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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09334     	-18108889907.03909	0.13421     	0           
  10          	0.09334     	-58630060.76646	0.13420     	0           
  100         	0.02909     	-11.41387   	0.04249     	1           
  300         	0.01947     	-0.65098    	0.02624     	3           
  500         	0.00936     	0.22636     	0.01432     	6           
  1000        	0.00824     	0.80116     	0.01248     	11          
---> try to load best state
              	0.00824     	0.80116     	0.01248     	11               eval state : test_on_restore;
              	0.00824     	0.80116     	0.01248     	11               eval state : test_on_last_epoch;
---> final result
              	0.00824     	0.80116     	0.01248     	11               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09518     	-18081427740.18344	0.13503     	0           
  10          	0.09508     	-17383791.54706	0.13493     	0           
  100         	0.02651     	-15.48354   	0.03619     	1           
  300         	0.01658     	-2.29435    	0.02812     	4           
  500         	0.01108     	-0.17333    	0.01693     	7           
  1000        	0.00618     	0.98253     	0.01034     	14          
---> try to load best state
              	0.00618     	0.98253     	0.01034     	14               eval state : test_on_restore;
              	0.00618     	0.98253     	0.01034     	14               eval state : test_on_last_epoch;
---> final result
              	0.00618     	0.98253     	0.01034     	14               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09315     	-18263077013.94181	0.13405     	0           
  10          	0.09311     	-36507802.28184	0.13401     	0           
  100         	0.02562     	-3.95930    	0.03640     	1           
  300         	0.01171     	0.70891     	0.01748     	5           
  500         	0.01041     	0.71980     	0.01558     	8           
  1000        	0.01114     	0.80696     	0.01664     	16          
---> try to load best state
              	0.01114     	0.80696     	0.01664     	16               eval state : test_on_restore;
              	0.01114     	0.80696     	0.01664     	16               eval state : test_on_last_epoch;
---> final result
              	0.01114     	0.80696     	0.01664     	16               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09337     	-22171055203.74228	0.13415     	0           
  10          	0.09323     	-50601648.22793	0.13406     	0           
  100         	0.02454     	-9.06870    	0.03415     	1           
  300         	0.01417     	0.39209     	0.02100     	4           
  500         	0.01486     	0.41420     	0.02026     	7           
  1000        	0.01065     	0.96129     	0.01893     	16          
---> try to load best state
              	0.01065     	0.96129     	0.01893     	16               eval state : test_on_restore;
              	0.01065     	0.96129     	0.01893     	16               eval state : test_on_last_epoch;
---> final result
              	0.01065     	0.96129     	0.01893     	16               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09375     	-19044356414.73597	0.13432     	0           
  10          	0.09364     	-50558170.25520	0.13425     	0           
  100         	0.02621     	-10.37122   	0.03553     	1           
  300         	0.01720     	-17.40728   	0.02325     	5           
  500         	0.01159     	-2.80540    	0.01711     	9           
  1000        	0.01027     	0.96648     	0.01718     	18          
---> try to load best state
              	0.01027     	0.96648     	0.01718     	18               eval state : test_on_restore;
              	0.01027     	0.96648     	0.01718     	18               eval state : test_on_last_epoch;
---> final result
              	0.01027     	0.96648     	0.01718     	18               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]', '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           	0.09491     	-17934583467.96097	0.13487     	0           
  10          	0.09495     	-16273769.43209	0.13486     	0           
  100         	0.02699     	-3.23562    	0.03745     	1           
  300         	0.01866     	0.28773     	0.02741     	5           
  500         	0.01294     	0.37200     	0.02140     	8           
  1000        	0.01206     	0.72018     	0.02879     	17          
---> try to load best state
              	0.01206     	0.72018     	0.02879     	17               eval state : test_on_restore;
              	0.01206     	0.72018     	0.02879     	17               eval state : test_on_last_epoch;
---> final result
