----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02495     	-16093976317.55157	0.06744     	0           
  10          	0.02496     	-102362943.24927	0.06744     	0           
  100         	0.00626     	0.63431     	0.02022     	0           
  300         	0.00605     	0.23860     	0.02032     	2           
  500         	0.00605     	0.51430     	0.01961     	3           
  1000        	0.00260     	0.94578     	0.00825     	7           
---> try to load best state
              	0.00260     	0.94578     	0.00825     	7                eval state : test_on_restore;
              	0.00260     	0.94578     	0.00825     	7                eval state : test_on_last_epoch;
---> final result
              	0.00260     	0.94578     	0.00825     	7                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02494     	-12540702305.43237	0.06744     	0           
  10          	0.02494     	-60864278.70224	0.06743     	0           
  100         	0.01113     	0.79077     	0.02987     	0           
  300         	0.00566     	0.44165     	0.01935     	2           
  500         	0.00623     	0.71765     	0.02212     	3           
  1000        	0.00427     	0.84666     	0.01422     	7           
---> try to load best state
              	0.00427     	0.84666     	0.01422     	7                eval state : test_on_restore;
              	0.00427     	0.84666     	0.01422     	7                eval state : test_on_last_epoch;
---> final result
              	0.00427     	0.84666     	0.01422     	7                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02490     	-7797753494.46056	0.06743     	0           
  10          	0.02487     	-79852260.85275	0.06741     	0           
  100         	0.01140     	0.43480     	0.02963     	0           
  300         	0.00740     	0.72631     	0.02197     	1           
  500         	0.00483     	0.90529     	0.01397     	3           
  1000        	0.00256     	0.93465     	0.00821     	6           
---> try to load best state
              	0.00256     	0.93465     	0.00821     	6                eval state : test_on_restore;
              	0.00256     	0.93465     	0.00821     	6                eval state : test_on_last_epoch;
---> final result
              	0.00256     	0.93465     	0.00821     	6                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02481     	-12227711380.89480	0.06740     	0           
  10          	0.02481     	-34834992.00579	0.06739     	0           
  100         	0.00832     	0.34707     	0.02658     	0           
  300         	0.00650     	0.72485     	0.02042     	1           
  500         	0.00513     	0.92108     	0.01782     	3           
  1000        	0.00399     	0.92097     	0.01068     	6           
---> try to load best state
              	0.00399     	0.92097     	0.01068     	6                eval state : test_on_restore;
              	0.00399     	0.92097     	0.01068     	6                eval state : test_on_last_epoch;
---> final result
              	0.00399     	0.92097     	0.01068     	6                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02459     	-8018406418.23971	0.06735     	0           
  10          	0.02457     	-8406003.53674	0.06733     	0           
  100         	0.00994     	0.41846     	0.02460     	0           
  300         	0.00721     	0.61068     	0.02372     	2           
  500         	0.00527     	0.89024     	0.01735     	3           
  1000        	0.00398     	0.92714     	0.01072     	6           
---> try to load best state
              	0.00398     	0.92714     	0.01072     	6                eval state : test_on_restore;
              	0.00398     	0.92714     	0.01072     	6                eval state : test_on_last_epoch;
---> final result
              	0.00398     	0.92714     	0.01072     	6                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02658     	-10529374125.56044	0.06797     	0           
  10          	0.02655     	-15668953.83515	0.06794     	0           
  100         	0.00915     	0.64449     	0.02371     	0           
  300         	0.01062     	-2.03810    	0.03150     	1           
  500         	0.00696     	0.82564     	0.02182     	3           
  1000        	0.00316     	0.96985     	0.00893     	6           
---> try to load best state
              	0.00316     	0.96985     	0.00893     	6                eval state : test_on_restore;
              	0.00316     	0.96985     	0.00893     	6                eval state : test_on_last_epoch;
---> final result
              	0.00316     	0.96985     	0.00893     	6                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02575     	-7489728763.51901	0.06746     	0           
  10          	0.02578     	-4303419.05891	0.06744     	0           
