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

  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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01488     	0.30943     	0.02438     	0           
  10          	0.01356     	0.42818     	0.02211     	0           
  100         	0.00692     	0.79300     	0.01096     	2           
  300         	0.00580     	0.85659     	0.00925     	7           
  500         	0.00524     	0.88225     	0.00845     	11          
  1000        	0.00463     	0.90633     	0.00753     	24          
---> try to load best state
              	0.00463     	0.90633     	0.00753     	24               eval state : test_on_restore;
              	0.00463     	0.90633     	0.00753     	24               eval state : test_on_last_epoch;
---> final result              	0.00463     	0.90633     	0.00753     	24               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	578.97858   	-89.54222   	820.17108   	0           
  10          	558.61896   	-79.26963   	741.04266   	0           
  100         	308.91129   	-94.07310   	452.75592   	1           
  300         	269.67865   	-1212.47863 	450.46613   	3           
  500         	268.73749   	-3910.54359 	452.20441   	5           
  1000        	248.29340   	-7387.89412 	447.25287   	10          
---> try to load best state
              	248.29340   	-7387.89412 	447.25287   	10               eval state : test_on_restore;
              	248.29340   	-7387.89412 	447.25287   	10               eval state : test_on_last_epoch;
---> final result              	248.29340   	-7387.89412 	447.25287   	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': 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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	487.91516   	-20.65898   	698.58636   	0           
  10          	466.04391   	-16.43431   	620.70300   	0           
  100         	231.65465   	-8.10178    	358.32175   	1           
  300         	214.14755   	-17.02566   	375.53104   	4           
  500         	202.45897   	-8.90212    	354.20905   	7           
  1000        	160.18413   	-2.56470    	298.12225   	14          
---> try to load best state
              	160.18413   	-2.56470    	298.12225   	14               eval state : test_on_restore;
              	160.18413   	-2.56470    	298.12225   	14               eval state : test_on_last_epoch;
---> final result              	160.18413   	-2.56470    	298.12225   	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': 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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	366.93182   	-8.13419    	546.71161   	0           
  10          	343.29236   	-5.71163    	471.62146   	0           
  100         	158.00812   	-1.52582    	262.48926   	1           
  300         	130.64886   	-1.36581    	252.56789   	5           
  500         	107.20235   	-0.43830    	209.98999   	8           
  1000        	78.11969    	0.09717     	157.67963   	18          
---> try to load best state
              	78.11969    	0.09717     	157.67963   	18               eval state : test_on_restore;
              	78.11969    	0.09717     	157.67963   	18               eval state : test_on_last_epoch;
---> final result              	78.11969    	0.09717     	157.67963   	18               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	242.01552   	-2.53963    	380.10928   	0           
  10          	223.07130   	-1.65967    	327.70148   	0           
  100         	106.01678   	-0.09261    	182.47636   	3           
  300         	79.22961    	0.05079     	161.66502   	9           
  500         	61.89229    	0.24271     	131.86835   	15          
  1000        	46.32226    	0.36501     	107.54661   	30          
---> try to load best state
              	46.32226    	0.36501     	107.54661   	30               eval state : test_on_restore;
              	46.32226    	0.36501     	107.54661   	30               eval state : test_on_last_epoch;
---> final result              	46.32226    	0.36501     	107.54661   	30               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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02180     	-0.35977    	0.03647     	0           
  10          	0.01931     	-0.04120    	0.03270     	0           
  100         	0.00873     	0.74631     	0.01417     	2           
  300         	0.00718     	0.81542     	0.01146     	7           
  500         	0.00647     	0.84975     	0.01029     	11          
  1000        	0.00564     	0.88507     	0.00892     	23          
---> try to load best state
              	0.00564     	0.88507     	0.00892     	23               eval state : test_on_restore;
              	0.00564     	0.88507     	0.00892     	23               eval state : test_on_last_epoch;
