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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	96.32738    	0.28563     	168.55647   	0           
  10          	82.32303    	0.33432     	143.09323   	0           
----------------------------------------

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	96.32738    	0.28563     	168.55647   	0           
  10          	82.32303    	0.33432     	143.09323   	0           
  100         	23.38271    	0.33114     	39.94157    	8           
  300         	18.50420    	0.46267     	35.02245    	24          
  500         	17.66773    	0.47629     	33.67623    	39          
  1000        	16.65862    	0.49190     	32.25457    	78          
---> try to load best state
              	16.65862    	0.49190     	32.25457    	78               eval state : test_on_restore;
              	16.65862    	0.49190     	32.25457    	78               eval state : test_on_last_epoch;
---> final result              	16.65862    	0.49190     	32.25457    	78               eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	459.45364   	-12.59721   	661.98132   	0           
  10          	146.46989   	-5.11146    	218.82896   	0           
  100         	19.55200    	0.36917     	38.20270    	1           
  300         	30.74103    	0.31958     	53.42914    	3           
  500         	19.29678    	0.43732     	40.00222    	5           
  1000        	19.17014    	0.22151     	36.53893    	9           
---> try to load best state
              	19.17014    	0.22151     	36.53893    	9                eval state : test_on_restore;
              	19.17014    	0.22151     	36.53893    	9                eval state : test_on_last_epoch;
---> final result              	19.17014    	0.22151     	36.53893    	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	349.32236   	-3.19632    	525.29474   	0           
  10          	107.25549   	-1.54622    	172.19615   	0           
  100         	18.07513    	0.44506     	37.07179    	1           
  300         	18.47921    	0.22668     	38.05341    	4           
  500         	17.66746    	0.48188     	35.80387    	6           
  1000        	17.96858    	0.43051     	36.63983    	13          
---> try to load best state
              	17.96858    	0.43051     	36.63983    	13               eval state : test_on_restore;
              	17.96858    	0.43051     	36.63983    	13               eval state : test_on_last_epoch;
---> final result              	17.96858    	0.43051     	36.63983    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	201.75928   	-0.92799    	335.74976   	0           
  10          	64.01841    	-0.31332    	113.16805   	0           
  100         	17.54323    	0.47456     	33.99277    	1           
  300         	18.77695    	0.41789     	36.59062    	5           
  500         	17.55192    	0.45343     	34.42358    	9           
  1000        	17.24519    	0.48266     	34.96657    	19          
---> try to load best state
              	17.24519    	0.48266     	34.96657    	19               eval state : test_on_restore;
              	17.24519    	0.48266     	34.96657    	19               eval state : test_on_last_epoch;
---> final result              	17.24519    	0.48266     	34.96657    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	113.59917   	0.01083     	194.95633   	0           
  10          	42.99294    	0.23349     	79.02020    	0           
  100         	17.36353    	0.48222     	33.58506    	3           
  300         	17.30772    	0.47102     	34.40825    	10          
  500         	17.76714    	0.41162     	36.09895    	17          
  1000        	19.44759    	0.46947     	39.45056    	33          
---> try to load best state
              	19.44759    	0.46947     	39.45056    	33               eval state : test_on_restore;
              	19.44759    	0.46947     	39.45056    	33               eval state : test_on_last_epoch;
---> final result              	19.44759    	0.46947     	39.45056    	33               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	81.30988    	0.23364     	139.87210   	0           
  10          	32.80674    	0.35461     	60.43316    	0           
  100         	17.04457    	0.48609     	33.45958    	7           
  300         	17.29486    	0.47976     	34.81177    	21          
  500         	17.26224    	0.47773     	35.29243    	35          
  1000        	17.45656    	0.48533     	35.60748    	71          
---> try to load best state
              	17.45656    	0.48533     	35.60748    	71               eval state : test_on_restore;