  100         	0.00840     	0.86638     	0.02337     	0           
  300         	0.01079     	0.85861     	0.02833     	2           
  500         	0.00966     	0.87519     	0.02781     	3           
  1000        	0.00400     	0.97278     	0.01157     	6           
---> try to load best state
              	0.00400     	0.97278     	0.01157     	6                eval state : test_on_restore;
              	0.00400     	0.97278     	0.01157     	6                eval state : test_on_last_epoch;
---> final result
              	0.00400     	0.97278     	0.01157     	6                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02628     	-6847338867.59288	0.06769     	0           
  10          	0.02630     	-13411601.60026	0.06769     	0           
  100         	0.00967     	0.88081     	0.02498     	0           
  300         	0.00781     	0.89270     	0.02316     	2           
  500         	0.00881     	0.90014     	0.01972     	3           
  1000        	0.00403     	0.97074     	0.01040     	7           
---> try to load best state
              	0.00403     	0.97074     	0.01040     	7                eval state : test_on_restore;
              	0.00403     	0.97074     	0.01040     	7                eval state : test_on_last_epoch;
---> final result
              	0.00403     	0.97074     	0.01040     	7                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02681     	-10851886938.35202	0.06802     	0           
  10          	0.02674     	-19553269.11916	0.06798     	0           
  100         	0.02070     	0.16500     	0.04559     	0           
  300         	0.00744     	0.91664     	0.02303     	1           
  500         	0.00628     	0.94676     	0.01989     	3           
  1000        	0.00351     	0.97265     	0.01051     	6           
---> try to load best state
              	0.00351     	0.97265     	0.01051     	6                eval state : test_on_restore;
              	0.00351     	0.97265     	0.01051     	6                eval state : test_on_last_epoch;
---> final result
              	0.00351     	0.97265     	0.01051     	6                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02311     	-8160050023.01640	0.06744     	0           
  10          	0.02310     	-37646035.65492	0.06743     	0           
  100         	0.00764     	0.18235     	0.02806     	0           
  300         	0.00639     	0.59418     	0.02214     	1           
  500         	0.00733     	0.89535     	0.02803     	3           
  1000        	0.00352     	0.96211     	0.01177     	6           
---> try to load best state
              	0.00352     	0.96211     	0.01177     	6                eval state : test_on_restore;
              	0.00352     	0.96211     	0.01177     	6                eval state : test_on_last_epoch;
---> final result
              	0.00352     	0.96211     	0.01177     	6                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02450     	-14477514990.43149	0.06758     	0           
  10          	0.02448     	-26357795.31175	0.06756     	0           
  100         	0.01097     	0.58247     	0.02725     	0           
  300         	0.00610     	0.86865     	0.02084     	1           
  500         	0.00537     	0.95281     	0.01546     	3           
  1000        	0.00485     	0.93155     	0.01560     	6           
---> try to load best state
              	0.00485     	0.93155     	0.01560     	6                eval state : test_on_restore;
              	0.00485     	0.93155     	0.01560     	6                eval state : test_on_last_epoch;
---> final result
              	0.00485     	0.93155     	0.01560     	6                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02400     	-8342442847.86546	0.06729     	0           
  10          	0.02402     	-14293618.11313	0.06727     	0           
  100         	0.00892     	0.05144     	0.02800     	0           
  300         	0.00792     	0.27502     	0.02786     	2           
  500         	0.00616     	0.86498     	0.02261     	3           
  1000        	0.00778     	0.93041     	0.01977     	7           
---> try to load best state
              	0.00778     	0.93041     	0.01977     	7                eval state : test_on_restore;
              	0.00778     	0.93041     	0.01977     	7                eval state : test_on_last_epoch;
---> final result
              	0.00778     	0.93041     	0.01977     	7                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02699     	-9769773328.93801	0.06801     	0           
  10          	0.02697     	-41573636.27954	0.06799     	0           
  100         	0.00872     	0.74603     	0.02184     	0           
  300         	0.00755     	0.79671     	0.02209     	2           