---> final result              	0.00564     	0.88507     	0.00892     	23               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	605.44110   	-54.62008   	775.53882   	0           
  10          	604.65363   	-45.17593   	742.47778   	0           
  100         	359.21686   	-38.96270   	498.54514   	1           
  300         	327.89749   	-170.30912  	492.73364   	3           
  500         	336.85516   	-119.23360  	497.22693   	5           
  1000        	311.55396   	-30.16944   	478.65460   	9           
---> try to load best state
              	311.55396   	-30.16944   	478.65460   	9                eval state : test_on_restore;
              	311.55396   	-30.16944   	478.65460   	9                eval state : test_on_last_epoch;
---> final result              	311.55396   	-30.16944   	478.65460   	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': 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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	498.16965   	-19.64942   	698.03174   	0           
  10          	476.04401   	-15.26008   	625.62292   	0           
  100         	249.16063   	-8.60522    	397.16852   	1           
  300         	226.63451   	-10.88436   	396.24475   	3           
  500         	213.93950   	-6.42532    	382.71072   	6           
  1000        	191.65033   	-3.02339    	363.00983   	12          
---> try to load best state
              	191.65033   	-3.02339    	363.00983   	12               eval state : test_on_restore;
              	191.65033   	-3.02339    	363.00983   	12               eval state : test_on_last_epoch;
---> final result              	191.65033   	-3.02339    	363.00983   	12               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	337.61154   	-5.69540    	542.43524   	0           
  10          	303.99231   	-3.88240    	463.97534   	0           
  100         	153.53674   	-1.41885    	280.23544   	1           
  300         	134.13971   	-1.29098    	273.78519   	5           
  500         	112.92818   	-0.37796    	236.97188   	10          
  1000        	85.59657    	0.13909     	188.75899   	19          
---> try to load best state
              	85.59657    	0.13909     	188.75899   	19               eval state : test_on_restore;
              	85.59657    	0.13909     	188.75899   	19               eval state : test_on_last_epoch;
---> final result              	85.59657    	0.13909     	188.75899   	19               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	288.07114   	-3.16840    	483.31238   	0           
  10          	261.29602   	-2.18712    	408.74109   	0           
  100         	117.31048   	-0.33103    	210.48964   	3           
  300         	87.75980    	-0.05270    	182.73360   	9           
  500         	71.13699    	0.16832     	152.84705   	16          
  1000        	54.32625    	0.31697     	123.21090   	29          
---> try to load best state
              	54.32625    	0.31697     	123.21090   	29               eval state : test_on_restore;
              	54.32625    	0.31697     	123.21090   	29               eval state : test_on_last_epoch;
---> final result              	54.32625    	0.31697     	123.21090   	29               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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01963     	-0.09295    	0.03130     	0           
  10          	0.01752     	0.12382     	0.02825     	0           
  100         	0.00822     	0.73074     	0.01286     	1           
  300         	0.00659     	0.81767     	0.01039     	5           
  500         	0.00598     	0.84541     	0.00953     	9           
  1000        	0.00539     	0.87079     	0.00866     	19          
---> try to load best state
              	0.00539     	0.87079     	0.00866     	19               eval state : test_on_restore;
              	0.00539     	0.87079     	0.00866     	19               eval state : test_on_last_epoch;
---> final result              	0.00539     	0.87079     	0.00866     	19               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	562.24286   	-51.81394   	811.54303   	0           
  10          	541.21747   	-44.81695   	730.75995   	0           
  100         	294.47223   	-28.12176   	441.07358   	0           
  300         	272.93970   	-210.98246  	458.08130   	2           
  500         	269.33643   	-280.81663  	452.87265   	4           
  1000        	250.69060   	-33.36726   	429.20117   	9           
---> try to load best state
              	250.69060   	-33.36726   	429.20117   	9                eval state : test_on_restore;
              	250.69060   	-33.36726   	429.20117   	9                eval state : test_on_last_epoch;
