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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02007     	-0.06653    	0.03427     	0           
  10          	0.01752     	0.19791     	0.03032     	0           
  100         	0.00645     	0.87696     	0.01147     	1           
  300         	0.00438     	0.95263     	0.00734     	5           
  500         	0.00415     	0.96051     	0.00690     	9           
---> try to load best state
              	0.00449     	0.91233     	0.00730     	15               eval state : test_on_restore;
              	0.00935     	-0.56386    	0.01261     	15               eval state : test_on_last_epoch;
---> final result              	0.00449     	0.91233     	0.00730     	15               eval state : final;
----------> finish x-yl training


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	196.80977   	-0.67992    	373.71100   	0           
  10          	160.09254   	-0.57335    	329.03723   	0           
  100         	46.70286    	0.22554     	103.22558   	5           
  300         	26.79902    	0.34651     	61.12722    	14          
  epoch 311 reach nan state
---> try to load best state
              	26.78045    	0.34657     	61.11333    	14               eval state : test_on_restore;
              	26.79902    	0.34651     	61.12722    	14               eval state : test_on_last_epoch;
---> final result
              	26.78045    	0.34657     	61.11333    	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01736     	0.19541     	0.02847     	0           
  10          	0.01515     	0.36529     	0.02519     	0           
  100         	0.00539     	0.89261     	0.00855     	2           
  300         	0.00321     	0.96731     	0.00512     	7           
  500         	0.00292     	0.97594     	0.00465     	12          
----------------------------------------

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02007     	-0.06653    	0.03427     	0           
  10          	0.01752     	0.19791     	0.03032     	0           
  100         	0.00645     	0.87696     	0.01147     	2           
  300         	0.00438     	0.95263     	0.00734     	6           
  500         	0.00415     	0.96051     	0.00690     	11          
---> try to load best state
              	0.00449     	0.91233     	0.00730     	18               eval state : test_on_restore;
              	0.00935     	-0.56386    	0.01261     	18               eval state : test_on_last_epoch;
---> final result              	0.00449     	0.91233     	0.00730     	18               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	530.88354   	-13.14663   	699.90631   	0           
  10          	453.31384   	-9.44771    	644.13208   	0           
  100         	206.57703   	-1.26721    	301.63177   	1           
  300         	155.68250   	-0.61136    	236.81091   	4           
  500         	151.58275   	-0.56816    	233.31689   	7           
  1000        	174.81136   	-0.83112    	257.71594   	14          
---> try to load best state
              	174.81136   	-0.83112    	257.71594   	14               eval state : test_on_restore;
              	174.81136   	-0.83112    	257.71594   	14               eval state : test_on_last_epoch;
---> final result
              	174.81136   	-0.83112    	257.71594   	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	400.21640   	-4.89957    	595.39343   	0           
  10          	331.33673   	-3.48936    	541.30090   	0           
  100         	150.15450   	-0.25201    	254.63953   	2           
  300         	109.01271   	0.00399     	188.53545   	5           
  500         	101.55631   	0.03978     	177.62645   	8           
  epoch 800 reach nan state
---> try to load best state
              	101.22071   	0.04134     	176.90562   	13               eval state : test_on_restore;
              	101.55631   	0.03978     	177.62645   	13               eval state : test_on_last_epoch;
---> final result
              	101.22071   	0.04134     	176.90562   	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	230.08713   	-0.71779    	429.72125   	0           
  10          	198.73015   	-0.48268    	392.67014   	0           
  100         	88.41675    	0.29834     	173.50343   	2           
  epoch 279 reach nan state
---> try to load best state
              	58.01330    	0.38246     	116.22771   	8                eval state : test_on_restore;
              	88.41675    	0.29834     	173.50343   	8                eval state : test_on_last_epoch;
---> final result
              	58.01330    	0.38246     	116.22771   	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	196.80977   	-0.67992    	373.71100   	0           
