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

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06940     	0.91897     	0.17700     	0           
  10          	0.06112     	0.93890     	0.16816     	0           
  100         	0.03402     	0.96914     	0.14813     	0           
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06940     	0.91897     	0.17700     	0           
  10          	0.06112     	0.93890     	0.16816     	0           
  100         	0.03402     	0.96914     	0.14813     	0           
  300         	0.03317     	0.97120     	0.14198     	0           
  500         	0.03244     	0.97282     	0.13746     	1           
  1000        	0.03178     	0.97505     	0.13098     	2           
---> try to load best state
              	0.03178     	0.97505     	0.13098     	2                eval state : test_on_restore;
              	0.03178     	0.97505     	0.13098     	2                eval state : test_on_last_epoch;
---> final result              	0.03178     	0.97505     	0.13098     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05619     	0.47236     	0.20725     	0           
  10          	0.07866     	-2.75962    	0.28864     	0           
  100         	0.15050     	-3.65712    	0.65822     	0           
  epoch 268 reach nan state
---> try to load best state
              	0.18749     	0.23391     	0.96975     	0                eval state : test_on_restore;
              	0.15050     	-3.65712    	0.65822     	0                eval state : test_on_last_epoch;
---> final result
              	0.15050     	-3.65712    	0.65822     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05222     	0.46408     	0.20657     	0           
  10          	0.07527     	-2.99578    	0.28969     	0           
  100         	0.12426     	0.06461     	0.56192     	0           
  300         	0.16386     	-1.00945    	0.79733     	0           
  500         	0.19585     	-0.03186    	1.01765     	1           
  1000        	0.21691     	0.08376     	1.14871     	3           
---> try to load best state
              	0.21691     	0.08376     	1.14871     	3                eval state : test_on_restore;
              	0.21691     	0.08376     	1.14871     	3                eval state : test_on_last_epoch;
---> final result
              	0.21691     	0.08376     	1.14871     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05086     	0.29897     	0.20596     	0           
  10          	0.05643     	-0.13852    	0.23562     	0           
  100         	0.05475     	-1.04426    	0.23081     	0           
  epoch 137 reach nan state
---> try to load best state
              	0.05461     	-0.99544    	0.23045     	0                eval state : test_on_restore;
              	0.05475     	-1.04426    	0.23081     	0                eval state : test_on_last_epoch;
---> final result
              	0.05461     	-0.99544    	0.23045     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.03704     	0.81955     	0.10960     	0           
  10          	0.02876     	0.85043     	0.09755     	0           
  100         	0.02445     	0.86481     	0.09378     	0           
  300         	0.02411     	0.86547     	0.09377     	0           
  500         	0.02376     	0.86538     	0.09335     	1           
  1000        	0.02377     	0.86537     	0.09306     	2           
---> try to load best state
              	0.02377     	0.86537     	0.09306     	2                eval state : test_on_restore;
              	0.02377     	0.86537     	0.09306     	2                eval state : test_on_last_epoch;
---> final result
              	0.02377     	0.86537     	0.09306     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05083     	0.21543     	0.34045     	0           
  10          	0.04893     	0.25034     	0.33785     	0           
  100         	0.05343     	-0.35381    	0.39272     	0           
  300         	0.04740     	0.06846     	0.36152     	0           
  500         	0.04402     	0.27269     	0.34096     	1           
  1000        	0.03979     	0.49381     	0.31152     	2           
---> try to load best state
              	0.03979     	0.49381     	0.31152     	2                eval state : test_on_restore;
              	0.03979     	0.49381     	0.31152     	2                eval state : test_on_last_epoch;
---> final result              	0.03979     	0.49381     	0.31152     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04832     	-0.24792    	0.25460     	0           
  10          	0.04780     	-0.15676    	0.25400     	0           
  100         	0.04781     	-0.09851    	0.24421     	0           
  epoch 147 reach nan state
---> try to load best state
              	0.04778     	-0.09364    	0.24410     	0                eval state : test_on_restore;
              	0.04781     	-0.09851    	0.24421     	0                eval state : test_on_last_epoch;
---> final result
              	0.04778     	-0.09364    	0.24410     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04777     	-0.09798    	0.25678     	0           
  10          	0.05051     	-0.31404    	0.26879     	0           
