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

  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           	182.51872   	0.38588     	290.10944   	0           
  10          	167.50978   	0.47074     	265.15985   	0           
  100         	55.19712    	0.82022     	86.74113    	3           
  300         	34.35799    	0.90599     	56.07136    	10          
  500         	33.00854    	0.91670     	53.97044    	16          
  1000        	31.66304    	0.92779     	51.85159    	32          
---> try to load best state
              	31.66304    	0.92779     	51.85159    	32               eval state : test_on_restore;
              	31.66304    	0.92779     	51.85159    	32               eval state : test_on_last_epoch;
---> final result              	31.66304    	0.92779     	51.85159    	32               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           	539.65619   	-14.37576   	774.44855   	0           
  10          	448.98792   	-7.07803    	685.94250   	0           
  100         	93.15965    	-1.01906    	130.06071   	1           
  300         	35.13992    	-0.93992    	51.53498    	5           
  500         	32.82689    	-4.28812    	58.39431    	8           
  1000        	24.67962    	-23.10930   	42.50930    	16          
---> try to load best state
              	24.67962    	-23.10930   	42.50930    	16               eval state : test_on_restore;
              	24.67962    	-23.10930   	42.50930    	17               eval state : test_on_last_epoch;
---> final result              	24.67962    	-23.10930   	42.50930    	17               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           	393.13531   	-3.43282    	593.52643   	0           
  10          	305.24057   	-1.22567    	480.28888   	0           
  100         	41.88005    	-0.06472    	62.49548    	4           
  300         	23.00101    	-0.35865    	40.90757    	8           
  500         	28.70815    	-2.46756    	49.92533    	12          
  1000        	23.52397    	-20.29591   	43.99209    	21          
---> try to load best state
              	23.52397    	-20.29591   	43.99209    	21               eval state : test_on_restore;
              	23.52397    	-20.29591   	43.99209    	21               eval state : test_on_last_epoch;
---> final result              	23.52397    	-20.29591   	43.99209    	21               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           	233.73468   	-0.96770    	381.08514   	0           
  10          	167.90269   	-0.26140    	294.47568   	0           
  100         	31.63042    	0.36039     	46.27941    	2           
  300         	22.24908    	0.27343     	43.84674    	8           
  500         	19.45382    	-0.27613    	40.08694    	11          
  1000        	19.03981    	-4.02410    	38.66105    	18          
---> try to load best state
              	19.03981    	-4.02410    	38.66105    	18               eval state : test_on_restore;
              	19.03981    	-4.02410    	38.66105    	18               eval state : test_on_last_epoch;
---> final result              	19.03981    	-4.02410    	38.66105    	18               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           	134.37276   	0.00133     	221.91896   	0           
  10          	96.93088    	0.23832     	174.61725   	0           
  100         	26.01539    	0.41437     	41.26904    	4           
  300         	20.76514    	0.41023     	38.65570    	15          
  500         	18.65602    	0.34684     	37.98825    	27          
  1000        	21.68598    	-0.28710    	41.85141    	57          
---> try to load best state
              	21.68598    	-0.28710    	41.85141    	57               eval state : test_on_restore;
              	21.68598    	-0.28710    	41.85141    	58               eval state : test_on_last_epoch;
---> final result              	21.68598    	-0.28710    	41.85141    	58               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           	269.03735   	-0.24210    	455.48245   	0           
  10          	233.79671   	0.04602     	403.71149   	0           
  100         	65.22202    	0.84391     	109.23157   	2           
  300         	45.25979    	0.89988     	69.44556    	7           
  500         	43.79570    	0.90956     	66.64994    	11          
  1000        	42.39856    	0.91915     	64.19317    	23          
---> try to load best state
              	42.39856    	0.91915     	64.19317    	23               eval state : test_on_restore;
              	42.39856    	0.91915     	64.19317    	23               eval state : test_on_last_epoch;
