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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 4
  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           	0.03743     	-15426236935.02559	0.07801     	0           
  10          	0.03734     	-5288875.88960	0.07788     	0           
  100         	0.00713     	0.16794     	0.02159     	1           
  300         	0.00811     	0.90670     	0.01988     	3           
  500         	0.00650     	0.93203     	0.01561     	5           
  1000        	0.00713     	0.93050     	0.01976     	10          
---> try to load best state
              	0.00713     	0.93050     	0.01976     	10               eval state : test_on_restore;
              	0.00713     	0.93050     	0.01976     	10               eval state : test_on_last_epoch;
---> final result
              	0.00713     	0.93050     	0.01976     	10               eval state : final;module_name : dmfal;subset : 2.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 8
  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           	0.02921     	-11691812675.06518	0.06888     	0           
  10          	0.02920     	-8841095.26412	0.06886     	0           
  100         	0.00707     	0.89747     	0.02272     	1           
  300         	0.00771     	0.79602     	0.02650     	3           
  500         	0.00731     	0.89971     	0.02418     	6           
  1000        	0.00358     	0.96290     	0.00988     	12          
---> try to load best state
              	0.00358     	0.96290     	0.00988     	12               eval state : test_on_restore;
              	0.00358     	0.96290     	0.00988     	12               eval state : test_on_last_epoch;
---> final result
              	0.00358     	0.96290     	0.00988     	12               eval state : final;module_name : dmfal;subset : 4.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 16
  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           	0.02684     	-16054069562.24791	0.06752     	0           
  10          	0.02681     	-15833621.41829	0.06749     	0           
  100         	0.02240     	0.56888     	0.04841     	1           
  300         	0.00706     	0.89868     	0.02290     	3           
  500         	0.00602     	0.86130     	0.02058     	5           
  1000        	0.00273     	0.98573     	0.00843     	11          
---> try to load best state
              	0.00273     	0.98573     	0.00843     	11               eval state : test_on_restore;
              	0.00273     	0.98573     	0.00843     	11               eval state : test_on_last_epoch;
---> final result
              	0.00273     	0.98573     	0.00843     	11               eval state : final;module_name : dmfal;subset : 8.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.02461     	-8366048535.61872	0.06686     	0           
  10          	0.02459     	-4403167.66220	0.06683     	0           
  100         	0.00676     	0.75172     	0.02279     	1           
  300         	0.00799     	0.77489     	0.02443     	3           
  500         	0.00623     	0.64121     	0.02126     	6           
  1000        	0.00326     	0.97970     	0.00863     	12          
---> try to load best state
              	0.00326     	0.97970     	0.00863     	12               eval state : test_on_restore;
              	0.00326     	0.97970     	0.00863     	12               eval state : test_on_last_epoch;
---> final result
              	0.00326     	0.97970     	0.00863     	12               eval state : final;module_name : dmfal;subset : 16.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 4
  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           	0.02656     	-12480263826.68771	0.06725     	0           
  10          	0.02656     	-13934124.54124	0.06724     	0           
  100         	0.00763     	0.66830     	0.02339     	1           
  300         	0.00831     	0.80557     	0.02200     	3           
  500         	0.01180     	0.65925     	0.03015     	5           
  1000        	0.01231     	0.57351     	0.03181     	10          
---> try to load best state
              	0.01231     	0.57351     	0.03181     	10               eval state : test_on_restore;
              	0.01231     	0.57351     	0.03181     	10               eval state : test_on_last_epoch;
---> final result
              	0.01231     	0.57351     	0.03181     	10               eval state : final;module_name : dmfal;subset : 2.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 8
  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           	0.02346     	-9225149333.06673	0.06690     	0           
  10          	0.02346     	-28195667.58690	0.06689     	0           
  100         	0.00759     	0.82167     	0.02283     	1           
  300         	0.00554     	0.83877     	0.01883     	3           
  500         	0.00649     	0.79091     	0.01874     	5           
  1000        	0.00486     	0.85712     	0.01342     	11          
---> try to load best state
              	0.00486     	0.85712     	0.01342     	11               eval state : test_on_restore;
              	0.00486     	0.85712     	0.01342     	11               eval state : test_on_last_epoch;
---> final result
              	0.00486     	0.85712     	0.01342     	11               eval state : final;module_name : dmfal;subset : 4.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 16
  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           	0.02294     	-21201209058.40373	0.06685     	0           
  10          	0.02291     	-19318198.49604	0.06685     	0           
  100         	0.00730     	0.70050     	0.02197     	1           
  300         	0.00561     	0.75192     	0.01910     	3           
  500         	0.00522     	0.71813     	0.01872     	5           
  1000        	0.00377     	0.88668     	0.00985     	11          
---> try to load best state
