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

  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           	0.01392     	0.47381     	0.02219     	0           
  10          	0.01281     	0.55371     	0.02032     	0           
  100         	0.00423     	0.93656     	0.00667     	2           
  300         	0.00261     	0.97245     	0.00414     	6           
  500         	0.00252     	0.97518     	0.00399     	11          
  1000        	0.00243     	0.97808     	0.00383     	23          
---> try to load best state
              	0.00243     	0.97808     	0.00383     	23               eval state : test_on_restore;
              	0.00243     	0.97808     	0.00383     	23               eval state : test_on_last_epoch;
---> final result              	0.00243     	0.97808     	0.00383     	23               eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	530.41193   	-23.86852   	777.93646   	0           
  10          	506.69781   	-21.10238   	695.25488   	0           
  100         	201.62778   	-3.13974    	333.95963   	1           
  300         	170.91580   	-2.83035    	317.39645   	3           
  500         	175.23317   	-3.00067    	322.49399   	5           
  1000        	176.17253   	-3.05916    	324.62561   	9           
---> try to load best state
              	176.17253   	-3.05916    	324.62561   	9                eval state : test_on_restore;
              	176.17253   	-3.05916    	324.62561   	9                eval state : test_on_last_epoch;
---> final result              	176.17253   	-3.05916    	324.62561   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	385.38379   	-4.52388    	597.80566   	0           
  10          	361.90015   	-3.39162    	519.34131   	0           
  100         	104.94159   	-0.38895    	188.63832   	1           
  300         	73.01257    	-0.07531    	165.12204   	4           
  500         	68.44336    	-0.00844    	161.47920   	7           
  1000        	66.59202    	0.01369     	159.96095   	13          
---> try to load best state
              	66.59202    	0.01369     	159.96095   	13               eval state : test_on_restore;
              	66.59202    	0.01369     	159.96095   	13               eval state : test_on_last_epoch;
---> final result              	66.59202    	0.01369     	159.96095   	13               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	229.91679   	-1.23191    	384.80338   	0           
  10          	207.68935   	-0.76645    	321.45825   	0           
  100         	48.15978    	0.31849     	85.75338    	2           
  300         	33.44378    	0.37788     	68.07324    	5           
  500         	31.15783    	0.39346     	65.79105    	9           
  1000        	29.85513    	0.40262     	64.67065    	18          
---> try to load best state
              	29.85513    	0.40262     	64.67065    	18               eval state : test_on_restore;
              	29.85513    	0.40262     	64.67065    	19               eval state : test_on_last_epoch;
---> final result              	29.85513    	0.40262     	64.67065    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	131.36263   	-0.05870    	227.13594   	0           
  10          	129.23773   	0.07539     	206.04402   	0           
  100         	34.62414    	0.41478     	65.71088    	3           
  300         	23.80576    	0.43626     	50.02323    	9           
  500         	22.05551    	0.44389     	47.96928    	15          
  1000        	20.76031    	0.45251     	47.20694    	31          
---> try to load best state
              	20.76031    	0.45251     	47.20694    	31               eval state : test_on_restore;
              	20.76031    	0.45251     	47.20694    	31               eval state : test_on_last_epoch;
---> final result              	20.76031    	0.45251     	47.20694    	31               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           	0.02007     	-0.06653    	0.03427     	0           
  10          	0.01750     	0.19959     	0.03029     	0           
  100         	0.00498     	0.93355     	0.00809     	2           
  300         	0.00346     	0.96586     	0.00498     	7           
  500         	0.00336     	0.96886     	0.00480     	12          
  1000        	0.00325     	0.97212     	0.00463     	24          
---> try to load best state
              	0.00325     	0.97212     	0.00463     	24               eval state : test_on_restore;
              	0.00325     	0.97212     	0.00463     	24               eval state : test_on_last_epoch;
---> final result              	0.00325     	0.97212     	0.00463     	24               eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	531.66388   	-13.33114   	700.69843   	0           
