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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09195     	-7241497854.05375	0.13340     	0           
  10          	0.09193     	-36128298.25141	0.13336     	0           
  100         	0.03036     	-13.27946   	0.04770     	0           
  300         	0.01126     	-2.48525    	0.01749     	1           
  500         	0.01115     	-0.72568    	0.01696     	2           
  1000        	0.01148     	0.95400     	0.01705     	5           
---> try to load best state
              	0.01148     	0.95400     	0.01705     	5                eval state : test_on_restore;
              	0.01148     	0.95400     	0.01705     	5                eval state : test_on_last_epoch;
---> final result
              	0.01148     	0.95400     	0.01705     	5                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09378     	-4601319870.12997	0.13420     	0           
  10          	0.09374     	-92695901.86399	0.13417     	0           
  100         	0.02511     	-19.87779   	0.03694     	0           
  300         	0.01775     	-3.74783    	0.02458     	1           
  500         	0.01053     	-1.00299    	0.01548     	2           
  1000        	0.00946     	0.97605     	0.01383     	4           
---> try to load best state
              	0.00946     	0.97605     	0.01383     	4                eval state : test_on_restore;
              	0.00946     	0.97605     	0.01383     	4                eval state : test_on_last_epoch;
---> final result
              	0.00946     	0.97605     	0.01383     	4                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09176     	-3670849538.27194	0.13323     	0           
  10          	0.09176     	-25644784.62825	0.13320     	0           
  100         	0.02824     	-10.84632   	0.03846     	0           
  300         	0.01374     	0.21175     	0.02044     	1           
  500         	0.01099     	0.56758     	0.01699     	2           
  1000        	0.00872     	0.97728     	0.01311     	4           
---> try to load best state
              	0.00872     	0.97728     	0.01311     	4                eval state : test_on_restore;
              	0.00872     	0.97728     	0.01311     	4                eval state : test_on_last_epoch;
---> final result
              	0.00872     	0.97728     	0.01311     	4                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09198     	-4771668639.97336	0.13333     	0           
  10          	0.09193     	-86570164.12884	0.13329     	0           
  100         	0.02788     	-13.59241   	0.04088     	0           
  300         	0.01791     	-1.23257    	0.02552     	1           
  500         	0.01117     	0.15921     	0.01718     	2           
  1000        	0.00971     	0.96832     	0.01470     	4           
---> try to load best state
              	0.00971     	0.96832     	0.01470     	4                eval state : test_on_restore;
              	0.00971     	0.96832     	0.01470     	4                eval state : test_on_last_epoch;
---> final result
              	0.00971     	0.96832     	0.01470     	4                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09236     	-4790755310.73306	0.13350     	0           
  10          	0.09232     	-56753575.14453	0.13346     	0           
  100         	0.02569     	-10.70712   	0.03847     	0           
  300         	0.01337     	-14.90680   	0.02034     	1           
  500         	0.01443     	-24.60203   	0.02310     	2           
  1000        	0.00970     	0.96400     	0.01654     	4           
---> try to load best state
              	0.00970     	0.96400     	0.01654     	4                eval state : test_on_restore;
              	0.00970     	0.96400     	0.01654     	4                eval state : test_on_last_epoch;
---> final result
              	0.00970     	0.96400     	0.01654     	4                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09356     	-3511586218.96532	0.13409     	0           
  10          	0.09354     	-33889870.39834	0.13404     	0           
  100         	0.02944     	-10.60552   	0.03933     	0           
  300         	0.01444     	-0.49264    	0.02335     	1           
  500         	0.01555     	-0.17084    	0.02859     	2           
  1000        	0.01226     	0.69022     	0.03009     	5           
---> try to load best state
              	0.01226     	0.69022     	0.03009     	5                eval state : test_on_restore;
              	0.01226     	0.69022     	0.03009     	5                eval state : test_on_last_epoch;
---> final result
              	0.01226     	0.69022     	0.03009     	5                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09595     	-5634595225.36963	0.13609     	0           
