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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42328    	0.30826     	159.23386   	0           
  10          	74.36810    	0.35025     	133.93761   	1           
  100         	21.95033    	0.35595     	36.60880    	9           
  300         	18.35363    	0.45822     	35.17606    	26          
  500         	17.55857    	0.47684     	33.94388    	44          
  1000        	16.54098    	0.49051     	32.48782    	87          
---> try to load best state
              	16.54098    	0.49051     	32.48782    	87               eval state : test_on_restore;
              	16.54098    	0.49051     	32.48782    	87               eval state : test_on_last_epoch;
---> final result              	16.54098    	0.49051     	32.48782    	87               eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	459.44046   	-12.60923   	661.84192   	0           
  10          	146.51871   	-5.11406    	218.96169   	0           
  100         	19.38572    	0.35284     	38.28617    	0           
  300         	30.11762    	0.28963     	52.72710    	2           
  500         	19.35673    	0.43454     	40.38580    	3           
  1000        	18.97624    	0.19356     	36.56799    	7           
---> try to load best state
              	18.97624    	0.19356     	36.56799    	7                eval state : test_on_restore;
              	18.97624    	0.19356     	36.56799    	7                eval state : test_on_last_epoch;
---> final result              	18.97624    	0.19356     	36.56799    	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	349.23889   	-3.20665    	525.08289   	0           
  10          	107.28918   	-1.55220    	172.30351   	0           
  100         	17.95902    	0.43792     	37.24675    	1           
  300         	18.48149    	0.23117     	38.41048    	3           
  500         	17.52416    	0.47998     	35.91152    	5           
  1000        	17.89151    	0.42554     	36.85276    	10          
---> try to load best state
              	17.89151    	0.42554     	36.85276    	10               eval state : test_on_restore;
              	17.89151    	0.42554     	36.85276    	10               eval state : test_on_last_epoch;
---> final result              	17.89151    	0.42554     	36.85276    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	201.67503   	-0.93323    	335.47971   	0           
  10          	64.00783    	-0.31646    	113.29640   	0           
  100         	17.38379    	0.47178     	34.03799    	1           
  300         	18.62141    	0.41815     	36.76427    	4           
  500         	17.31672    	0.45229     	34.39494    	6           
  1000        	17.05423    	0.48082     	35.04652    	14          
---> try to load best state
              	17.05423    	0.48082     	35.04652    	14               eval state : test_on_restore;
              	17.05423    	0.48082     	35.04652    	14               eval state : test_on_last_epoch;
---> final result              	17.05423    	0.48082     	35.04652    	14               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	113.77256   	0.00966     	194.98639   	0           
  10          	42.94567    	0.23217     	79.06213    	0           
  100         	17.19919    	0.48011     	33.71969    	3           
  300         	17.13405    	0.47026     	34.52235    	8           
  500         	17.62020    	0.41301     	36.28292    	13          
  1000        	19.46912    	0.46821     	39.83002    	25          
---> try to load best state
              	19.46912    	0.46821     	39.83002    	25               eval state : test_on_restore;
              	19.46912    	0.46821     	39.83002    	25               eval state : test_on_last_epoch;
---> final result              	19.46912    	0.46821     	39.83002    	25               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	81.34906    	0.23274     	139.85095   	0           
  10          	32.74698    	0.35326     	60.51709    	0           
  100         	16.87563    	0.48491     	33.60126    	6           
  300         	17.12965    	0.47942     	34.94961    	18          
  500         	17.09323    	0.47729     	35.43233    	30          
  1000        	17.32583    	0.48412     	35.80556    	61          
---> try to load best state
              	17.32583    	0.48412     	35.80556    	61               eval state : test_on_restore;
              	17.32583    	0.48412     	35.80556    	61               eval state : test_on_last_epoch;
---> final result              	17.32583    	0.48412     	35.80556    	61               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.32615    	0.25342     	131.59653   	0           
  10          	30.69051    	0.35998     	58.58867    	0           
  100         	16.83930    	0.48439     	33.48861    	8           
  300         	17.02100    	0.47714     	34.52335    	24          
  500         	17.11681    	0.48568     	34.05086    	39          
  1000        	16.95401    	0.48421     	34.44962    	78          
---> try to load best state
              	16.95401    	0.48421     	34.44962    	78               eval state : test_on_restore;
              	16.95401    	0.48421     	34.44962    	78               eval state : test_on_last_epoch;
