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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	130.65968   	0.04878     	230.48550   	0           
  10          	115.67155   	0.16104     	207.17157   	0           
  100         	34.86905    	0.41580     	63.66299    	3           
  300         	23.44057    	0.47873     	42.47451    	11          
  500         	22.32258    	0.47965     	40.69336    	19          
  1000        	21.33553    	0.47236     	39.21212    	40          
---> try to load best state
              	21.33553    	0.47236     	39.21212    	40               eval state : test_on_restore;
              	21.33553    	0.47236     	39.21212    	40               eval state : test_on_last_epoch;
---> final result              	21.33553    	0.47236     	39.21212    	40               eval state : final;
---> end


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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           	459.47284   	-12.50315   	664.74524   	0           
  10          	147.03603   	-4.81322    	220.33482   	0           
  100         	23.28677    	0.41939     	42.87674    	1           
  300         	26.76263    	0.45181     	47.03765    	3           
  500         	22.68525    	0.38496     	41.66799    	5           
  1000        	28.17383    	-0.08423    	47.01294    	10          
---> try to load best state
              	28.17383    	-0.08423    	47.01294    	10               eval state : test_on_restore;
              	28.17383    	-0.08423    	47.01294    	10               eval state : test_on_last_epoch;
---> final result              	28.17383    	-0.08423    	47.01294    	10               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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           	350.30991   	-3.15862    	527.84784   	0           
  10          	108.09771   	-1.45004    	173.71793   	0           
  100         	22.12031    	0.44660     	42.72755    	2           
  300         	21.61222    	0.22429     	42.02994    	5           
  500         	29.59867    	0.46980     	49.93407    	7           
  1000        	22.66995    	0.45229     	41.59954    	13          
---> try to load best state
              	22.66995    	0.45229     	41.59954    	13               eval state : test_on_restore;
              	22.66995    	0.45229     	41.59954    	13               eval state : test_on_last_epoch;
---> final result              	22.66995    	0.45229     	41.59954    	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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           	202.76457   	-0.90569    	338.17035   	0           
  10          	64.89928    	-0.27568    	115.00911   	0           
  100         	21.63208    	0.45454     	39.66254    	1           
  300         	22.41000    	0.38530     	41.68190    	4           
  500         	22.08973    	0.44162     	42.25426    	7           
  1000        	21.93332    	0.14883     	40.83259    	15          
---> try to load best state
              	21.93332    	0.14883     	40.83259    	15               eval state : test_on_restore;
              	21.93332    	0.14883     	40.83259    	15               eval state : test_on_last_epoch;
---> final result              	21.93332    	0.14883     	40.83259    	15               eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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           	113.58870   	0.02290     	197.66023   	0           
  10          	45.31610    	0.23680     	83.11623    	0           
  100         	21.50675    	0.45410     	39.42209    	4           
  300         	21.25083    	0.46325     	38.65859    	13          
  500         	22.43271    	0.43647     	39.50686    	21          
  1000        	21.27167    	0.44360     	40.14602    	44          
---> try to load best state
              	21.27167    	0.44360     	40.14602    	44               eval state : test_on_restore;
              	21.27167    	0.44360     	40.14602    	44               eval state : test_on_last_epoch;
---> final result              	21.27167    	0.44360     	40.14602    	44               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	196.75124   	-0.58729    	378.20691   	0           
  10          	166.62883   	-0.30626    	331.11221   	0           
  100         	40.13585    	0.44228     	81.59708    	4           
  300         	29.69427    	0.45355     	52.95094    	12          
  500         	28.45393    	0.45430     	50.18145    	20          
  1000        	27.35421    	0.45001     	47.93501    	40          
---> try to load best state
              	27.35421    	0.45001     	47.93501    	40               eval state : test_on_restore;
              	27.35421    	0.45001     	47.93501    	40               eval state : test_on_last_epoch;
---> final result              	27.35421    	0.45001     	47.93501    	40               eval state : final;
---> end


---------->
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: 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           	454.48331   	-6.94183    	604.75220   	0           
