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

  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': 128, '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_normalize: 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}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03813     	0.93242     	0.13297     	0           
  10          	0.03542     	0.94574     	0.11981     	0           
  100         	0.00933     	0.99827     	0.02673     	0           
  300         	0.00506     	0.99944     	0.01469     	0           
  500         	0.00545     	0.99938     	0.01535     	1           
---> try to load best state
              	0.01198     	0.99738     	0.03034     	2                eval state : test_on_restore;
              	0.05807     	0.89909     	0.15005     	2                eval state : test_on_last_epoch;
---> final result              	0.01198     	0.99738     	0.03034     	2                eval state : final;
----------> finish x-yl training


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03620     	0.37431     	0.15637     	0           
  10          	0.03533     	0.47853     	0.15038     	0           
  100         	0.04251     	-1.44771    	0.18054     	0           
  300         	0.04192     	-1.17699    	0.18120     	0           
  500         	0.03894     	-0.05574    	0.17283     	1           
  1000        	0.03720     	0.42803     	0.17086     	2           
---> try to load best state
              	0.03720     	0.42803     	0.17086     	2                eval state : test_on_restore;
              	0.03720     	0.42803     	0.17086     	2                eval state : test_on_last_epoch;
---> final result
              	0.03720     	0.42803     	0.17086     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03529     	0.54576     	0.14565     	0           
  10          	0.03477     	0.48609     	0.13839     	0           
  100         	0.04101     	-1.04119    	0.16946     	0           
  300         	0.04303     	-2.03387    	0.18131     	0           
  500         	0.03750     	0.02804     	0.15968     	1           
  1000        	0.03607     	0.44524     	0.15540     	2           
---> try to load best state
              	0.03607     	0.44524     	0.15540     	2                eval state : test_on_restore;
              	0.03607     	0.44524     	0.15540     	2                eval state : test_on_last_epoch;
---> final result
              	0.03607     	0.44524     	0.15540     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03682     	0.38667     	0.15759     	0           
  10          	0.04347     	-0.45060    	0.19822     	0           
  100         	0.04292     	0.30833     	0.20539     	0           
  300         	0.04881     	-0.73107    	0.23385     	1           
  500         	0.04805     	-0.25092    	0.23259     	1           
  1000        	0.04723     	0.38152     	0.23595     	2           
---> try to load best state
              	0.04723     	0.38152     	0.23595     	2                eval state : test_on_restore;
              	0.04723     	0.38152     	0.23595     	2                eval state : test_on_last_epoch;
---> final result
              	0.04723     	0.38152     	0.23595     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04295     	0.24440     	0.16362     	0           
  10          	0.03943     	0.64496     	0.14952     	0           
  100         	0.04613     	0.05748     	0.18667     	0           
  300         	0.04650     	0.23808     	0.19072     	0           
  500         	0.05155     	-0.24782    	0.23240     	1           
  1000        	0.05107     	-0.13086    	0.22985     	3           
---> try to load best state
              	0.05107     	-0.13086    	0.22985     	3                eval state : test_on_restore;
              	0.05107     	-0.13086    	0.22985     	3                eval state : test_on_last_epoch;
---> final result
              	0.05107     	-0.13086    	0.22985     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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': ['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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04313     	0.71675     	0.13465     	0           
  10          	0.04000     	0.74394     	0.12605     	0           
  100         	0.02752     	0.71553     	0.11390     	0           
  300         	0.03130     	0.71824     	0.11694     	1           
  500         	0.04290     	0.71716     	0.12849     	1           
  1000        	0.04227     	0.74634     	0.12169     	3           
---> try to load best state
              	0.04227     	0.74634     	0.12169     	3                eval state : test_on_restore;
              	0.04227     	0.74634     	0.12169     	3                eval state : test_on_last_epoch;
---> final result
              	0.04227     	0.74634     	0.12169     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, '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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03770     	0.64659     	0.13290     	0           
  10          	0.03559     	0.69865     	0.12008     	0           
  100         	0.02249     	0.69505     	0.10643     	0           
  300         	0.02444     	0.69386     	0.11035     	1           
  500         	0.02881     	0.69072     	0.11421     	2           
  1000        	0.02821     	0.70327     	0.11025     	4           
---> try to load best state
              	0.02821     	0.70327     	0.11025     	4                eval state : test_on_restore;
              	0.02821     	0.70327     	0.11025     	4                eval state : test_on_last_epoch;
---> final result