---> final result              	16.95401    	0.48421     	34.44962    	78               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42328    	0.30826     	159.23380   	0           
  10          	74.36810    	0.35025     	133.93759   	0           
  100         	21.94864    	0.35590     	36.60486    	8           
  300         	18.35261    	0.45814     	35.17370    	26          
  500         	17.56190    	0.47673     	33.94807    	44          
  1000        	16.55151    	0.49043     	32.52464    	89          
---> try to load best state
              	16.55151    	0.49043     	32.52464    	89               eval state : test_on_restore;
              	16.55151    	0.49043     	32.52464    	89               eval state : test_on_last_epoch;
---> final result              	16.55151    	0.49043     	32.52464    	89               eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	461.07309   	-7.10022    	615.18182   	0           
  10          	120.18378   	-4.09269    	168.17996   	0           
  100         	20.86531    	0.32485     	40.04476    	0           
  300         	21.21424    	0.34975     	36.82434    	2           
  500         	26.27310    	-0.11450    	44.63089    	3           
  1000        	20.87883    	-1.43208    	37.25588    	7           
---> try to load best state
              	20.87883    	-1.43208    	37.25588    	7                eval state : test_on_restore;
              	20.87883    	-1.43208    	37.25588    	7                eval state : test_on_last_epoch;
---> final result              	20.87883    	-1.43208    	37.25588    	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	335.94879   	-3.19626    	501.53867   	0           
  10          	99.89718    	-1.37151    	157.96759   	0           
  100         	19.78135    	0.45073     	39.01910    	0           
  300         	18.63264    	0.47150     	36.81369    	2           
  500         	18.22722    	0.34958     	35.83178    	4           
  1000        	34.48269    	-4.78526    	63.58966    	9           
---> try to load best state
              	34.48269    	-4.78526    	63.58966    	9                eval state : test_on_restore;
              	34.48269    	-4.78526    	63.58966    	9                eval state : test_on_last_epoch;
---> final result              	34.48269    	-4.78526    	63.58966    	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	189.46692   	-0.37947    	351.23929   	0           
  10          	70.33378    	0.03072     	130.23198   	0           
  100         	17.56249    	0.47373     	36.34285    	1           
  300         	18.89403    	0.38104     	38.51759    	4           
  500         	17.83155    	0.39563     	36.08731    	7           
  1000        	17.39470    	0.38513     	34.66872    	14          
---> try to load best state
              	17.39470    	0.38513     	34.66872    	14               eval state : test_on_restore;
              	17.39470    	0.38513     	34.66872    	14               eval state : test_on_last_epoch;
---> final result              	17.39470    	0.38513     	34.66872    	14               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	164.08501   	-0.39036    	309.74805   	0           
  10          	56.44961    	0.02355     	107.05038   	0           
  100         	17.17782    	0.48200     	34.26298    	3           
  300         	17.41719    	0.45156     	34.18914    	8           
  500         	22.99757    	0.40071     	48.05861    	14          
  1000        	17.55313    	0.47157     	35.88770    	27          
---> try to load best state
              	17.55313    	0.47157     	35.88770    	27               eval state : test_on_restore;
              	17.55313    	0.47157     	35.88770    	27               eval state : test_on_last_epoch;
---> final result              	17.55313    	0.47157     	35.88770    	27               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	81.45575    	0.25654     	140.46930   	0           
  10          	33.11695    	0.37410     	60.39120    	0           
  100         	16.93120    	0.48456     	33.73275    	6           
  300         	17.03219    	0.48564     	34.40870    	19          
  500         	17.06180    	0.48575     	34.52852    	32          
  1000        	16.97002    	0.48847     	34.62941    	64          
---> try to load best state
              	16.97002    	0.48847     	34.62941    	64               eval state : test_on_restore;
              	16.97002    	0.48847     	34.62941    	64               eval state : test_on_last_epoch;
---> final result              	16.97002    	0.48847     	34.62941    	64               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.33247    	0.25337     	131.60988   	0           
  10          	30.69235    	0.35988     	58.60409    	0           
  100         	16.85304    	0.48430     	33.54009    	8           
  300         	17.16796    	0.48688     	34.91696    	24          
  500         	17.05878    	0.47883     	34.18541    	40          
  1000        	17.30486    	0.48152     	35.17176    	79          
---> try to load best state
              	17.30486    	0.48152     	35.17176    	80               eval state : test_on_restore;
              	17.30486    	0.48152     	35.17176    	80               eval state : test_on_last_epoch;