  10          	531.29016   	-10.53020   	676.48322   	0           
  100         	245.07230   	-1.75444    	388.13223   	1           
  300         	250.36169   	-2.58757    	403.34503   	2           
  500         	257.82651   	-2.80804    	415.53720   	4           
  1000        	253.08804   	-2.71009    	402.56784   	9           
---> try to load best state
              	253.08804   	-2.71009    	402.56784   	9                eval state : test_on_restore;
              	253.08804   	-2.71009    	402.56784   	9                eval state : test_on_last_epoch;
---> final result              	253.08804   	-2.71009    	402.56784   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	401.14874   	-4.96935    	596.39868   	0           
  10          	378.67105   	-3.61756    	529.14832   	0           
  100         	155.30721   	-0.24195    	275.18427   	1           
  300         	130.77887   	-0.18801    	254.57838   	3           
  500         	128.41377   	-0.16338    	250.87325   	6           
  1000        	127.37823   	-0.15296    	248.83876   	12          
---> try to load best state
              	127.37823   	-0.15296    	248.83876   	12               eval state : test_on_restore;
              	127.37823   	-0.15296    	248.83876   	12               eval state : test_on_last_epoch;
---> final result              	127.37823   	-0.15296    	248.83876   	12               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 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           	230.39040   	-0.73681    	430.50888   	0           
  10          	212.58495   	-0.34647    	363.83133   	0           
  100         	63.90076    	0.37745     	119.56473   	2           
  300         	43.52004    	0.41173     	93.35431    	6           
  500         	39.73509    	0.42163     	87.29454    	9           
  1000        	37.25232    	0.42757     	83.19332    	19          
---> try to load best state
              	37.25232    	0.42757     	83.19332    	19               eval state : test_on_restore;
              	37.25232    	0.42757     	83.19332    	19               eval state : test_on_last_epoch;
---> final result              	37.25232    	0.42757     	83.19332    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	197.76512   	-0.69312    	374.82632   	0           
  10          	175.81830   	-0.36225    	306.98212   	0           
  100         	42.23511    	0.35395     	79.66726    	3           
  300         	29.10827    	0.39552     	58.16201    	9           
  500         	26.40306    	0.40701     	53.65824    	16          
  1000        	24.24689    	0.41760     	50.53915    	31          
---> try to load best state
              	24.24689    	0.41760     	50.53915    	31               eval state : test_on_restore;
              	24.24689    	0.41760     	50.53915    	31               eval state : test_on_last_epoch;
---> final result              	24.24689    	0.41760     	50.53915    	31               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           	0.01736     	0.19541     	0.02847     	0           
  10          	0.01514     	0.36659     	0.02517     	0           
  100         	0.00443     	0.93134     	0.00689     	2           
  300         	0.00382     	0.93670     	0.00621     	6           
  500         	0.00374     	0.94083     	0.00614     	11          
  1000        	0.00363     	0.94534     	0.00606     	23          
---> try to load best state
              	0.00363     	0.94534     	0.00606     	23               eval state : test_on_restore;
              	0.00363     	0.94534     	0.00606     	23               eval state : test_on_last_epoch;
---> final result              	0.00363     	0.94534     	0.00606     	23               eval state : final;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	494.16925   	-11.52944   	760.38623   	0           
  10          	472.19589   	-9.98647    	676.01642   	0           
  100         	211.14435   	-1.80784    	367.03479   	1           
  300         	190.70862   	-1.67075    	361.68976   	3           
  500         	192.23097   	-1.70980    	362.78848   	4           
  1000        	193.24626   	-1.73741    	363.22873   	9           
---> try to load best state
              	193.24626   	-1.73741    	363.22873   	9                eval state : test_on_restore;
              	193.24626   	-1.73741    	363.22873   	9                eval state : test_on_last_epoch;
---> final result              	193.24626   	-1.73741    	363.22873   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 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           	460.47189   	-9.58347    	751.98224   	0           