---> final result              	250.69060   	-33.36726   	429.20117   	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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	531.90302   	-36.05299   	798.27966   	0           
  10          	508.88110   	-31.43923   	715.74286   	0           
  100         	265.43335   	-15.05879   	418.55118   	1           
  300         	230.62100   	-75.16767   	433.04697   	3           
  500         	229.80513   	-169.82698  	440.78860   	5           
  1000        	216.48006   	-116.43198  	435.11856   	11          
---> try to load best state
              	216.48006   	-116.43198  	435.11856   	11               eval state : test_on_restore;
              	216.48006   	-116.43198  	435.11856   	11               eval state : test_on_last_epoch;
---> final result              	216.48006   	-116.43198  	435.11856   	11               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	389.79016   	-8.87389    	579.92480   	0           
  10          	366.13376   	-6.49697    	508.43378   	0           
  100         	179.23703   	-2.47975    	294.55304   	1           
  300         	168.82056   	-5.22376    	319.67133   	4           
  500         	148.51810   	-2.38954    	284.29208   	7           
  1000        	111.81456   	-0.55653    	221.39601   	14          
---> try to load best state
              	111.81456   	-0.55653    	221.39601   	14               eval state : test_on_restore;
              	111.81456   	-0.55653    	221.39601   	14               eval state : test_on_last_epoch;
---> final result              	111.81456   	-0.55653    	221.39601   	14               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	273.96915   	-3.02482    	434.47818   	0           
  10          	254.42395   	-2.03431    	375.25146   	0           
  100         	115.65043   	-0.22319    	194.25084   	2           
  300         	87.03088    	-0.06787    	172.86163   	7           
  500         	68.23037    	0.16363     	141.18706   	12          
  1000        	49.48960    	0.31468     	111.71832   	25          
---> try to load best state
              	49.48960    	0.31468     	111.71832   	25               eval state : test_on_restore;
              	49.48960    	0.31468     	111.71832   	25               eval state : test_on_last_epoch;
---> final result              	49.48960    	0.31468     	111.71832   	25               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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02215     	-0.21049    	0.03634     	0           
  10          	0.01968     	0.04247     	0.03290     	0           
  100         	0.00859     	0.73952     	0.01456     	1           
  300         	0.00662     	0.82744     	0.01112     	6           
  500         	0.00600     	0.85716     	0.01020     	12          
  1000        	0.00544     	0.88389     	0.00933     	22          
---> try to load best state
              	0.00544     	0.88389     	0.00933     	22               eval state : test_on_restore;
              	0.00544     	0.88389     	0.00933     	22               eval state : test_on_last_epoch;
---> final result              	0.00544     	0.88389     	0.00933     	22               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	658.56018   	-585.81418  	872.01086   	0           
  10          	649.69873   	-420.86326  	814.56952   	0           
  100         	425.82379   	-123.48486  	534.65082   	0           
  300         	318.03070   	-254.25508  	469.34601   	3           
  500         	303.82855   	-502.09833  	462.19229   	4           
  1000        	270.02121   	-446.19802  	445.22888   	9           
---> try to load best state
              	270.02121   	-446.19802  	445.22888   	9                eval state : test_on_restore;
              	270.02121   	-446.19802  	445.22888   	9                eval state : test_on_last_epoch;
---> final result              	270.02121   	-446.19802  	445.22888   	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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	519.45337   	-26.95516   	806.39380   	0           
  10          	485.21420   	-21.70449   	719.60944   	0           
  100         	255.22729   	-8.99014    	435.50772   	1           
  300         	228.82880   	-10.19085   	433.91574   	3           
  500         	222.95308   	-6.62098    	425.08142   	6           
  1000        	193.86258   	-2.78773    	387.69739   	12          
---> try to load best state
              	193.86258   	-2.78773    	387.69739   	12               eval state : test_on_restore;
              	193.86258   	-2.78773    	387.69739   	12               eval state : test_on_last_epoch;
---> final result              	193.86258   	-2.78773    	387.69739   	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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	435.35669   	-9.14364    	671.12292   	0           