  500         	0.00593     	0.86621     	0.02063     	3           
  1000        	0.00425     	0.95704     	0.01220     	7           
---> try to load best state
              	0.00425     	0.95704     	0.01220     	7                eval state : test_on_restore;
              	0.00425     	0.95704     	0.01220     	7                eval state : test_on_last_epoch;
---> final result
              	0.00425     	0.95704     	0.01220     	7                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02485     	-5811256985.94108	0.06731     	0           
  10          	0.02487     	-9448346.67291	0.06729     	0           
  100         	0.00941     	-1.42912    	0.02206     	0           
  300         	0.00841     	0.82144     	0.02298     	2           
  500         	0.00519     	0.90872     	0.01628     	4           
  1000        	0.00541     	0.94913     	0.01467     	8           
---> try to load best state
              	0.00541     	0.94913     	0.01467     	8                eval state : test_on_restore;
              	0.00541     	0.94913     	0.01467     	8                eval state : test_on_last_epoch;
---> final result
              	0.00541     	0.94913     	0.01467     	8                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02437     	-6668323388.62057	0.06730     	0           
  10          	0.02434     	-10802324.17930	0.06729     	0           
  100         	0.00860     	0.35431     	0.02297     	0           
  300         	0.00605     	0.39759     	0.02022     	1           
  500         	0.00583     	0.76035     	0.02015     	2           
  1000        	0.00373     	0.96935     	0.01111     	5           
---> try to load best state
              	0.00373     	0.96935     	0.01111     	5                eval state : test_on_restore;
              	0.00373     	0.96935     	0.01111     	5                eval state : test_on_last_epoch;
---> final result
              	0.00373     	0.96935     	0.01111     	5                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02354     	-9628061274.03824	0.06748     	0           
  10          	0.02357     	-6299384.83580	0.06744     	0           
  100         	0.00740     	-0.61714    	0.02268     	0           
  300         	0.00718     	0.86992     	0.02088     	1           
  500         	0.00445     	0.94729     	0.01567     	3           
  1000        	0.00255     	0.98285     	0.00868     	6           
---> try to load best state
              	0.00255     	0.98285     	0.00868     	6                eval state : test_on_restore;
              	0.00255     	0.98285     	0.00868     	6                eval state : test_on_last_epoch;
---> final result
              	0.00255     	0.98285     	0.00868     	6                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02177     	-17609186363.30677	0.06842     	0           
  10          	0.02176     	-91162074.77150	0.06842     	0           
  100         	0.00676     	-0.01875    	0.02479     	0           
  300         	0.00580     	0.35662     	0.02097     	1           
  500         	0.00526     	0.60996     	0.01945     	3           
  1000        	0.00381     	0.88130     	0.01244     	6           
---> try to load best state
              	0.00381     	0.88130     	0.01244     	6                eval state : test_on_restore;
              	0.00381     	0.88130     	0.01244     	6                eval state : test_on_last_epoch;
---> final result
              	0.00381     	0.88130     	0.01244     	6                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02354     	-12731389945.90581	0.06737     	0           
  10          	0.02354     	-11596659.02819	0.06735     	0           
  100         	0.02126     	0.34736     	0.04739     	0           
  300         	0.00626     	0.62946     	0.02287     	1           
  500         	0.00802     	0.91007     	0.02525     	2           
  1000        	0.00298     	0.96721     	0.00955     	7           
---> try to load best state
              	0.00298     	0.96721     	0.00955     	7                eval state : test_on_restore;
              	0.00298     	0.96721     	0.00955     	7                eval state : test_on_last_epoch;
---> final result
              	0.00298     	0.96721     	0.00955     	7                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02768     	-7853749875.98788	0.06894     	0           
  10          	0.02764     	-5080852.63403	0.06889     	0           
  100         	0.00673     	0.88910     	0.02149     	0           
  300         	0.00773     	0.81733     	0.02548     	2           
  500         	0.00538     	0.92353     	0.01924     	3           
  1000        	0.00322     	0.96698     	0.01021     	7           