  10          	439.07111   	-8.56084    	666.30579   	0           
  100         	133.80521   	-0.73582    	259.64917   	1           
  300         	85.36881    	-0.29891    	190.16052   	3           
  500         	78.85293    	-0.24923    	179.34807   	6           
  1000        	73.92593    	-0.21596    	171.35248   	12          
---> try to load best state
              	73.92593    	-0.21596    	171.35248   	12               eval state : test_on_restore;
              	73.92593    	-0.21596    	171.35248   	12               eval state : test_on_last_epoch;
---> final result              	73.92593    	-0.21596    	171.35248   	12               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	265.27448   	-1.60525    	450.75439   	0           
  10          	248.59399   	-1.04682    	384.29990   	0           
  100         	59.48376    	0.25772     	107.61366   	1           
  300         	40.55992    	0.31492     	87.98077    	5           
  500         	37.01846    	0.33789     	83.51582    	7           
  1000        	34.86425    	0.35247     	80.74883    	14          
---> try to load best state
              	34.86425    	0.35247     	80.74883    	14               eval state : test_on_restore;
              	34.86425    	0.35247     	80.74883    	14               eval state : test_on_last_epoch;
---> final result              	34.86425    	0.35247     	80.74883    	14               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 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           	162.09399   	-0.44135    	304.87427   	0           
  10          	151.17694   	-0.21018    	254.29745   	0           
  100         	41.45646    	0.35469     	79.08715    	2           
  300         	30.39011    	0.37923     	69.52660    	7           
  500         	28.30137    	0.39117     	67.36190    	14          
  1000        	26.63983    	0.40417     	65.98053    	31          
---> try to load best state
              	26.63983    	0.40417     	65.98053    	31               eval state : test_on_restore;
              	26.63983    	0.40417     	65.98053    	31               eval state : test_on_last_epoch;
---> final result              	26.63983    	0.40417     	65.98053    	31               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           	0.01979     	0.08864     	0.03362     	0           
  10          	0.01732     	0.29119     	0.02993     	0           
  100         	0.00441     	0.93816     	0.00775     	2           
  300         	0.00403     	0.95583     	0.00650     	7           
  500         	0.00393     	0.95949     	0.00629     	12          
  1000        	0.00381     	0.96316     	0.00608     	24          
---> try to load best state
              	0.00381     	0.96316     	0.00608     	24               eval state : test_on_restore;
              	0.00381     	0.96316     	0.00608     	24               eval state : test_on_last_epoch;
---> final result              	0.00381     	0.96316     	0.00608     	24               eval state : final;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	649.45258   	-163.06774  	864.71478   	0           
  10          	640.59229   	-124.96678  	807.80316   	0           
  100         	373.17084   	-10.03598   	485.86008   	1           
  300         	265.12561   	-4.60136    	400.20361   	2           
  500         	266.89243   	-4.70123    	402.04703   	4           
  1000        	266.59259   	-4.64545    	401.85889   	9           
---> try to load best state
              	266.59259   	-4.64545    	401.85889   	9                eval state : test_on_restore;
              	266.59259   	-4.64545    	401.85889   	9                eval state : test_on_last_epoch;
---> final result              	266.59259   	-4.64545    	401.85889   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 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           	461.04306   	-8.07992    	765.41907   	0           
  10          	421.78949   	-6.42667    	675.77539   	0           
  100         	153.50169   	-0.51454    	286.99783   	1           
  300         	91.80782    	-0.18970    	209.41396   	3           
  500         	80.19153    	-0.13794    	190.85233   	6           
  1000        	73.33094    	-0.10716    	180.84267   	12          
---> try to load best state
              	73.33094    	-0.10716    	180.84267   	12               eval state : test_on_restore;
              	73.33094    	-0.10716    	180.84267   	12               eval state : test_on_last_epoch;
---> final result              	73.33094    	-0.10716    	180.84267   	12               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	338.14526   	-2.17365    	578.02576   	0           
