shape temp_raw=(840960, 19), temp_all_sky=(840960, 14)
DEBUG - 2022-03-01 13:18:28,319 [data_handlers.py:106] : 	Time step is 5 minutes
DEBUG - 2022-03-01 13:18:28,319 [data_handlers.py:110] : 	Grabbing surface data for 2018 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:28,322 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2018.h5
DEBUG - 2022-03-01 13:18:28,988 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:28,991 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2018.h5
DEBUG - 2022-03-01 13:18:29,653 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:29,656 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2018.h5
DEBUG - 2022-03-01 13:18:30,374 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:30,378 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2018.h5
DEBUG - 2022-03-01 13:18:31,073 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:31,076 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2018.h5
DEBUG - 2022-03-01 13:18:31,742 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:31,746 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2018.h5
DEBUG - 2022-03-01 13:18:32,425 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:32,428 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2018.h5
DEBUG - 2022-03-01 13:18:33,099 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:33,102 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2018.h5
DEBUG - 2022-03-01 13:18:33,783 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:33,783 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2018_west_v322/mlclouds_surfrad_west_2018.h5
DEBUG - 2022-03-01 13:18:34,946 [data_handlers.py:103] : 	Shape temp_raw=(140160, 19), temp_all_sky=(140160, 14)
DEBUG - 2022-03-01 13:18:34,950 [data_handlers.py:106] : 	Time step is 30 minutes
DEBUG - 2022-03-01 13:18:34,950 [data_handlers.py:110] : 	Grabbing surface data for 2018 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:34,953 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2018.h5
DEBUG - 2022-03-01 13:18:35,599 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:35,602 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2018.h5
DEBUG - 2022-03-01 13:18:36,256 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:36,259 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2018.h5
DEBUG - 2022-03-01 13:18:36,905 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:36,908 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2018.h5
DEBUG - 2022-03-01 13:18:37,563 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:37,566 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2018.h5
DEBUG - 2022-03-01 13:18:38,210 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:38,213 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2018.h5
DEBUG - 2022-03-01 13:18:38,868 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:38,871 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2018.h5
DEBUG - 2022-03-01 13:18:39,524 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:39,527 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2018.h5
DEBUG - 2022-03-01 13:18:40,185 [data_handlers.py:134] : 	Shape: temp_surf=(17520, 5)
DEBUG - 2022-03-01 13:18:40,185 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2019_east_v322/mlclouds_surfrad_east_2019.h5
DEBUG - 2022-03-01 13:18:46,231 [data_handlers.py:103] : 	Shape temp_raw=(840960, 19), temp_all_sky=(840960, 14)
DEBUG - 2022-03-01 13:18:46,251 [data_handlers.py:106] : 	Time step is 5 minutes
DEBUG - 2022-03-01 13:18:46,251 [data_handlers.py:110] : 	Grabbing surface data for 2019 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:46,254 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2019.h5
DEBUG - 2022-03-01 13:18:46,927 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:46,931 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2019.h5
DEBUG - 2022-03-01 13:18:47,681 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:47,684 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2019.h5
DEBUG - 2022-03-01 13:18:48,505 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:48,509 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2019.h5
DEBUG - 2022-03-01 13:18:49,193 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:49,196 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2019.h5
DEBUG - 2022-03-01 13:18:49,878 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:49,882 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2019.h5
DEBUG - 2022-03-01 13:18:50,568 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:50,571 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2019.h5
DEBUG - 2022-03-01 13:18:51,261 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:51,264 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2019.h5
DEBUG - 2022-03-01 13:18:51,965 [data_handlers.py:134] : 	Shape: temp_surf=(105120, 5)
DEBUG - 2022-03-01 13:18:51,965 [data_handlers.py:85] : Loading data for site(s) [0, 1, 3, 4, 5, 6, 7, 8], from /projects/pxs/mlclouds/training_data/2019_west_v322/mlclouds_surfrad_west_2019.h5
DEBUG - 2022-03-01 13:18:55,238 [data_handlers.py:103] : 	Shape temp_raw=(420480, 19), temp_all_sky=(420480, 14)
DEBUG - 2022-03-01 13:18:55,248 [data_handlers.py:106] : 	Time step is 10 minutes
DEBUG - 2022-03-01 13:18:55,248 [data_handlers.py:110] : 	Grabbing surface data for 2019 and [0, 1, 3, 4, 5, 6, 7, 8]
DEBUG - 2022-03-01 13:18:55,251 [data_handlers.py:117] : 		Grabbing surface data for bon from /projects/pxs/surfrad/h5/bon_2019.h5
DEBUG - 2022-03-01 13:18:55,919 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:55,923 [data_handlers.py:117] : 		Grabbing surface data for tbl from /projects/pxs/surfrad/h5/tbl_2019.h5
DEBUG - 2022-03-01 13:18:56,595 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:56,599 [data_handlers.py:117] : 		Grabbing surface data for fpk from /projects/pxs/surfrad/h5/fpk_2019.h5
DEBUG - 2022-03-01 13:18:57,271 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:57,275 [data_handlers.py:117] : 		Grabbing surface data for gwn from /projects/pxs/surfrad/h5/gwn_2019.h5
DEBUG - 2022-03-01 13:18:57,946 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:57,949 [data_handlers.py:117] : 		Grabbing surface data for psu from /projects/pxs/surfrad/h5/psu_2019.h5
DEBUG - 2022-03-01 13:18:58,622 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:58,626 [data_handlers.py:117] : 		Grabbing surface data for sxf from /projects/pxs/surfrad/h5/sxf_2019.h5
DEBUG - 2022-03-01 13:18:59,299 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:59,302 [data_handlers.py:117] : 		Grabbing surface data for sgp from /projects/pxs/surfrad/h5/sgp_2019.h5
DEBUG - 2022-03-01 13:18:59,977 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:18:59,980 [data_handlers.py:117] : 		Grabbing surface data for srrl from /projects/pxs/surfrad/h5/srrl_2019.h5
DEBUG - 2022-03-01 13:19:00,658 [data_handlers.py:134] : 	Shape: temp_surf=(52560, 5)
DEBUG - 2022-03-01 13:19:00,658 [data_handlers.py:136] : Data load complete. Shape df_raw=(2803968, 19), df_all_sky=(2803968, 14), df_surf=(2803968, 5)
DEBUG - 2022-03-01 13:19:01,524 [data_handlers.py:159] : Extracting 2D arrays to run rest2 for clearsky PhyGNN inputs.