              	0.01206     	0.72018     	0.02879     	17               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09739     	-14780066301.10861	0.13693     	0           
  10          	0.09749     	-21452759.48453	0.13699     	0           
  100         	0.02224     	-0.07455    	0.03176     	1           
  300         	0.01347     	0.07303     	0.02249     	3           
  500         	0.01176     	0.43867     	0.01801     	6           
  1000        	0.01271     	-0.27083    	0.01878     	14          
---> try to load best state
              	0.01271     	-0.27083    	0.01878     	14               eval state : test_on_restore;
              	0.01271     	-0.27083    	0.01878     	14               eval state : test_on_last_epoch;
---> final result
              	0.01271     	-0.27083    	0.01878     	14               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09785     	-9075108553.98819	0.13738     	0           
  10          	0.09776     	-10111014.20292	0.13729     	0           
  100         	0.02844     	-0.94267    	0.03776     	1           
  300         	0.02166     	0.33552     	0.03100     	5           
  500         	0.01452     	0.57110     	0.02100     	8           
  1000        	0.01060     	0.53742     	0.01711     	16          
---> try to load best state
              	0.01060     	0.53742     	0.01711     	17               eval state : test_on_restore;
              	0.01060     	0.53742     	0.01711     	17               eval state : test_on_last_epoch;
---> final result
              	0.01060     	0.53742     	0.01711     	17               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09624     	-13519033889.51522	0.13613     	0           
  10          	0.09632     	-25761278.72849	0.13617     	0           
  100         	0.02680     	-1.05053    	0.03619     	1           
  300         	0.01776     	0.57899     	0.02546     	4           
  500         	0.01609     	0.41920     	0.02240     	8           
  1000        	0.01832     	-0.83987    	0.02614     	17          
---> try to load best state
              	0.01832     	-0.83987    	0.02614     	17               eval state : test_on_restore;
              	0.01832     	-0.83987    	0.02614     	17               eval state : test_on_last_epoch;
---> final result
              	0.01832     	-0.83987    	0.02614     	17               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09098     	-27221744478.33691	0.13382     	0           
  10          	0.09097     	-29329585.56854	0.13378     	0           
  100         	0.02210     	-0.14783    	0.03244     	1           
  300         	0.01142     	-2.01230    	0.01893     	4           
  500         	0.01201     	0.60851     	0.01953     	8           
  1000        	0.00915     	0.96519     	0.01568     	16          
---> try to load best state
              	0.00915     	0.96519     	0.01568     	16               eval state : test_on_restore;
              	0.00915     	0.96519     	0.01568     	16               eval state : test_on_last_epoch;
---> final result
              	0.00915     	0.96519     	0.01568     	16               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09129     	-26334650255.20578	0.13393     	0           
  10          	0.09121     	-49656038.22491	0.13388     	0           
  100         	0.02280     	-1.72059    	0.03812     	1           
  300         	0.01327     	0.78463     	0.02046     	3           
  500         	0.01920     	0.77979     	0.02688     	6           
  1000        	0.01314     	0.94460     	0.02079     	12          
---> try to load best state
              	0.01314     	0.94460     	0.02079     	12               eval state : test_on_restore;
              	0.01314     	0.94460     	0.02079     	12               eval state : test_on_last_epoch;
---> final result
              	0.01314     	0.94460     	0.02079     	12               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09081     	-25957598871.92462	0.13389     	0           
  10          	0.09074     	-25223681.93903	0.13384     	0           
  100         	0.02999     	-18.96814   	0.05181     	1           
  300         	0.01327     	0.01095     	0.02797     	4           
  500         	0.01287     	0.27545     	0.02925     	6           
  1000        	0.01009     	0.88003     	0.02602     	14          
---> try to load best state
              	0.01009     	0.88003     	0.02602     	14               eval state : test_on_restore;
              	0.01009     	0.88003     	0.02602     	14               eval state : test_on_last_epoch;
---> final result