              	17.45656    	0.48533     	35.60748    	71               eval state : test_on_last_epoch;
---> final result              	17.45656    	0.48533     	35.60748    	71               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	97.42626    	0.30637     	168.33960   	0           
  10          	84.75243    	0.35632     	145.07048   	0           
  100         	24.94124    	0.29919     	40.58601    	7           
  300         	19.11818    	0.47481     	35.63847    	23          
  500         	18.27313    	0.48282     	34.30083    	39          
  1000        	17.27289    	0.49134     	32.90572    	78          
---> try to load best state
              	17.27289    	0.49134     	32.90572    	78               eval state : test_on_restore;
              	17.27289    	0.49134     	32.90572    	78               eval state : test_on_last_epoch;
---> final result              	17.27289    	0.49134     	32.90572    	78               eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	459.84885   	-7.05681    	613.46411   	0           
  10          	119.94389   	-4.04279    	167.74593   	0           
  100         	21.48409    	0.34439     	40.68505    	1           
  300         	21.57487    	0.37933     	36.82698    	2           
  500         	26.46391    	0.06244     	44.06546    	4           
  1000        	21.78086    	-1.03356    	38.35231    	9           
---> try to load best state
              	21.78086    	-1.03356    	38.35231    	9                eval state : test_on_restore;
              	21.78086    	-1.03356    	38.35231    	9                eval state : test_on_last_epoch;
---> final result              	21.78086    	-1.03356    	38.35231    	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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	334.68536   	-3.18076    	499.64914   	0           
  10          	99.46699    	-1.35339    	157.26419   	0           
  100         	20.48841    	0.45540     	39.67541    	1           
  300         	19.42654    	0.47080     	37.53386    	3           
  500         	18.89335    	0.34379     	36.44391    	5           
  1000        	35.64357    	-3.87652    	64.41965    	11          
---> try to load best state
              	35.64357    	-3.87652    	64.41965    	11               eval state : test_on_restore;
              	35.64357    	-3.87652    	64.41965    	11               eval state : test_on_last_epoch;
---> final result              	35.64357    	-3.87652    	64.41965    	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	188.64714   	-0.37141    	349.35876   	0           
  10          	69.88145    	0.03157     	129.40953   	0           
  100         	18.24806    	0.47414     	36.79137    	2           
  300         	19.73598    	0.37926     	39.12931    	6           
  500         	18.52758    	0.39786     	36.62099    	10          
  1000        	17.90354    	0.38223     	35.05278    	19          
---> try to load best state
              	17.90354    	0.38223     	35.05278    	19               eval state : test_on_restore;
              	17.90354    	0.38223     	35.05278    	19               eval state : test_on_last_epoch;
---> final result              	17.90354    	0.38223     	35.05278    	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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	163.32573   	-0.37939    	308.47543   	0           
  10          	56.06236    	0.02687     	106.44546   	0           
  100         	17.70630    	0.48140     	34.52666    	3           
  300         	17.96826    	0.44915     	34.63602    	8           
  500         	23.70935    	0.39349     	48.52027    	15          
  1000        	18.25893    	0.46819     	36.48120    	30          
---> try to load best state
              	18.25893    	0.46819     	36.48120    	30               eval state : test_on_restore;
              	18.25893    	0.46819     	36.48120    	30               eval state : test_on_last_epoch;
---> final result              	18.25893    	0.46819     	36.48120    	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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	81.51115    	0.26146     	140.08318   	0           
  10          	33.14146    	0.37386     	60.37895    	0           
  100         	17.56443    	0.48423     	34.25194    	7           
  300         	17.64153    	0.48834     	34.95025    	21          
  500         	17.66675    	0.48623     	35.06067    	37          
  1000        	17.58248    	0.48908     	35.17067    	73          
---> try to load best state
              	17.58248    	0.48908     	35.17067    	73               eval state : test_on_restore;