DEBUG - 2022-03-01 13:19:14,138 [data_handlers.py:176] : Running rest2 for clearsky PhyGNN inputs.
DEBUG - 2022-03-01 13:21:07,947 [data_handlers.py:194] : Completed rest2 run for clearsky PhyGNN inputs.
INFO - 2022-03-01 13:21:09,635 [data_handlers.py:62] : Prepping training data
DEBUG - 2022-03-01 13:21:09,635 [data_handlers.py:214] : Training data clean kwargs: {'filter_daylight': True, 'filter_clear': False, 'add_cloud_flag': True, 'sza_lim': 89, 'nan_option': 'interp'}
DEBUG - 2022-03-01 13:21:09,635 [data_handlers.py:215] : Shape before cleaning: df_raw=(2803968, 19)
INFO - 2022-03-01 13:21:09,938 [data_cleaners.py:36] : 49.69% of timesteps are daylight
INFO - 2022-03-01 13:21:09,943 [data_cleaners.py:38] : 54.91% of daylight timesteps are cloudy
INFO - 2022-03-01 13:21:09,947 [data_cleaners.py:40] : 3.59% of daylight timesteps are missing cloud type
INFO - 2022-03-01 13:21:09,952 [data_cleaners.py:42] : 34.03% of cloudy daylight timesteps are missing cloud opd
INFO - 2022-03-01 13:21:09,957 [data_cleaners.py:44] : 34.26% of cloudy daylight timesteps are missing cloud reff
DEBUG - 2022-03-01 13:21:09,957 [data_cleaners.py:47] : Column NaN values:
DEBUG - 2022-03-01 13:21:09,960 [data_cleaners.py:50] : 	"gid" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:09,965 [data_cleaners.py:50] : 	"time_index" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:09,969 [data_cleaners.py:50] : 	"solar_zenith_angle" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:09,974 [data_cleaners.py:50] : 	"cloud_type" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:09,978 [data_cleaners.py:50] : 	"refl_0_65um_nom" has 51.62% NaN values
DEBUG - 2022-03-01 13:21:09,982 [data_cleaners.py:50] : 	"refl_0_65um_nom_stddev_3x3" has 51.62% NaN values
DEBUG - 2022-03-01 13:21:09,986 [data_cleaners.py:50] : 	"refl_3_75um_nom" has 3.63% NaN values
DEBUG - 2022-03-01 13:21:09,990 [data_cleaners.py:50] : 	"temp_3_75um_nom" has 3.53% NaN values
DEBUG - 2022-03-01 13:21:09,994 [data_cleaners.py:50] : 	"temp_11_0um_nom" has 3.53% NaN values
DEBUG - 2022-03-01 13:21:09,997 [data_cleaners.py:50] : 	"temp_11_0um_nom_stddev_3x3" has 3.61% NaN values
DEBUG - 2022-03-01 13:21:10,001 [data_cleaners.py:50] : 	"cloud_probability" has 3.61% NaN values
DEBUG - 2022-03-01 13:21:10,005 [data_cleaners.py:50] : 	"cloud_fraction" has 3.61% NaN values
DEBUG - 2022-03-01 13:21:10,009 [data_cleaners.py:50] : 	"air_temperature" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:10,013 [data_cleaners.py:50] : 	"dew_point" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:10,017 [data_cleaners.py:50] : 	"relative_humidity" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:10,021 [data_cleaners.py:50] : 	"total_precipitable_water" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:10,025 [data_cleaners.py:50] : 	"surface_albedo" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:10,029 [data_cleaners.py:50] : 	"cld_opd_dcomp" has 82.00% NaN values
DEBUG - 2022-03-01 13:21:10,032 [data_cleaners.py:50] : 	"cld_reff_dcomp" has 82.06% NaN values
DEBUG - 2022-03-01 13:21:10,033 [data_cleaners.py:53] : Interpolating opd and reff
DEBUG - 2022-03-01 13:21:13,231 [data_cleaners.py:79] : Adding cloud type flag (e.g. flag=[night, clear, ice_cloud, water_cloud, bad_cloud])
INFO - 2022-03-01 13:21:13,511 [data_cleaners.py:99] : Data reduced from 2803968 rows to 1393424 after filters (49.69% of original)
DEBUG - 2022-03-01 13:21:13,636 [data_cleaners.py:105] : Feature flag column has these values: ['clear' 'bad_cloud' 'water_cloud' 'ice_cloud']
INFO - 2022-03-01 13:21:13,637 [data_cleaners.py:107] : Cleaning took 4.0 seconds
DEBUG - 2022-03-01 13:21:13,637 [data_handlers.py:218] : Shape after cleaning: df_train=(1393424, 20)
DEBUG - 2022-03-01 13:21:13,637 [data_handlers.py:221] : Cleaning df_all_sky training data (for pfun).