  10          	160.09254   	-0.57335    	329.03723   	0           
  100         	46.70286    	0.22554     	103.22558   	5           
  300         	26.79902    	0.34651     	61.12722    	14          
  epoch 311 reach nan state
---> try to load best state
              	26.78045    	0.34657     	61.11333    	15               eval state : test_on_restore;
              	26.79902    	0.34651     	61.12722    	15               eval state : test_on_last_epoch;
---> final result
              	26.78045    	0.34657     	61.11333    	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01736     	0.19541     	0.02847     	0           
  10          	0.01515     	0.36529     	0.02519     	0           
  100         	0.00539     	0.89261     	0.00855     	2           
  300         	0.00321     	0.96731     	0.00512     	6           
  500         	0.00292     	0.97594     	0.00465     	9           
---> try to load best state
              	0.00373     	0.85694     	0.00512     	16               eval state : test_on_restore;
              	0.01544     	-2.40533    	0.01929     	16               eval state : test_on_last_epoch;
---> final result              	0.00373     	0.85694     	0.00512     	16               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	493.41464   	-11.36551   	759.86676   	0           
  10          	433.80536   	-9.08306    	731.32953   	0           
  100         	230.30757   	-1.97327    	422.32996   	1           
  300         	180.48289   	-1.11036    	349.99597   	4           
  500         	176.90562   	-1.05464    	346.90225   	6           
  epoch 868 reach nan state
---> try to load best state
              	179.10275   	-1.08775    	348.83536   	10               eval state : test_on_restore;
              	176.90562   	-1.05464    	346.90225   	11               eval state : test_on_last_epoch;
---> final result
              	176.90562   	-1.05464    	346.90225   	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	459.79651   	-9.46327    	751.54730   	0           
  10          	405.24744   	-7.53806    	729.40228   	0           
  100         	192.16084   	-1.43992    	408.05930   	1           
  300         	76.78237    	0.06706     	183.51047   	5           
  epoch 321 reach nan state
---> try to load best state
              	76.59244    	0.06835     	183.08159   	5                eval state : test_on_restore;
              	76.78237    	0.06706     	183.51047   	5                eval state : test_on_last_epoch;
---> final result
              	76.59244    	0.06835     	183.08159   	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	264.31580   	-1.57212    	449.55630   	0           
  10          	219.45367   	-1.16872    	403.08606   	0           
  100         	68.38303    	0.09297     	127.88320   	2           
  300         	34.34078    	0.32302     	70.72342    	7           
  epoch 316 reach nan state
---> try to load best state
              	34.29626    	0.32349     	70.65958    	7                eval state : test_on_restore;
              	34.34078    	0.32302     	70.72342    	7                eval state : test_on_last_epoch;
---> final result
              	34.29626    	0.32349     	70.65958    	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	160.88492   	-0.42663    	303.34036   	0           
  10          	129.96992   	-0.42074    	265.49884   	0           
  100         	38.73856    	0.18469     	71.27725    	4           
  epoch 273 reach nan state
---> try to load best state
              	17.77027    	0.39658     	40.68792    	12               eval state : test_on_restore;
              	38.73856    	0.18469     	71.27725    	12               eval state : test_on_last_epoch;
---> final result
              	17.77027    	0.39658     	40.68792    	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01979     	0.08864     	0.03362     	0           
  10          	0.01733     	0.28976     	0.02995     	0           
  100         	0.00573     	0.88293     	0.01048     	1           
  300         	0.00330     	0.96580     	0.00578     	7           
  500         	0.00296     	0.97534     	0.00504     	12          
---> try to load best state
              	0.00386     	0.85911     	0.00588     	19               eval state : test_on_restore;
              	0.01580     	-2.23351    	0.01997     	19               eval state : test_on_last_epoch;
---> final result              	0.00386     	0.85911     	0.00588     	19               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	649.33722   	-160.85356  	864.63519   	0           