  10          	120.18361   	-3.29723    	169.32736   	0           
  100         	29.27736    	0.38318     	49.51538    	1           
  300         	29.56055    	0.38987     	46.90698    	3           
  500         	31.64578    	0.26747     	53.05549    	5           
  1000        	29.75493    	0.08767     	48.00343    	10          
---> try to load best state
              	29.75493    	0.08767     	48.00343    	10               eval state : test_on_restore;
              	29.75493    	0.08767     	48.00343    	10               eval state : test_on_last_epoch;
---> final result              	29.75493    	0.08767     	48.00343    	10               eval state : final;module_name : SGAR;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: 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           	328.16696   	-2.87664    	492.24042   	0           
  10          	101.50294   	-1.11209    	160.02722   	0           
  100         	29.12269    	0.42008     	49.45962    	1           
  300         	28.75325    	0.42281     	49.30556    	3           
  500         	28.43363    	0.43196     	48.59864    	5           
  1000        	29.08512    	0.38545     	49.15213    	11          
---> try to load best state
              	29.08512    	0.38545     	49.15213    	11               eval state : test_on_restore;
              	29.08512    	0.38545     	49.15213    	11               eval state : test_on_last_epoch;
---> final result              	29.08512    	0.38545     	49.15213    	11               eval state : final;module_name : SGAR;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: 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           	184.65825   	-0.32605    	343.24905   	0           
  10          	73.22555    	0.07206     	133.19479   	0           
  100         	27.50148    	0.42506     	48.33686    	1           
  300         	28.70830    	0.34784     	50.45527    	4           
  500         	33.38269    	0.32710     	61.00717    	7           
  1000        	26.95068    	0.42860     	46.99360    	15          
---> try to load best state
              	26.95068    	0.42860     	46.99360    	15               eval state : test_on_restore;
              	26.95068    	0.42860     	46.99360    	15               eval state : test_on_last_epoch;
---> final result              	26.95068    	0.42860     	46.99360    	15               eval state : final;module_name : SGAR;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: 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           	158.60385   	-0.32979    	300.77103   	0           
  10          	60.05916    	0.08413     	111.81239   	0           
  100         	27.00091    	0.42945     	46.52231    	4           
  300         	27.39540    	0.43360     	48.29856    	13          
  500         	26.96617    	0.43789     	45.15811    	22          
  1000        	26.92039    	0.43267     	46.17223    	43          
---> try to load best state
              	26.92039    	0.43267     	46.17223    	43               eval state : test_on_restore;
              	26.92039    	0.43267     	46.17223    	43               eval state : test_on_last_epoch;
---> final result              	26.92039    	0.43267     	46.17223    	43               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	160.41031   	-0.31743    	307.53506   	0           
  10          	136.01427   	-0.12002    	268.12885   	0           
  100         	36.24395    	0.43481     	65.94878    	3           
  300         	29.66304    	0.43449     	59.69798    	11          
  500         	28.71803    	0.43622     	58.97666    	19          
  1000        	27.84501    	0.42886     	58.41724    	39          
---> try to load best state
              	27.84501    	0.42886     	58.41724    	39               eval state : test_on_restore;
              	27.84501    	0.42886     	58.41724    	39               eval state : test_on_last_epoch;
---> final result              	27.84501    	0.42886     	58.41724    	39               eval state : final;
---> end


---------->
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: 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           	468.65900   	-8.09840    	707.70264   	0           
  10          	172.88968   	-2.58086    	279.56009   	0           
  100         	27.81229    	0.39176     	58.69707    	1           
  300         	29.04922    	0.36840     	60.94811    	3           
  500         	31.87188    	0.43629     	64.50512    	4           
  1000        	29.70951    	-0.03612    	61.38671    	9           
---> try to load best state
              	29.70951    	-0.03612    	61.38671    	9                eval state : test_on_restore;
              	29.70951    	-0.03612    	61.38671    	9                eval state : test_on_last_epoch;
---> final result              	29.70951    	-0.03612    	61.38671    	9                eval state : final;module_name : SGAR;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: 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           	433.81641   	-6.76207    	693.48456   	0           