---> try to load best state
              	0.00322     	0.96698     	0.01021     	7                eval state : test_on_restore;
              	0.00322     	0.96698     	0.01021     	7                eval state : test_on_last_epoch;
---> final result
              	0.00322     	0.96698     	0.01021     	7                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02856     	-8219804191.43376	0.06956     	0           
  10          	0.02855     	-20483532.52243	0.06954     	0           
  100         	0.01113     	-0.18645    	0.02605     	0           
  300         	0.00801     	0.86240     	0.02276     	2           
  500         	0.00663     	0.91491     	0.02207     	3           
  1000        	0.00493     	0.89847     	0.01421     	7           
---> try to load best state
              	0.00493     	0.89847     	0.01421     	7                eval state : test_on_restore;
              	0.00493     	0.89847     	0.01421     	7                eval state : test_on_last_epoch;
---> final result
              	0.00493     	0.89847     	0.01421     	7                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02888     	-9072967303.21416	0.06987     	0           
  10          	0.02886     	-9824318.30935	0.06984     	0           
  100         	0.02818     	-0.72944    	0.05867     	0           
  300         	0.00666     	0.88871     	0.02078     	2           
  500         	0.00652     	0.91664     	0.02133     	3           
  1000        	0.00455     	0.96561     	0.01338     	7           
---> try to load best state
              	0.00455     	0.96561     	0.01338     	7                eval state : test_on_restore;
              	0.00455     	0.96561     	0.01338     	7                eval state : test_on_last_epoch;
---> final result
              	0.00455     	0.96561     	0.01338     	7                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02674     	-11980055712.92581	0.06837     	0           
  10          	0.02671     	-18838825.55265	0.06834     	0           
  100         	0.00709     	0.74233     	0.01979     	0           
  300         	0.00883     	0.71093     	0.02502     	2           
  500         	0.00455     	0.91426     	0.01328     	3           
  1000        	0.00359     	0.90698     	0.01180     	7           
---> try to load best state
              	0.00359     	0.90698     	0.01180     	7                eval state : test_on_restore;
              	0.00359     	0.90698     	0.01180     	7                eval state : test_on_last_epoch;
---> final result
              	0.00359     	0.90698     	0.01180     	7                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02737     	-9486256999.30109	0.06886     	0           
  10          	0.02738     	-16148083.71337	0.06885     	0           
  100         	0.01199     	0.32438     	0.03141     	0           
  300         	0.00649     	0.90028     	0.01942     	1           
  500         	0.00777     	0.88699     	0.01681     	3           
  1000        	0.00306     	0.97639     	0.00947     	7           
---> try to load best state
              	0.00306     	0.97639     	0.00947     	7                eval state : test_on_restore;
              	0.00306     	0.97639     	0.00947     	7                eval state : test_on_last_epoch;
---> final result
              	0.00306     	0.97639     	0.00947     	7                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02886     	-6618226086.04209	0.07037     	0           
  10          	0.02881     	-9558236.02243	0.07029     	0           
  100         	0.00600     	0.68344     	0.01946     	0           
  300         	0.00804     	0.86096     	0.02514     	2           
  500         	0.00686     	0.79409     	0.02199     	3           
  1000        	0.00296     	0.97935     	0.00896     	7           
---> try to load best state
              	0.00296     	0.97935     	0.00896     	7                eval state : test_on_restore;
              	0.00296     	0.97935     	0.00896     	7                eval state : test_on_last_epoch;
---> final result
              	0.00296     	0.97935     	0.00896     	7                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02737     	-12161967465.45098	0.06827     	0           
  10          	0.02737     	-47080737.65626	0.06827     	0           
  100         	0.01013     	0.36427     	0.03063     	0           
  300         	0.00915     	0.34084     	0.03228     	2           
  500         	0.00779     	0.92067     	0.02116     	3           
  1000        	0.00391     	0.97855     	0.01255     	7           
---> try to load best state
              	0.00391     	0.97855     	0.01255     	7                eval state : test_on_restore;