---> final result              	42.39856    	0.91915     	64.19317    	23               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           	534.41602   	-9.25479    	699.69312   	0           
  10          	407.86661   	-4.09125    	592.82574   	0           
  100         	67.03062    	-0.07019    	92.95284    	1           
  300         	37.28691    	-1.14667    	52.24847    	4           
  500         	31.82595    	-4.16801    	47.90692    	6           
  1000        	28.48627    	-25.81591   	47.56768    	12          
---> try to load best state
              	28.48627    	-25.81591   	47.56768    	12               eval state : test_on_restore;
              	28.48627    	-25.81591   	47.56768    	12               eval state : test_on_last_epoch;
---> final result              	28.48627    	-25.81591   	47.56768    	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': 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           	405.30124   	-3.79701    	594.85620   	0           
  10          	313.66577   	-1.65070    	481.04782   	0           
  100         	53.12389    	0.23400     	75.32791    	1           
  300         	30.25095    	-0.00022    	50.85934    	4           
  500         	29.83607    	-1.78551    	54.42348    	7           
  1000        	24.18643    	-11.27483   	45.67664    	14          
---> try to load best state
              	24.18643    	-11.27483   	45.67664    	14               eval state : test_on_restore;
              	24.18643    	-11.27483   	45.67664    	14               eval state : test_on_last_epoch;
---> final result              	24.18643    	-11.27483   	45.67664    	14               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           	237.79883   	-0.58683    	429.35394   	0           
  10          	189.15582   	-0.07291    	337.27136   	0           
  100         	41.07241    	0.40868     	59.70145    	2           
  300         	25.13857    	0.40786     	49.28892    	5           
  500         	28.96574    	-0.00631    	53.63853    	9           
  1000        	22.76947    	-4.77146    	44.62617    	20          
---> try to load best state
              	22.76947    	-4.77146    	44.62617    	20               eval state : test_on_restore;
              	22.76947    	-4.77146    	44.62617    	20               eval state : test_on_last_epoch;
---> final result              	22.76947    	-4.77146    	44.62617    	20               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           	201.86258   	-0.58039    	373.24512   	0           
  10          	150.78563   	-0.07334    	284.98642   	0           
  100         	33.10550    	0.35786     	50.37465    	6           
  300         	25.23000    	0.39966     	45.51601    	19          
  500         	23.11851    	0.36581     	44.58140    	31          
  1000        	26.42913    	-0.25317    	50.23166    	57          
---> try to load best state
              	26.42913    	-0.25317    	50.23166    	57               eval state : test_on_restore;
              	26.42913    	-0.25317    	50.23166    	57               eval state : test_on_last_epoch;
---> final result              	26.42913    	-0.25317    	50.23166    	57               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           	230.21683   	0.09018     	373.35269   	0           
  10          	199.96593   	0.26622     	329.82632   	0           
  100         	57.14434    	0.83379     	89.20300    	2           
  300         	49.40046    	0.84525     	83.20367    	6           
  500         	48.20353    	0.85617     	82.19343    	11          
  1000        	46.81979    	0.86888     	81.12826    	21          
---> try to load best state
              	46.81979    	0.86888     	81.12826    	21               eval state : test_on_restore;
              	46.81979    	0.86888     	81.12826    	21               eval state : test_on_last_epoch;
---> final result              	46.81979    	0.86888     	81.12826    	21               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           	503.88586   	-8.04854    	756.09454   	0           
  10          	403.20969   	-4.65990    	656.53540   	0           
  100         	70.94977    	-0.18429    	106.09227   	1           
  300         	36.52188    	-1.05749    	64.95267    	3           
  500         	30.89503    	-3.70579    	60.92895    	6           
  1000        	31.29533    	-14.99587   	63.74314    	13          
---> try to load best state
              	31.29533    	-14.99587   	63.74314    	13               eval state : test_on_restore;
              	31.29533    	-14.99587   	63.74314    	13               eval state : test_on_last_epoch;