---> final result              	17.30486    	0.48152     	35.17176    	80               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42329    	0.30826     	159.23396   	0           
  10          	74.36797    	0.35025     	133.93753   	0           
  100         	21.94967    	0.35591     	36.60863    	9           
  300         	18.35430    	0.45830     	35.17985    	26          
  500         	17.56032    	0.47664     	33.94918    	44          
  1000        	16.55438    	0.49051     	32.51887    	300         
---> try to load best state
              	16.55438    	0.49051     	32.51887    	300              eval state : test_on_restore;
              	16.55438    	0.49051     	32.51887    	300              eval state : test_on_last_epoch;
---> final result              	16.55438    	0.49051     	32.51887    	300              eval state : final;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	467.15149   	-8.05984    	704.71497   	0           
  10          	171.55278   	-2.72861    	277.63620   	0           
  100         	18.66835    	0.43343     	38.68807    	0           
  300         	20.19919    	0.40807     	39.38445    	2           
  500         	26.36795    	-0.15086    	55.03172    	4           
  1000        	18.99227    	0.21944     	38.97219    	9           
---> try to load best state
              	18.99227    	0.21944     	38.97219    	9                eval state : test_on_restore;
              	18.99227    	0.21944     	38.97219    	9                eval state : test_on_last_epoch;
---> final result              	18.99227    	0.21944     	38.97219    	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	431.79779   	-6.75963    	689.77087   	0           
  10          	155.14780   	-2.27137    	267.55032   	0           
  100         	18.63074    	0.43721     	38.24727    	1           
  300         	17.85020    	0.44229     	37.36140    	3           
  500         	17.93569    	0.46141     	37.68942    	5           
  1000        	19.36995    	0.25569     	38.01160    	10          
---> try to load best state
              	19.36995    	0.25569     	38.01160    	10               eval state : test_on_restore;
              	19.36995    	0.25569     	38.01160    	10               eval state : test_on_last_epoch;
---> final result              	19.36995    	0.25569     	38.01160    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	232.64432   	-1.05123    	386.52512   	0           
  10          	74.47202    	-0.34839    	132.82874   	0           
  100         	17.85045    	0.46377     	35.43885    	1           
  300         	17.52869    	0.48254     	34.51221    	4           
  500         	17.90351    	0.36117     	36.57747    	7           
  1000        	17.35911    	0.46235     	34.36310    	15          
---> try to load best state
              	17.35911    	0.46235     	34.36310    	15               eval state : test_on_restore;
              	17.35911    	0.46235     	34.36310    	15               eval state : test_on_last_epoch;
---> final result              	17.35911    	0.46235     	34.36310    	15               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	141.29019   	-0.27470    	260.84998   	0           
  10          	49.21506    	0.05583     	96.82578    	0           
  100         	17.40639    	0.47883     	35.82170    	2           
  300         	17.31674    	0.46745     	35.31366    	8           
  500         	17.78831    	0.41575     	36.26683    	14          
  1000        	17.24861    	0.47078     	34.36974    	28          
---> try to load best state
              	17.24861    	0.47078     	34.36974    	28               eval state : test_on_restore;
              	17.24861    	0.47078     	34.36974    	28               eval state : test_on_last_epoch;
---> final result              	17.24861    	0.47078     	34.36974    	28               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	91.70017    	0.16846     	168.97298   	0           
  10          	35.92286    	0.30335     	72.30319    	0           
  100         	16.92319    	0.48203     	34.68136    	7           
  300         	17.36217    	0.48166     	35.78939    	23          
  500         	17.87500    	0.48134     	37.65385    	37          
  1000        	17.03625    	0.48513     	34.75060    	72          
---> try to load best state
              	17.03625    	0.48513     	34.75060    	72               eval state : test_on_restore;
              	17.03625    	0.48513     	34.75060    	72               eval state : test_on_last_epoch;
---> final result              	17.03625    	0.48513     	34.75060    	72               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.32282    	0.25345     	131.58812   	0           
  10          	30.69440    	0.35995     	58.60117    	1           
  100         	16.85603    	0.48438     	33.52408    	8           
  300         	17.11051    	0.48791     	34.94430    	26          
  500         	16.96201    	0.48802     	34.42093    	43          
  1000        	16.94866    	0.48455     	34.67982    	86          
---> try to load best state
              	16.94866    	0.48455     	34.67982    	87               eval state : test_on_restore;
              	16.94866    	0.48455     	34.67982    	87               eval state : test_on_last_epoch;