  10          	156.31822   	-2.21880    	269.58908   	0           
  100         	27.81940    	0.40132     	58.21811    	1           
  300         	27.16148    	0.38926     	57.59824    	3           
  500         	27.24452    	0.40503     	57.88764    	5           
  1000        	27.05359    	0.26818     	58.32041    	11          
---> try to load best state
              	27.05359    	0.26818     	58.32041    	11               eval state : test_on_restore;
              	27.05359    	0.26818     	58.32041    	11               eval state : test_on_last_epoch;
---> final result              	27.05359    	0.26818     	58.32041    	11               eval state : final;module_name : SGAR;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: 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           	238.39055   	-1.06252    	398.61298   	0           
  10          	78.00686    	-0.32717    	140.10774   	0           
  100         	27.30775    	0.40708     	56.63530    	1           
  300         	26.79201    	0.40239     	56.36014    	4           
  500         	28.21412    	0.42594     	56.59288    	7           
  1000        	26.77519    	0.40925     	56.77924    	14          
---> try to load best state
              	26.77519    	0.40925     	56.77924    	14               eval state : test_on_restore;
              	26.77519    	0.40925     	56.77924    	15               eval state : test_on_last_epoch;
---> final result              	26.77519    	0.40925     	56.77924    	15               eval state : final;module_name : SGAR;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: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	146.29518   	-0.27803    	272.08853   	0           
  10          	53.84217    	0.06473     	106.61666   	0           
  100         	27.42947    	0.40712     	58.40511    	4           
  300         	27.12935    	0.41201     	58.20963    	13          
  500         	27.75233    	0.38034     	58.69624    	22          
  1000        	26.66959    	0.41690     	56.81594    	46          
---> try to load best state
              	26.66959    	0.41690     	56.81594    	46               eval state : test_on_restore;
              	26.66959    	0.41690     	56.81594    	46               eval state : test_on_last_epoch;
---> final result              	26.66959    	0.41690     	56.81594    	46               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	203.33586   	-0.56942    	375.68076   	0           
  10          	174.26385   	-0.32653    	331.25751   	0           
  100         	37.79172    	0.44281     	80.45721    	4           
  300         	32.35707    	0.43559     	67.68114    	12          
  500         	31.20047    	0.43594     	65.44447    	20          
  1000        	30.20268    	0.42173     	63.47687    	40          
---> try to load best state
              	30.20268    	0.42173     	63.47687    	40               eval state : test_on_restore;
              	30.20268    	0.42173     	63.47687    	40               eval state : test_on_last_epoch;
---> final result              	30.20268    	0.42173     	63.47687    	40               eval state : final;
---> end


---------->
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: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	624.96777   	-69.74434   	819.46313   	0           
  10          	250.56297   	-23.16014   	344.21088   	0           
  100         	30.22539    	0.20030     	62.95710    	1           
  300         	32.78707    	0.24826     	69.75146    	2           
  500         	30.52336    	0.13823     	62.03513    	4           
  1000        	32.20417    	0.29564     	63.27992    	9           
---> try to load best state
              	32.20417    	0.29564     	63.27992    	9                eval state : test_on_restore;
              	32.20417    	0.29564     	63.27992    	9                eval state : test_on_last_epoch;
---> final result              	32.20417    	0.29564     	63.27992    	9                eval state : final;module_name : SGAR;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: 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           	448.48349   	-6.18547    	729.14349   	0           
  10          	158.80377   	-2.20551    	273.77896   	0           
  100         	29.10620    	0.38384     	61.19275    	1           
  300         	28.94169    	0.41136     	59.99290    	3           
  500         	28.75000    	0.40772     	60.25790    	5           
  1000        	28.82855    	0.39331     	60.11819    	12          
---> try to load best state
              	28.82855    	0.39331     	60.11819    	12               eval state : test_on_restore;
              	28.82855    	0.39331     	60.11819    	12               eval state : test_on_last_epoch;
---> final result              	28.82855    	0.39331     	60.11819    	12               eval state : final;module_name : SGAR;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: 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           	315.01080   	-1.63421    	520.81262   	0           
