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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
----------------------------------------

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
----------------------------------------

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	138.77553   	250.73946   	0.74367     	0           
  10          	138.07928   	249.10339   	0.74967     	0           
  100         	131.43466   	234.79117   	0.79442     	0           
----------------------------------------

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05164     	0.80804     	0.08467     	0           
  10          	0.04908     	0.82902     	0.08020     	0           
  100         	0.04591     	0.85774     	0.07361     	0           
  300         	0.04715     	0.84904     	0.07558     	1           
  500         	0.04756     	0.84604     	0.07617     	2           
  1000        	0.04778     	0.84427     	0.07647     	4           
---> try to load best state
              	0.04778     	0.84427     	0.07647     	4                eval state : test_on_restore;
              	0.04778     	0.84427     	0.07647     	4                eval state : test_on_last_epoch;
---> final result              	0.04778     	0.84427     	0.07647     	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.20031     	-0.37504    	0.26822     	0           
  10          	0.19257     	-0.12661    	0.26161     	0           
  100         	0.20386     	-0.04887    	0.27814     	0           
  300         	0.20380     	-0.01607    	0.27545     	2           
  500         	0.20364     	-0.00673    	0.27467     	3           
  1000        	0.20464     	-0.02139    	0.27528     	6           
---> try to load best state
              	0.20464     	-0.02139    	0.27528     	6                eval state : test_on_restore;
              	0.20464     	-0.02139    	0.27528     	6                eval state : test_on_last_epoch;
---> final result
              	0.20464     	-0.02139    	0.27528     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.18334     	-0.11820    	0.25722     	0           
  10          	0.18134     	-0.03228    	0.25564     	0           
  100         	0.17984     	0.06693     	0.25118     	0           
  300         	0.18342     	0.08791     	0.25084     	2           
  500         	0.18465     	0.08851     	0.25158     	4           
  1000        	0.18592     	0.08690     	0.25270     	8           
---> try to load best state
              	0.18592     	0.08690     	0.25270     	8                eval state : test_on_restore;
              	0.18592     	0.08690     	0.25270     	9                eval state : test_on_last_epoch;
---> final result
              	0.18592     	0.08690     	0.25270     	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.18707     	0.01864     	0.27431     	0           
  10          	0.19375     	0.00414     	0.28479     	0           
  100         	0.17947     	0.06986     	0.24908     	1           
  300         	0.18308     	0.06890     	0.24980     	4           
  500         	0.18427     	0.07024     	0.25061     	6           
  1000        	0.18517     	0.07262     	0.25129     	13          
---> try to load best state
              	0.18517     	0.07262     	0.25129     	13               eval state : test_on_restore;
              	0.18517     	0.07262     	0.25129     	13               eval state : test_on_last_epoch;
---> final result
              	0.18517     	0.07262     	0.25129     	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.16820     	0.11649     	0.26227     	0           
  10          	0.16747     	0.10841     	0.26134     	0           
  100         	0.14821     	0.23402     	0.21866     	2           
  300         	0.15231     	0.22250     	0.22019     	5           
  500         	0.15386     	0.21883     	0.22103     	9           
  1000        	0.15507     	0.21595     	0.22174     	19          
---> try to load best state
              	0.15507     	0.21595     	0.22174     	19               eval state : test_on_restore;
              	0.15507     	0.21595     	0.22174     	19               eval state : test_on_last_epoch;
---> final result
              	0.15507     	0.21595     	0.22174     	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05143     	0.80382     	0.07772     	0           
  10          	0.04846     	0.82351     	0.07345     	0           
  100         	0.04652     	0.83507     	0.07049     	0           
  300         	0.04961     	0.81580     	0.07432     	1           
  500         	0.05040     	0.81017     	0.07534     	2           
  1000        	0.05080     	0.80760     	0.07583     	4           
---> try to load best state
