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

  Demo NAR cigp 
  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': 64, '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           	0.05141     	0.19435     	0.92313     	0           
  10          	0.05056     	0.19301     	0.92420     	0           
  100         	0.04365     	0.17950     	0.93438     	0           
  300         	0.03354     	0.15086     	0.95282     	0           
  500         	0.02909     	0.12885     	0.96488     	1           
  1000        	0.02842     	0.11456     	0.97203     	1           
---> try to load best state
              	0.02842     	0.11456     	0.97203     	1                eval state : test_on_restore;
              	0.02842     	0.11456     	0.97203     	1                eval state : test_on_last_epoch;
---> final result
              	0.02842     	0.11456     	0.97203     	1                eval state : final;
----------> finish x-yl training


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.16678     	0.53592     	-4535.37363 	0           
  10          	0.16675     	0.53592     	-4507.65741 	0           
  100         	0.16657     	0.53591     	-4272.96113 	0           
  300         	0.16633     	0.53590     	-3969.33360 	0           
  500         	0.16620     	0.53589     	-3810.11692 	0           
  1000        	0.16593     	0.53588     	-3522.66231 	1           
---> try to load best state
              	0.16593     	0.53588     	-3522.66231 	1                eval state : test_on_restore;
              	0.16593     	0.53588     	-3522.66231 	1                eval state : test_on_last_epoch;
---> final result
              	0.16593     	0.53588     	-3522.66231 	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.16756     	0.53625     	-5060.80786 	0           
  10          	0.16754     	0.53625     	-5038.72761 	0           
  100         	0.16733     	0.53625     	-4844.00906 	0           
  300         	0.16697     	0.53624     	-4534.05893 	0           
  500         	0.16672     	0.53624     	-4293.91894 	0           
  1000        	0.16625     	0.53622     	-3823.38230 	1           
---> try to load best state
              	0.16625     	0.53622     	-3823.38230 	1                eval state : test_on_restore;
              	0.16625     	0.53622     	-3823.38230 	1                eval state : test_on_last_epoch;
---> final result
              	0.16625     	0.53622     	-3823.38230 	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.14464     	0.52535     	-108.97636  	0           
  10          	0.14447     	0.52532     	-107.53985  	0           
  100         	0.14295     	0.52504     	-95.43667   	0           
  300         	0.14033     	0.52455     	-78.00830   	0           
  500         	0.13829     	0.52418     	-67.10428   	1           
  1000        	0.13706     	0.52353     	-51.94536   	2           
---> try to load best state
              	0.13706     	0.52353     	-51.94536   	2                eval state : test_on_restore;
              	0.13706     	0.52353     	-51.94536   	2                eval state : test_on_last_epoch;
---> final result
              	0.13706     	0.52353     	-51.94536   	2                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.12777     	0.50996     	-45.51609   	0           
  10          	0.12761     	0.50979     	-45.07565   	0           
  100         	0.12607     	0.50811     	-41.00680   	0           
  300         	0.12285     	0.50483     	-33.73043   	0           
  500         	0.11975     	0.50213     	-28.19443   	0           
  1000        	0.11366     	0.49752     	-19.95650   	1           
---> try to load best state
              	0.11366     	0.49752     	-19.95650   	1                eval state : test_on_restore;
              	0.11366     	0.49752     	-19.95650   	1                eval state : test_on_last_epoch;
---> final result
              	0.11366     	0.49752     	-19.95650   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.04102     	0.21454     	0.49972     	0           
  10          	0.04076     	0.21209     	0.51042     	0           
  100         	0.03833     	0.19124     	0.60176     	0           
  300         	0.03406     	0.16969     	0.72050     	0           
  500         	0.03477     	0.16926     	0.76839     	0           
  1000        	0.03822     	0.17380     	0.79202     	1           
---> try to load best state
              	0.03822     	0.17380     	0.79202     	1                eval state : test_on_restore;
              	0.03822     	0.17380     	0.79202     	1                eval state : test_on_last_epoch;
---> final result
              	0.03822     	0.17380     	0.79202     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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': 64, '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           	0.04675     	0.29876     	0.54222     	0           
  10          	0.04639     	0.29804     	0.54673     	0           
  100         	0.04317     	0.29150     	0.58621     	0           
  300         	0.03917     	0.28263     	0.63806     	0           
  500         	0.03687     	0.27863     	0.65947     	0           
  1000        	0.03572     	0.27323     	0.68249     	1           
---> try to load best state
              	0.03572     	0.27323     	0.68249     	1                eval state : test_on_restore;
              	0.03572     	0.27323     	0.68249     	1                eval state : test_on_last_epoch;
---> final result
              	0.03572     	0.27323     	0.68249     	1                eval state : final;
----------> finish x-yl training