DEBUG - 2022-03-01 13:21:13,637 [data_handlers.py:222] : Shape before cleaning: df_all_sky=(2803968, 25)
INFO - 2022-03-01 13:21:14,011 [data_cleaners.py:36] : 49.69% of timesteps are daylight
INFO - 2022-03-01 13:21:14,015 [data_cleaners.py:38] : 54.91% of daylight timesteps are cloudy
INFO - 2022-03-01 13:21:14,020 [data_cleaners.py:40] : 3.59% of daylight timesteps are missing cloud type
INFO - 2022-03-01 13:21:14,025 [data_cleaners.py:42] : 34.03% of cloudy daylight timesteps are missing cloud opd
INFO - 2022-03-01 13:21:14,029 [data_cleaners.py:44] : 34.26% of cloudy daylight timesteps are missing cloud reff
DEBUG - 2022-03-01 13:21:14,029 [data_cleaners.py:47] : Column NaN values:
DEBUG - 2022-03-01 13:21:14,032 [data_cleaners.py:50] : 	"gid" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,036 [data_cleaners.py:50] : 	"alpha" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,040 [data_cleaners.py:50] : 	"aod" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,044 [data_cleaners.py:50] : 	"asymmetry" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,049 [data_cleaners.py:50] : 	"cloud_type" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,053 [data_cleaners.py:50] : 	"cld_opd_dcomp" has 82.00% NaN values
DEBUG - 2022-03-01 13:21:14,057 [data_cleaners.py:50] : 	"cld_reff_dcomp" has 82.06% NaN values
DEBUG - 2022-03-01 13:21:14,061 [data_cleaners.py:50] : 	"ozone" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,065 [data_cleaners.py:50] : 	"solar_zenith_angle" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,069 [data_cleaners.py:50] : 	"ssa" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,072 [data_cleaners.py:50] : 	"surface_albedo" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,075 [data_cleaners.py:50] : 	"surface_pressure" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,079 [data_cleaners.py:50] : 	"total_precipitable_water" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,084 [data_cleaners.py:50] : 	"surfrad_dhi" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,089 [data_cleaners.py:50] : 	"surfrad_dni" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,095 [data_cleaners.py:50] : 	"surfrad_ghi" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,097 [data_cleaners.py:50] : 	"doy" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,102 [data_cleaners.py:50] : 	"radius" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,108 [data_cleaners.py:50] : 	"Tuuclr" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,113 [data_cleaners.py:50] : 	"clearsky_ghi" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,118 [data_cleaners.py:50] : 	"clearsky_dni" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,123 [data_cleaners.py:50] : 	"Ruuclr" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,128 [data_cleaners.py:50] : 	"Tddclr" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,134 [data_cleaners.py:50] : 	"Tduclr" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,139 [data_cleaners.py:50] : 	"time_index" has 0.00% NaN values
DEBUG - 2022-03-01 13:21:14,139 [data_cleaners.py:53] : Interpolating opd and reff
DEBUG - 2022-03-01 13:21:16,766 [data_cleaners.py:79] : Adding cloud type flag (e.g. flag=[night, clear, ice_cloud, water_cloud, bad_cloud])
INFO - 2022-03-01 13:21:17,046 [data_cleaners.py:99] : Data reduced from 2803968 rows to 1393424 after filters (49.69% of original)
DEBUG - 2022-03-01 13:21:17,226 [data_cleaners.py:105] : Feature flag column has these values: ['clear' 'bad_cloud' 'water_cloud' 'ice_cloud']
INFO - 2022-03-01 13:21:17,226 [data_cleaners.py:107] : Cleaning took 3.6 seconds
DEBUG - 2022-03-01 13:21:17,227 [data_handlers.py:226] : Shape after cleaning: df_all_sky=(1393424, 26)
DEBUG - 2022-03-01 13:21:17,322 [data_handlers.py:240] : **Shape: df_train=(1393424, 17)
DEBUG - 2022-03-01 13:21:17,350 [data_handlers.py:250] : Shapes: x=(1393424, 15), y=(1393424, 2), p=(1393424, 26)
DEBUG - 2022-03-01 13:21:17,350 [data_handlers.py:253] : Training features: ['solar_zenith_angle', 'refl_0_65um_nom', 'refl_0_65um_nom_stddev_3x3', 'refl_3_75um_nom', 'temp_3_75um_nom', 'temp_11_0um_nom', 'temp_11_0um_nom_stddev_3x3', 'cloud_probability', 'cloud_fraction', 'air_temperature', 'dew_point', 'relative_humidity', 'total_precipitable_water', 'surface_albedo', 'flag']
