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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 4
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02102     	-38567023836.84882	0.07780     	0           
  10          	0.02102     	-1768718548.27255	0.07778     	0           
  100         	0.01432     	-85.56086   	0.05866     	0           
  300         	0.01346     	-84.50932   	0.05727     	1           
  500         	0.01295     	-68.20061   	0.05530     	2           
  1000        	0.01161     	-43.13370   	0.05144     	5           
---> try to load best state
              	0.01161     	-43.13370   	0.05144     	5                eval state : test_on_restore;
              	0.01161     	-43.13370   	0.05144     	5                eval state : test_on_last_epoch;
---> final result
              	0.01161     	-43.13370   	0.05144     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 8
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02040     	-25352925832.68346	0.07319     	0           
  10          	0.02040     	-123162374.82033	0.07317     	0           
  100         	0.00993     	-8.32258    	0.03830     	0           
  300         	0.00920     	-11.24987   	0.04193     	2           
  500         	0.00927     	-12.03960   	0.04193     	3           
  1000        	0.00755     	-5.86824    	0.03594     	7           
---> try to load best state
              	0.00755     	-5.86824    	0.03594     	7                eval state : test_on_restore;
              	0.00755     	-5.86824    	0.03594     	7                eval state : test_on_last_epoch;
---> final result
              	0.00755     	-5.86824    	0.03594     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 16
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02491     	-5209101303.20440	0.06729     	0           
  10          	0.02490     	-3193815.73432	0.06726     	0           
  100         	0.00962     	0.82673     	0.02422     	0           
  300         	0.00780     	0.89567     	0.02452     	2           
  500         	0.00499     	0.94442     	0.01203     	3           
  1000        	0.00307     	0.96912     	0.00910     	7           
---> try to load best state
              	0.00307     	0.96912     	0.00910     	7                eval state : test_on_restore;
              	0.00307     	0.96912     	0.00910     	7                eval state : test_on_last_epoch;
---> final result
              	0.00307     	0.96912     	0.00910     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: False
  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           	0.02399     	-4803611580.41465	0.06729     	0           
  10          	0.02399     	-3836897.79777	0.06726     	0           
  100         	0.01487     	0.25408     	0.03341     	0           
  300         	0.00687     	0.72580     	0.02350     	2           
  500         	0.00412     	0.94084     	0.01228     	3           
  1000        	0.00359     	0.95566     	0.01024     	6           
---> try to load best state
              	0.00359     	0.95566     	0.01024     	6                eval state : test_on_restore;
              	0.00359     	0.95566     	0.01024     	6                eval state : test_on_last_epoch;
---> final result
              	0.00359     	0.95566     	0.01024     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 4
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02070     	-43154625192.89065	0.07614     	0           
  10          	0.02070     	-465713422.22053	0.07614     	0           
  100         	0.01168     	-35.25562   	0.05128     	0           
  300         	0.01180     	-33.60581   	0.05128     	1           
  500         	0.01124     	-24.22680   	0.04883     	2           
  1000        	0.00968     	-18.45809   	0.04511     	6           
---> try to load best state
              	0.00968     	-18.45809   	0.04511     	6                eval state : test_on_restore;
              	0.00968     	-18.45809   	0.04511     	6                eval state : test_on_last_epoch;
---> final result
              	0.00968     	-18.45809   	0.04511     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 8
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02081     	-23775535339.31715	0.07195     	0           
  10          	0.02081     	-274150766.80011	0.07192     	0           
  100         	0.00816     	-0.67277    	0.02826     	0           
  300         	0.00734     	-0.14128    	0.02606     	2           
  500         	0.00714     	-0.54963    	0.02748     	3           
  1000        	0.00585     	-0.40510    	0.02429     	6           
---> try to load best state
              	0.00585     	-0.40510    	0.02429     	6                eval state : test_on_restore;
              	0.00585     	-0.40510    	0.02429     	6                eval state : test_on_last_epoch;
---> final result
              	0.00585     	-0.40510    	0.02429     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 16
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02608     	-5718917296.93014	0.06772     	0           
  10          	0.02607     	-8209274.83796	0.06770     	0           
  100         	0.01999     	-0.59857    	0.04887     	0           
  300         	0.00808     	0.85914     	0.02380     	2           
  500         	0.00741     	0.90923     	0.01932     	3           
  1000        	0.00388     	0.95405     	0.01043     	7           
---> try to load best state
              	0.00388     	0.95405     	0.01043     	7                eval state : test_on_restore;