---> final result              	31.29533    	-14.99587   	63.74314    	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': 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           	470.33459   	-7.01839    	747.66974   	0           
  10          	381.79120   	-4.17743    	654.58734   	0           
  100         	70.03778    	-0.06471    	110.83945   	1           
  300         	34.85274    	-0.13303    	63.34515    	4           
  500         	34.31635    	-0.81890    	65.41908    	7           
  1000        	27.80465    	-3.91506    	59.99823    	14          
---> try to load best state
              	27.80465    	-3.91506    	59.99823    	14               eval state : test_on_restore;
              	27.80465    	-3.91506    	59.99823    	14               eval state : test_on_last_epoch;
---> final result              	27.80465    	-3.91506    	59.99823    	14               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           	269.69388   	-1.28672    	444.98315   	0           
  10          	200.66290   	-0.32082    	349.15894   	0           
  100         	40.15344    	0.29540     	65.36506    	1           
  300         	26.73304    	0.25560     	56.53156    	5           
  500         	25.97717    	-0.12817    	56.92000    	9           
  1000        	24.68126    	-3.61234    	56.22407    	19          
---> try to load best state
              	24.68126    	-3.61234    	56.22407    	19               eval state : test_on_restore;
              	24.68126    	-3.61234    	56.22407    	19               eval state : test_on_last_epoch;
---> final result              	24.68126    	-3.61234    	56.22407    	19               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           	164.13976   	-0.31499    	299.22302   	0           
  10          	115.37845   	0.08378     	228.15422   	0           
  100         	35.22591    	0.34126     	62.02787    	5           
  300         	27.22566    	0.36799     	57.28208    	17          
  500         	25.32357    	0.28186     	57.42647    	29          
  1000        	24.67068    	-0.38905    	56.74318    	58          
---> try to load best state
              	24.67068    	-0.38905    	56.74318    	58               eval state : test_on_restore;
              	24.67068    	-0.38905    	56.74318    	58               eval state : test_on_last_epoch;
---> final result              	24.67068    	-0.38905    	56.74318    	58               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           	268.73587   	-0.07954    	444.75806   	0           
  10          	234.18700   	0.13902     	395.81964   	0           
  100         	58.17985    	0.84569     	104.48888   	2           
  300         	53.40879    	0.89324     	90.91120    	6           
  500         	51.81328    	0.90534     	88.10812    	10          
  1000        	50.06139    	0.91750     	85.05940    	20          
---> try to load best state
              	50.06139    	0.91750     	85.05940    	20               eval state : test_on_restore;
              	50.06139    	0.91750     	85.05940    	20               eval state : test_on_last_epoch;
---> final result              	50.06139    	0.91750     	85.05940    	20               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           	655.80939   	-55.03278   	861.03534   	0           
  10          	527.50342   	-19.44409   	767.19922   	0           
  100         	240.43546   	-2.56944    	326.18619   	1           
  300         	62.54927    	-0.36499    	87.47758    	2           
  500         	43.44455    	-0.38678    	68.06956    	5           
  1000        	35.01273    	-1.09008    	63.07377    	10          
---> try to load best state
              	35.01273    	-1.09008    	63.07377    	10               eval state : test_on_restore;
              	35.01273    	-1.09008    	63.07377    	10               eval state : test_on_last_epoch;
---> final result              	35.01273    	-1.09008    	63.07377    	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': 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           	473.73914   	-5.93589    	761.20807   	0           
  10          	408.53976   	-1.85797    	623.80133   	0           
  100         	87.65833    	-0.12146    	131.41586   	1           
  300         	31.70017    	-0.38314    	58.70175    	5           
  500         	27.48083    	-1.61153    	58.71559    	8           
  1000        	25.99044    	-9.83002    	60.99739    	18          
---> try to load best state
              	25.99044    	-9.83002    	60.99739    	18               eval state : test_on_restore;
              	25.99044    	-9.83002    	60.99739    	18               eval state : test_on_last_epoch;