              	0.00377     	0.88668     	0.00985     	11               eval state : test_on_restore;
              	0.00377     	0.88668     	0.00985     	11               eval state : test_on_last_epoch;
---> final result
              	0.00377     	0.88668     	0.00985     	11               eval state : final;module_name : dmfal;subset : 8.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.02185     	-17825074211.37663	0.06772     	0           
  10          	0.02183     	-27066313.40294	0.06773     	0           
  100         	0.00604     	0.66550     	0.02049     	1           
  300         	0.00874     	0.67009     	0.02296     	3           
  500         	0.00623     	0.49462     	0.01993     	6           
  1000        	0.00384     	0.85519     	0.01213     	12          
---> try to load best state
              	0.00384     	0.85519     	0.01213     	12               eval state : test_on_restore;
              	0.00384     	0.85519     	0.01213     	12               eval state : test_on_last_epoch;
---> final result
              	0.00384     	0.85519     	0.01213     	13               eval state : final;module_name : dmfal;subset : 16.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 4
  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           	0.03474     	-9277927507.71939	0.08009     	0           
  10          	0.03469     	-4172926.27787	0.07999     	0           
  100         	0.01195     	0.61442     	0.03025     	1           
  300         	0.01077     	0.83429     	0.02710     	3           
  500         	0.01071     	0.80792     	0.02708     	5           
  1000        	0.00941     	0.89656     	0.02707     	10          
---> try to load best state
              	0.00941     	0.89656     	0.02707     	10               eval state : test_on_restore;
              	0.00941     	0.89656     	0.02707     	10               eval state : test_on_last_epoch;
---> final result
              	0.00941     	0.89656     	0.02707     	10               eval state : final;module_name : dmfal;subset : 2.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 8
  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           	0.03329     	-16682122769.75423	0.07603     	0           
  10          	0.03336     	-11032020.95370	0.07614     	0           
  100         	0.00721     	0.78613     	0.01979     	1           
  300         	0.00678     	0.83616     	0.01977     	5           
  500         	0.00580     	0.84463     	0.01844     	7           
  1000        	0.00445     	0.89539     	0.01292     	13          
---> try to load best state
              	0.00445     	0.89539     	0.01292     	13               eval state : test_on_restore;
              	0.00445     	0.89539     	0.01292     	13               eval state : test_on_last_epoch;
---> final result
              	0.00445     	0.89539     	0.01292     	13               eval state : final;module_name : dmfal;subset : 4.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 16
  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           	0.02612     	-13069068452.56322	0.06754     	0           
  10          	0.02607     	-15809743.08003	0.06750     	0           
  100         	0.00610     	0.80978     	0.01968     	1           
  300         	0.00561     	0.79367     	0.01864     	3           
  500         	0.00550     	0.83814     	0.01716     	5           
  1000        	0.00341     	0.89327     	0.01069     	11          
---> try to load best state
              	0.00341     	0.89327     	0.01069     	11               eval state : test_on_restore;
              	0.00341     	0.89327     	0.01069     	11               eval state : test_on_last_epoch;
---> final result
              	0.00341     	0.89327     	0.01069     	11               eval state : final;module_name : dmfal;subset : 8.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.02749     	-8325710110.98805	0.06814     	0           
  10          	0.02752     	-8086410.12465	0.06815     	0           
  100         	0.00679     	0.90328     	0.02010     	1           
  300         	0.00631     	0.80599     	0.02015     	3           
  500         	0.00887     	0.87073     	0.02472     	6           
  1000        	0.00277     	0.98598     	0.00835     	12          
---> try to load best state
              	0.00277     	0.98598     	0.00835     	12               eval state : test_on_restore;
              	0.00277     	0.98598     	0.00835     	12               eval state : test_on_last_epoch;
---> final result
              	0.00277     	0.98598     	0.00835     	12               eval state : final;module_name : dmfal;subset : 16.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 4
  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           	0.03289     	-17109300508.49859	0.07342     	0           
  10          	0.03283     	-9371482.33180	0.07334     	0           
  100         	0.00792     	0.48446     	0.02626     	1           
  300         	0.00738     	0.86314     	0.02097     	4           
  500         	0.00780     	0.85900     	0.01985     	6           
  1000        	0.00670     	0.83819     	0.01845     	11          
---> try to load best state
              	0.00670     	0.83819     	0.01845     	12               eval state : test_on_restore;
              	0.00670     	0.83819     	0.01845     	12               eval state : test_on_last_epoch;
---> final result
              	0.00670     	0.83819     	0.01845     	12               eval state : final;module_name : dmfal;subset : 2.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 8
  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           	0.04106     	-8459739459.58522	0.08429     	0           
  10          	0.04110     	-3754318.67076	0.08436     	0           
  100         	0.00937     	0.83451     	0.02915     	1           
  300         	0.01808     	0.60656     	0.03782     	3           