  10          	599.04181   	-96.01365   	852.64459   	0           
  100         	531.73285   	-9.95613    	752.33350   	1           
  300         	350.55371   	-2.79201    	536.54425   	4           
  500         	303.75815   	-2.10392    	475.71585   	7           
  epoch 723 reach nan state
---> try to load best state
              	326.30875   	-2.41172    	505.15491   	11               eval state : test_on_restore;
              	303.75815   	-2.10392    	475.71585   	11               eval state : test_on_last_epoch;
---> final result
              	303.75815   	-2.10392    	475.71585   	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	460.41080   	-7.97885    	764.96619   	0           
  10          	417.75101   	-6.19699    	742.91779   	0           
  100         	238.10590   	-0.94812    	454.34598   	2           
  300         	138.25174   	-0.25278    	283.23294   	6           
  500         	116.84796   	-0.15952    	243.60338   	9           
  epoch 654 reach nan state
---> try to load best state
              	113.68742   	-0.14520    	238.30205   	11               eval state : test_on_restore;
              	116.84796   	-0.15952    	243.60338   	11               eval state : test_on_last_epoch;
---> final result
              	113.68742   	-0.14520    	238.30205   	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	337.32999   	-2.13867    	577.06494   	0           
  10          	286.52771   	-1.59917    	534.22632   	0           
  100         	90.94925    	-0.12939    	194.51602   	2           
  epoch 240 reach nan state
---> try to load best state
              	52.62835    	0.02770     	120.83544   	5                eval state : test_on_restore;
              	90.94925    	-0.12939    	194.51602   	5                eval state : test_on_last_epoch;
---> final result
              	52.62835    	0.02770     	120.83544   	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	206.30679   	-0.69664    	373.28482   	0           
  10          	168.55208   	-0.53689    	330.36182   	0           
  100         	46.46835    	0.08225     	95.38928    	4           
  300         	18.18561    	0.21350     	41.91584    	13          
  epoch 325 reach nan state
---> try to load best state
              	18.06147    	0.21845     	41.75979    	15               eval state : test_on_restore;
              	18.18561    	0.21350     	41.91584    	15               eval state : test_on_last_epoch;
---> final result
              	18.06147    	0.21845     	41.75979    	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01822     	0.04472     	0.03140     	0           
  10          	0.01604     	0.27893     	0.02732     	0           
  100         	0.00583     	0.90082     	0.00978     	2           
  300         	0.00370     	0.96824     	0.00607     	5           
  500         	0.00337     	0.97507     	0.00551     	9           
---> try to load best state
              	0.00395     	0.87156     	0.00608     	15               eval state : test_on_restore;
              	0.01452     	-3.06462    	0.01834     	15               eval state : test_on_last_epoch;
---> final result              	0.00395     	0.87156     	0.00608     	15               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	557.84613   	-41.24175   	844.58282   	0           
  10          	526.28473   	-33.89753   	838.09998   	0           
  100         	446.73581   	-8.37820    	746.73730   	1           
  300         	364.91769   	-5.82540    	640.10852   	4           
  500         	368.43692   	-5.91432    	644.70667   	6           
  epoch 928 reach nan state
---> try to load best state
              	423.45364   	-7.67593    	711.61176   	11               eval state : test_on_restore;
              	368.43692   	-5.91432    	644.70667   	11               eval state : test_on_last_epoch;
---> final result
              	368.43692   	-5.91432    	644.70667   	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	424.97379   	-10.81116   	687.29187   	0           
  10          	358.43680   	-8.04859    	636.28210   	0           
  100         	139.96146   	-0.33208    	274.38892   	2           
  300         	101.21569   	0.02153     	206.86990   	5           
  500         	95.27175    	0.06101     	199.22485   	10          
  epoch 610 reach nan state
---> try to load best state
              	97.27915    	0.04754     	201.67111   	12               eval state : test_on_restore;
              	95.27175    	0.06101     	199.22485   	12               eval state : test_on_last_epoch;
---> final result