              	0.01009     	0.88003     	0.02602     	14               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08964     	-23723001287.54067	0.13368     	0           
  10          	0.08967     	-26614151.85852	0.13363     	0           
  100         	0.02396     	-3.35173    	0.03516     	1           
  300         	0.01458     	0.69348     	0.02357     	4           
  500         	0.01307     	0.75163     	0.02349     	7           
  1000        	0.01102     	0.94211     	0.02211     	15          
---> try to load best state
              	0.01102     	0.94211     	0.02211     	15               eval state : test_on_restore;
              	0.01102     	0.94211     	0.02211     	15               eval state : test_on_last_epoch;
---> final result
              	0.01102     	0.94211     	0.02211     	16               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08878     	-28877804334.51575	0.13394     	0           
  10          	0.08881     	-47946337.35342	0.13387     	0           
  100         	0.02412     	-2.45537    	0.03536     	1           
  300         	0.01329     	0.67650     	0.02215     	4           
  500         	0.01277     	0.60543     	0.02542     	8           
  1000        	0.00960     	0.95738     	0.01736     	15          
---> try to load best state
              	0.00960     	0.95738     	0.01736     	15               eval state : test_on_restore;
              	0.00960     	0.95738     	0.01736     	15               eval state : test_on_last_epoch;
---> final result
              	0.00960     	0.95738     	0.01736     	15               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08831     	-16922102768.26018	0.13427     	0           
  10          	0.08834     	-9951149.70273	0.13415     	0           
  100         	0.02434     	-1.94836    	0.03713     	1           
  300         	0.01495     	0.07514     	0.02439     	4           
  500         	0.01201     	-0.84410    	0.02239     	8           
  1000        	0.00898     	0.93953     	0.01823     	14          
---> try to load best state
              	0.00898     	0.93953     	0.01823     	14               eval state : test_on_restore;
              	0.00898     	0.93953     	0.01823     	14               eval state : test_on_last_epoch;
---> final result
              	0.00898     	0.93953     	0.01823     	14               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09075     	-22428077599.50574	0.13353     	0           
  10          	0.09079     	-25646133.64203	0.13350     	0           
  100         	0.02446     	-74.19289   	0.03789     	1           
  300         	0.01247     	-0.62097    	0.02106     	3           
  500         	0.01430     	-3.38372    	0.02686     	6           
  1000        	0.00926     	0.95120     	0.01792     	16          
---> try to load best state
              	0.00926     	0.95120     	0.01792     	16               eval state : test_on_restore;
              	0.00926     	0.95120     	0.01792     	16               eval state : test_on_last_epoch;
---> final result
              	0.00926     	0.95120     	0.01792     	16               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09251     	-22744924357.57961	0.13376     	0           
  10          	0.09252     	-39794992.61961	0.13373     	0           
  100         	0.02609     	-11.43250   	0.03878     	1           
  300         	0.01311     	0.53117     	0.02161     	4           
  500         	0.01133     	0.43757     	0.02146     	8           
  1000        	0.01110     	0.54796     	0.01970     	16          
---> try to load best state
              	0.01110     	0.54796     	0.01970     	16               eval state : test_on_restore;
              	0.01110     	0.54796     	0.01970     	16               eval state : test_on_last_epoch;
---> final result
              	0.01110     	0.54796     	0.01970     	16               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09165     	-23295429318.65723	0.13357     	0           
  10          	0.09154     	-75749441.72746	0.13353     	0           
  100         	0.02535     	-2.08887    	0.03850     	1           
  300         	0.01638     	0.61631     	0.02796     	5           
  500         	0.01804     	0.74721     	0.02874     	9           
  1000        	0.00989     	0.94345     	0.02051     	17          
---> try to load best state
              	0.00989     	0.94345     	0.02051     	17               eval state : test_on_restore;
              	0.00989     	0.94345     	0.02051     	17               eval state : test_on_last_epoch;
---> final result
              	0.00989     	0.94345     	0.02051     	17               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08689     	-34521868519.77720	0.13777     	0           