  10          	0.09595     	-20031655.73695	0.13606     	0           
  100         	0.02279     	-1.66198    	0.03151     	0           
  300         	0.01225     	-0.21199    	0.01950     	1           
  500         	0.01230     	-1.25534    	0.01845     	2           
  1000        	0.01371     	-1.09104    	0.02343     	4           
---> try to load best state
              	0.01371     	-1.09104    	0.02343     	4                eval state : test_on_restore;
              	0.01371     	-1.09104    	0.02343     	4                eval state : test_on_last_epoch;
---> final result
              	0.01371     	-1.09104    	0.02343     	4                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09640     	-4230672265.94162	0.13653     	0           
  10          	0.09638     	-81076989.01177	0.13650     	0           
  100         	0.02311     	-0.45222    	0.03292     	0           
  300         	0.01432     	-0.33951    	0.02069     	1           
  500         	0.01232     	-0.33003    	0.02005     	2           
  1000        	0.01006     	-0.38244    	0.01731     	5           
---> try to load best state
              	0.01006     	-0.38244    	0.01731     	5                eval state : test_on_restore;
              	0.01006     	-0.38244    	0.01731     	5                eval state : test_on_last_epoch;
---> final result
              	0.01006     	-0.38244    	0.01731     	5                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09482     	-4767018328.06971	0.13529     	0           
  10          	0.09472     	-35030185.81834	0.13520     	0           
  100         	0.02393     	-1.49379    	0.03371     	0           
  300         	0.01971     	-0.24989    	0.03218     	1           
  500         	0.01567     	-0.54553    	0.02700     	2           
  1000        	0.01230     	-4.19777    	0.01900     	3           
---> try to load best state
              	0.01230     	-4.19777    	0.01900     	3                eval state : test_on_restore;
              	0.01230     	-4.19777    	0.01900     	3                eval state : test_on_last_epoch;
---> final result
              	0.01230     	-4.19777    	0.01900     	3                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08958     	-5450936279.65819	0.13301     	0           
  10          	0.08953     	-39914263.37748	0.13297     	0           
  100         	0.02131     	-4.36645    	0.03199     	0           
  300         	0.02071     	-1.74063    	0.03050     	1           
  500         	0.01171     	-0.31838    	0.02043     	1           
  1000        	0.01008     	0.96319     	0.01710     	3           
---> try to load best state
              	0.01008     	0.96319     	0.01710     	3                eval state : test_on_restore;
              	0.01008     	0.96319     	0.01710     	3                eval state : test_on_last_epoch;
---> final result
              	0.01008     	0.96319     	0.01710     	3                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08989     	-5624965996.59575	0.13311     	0           
  10          	0.08992     	-76560988.28384	0.13310     	0           
  100         	0.02829     	-1.07845    	0.04816     	0           
  300         	0.01318     	0.58939     	0.02217     	1           
  500         	0.01168     	0.64317     	0.01917     	1           
  1000        	0.01719     	0.90416     	0.02848     	3           
---> try to load best state
              	0.01719     	0.90416     	0.02848     	3                eval state : test_on_restore;
              	0.01719     	0.90416     	0.02848     	3                eval state : test_on_last_epoch;
---> final result
              	0.01719     	0.90416     	0.02848     	3                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08940     	-10720122195.96204	0.13309     	0           
  10          	0.08940     	-321195142.71414	0.13308     	0           
  100         	0.02551     	-0.66921    	0.04264     	0           
  300         	0.01887     	0.39145     	0.03738     	1           
  500         	0.01271     	0.46528     	0.02769     	1           
  1000        	0.01004     	0.89098     	0.02487     	3           
---> try to load best state
              	0.01004     	0.89098     	0.02487     	3                eval state : test_on_restore;
              	0.01004     	0.89098     	0.02487     	3                eval state : test_on_last_epoch;
---> final result
              	0.01004     	0.89098     	0.02487     	3                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08827     	-4877230160.89856	0.13287     	0           
  10          	0.08825     	-17839957.45324	0.13281     	0           