  10          	305.79480   	-1.41657    	489.91541   	0           
  100         	77.50024    	0.06837     	155.03986   	2           
  300         	49.17988    	0.13122     	141.04167   	6           
  500         	46.82692    	0.14939     	146.13167   	10          
  1000        	46.28079    	0.16110     	152.02609   	19          
---> try to load best state
              	46.28079    	0.16110     	152.02609   	19               eval state : test_on_restore;
              	46.28079    	0.16110     	152.02609   	19               eval state : test_on_last_epoch;
---> final result              	46.28079    	0.16110     	152.02609   	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 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           	207.54770   	-0.71465    	374.72714   	0           
  10          	183.80428   	-0.40543    	310.97269   	0           
  100         	44.53199    	0.22122     	91.87317    	3           
  300         	32.63552    	0.30411     	74.51935    	9           
  500         	30.66316    	0.32090     	71.51303    	16          
  1000        	29.41304    	0.33792     	70.24979    	32          
---> try to load best state
              	29.41304    	0.33792     	70.24979    	32               eval state : test_on_restore;
              	29.41304    	0.33792     	70.24979    	32               eval state : test_on_last_epoch;
---> final result              	29.41304    	0.33792     	70.24979    	32               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           	0.01822     	0.04472     	0.03140     	0           
  10          	0.01602     	0.28031     	0.02730     	0           
  100         	0.00479     	0.93440     	0.00783     	2           
  300         	0.00341     	0.95873     	0.00563     	6           
  500         	0.00328     	0.96229     	0.00544     	9           
  1000        	0.00313     	0.96628     	0.00523     	19          
---> try to load best state
              	0.00313     	0.96628     	0.00523     	19               eval state : test_on_restore;
              	0.00313     	0.96628     	0.00523     	19               eval state : test_on_last_epoch;
---> final result              	0.00313     	0.96628     	0.00523     	19               eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	558.07062   	-41.67565   	844.72736   	0           
  10          	551.25262   	-41.92763   	760.29950   	0           
  100         	315.34836   	-16.62979   	475.53745   	0           
  300         	287.44034   	-16.70571   	463.57629   	2           
  500         	290.05066   	-19.02054   	467.23303   	4           
  1000        	290.07260   	-18.97998   	467.25620   	8           
---> try to load best state
              	290.07260   	-18.97998   	467.25620   	8                eval state : test_on_restore;
              	290.07260   	-18.97998   	467.25620   	8                eval state : test_on_last_epoch;
---> final result              	290.07260   	-18.97998   	467.25620   	8                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	425.87134   	-10.95952   	688.17419   	0           
  10          	398.02597   	-8.96701    	586.59143   	0           
  100         	146.03235   	-0.34319    	276.61563   	1           
  300         	126.58511   	-0.18275    	256.47318   	2           
  500         	123.91637   	-0.14533    	252.12946   	5           
  1000        	122.26472   	-0.12311    	249.40556   	9           
---> try to load best state
              	122.26472   	-0.12311    	249.40556   	9                eval state : test_on_restore;
              	122.26472   	-0.12311    	249.40556   	9                eval state : test_on_last_epoch;
---> final result              	122.26472   	-0.12311    	249.40556   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 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           	261.67206   	-1.85539    	465.13034   	0           
  10          	240.33569   	-1.19060    	391.19757   	0           
  100         	73.75820    	0.26904     	142.13530   	1           
  300         	50.99539    	0.33338     	113.28588   	4           
  500         	46.85832    	0.34968     	107.78097   	8           
  1000        	44.45662    	0.36043     	104.92163   	17          
---> try to load best state
              	44.45662    	0.36043     	104.92163   	17               eval state : test_on_restore;
              	44.45662    	0.36043     	104.92163   	17               eval state : test_on_last_epoch;
---> final result              	44.45662    	0.36043     	104.92163   	17               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	174.09807   	-0.54998    	341.34058   	0           