              	0.00391     	0.97855     	0.01255     	7                eval state : test_on_last_epoch;
---> final result
              	0.00391     	0.97855     	0.01255     	7                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02771     	-6555608167.57772	0.06862     	0           
  10          	0.02772     	-10666641.77469	0.06861     	0           
  100         	0.00670     	0.67043     	0.02004     	0           
  300         	0.01118     	0.81668     	0.02843     	1           
  500         	0.00499     	0.94942     	0.01405     	2           
  1000        	0.00417     	0.96543     	0.01133     	5           
---> try to load best state
              	0.00417     	0.96543     	0.01133     	5                eval state : test_on_restore;
              	0.00417     	0.96543     	0.01133     	5                eval state : test_on_last_epoch;
---> final result
              	0.00417     	0.96543     	0.01133     	5                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02864     	-7324652383.15054	0.06939     	0           
  10          	0.02860     	-4510954.10418	0.06934     	0           
  100         	0.01938     	-1.11845    	0.04598     	0           
  300         	0.00715     	0.20014     	0.02395     	1           
  500         	0.00859     	0.34437     	0.02887     	2           
  1000        	0.00335     	0.93148     	0.00841     	5           
---> try to load best state
              	0.00335     	0.93148     	0.00841     	5                eval state : test_on_restore;
              	0.00335     	0.93148     	0.00841     	5                eval state : test_on_last_epoch;
---> final result
              	0.00335     	0.93148     	0.00841     	5                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02809     	-8229701492.63676	0.06905     	0           
  10          	0.02806     	-5332943.16415	0.06901     	0           
  100         	0.00701     	0.72084     	0.02054     	0           
  300         	0.00683     	0.84783     	0.02366     	1           
  500         	0.00615     	0.91453     	0.02316     	2           
  1000        	0.00268     	0.98298     	0.00853     	5           
---> try to load best state
              	0.00268     	0.98298     	0.00853     	5                eval state : test_on_restore;
              	0.00268     	0.98298     	0.00853     	5                eval state : test_on_last_epoch;
---> final result
              	0.00268     	0.98298     	0.00853     	5                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02731     	-8452199603.74552	0.06835     	0           
  10          	0.02729     	-14320910.06217	0.06833     	0           
  100         	0.00716     	0.80002     	0.02375     	0           
  300         	0.00704     	0.86671     	0.02300     	1           
  500         	0.00649     	0.88128     	0.02263     	2           
  1000        	0.00272     	0.97209     	0.00862     	5           
---> try to load best state
              	0.00272     	0.97209     	0.00862     	5                eval state : test_on_restore;
              	0.00272     	0.97209     	0.00862     	5                eval state : test_on_last_epoch;
---> final result
              	0.00272     	0.97209     	0.00862     	5                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02688     	-6052232735.47769	0.06859     	0           
  10          	0.02683     	-12066951.88691	0.06853     	0           
  100         	0.01064     	0.75929     	0.02360     	0           
  300         	0.00652     	0.89425     	0.02107     	1           
  500         	0.00683     	0.92096     	0.02108     	2           
  1000        	0.00286     	0.98157     	0.00967     	5           
---> try to load best state
              	0.00286     	0.98157     	0.00967     	5                eval state : test_on_restore;
              	0.00286     	0.98157     	0.00967     	5                eval state : test_on_last_epoch;
---> final result
              	0.00286     	0.98157     	0.00967     	5                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 31
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02710     	-8846494280.10164	0.06855     	0           
  10          	0.02709     	-14799687.32162	0.06853     	0           
  100         	0.00999     	0.34079     	0.02722     	0           
  300         	0.00759     	0.45316     	0.02301     	1           
  500         	0.00835     	0.01248     	0.02192     	2           
  1000        	0.00674     	0.85874     	0.01727     	5           
---> try to load best state
              	0.00674     	0.85874     	0.01727     	5                eval state : test_on_restore;
              	0.00674     	0.85874     	0.01727     	5                eval state : test_on_last_epoch;
---> final result