  100         	0.02433     	-3.98608    	0.03716     	0           
  300         	0.01649     	0.62145     	0.02459     	1           
  500         	0.01107     	0.50030     	0.01917     	1           
  1000        	0.01196     	0.95236     	0.02042     	3           
---> try to load best state
              	0.01196     	0.95236     	0.02042     	3                eval state : test_on_restore;
              	0.01196     	0.95236     	0.02042     	3                eval state : test_on_last_epoch;
---> final result
              	0.01196     	0.95236     	0.02042     	3                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08741     	-10328111632.97754	0.13313     	0           
  10          	0.08744     	-49934700.31821	0.13308     	0           
  100         	0.02478     	-11.62118   	0.03614     	0           
  300         	0.01341     	0.48065     	0.02437     	1           
  500         	0.01049     	0.47496     	0.01881     	1           
  1000        	0.01165     	0.94428     	0.02071     	3           
---> try to load best state
              	0.01165     	0.94428     	0.02071     	3                eval state : test_on_restore;
              	0.01165     	0.94428     	0.02071     	3                eval state : test_on_last_epoch;
---> final result
              	0.01165     	0.94428     	0.02071     	3                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08695     	-7719970555.14859	0.13346     	0           
  10          	0.08694     	-146880705.11330	0.13344     	0           
  100         	0.02241     	-2.09323    	0.03404     	0           
  300         	0.01152     	-2.81687    	0.02232     	0           
  500         	0.01363     	-0.70243    	0.02300     	1           
  1000        	0.00842     	0.95533     	0.01806     	3           
---> try to load best state
              	0.00842     	0.95533     	0.01806     	3                eval state : test_on_restore;
              	0.00842     	0.95533     	0.01806     	3                eval state : test_on_last_epoch;
---> final result
              	0.00842     	0.95533     	0.01806     	3                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08935     	-7165932905.59460	0.13273     	0           
  10          	0.08932     	-75714290.94949	0.13269     	0           
  100         	0.03295     	-19.91873   	0.04494     	0           
  300         	0.01609     	-1.55067    	0.02501     	1           
  500         	0.01094     	-7.67675    	0.01826     	1           
  1000        	0.00825     	0.96585     	0.01646     	3           
---> try to load best state
              	0.00825     	0.96585     	0.01646     	3                eval state : test_on_restore;
              	0.00825     	0.96585     	0.01646     	3                eval state : test_on_last_epoch;
---> final result
              	0.00825     	0.96585     	0.01646     	3                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09110     	-3611481776.45633	0.13294     	0           
  10          	0.09108     	-171171402.98535	0.13293     	0           
  100         	0.02474     	-32.72748   	0.03619     	0           
  300         	0.01267     	0.31761     	0.02265     	1           
  500         	0.01275     	-0.09458    	0.02144     	1           
  1000        	0.01040     	0.93761     	0.02053     	3           
---> try to load best state
              	0.01040     	0.93761     	0.02053     	3                eval state : test_on_restore;
              	0.01040     	0.93761     	0.02053     	3                eval state : test_on_last_epoch;
---> final result
              	0.01040     	0.93761     	0.02053     	3                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09025     	-4630922366.18186	0.13276     	0           
  10          	0.09025     	-53648489.82242	0.13274     	0           
  100         	0.02591     	-7.62716    	0.04116     	0           
  300         	0.01742     	0.25455     	0.03314     	0           
  500         	0.01288     	0.11476     	0.02597     	1           
  1000        	0.00902     	0.93289     	0.02122     	3           
---> try to load best state
              	0.00902     	0.93289     	0.02122     	3                eval state : test_on_restore;
              	0.00902     	0.93289     	0.02122     	3                eval state : test_on_last_epoch;
---> final result
              	0.00902     	0.93289     	0.02122     	3                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08552     	-7354435565.29129	0.13694     	0           
  10          	0.08551     	-65633539.10285	0.13691     	0           
  100         	0.02553     	-16.88645   	0.04879     	0           