              	17.58248    	0.48908     	35.17067    	73               eval state : test_on_last_epoch;
---> final result              	17.58248    	0.48908     	35.17067    	74               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	107.51945   	0.20802     	205.89905   	0           
  10          	91.49305    	0.27879     	174.86775   	0           
  100         	23.87482    	0.36961     	41.68071    	7           
  300         	19.68057    	0.46675     	40.33884    	23          
  500         	18.86031    	0.47882     	39.21106    	38          
  1000        	17.86184    	0.48664     	37.88418    	76          
---> try to load best state
              	17.86184    	0.48664     	37.88418    	76               eval state : test_on_restore;
              	17.86184    	0.48664     	37.88418    	76               eval state : test_on_last_epoch;
---> final result              	17.86184    	0.48664     	37.88418    	76               eval state : final;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	467.58200   	-8.12971    	705.25641   	0           
  10          	171.21176   	-2.69643    	277.14725   	0           
  100         	19.97139    	0.43072     	43.90690    	1           
  300         	21.43634    	0.41234     	44.80851    	3           
  500         	27.79251    	-0.11611    	59.25146    	5           
  1000        	20.41592    	0.25838     	45.05445    	9           
---> try to load best state
              	20.41592    	0.25838     	45.05445    	9                eval state : test_on_restore;
              	20.41592    	0.25838     	45.05445    	10               eval state : test_on_last_epoch;
---> final result              	20.41592    	0.25838     	45.05445    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	432.42361   	-6.80637    	690.56488   	0           
  10          	154.75371   	-2.26398    	267.15637   	0           
  100         	19.94385    	0.43608     	43.39535    	1           
  300         	19.10564    	0.43934     	42.56820    	4           
  500         	19.25385    	0.45682     	43.07699    	6           
  1000        	20.47139    	0.29572     	44.11124    	13          
---> try to load best state
              	20.47139    	0.29572     	44.11124    	13               eval state : test_on_restore;
              	20.47139    	0.29572     	44.11124    	13               eval state : test_on_last_epoch;
---> final result              	20.47139    	0.29572     	44.11124    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	234.73645   	-1.05272    	390.23618   	0           
  10          	74.71872    	-0.34459    	133.97838   	0           
  100         	18.73820    	0.45664     	39.65871    	2           
  300         	18.39292    	0.47667     	40.08077    	6           
  500         	19.13438    	0.36612     	42.30853    	9           
  1000        	18.19962    	0.45958     	40.07661    	20          
---> try to load best state
              	18.19962    	0.45958     	40.07661    	20               eval state : test_on_restore;
              	18.19962    	0.45958     	40.07661    	20               eval state : test_on_last_epoch;
---> final result              	18.19962    	0.45958     	40.07661    	20               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	142.85719   	-0.27676    	264.39429   	0           
  10          	49.67321    	0.05724     	98.92677    	0           
  100         	18.67064    	0.47242     	41.58270    	3           
  300         	18.67170    	0.46182     	42.08759    	10          
  500         	19.23949    	0.40619     	42.87801    	17          
  1000        	18.25281    	0.46647     	40.65501    	34          
---> try to load best state
              	18.25281    	0.46647     	40.65501    	34               eval state : test_on_restore;
              	18.25281    	0.46647     	40.65501    	34               eval state : test_on_last_epoch;
---> final result              	18.25281    	0.46647     	40.65501    	34               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	93.91090    	0.16548     	173.69164   	0           
  10          	36.63359    	0.30066     	75.04576    	0           
  100         	18.29357    	0.47579     	40.91838    	7           
  300         	18.73407    	0.47351     	42.62306    	22          
  500         	19.55936    	0.47357     	44.71483    	36          
  1000        	18.19044    	0.47938     	41.22825    	71          
---> try to load best state
              	18.19044    	0.47938     	41.22825    	72               eval state : test_on_restore;
              	18.19044    	0.47938     	41.22825    	72               eval state : test_on_last_epoch;