              	0.00674     	0.85874     	0.01727     	5                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 30
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02745     	-9985187643.40146	0.06892     	0           
  10          	0.02743     	-63411350.75498	0.06890     	0           
  100         	0.00830     	0.53931     	0.02154     	0           
  300         	0.00604     	0.55466     	0.01870     	1           
  500         	0.00731     	0.43391     	0.02095     	2           
  1000        	0.00348     	0.88806     	0.01062     	5           
---> try to load best state
              	0.00348     	0.88806     	0.01062     	5                eval state : test_on_restore;
              	0.00348     	0.88806     	0.01062     	5                eval state : test_on_last_epoch;
---> final result
              	0.00348     	0.88806     	0.01062     	5                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 28
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02678     	-11150033335.71329	0.06853     	0           
  10          	0.02681     	-45045160.49838	0.06854     	0           
  100         	0.00631     	0.71410     	0.02022     	0           
  300         	0.00678     	0.71580     	0.01925     	1           
  500         	0.00711     	0.61003     	0.02111     	2           
  1000        	0.00542     	0.84413     	0.01569     	6           
---> try to load best state
              	0.00542     	0.84413     	0.01569     	6                eval state : test_on_restore;
              	0.00542     	0.84413     	0.01569     	6                eval state : test_on_last_epoch;
---> final result
              	0.00542     	0.84413     	0.01569     	6                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 24
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02724     	-10340627812.12732	0.06871     	0           
  10          	0.02727     	-80015562.20505	0.06873     	0           
  100         	0.01006     	0.77224     	0.02983     	1           
  300         	0.01154     	0.59942     	0.03009     	2           
  500         	0.00676     	0.67488     	0.02107     	3           
  1000        	0.00278     	0.96487     	0.00920     	5           
---> try to load best state
              	0.00278     	0.96487     	0.00920     	5                eval state : test_on_restore;
              	0.00278     	0.96487     	0.00920     	6                eval state : test_on_last_epoch;
---> final result
              	0.00278     	0.96487     	0.00920     	6                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 16
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02441     	-14581272609.68886	0.06728     	0           
  10          	0.02439     	-40570157.97739	0.06727     	0           
  100         	0.01300     	-0.18214    	0.03425     	0           
  300         	0.00623     	0.24075     	0.02039     	1           
  500         	0.00572     	0.61105     	0.02001     	3           
  1000        	0.00599     	0.94693     	0.01566     	5           
---> try to load best state
              	0.00599     	0.94693     	0.01566     	6                eval state : test_on_restore;
              	0.00599     	0.94693     	0.01566     	6                eval state : test_on_last_epoch;
---> final result
              	0.00599     	0.94693     	0.01566     	6                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

----------------------------------------

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Burget_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]', 'y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  second_fidelity_sample: 32
  second_fidelity_start_index: 0
  non_subset: True
  lr: {'opt_param': 0.01}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  nn_param: {'hlayers_w': [40, 40], 'hlayers_d': [2, 2], 'base_dim': [32, 32], 'activation': 'relu', 'M': 2}
  reg_strength: 0.001
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02477     	-12568413865.18505	0.06748     	0           
  10          	0.02476     	-18341065.71663	0.06746     	0           
  100         	0.00713     	0.50212     	0.02129     	0           
  300         	0.00754     	0.53745     	0.02218     	1           
  500         	0.00662     	0.60977     	0.02136     	2           
  1000        	0.00488     	0.85174     	0.01427     	5           
---> try to load best state
              	0.00488     	0.85174     	0.01427     	5                eval state : test_on_restore;
              	0.00488     	0.85174     	0.01427     	5                eval state : test_on_last_epoch;
---> final result
              	0.00488     	0.85174     	0.01427     	5                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