---> final result              	16.94866    	0.48455     	34.67982    	87               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42329    	0.30826     	159.23401   	0           
  10          	74.36795    	0.35025     	133.93739   	1           
  100         	21.95197    	0.35613     	36.60974    	9           
  300         	18.35040    	0.45825     	35.17070    	28          
  500         	17.55400    	0.47656     	33.93250    	46          
  1000        	16.53770    	0.49043     	32.48122    	93          
---> try to load best state
              	16.53770    	0.49043     	32.48122    	93               eval state : test_on_restore;
              	16.53770    	0.49043     	32.48122    	93               eval state : test_on_last_epoch;
---> final result              	16.53770    	0.49043     	32.48122    	93               eval state : final;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	624.22937   	-69.75884   	817.96185   	0           
  10          	248.62575   	-25.75992   	340.01508   	0           
  100         	19.32853    	0.03672     	39.54661    	1           
  300         	44.76085    	-0.27865    	82.09837    	3           
  500         	18.11228    	0.45235     	38.33444    	5           
  1000        	22.28536    	-2.40809    	44.27187    	9           
---> try to load best state
              	22.28536    	-2.40809    	44.27187    	9                eval state : test_on_restore;
              	22.28536    	-2.40809    	44.27187    	9                eval state : test_on_last_epoch;
---> final result              	22.28536    	-2.40809    	44.27187    	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	448.07025   	-6.15349    	728.03955   	0           
  10          	156.21382   	-2.28423    	268.96112   	0           
  100         	18.46501    	0.43282     	37.62959    	1           
  300         	17.96984    	0.47725     	36.75883    	3           
  500         	17.82359    	0.48334     	36.91967    	6           
  1000        	18.65601    	0.37639     	39.12617    	13          
---> try to load best state
              	18.65601    	0.37639     	39.12617    	13               eval state : test_on_restore;
              	18.65601    	0.37639     	39.12617    	13               eval state : test_on_last_epoch;
---> final result              	18.65601    	0.37639     	39.12617    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	312.12851   	-1.64868    	514.72845   	0           
  10          	101.95519   	-0.59514    	182.77338   	0           
  100         	17.52081    	0.46657     	38.78075    	2           
  300         	18.28699    	0.45921     	37.58697    	5           
  500         	17.95751    	0.47377     	40.05785    	10          
  1000        	17.49417    	0.46677     	38.83987    	19          
---> try to load best state
              	17.49417    	0.46677     	38.83987    	19               eval state : test_on_restore;
              	17.49417    	0.46677     	38.83987    	19               eval state : test_on_last_epoch;
---> final result              	17.49417    	0.46677     	38.83987    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	182.13513   	-0.52605    	319.79410   	0           
  10          	59.83457    	-0.09280    	111.31214   	0           
  100         	17.02088    	0.47318     	33.98454    	3           
  300         	17.33181    	0.45987     	33.96881    	10          
  500         	17.79239    	0.45450     	33.99272    	17          
  1000        	17.84122    	0.40988     	37.06976    	34          
---> try to load best state
              	17.84122    	0.40988     	37.06976    	34               eval state : test_on_restore;
              	17.84122    	0.40988     	37.06976    	34               eval state : test_on_last_epoch;
---> final result              	17.84122    	0.40988     	37.06976    	34               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	96.36682    	0.08435     	183.69804   	0           
  10          	36.40873    	0.25574     	70.51331    	0           
  100         	16.84376    	0.48294     	33.33478    	7           
  300         	17.04854    	0.48019     	34.57885    	21          
  500         	17.05141    	0.48652     	34.67187    	35          
  1000        	17.12898    	0.47916     	35.04208    	70          
---> try to load best state
              	17.12898    	0.47916     	35.04208    	70               eval state : test_on_restore;
              	17.12898    	0.47916     	35.04208    	70               eval state : test_on_last_epoch;
---> final result              	17.12898    	0.47916     	35.04208    	70               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.32956    	0.25340     	131.59846   	0           
  10          	30.68315    	0.35999     	58.57993    	1           
  100         	16.83517    	0.48430     	33.47989    	8           
  300         	17.04713    	0.47790     	34.48347    	26          
  500         	16.94033    	0.48241     	34.54129    	42          
  1000        	17.05929    	0.48623     	35.17399    	84          
---> try to load best state
              	17.05929    	0.48623     	35.17399    	84               eval state : test_on_restore;
              	17.05929    	0.48623     	35.17399    	84               eval state : test_on_last_epoch;
---> final result              	17.05929    	0.48623     	35.17399    	84               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42330    	0.30826     	159.23381   	0           
