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

  Demo res cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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           	0.05872     	0.22776     	0.85305     	0           
  10          	0.04901     	0.21540     	0.86947     	0           
  100         	0.04040     	0.18869     	0.90488     	0           
  300         	0.03955     	0.17833     	0.91806     	0           
  500         	0.03991     	0.17130     	0.92605     	0           
  1000        	0.04098     	0.16046     	0.93726     	1           
---> try to load best state
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_restore;
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_last_epoch;
---> final result
              	0.04098     	0.16046     	0.93726     	1                eval state : final;
----------> finish x-yl training


---------->
res cigp 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04311     	0.13914     	0.48304     	0           
  10          	0.04311     	0.13914     	0.48304     	0           
----------------------------------------

  Demo res cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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           	0.05872     	0.22776     	0.85305     	0           
  10          	0.04901     	0.21540     	0.86947     	0           
  100         	0.04040     	0.18869     	0.90488     	0           
  300         	0.03955     	0.17833     	0.91806     	0           
  500         	0.03991     	0.17130     	0.92605     	0           
  1000        	0.04098     	0.16046     	0.93726     	1           
---> try to load best state
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_restore;
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_last_epoch;
---> final result
              	0.04098     	0.16046     	0.93726     	1                eval state : final;
----------> finish x-yl training


---------->
res cigp 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04663     	0.15299     	0.37624     	0           
  10          	0.04643     	0.15256     	0.37622     	0           
  100         	0.04160     	0.14216     	0.39957     	0           
  300         	0.04063     	0.13983     	0.41662     	0           
  500         	0.04064     	0.13989     	0.41613     	0           
  1000        	0.04066     	0.14003     	0.41505     	1           
---> try to load best state
              	0.04066     	0.14003     	0.41505     	1                eval state : test_on_restore;
              	0.04066     	0.14003     	0.41505     	1                eval state : test_on_last_epoch;
---> final result
              	0.04066     	0.14003     	0.41505     	1                eval state : final;module_name : ResGP;cp_record_file : True;
---> end


---------->
res cigp 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05627     	0.15525     	0.52234     	0           
  10          	0.05546     	0.15317     	0.52385     	0           
----------------------------------------

  Demo res cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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           	0.05872     	0.22776     	0.85305     	0           
  10          	0.04901     	0.21540     	0.86947     	0           
  100         	0.04040     	0.18869     	0.90488     	0           
  300         	0.03955     	0.17833     	0.91806     	0           
  500         	0.03991     	0.17130     	0.92605     	0           
  1000        	0.04098     	0.16046     	0.93726     	1           
---> try to load best state
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_restore;
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_last_epoch;
---> final result
              	0.04098     	0.16046     	0.93726     	1                eval state : final;
----------> finish x-yl training


---------->
res cigp 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05627     	0.15525     	0.52234     	0           
  10          	0.05546     	0.15317     	0.52385     	0           
----------------------------------------

  Demo res cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  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           	0.05872     	0.22776     	0.85305     	0           
  10          	0.04901     	0.21540     	0.86947     	0           
  100         	0.04040     	0.18869     	0.90488     	0           
  300         	0.03955     	0.17833     	0.91806     	0           
  500         	0.03991     	0.17130     	0.92605     	0           
  1000        	0.04098     	0.16046     	0.93726     	1           
---> try to load best state
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_restore;
              	0.04098     	0.16046     	0.93726     	1                eval state : test_on_last_epoch;
---> final result
              	0.04098     	0.16046     	0.93726     	1                eval state : final;
----------> finish x-yl training


---------->
res cigp 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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04708     	0.11620     	0.86033     	0           
  10          	0.05008     	0.11903     	0.86879     	0           
  100         	0.04293     	0.14086     	0.91513     	0           
  300         	0.04524     	0.13984     	0.90889     	0           
  500         	0.04105     	0.12591     	0.84506     	0           
  1000        	0.04106     	0.12593     	0.84489     	1           
---> try to load best state
              	0.04106     	0.12593     	0.84489     	1                eval state : test_on_restore;
              	0.04106     	0.12593     	0.84489     	1                eval state : test_on_last_epoch;
---> final result
              	0.04106     	0.12593     	0.84489     	1                eval state : final;module_name : ResGP;cp_record_file : True;