  100         	0.04754     	-0.10872    	0.24804     	0           
  epoch 131 reach nan state
---> try to load best state
              	0.04748     	-0.09770    	0.24797     	0                eval state : test_on_restore;
              	0.04754     	-0.10872    	0.24804     	0                eval state : test_on_last_epoch;
---> final result
              	0.04748     	-0.09770    	0.24797     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04945     	-0.20964    	0.25333     	0           
  10          	0.05217     	-0.81984    	0.24765     	0           
  100         	0.05010     	-0.30792    	0.23822     	0           
  300         	0.05017     	0.04251     	0.23080     	0           
  500         	0.05016     	0.04533     	0.23075     	1           
  1000        	0.05036     	0.06867     	0.23040     	2           
---> try to load best state
              	0.05036     	0.06867     	0.23040     	2                eval state : test_on_restore;
              	0.05036     	0.06867     	0.23040     	2                eval state : test_on_last_epoch;
---> final result
              	0.05036     	0.06867     	0.23040     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05953     	-1.08712    	0.30806     	0           
  10          	0.05974     	-0.91770    	0.31447     	0           
  100         	0.06126     	-1.13706    	0.31659     	0           
  300         	0.05831     	-0.83810    	0.30349     	0           
  500         	0.05731     	-0.70841    	0.29904     	1           
  1000        	0.05734     	-0.69301    	0.29780     	2           
---> try to load best state
              	0.05734     	-0.69301    	0.29780     	2                eval state : test_on_restore;
              	0.05734     	-0.69301    	0.29780     	2                eval state : test_on_last_epoch;
---> final result
              	0.05734     	-0.69301    	0.29780     	2                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': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04721     	0.41646     	0.31487     	0           
  10          	0.04470     	0.45694     	0.30993     	0           
  100         	0.04237     	0.33181     	0.33378     	0           
  300         	0.04068     	0.40756     	0.32299     	0           
  500         	0.03877     	0.46938     	0.31323     	1           
  1000        	0.03762     	0.56128     	0.29630     	2           
---> try to load best state
              	0.03762     	0.56128     	0.29630     	2                eval state : test_on_restore;
              	0.03762     	0.56128     	0.29630     	2                eval state : test_on_last_epoch;
---> final result              	0.03762     	0.56128     	0.29630     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04489     	0.30875     	0.23178     	0           
  10          	0.04442     	0.29047     	0.22167     	0           
  100         	0.04394     	0.36539     	0.22739     	0           
  epoch 165 reach nan state
---> try to load best state
              	0.04377     	0.40257     	0.22607     	0                eval state : test_on_restore;
              	0.04394     	0.36539     	0.22739     	0                eval state : test_on_last_epoch;
---> final result
              	0.04377     	0.40257     	0.22607     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04806     	0.30974     	0.24586     	0           
  10          	0.05152     	0.34044     	0.26095     	0           
  100         	0.05171     	0.19525     	0.26515     	0           
  epoch 140 reach nan state
---> try to load best state
              	0.05175     	0.19728     	0.26505     	0                eval state : test_on_restore;
              	0.05171     	0.19525     	0.26515     	0                eval state : test_on_last_epoch;
---> final result
              	0.05175     	0.19728     	0.26505     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04845     	0.18762     	0.25381     	0           
  10          	0.04844     	0.15302     	0.25883     	0           
  100         	0.04751     	0.25986     	0.25027     	0           
  epoch 127 reach nan state
---> try to load best state
              	0.04745     	0.26680     	0.25015     	0                eval state : test_on_restore;
              	0.04751     	0.25986     	0.25027     	0                eval state : test_on_last_epoch;
---> final result
              	0.04745     	0.26680     	0.25015     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04515     	0.31129     	0.23321     	0           
  10          	0.04535     	0.25980     	0.23359     	0           
  100         	0.04514     	0.38885     	0.23181     	0           
  epoch 183 reach nan state
---> try to load best state
              	0.04603     	0.34822     	0.23744     	0                eval state : test_on_restore;
              	0.04514     	0.38885     	0.23181     	0                eval state : test_on_last_epoch;
---> final result
              	0.04514     	0.38885     	0.23181     	0                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': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06946     	0.88362     	0.15315     	0           