  300         	0.01566     	0.56300     	0.03173     	0           
  500         	0.01447     	0.66619     	0.02784     	1           
  1000        	0.01066     	0.90429     	0.02417     	3           
---> try to load best state
              	0.01066     	0.90429     	0.02417     	3                eval state : test_on_restore;
              	0.01066     	0.90429     	0.02417     	3                eval state : test_on_last_epoch;
---> final result
              	0.01066     	0.90429     	0.02417     	3                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08553     	-7444579635.33930	0.13643     	0           
  10          	0.08551     	-33607649.87187	0.13641     	0           
  100         	0.02503     	-9.96516    	0.04586     	0           
  300         	0.01610     	0.44008     	0.03142     	1           
  500         	0.01357     	0.84198     	0.03124     	1           
  1000        	0.01657     	0.77163     	0.03616     	3           
---> try to load best state
              	0.01657     	0.77163     	0.03616     	3                eval state : test_on_restore;
              	0.01657     	0.77163     	0.03616     	3                eval state : test_on_last_epoch;
---> final result
              	0.01657     	0.77163     	0.03616     	3                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08589     	-11328606535.23407	0.13486     	0           
  10          	0.08586     	-47979634.13118	0.13484     	0           
  100         	0.02410     	-24.22093   	0.04444     	0           
  300         	0.01529     	0.41594     	0.03000     	1           
  500         	0.01231     	0.67101     	0.02549     	1           
  1000        	0.00982     	0.90811     	0.02275     	3           
---> try to load best state
              	0.00982     	0.90811     	0.02275     	3                eval state : test_on_restore;
              	0.00982     	0.90811     	0.02275     	3                eval state : test_on_last_epoch;
---> final result
              	0.00982     	0.90811     	0.02275     	3                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08961     	-4132115203.31688	0.13277     	0           
  10          	0.08954     	-18863999.62638	0.13271     	0           
  100         	0.02570     	-16.71297   	0.03705     	0           
  300         	0.01400     	0.05794     	0.02128     	1           
  500         	0.01234     	0.12583     	0.02068     	1           
  1000        	0.01013     	0.96656     	0.01673     	3           
---> try to load best state
              	0.01013     	0.96656     	0.01673     	3                eval state : test_on_restore;
              	0.01013     	0.96656     	0.01673     	3                eval state : test_on_last_epoch;
---> final result
              	0.01013     	0.96656     	0.01673     	3                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09072     	-6555662122.82926	0.13287     	0           
  10          	0.09073     	-79094239.57100	0.13286     	0           
  100         	0.02682     	-0.56863    	0.03805     	0           
  300         	0.01933     	0.54558     	0.02835     	0           
  500         	0.01599     	0.49023     	0.02392     	1           
  1000        	0.00861     	0.94856     	0.01440     	3           
---> try to load best state
              	0.00861     	0.94856     	0.01440     	3                eval state : test_on_restore;
              	0.00861     	0.94856     	0.01440     	3                eval state : test_on_last_epoch;
---> final result
              	0.00861     	0.94856     	0.01440     	3                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09010     	-4244823049.57830	0.13281     	0           
  10          	0.09008     	-45819937.43270	0.13279     	0           
  100         	0.03749     	-1.92048    	0.05580     	0           
  300         	0.01993     	0.56161     	0.03789     	0           
  500         	0.01543     	0.68579     	0.03090     	1           
  1000        	0.01088     	0.88945     	0.02286     	3           
---> try to load best state
              	0.01088     	0.88945     	0.02286     	3                eval state : test_on_restore;
              	0.01088     	0.88945     	0.02286     	3                eval state : test_on_last_epoch;
---> final result
              	0.01088     	0.88945     	0.02286     	3                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08812     	-6741979295.83892	0.13309     	0           
  10          	0.08811     	-56171886.74578	0.13306     	0           
  100         	0.02454     	-7.29028    	0.04209     	0           
  300         	0.01646     	-1.76870    	0.03598     	1           
  500         	0.01412     	-1.55742    	0.03180     	1           