  10          	0.08686     	-40052467.08064	0.13762     	0           
  100         	0.02437     	-2.81194    	0.04163     	1           
  300         	0.01233     	0.72580     	0.02509     	4           
  500         	0.01107     	0.76062     	0.02176     	8           
  1000        	0.01113     	0.94843     	0.02381     	15          
---> try to load best state
              	0.01113     	0.94843     	0.02381     	15               eval state : test_on_restore;
              	0.01113     	0.94843     	0.02381     	15               eval state : test_on_last_epoch;
---> final result
              	0.01113     	0.94843     	0.02381     	15               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08689     	-20959453566.58103	0.13725     	0           
  10          	0.08686     	-24742442.02946	0.13722     	0           
  100         	0.02459     	-4.13836    	0.04320     	1           
  300         	0.01483     	0.65371     	0.02746     	3           
  500         	0.01259     	0.73043     	0.02331     	5           
  1000        	0.01028     	0.94112     	0.02093     	12          
---> try to load best state
              	0.01028     	0.94112     	0.02093     	12               eval state : test_on_restore;
              	0.01028     	0.94112     	0.02093     	12               eval state : test_on_last_epoch;
---> final result
              	0.01028     	0.94112     	0.02093     	12               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08725     	-21047353863.46975	0.13567     	0           
  10          	0.08723     	-39685033.56256	0.13563     	0           
  100         	0.02323     	-2.81031    	0.04025     	1           
  300         	0.01579     	0.71898     	0.03168     	3           
  500         	0.01019     	0.72419     	0.02068     	6           
  1000        	0.00807     	0.92578     	0.01754     	12          
---> try to load best state
              	0.00807     	0.92578     	0.01754     	12               eval state : test_on_restore;
              	0.00807     	0.92578     	0.01754     	12               eval state : test_on_last_epoch;
---> final result
              	0.00807     	0.92578     	0.01754     	12               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09098     	-20424530175.86467	0.13358     	0           
  10          	0.09099     	-59510087.33930	0.13356     	0           
  100         	0.02211     	-2.86738    	0.03443     	1           
  300         	0.01594     	0.38284     	0.02700     	3           
  500         	0.01226     	0.60648     	0.01833     	5           
  1000        	0.00968     	0.96602     	0.01518     	12          
---> try to load best state
              	0.00968     	0.96602     	0.01518     	12               eval state : test_on_restore;
              	0.00968     	0.96602     	0.01518     	12               eval state : test_on_last_epoch;
---> final result
              	0.00968     	0.96602     	0.01518     	12               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09208     	-19797446777.78981	0.13368     	0           
  10          	0.09206     	-19893563.48266	0.13362     	0           
  100         	0.02634     	-1.78368    	0.03761     	1           
  300         	0.01173     	0.83121     	0.01886     	3           
  500         	0.01236     	0.82941     	0.01962     	6           
  1000        	0.01008     	0.92631     	0.01600     	12          
---> try to load best state
              	0.01008     	0.92631     	0.01600     	13               eval state : test_on_restore;
              	0.01008     	0.92631     	0.01600     	13               eval state : test_on_last_epoch;
---> final result
              	0.01008     	0.92631     	0.01600     	13               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09145     	-31037038039.63018	0.13362     	0           
  10          	0.09140     	-54987050.99539	0.13359     	0           
  100         	0.02666     	-0.01789    	0.03988     	1           
  300         	0.01822     	0.85395     	0.02795     	3           
  500         	0.01622     	0.81258     	0.02926     	6           
  1000        	0.01009     	0.91000     	0.01912     	13          
---> try to load best state
              	0.01009     	0.91000     	0.01912     	13               eval state : test_on_restore;
              	0.01009     	0.91000     	0.01912     	13               eval state : test_on_last_epoch;
---> final result
              	0.01009     	0.91000     	0.01912     	13               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08950     	-16145798766.56026	0.13389     	0           