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.16058     	0.53176     	-1027.50532 	0           
  10          	0.16053     	0.53176     	-1023.69723 	0           
  100         	0.16003     	0.53173     	-987.68741  	0           
  300         	0.15932     	0.53168     	-929.77275  	0           
  500         	0.15897     	0.53164     	-894.11094  	0           
  1000        	0.15824     	0.53155     	-801.01173  	1           
---> try to load best state
              	0.15824     	0.53155     	-801.01173  	1                eval state : test_on_restore;
              	0.15824     	0.53155     	-801.01173  	1                eval state : test_on_last_epoch;
---> final result
              	0.15824     	0.53155     	-801.01173  	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.13688     	0.51212     	-74.06026   	0           
  10          	0.13673     	0.51209     	-73.49042   	0           
  100         	0.13511     	0.51180     	-67.22282   	0           
  300         	0.13224     	0.51126     	-57.44064   	0           
  500         	0.13040     	0.51081     	-51.94047   	0           
  1000        	0.12831     	0.50982     	-45.91804   	1           
---> try to load best state
              	0.12831     	0.50982     	-45.91804   	1                eval state : test_on_restore;
              	0.12831     	0.50982     	-45.91804   	1                eval state : test_on_last_epoch;
---> final result
              	0.12831     	0.50982     	-45.91804   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.10391     	0.47924     	-11.23541   	0           
  10          	0.10373     	0.47904     	-11.12512   	0           
  100         	0.10214     	0.47711     	-10.18165   	0           
  300         	0.10042     	0.47276     	-8.79913    	0           
  500         	0.09884     	0.46804     	-7.93844    	0           
  1000        	0.09549     	0.45480     	-6.62041    	1           
---> try to load best state
              	0.09549     	0.45480     	-6.62041    	1                eval state : test_on_restore;
              	0.09549     	0.45480     	-6.62041    	1                eval state : test_on_last_epoch;
---> final result
              	0.09549     	0.45480     	-6.62041    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.09380     	0.46095     	-7.00835    	0           
  10          	0.09365     	0.46061     	-6.95530    	0           
  100         	0.09231     	0.45707     	-6.48484    	0           
  300         	0.08987     	0.44886     	-5.71518    	0           
  500         	0.08764     	0.44065     	-5.15240    	0           
  1000        	0.08350     	0.42164     	-4.16087    	1           
---> try to load best state
              	0.08350     	0.42164     	-4.16087    	1                eval state : test_on_restore;
              	0.08350     	0.42164     	-4.16087    	1                eval state : test_on_last_epoch;
---> final result
              	0.08350     	0.42164     	-4.16087    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.04293     	0.23388     	-0.00049    	0           
  10          	0.04250     	0.23267     	0.01452     	0           
  100         	0.03884     	0.22297     	0.13356     	0           
  300         	0.03407     	0.21195     	0.27158     	0           
  500         	0.03292     	0.20733     	0.33098     	0           
  1000        	0.03245     	0.20262     	0.37683     	1           
---> try to load best state
              	0.03245     	0.20262     	0.37683     	1                eval state : test_on_restore;
              	0.03245     	0.20262     	0.37683     	1                eval state : test_on_last_epoch;
---> final result
              	0.03245     	0.20262     	0.37683     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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': 64, '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           	0.05561     	0.18846     	0.93115     	0           
  10          	0.05511     	0.18725     	0.93223     	0           
  100         	0.05076     	0.17591     	0.94187     	0           
  300         	0.04213     	0.15371     	0.95718     	0           
  500         	0.03581     	0.13635     	0.96673     	0           
  1000        	0.03338     	0.12640     	0.97160     	1           
---> try to load best state
              	0.03338     	0.12640     	0.97160     	1                eval state : test_on_restore;
              	0.03338     	0.12640     	0.97160     	1                eval state : test_on_last_epoch;
---> final result
              	0.03338     	0.12640     	0.97160     	1                eval state : final;
----------> finish x-yl training