              	0.05080     	0.80760     	0.07583     	4                eval state : test_on_restore;
              	0.05080     	0.80760     	0.07583     	4                eval state : test_on_last_epoch;
---> final result              	0.05080     	0.80760     	0.07583     	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.20489     	-0.52163    	0.26945     	0           
  10          	0.19891     	-0.23058    	0.26506     	0           
  100         	0.21926     	0.08880     	0.29342     	0           
  300         	0.22802     	0.03610     	0.30353     	2           
  500         	0.22824     	0.04037     	0.30309     	4           
  1000        	0.22913     	0.03152     	0.30308     	7           
---> try to load best state
              	0.22913     	0.03152     	0.30308     	7                eval state : test_on_restore;
              	0.22913     	0.03152     	0.30308     	7                eval state : test_on_last_epoch;
---> final result
              	0.22913     	0.03152     	0.30308     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17682     	-0.07004    	0.24525     	0           
  10          	0.17620     	0.00036     	0.24532     	0           
  100         	0.18073     	0.08854     	0.24678     	0           
  300         	0.18138     	0.05741     	0.24240     	2           
  500         	0.18155     	0.04115     	0.24223     	4           
  1000        	0.18126     	0.01922     	0.24176     	8           
---> try to load best state
              	0.18126     	0.01922     	0.24176     	8                eval state : test_on_restore;
              	0.18126     	0.01922     	0.24176     	8                eval state : test_on_last_epoch;
---> final result
              	0.18126     	0.01922     	0.24176     	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.16871     	0.06663     	0.24466     	0           
  10          	0.17178     	0.09186     	0.25065     	0           
  100         	0.16845     	0.11689     	0.23498     	1           
  300         	0.18248     	0.04882     	0.24191     	4           
  epoch 449 reach nan state
---> try to load best state
              	0.18723     	0.00818     	0.24568     	6                eval state : test_on_restore;
              	0.18248     	0.04882     	0.24191     	6                eval state : test_on_last_epoch;
---> final result
              	0.18248     	0.04882     	0.24191     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17102     	0.24070     	0.26209     	0           
  10          	0.16837     	0.24422     	0.25754     	0           
  100         	0.15025     	0.22530     	0.21071     	2           
  300         	0.15557     	0.18178     	0.21369     	6           
  500         	0.15779     	0.16093     	0.21537     	10          
  1000        	0.16027     	0.13775     	0.21737     	19          
---> try to load best state
              	0.16027     	0.13775     	0.21737     	20               eval state : test_on_restore;
              	0.16027     	0.13775     	0.21737     	20               eval state : test_on_last_epoch;
---> final result
              	0.16027     	0.13775     	0.21737     	20               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05055     	0.75243     	0.09103     	0           
  10          	0.04699     	0.78292     	0.08728     	0           
  100         	0.04269     	0.81943     	0.08373     	0           
  300         	0.04340     	0.81797     	0.08337     	1           
  500         	0.04392     	0.81552     	0.08345     	2           
  1000        	0.04434     	0.81379     	0.08365     	5           
---> try to load best state
              	0.04434     	0.81379     	0.08365     	5                eval state : test_on_restore;
              	0.04434     	0.81379     	0.08365     	5                eval state : test_on_last_epoch;
---> final result              	0.04434     	0.81379     	0.08365     	5                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.21194     	-0.81745    	0.28610     	0           
  10          	0.20646     	-0.70892    	0.28456     	0           
  100         	0.22279     	-0.52787    	0.30659     	0           
  300         	0.24877     	-0.53159    	0.33853     	2           
  500         	0.25407     	-0.53212    	0.34558     	3           
  1000        	0.25994     	-0.58444    	0.35344     	7           
---> try to load best state
              	0.25994     	-0.58444    	0.35344     	7                eval state : test_on_restore;
              	0.25994     	-0.58444    	0.35344     	7                eval state : test_on_last_epoch;
---> final result
              	0.25994     	-0.58444    	0.35344     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.19339     	-0.37430    	0.27555     	0           