  10          	0.08944     	-33412888.40030	0.13386     	0           
  100         	0.02129     	-2.85581    	0.04071     	1           
  300         	0.01309     	-0.28871    	0.03057     	5           
  500         	0.01243     	0.61057     	0.02815     	9           
  1000        	0.01010     	0.88456     	0.02523     	17          
---> try to load best state
              	0.01010     	0.88456     	0.02523     	17               eval state : test_on_restore;
              	0.01010     	0.88456     	0.02523     	17               eval state : test_on_last_epoch;
---> final result
              	0.01010     	0.88456     	0.02523     	17               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08990     	-27856402885.15168	0.13378     	0           
  10          	0.08988     	-75409768.61532	0.13376     	0           
  100         	0.02862     	-11.79306   	0.05474     	1           
  300         	0.01572     	-4.90006    	0.02904     	3           
  500         	0.01147     	0.46748     	0.02679     	6           
  1000        	0.01302     	0.87686     	0.02705     	12          
---> try to load best state
              	0.01302     	0.87686     	0.02705     	12               eval state : test_on_restore;
              	0.01302     	0.87686     	0.02705     	12               eval state : test_on_last_epoch;
---> final result
              	0.01302     	0.87686     	0.02705     	12               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09283     	-13878321250.06496	0.13451     	0           
  10          	0.09279     	-14041261.84006	0.13445     	0           
  100         	0.02866     	-4.62049    	0.04181     	1           
  300         	0.01625     	0.19166     	0.02880     	3           
  500         	0.01177     	0.48858     	0.02568     	5           
  1000        	0.00981     	0.92002     	0.02512     	12          
---> try to load best state
              	0.00981     	0.92002     	0.02512     	12               eval state : test_on_restore;
              	0.00981     	0.92002     	0.02512     	12               eval state : test_on_last_epoch;
---> final result
              	0.00981     	0.92002     	0.02512     	12               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09540     	-18438288568.32386	0.13553     	0           
  10          	0.09535     	-19264438.33835	0.13547     	0           
  100         	0.02632     	-5.27667    	0.04228     	1           
  300         	0.01325     	0.62563     	0.02086     	3           
  500         	0.01661     	0.61996     	0.02265     	6           
  1000        	0.01099     	0.90099     	0.02004     	12          
---> try to load best state
              	0.01099     	0.90099     	0.02004     	12               eval state : test_on_restore;
              	0.01099     	0.90099     	0.02004     	12               eval state : test_on_last_epoch;
---> final result
              	0.01099     	0.90099     	0.02004     	12               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.10091     	-17051494685.78242	0.14054     	0           
  10          	0.10076     	-43920594.11834	0.14038     	0           
  100         	0.02169     	-1.76488    	0.03329     	1           
  300         	0.01262     	0.46171     	0.01898     	3           
  500         	0.01423     	-0.05642    	0.02009     	6           
  1000        	0.00778     	0.97924     	0.01364     	14          
---> try to load best state
              	0.00778     	0.97924     	0.01364     	14               eval state : test_on_restore;
              	0.00778     	0.97924     	0.01364     	14               eval state : test_on_last_epoch;
---> final result
              	0.00778     	0.97924     	0.01364     	14               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09828     	-34285796325.59674	0.13798     	0           
  10          	0.09810     	-145518954.05459	0.13782     	0           
  100         	0.02634     	-10.03749   	0.04020     	1           
  300         	0.01448     	-0.32545    	0.02217     	5           
  500         	0.01417     	0.26489     	0.02194     	8           
  1000        	0.00761     	0.95169     	0.01666     	17          
---> try to load best state
              	0.00761     	0.95169     	0.01666     	17               eval state : test_on_restore;
              	0.00761     	0.95169     	0.01666     	17               eval state : test_on_last_epoch;
---> final result
              	0.00761     	0.95169     	0.01666     	17               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08916     	-32310019765.58531	0.13381     	0           