              	0.02821     	0.70327     	0.11025     	4                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': 128, '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_normalize: 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}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03042     	0.87878     	0.19811     	0           
  10          	0.02731     	0.89185     	0.18994     	0           
  100         	0.01268     	0.98199     	0.08807     	0           
  300         	0.00739     	0.99193     	0.06010     	1           
  500         	0.00683     	0.99410     	0.05108     	1           
---> try to load best state
              	0.00843     	0.99416     	0.04877     	3                eval state : test_on_restore;
              	0.03866     	0.94935     	0.11342     	3                eval state : test_on_last_epoch;
---> final result              	0.00843     	0.99416     	0.04877     	3                eval state : final;
----------> finish x-yl training


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03138     	0.36307     	0.14378     	0           
  10          	0.03342     	0.21727     	0.16054     	0           
  100         	0.03452     	0.34747     	0.17199     	0           
  300         	0.03179     	0.64890     	0.15284     	0           
  500         	0.03111     	0.63332     	0.14490     	1           
  1000        	0.03112     	0.60709     	0.14455     	2           
---> try to load best state
              	0.03112     	0.60709     	0.14455     	2                eval state : test_on_restore;
              	0.03112     	0.60709     	0.14455     	2                eval state : test_on_last_epoch;
---> final result
              	0.03112     	0.60709     	0.14455     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03239     	0.54900     	0.13363     	0           
  10          	0.03021     	0.82460     	0.11527     	0           
  100         	0.03289     	0.70502     	0.13666     	0           
  300         	0.03603     	0.54264     	0.15510     	0           
  500         	0.03611     	0.61937     	0.15528     	1           
  1000        	0.03622     	0.62872     	0.15596     	2           
---> try to load best state
              	0.03622     	0.62872     	0.15596     	2                eval state : test_on_restore;
              	0.03622     	0.62872     	0.15596     	2                eval state : test_on_last_epoch;
---> final result
              	0.03622     	0.62872     	0.15596     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03147     	0.43638     	0.13698     	0           
  10          	0.03068     	0.27566     	0.14256     	0           
  100         	0.02983     	0.30379     	0.14178     	0           
  300         	0.03573     	-0.42337    	0.17738     	0           
  500         	0.03205     	-0.00576    	0.15707     	1           
  1000        	0.02947     	0.36108     	0.13869     	2           
---> try to load best state
              	0.02947     	0.36108     	0.13869     	2                eval state : test_on_restore;
              	0.02947     	0.36108     	0.13869     	2                eval state : test_on_last_epoch;
---> final result
              	0.02947     	0.36108     	0.13869     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03312     	0.39124     	0.14289     	0           
  10          	0.03429     	0.07553     	0.15394     	0           
  100         	0.02830     	0.55078     	0.12503     	0           
  300         	0.03350     	0.67480     	0.14961     	0           
  500         	0.03422     	0.67836     	0.15459     	1           
  1000        	0.03428     	0.67984     	0.15506     	2           
---> try to load best state
              	0.03428     	0.67984     	0.15506     	2                eval state : test_on_restore;
              	0.03428     	0.67984     	0.15506     	2                eval state : test_on_last_epoch;
---> final result
              	0.03428     	0.67984     	0.15506     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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': ['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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03125     	0.66681     	0.13286     	0           
  10          	0.02856     	0.80523     	0.11150     	0           
  100         	0.02418     	0.86275     	0.09174     	0           
  300         	0.02380     	0.75304     	0.10670     	1           
  500         	0.02385     	0.74255     	0.11149     	1           
  1000        	0.02385     	0.74289     	0.11144     	3           
---> try to load best state
              	0.02385     	0.74289     	0.11144     	3                eval state : test_on_restore;
              	0.02385     	0.74289     	0.11144     	3                eval state : test_on_last_epoch;
---> final result
              	0.02385     	0.74289     	0.11144     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, '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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03585     	0.81822     	0.15788     	0           
  10          	0.03627     	0.87486     	0.16704     	0           
  100         	0.04230     	0.91998     	0.22782     	0           
  300         	0.04490     	0.91572     	0.23041     	1           
  500         	0.04580     	0.92034     	0.22338     	2           
  1000        	0.04728     	0.90865     	0.24192     	4           
---> try to load best state
              	0.04728     	0.90865     	0.24192     	4                eval state : test_on_restore;
              	0.04728     	0.90865     	0.24192     	4                eval state : test_on_last_epoch;
---> final result