  10          	160.55127   	-0.27635    	276.43784   	0           
  100         	40.42117    	0.34935     	80.26478    	3           
  300         	29.81232    	0.39437     	65.60376    	9           
  500         	27.07362    	0.40840     	61.15454    	15          
  1000        	24.64436    	0.42051     	57.47450    	32          
---> try to load best state
              	24.64436    	0.42051     	57.47450    	32               eval state : test_on_restore;
              	24.64436    	0.42051     	57.47450    	33               eval state : test_on_last_epoch;
---> final result              	24.64436    	0.42051     	57.47450    	33               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 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           	0.01418     	0.57671     	0.02301     	0           
  10          	0.01305     	0.66003     	0.02074     	0           
  100         	0.00467     	0.94476     	0.00701     	2           
  300         	0.00358     	0.96478     	0.00562     	6           
  500         	0.00348     	0.96684     	0.00553     	11          
  1000        	0.00338     	0.96943     	0.00542     	23          
---> try to load best state
              	0.00338     	0.96943     	0.00542     	23               eval state : test_on_restore;
              	0.00338     	0.96943     	0.00542     	23               eval state : test_on_last_epoch;
---> final result              	0.00338     	0.96943     	0.00542     	23               eval state : final;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	601.46381   	-161.00265  	877.07086   	0           
  10          	589.33667   	-122.37686  	797.92419   	0           
  100         	357.98010   	-26.08418   	520.39594   	1           
  300         	318.41699   	-61.18546   	515.73010   	3           
  500         	319.39020   	-64.98895   	516.56818   	4           
  1000        	320.43405   	-61.19726   	519.50922   	9           
---> try to load best state
              	320.43405   	-61.19726   	519.50922   	9                eval state : test_on_restore;
              	320.43405   	-61.19726   	519.50922   	9                eval state : test_on_last_epoch;
---> final result              	320.43405   	-61.19726   	519.50922   	9                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	329.93149   	-4.96228    	534.14252   	0           
  10          	306.83499   	-3.42252    	454.78741   	0           
  100         	77.62451    	0.07749     	146.51309   	1           
  300         	53.79892    	0.24205     	124.52243   	3           
  500         	51.92165    	0.27161     	123.50856   	6           
  1000        	51.32500    	0.28433     	123.38861   	12          
---> try to load best state
              	51.32500    	0.28433     	123.38861   	12               eval state : test_on_restore;
              	51.32500    	0.28433     	123.38861   	12               eval state : test_on_last_epoch;
---> final result              	51.32500    	0.28433     	123.38861   	12               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	238.27100   	-1.24871    	434.26053   	0           
  10          	214.18733   	-0.71174    	364.46713   	0           
  100         	66.51243    	0.36273     	120.63538   	1           
  300         	45.54219    	0.41134     	100.23979   	5           
  500         	42.39736    	0.41839     	97.19814    	9           
  1000        	40.57113    	0.42419     	95.63876    	19          
---> try to load best state
              	40.57113    	0.42419     	95.63876    	19               eval state : test_on_restore;
              	40.57113    	0.42419     	95.63876    	19               eval state : test_on_last_epoch;
---> final result              	40.57113    	0.42419     	95.63876    	19               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	129.78905   	-0.02080    	239.28477   	0           
  10          	120.08173   	0.11882     	201.46408   	0           
  100         	36.97460    	0.43868     	71.59603    	3           
  300         	28.40414    	0.45205     	68.80207    	9           
  500         	27.23710    	0.45777     	69.93667    	16          
  1000        	26.54151    	0.46442     	72.40493    	34          
---> try to load best state
              	26.54151    	0.46442     	72.40493    	34               eval state : test_on_restore;
              	26.54151    	0.46442     	72.40493    	34               eval state : test_on_last_epoch;
---> final result              	26.54151    	0.46442     	72.40493    	34               eval state : final;module_name : SGAR;cp_record_file : True;