  10          	0.06386     	0.90811     	0.13847     	0           
  100         	0.01246     	0.99741     	0.03377     	0           
  300         	0.01613     	0.99520     	0.04884     	0           
  500         	0.01784     	0.99404     	0.05394     	1           
  1000        	0.01978     	0.99234     	0.05824     	2           
---> try to load best state
              	0.01978     	0.99234     	0.05824     	2                eval state : test_on_restore;
              	0.01978     	0.99234     	0.05824     	2                eval state : test_on_last_epoch;
---> final result              	0.01978     	0.99234     	0.05824     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.07105     	0.50280     	0.19229     	0           
  10          	0.08241     	0.83403     	0.16777     	0           
  100         	0.08296     	0.12649     	0.22413     	0           
  300         	0.07929     	0.31467     	0.23517     	0           
  500         	0.07995     	0.29232     	0.23926     	1           
  1000        	0.08049     	0.27793     	0.24182     	2           
---> try to load best state
              	0.08049     	0.27793     	0.24182     	2                eval state : test_on_restore;
              	0.08049     	0.27793     	0.24182     	2                eval state : test_on_last_epoch;
---> final result
              	0.08049     	0.27793     	0.24182     	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': 'plasmonic2_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           	0.05125     	0.48617     	0.16362     	0           
  10          	0.04669     	0.82469     	0.13416     	0           
  100         	0.04916     	-0.35129    	0.18186     	0           
  epoch 245 reach nan state
---> try to load best state
              	0.04074     	0.68239     	0.13087     	0                eval state : test_on_restore;
              	0.04916     	-0.35129    	0.18186     	0                eval state : test_on_last_epoch;
---> final result
              	0.04074     	0.68239     	0.13087     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04944     	0.56673     	0.18731     	0           
  10          	0.05497     	0.17306     	0.22325     	0           
  100         	0.04785     	0.26015     	0.17748     	0           
  300         	0.03760     	0.11932     	0.14881     	0           
  500         	0.04013     	0.66008     	0.15907     	1           
  1000        	0.05024     	0.48260     	0.24057     	2           
---> try to load best state
              	0.05024     	0.48260     	0.24057     	2                eval state : test_on_restore;
              	0.05024     	0.48260     	0.24057     	2                eval state : test_on_last_epoch;
---> final result
              	0.05024     	0.48260     	0.24057     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05294     	0.61558     	0.15396     	0           
  10          	0.04266     	0.68894     	0.13365     	0           
  100         	0.03571     	0.73571     	0.11647     	0           
  300         	0.03607     	0.72935     	0.11965     	0           
  500         	0.03653     	0.72971     	0.11999     	1           
  1000        	0.03690     	0.73009     	0.11956     	2           
---> try to load best state
              	0.03690     	0.73009     	0.11956     	2                eval state : test_on_restore;
              	0.03690     	0.73009     	0.11956     	2                eval state : test_on_last_epoch;
---> final result
              	0.03690     	0.73009     	0.11956     	2                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': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07214     	0.87174     	0.16540     	0           
  10          	0.06362     	0.91334     	0.15080     	0           
  100         	0.01691     	0.99416     	0.04157     	0           
  300         	0.01542     	0.99450     	0.03666     	0           
  500         	0.01563     	0.99444     	0.03653     	1           
  1000        	0.01638     	0.99403     	0.03709     	2           
---> try to load best state
              	0.01638     	0.99403     	0.03709     	2                eval state : test_on_restore;
              	0.01638     	0.99403     	0.03709     	2                eval state : test_on_last_epoch;
---> final result              	0.01638     	0.99403     	0.03709     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05083     	0.47845     	0.15150     	0           
  10          	0.04175     	0.32826     	0.14478     	0           
  100         	0.03969     	0.08393     	0.14807     	0           
  epoch 152 reach nan state
---> try to load best state
              	0.03968     	0.08132     	0.14808     	0                eval state : test_on_restore;
              	0.03969     	0.08393     	0.14807     	0                eval state : test_on_last_epoch;
---> final result
              	0.03969     	0.08393     	0.14807     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04116     	0.47178     	0.14870     	0           
  10          	0.03917     	0.29767     	0.14474     	0           
  100         	0.04022     	-0.01136    	0.15073     	0           