  10          	106.51645   	-0.55699    	190.91600   	0           
  100         	29.23691    	0.40311     	63.11315    	1           
  300         	29.79729    	0.42361     	60.89280    	5           
  500         	32.87089    	0.36184     	63.28535    	8           
  1000        	31.81271    	0.38399     	63.23068    	15          
---> try to load best state
              	31.81271    	0.38399     	63.23068    	15               eval state : test_on_restore;
              	31.81271    	0.38399     	63.23068    	15               eval state : test_on_last_epoch;
---> final result              	31.81271    	0.38399     	63.23068    	15               eval state : final;module_name : SGAR;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: 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           	185.85837   	-0.51272    	327.54419   	0           
  10          	65.90424    	-0.06549    	123.50477   	0           
  100         	28.70978    	0.40754     	59.14236    	4           
  300         	28.36879    	0.41309     	57.56079    	14          
  500         	28.40481    	0.39222     	58.30977    	24          
  1000        	27.75491    	0.40558     	59.37024    	47          
---> try to load best state
              	27.75491    	0.40558     	59.37024    	47               eval state : test_on_restore;
              	27.75491    	0.40558     	59.37024    	47               eval state : test_on_last_epoch;
---> final result              	27.75491    	0.40558     	59.37024    	47               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	174.21561   	-0.43841    	345.09113   	0           
  10          	149.35500   	-0.19959    	297.72037   	0           
  100         	37.72984    	0.41940     	80.16168    	4           
  300         	27.45801    	0.45970     	58.27104    	12          
  500         	26.13308    	0.46047     	55.76568    	20          
  1000        	24.93752    	0.45112     	53.59333    	42          
---> try to load best state
              	24.93752    	0.45112     	53.59333    	42               eval state : test_on_restore;
              	24.93752    	0.45112     	53.59333    	42               eval state : test_on_last_epoch;
---> final result              	24.93752    	0.45112     	53.59333    	42               eval state : final;
---> end


---------->
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: 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           	450.43195   	-11.44092   	678.00848   	0           
  10          	164.62645   	-4.42327    	248.61630   	0           
  100         	28.32973    	0.30550     	58.69168    	1           
  300         	30.76585    	0.31948     	65.53329    	3           
  500         	32.11418    	0.03731     	68.81161    	5           
  1000        	28.78469    	0.25483     	56.61756    	9           
---> try to load best state
              	28.78469    	0.25483     	56.61756    	9                eval state : test_on_restore;
              	28.78469    	0.25483     	56.61756    	9                eval state : test_on_last_epoch;
---> final result              	28.78469    	0.25483     	56.61756    	9                eval state : final;module_name : SGAR;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: 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           	357.03275   	-5.50232    	565.95715   	0           
  10          	113.65141   	-1.95688    	187.89943   	0           
  100         	26.16997    	0.42142     	54.13212    	1           
  300         	30.04112    	0.35628     	61.04076    	3           
  500         	25.67181    	0.42440     	52.23971    	5           
  1000        	25.89215    	0.32925     	51.87083    	10          
---> try to load best state
              	25.89215    	0.32925     	51.87083    	10               eval state : test_on_restore;
              	25.89215    	0.32925     	51.87083    	10               eval state : test_on_last_epoch;
---> final result              	25.89215    	0.32925     	51.87083    	10               eval state : final;module_name : SGAR;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: 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           	217.54068   	-0.94936    	378.23572   	0           
  10          	68.90298    	-0.24647    	126.40801   	0           
  100         	24.21109    	0.42839     	49.49129    	1           
  300         	27.45497    	0.39517     	47.53384    	4           
  500         	23.60469    	0.40361     	48.17371    	6           
  1000        	23.38828    	0.44888     	48.07720    	13          
---> try to load best state
              	23.38828    	0.44888     	48.07720    	13               eval state : test_on_restore;
              	23.38828    	0.44888     	48.07720    	13               eval state : test_on_last_epoch;
---> final result              	23.38828    	0.44888     	48.07720    	13               eval state : final;module_name : SGAR;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: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	141.81842   	-0.25751    	269.81677   	0           