---> final result              	18.19044    	0.47938     	41.22825    	72               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	115.06655   	0.13120     	222.28355   	0           
  10          	97.21147    	0.21420     	189.45697   	0           
  100         	26.17438    	0.42411     	49.11699    	8           
  300         	20.96404    	0.48368     	42.06687    	24          
  500         	20.02805    	0.48516     	40.24348    	39          
  1000        	18.92433    	0.48126     	38.21674    	79          
---> try to load best state
              	18.92433    	0.48126     	38.21674    	79               eval state : test_on_restore;
              	18.92433    	0.48126     	38.21674    	79               eval state : test_on_last_epoch;
---> final result              	18.92433    	0.48126     	38.21674    	79               eval state : final;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	623.95532   	-69.23054   	817.60742   	0           
  10          	249.23503   	-25.16614   	341.52603   	0           
  100         	21.40394    	0.14020     	43.69552    	0           
  300         	46.61796    	-0.21938    	86.03538    	2           
  500         	20.42587    	0.46377     	42.36034    	4           
  1000        	24.71139    	-1.26131    	48.53067    	7           
---> try to load best state
              	24.71139    	-1.26131    	48.53067    	7                eval state : test_on_restore;
              	24.71139    	-1.26131    	48.53067    	7                eval state : test_on_last_epoch;
---> final result              	24.71139    	-1.26131    	48.53067    	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	448.01901   	-6.12791    	727.95172   	0           
  10          	156.94237   	-2.26877    	270.56467   	0           
  100         	20.58717    	0.43632     	42.33952    	1           
  300         	20.24451    	0.46711     	41.30759    	2           
  500         	20.14579    	0.47200     	41.57284    	4           
  1000        	21.01007    	0.37182     	43.99397    	9           
---> try to load best state
              	21.01007    	0.37182     	43.99397    	9                eval state : test_on_restore;
              	21.01007    	0.37182     	43.99397    	9                eval state : test_on_last_epoch;
---> final result              	21.01007    	0.37182     	43.99397    	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': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	310.78851   	-1.65004    	512.13751   	0           
  10          	102.43070   	-0.58983    	184.22672   	0           
  100         	19.81970    	0.46605     	43.08880    	1           
  300         	20.36752    	0.48121     	40.76450    	4           
  500         	20.19096    	0.45630     	44.21500    	7           
  1000        	19.65800    	0.45864     	42.63384    	15          
---> try to load best state
              	19.65800    	0.45864     	42.63384    	15               eval state : test_on_restore;
              	19.65800    	0.45864     	42.63384    	15               eval state : test_on_last_epoch;
---> final result              	19.65800    	0.45864     	42.63384    	15               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	180.46507   	-0.52448    	316.33099   	0           
  10          	60.46274    	-0.09096    	113.30032   	0           
  100         	19.08191    	0.46950     	38.22065    	2           
  300         	19.28209    	0.47188     	37.56679    	8           
  500         	19.76576    	0.45759     	37.37144    	13          
  1000        	19.81481    	0.40559     	41.46511    	26          
---> try to load best state
              	19.81481    	0.40559     	41.46511    	26               eval state : test_on_restore;
              	19.81481    	0.40559     	41.46511    	26               eval state : test_on_last_epoch;
---> final result              	19.81481    	0.40559     	41.46511    	26               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	95.22535    	0.09421     	180.95926   	0           
  10          	37.39798    	0.25762     	73.12031    	0           
  100         	18.95785    	0.47407     	37.69622    	6           
  300         	19.06952    	0.47165     	38.76118    	20          
  500         	19.07664    	0.47691     	38.86937    	33          
  1000        	19.16444    	0.46885     	39.34234    	65          
---> try to load best state
              	19.16444    	0.46885     	39.34234    	65               eval state : test_on_restore;
              	19.16444    	0.46885     	39.34234    	65               eval state : test_on_last_epoch;