  10          	0.19174     	-0.32874    	0.27611     	0           
  100         	0.19247     	-0.21231    	0.27583     	0           
  300         	0.19819     	-0.25549    	0.27536     	2           
  500         	0.20262     	-0.28373    	0.27836     	4           
  1000        	0.21178     	-0.36691    	0.28504     	8           
---> try to load best state
              	0.21178     	-0.36691    	0.28504     	8                eval state : test_on_restore;
              	0.21178     	-0.36691    	0.28504     	8                eval state : test_on_last_epoch;
---> final result
              	0.21178     	-0.36691    	0.28504     	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17459     	-0.01512    	0.26317     	0           
  10          	0.17916     	-0.05203    	0.27078     	0           
  100         	0.16788     	0.07653     	0.24595     	1           
  300         	0.18257     	-0.01261    	0.25338     	4           
  epoch 413 reach nan state
---> try to load best state
              	0.18639     	-0.04406    	0.25618     	5                eval state : test_on_restore;
              	0.18257     	-0.01261    	0.25338     	5                eval state : test_on_last_epoch;
---> final result
              	0.18257     	-0.01261    	0.25338     	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.16946     	0.10280     	0.26697     	0           
  10          	0.17121     	0.07978     	0.27056     	0           
  100         	0.15205     	0.20124     	0.22596     	2           
  300         	0.15678     	0.15606     	0.23015     	5           
  500         	0.15948     	0.13270     	0.23231     	9           
  1000        	0.16136     	0.11217     	0.23410     	19          
---> try to load best state
              	0.16136     	0.11217     	0.23410     	19               eval state : test_on_restore;
              	0.16136     	0.11217     	0.23410     	19               eval state : test_on_last_epoch;
---> final result
              	0.16136     	0.11217     	0.23410     	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05542     	0.83605     	0.08245     	0           
  10          	0.05368     	0.84704     	0.08004     	0           
  100         	0.05410     	0.84487     	0.08022     	0           
  300         	0.05584     	0.83414     	0.08254     	1           
  500         	0.05660     	0.82834     	0.08367     	2           
  1000        	0.05699     	0.82529     	0.08426     	4           
---> try to load best state
              	0.05699     	0.82529     	0.08426     	4                eval state : test_on_restore;
              	0.05699     	0.82529     	0.08426     	4                eval state : test_on_last_epoch;
---> final result              	0.05699     	0.82529     	0.08426     	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.22480     	-0.82834    	0.29908     	0           
  10          	0.22212     	-0.67220    	0.30082     	0           
  100         	0.23123     	-0.37731    	0.30842     	0           
  300         	0.22447     	-0.36554    	0.29397     	2           
  500         	0.22577     	-0.38598    	0.29463     	3           
  1000        	0.23054     	-0.52587    	0.29856     	7           
---> try to load best state
              	0.23054     	-0.52587    	0.29856     	7                eval state : test_on_restore;
              	0.23054     	-0.52587    	0.29856     	7                eval state : test_on_last_epoch;
---> final result
              	0.23054     	-0.52587    	0.29856     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.20251     	-0.38676    	0.27956     	0           
  10          	0.20017     	-0.32205    	0.27842     	0           
  100         	0.19777     	-0.16426    	0.26765     	0           
  300         	0.19960     	-0.12931    	0.26565     	2           
  500         	0.20076     	-0.12528    	0.26644     	4           
  1000        	0.20232     	-0.13074    	0.26796     	8           
---> try to load best state
              	0.20232     	-0.13074    	0.26796     	8                eval state : test_on_restore;
              	0.20232     	-0.13074    	0.26796     	8                eval state : test_on_last_epoch;
---> final result
              	0.20232     	-0.13074    	0.26796     	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17878     	0.07178     	0.25895     	0           
  10          	0.17893     	0.11073     	0.26046     	0           
  100         	0.17138     	0.16699     	0.24554     	1           
  300         	0.16940     	0.15812     	0.23908     	4           
  500         	0.16957     	0.15639     	0.23892     	6           
  1000        	0.16955     	0.15743     	0.23903     	12          