  10          	53.82594    	0.10127     	101.86703   	0           
  100         	24.23038    	0.42823     	49.99243    	4           
  300         	24.71583    	0.41247     	52.02864    	12          
  500         	24.60187    	0.41625     	51.56563    	21          
  1000        	23.34633    	0.43115     	46.71854    	46          
---> try to load best state
              	23.34633    	0.43115     	46.71854    	46               eval state : test_on_restore;
              	23.34633    	0.43115     	46.71854    	47               eval state : test_on_last_epoch;
---> final result              	23.34633    	0.43115     	46.71854    	47               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 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           	129.60701   	0.08387     	242.31181   	0           
  10          	114.40147   	0.19727     	213.61964   	0           
  100         	35.71910    	0.32864     	66.28648    	5           
  300         	27.15225    	0.46390     	55.47803    	17          
  500         	26.29875    	0.46878     	54.81042    	29          
  1000        	25.54338    	0.47717     	54.36930    	60          
---> try to load best state
              	25.54338    	0.47717     	54.36930    	60               eval state : test_on_restore;
              	25.54338    	0.47717     	54.36930    	60               eval state : test_on_last_epoch;
---> final result              	25.54338    	0.47717     	54.36930    	60               eval state : final;
---> end


---------->
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: 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           	600.99286   	-70.23885   	855.83911   	0           
  10          	228.25830   	-29.80966   	328.52463   	0           
  100         	27.87356    	0.27586     	55.19459    	1           
  300         	30.66568    	0.32012     	63.88886    	4           
  500         	27.40308    	0.44497     	58.15341    	6           
  1000        	50.41058    	-5.00532    	83.88171    	13          
---> try to load best state
              	50.41058    	-5.00532    	83.88171    	13               eval state : test_on_restore;
              	50.41058    	-5.00532    	83.88171    	13               eval state : test_on_last_epoch;
---> final result              	50.41058    	-5.00532    	83.88171    	13               eval state : final;module_name : SGAR;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: 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           	298.40881   	-3.26838    	469.02078   	0           
  10          	92.66814    	-1.14757    	155.42336   	0           
  100         	26.78628    	0.45466     	60.71006    	1           
  300         	26.09188    	0.46713     	57.87816    	4           
  500         	25.51253    	0.46390     	57.28380    	7           
  1000        	32.33565    	-0.07260    	64.32855    	15          
---> try to load best state
              	32.33565    	-0.07260    	64.32855    	15               eval state : test_on_restore;
              	32.33565    	-0.07260    	64.32855    	15               eval state : test_on_last_epoch;
---> final result              	32.33565    	-0.07260    	64.32855    	15               eval state : final;module_name : SGAR;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: 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           	211.96796   	-0.83282    	382.21094   	0           
  10          	71.58806    	-0.14853    	134.07201   	0           
  100         	25.44526    	0.46236     	54.85426    	2           
  300         	25.70591    	0.41956     	54.38249    	6           
  500         	26.05710    	0.33930     	55.64391    	10          
  1000        	24.91601    	0.42891     	54.38921    	21          
---> try to load best state
              	24.91601    	0.42891     	54.38921    	21               eval state : test_on_restore;
              	24.91601    	0.42891     	54.38921    	21               eval state : test_on_last_epoch;
---> final result              	24.91601    	0.42891     	54.38921    	21               eval state : final;module_name : SGAR;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: 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           	120.44823   	0.04325     	219.01003   	0           
  10          	48.73887    	0.26001     	98.10188    	0           
  100         	25.19884    	0.46248     	54.79819    	6           
  300         	24.85470    	0.46970     	54.35545    	19          
  500         	25.77574    	0.46593     	54.99122    	31          
  1000        	24.85872    	0.46536     	55.03876    	60          
---> try to load best state
              	24.85872    	0.46536     	55.03876    	60               eval state : test_on_restore;
              	24.85872    	0.46536     	55.03876    	61               eval state : test_on_last_epoch;
---> final result              	24.85872    	0.46536     	55.03876    	61               eval state : final;module_name : SGAR;cp_record_file : True;