              	95.27175    	0.06101     	199.22485   	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	260.65250   	-1.82226    	463.86575   	0           
  10          	210.62915   	-1.26102    	413.14536   	0           
  100         	81.88178    	0.11650     	169.42560   	2           
  epoch 297 reach nan state
---> try to load best state
              	55.93326    	0.26929     	126.40432   	7                eval state : test_on_restore;
              	81.88178    	0.11650     	169.42560   	7                eval state : test_on_last_epoch;
---> final result
              	55.93326    	0.26929     	126.40432   	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	173.09853   	-0.53585    	339.91589   	0           
  10          	142.54320   	-0.44066    	294.49005   	0           
  100         	41.87039    	0.14088     	90.25359    	4           
  epoch 232 reach nan state
---> try to load best state
              	27.53270    	0.33381     	61.79375    	10               eval state : test_on_restore;
              	41.87039    	0.14088     	90.25359    	10               eval state : test_on_last_epoch;
---> final result
              	27.53270    	0.33381     	61.79375    	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01418     	0.57671     	0.02301     	0           
  10          	0.01306     	0.65945     	0.02076     	0           
  100         	0.00586     	0.93012     	0.00889     	1           
  300         	0.00372     	0.97494     	0.00577     	5           
  500         	0.00337     	0.98083     	0.00524     	9           
---> try to load best state
              	0.00384     	0.87466     	0.00564     	17               eval state : test_on_restore;
              	0.01602     	-2.96050    	0.01956     	17               eval state : test_on_last_epoch;
---> final result              	0.00384     	0.87466     	0.00564     	17               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	601.37262   	-163.41789  	877.01514   	0           
  10          	562.57281   	-187.02824  	877.34631   	0           
  100         	483.54059   	-14.98664   	745.49890   	1           
  300         	386.20740   	-6.25167    	622.39581   	5           
  500         	396.46838   	-6.70725    	636.25275   	8           
  epoch 817 reach nan state
---> try to load best state
              	420.33984   	-7.85731    	668.77435   	12               eval state : test_on_restore;
              	396.46838   	-6.70725    	636.25275   	12               eval state : test_on_last_epoch;
---> final result
              	396.46838   	-6.70725    	636.25275   	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	328.90448   	-4.88144    	532.73273   	0           
  10          	268.76047   	-3.44715    	478.27875   	0           
  100         	106.09580   	-0.10220    	198.19162   	1           
  300         	66.27769    	0.18699     	131.73322   	4           
  500         	62.62676    	0.21425     	127.60477   	8           
  epoch 753 reach nan state
---> try to load best state
              	62.30011    	0.21691     	127.30412   	13               eval state : test_on_restore;
              	62.62676    	0.21425     	127.60477   	13               eval state : test_on_last_epoch;
---> final result
              	62.30011    	0.21691     	127.30412   	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	237.38600   	-1.22206    	433.37445   	0           
  10          	195.75661   	-0.85572    	392.32141   	0           
  100         	82.70663    	0.25595     	158.93781   	2           
  300         	47.23485    	0.36753     	101.97713   	7           
  500         	42.36327    	0.38600     	94.78571    	12          
  epoch 539 reach nan state
---> try to load best state
              	42.36647    	0.38583     	94.77090    	13               eval state : test_on_restore;
              	42.36327    	0.38600     	94.78571    	13               eval state : test_on_last_epoch;
---> final result
              	42.36647    	0.38583     	94.77090    	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	129.18660   	-0.01234    	238.02776   	0           
  10          	110.00760   	-0.14507    	210.50404   	0           
  100         	40.32611    	0.17221     	77.13207    	5           
  epoch 210 reach nan state
---> try to load best state
              	26.14135    	0.42240     	54.52454    	9                eval state : test_on_restore;
              	40.32611    	0.17221     	77.13207    	10               eval state : test_on_last_epoch;
---> final result
              	26.14135    	0.42240     	54.52454    	10               eval state : final;module_name : GAR;cp_record_file : True;