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.23593     	0.49157     	-34.19583   	0           
  10          	0.23546     	0.49140     	-34.03840   	0           
  100         	0.23115     	0.48981     	-32.70466   	0           
  300         	0.22295     	0.48685     	-30.45730   	0           
  500         	0.21520     	0.48418     	-28.42155   	0           
  1000        	0.19922     	0.47847     	-25.38560   	1           
---> try to load best state
              	0.19922     	0.47847     	-25.38560   	1                eval state : test_on_restore;
              	0.19922     	0.47847     	-25.38560   	1                eval state : test_on_last_epoch;
---> final result
              	0.19922     	0.47847     	-25.38560   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.12276     	0.48941     	-28.76165   	0           
  10          	0.12272     	0.48939     	-28.67908   	0           
  100         	0.12244     	0.48916     	-27.93865   	0           
  300         	0.12222     	0.48871     	-26.74087   	0           
  500         	0.12193     	0.48829     	-25.85220   	0           
  1000        	0.12055     	0.48723     	-23.68198   	1           
---> try to load best state
              	0.12055     	0.48723     	-23.68198   	1                eval state : test_on_restore;
              	0.12055     	0.48723     	-23.68198   	1                eval state : test_on_last_epoch;
---> final result
              	0.12055     	0.48723     	-23.68198   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.10117     	0.46399     	-7.91631    	0           
  10          	0.10090     	0.46383     	-7.83126    	0           
  100         	0.09844     	0.46245     	-7.09471    	0           
  300         	0.09569     	0.46002     	-6.01611    	0           
  500         	0.09392     	0.45800     	-5.39176    	0           
  1000        	0.09273     	0.45289     	-4.61063    	1           
---> try to load best state
              	0.09273     	0.45289     	-4.61063    	1                eval state : test_on_restore;
              	0.09273     	0.45289     	-4.61063    	1                eval state : test_on_last_epoch;
---> final result
              	0.09273     	0.45289     	-4.61063    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.03283     	0.16042     	0.45028     	0           
  10          	0.03255     	0.15909     	0.45793     	0           
  100         	0.03000     	0.14789     	0.52082     	0           
  300         	0.02840     	0.13446     	0.59724     	0           
  500         	0.02772     	0.12996     	0.62764     	0           
  1000        	0.02788     	0.12901     	0.64294     	1           
---> try to load best state
              	0.02788     	0.12901     	0.64294     	1                eval state : test_on_restore;
              	0.02788     	0.12901     	0.64294     	1                eval state : test_on_last_epoch;
---> final result
              	0.02788     	0.12901     	0.64294     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.02117     	0.07695     	0.94512     	0           
  10          	0.02123     	0.07755     	0.94474     	0           
  100         	0.02234     	0.08311     	0.94087     	0           
  300         	0.02424     	0.09120     	0.93452     	0           
  500         	0.02529     	0.09542     	0.93102     	0           
  1000        	0.02633     	0.09962     	0.92793     	1           
---> try to load best state
              	0.02633     	0.09962     	0.92793     	1                eval state : test_on_restore;
              	0.02633     	0.09962     	0.92793     	1                eval state : test_on_last_epoch;
---> final result
              	0.02633     	0.09962     	0.92793     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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': 64, '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           	0.06663     	0.22225     	0.91689     	0           
  10          	0.06580     	0.22103     	0.91786     	0           
  100         	0.05853     	0.20930     	0.92662     	0           
  300         	0.04876     	0.18855     	0.93993     	0           
  500         	0.04290     	0.17674     	0.94666     	0           
  1000        	0.04134     	0.17049     	0.95046     	1           
---> try to load best state
              	0.04134     	0.17049     	0.95046     	1                eval state : test_on_restore;
              	0.04134     	0.17049     	0.95046     	1                eval state : test_on_last_epoch;
---> final result
              	0.04134     	0.17049     	0.95046     	1                eval state : final;
----------> finish x-yl training