              	0.00388     	0.95405     	0.01043     	7                eval state : test_on_last_epoch;
---> final result
              	0.00388     	0.95405     	0.01043     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: False
  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     	-7546449609.44405	0.06737     	0           
  10          	0.02356     	-34411803.77415	0.06735     	0           
  100         	0.01239     	0.09306     	0.02849     	0           
  300         	0.00595     	0.64505     	0.02035     	2           
  500         	0.00602     	0.88971     	0.02029     	4           
  1000        	0.00343     	0.96858     	0.01048     	7           
---> try to load best state
              	0.00343     	0.96858     	0.01048     	7                eval state : test_on_restore;
              	0.00343     	0.96858     	0.01048     	7                eval state : test_on_last_epoch;
---> final result
              	0.00343     	0.96858     	0.01048     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 4
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02667     	-17844096858.94516	0.06911     	0           
  10          	0.02668     	-37369810.11331	0.06910     	0           
  100         	0.00861     	-0.19393    	0.02798     	0           
  300         	0.00781     	-0.60009    	0.02777     	1           
  500         	0.00592     	0.28442     	0.02275     	2           
  1000        	0.00670     	0.27696     	0.02265     	5           
---> try to load best state
              	0.00670     	0.27696     	0.02265     	5                eval state : test_on_restore;
              	0.00670     	0.27696     	0.02265     	5                eval state : test_on_last_epoch;
---> final result
              	0.00670     	0.27696     	0.02265     	5                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 8
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02356     	-9832789560.44422	0.06741     	0           
  10          	0.02354     	-14047645.81471	0.06739     	0           
  100         	0.00934     	-1.52312    	0.03438     	0           
  300         	0.00778     	-0.37748    	0.02688     	1           
  500         	0.00773     	-0.31584    	0.02966     	3           
  1000        	0.00654     	-0.22285    	0.02514     	6           
---> try to load best state
              	0.00654     	-0.22285    	0.02514     	6                eval state : test_on_restore;
              	0.00654     	-0.22285    	0.02514     	6                eval state : test_on_last_epoch;
---> final result
              	0.00654     	-0.22285    	0.02514     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 16
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02598     	-11025963209.14210	0.06816     	0           
  10          	0.02601     	-18543975.51156	0.06816     	0           
  100         	0.01323     	-1.94428    	0.04017     	0           
  300         	0.00851     	-0.46903    	0.02736     	2           
  500         	0.00538     	0.66215     	0.01957     	3           
  1000        	0.00518     	0.76565     	0.01862     	7           
---> try to load best state
              	0.00518     	0.76565     	0.01862     	7                eval state : test_on_restore;
              	0.00518     	0.76565     	0.01862     	7                eval state : test_on_last_epoch;
---> final result
              	0.00518     	0.76565     	0.01862     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: False
  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     	-12183169723.32310	0.07037     	0           
  10          	0.02884     	-33134550.60309	0.07033     	0           
  100         	0.00680     	-0.22577    	0.01937     	0           
  300         	0.00668     	0.78442     	0.02030     	2           
  500         	0.00461     	0.96214     	0.01396     	3           
  1000        	0.00262     	0.98518     	0.00873     	7           
---> try to load best state
              	0.00262     	0.98518     	0.00873     	7                eval state : test_on_restore;
              	0.00262     	0.98518     	0.00873     	7                eval state : test_on_last_epoch;
---> final result
              	0.00262     	0.98518     	0.00873     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 4
  second_fidelity_start_index: 0
  non_subset: False
  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     	-16983246371.67373	0.06970     	0           
  10          	0.02740     	-28492387.96416	0.06973     	0           
  100         	0.01393     	-1.00574    	0.03881     	0           
  300         	0.01087     	-0.26060    	0.03204     	1           
  500         	0.00865     	-0.06106    	0.02781     	3           
  1000        	0.00777     	0.37501     	0.02436     	5           
---> try to load best state
              	0.00777     	0.37501     	0.02436     	6                eval state : test_on_restore;
              	0.00777     	0.37501     	0.02436     	6                eval state : test_on_last_epoch;
---> final result
              	0.00777     	0.37501     	0.02436     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 8
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02261     	-16373622599.32073	0.06785     	0           
  10          	0.02260     	-40936865.69494	0.06784     	0           
  100         	0.00757     	-1.18951    	0.02892     	0           
  300         	0.00673     	-0.32457    	0.02693     	1           
  500         	0.00695     	-0.64937    	0.02732     	3           