---> final result              	19.16444    	0.46885     	39.34234    	65               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	101.36311   	0.26777     	186.72198   	0           
  10          	87.14536    	0.31845     	162.25919   	0           
  100         	24.77588    	0.36759     	43.05816    	7           
  300         	19.23368    	0.47131     	36.71386    	21          
  500         	18.25365    	0.48054     	35.10369    	35          
  1000        	17.09836    	0.49113     	33.44183    	71          
---> try to load best state
              	17.09836    	0.49113     	33.44183    	71               eval state : test_on_restore;
              	17.09836    	0.49113     	33.44183    	71               eval state : test_on_last_epoch;
---> final result              	17.09836    	0.49113     	33.44183    	71               eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	455.35034   	-11.77264   	689.41364   	0           
  10          	164.84157   	-4.64161    	248.06349   	0           
  100         	22.18342    	0.28284     	41.55470    	0           
  300         	24.65360    	0.20662     	48.49723    	2           
  500         	22.47289    	0.34689     	45.41979    	3           
  1000        	23.94712    	-0.64075    	42.16996    	7           
---> try to load best state
              	23.94712    	-0.64075    	42.16996    	7                eval state : test_on_restore;
              	23.94712    	-0.64075    	42.16996    	7                eval state : test_on_last_epoch;
---> final result              	23.94712    	-0.64075    	42.16996    	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	362.26614   	-5.68060    	578.55823   	0           
  10          	113.43327   	-2.02251    	185.17772   	0           
  100         	19.88727    	0.47319     	37.65078    	1           
  300         	23.03133    	0.41857     	46.40507    	2           
  500         	18.39956    	0.47169     	37.32198    	4           
  1000        	22.16109    	0.19268     	40.47888    	11          
---> try to load best state
              	22.16109    	0.19268     	40.47888    	11               eval state : test_on_restore;
              	22.16109    	0.19268     	40.47888    	11               eval state : test_on_last_epoch;
---> final result              	22.16109    	0.19268     	40.47888    	11               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	223.20117   	-0.99069    	393.28763   	0           
  10          	65.91999    	-0.25776    	120.18636   	0           
  100         	18.04589    	0.48008     	35.59721    	1           
  300         	17.96646    	0.45406     	35.81654    	5           
  500         	18.18612    	0.47635     	34.80267    	9           
  1000        	22.56324    	0.34787     	39.36034    	19          
---> try to load best state
              	22.56324    	0.34787     	39.36034    	19               eval state : test_on_restore;
              	22.56324    	0.34787     	39.36034    	19               eval state : test_on_last_epoch;
---> final result              	22.56324    	0.34787     	39.36034    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	145.74167   	-0.29690    	283.95193   	0           
  10          	50.56976    	0.10395     	94.36742    	0           
  100         	17.72541    	0.48251     	34.96161    	3           
  300         	18.22118    	0.47728     	37.09632    	10          
  500         	17.82224    	0.46190     	35.16250    	17          
  1000        	17.72278    	0.45662     	35.38215    	33          
---> try to load best state
              	17.72278    	0.45662     	35.38215    	33               eval state : test_on_restore;
              	17.72278    	0.45662     	35.38215    	33               eval state : test_on_last_epoch;
---> final result              	17.72278    	0.45662     	35.38215    	33               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	83.00896    	0.22409     	152.35315   	0           
  10          	34.64775    	0.34758     	65.63518    	0           
  100         	17.48093    	0.48435     	34.45435    	7           
  300         	17.67145    	0.48521     	34.96727    	22          
  500         	17.68469    	0.48031     	35.30741    	36          
  1000        	17.50084    	0.48693     	35.10929    	71          
---> try to load best state
              	17.50084    	0.48693     	35.10929    	71               eval state : test_on_restore;
              	17.50084    	0.48693     	35.10929    	71               eval state : test_on_last_epoch;