DEBUG - 2022-03-01 13:21:17,350 [trainer.py:67] : Building PHYGNN model
INFO - 2022-03-01 13:21:17,350 [trainer.py:70] : Using p_fun: <function p_fun_all_sky at 0x2b0c6ed0f8b0>
INFO - 2022-03-01 13:21:17,351 [base.py:152] : Active python environment versions: 
{   'numpy': '1.22.2',
    'pandas': '1.2.4',
    'phygnn': '0.0.14',
    'python': '3.8.8 (default, Feb 24 2021, 21:46:12) \n[GCC 7.3.0]',
    'sklearn': '0.24.1',
    'tensorflow': '2.8.0'}
INFO - 2022-03-01 13:21:17,366 [base.py:111] : Successfully initialized model with 17 layers
INFO - 2022-03-01 13:21:17,366 [trainer.py:84] : Training part A - pure data. Loss is [1, 0]
2022-03-01 13:21:26.444404: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.445403: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.446136: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.446869: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.447631: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcurand.so.10'; dlerror: libcurand.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.448365: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusolver.so.11'; dlerror: libcusolver.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.449102: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.449827: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nopt/slurm/current/lib:/nopt/slurm/current/lib:
2022-03-01 13:21:26.449846: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2022-03-01 13:21:26.450233: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO - 2022-03-01 13:21:34,951 [phygnn.py:576] : Epoch 0 train loss: 7.00e-01 val loss: 6.90e-01 for "phygnn"
INFO - 2022-03-01 13:21:43,686 [phygnn.py:576] : Epoch 1 train loss: 6.37e-01 val loss: 6.26e-01 for "phygnn"
INFO - 2022-03-01 13:21:52,170 [phygnn.py:576] : Epoch 2 train loss: 5.63e-01 val loss: 5.52e-01 for "phygnn"
INFO - 2022-03-01 13:22:00,850 [phygnn.py:576] : Epoch 3 train loss: 5.31e-01 val loss: 5.16e-01 for "phygnn"
INFO - 2022-03-01 13:22:09,561 [phygnn.py:576] : Epoch 4 train loss: 5.18e-01 val loss: 4.95e-01 for "phygnn"
INFO - 2022-03-01 13:22:18,143 [phygnn.py:576] : Epoch 5 train loss: 5.02e-01 val loss: 4.86e-01 for "phygnn"
INFO - 2022-03-01 13:22:26,760 [phygnn.py:576] : Epoch 6 train loss: 4.94e-01 val loss: 4.79e-01 for "phygnn"
INFO - 2022-03-01 13:22:35,633 [phygnn.py:576] : Epoch 7 train loss: 4.85e-01 val loss: 4.75e-01 for "phygnn"
INFO - 2022-03-01 13:22:44,340 [phygnn.py:576] : Epoch 8 train loss: 4.83e-01 val loss: 4.71e-01 for "phygnn"
INFO - 2022-03-01 13:22:52,697 [phygnn.py:576] : Epoch 9 train loss: 4.83e-01 val loss: 4.68e-01 for "phygnn"
INFO - 2022-03-01 13:23:01,305 [phygnn.py:576] : Epoch 10 train loss: 4.79e-01 val loss: 4.66e-01 for "phygnn"
INFO - 2022-03-01 13:23:09,759 [phygnn.py:576] : Epoch 11 train loss: 4.74e-01 val loss: 4.65e-01 for "phygnn"
INFO - 2022-03-01 13:23:18,669 [phygnn.py:576] : Epoch 12 train loss: 4.73e-01 val loss: 4.62e-01 for "phygnn"
INFO - 2022-03-01 13:23:27,492 [phygnn.py:576] : Epoch 13 train loss: 4.74e-01 val loss: 4.61e-01 for "phygnn"
INFO - 2022-03-01 13:23:35,992 [phygnn.py:576] : Epoch 14 train loss: 4.68e-01 val loss: 4.57e-01 for "phygnn"
INFO - 2022-03-01 13:23:44,711 [phygnn.py:576] : Epoch 15 train loss: 4.71e-01 val loss: 4.56e-01 for "phygnn"
INFO - 2022-03-01 13:23:53,446 [phygnn.py:576] : Epoch 16 train loss: 4.62e-01 val loss: 4.55e-01 for "phygnn"
INFO - 2022-03-01 13:24:01,897 [phygnn.py:576] : Epoch 17 train loss: 4.65e-01 val loss: 4.53e-01 for "phygnn"
INFO - 2022-03-01 13:24:10,804 [phygnn.py:576] : Epoch 18 train loss: 4.62e-01 val loss: 4.51e-01 for "phygnn"
INFO - 2022-03-01 13:24:19,276 [phygnn.py:576] : Epoch 19 train loss: 4.62e-01 val loss: 4.50e-01 for "phygnn"
INFO - 2022-03-01 13:24:27,847 [phygnn.py:576] : Epoch 20 train loss: 4.56e-01 val loss: 4.49e-01 for "phygnn"
INFO - 2022-03-01 13:24:36,324 [phygnn.py:576] : Epoch 21 train loss: 4.57e-01 val loss: 4.47e-01 for "phygnn"
INFO - 2022-03-01 13:24:44,804 [phygnn.py:576] : Epoch 22 train loss: 4.54e-01 val loss: 4.44e-01 for "phygnn"