  10          	74.36797    	0.35025     	133.93752   	0           
  100         	21.94917    	0.35599     	36.60661    	9           
  300         	18.35187    	0.45828     	35.17191    	28          
  500         	17.55855    	0.47677     	33.93895    	47          
  1000        	16.53927    	0.49044     	32.49308    	92          
---> try to load best state
              	16.53927    	0.49044     	32.49308    	92               eval state : test_on_restore;
              	16.53927    	0.49044     	32.49308    	92               eval state : test_on_last_epoch;
---> final result              	16.53927    	0.49044     	32.49308    	92               eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	456.15170   	-11.78619   	690.66254   	0           
  10          	164.88338   	-4.66109    	248.42516   	0           
  100         	21.37645    	0.27300     	40.08874    	0           
  300         	23.94740    	0.20888     	47.22315    	2           
  500         	21.65995    	0.34913     	43.89114    	3           
  1000        	23.20153    	-0.81894    	40.86836    	7           
---> try to load best state
              	23.20153    	-0.81894    	40.86836    	7                eval state : test_on_restore;
              	23.20153    	-0.81894    	40.86836    	7                eval state : test_on_last_epoch;
---> final result              	23.20153    	-0.81894    	40.86836    	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	363.46719   	-5.69606    	579.80371   	0           
  10          	113.50624   	-2.03106    	185.45421   	0           
  100         	19.29605    	0.47146     	36.59081    	1           
  300         	22.49001    	0.42223     	45.45772    	2           
  500         	17.70929    	0.47036     	36.36058    	4           
  1000        	21.64736    	0.11605     	39.84008    	9           
---> try to load best state
              	21.64736    	0.11605     	39.84008    	9                eval state : test_on_restore;
              	21.64736    	0.11605     	39.84008    	9                eval state : test_on_last_epoch;
---> final result              	21.64736    	0.11605     	39.84008    	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	224.34050   	-0.99060    	394.21683   	0           
  10          	65.75008    	-0.26459    	120.22713   	0           
  100         	17.47061    	0.47905     	34.76820    	1           
  300         	17.34907    	0.45528     	34.94162    	4           
  500         	17.63856    	0.47381     	34.01121    	7           
  1000        	22.31931    	0.32806     	39.11689    	15          
---> try to load best state
              	22.31931    	0.32806     	39.11689    	15               eval state : test_on_restore;
              	22.31931    	0.32806     	39.11689    	15               eval state : test_on_last_epoch;
---> final result              	22.31931    	0.32806     	39.11689    	15               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	146.37292   	-0.29379    	284.80109   	0           
  10          	50.24286    	0.10201     	94.21791    	0           
  100         	17.12915    	0.48251     	33.95723    	2           
  300         	17.65392    	0.47925     	36.48841    	8           
  500         	17.23240    	0.46084     	34.38622    	14          
  1000        	17.15797    	0.45574     	34.70280    	29          
---> try to load best state
              	17.15797    	0.45574     	34.70280    	29               eval state : test_on_restore;
              	17.15797    	0.45574     	34.70280    	29               eval state : test_on_last_epoch;
---> final result              	17.15797    	0.45574     	34.70280    	29               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	83.39696    	0.22232     	153.32939   	0           
  10          	34.31979    	0.34766     	65.32404    	0           
  100         	16.89981    	0.48374     	33.62005    	6           
  300         	17.02617    	0.48077     	34.00985    	21          
  500         	17.10112    	0.48012     	34.55808    	35          
  1000        	16.93301    	0.48630     	34.42769    	67          
---> try to load best state
              	16.93301    	0.48630     	34.42769    	67               eval state : test_on_restore;
              	16.93301    	0.48630     	34.42769    	67               eval state : test_on_last_epoch;
---> final result              	16.93301    	0.48630     	34.42769    	67               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.32503    	0.25341     	131.59219   	0           
  10          	30.68755    	0.35997     	58.58819    	0           
  100         	16.83984    	0.48430     	33.49686    	8           
  300         	17.03131    	0.48478     	34.21221    	25          
  500         	17.20162    	0.48002     	35.51639    	41          
  1000        	17.00635    	0.45362     	34.51074    	82          
---> try to load best state
              	17.00635    	0.45362     	34.51074    	82               eval state : test_on_restore;
              	17.00635    	0.45362     	34.51074    	82               eval state : test_on_last_epoch;
---> final result              	17.00635    	0.45362     	34.51074    	82               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	88.42331    	0.30826     	159.23386   	0           