---> final result              	17.50084    	0.48693     	35.10929    	71               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	96.31594    	0.25963     	164.93346   	0           
  10          	84.62469    	0.31585     	142.25107   	0           
  100         	24.96504    	0.34093     	41.87341    	7           
  300         	19.42656    	0.47341     	35.84741    	23          
  500         	18.59424    	0.48289     	34.60974    	39          
  1000        	17.53908    	0.49223     	33.24770    	78          
---> try to load best state
              	17.53908    	0.49223     	33.24770    	78               eval state : test_on_restore;
              	17.53908    	0.49223     	33.24770    	78               eval state : test_on_last_epoch;
---> final result              	17.53908    	0.49223     	33.24770    	78               eval state : final;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	601.16211   	-68.58718   	855.48230   	0           
  10          	228.60374   	-31.13601   	328.99011   	0           
  100         	22.14985    	0.12001     	42.38073    	1           
  300         	21.94409    	0.26426     	45.11877    	3           
  500         	20.13360    	0.45475     	38.85210    	5           
  1000        	20.28771    	0.32546     	38.50899    	10          
---> try to load best state
              	20.28771    	0.32546     	38.50899    	10               eval state : test_on_restore;
              	20.28771    	0.32546     	38.50899    	10               eval state : test_on_last_epoch;
---> final result              	20.28771    	0.32546     	38.50899    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	292.29092   	-3.30185    	458.77698   	0           
  10          	89.84311    	-1.26966    	149.05685   	0           
  100         	18.91978    	0.47818     	40.31490    	1           
  300         	18.59753    	0.48817     	37.01217    	4           
  500         	19.43524    	0.38585     	39.61908    	6           
  1000        	19.88412    	0.37827     	37.87170    	13          
---> try to load best state
              	19.88412    	0.37827     	37.87170    	13               eval state : test_on_restore;
              	19.88412    	0.37827     	37.87170    	13               eval state : test_on_last_epoch;
---> final result              	19.88412    	0.37827     	37.87170    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	206.97610   	-0.83058    	371.31274   	0           
  10          	68.26813    	-0.17758    	126.88060   	0           
  100         	18.37775    	0.48273     	35.75229    	2           
  300         	18.57742    	0.47833     	35.36884    	5           
  500         	18.40334    	0.47952     	35.36888    	9           
  1000        	18.10549    	0.47870     	35.71429    	19          
---> try to load best state
              	18.10549    	0.47870     	35.71429    	19               eval state : test_on_restore;
              	18.10549    	0.47870     	35.71429    	19               eval state : test_on_last_epoch;
---> final result              	18.10549    	0.47870     	35.71429    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	113.17335   	0.05290     	199.10123   	0           
  10          	45.10439    	0.24671     	88.87514    	0           
  100         	18.16945    	0.48377     	35.63118    	3           
  300         	19.11965    	0.48004     	41.58686    	10          
  500         	18.33175    	0.48484     	35.42159    	17          
  1000        	17.94435    	0.48800     	36.00440    	33          
---> try to load best state
              	17.94435    	0.48800     	36.00440    	33               eval state : test_on_restore;
              	17.94435    	0.48800     	36.00440    	33               eval state : test_on_last_epoch;
---> final result              	17.94435    	0.48800     	36.00440    	33               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	78.54905    	0.20742     	134.83508   	0           
  10          	33.03790    	0.34006     	60.23267    	0           
  100         	17.92606    	0.48635     	34.48526    	7           
  300         	18.26362    	0.46864     	36.17373    	21          
  500         	18.48913    	0.48573     	35.37694    	35          
  1000        	18.11666    	0.44929     	35.06570    	70          
---> try to load best state
              	18.11666    	0.44929     	35.06570    	70               eval state : test_on_restore;
              	18.11666    	0.44929     	35.06570    	71               eval state : test_on_last_epoch;
---> final result              	18.11666    	0.44929     	35.06570    	71               eval state : final;module_name : SGAR;cp_record_file : True;