---> final result              	25.99044    	-9.83002    	60.99739    	18               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           	347.39154   	-1.68865    	572.00568   	0           
  10          	271.17291   	-0.94656    	461.72986   	0           
  100         	50.62885    	0.16823     	80.43132    	2           
  300         	30.18283    	-0.03239    	64.46269    	8           
  500         	27.71211    	-0.48430    	63.80728    	13          
  1000        	31.75754    	-2.76692    	64.31021    	26          
---> try to load best state
              	31.75754    	-2.76692    	64.31021    	26               eval state : test_on_restore;
              	31.75754    	-2.76692    	64.31021    	26               eval state : test_on_last_epoch;
---> final result              	31.75754    	-2.76692    	64.31021    	26               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           	210.96335   	-0.55199    	370.66898   	0           
  10          	155.19986   	-0.13568    	283.07217   	0           
  100         	35.02397    	0.23379     	61.15014    	7           
  300         	29.37641    	0.28532     	55.03613    	20          
  500         	26.20725    	0.18300     	55.92381    	34          
  1000        	24.43021    	-0.47836    	55.87256    	68          
---> try to load best state
              	24.43021    	-0.47836    	55.87256    	68               eval state : test_on_restore;
              	24.43021    	-0.47836    	55.87256    	68               eval state : test_on_last_epoch;
---> final result              	24.43021    	-0.47836    	55.87256    	68               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           	240.75203   	-0.09779    	419.63184   	0           
  10          	211.11670   	0.15552     	365.81656   	0           
  100         	61.94029    	0.84471     	106.32861   	3           
  300         	44.80644    	0.89798     	78.63695    	9           
  500         	43.00061    	0.90953     	75.92935    	15          
  1000        	41.01758    	0.92182     	73.02196    	30          
---> try to load best state
              	41.01758    	0.92182     	73.02196    	30               eval state : test_on_restore;
              	41.01758    	0.92182     	73.02196    	30               eval state : test_on_last_epoch;
---> final result              	41.01758    	0.92182     	73.02196    	30               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           	571.90729   	-20.09009   	844.31592   	0           
  10          	511.76724   	-11.90802   	797.69562   	0           
  100         	142.95135   	-0.63742    	215.01923   	1           
  300         	40.64149    	-0.01688    	67.07954    	5           
  500         	32.24657    	-0.18154    	60.90778    	8           
  1000        	36.98626    	-2.19311    	73.89453    	17          
---> try to load best state
              	36.98626    	-2.19311    	73.89453    	17               eval state : test_on_restore;
              	36.98626    	-2.19311    	73.89453    	17               eval state : test_on_last_epoch;
---> final result              	36.98626    	-2.19311    	73.89453    	17               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           	438.07495   	-7.30562    	687.93329   	0           
  10          	351.39731   	-3.94156    	582.98584   	0           
  100         	41.94714    	0.37902     	65.87797    	1           
  300         	26.25951    	0.05817     	48.33307    	5           
  500         	28.34070    	-1.51651    	56.66272    	9           
  1000        	22.22443    	-12.79613   	47.43006    	18          
---> try to load best state
              	22.22443    	-12.79613   	47.43006    	18               eval state : test_on_restore;
              	22.22443    	-12.79613   	47.43006    	19               eval state : test_on_last_epoch;
---> final result              	22.22443    	-12.79613   	47.43006    	19               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           	264.42899   	-1.55267    	463.62665   	0           
  10          	203.34760   	-0.59996    	370.28732   	0           
  100         	38.11960    	0.38851     	60.91682    	2           
  300         	24.28360    	0.33159     	51.13984    	6           
  500         	22.30961    	-0.16571    	45.39106    	9           
  1000        	23.88426    	-3.68717    	46.38987    	17          
---> try to load best state
              	23.88426    	-3.68717    	46.38987    	17               eval state : test_on_restore;
              	23.88426    	-3.68717    	46.38987    	17               eval state : test_on_last_epoch;