  10          	74.36783    	0.35024     	133.93745   	1           
  100         	21.94892    	0.35613     	36.60741    	9           
  300         	18.35144    	0.45835     	35.17222    	28          
  500         	17.55799    	0.47678     	33.93819    	47          
  1000        	16.53940    	0.49043     	32.49127    	93          
---> try to load best state
              	16.53940    	0.49043     	32.49127    	93               eval state : test_on_restore;
              	16.53940    	0.49043     	32.49127    	93               eval state : test_on_last_epoch;
---> final result              	16.53940    	0.49043     	32.49127    	93               eval state : final;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	601.15381   	-68.87992   	855.48138   	0           
  10          	228.55583   	-31.28697   	329.15842   	0           
  100         	21.46759    	0.08708     	41.88476    	1           
  300         	21.33813    	0.26011     	44.99526    	3           
  500         	19.16569    	0.43809     	38.06581    	4           
  1000        	19.29515    	0.26882     	37.76915    	9           
---> try to load best state
              	19.29515    	0.26882     	37.76915    	9                eval state : test_on_restore;
              	19.29515    	0.26882     	37.76915    	10               eval state : test_on_last_epoch;
---> final result              	19.29515    	0.26882     	37.76915    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	292.06140   	-3.29366    	458.16830   	0           
  10          	89.66408    	-1.28407    	149.10258   	0           
  100         	18.04447    	0.47552     	39.86043    	1           
  300         	17.64521    	0.48573     	36.33345    	4           
  500         	18.59785    	0.38181     	39.23533    	6           
  1000        	18.89953    	0.34533     	37.13477    	13          
---> try to load best state
              	18.89953    	0.34533     	37.13477    	13               eval state : test_on_restore;
              	18.89953    	0.34533     	37.13477    	13               eval state : test_on_last_epoch;
---> final result              	18.89953    	0.34533     	37.13477    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	206.78545   	-0.83358    	370.85416   	0           
  10          	68.02684    	-0.18101    	126.89266   	0           
  100         	17.31935    	0.48025     	34.81969    	2           
  300         	17.44618    	0.47523     	34.21955    	6           
  500         	17.37650    	0.47664     	34.35668    	10          
  1000        	17.08377    	0.47614     	34.84370    	21          
---> try to load best state
              	17.08377    	0.47614     	34.84370    	21               eval state : test_on_restore;
              	17.08377    	0.47614     	34.84370    	21               eval state : test_on_last_epoch;
---> final result              	17.08377    	0.47614     	34.84370    	21               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	113.38767   	0.05248     	199.71399   	0           
  10          	44.73022    	0.24532     	88.65874    	0           
  100         	17.11489    	0.48118     	34.74905    	3           
  300         	18.71187    	0.48027     	41.77832    	10          
  500         	17.22130    	0.48253     	34.37917    	17          
  1000        	16.93921    	0.48648     	35.23760    	33          
---> try to load best state
              	16.93921    	0.48648     	35.23760    	33               eval state : test_on_restore;
              	16.93921    	0.48648     	35.23760    	33               eval state : test_on_last_epoch;
---> final result              	16.93921    	0.48648     	35.23760    	33               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	78.96090    	0.20633     	135.28825   	0           
  10          	32.55986    	0.33852     	59.81472    	0           
  100         	16.87888    	0.48438     	33.55703    	7           
  300         	17.40819    	0.46914     	35.54706    	22          
  500         	17.22094    	0.48170     	33.92653    	37          
  1000        	17.07950    	0.44713     	34.06507    	72          
---> try to load best state
              	17.07950    	0.44713     	34.06507    	72               eval state : test_on_restore;
              	17.07950    	0.44713     	34.06507    	72               eval state : test_on_last_epoch;
---> final result              	17.07950    	0.44713     	34.06507    	72               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	74.32537    	0.25340     	131.59262   	0           
  10          	30.68669    	0.35998     	58.58635    	1           
  100         	16.83984    	0.48429     	33.49533    	9           
  300         	17.35965    	0.48540     	35.49598    	26          
  500         	17.04406    	0.47680     	34.80231    	43          
  1000        	17.01500    	0.45260     	34.66873    	86          
---> try to load best state
              	17.01500    	0.45260     	34.66873    	86               eval state : test_on_restore;
              	17.01500    	0.45260     	34.66873    	86               eval state : test_on_last_epoch;
---> final result              	17.01500    	0.45260     	34.66873    	86               eval state : final;module_name : SGAR;cp_record_file : True;