INFO - 2022-03-01 13:24:53,695 [phygnn.py:576] : Epoch 23 train loss: 4.57e-01 val loss: 4.44e-01 for "phygnn"
INFO - 2022-03-01 13:25:02,136 [phygnn.py:576] : Epoch 24 train loss: 4.55e-01 val loss: 4.44e-01 for "phygnn"
INFO - 2022-03-01 13:25:10,812 [phygnn.py:576] : Epoch 25 train loss: 4.51e-01 val loss: 4.41e-01 for "phygnn"
INFO - 2022-03-01 13:25:19,350 [phygnn.py:576] : Epoch 26 train loss: 4.46e-01 val loss: 4.39e-01 for "phygnn"
INFO - 2022-03-01 13:25:27,839 [phygnn.py:576] : Epoch 27 train loss: 4.49e-01 val loss: 4.39e-01 for "phygnn"
INFO - 2022-03-01 13:25:36,577 [phygnn.py:576] : Epoch 28 train loss: 4.49e-01 val loss: 4.37e-01 for "phygnn"
INFO - 2022-03-01 13:25:45,313 [phygnn.py:576] : Epoch 29 train loss: 4.39e-01 val loss: 4.34e-01 for "phygnn"
INFO - 2022-03-01 13:25:54,318 [phygnn.py:576] : Epoch 30 train loss: 4.49e-01 val loss: 4.33e-01 for "phygnn"
INFO - 2022-03-01 13:26:02,769 [phygnn.py:576] : Epoch 31 train loss: 4.47e-01 val loss: 4.33e-01 for "phygnn"
INFO - 2022-03-01 13:26:11,360 [phygnn.py:576] : Epoch 32 train loss: 4.48e-01 val loss: 4.33e-01 for "phygnn"
INFO - 2022-03-01 13:26:19,847 [phygnn.py:576] : Epoch 33 train loss: 4.52e-01 val loss: 4.33e-01 for "phygnn"
INFO - 2022-03-01 13:26:28,298 [phygnn.py:576] : Epoch 34 train loss: 4.40e-01 val loss: 4.31e-01 for "phygnn"
INFO - 2022-03-01 13:26:36,709 [phygnn.py:576] : Epoch 35 train loss: 4.49e-01 val loss: 4.30e-01 for "phygnn"
INFO - 2022-03-01 13:26:45,796 [phygnn.py:576] : Epoch 36 train loss: 4.39e-01 val loss: 4.31e-01 for "phygnn"
INFO - 2022-03-01 13:26:54,429 [phygnn.py:576] : Epoch 37 train loss: 4.44e-01 val loss: 4.28e-01 for "phygnn"
INFO - 2022-03-01 13:27:03,087 [phygnn.py:576] : Epoch 38 train loss: 4.50e-01 val loss: 4.27e-01 for "phygnn"
INFO - 2022-03-01 13:27:11,910 [phygnn.py:576] : Epoch 39 train loss: 4.44e-01 val loss: 4.27e-01 for "phygnn"
INFO - 2022-03-01 13:27:20,542 [phygnn.py:576] : Epoch 40 train loss: 4.39e-01 val loss: 4.25e-01 for "phygnn"
INFO - 2022-03-01 13:27:29,354 [phygnn.py:576] : Epoch 41 train loss: 4.34e-01 val loss: 4.24e-01 for "phygnn"
INFO - 2022-03-01 13:27:37,983 [phygnn.py:576] : Epoch 42 train loss: 4.37e-01 val loss: 4.24e-01 for "phygnn"
INFO - 2022-03-01 13:27:46,832 [phygnn.py:576] : Epoch 43 train loss: 4.38e-01 val loss: 4.23e-01 for "phygnn"
INFO - 2022-03-01 13:27:55,526 [phygnn.py:576] : Epoch 44 train loss: 4.34e-01 val loss: 4.25e-01 for "phygnn"
INFO - 2022-03-01 13:28:04,054 [phygnn.py:576] : Epoch 45 train loss: 4.33e-01 val loss: 4.25e-01 for "phygnn"
INFO - 2022-03-01 13:28:12,999 [phygnn.py:576] : Epoch 46 train loss: 4.34e-01 val loss: 4.20e-01 for "phygnn"
INFO - 2022-03-01 13:28:21,906 [phygnn.py:576] : Epoch 47 train loss: 4.31e-01 val loss: 4.20e-01 for "phygnn"
INFO - 2022-03-01 13:28:30,603 [phygnn.py:576] : Epoch 48 train loss: 4.33e-01 val loss: 4.21e-01 for "phygnn"
INFO - 2022-03-01 13:28:39,653 [phygnn.py:576] : Epoch 49 train loss: 4.29e-01 val loss: 4.23e-01 for "phygnn"
INFO - 2022-03-01 13:28:48,415 [phygnn.py:576] : Epoch 50 train loss: 4.25e-01 val loss: 4.19e-01 for "phygnn"
INFO - 2022-03-01 13:28:56,955 [phygnn.py:576] : Epoch 51 train loss: 4.35e-01 val loss: 4.18e-01 for "phygnn"
INFO - 2022-03-01 13:29:06,103 [phygnn.py:576] : Epoch 52 train loss: 4.32e-01 val loss: 4.17e-01 for "phygnn"
INFO - 2022-03-01 13:29:15,229 [phygnn.py:576] : Epoch 53 train loss: 4.33e-01 val loss: 4.18e-01 for "phygnn"
INFO - 2022-03-01 13:29:24,025 [phygnn.py:576] : Epoch 54 train loss: 4.29e-01 val loss: 4.20e-01 for "phygnn"
INFO - 2022-03-01 13:29:32,818 [phygnn.py:576] : Epoch 55 train loss: 4.30e-01 val loss: 4.15e-01 for "phygnn"
INFO - 2022-03-01 13:29:41,722 [phygnn.py:576] : Epoch 56 train loss: 4.30e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:29:50,591 [phygnn.py:576] : Epoch 57 train loss: 4.24e-01 val loss: 4.15e-01 for "phygnn"
INFO - 2022-03-01 13:29:59,499 [phygnn.py:576] : Epoch 58 train loss: 4.29e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:30:08,252 [phygnn.py:576] : Epoch 59 train loss: 4.28e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:30:17,329 [phygnn.py:576] : Epoch 60 train loss: 4.27e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:30:26,121 [phygnn.py:576] : Epoch 61 train loss: 4.25e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:30:34,727 [phygnn.py:576] : Epoch 62 train loss: 4.25e-01 val loss: 4.12e-01 for "phygnn"