  epoch 137 reach nan state
---> try to load best state
              	0.04014     	0.00001     	0.15037     	0                eval state : test_on_restore;
              	0.04022     	-0.01136    	0.15073     	0                eval state : test_on_last_epoch;
---> final result
              	0.04014     	0.00001     	0.15037     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04271     	0.26585     	0.15441     	0           
  10          	0.04067     	0.06802     	0.15149     	0           
  100         	0.03988     	0.34507     	0.14334     	0           
  epoch 148 reach nan state
---> try to load best state
              	0.03995     	0.34983     	0.14298     	0                eval state : test_on_restore;
              	0.03988     	0.34507     	0.14334     	0                eval state : test_on_last_epoch;
---> final result
              	0.03995     	0.34983     	0.14298     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05512     	0.90993     	0.12941     	0           
  10          	0.04594     	0.93301     	0.11254     	0           
  100         	0.04852     	0.93866     	0.11199     	0           
  epoch 147 reach nan state
---> try to load best state
              	0.04832     	0.93833     	0.11187     	0                eval state : test_on_restore;
              	0.04852     	0.93866     	0.11199     	0                eval state : test_on_last_epoch;
---> final result
              	0.04832     	0.93833     	0.11187     	0                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': 'plasmonic2_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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05062     	0.88726     	0.16660     	0           
  10          	0.04725     	0.91663     	0.15566     	0           
  100         	0.02497     	0.97509     	0.05872     	0           
  300         	0.02445     	0.97865     	0.05812     	0           
  500         	0.02455     	0.98041     	0.05822     	1           
  1000        	0.02520     	0.98211     	0.05979     	2           
---> try to load best state
              	0.02520     	0.98211     	0.05979     	2                eval state : test_on_restore;
              	0.02520     	0.98211     	0.05979     	2                eval state : test_on_last_epoch;
---> final result              	0.02520     	0.98211     	0.05979     	2                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.04556     	0.58223     	0.15164     	0           
  10          	0.04574     	0.46873     	0.15170     	0           
  100         	0.04618     	0.52115     	0.15739     	0           
  epoch 166 reach nan state
---> try to load best state
              	0.04616     	0.52514     	0.15724     	0                eval state : test_on_restore;
              	0.04618     	0.52115     	0.15739     	0                eval state : test_on_last_epoch;
---> final result
              	0.04616     	0.52514     	0.15724     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05753     	0.40402     	0.17182     	0           
  10          	0.05742     	0.38110     	0.17935     	0           
  100         	0.04618     	0.29628     	0.14712     	0           
  epoch 141 reach nan state
---> try to load best state
              	0.04543     	0.30877     	0.14652     	0                eval state : test_on_restore;
              	0.04618     	0.29628     	0.14712     	0                eval state : test_on_last_epoch;
---> final result
              	0.04543     	0.30877     	0.14652     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.06803     	0.54539     	0.17900     	0           
  10          	0.06975     	0.34422     	0.19740     	0           
  100         	0.05103     	0.42405     	0.14330     	0           
  300         	0.05237     	0.28308     	0.14879     	0           
  500         	0.05564     	-0.40759    	0.18036     	1           
  1000        	0.08769     	-0.83868    	0.32835     	2           
---> try to load best state
              	0.08769     	-0.83868    	0.32835     	2                eval state : test_on_restore;
              	0.08769     	-0.83868    	0.32835     	2                eval state : test_on_last_epoch;
---> final result
              	0.08769     	-0.83868    	0.32835     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'plasmonic2_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           	0.05242     	0.86036     	0.12974     	0           
  10          	0.05006     	0.89909     	0.11340     	0           
  100         	0.04001     	0.94974     	0.08480     	0           
  300         	0.03880     	0.95724     	0.08292     	0           
  500         	0.03951     	0.95753     	0.08316     	1           
  1000        	0.04061     	0.95713     	0.08436     	2           
---> try to load best state
              	0.04061     	0.95713     	0.08436     	2                eval state : test_on_restore;
              	0.04061     	0.95713     	0.08436     	2                eval state : test_on_last_epoch;
---> final result
              	0.04061     	0.95713     	0.08436     	2                eval state : final;module_name : GAR;cp_record_file : True;