---> try to load best state
              	0.16955     	0.15743     	0.23903     	12               eval state : test_on_restore;
              	0.16955     	0.15743     	0.23903     	12               eval state : test_on_last_epoch;
---> final result
              	0.16955     	0.15743     	0.23903     	12               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17283     	0.25138     	0.26412     	0           
  10          	0.17633     	0.24463     	0.26987     	0           
  100         	0.15191     	0.22302     	0.22187     	2           
  300         	0.15413     	0.20786     	0.22162     	6           
  500         	0.15608     	0.19326     	0.22257     	10          
  1000        	0.15763     	0.17867     	0.22349     	20          
---> try to load best state
              	0.15763     	0.17867     	0.22349     	20               eval state : test_on_restore;
              	0.15763     	0.17867     	0.22349     	20               eval state : test_on_last_epoch;
---> final result
              	0.15763     	0.17867     	0.22349     	20               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04873     	0.80350     	0.07883     	0           
  10          	0.04690     	0.82141     	0.07577     	0           
  100         	0.04679     	0.82111     	0.07521     	0           
  300         	0.04883     	0.80260     	0.07844     	1           
  500         	0.04955     	0.79594     	0.07957     	2           
  1000        	0.05008     	0.79087     	0.08042     	4           
---> try to load best state
              	0.05008     	0.79087     	0.08042     	4                eval state : test_on_restore;
              	0.05008     	0.79087     	0.08042     	4                eval state : test_on_last_epoch;
---> final result              	0.05008     	0.79087     	0.08042     	4                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.21595     	-0.68959    	0.28995     	0           
  10          	0.21462     	-0.49509    	0.29261     	0           
  100         	0.22536     	-0.25122    	0.30212     	0           
  epoch 255 reach nan state
---> try to load best state
              	0.22979     	-0.34523    	0.29996     	2                eval state : test_on_restore;
              	0.22536     	-0.25122    	0.30212     	2                eval state : test_on_last_epoch;
---> final result
              	0.22979     	-0.34523    	0.29996     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.19366     	-0.39344    	0.27061     	0           
  10          	0.19139     	-0.26512    	0.26836     	0           
  100         	0.19197     	-0.13647    	0.26105     	0           
  300         	0.19749     	-0.15958    	0.26108     	3           
  500         	0.19835     	-0.16811    	0.26207     	4           
  1000        	0.19971     	-0.17773    	0.26360     	9           
---> try to load best state
              	0.19971     	-0.17773    	0.26360     	9                eval state : test_on_restore;
              	0.19971     	-0.17773    	0.26360     	9                eval state : test_on_last_epoch;
---> final result
              	0.19971     	-0.17773    	0.26360     	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.17175     	0.06298     	0.25196     	0           
  10          	0.17525     	0.06921     	0.25614     	0           
  100         	0.16067     	0.14325     	0.23267     	1           
  300         	0.17445     	0.01807     	0.23724     	4           
  500         	0.17851     	-0.01951    	0.24040     	7           
  1000        	0.18186     	-0.05336    	0.24324     	13          
---> try to load best state
              	0.18186     	-0.05336    	0.24324     	13               eval state : test_on_restore;
              	0.18186     	-0.05336    	0.24324     	13               eval state : test_on_last_epoch;
---> final result
              	0.18186     	-0.05336    	0.24324     	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.16189     	0.25251     	0.24863     	0           
  10          	0.16639     	0.24247     	0.25584     	0           
  100         	0.14408     	0.22448     	0.21279     	2           
  300         	0.14861     	0.18943     	0.21431     	5           
  500         	0.15104     	0.17248     	0.21571     	9           
  1000        	0.15305     	0.15784     	0.21697     	19          
---> try to load best state
              	0.15305     	0.15784     	0.21697     	19               eval state : test_on_restore;
              	0.15305     	0.15784     	0.21697     	19               eval state : test_on_last_epoch;
---> final result
              	0.15305     	0.15784     	0.21697     	19               eval state : final;module_name : GAR;cp_record_file : True;