  1000        	0.00602     	0.13514     	0.02264     	6           
---> try to load best state
              	0.00602     	0.13514     	0.02264     	6                eval state : test_on_restore;
              	0.00602     	0.13514     	0.02264     	6                eval state : test_on_last_epoch;
---> final result
              	0.00602     	0.13514     	0.02264     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 16
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02792     	-9450798301.18085	0.06933     	0           
  10          	0.02795     	-14126458.69748	0.06935     	0           
  100         	0.00744     	0.42114     	0.02439     	0           
  300         	0.00655     	0.82535     	0.02181     	2           
  500         	0.00532     	0.95478     	0.01497     	3           
  1000        	0.00353     	0.96795     	0.00980     	6           
---> try to load best state
              	0.00353     	0.96795     	0.00980     	6                eval state : test_on_restore;
              	0.00353     	0.96795     	0.00980     	6                eval state : test_on_last_epoch;
---> final result
              	0.00353     	0.96795     	0.00980     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: False
  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     	-17063201031.97514	0.06858     	0           
  10          	0.02688     	-44067283.28639	0.06857     	0           
  100         	0.00720     	0.50033     	0.01941     	0           
  300         	0.00589     	0.51589     	0.02022     	2           
  500         	0.00585     	0.70555     	0.02044     	3           
  1000        	0.00277     	0.98750     	0.00854     	7           
---> try to load best state
              	0.00277     	0.98750     	0.00854     	7                eval state : test_on_restore;
              	0.00277     	0.98750     	0.00854     	7                eval state : test_on_last_epoch;
---> final result
              	0.00277     	0.98750     	0.00854     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 4
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.03555     	-8326771449.37569	0.08437     	0           
  10          	0.03549     	-3745194.71549	0.08426     	0           
  100         	0.01342     	0.72214     	0.03503     	0           
  300         	0.01284     	0.52493     	0.03491     	1           
  500         	0.01718     	0.64427     	0.04162     	3           
  1000        	0.01124     	0.75679     	0.03057     	5           
---> try to load best state
              	0.01124     	0.75679     	0.03057     	5                eval state : test_on_restore;
              	0.01124     	0.75679     	0.03057     	5                eval state : test_on_last_epoch;
---> final result
              	0.01124     	0.75679     	0.03057     	6                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 8
  second_fidelity_start_index: 0
  non_subset: False
  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           	0.02589     	-10267511020.49130	0.06841     	0           
  10          	0.02587     	-44080416.60632	0.06839     	0           
  100         	0.00738     	0.47836     	0.02464     	0           
  300         	0.00689     	0.75983     	0.02094     	2           
  500         	0.00666     	0.79372     	0.01933     	4           
  1000        	0.00440     	0.82610     	0.01325     	7           
---> try to load best state
              	0.00440     	0.82610     	0.01325     	7                eval state : test_on_restore;
              	0.00440     	0.82610     	0.01325     	7                eval state : test_on_last_epoch;
---> final result
              	0.00440     	0.82610     	0.01325     	7                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: 16
  second_fidelity_start_index: 0
  non_subset: False
  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     	-10099698264.22919	0.06763     	0           
  10          	0.02448     	-51811093.61603	0.06761     	0           
  100         	0.00870     	0.67832     	0.02441     	0           
  300         	0.00855     	0.78421     	0.02655     	2           
  500         	0.00537     	0.74447     	0.01727     	4           
  1000        	0.00383     	0.78750     	0.01367     	8           
---> try to load best state
              	0.00383     	0.78750     	0.01367     	8                eval state : test_on_restore;
              	0.00383     	0.78750     	0.01367     	8                eval state : test_on_last_epoch;
---> final result
              	0.00383     	0.78750     	0.01367     	8                eval state : final;module_name : dmfal;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', '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: False
  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     	-12025593774.67532	0.06748     	0           
  10          	0.02477     	-32286403.80157	0.06747     	0           
  100         	0.00842     	0.71204     	0.02063     	0           
  300         	0.00586     	-0.45286    	0.01923     	2           
  500         	0.00661     	0.84035     	0.02247     	4           
  1000        	0.00356     	0.96373     	0.00975     	8           
---> try to load best state
              	0.00356     	0.96373     	0.00975     	8                eval state : test_on_restore;
              	0.00356     	0.96373     	0.00975     	8                eval state : test_on_last_epoch;
---> final result
              	0.00356     	0.96373     	0.00975     	8                eval state : final;module_name : dmfal;cp_record_file : True;