              	0.04728     	0.90865     	0.24192     	4                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': 128, '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_normalize: 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}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04551     	0.94823     	0.11817     	0           
  10          	0.03932     	0.96259     	0.10563     	0           
  100         	0.01147     	0.99731     	0.03260     	0           
  300         	0.00682     	0.99902     	0.01963     	1           
  500         	0.00702     	0.99898     	0.02005     	2           
---> try to load best state
              	0.00755     	0.99921     	0.01580     	3                eval state : test_on_restore;
              	0.03079     	0.98413     	0.06242     	3                eval state : test_on_last_epoch;
---> final result              	0.00755     	0.99921     	0.01580     	3                eval state : final;
----------> finish x-yl training


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05950     	0.49638     	0.16974     	0           
  10          	0.06981     	0.84754     	0.14570     	0           
  100         	0.09680     	0.06226     	0.24940     	0           
  300         	0.08057     	-0.08549    	0.28167     	1           
  500         	0.07770     	-0.08541    	0.28346     	1           
  1000        	0.07639     	-0.07978    	0.28407     	3           
---> try to load best state
              	0.07639     	-0.07978    	0.28407     	3                eval state : test_on_restore;
              	0.07639     	-0.07978    	0.28407     	3                eval state : test_on_last_epoch;
---> final result
              	0.07639     	-0.07978    	0.28407     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03974     	0.46989     	0.15212     	0           
  10          	0.03519     	0.82699     	0.12230     	0           
  100         	0.04719     	-0.62577    	0.18895     	0           
  300         	0.03614     	-0.16120    	0.16936     	0           
  500         	0.04047     	-0.62892    	0.19323     	1           
  1000        	0.04835     	-1.14172    	0.23241     	2           
---> try to load best state
              	0.04835     	-1.14172    	0.23241     	2                eval state : test_on_restore;
              	0.04835     	-1.14172    	0.23241     	2                eval state : test_on_last_epoch;
---> final result
              	0.04835     	-1.14172    	0.23241     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03823     	0.55467     	0.17771     	0           
  10          	0.04314     	0.15137     	0.21261     	0           
  100         	0.03510     	0.63039     	0.15515     	0           
  300         	0.03237     	-0.29665    	0.15914     	0           
  500         	0.04941     	0.35173     	0.26640     	1           
  1000        	0.04942     	0.35035     	0.26563     	2           
---> try to load best state
              	0.04942     	0.35035     	0.26563     	2                eval state : test_on_restore;
              	0.04942     	0.35035     	0.26563     	2                eval state : test_on_last_epoch;
---> final result
              	0.04942     	0.35035     	0.26563     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04684     	0.60199     	0.15052     	0           
  10          	0.04231     	0.66884     	0.13720     	0           
  100         	0.03244     	0.72338     	0.11731     	0           
  300         	0.02958     	0.72431     	0.11694     	0           
  500         	0.02896     	0.72527     	0.11510     	1           
  1000        	0.02860     	0.72263     	0.11526     	2           
---> try to load best state
              	0.02860     	0.72263     	0.11526     	2                eval state : test_on_restore;
              	0.02860     	0.72263     	0.11526     	2                eval state : test_on_last_epoch;
---> final result
              	0.02860     	0.72263     	0.11526     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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': ['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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03990     	0.85793     	0.12255     	0           
  10          	0.03253     	0.89617     	0.10536     	0           
  100         	0.02562     	0.93198     	0.07971     	0           
  300         	0.02512     	0.93275     	0.08035     	0           
  500         	0.02510     	0.93320     	0.07900     	1           
  1000        	0.02482     	0.93389     	0.07912     	2           
---> try to load best state
              	0.02482     	0.93389     	0.07912     	2                eval state : test_on_restore;
              	0.02482     	0.93389     	0.07912     	2                eval state : test_on_last_epoch;
---> final result
              	0.02482     	0.93389     	0.07912     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, '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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03876     	0.91494     	0.10924     	0           
  10          	0.03329     	0.94107     	0.09213     	0           
  100         	0.02302     	0.97088     	0.06304     	0           
  300         	0.02485     	0.96815     	0.06721     	1           
  500         	0.02551     	0.96858     	0.06703     	2           
  1000        	0.02443     	0.96889     	0.06749     	3           
---> try to load best state
              	0.02443     	0.96889     	0.06749     	3                eval state : test_on_restore;
              	0.02443     	0.96889     	0.06749     	3                eval state : test_on_last_epoch;
---> final result