  10          	0.08917     	-45179254.17349	0.13374     	0           
  100         	0.02744     	-5.20706    	0.03806     	1           
  300         	0.01163     	0.54942     	0.01957     	5           
  500         	0.01401     	0.23730     	0.02041     	8           
  1000        	0.01145     	0.96538     	0.02109     	15          
---> try to load best state
              	0.01145     	0.96538     	0.02109     	15               eval state : test_on_restore;
              	0.01145     	0.96538     	0.02109     	15               eval state : test_on_last_epoch;
---> final result
              	0.01145     	0.96538     	0.02109     	15               eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.08948     	-19082113789.65180	0.13367     	0           
  10          	0.08941     	-20444823.73371	0.13362     	0           
  100         	0.02605     	-2.46364    	0.03628     	1           
  300         	0.01701     	0.56456     	0.02745     	4           
  500         	0.01151     	0.58969     	0.02031     	7           
  1000        	0.01163     	0.95642     	0.02095     	13          
---> try to load best state
              	0.01163     	0.95642     	0.02095     	13               eval state : test_on_restore;
              	0.01163     	0.95642     	0.02095     	13               eval state : test_on_last_epoch;
---> final result
              	0.01163     	0.95642     	0.02095     	13               eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09020     	-34724614307.78537	0.13350     	0           
  10          	0.09025     	-81358471.38695	0.13347     	0           
  100         	0.02612     	-30.70840   	0.03644     	1           
  300         	0.01204     	-2.82610    	0.01872     	3           
  500         	0.01371     	0.02648     	0.02284     	5           
  1000        	0.00903     	0.96020     	0.01656     	11          
---> try to load best state
              	0.00903     	0.96020     	0.01656     	11               eval state : test_on_restore;
              	0.00903     	0.96020     	0.01656     	11               eval state : test_on_last_epoch;
---> final result
              	0.00903     	0.96020     	0.01656     	11               eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09009     	-17851240654.28451	0.13354     	0           
  10          	0.09009     	-31653863.98874	0.13349     	0           
  100         	0.02851     	-9.44684    	0.04365     	1           
  300         	0.01846     	-5.38705    	0.02534     	3           
  500         	0.01278     	0.51811     	0.01826     	6           
  1000        	0.00948     	0.96931     	0.01717     	12          
---> try to load best state
              	0.00948     	0.96931     	0.01717     	12               eval state : test_on_restore;
              	0.00948     	0.96931     	0.01717     	12               eval state : test_on_last_epoch;
---> final result
              	0.00948     	0.96931     	0.01717     	12               eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.09106     	-18404660173.69043	0.13373     	0           
  10          	0.09102     	-18093430.84484	0.13365     	0           
  100         	0.02458     	-387.37836  	0.04218     	1           
  300         	0.01427     	0.22223     	0.02792     	3           
  500         	0.01358     	0.59241     	0.02595     	6           
  1000        	0.00928     	0.93042     	0.02163     	12          
---> try to load best state
              	0.00928     	0.93042     	0.02163     	12               eval state : test_on_restore;
              	0.00928     	0.93042     	0.02163     	12               eval state : test_on_last_epoch;
---> final result
              	0.00928     	0.93042     	0.02163     	12               eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', '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[1]', '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           	0.10865     	-18964127283.04621	0.14889     	0           
  10          	0.10877     	-15837842.47990	0.14903     	0           
  100         	0.02614     	-2.05920    	0.03512     	1           
  300         	0.01538     	0.48430     	0.02092     	3           
  500         	0.01428     	0.35634     	0.01898     	5           
  1000        	0.00997     	0.93048     	0.01664     	15          
---> try to load best state
              	0.00997     	0.93048     	0.01664     	15               eval state : test_on_restore;
              	0.00997     	0.93048     	0.01664     	15               eval state : test_on_last_epoch;
---> final result
              	0.00997     	0.93048     	0.01664     	15               eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