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.11685     	0.46047     	-10.91075   	0           
  10          	0.11624     	0.46028     	-10.80252   	0           
  100         	0.11098     	0.45853     	-9.88889    	0           
  300         	0.10360     	0.45541     	-8.61968    	0           
  500         	0.10172     	0.45288     	-7.81031    	0           
  1000        	0.10107     	0.44820     	-6.74570    	1           
---> try to load best state
              	0.10107     	0.44820     	-6.74570    	1                eval state : test_on_restore;
              	0.10107     	0.44820     	-6.74570    	1                eval state : test_on_last_epoch;
---> final result
              	0.10107     	0.44820     	-6.74570    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.11903     	0.48440     	-17.87439   	0           
  10          	0.11865     	0.48423     	-17.61532   	0           
  100         	0.11535     	0.48266     	-15.42947   	0           
  300         	0.11007     	0.47962     	-12.35537   	0           
  500         	0.10827     	0.47692     	-10.60000   	0           
  1000        	0.10425     	0.47048     	-8.48178    	1           
---> try to load best state
              	0.10425     	0.47048     	-8.48178    	1                eval state : test_on_restore;
              	0.10425     	0.47048     	-8.48178    	1                eval state : test_on_last_epoch;
---> final result
              	0.10425     	0.47048     	-8.48178    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.11408     	0.49307     	-18.89974   	0           
  10          	0.11383     	0.49294     	-18.63792   	0           
  100         	0.11205     	0.49173     	-16.44205   	0           
  300         	0.11066     	0.48970     	-13.43447   	0           
  500         	0.11019     	0.48805     	-11.82894   	0           
  1000        	0.10859     	0.48405     	-10.01661   	1           
---> try to load best state
              	0.10859     	0.48405     	-10.01661   	1                eval state : test_on_restore;
              	0.10859     	0.48405     	-10.01661   	1                eval state : test_on_last_epoch;
---> final result
              	0.10859     	0.48405     	-10.01661   	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.02651     	0.08131     	0.95732     	0           
  10          	0.02666     	0.08179     	0.95697     	0           
  100         	0.02802     	0.08625     	0.95351     	0           
  300         	0.03012     	0.09400     	0.94702     	0           
  500         	0.03114     	0.09983     	0.94185     	0           
  1000        	0.03252     	0.11012     	0.93230     	1           
---> try to load best state
              	0.03252     	0.11012     	0.93230     	1                eval state : test_on_restore;
              	0.03252     	0.11012     	0.93230     	1                eval state : test_on_last_epoch;
---> final result
              	0.03252     	0.11012     	0.93230     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.05975     	0.26546     	0.85363     	0           
  10          	0.05978     	0.26532     	0.85388     	0           
  100         	0.05982     	0.26335     	0.85646     	0           
  300         	0.05971     	0.25760     	0.86210     	0           
  500         	0.06081     	0.25159     	0.86677     	0           
  1000        	0.06303     	0.24352     	0.87056     	1           
---> try to load best state
              	0.06303     	0.24352     	0.87056     	1                eval state : test_on_restore;
              	0.06303     	0.24352     	0.87056     	1                eval state : test_on_last_epoch;
---> final result
              	0.06303     	0.24352     	0.87056     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end