              	0.02443     	0.96889     	0.06749     	3                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': 128, '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_normalize: 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}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03998     	0.93731     	0.13096     	0           
  10          	0.03502     	0.94940     	0.11790     	0           
  100         	0.01148     	0.99608     	0.03389     	0           
  300         	0.00583     	0.99858     	0.01846     	1           
  500         	0.00642     	0.99832     	0.02165     	2           
---> try to load best state
              	0.01367     	0.99705     	0.02914     	4                eval state : test_on_restore;
              	0.07401     	0.79720     	0.19738     	4                eval state : test_on_last_epoch;
---> final result              	0.01367     	0.99705     	0.02914     	4                eval state : final;
----------> finish x-yl training


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05229     	0.43455     	0.16225     	0           
  10          	0.04249     	0.27433     	0.15621     	0           
  100         	0.03959     	0.10963     	0.15639     	0           
  300         	0.03971     	0.02428     	0.15817     	1           
  500         	0.03949     	0.06398     	0.15731     	1           
  1000        	0.03966     	0.01479     	0.15842     	3           
---> try to load best state
              	0.03966     	0.01479     	0.15842     	3                eval state : test_on_restore;
              	0.03966     	0.01479     	0.15842     	3                eval state : test_on_last_epoch;
---> final result
              	0.03966     	0.01479     	0.15842     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04239     	0.42687     	0.16047     	0           
  10          	0.03988     	0.24107     	0.15644     	0           
  100         	0.03851     	0.26906     	0.15217     	0           
  300         	0.04040     	-0.13901    	0.16191     	1           
  500         	0.04079     	-0.48713    	0.17026     	1           
  1000        	0.04073     	-0.46878    	0.16990     	3           
---> try to load best state
              	0.04073     	-0.46878    	0.16990     	3                eval state : test_on_restore;
              	0.04073     	-0.46878    	0.16990     	3                eval state : test_on_last_epoch;
---> final result
              	0.04073     	-0.46878    	0.16990     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04325     	0.20580     	0.16617     	0           
  10          	0.04123     	-0.00131    	0.16295     	0           
  100         	0.03654     	0.66756     	0.13879     	0           
  300         	0.04060     	0.93566     	0.17441     	1           
  500         	0.04109     	0.93612     	0.17830     	1           
  1000        	0.04049     	0.93617     	0.17384     	3           
---> try to load best state
              	0.04049     	0.93617     	0.17384     	3                eval state : test_on_restore;
              	0.04049     	0.93617     	0.17384     	3                eval state : test_on_last_epoch;
---> final result
              	0.04049     	0.93617     	0.17384     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05532     	0.90344     	0.14238     	0           
  10          	0.04541     	0.93365     	0.12032     	0           
  100         	0.03825     	0.96161     	0.10733     	0           
  300         	0.03411     	0.96299     	0.10390     	1           
  500         	0.03543     	0.96275     	0.10385     	1           
  1000        	0.03674     	0.96228     	0.10414     	3           
---> try to load best state
              	0.03674     	0.96228     	0.10414     	3                eval state : test_on_restore;
              	0.03674     	0.96228     	0.10414     	3                eval state : test_on_last_epoch;
---> final result
              	0.03674     	0.96228     	0.10414     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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': ['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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04946     	0.90399     	0.15352     	0           
  10          	0.04283     	0.93159     	0.13751     	0           
  100         	0.03945     	0.96458     	0.11776     	0           
  300         	0.03492     	0.95969     	0.12316     	1           
  500         	0.03449     	0.96193     	0.12240     	1           
  1000        	0.03734     	0.96206     	0.12287     	3           
---> try to load best state
              	0.03734     	0.96206     	0.12287     	3                eval state : test_on_restore;
              	0.03734     	0.96206     	0.12287     	3                eval state : test_on_last_epoch;
---> final result
              	0.03734     	0.96206     	0.12287     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 128, '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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04205     	0.92021     	0.13983     	0           
  10          	0.03610     	0.94863     	0.12266     	0           
  100         	0.03312     	0.97046     	0.10548     	0           
  300         	0.03337     	0.96635     	0.11427     	1           
  500         	0.03291     	0.96597     	0.11456     	2           
  1000        	0.03349     	0.96507     	0.11538     	4           
---> try to load best state
              	0.03349     	0.96507     	0.11538     	4                eval state : test_on_restore;
              	0.03349     	0.96507     	0.11538     	4                eval state : test_on_last_epoch;
---> final result