INFO - 2022-03-01 13:30:43,300 [phygnn.py:576] : Epoch 63 train loss: 4.26e-01 val loss: 4.14e-01 for "phygnn"
INFO - 2022-03-01 13:30:51,765 [phygnn.py:576] : Epoch 64 train loss: 4.21e-01 val loss: 4.13e-01 for "phygnn"
INFO - 2022-03-01 13:31:00,146 [phygnn.py:576] : Epoch 65 train loss: 4.26e-01 val loss: 4.12e-01 for "phygnn"
INFO - 2022-03-01 13:31:08,594 [phygnn.py:576] : Epoch 66 train loss: 4.24e-01 val loss: 4.10e-01 for "phygnn"
INFO - 2022-03-01 13:31:17,030 [phygnn.py:576] : Epoch 67 train loss: 4.26e-01 val loss: 4.11e-01 for "phygnn"
INFO - 2022-03-01 13:31:25,619 [phygnn.py:576] : Epoch 68 train loss: 4.24e-01 val loss: 4.10e-01 for "phygnn"
INFO - 2022-03-01 13:31:34,254 [phygnn.py:576] : Epoch 69 train loss: 4.25e-01 val loss: 4.09e-01 for "phygnn"
INFO - 2022-03-01 13:31:43,029 [phygnn.py:576] : Epoch 70 train loss: 4.26e-01 val loss: 4.08e-01 for "phygnn"
INFO - 2022-03-01 13:31:51,845 [phygnn.py:576] : Epoch 71 train loss: 4.28e-01 val loss: 4.08e-01 for "phygnn"
INFO - 2022-03-01 13:32:00,691 [phygnn.py:576] : Epoch 72 train loss: 4.24e-01 val loss: 4.09e-01 for "phygnn"
INFO - 2022-03-01 13:32:09,468 [phygnn.py:576] : Epoch 73 train loss: 4.23e-01 val loss: 4.06e-01 for "phygnn"
INFO - 2022-03-01 13:32:18,421 [phygnn.py:576] : Epoch 74 train loss: 4.18e-01 val loss: 4.07e-01 for "phygnn"
INFO - 2022-03-01 13:32:27,087 [phygnn.py:576] : Epoch 75 train loss: 4.20e-01 val loss: 4.06e-01 for "phygnn"
INFO - 2022-03-01 13:32:35,976 [phygnn.py:576] : Epoch 76 train loss: 4.18e-01 val loss: 4.08e-01 for "phygnn"
INFO - 2022-03-01 13:32:44,671 [phygnn.py:576] : Epoch 77 train loss: 4.18e-01 val loss: 4.05e-01 for "phygnn"
INFO - 2022-03-01 13:32:53,358 [phygnn.py:576] : Epoch 78 train loss: 4.20e-01 val loss: 4.08e-01 for "phygnn"
INFO - 2022-03-01 13:33:02,181 [phygnn.py:576] : Epoch 79 train loss: 4.20e-01 val loss: 4.05e-01 for "phygnn"
INFO - 2022-03-01 13:33:11,073 [phygnn.py:576] : Epoch 80 train loss: 4.18e-01 val loss: 4.05e-01 for "phygnn"
INFO - 2022-03-01 13:33:19,945 [phygnn.py:576] : Epoch 81 train loss: 4.23e-01 val loss: 4.05e-01 for "phygnn"
INFO - 2022-03-01 13:33:28,465 [phygnn.py:576] : Epoch 82 train loss: 4.15e-01 val loss: 4.05e-01 for "phygnn"
INFO - 2022-03-01 13:33:37,220 [phygnn.py:576] : Epoch 83 train loss: 4.19e-01 val loss: 4.04e-01 for "phygnn"
INFO - 2022-03-01 13:33:46,172 [phygnn.py:576] : Epoch 84 train loss: 4.09e-01 val loss: 4.04e-01 for "phygnn"
INFO - 2022-03-01 13:33:54,811 [phygnn.py:576] : Epoch 85 train loss: 4.09e-01 val loss: 4.02e-01 for "phygnn"
INFO - 2022-03-01 13:34:03,256 [phygnn.py:576] : Epoch 86 train loss: 4.11e-01 val loss: 4.03e-01 for "phygnn"
INFO - 2022-03-01 13:34:12,012 [phygnn.py:576] : Epoch 87 train loss: 4.14e-01 val loss: 4.02e-01 for "phygnn"
INFO - 2022-03-01 13:34:20,531 [phygnn.py:576] : Epoch 88 train loss: 4.12e-01 val loss: 4.03e-01 for "phygnn"
INFO - 2022-03-01 13:34:29,206 [phygnn.py:576] : Epoch 89 train loss: 4.16e-01 val loss: 4.02e-01 for "phygnn"
INFO - 2022-03-01 13:34:37,881 [phygnn.py:576] : Epoch 90 train loss: 4.16e-01 val loss: 4.00e-01 for "phygnn"
INFO - 2022-03-01 13:34:46,364 [phygnn.py:576] : Epoch 91 train loss: 4.15e-01 val loss: 4.02e-01 for "phygnn"
INFO - 2022-03-01 13:34:54,680 [phygnn.py:576] : Epoch 92 train loss: 4.11e-01 val loss: 4.03e-01 for "phygnn"
INFO - 2022-03-01 13:35:03,126 [phygnn.py:576] : Epoch 93 train loss: 4.16e-01 val loss: 4.01e-01 for "phygnn"
INFO - 2022-03-01 13:35:11,710 [phygnn.py:576] : Epoch 94 train loss: 4.13e-01 val loss: 4.00e-01 for "phygnn"
INFO - 2022-03-01 13:35:20,800 [phygnn.py:576] : Epoch 95 train loss: 4.13e-01 val loss: 4.00e-01 for "phygnn"
INFO - 2022-03-01 13:35:29,484 [phygnn.py:576] : Epoch 96 train loss: 4.15e-01 val loss: 4.00e-01 for "phygnn"
INFO - 2022-03-01 13:35:37,861 [phygnn.py:576] : Epoch 97 train loss: 4.08e-01 val loss: 3.99e-01 for "phygnn"
INFO - 2022-03-01 13:35:46,224 [phygnn.py:576] : Epoch 98 train loss: 4.16e-01 val loss: 4.00e-01 for "phygnn"