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

  Demo NAR cigp 
  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': 64, '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           	0.04568     	0.22600     	0.84686     	0           
  10          	0.04488     	0.22492     	0.84867     	0           
  100         	0.03769     	0.21437     	0.86588     	0           
  300         	0.03404     	0.19619     	0.89272     	0           
  500         	0.03060     	0.18524     	0.90583     	0           
  1000        	0.02819     	0.17366     	0.91916     	1           
---> try to load best state
              	0.02819     	0.17366     	0.91916     	1                eval state : test_on_restore;
              	0.02819     	0.17366     	0.91916     	1                eval state : test_on_last_epoch;
---> final result
              	0.02819     	0.17366     	0.91916     	1                eval state : final;
----------> finish x-yl training


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.17057     	0.53727     	-29568.56222	0           
  10          	0.17057     	0.53727     	-29435.01044	0           
  100         	0.17050     	0.53727     	-28265.45014	0           
  300         	0.17038     	0.53727     	-26464.06422	0           
  500         	0.17027     	0.53727     	-25167.39768	0           
  1000        	0.16997     	0.53726     	-21314.00683	1           
---> try to load best state
              	0.16997     	0.53726     	-21314.00683	1                eval state : test_on_restore;
              	0.16997     	0.53726     	-21314.00683	1                eval state : test_on_last_epoch;
---> final result
              	0.16997     	0.53726     	-21314.00683	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.10575     	0.40403     	-3.39717    	0           
  10          	0.10533     	0.40345     	-3.37607    	0           
  100         	0.10124     	0.39783     	-3.17283    	0           
  300         	0.09411     	0.38644     	-2.76789    	0           
  500         	0.08923     	0.37652     	-2.44991    	0           
  1000        	0.08045     	0.35484     	-1.84893    	1           
---> try to load best state
              	0.08045     	0.35484     	-1.84893    	1                eval state : test_on_restore;
              	0.08045     	0.35484     	-1.84893    	1                eval state : test_on_last_epoch;
---> final result
              	0.08045     	0.35484     	-1.84893    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.08246     	0.40262     	-2.54797    	0           
  10          	0.08218     	0.40205     	-2.53196    	0           
  100         	0.07931     	0.39663     	-2.38253    	0           
  300         	0.07447     	0.38593     	-2.09596    	0           
  500         	0.07223     	0.37639     	-1.85082    	0           
  1000        	0.06986     	0.35454     	-1.35481    	1           
---> try to load best state
              	0.06986     	0.35454     	-1.35481    	1                eval state : test_on_restore;
              	0.06986     	0.35454     	-1.35481    	1                eval state : test_on_last_epoch;
---> final result
              	0.06986     	0.35454     	-1.35481    	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR 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': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.03094     	0.14751     	0.75946     	0           
  10          	0.03069     	0.14601     	0.76456     	0           
  100         	0.02885     	0.13406     	0.80260     	0           
  300         	0.02827     	0.12112     	0.83918     	0           
  500         	0.02788     	0.11664     	0.85115     	0           
  1000        	0.02747     	0.11481     	0.85655     	1           
---> try to load best state
              	0.02747     	0.11481     	0.85655     	1                eval state : test_on_restore;
              	0.02747     	0.11481     	0.85655     	1                eval state : test_on_last_epoch;
---> final result
              	0.02747     	0.11481     	0.85655     	1                eval state : final;module_name : NAR;cp_record_file : True;
---> end


---------->
NAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['np.concatenate([x[0],y[0]], axis=1)'], 'outputs_format': ['y[-1]'], '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           	0.03546     	0.20538     	0.51696     	0           
  10          	0.03513     	0.20303     	0.52995     	0           
  100         	0.03204     	0.18147     	0.63705     	0           
  300         	0.02713     	0.14738     	0.76753     	0           
  500         	0.02494     	0.12907     	0.82117     	0           
  1000        	0.02553     	0.11494     	0.85645     	1           
---> try to load best state
              	0.02553     	0.11494     	0.85645     	1                eval state : test_on_restore;
              	0.02553     	0.11494     	0.85645     	1                eval state : test_on_last_epoch;
---> final result
              	0.02553     	0.11494     	0.85645     	1                eval state : final;module_name : NAR;cp_record_file : True;