              	0.03349     	0.96507     	0.11538     	4                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': 128, '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_normalize: 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}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04620     	0.93977     	0.11369     	0           
  10          	0.04050     	0.95513     	0.10150     	0           
  100         	0.01053     	0.99802     	0.02685     	0           
  300         	0.00714     	0.99902     	0.01899     	1           
  500         	0.00734     	0.99898     	0.01944     	2           
---> try to load best state
              	0.00841     	0.99895     	0.01873     	3                eval state : test_on_restore;
              	0.02910     	0.98651     	0.05890     	3                eval state : test_on_last_epoch;
---> final result              	0.00841     	0.99895     	0.01873     	3                eval state : final;
----------> finish x-yl training


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03453     	0.59163     	0.15705     	0           
  10          	0.03476     	0.47514     	0.15593     	0           
  100         	0.04254     	-0.74634    	0.17192     	0           
  300         	0.03518     	0.51278     	0.16321     	0           
  500         	0.03518     	0.52571     	0.16344     	1           
  1000        	0.03515     	0.53164     	0.16314     	3           
---> try to load best state
              	0.03515     	0.53164     	0.16314     	3                eval state : test_on_restore;
              	0.03515     	0.53164     	0.16314     	3                eval state : test_on_last_epoch;
---> final result
              	0.03515     	0.53164     	0.16314     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06100     	0.40088     	0.18656     	0           
  10          	0.06226     	0.37443     	0.19573     	0           
  100         	0.05592     	0.74402     	0.13552     	0           
  300         	0.05160     	0.14027     	0.18545     	0           
  500         	0.06089     	-0.55430    	0.34971     	1           
  1000        	0.05931     	-0.50495    	0.33963     	3           
---> try to load best state
              	0.05931     	-0.50495    	0.33963     	3                eval state : test_on_restore;
              	0.05931     	-0.50495    	0.33963     	3                eval state : test_on_last_epoch;
---> final result
              	0.05931     	-0.50495    	0.33963     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07111     	0.53949     	0.19822     	0           
  10          	0.07252     	0.30808     	0.21802     	0           
  100         	0.05112     	0.69306     	0.14084     	0           
  300         	0.04910     	-1.12149    	0.24799     	0           
  500         	0.07396     	-0.70626    	0.37010     	1           
  1000        	0.09558     	-0.59892    	0.46181     	3           
---> try to load best state
              	0.09558     	-0.59892    	0.46181     	3                eval state : test_on_restore;
              	0.09558     	-0.59892    	0.46181     	3                eval state : test_on_last_epoch;
---> final result
              	0.09558     	-0.59892    	0.46181     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04106     	0.86603     	0.13477     	0           
  10          	0.03830     	0.91475     	0.11458     	0           
  100         	0.03228     	0.96781     	0.08146     	0           
  300         	0.03180     	0.97547     	0.07073     	1           
  500         	0.03220     	0.97568     	0.07060     	1           
  1000        	0.03212     	0.97551     	0.07125     	3           
---> try to load best state
              	0.03212     	0.97551     	0.07125     	3                eval state : test_on_restore;
              	0.03212     	0.97551     	0.07125     	3                eval state : test_on_last_epoch;
---> final result
              	0.03212     	0.97551     	0.07125     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR 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': ['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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03890     	0.86553     	0.13603     	0           
  10          	0.03621     	0.91866     	0.11459     	0           
  100         	0.02944     	0.97418     	0.07242     	0           
  300         	0.02863     	0.97544     	0.06965     	1           
  500         	0.02871     	0.97553     	0.06992     	2           
  1000        	0.02871     	0.97553     	0.06992     	3           
---> try to load best state
              	0.02871     	0.97553     	0.06992     	3                eval state : test_on_restore;
              	0.02871     	0.97553     	0.06992     	3                eval state : test_on_last_epoch;
---> final result
              	0.02871     	0.97553     	0.06992     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 128, '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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04038     	0.91510     	0.11204     	0           
  10          	0.03452     	0.94151     	0.09456     	0           
  100         	0.02500     	0.97109     	0.06476     	0           
  300         	0.02549     	0.97012     	0.06758     	1           
  500         	0.02563     	0.97110     	0.06608     	1           
  1000        	0.02512     	0.97137     	0.06680     	3           
---> try to load best state
              	0.02512     	0.97137     	0.06680     	3                eval state : test_on_restore;
              	0.02512     	0.97137     	0.06680     	3                eval state : test_on_last_epoch;
---> final result
              	0.02512     	0.97137     	0.06680     	3                eval state : final;module_name : GAR;cp_record_file : True;