INFO - 2022-03-01 13:35:54,895 [phygnn.py:576] : Epoch 99 train loss: 4.16e-01 val loss: 3.99e-01 for "phygnn"
INFO - 2022-03-01 13:35:55,779 [trainer.py:92] : Training part B - data and phygnn. Loss is [0.5, 0.5]
INFO - 2022-03-01 13:36:19,425 [phygnn.py:576] : Epoch 100 train loss: 2.86e-01 val loss: 2.80e-01 for "phygnn"
INFO - 2022-03-01 13:36:34,727 [phygnn.py:576] : Epoch 101 train loss: 2.89e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:36:48,749 [phygnn.py:576] : Epoch 102 train loss: 2.91e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:37:02,036 [phygnn.py:576] : Epoch 103 train loss: 2.90e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:37:15,438 [phygnn.py:576] : Epoch 104 train loss: 2.90e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:37:28,553 [phygnn.py:576] : Epoch 105 train loss: 2.92e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:37:42,671 [phygnn.py:576] : Epoch 106 train loss: 2.89e-01 val loss: 2.81e-01 for "phygnn"
INFO - 2022-03-01 13:37:56,874 [phygnn.py:576] : Epoch 107 train loss: 2.90e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:38:10,658 [phygnn.py:576] : Epoch 108 train loss: 2.87e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:38:24,709 [phygnn.py:576] : Epoch 109 train loss: 2.88e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:38:37,913 [phygnn.py:576] : Epoch 110 train loss: 2.86e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:38:52,216 [phygnn.py:576] : Epoch 111 train loss: 2.89e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:39:06,810 [phygnn.py:576] : Epoch 112 train loss: 2.88e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:39:20,553 [phygnn.py:576] : Epoch 113 train loss: 2.87e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:39:34,341 [phygnn.py:576] : Epoch 114 train loss: 2.85e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:39:48,118 [phygnn.py:576] : Epoch 115 train loss: 2.90e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:40:02,712 [phygnn.py:576] : Epoch 116 train loss: 2.90e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:40:16,228 [phygnn.py:576] : Epoch 117 train loss: 2.88e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:40:30,095 [phygnn.py:576] : Epoch 118 train loss: 2.85e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:40:43,517 [phygnn.py:576] : Epoch 119 train loss: 2.86e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:40:56,963 [phygnn.py:576] : Epoch 120 train loss: 2.89e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:41:10,578 [phygnn.py:576] : Epoch 121 train loss: 2.89e-01 val loss: 2.81e-01 for "phygnn"
INFO - 2022-03-01 13:41:23,657 [phygnn.py:576] : Epoch 122 train loss: 2.87e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:41:37,778 [phygnn.py:576] : Epoch 123 train loss: 2.88e-01 val loss: 2.79e-01 for "phygnn"
INFO - 2022-03-01 13:41:52,598 [phygnn.py:576] : Epoch 124 train loss: 2.88e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:42:07,078 [phygnn.py:576] : Epoch 125 train loss: 2.86e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:42:20,753 [phygnn.py:576] : Epoch 126 train loss: 2.91e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:42:34,048 [phygnn.py:576] : Epoch 127 train loss: 2.89e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:42:48,411 [phygnn.py:576] : Epoch 128 train loss: 2.87e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:43:01,568 [phygnn.py:576] : Epoch 129 train loss: 2.88e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:43:16,243 [phygnn.py:576] : Epoch 130 train loss: 2.88e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:43:29,476 [phygnn.py:576] : Epoch 131 train loss: 2.89e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:43:43,060 [phygnn.py:576] : Epoch 132 train loss: 2.87e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:43:56,615 [phygnn.py:576] : Epoch 133 train loss: 2.83e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:44:10,468 [phygnn.py:576] : Epoch 134 train loss: 2.87e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:44:25,066 [phygnn.py:576] : Epoch 135 train loss: 2.83e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:44:38,726 [phygnn.py:576] : Epoch 136 train loss: 2.85e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:44:51,839 [phygnn.py:576] : Epoch 137 train loss: 2.85e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:45:05,277 [phygnn.py:576] : Epoch 138 train loss: 2.87e-01 val loss: 2.78e-01 for "phygnn"