  10          	402.41296   	-6.55458    	582.12610   	0           
  100         	175.03564   	-1.73960    	290.68448   	2           
  300         	125.73586   	-1.03199    	248.92000   	5           
  500         	100.21111   	-0.46097    	208.12614   	9           
  1000        	70.10384    	-0.09419    	164.65680   	19          
---> try to load best state
              	70.10384    	-0.09419    	164.65680   	19               eval state : test_on_restore;
              	70.10384    	-0.09419    	164.65680   	19               eval state : test_on_last_epoch;
---> final result              	70.10384    	-0.09419    	164.65680   	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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	314.13766   	-3.34277    	496.28949   	0           
  10          	286.48471   	-2.36129    	426.04703   	0           
  100         	120.78607   	-0.39481    	201.36777   	3           
  300         	83.07889    	-0.14451    	165.77258   	9           
  500         	64.45483    	0.07584     	136.49387   	16          
  1000        	47.81115    	0.23118     	112.23323   	31          
---> try to load best state
              	47.81115    	0.23118     	112.23323   	31               eval state : test_on_restore;
              	47.81115    	0.23118     	112.23323   	31               eval state : test_on_last_epoch;
---> final result              	47.81115    	0.23118     	112.23323   	31               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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02011     	-0.29680    	0.03417     	0           
  10          	0.01798     	0.00937     	0.03028     	0           
  100         	0.00911     	0.67529     	0.01404     	2           
  300         	0.00782     	0.73873     	0.01216     	7           
  500         	0.00704     	0.78592     	0.01100     	11          
  1000        	0.00615     	0.83470     	0.00964     	23          
---> try to load best state
              	0.00615     	0.83470     	0.00964     	23               eval state : test_on_restore;
              	0.00615     	0.83470     	0.00964     	23               eval state : test_on_last_epoch;
---> final result              	0.00615     	0.83470     	0.00964     	23               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	584.80139   	-143.48232  	860.32104   	0           
  10          	575.57361   	-137.22895  	777.89807   	0           
  100         	360.16019   	-221.19359  	514.20813   	1           
  300         	294.87790   	-880.57654  	485.89575   	2           
  500         	291.56201   	-2046.40137 	486.17661   	4           
  1000        	275.42133   	-1150.32522 	479.94000   	9           
---> try to load best state
              	275.42133   	-1150.32522 	479.94000   	9                eval state : test_on_restore;
              	275.42133   	-1150.32522 	479.94000   	9                eval state : test_on_last_epoch;
---> final result              	275.42133   	-1150.32522 	479.94000   	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': 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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	510.48166   	-42.90024   	767.73749   	0           
  10          	489.74759   	-39.94002   	673.62085   	0           
  100         	239.32970   	-10.16697   	393.80563   	1           
  300         	222.54926   	-22.49944   	405.26886   	4           
  500         	217.44466   	-12.01023   	393.20166   	6           
  1000        	191.54695   	-4.44669    	360.80585   	13          
---> try to load best state
              	191.54695   	-4.44669    	360.80585   	13               eval state : test_on_restore;
              	191.54695   	-4.44669    	360.80585   	13               eval state : test_on_last_epoch;
---> final result              	191.54695   	-4.44669    	360.80585   	13               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	383.26389   	-9.70915    	598.67871   	0           
  10          	356.35141   	-7.01129    	512.89728   	0           
  100         	165.77548   	-2.12366    	282.36819   	2           
  300         	139.89653   	-1.97628    	271.49368   	5           
  500         	117.14213   	-0.80466    	233.13016   	9           
  1000        	85.63918    	-0.06822    	181.09628   	18          
---> try to load best state
              	85.63918    	-0.06822    	181.09628   	18               eval state : test_on_restore;
              	85.63918    	-0.06822    	181.09628   	18               eval state : test_on_last_epoch;
---> final result              	85.63918    	-0.06822    	181.09628   	18               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	276.34830   	-3.63197    	475.34564   	0           