  1000        	0.01077     	0.83810     	0.03054     	3           
---> try to load best state
              	0.01077     	0.83810     	0.03054     	3                eval state : test_on_restore;
              	0.01077     	0.83810     	0.03054     	3                eval state : test_on_last_epoch;
---> final result
              	0.01077     	0.83810     	0.03054     	3                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08852     	-10342474176.07967	0.13298     	0           
  10          	0.08850     	-61678390.23048	0.13294     	0           
  100         	0.02276     	-21.27756   	0.03976     	0           
  300         	0.01378     	-43.56836   	0.03203     	0           
  500         	0.01311     	-10.12274   	0.02962     	1           
  1000        	0.01120     	0.87721     	0.02756     	3           
---> try to load best state
              	0.01120     	0.87721     	0.02756     	3                eval state : test_on_restore;
              	0.01120     	0.87721     	0.02756     	3                eval state : test_on_last_epoch;
---> final result
              	0.01120     	0.87721     	0.02756     	3                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09141     	-7383181172.02111	0.13370     	0           
  10          	0.09139     	-43690199.59775	0.13367     	0           
  100         	0.02240     	-6.25991    	0.03860     	0           
  300         	0.01594     	-0.27598    	0.03216     	1           
  500         	0.01477     	0.32467     	0.03014     	1           
  1000        	0.01272     	0.92042     	0.02503     	4           
---> try to load best state
              	0.01272     	0.92042     	0.02503     	4                eval state : test_on_restore;
              	0.01272     	0.92042     	0.02503     	4                eval state : test_on_last_epoch;
---> final result
              	0.01272     	0.92042     	0.02503     	4                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09397     	-6827128833.26734	0.13470     	0           
  10          	0.09397     	-35507357.14918	0.13469     	0           
  100         	0.02446     	-3.81942    	0.03681     	0           
  300         	0.01557     	0.27708     	0.02357     	1           
  500         	0.01535     	0.01239     	0.02145     	2           
  1000        	0.00723     	0.96069     	0.01129     	4           
---> try to load best state
              	0.00723     	0.96069     	0.01129     	4                eval state : test_on_restore;
              	0.00723     	0.96069     	0.01129     	4                eval state : test_on_last_epoch;
---> final result
              	0.00723     	0.96069     	0.01129     	4                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09939     	-4880267767.41125	0.13965     	0           
  10          	0.09939     	-27871912.68813	0.13964     	0           
  100         	0.02423     	-5.86327    	0.03761     	0           
  300         	0.01266     	0.03707     	0.01966     	1           
  500         	0.01150     	-0.70863    	0.01747     	1           
  1000        	0.00953     	0.94407     	0.01484     	3           
---> try to load best state
              	0.00953     	0.94407     	0.01484     	3                eval state : test_on_restore;
              	0.00953     	0.94407     	0.01484     	3                eval state : test_on_last_epoch;
---> final result
              	0.00953     	0.94407     	0.01484     	3                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.09681     	-4966224171.96213	0.13712     	0           
  10          	0.09682     	-25460936.13879	0.13711     	0           
  100         	0.02323     	-9.04345    	0.03634     	0           
  300         	0.01404     	-0.87979    	0.02418     	0           
  500         	0.01756     	-1.86874    	0.02685     	1           
  1000        	0.00734     	0.95733     	0.01672     	3           
---> try to load best state
              	0.00734     	0.95733     	0.01672     	3                eval state : test_on_restore;
              	0.00734     	0.95733     	0.01672     	3                eval state : test_on_last_epoch;
---> final result
              	0.00734     	0.95733     	0.01672     	3                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08781     	-6125529058.46748	0.13300     	0           
  10          	0.08774     	-11704622.24811	0.13296     	0           
  100         	0.02708     	-6.23973    	0.03965     	0           
  300         	0.01655     	-0.08743    	0.02272     	0           
  500         	0.01404     	0.33670     	0.02374     	1           
  1000        	0.01229     	0.95271     	0.01998     	3           