INFO - 2022-03-01 13:45:18,656 [phygnn.py:576] : Epoch 139 train loss: 2.86e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:45:32,684 [phygnn.py:576] : Epoch 140 train loss: 2.85e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:45:46,942 [phygnn.py:576] : Epoch 141 train loss: 2.85e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:46:00,578 [phygnn.py:576] : Epoch 142 train loss: 2.83e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:46:14,478 [phygnn.py:576] : Epoch 143 train loss: 2.85e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:46:28,366 [phygnn.py:576] : Epoch 144 train loss: 2.87e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:46:42,692 [phygnn.py:576] : Epoch 145 train loss: 2.82e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:46:57,363 [phygnn.py:576] : Epoch 146 train loss: 2.85e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:47:11,010 [phygnn.py:576] : Epoch 147 train loss: 2.84e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:47:25,351 [phygnn.py:576] : Epoch 148 train loss: 2.86e-01 val loss: 2.77e-01 for "phygnn"
INFO - 2022-03-01 13:47:39,036 [phygnn.py:576] : Epoch 149 train loss: 2.86e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:47:52,167 [phygnn.py:576] : Epoch 150 train loss: 2.83e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:48:05,433 [phygnn.py:576] : Epoch 151 train loss: 2.86e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:48:19,358 [phygnn.py:576] : Epoch 152 train loss: 2.85e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:48:32,813 [phygnn.py:576] : Epoch 153 train loss: 2.89e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:48:46,916 [phygnn.py:576] : Epoch 154 train loss: 2.85e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:49:00,006 [phygnn.py:576] : Epoch 155 train loss: 2.82e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:49:13,684 [phygnn.py:576] : Epoch 156 train loss: 2.85e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:49:27,761 [phygnn.py:576] : Epoch 157 train loss: 2.85e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:49:41,640 [phygnn.py:576] : Epoch 158 train loss: 2.85e-01 val loss: 2.76e-01 for "phygnn"
INFO - 2022-03-01 13:49:55,118 [phygnn.py:576] : Epoch 159 train loss: 2.83e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:50:08,083 [phygnn.py:576] : Epoch 160 train loss: 2.85e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:50:22,206 [phygnn.py:576] : Epoch 161 train loss: 2.82e-01 val loss: 2.75e-01 for "phygnn"
INFO - 2022-03-01 13:50:35,587 [phygnn.py:576] : Epoch 162 train loss: 2.82e-01 val loss: 2.75e-01 for "phygnn"
ERROR - 2022-03-01 13:50:37,399 [phygnn.py:461] : phygnn calculated a NaN loss value!
Traceback (most recent call last):
  File "k_fold.py", line 50, in <module>
    t = Trainer(train_sites=train_sites, train_files=files, config=config)
  File "/home/gbuster/code/mlclouds/mlclouds/trainer.py", line 95, in __init__
    out = model.train_model(self.x, self.y, self.p,
  File "/home/gbuster/miniconda3/envs/nsrdb/lib/python3.8/site-packages/phygnn/model_interfaces/phygnn_model.py", line 187, in train_model
    diagnostics = self.model.fit(x, y, p,
  File "/home/gbuster/miniconda3/envs/nsrdb/lib/python3.8/site-packages/phygnn/phygnn.py", line 570, in fit
    tr_loss, tr_nn_loss, tr_p_loss = self.run_gradient_descent(
  File "/home/gbuster/miniconda3/envs/nsrdb/lib/python3.8/site-packages/phygnn/phygnn.py", line 482, in run_gradient_descent
    grad, loss, nn_loss, p_loss = self._get_grad(x, y_true, p, p_kwargs)
  File "/home/gbuster/miniconda3/envs/nsrdb/lib/python3.8/site-packages/phygnn/phygnn.py", line 473, in _get_grad
    loss, nn_loss, p_loss = self.calc_loss(y_true, y_predicted,
  File "/home/gbuster/miniconda3/envs/nsrdb/lib/python3.8/site-packages/phygnn/phygnn.py", line 462, in calc_loss
    raise ArithmeticError(msg)
ArithmeticError: phygnn calculated a NaN loss value!