  10          	256.33600   	-2.43606    	402.91306   	0           
  100         	125.83713   	-0.40861    	219.94127   	3           
  300         	99.99969    	-0.28110    	204.10687   	9           
  500         	81.13475    	0.03445     	171.22911   	14          
  1000        	61.10820    	0.24470     	137.61346   	27          
---> try to load best state
              	61.10820    	0.24470     	137.61346   	27               eval state : test_on_restore;
              	61.10820    	0.24470     	137.61346   	27               eval state : test_on_last_epoch;
---> final result              	61.10820    	0.24470     	137.61346   	27               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]'], '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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01514     	0.43667     	0.02511     	0           
  10          	0.01379     	0.55138     	0.02275     	0           
  100         	0.00707     	0.82588     	0.01140     	1           
  300         	0.00592     	0.86066     	0.01009     	5           
  500         	0.00554     	0.87813     	0.00955     	9           
  1000        	0.00515     	0.89675     	0.00892     	19          
---> try to load best state
              	0.00515     	0.89675     	0.00892     	19               eval state : test_on_restore;
              	0.00515     	0.89675     	0.00892     	19               eval state : test_on_last_epoch;
---> final result              	0.00515     	0.89675     	0.00892     	19               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	608.99792   	-647.08798  	882.47754   	0           
  10          	593.79303   	-453.16455  	804.94708   	0           
  100         	377.77655   	-218.36629  	538.61475   	0           
  300         	292.30447   	-407.17868  	505.51520   	2           
  500         	293.81516   	-788.07781  	516.21600   	3           
  1000        	271.03702   	-532.86902  	496.78275   	7           
---> try to load best state
              	271.03702   	-532.86902  	496.78275   	7                eval state : test_on_restore;
              	271.03702   	-532.86902  	496.78275   	7                eval state : test_on_last_epoch;
---> final result              	271.03702   	-532.86902  	496.78275   	7                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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	454.68961   	-25.28522   	670.69263   	0           
  10          	427.72659   	-19.27927   	587.44366   	0           
  100         	216.09665   	-8.01715    	350.13593   	0           
  300         	216.74513   	-35.02668   	395.40366   	3           
  500         	210.95261   	-22.68984   	383.49261   	6           
  1000        	162.82751   	-3.47054    	306.94629   	13          
---> try to load best state
              	162.82751   	-3.47054    	306.94629   	13               eval state : test_on_restore;
              	162.82751   	-3.47054    	306.94629   	13               eval state : test_on_last_epoch;
---> final result              	162.82751   	-3.47054    	306.94629   	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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	351.11072   	-7.06621    	549.32654   	0           
  10          	321.48233   	-5.02437    	471.21497   	0           
  100         	149.60066   	-1.35982    	265.89523   	2           
  300         	127.66508   	-1.14410    	257.51416   	5           
  500         	104.27829   	-0.27813    	216.82384   	10          
  1000        	75.86499    	0.18682     	167.66179   	20          
---> try to load best state
              	75.86499    	0.18682     	167.66179   	20               eval state : test_on_restore;
              	75.86499    	0.18682     	167.66179   	20               eval state : test_on_last_epoch;
---> final result              	75.86499    	0.18682     	167.66179   	20               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: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	226.48677   	-2.09090    	372.04590   	0           
  10          	201.89091   	-1.26434    	314.63580   	0           
  100         	96.28455    	0.08935     	172.45474   	3           
  300         	69.14834    	0.24040     	147.74432   	9           
  500         	54.37740    	0.34654     	125.94485   	15          
  1000        	42.51632    	0.41006     	113.41890   	31          
---> try to load best state
              	42.51632    	0.41006     	113.41890   	31               eval state : test_on_restore;
              	42.51632    	0.41006     	113.41890   	31               eval state : test_on_last_epoch;
---> final result              	42.51632    	0.41006     	113.41890   	31               eval state : final;module_name : SGAR;cp_record_file : True;