---> try to load best state
              	0.01229     	0.95271     	0.01998     	3                eval state : test_on_restore;
              	0.01229     	0.95271     	0.01998     	3                eval state : test_on_last_epoch;
---> final result
              	0.01229     	0.95271     	0.01998     	3                eval state : final;module_name : dmfal;subset : 1;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08813     	-4304960178.64309	0.13286     	0           
  10          	0.08813     	-43951279.05175	0.13280     	0           
  100         	0.02958     	-3.33117    	0.04390     	0           
  300         	0.01444     	-0.39008    	0.02178     	0           
  500         	0.01511     	0.26002     	0.02716     	1           
  1000        	0.01021     	0.93653     	0.01859     	3           
---> try to load best state
              	0.01021     	0.93653     	0.01859     	3                eval state : test_on_restore;
              	0.01021     	0.93653     	0.01859     	3                eval state : test_on_last_epoch;
---> final result
              	0.01021     	0.93653     	0.01859     	3                eval state : final;module_name : dmfal;subset : 2;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08884     	-3428548091.39841	0.13269     	0           
  10          	0.08881     	-33358567.02954	0.13264     	0           
  100         	0.02535     	-4.18821    	0.03510     	0           
  300         	0.01215     	-13.23658   	0.02017     	1           
  500         	0.01237     	-5.80315    	0.02096     	1           
  1000        	0.01077     	0.95357     	0.01885     	3           
---> try to load best state
              	0.01077     	0.95357     	0.01885     	3                eval state : test_on_restore;
              	0.01077     	0.95357     	0.01885     	3                eval state : test_on_last_epoch;
---> final result
              	0.01077     	0.95357     	0.01885     	3                eval state : final;module_name : dmfal;subset : 4;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08873     	-6256840796.02287	0.13273     	0           
  10          	0.08874     	-48143550.87473	0.13270     	0           
  100         	0.02419     	-381.58880  	0.03631     	0           
  300         	0.01848     	-1.91624    	0.02668     	0           
  500         	0.01141     	-2.50428    	0.01831     	1           
  1000        	0.00934     	0.96900     	0.01828     	3           
---> try to load best state
              	0.00934     	0.96900     	0.01828     	3                eval state : test_on_restore;
              	0.00934     	0.96900     	0.01828     	3                eval state : test_on_last_epoch;
---> final result
              	0.00934     	0.96900     	0.01828     	3                eval state : final;module_name : dmfal;subset : 8;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.08965     	-5024344334.26328	0.13292     	0           
  10          	0.08962     	-25040470.69380	0.13285     	0           
  100         	0.02970     	-33.90007   	0.05488     	0           
  300         	0.01805     	-4.32251    	0.03072     	1           
  500         	0.01250     	-0.00826    	0.02877     	1           
  1000        	0.01179     	0.84910     	0.02886     	3           
---> try to load best state
              	0.01179     	0.84910     	0.02886     	3                eval state : test_on_restore;
              	0.01179     	0.84910     	0.02886     	3                eval state : test_on_last_epoch;
---> final result
              	0.01179     	0.84910     	0.02886     	3                eval state : final;module_name : dmfal;subset : 16;cp_record_file : True;
---> end

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

  Demo DMFAL 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'Heat_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[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           	0.10707     	-6682397132.84242	0.14797     	0           
  10          	0.10697     	-45172671.21380	0.14783     	0           
  100         	0.02610     	-3.36210    	0.03481     	0           
  300         	0.01644     	-0.02798    	0.02435     	0           
  500         	0.01747     	-0.19259    	0.02419     	1           
  1000        	0.01219     	-0.02088    	0.01879     	3           
---> try to load best state
              	0.01219     	-0.02088    	0.01879     	3                eval state : test_on_restore;
              	0.01219     	-0.02088    	0.01879     	3                eval state : test_on_last_epoch;
---> final result
              	0.01219     	-0.02088    	0.01879     	3                eval state : final;module_name : dmfal;subset : 32;cp_record_file : True;