  500         	0.00743     	0.92379     	0.02142     	5           
  1000        	0.00530     	0.96114     	0.01125     	11          
---> try to load best state
              	0.00530     	0.96114     	0.01125     	11               eval state : test_on_restore;
              	0.00530     	0.96114     	0.01125     	11               eval state : test_on_last_epoch;
---> final result
              	0.00530     	0.96114     	0.01125     	11               eval state : final;module_name : dmfal;subset : 4.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 16
  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           	0.03006     	-20699567843.53417	0.06966     	0           
  10          	0.03007     	-11844461.91018	0.06966     	0           
  100         	0.00920     	0.84083     	0.02990     	1           
  300         	0.00864     	0.78508     	0.02715     	3           
  500         	0.00891     	0.91398     	0.02317     	5           
  1000        	0.00309     	0.98911     	0.00920     	13          
---> try to load best state
              	0.00309     	0.98911     	0.00920     	13               eval state : test_on_restore;
              	0.00309     	0.98911     	0.00920     	13               eval state : test_on_last_epoch;
---> final result
              	0.00309     	0.98911     	0.00920     	13               eval state : final;module_name : dmfal;subset : 8.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.02743     	-8148231191.48388	0.06764     	0           
  10          	0.02747     	-11419761.42716	0.06765     	0           
  100         	0.00692     	0.79826     	0.02163     	1           
  300         	0.00732     	0.45823     	0.02211     	3           
  500         	0.00656     	0.82716     	0.02239     	6           
  1000        	0.00598     	0.94893     	0.01724     	12          
---> try to load best state
              	0.00598     	0.94893     	0.01724     	12               eval state : test_on_restore;
              	0.00598     	0.94893     	0.01724     	12               eval state : test_on_last_epoch;
---> final result
              	0.00598     	0.94893     	0.01724     	12               eval state : final;module_name : dmfal;subset : 16.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 4
  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           	0.02060     	-33083614657.43884	0.07112     	0           
  10          	0.02060     	-101490549.15960	0.07115     	0           
  100         	0.00843     	-6.75602    	0.03538     	1           
  300         	0.00773     	-5.59998    	0.03396     	3           
  500         	0.00804     	-6.59847    	0.03437     	6           
  1000        	0.00773     	-3.56197    	0.03149     	12          
---> try to load best state
              	0.00773     	-3.56197    	0.03149     	12               eval state : test_on_restore;
              	0.00773     	-3.56197    	0.03149     	12               eval state : test_on_last_epoch;
---> final result
              	0.00773     	-3.56197    	0.03149     	12               eval state : final;module_name : dmfal;subset : 2.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 8
  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           	0.02304     	-13890609212.78260	0.06674     	0           
  10          	0.02303     	-38194255.61568	0.06673     	0           
  100         	0.00666     	-0.27573    	0.02597     	1           
  300         	0.00665     	-0.57909    	0.02562     	3           
  500         	0.00617     	0.39307     	0.02245     	5           
  1000        	0.00568     	0.64154     	0.02388     	11          
---> try to load best state
              	0.00568     	0.64154     	0.02388     	11               eval state : test_on_restore;
              	0.00568     	0.64154     	0.02388     	11               eval state : test_on_last_epoch;
---> final result
              	0.00568     	0.64154     	0.02388     	11               eval state : final;module_name : dmfal;subset : 4.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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: 16
  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           	0.02337     	-18536402356.33820	0.06705     	0           
  10          	0.02336     	-75271310.17617	0.06705     	0           
  100         	0.01804     	0.25047     	0.04540     	1           
  300         	0.00751     	0.81989     	0.02248     	3           
  500         	0.00598     	0.75025     	0.01970     	5           
  1000        	0.00462     	0.96096     	0.01351     	12          
---> try to load best state
              	0.00462     	0.96096     	0.01351     	12               eval state : test_on_restore;
              	0.00462     	0.96096     	0.01351     	12               eval state : test_on_last_epoch;
---> final result
              	0.00462     	0.96096     	0.01351     	12               eval state : final;module_name : dmfal;subset : 8.0;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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           	0.02451     	-11946633197.78850	0.06658     	0           
  10          	0.02452     	-7287959.58413	0.06656     	0           
  100         	0.00751     	0.73092     	0.02589     	1           
  300         	0.00832     	0.78059     	0.02386     	3           
  500         	0.00529     	0.86350     	0.01841     	6           
  1000        	0.00340     	0.95689     	0.00941     	12          
---> try to load best state
              	0.00340     	0.95689     	0.00941     	12               eval state : test_on_restore;
              	0.00340     	0.95689     	0.00941     	12               eval state : test_on_last_epoch;
---> final result
              	0.00340     	0.95689     	0.00941     	12               eval state : final;module_name : dmfal;subset : 16.0;cp_record_file : True;