---> final result              	23.88426    	-3.68717    	46.38987    	17               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           	176.83528   	-0.44995    	340.03827   	0           
  10          	134.45589   	-0.03925    	261.71899   	0           
  100         	29.89954    	0.36298     	52.72239    	6           
  300         	25.12905    	0.38724     	52.45524    	17          
  500         	22.58003    	0.33101     	49.80825    	29          
  1000        	20.60364    	-0.26510    	45.98001    	60          
---> try to load best state
              	20.60364    	-0.26510    	45.98001    	60               eval state : test_on_restore;
              	20.60364    	-0.26510    	45.98001    	61               eval state : test_on_last_epoch;
---> final result              	20.60364    	-0.26510    	45.98001    	61               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           	186.15102   	0.45236     	299.77625   	0           
  10          	169.64270   	0.54523     	269.22269   	0           
  100         	58.75901    	0.84382     	90.35870    	2           
  300         	45.09742    	0.91191     	75.77855    	7           
  500         	43.77182    	0.92064     	74.43163    	12          
  1000        	42.21086    	0.93134     	72.94093    	23          
---> try to load best state
              	42.21086    	0.93134     	72.94093    	23               eval state : test_on_restore;
              	42.21086    	0.93134     	72.94093    	23               eval state : test_on_last_epoch;
---> final result              	42.21086    	0.93134     	72.94093    	23               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           	612.45282   	-50.01351   	872.82324   	0           
  10          	526.83600   	-21.29842   	792.21985   	0           
  100         	275.88910   	-3.18997    	346.80316   	1           
  300         	43.06482    	-0.42161    	64.71163    	4           
  500         	32.99280    	-0.48296    	56.68520    	6           
  1000        	28.13234    	-1.69160    	54.18009    	13          
---> try to load best state
              	28.13234    	-1.69160    	54.18009    	13               eval state : test_on_restore;
              	28.13234    	-1.69160    	54.18009    	13               eval state : test_on_last_epoch;
---> final result              	28.13234    	-1.69160    	54.18009    	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           	333.84393   	-3.76491    	528.88245   	0           
  10          	253.94620   	-1.05177    	417.36774   	0           
  100         	44.57473    	0.25067     	70.33057    	1           
  300         	30.74909    	-0.14125    	61.92451    	4           
  500         	29.35896    	-2.10460    	60.78397    	7           
  1000        	29.50521    	-17.47956   	63.15491    	15          
---> try to load best state
              	29.50521    	-17.47956   	63.15491    	15               eval state : test_on_restore;
              	29.50521    	-17.47956   	63.15491    	15               eval state : test_on_last_epoch;
---> final result              	29.50521    	-17.47956   	63.15491    	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           	241.01915   	-1.01748    	429.15240   	0           
  10          	182.31253   	-0.12326    	334.96756   	0           
  100         	41.65835    	0.41994     	63.43301    	2           
  300         	23.92863    	0.39252     	52.87706    	5           
  500         	23.12928    	-0.04544    	52.93933    	9           
  1000        	22.86364    	-4.72615    	52.91095    	19          
---> try to load best state
              	22.86364    	-4.72615    	52.91095    	19               eval state : test_on_restore;
              	22.86364    	-4.72615    	52.91095    	19               eval state : test_on_last_epoch;
---> final result              	22.86364    	-4.72615    	52.91095    	19               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           	133.73123   	0.05332     	229.83635   	0           
  10          	101.34189   	0.25646     	183.84576   	0           
  100         	29.81701    	0.43139     	54.72979    	5           
  300         	25.60952    	0.43858     	55.96906    	18          
  500         	24.95413    	0.40019     	55.10876    	31          
  1000        	22.00724    	-0.07096    	53.10164    	63          
---> try to load best state
              	22.00724    	-0.07096    	53.10164    	63               eval state : test_on_restore;
              	22.00724    	-0.07096    	53.10164    	63               eval state : test_on_last_epoch;
---> final result              	22.00724    	-0.07096    	53.10164    	63               eval state : final;module_name : SGAR;cp_record_file : True;
