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

  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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04624     	0.90468     	0.13017     	0           
  10          	0.04325     	0.92490     	0.11862     	0           
  100         	0.01409     	0.99557     	0.04419     	0           
  300         	0.00944     	0.99812     	0.02850     	1           
  500         	0.00991     	0.99797     	0.02951     	1           
---> try to load best state
              	0.01862     	0.99401     	0.04732     	3                eval state : test_on_restore;
              	0.07662     	0.80693     	0.19492     	3                eval state : test_on_last_epoch;
---> final result              	0.01862     	0.99401     	0.04732     	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': 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           	0.03720     	0.36625     	0.15372     	0           
  10          	0.03646     	0.47023     	0.14794     	0           
  100         	0.04329     	-1.38473    	0.17864     	0           
  300         	0.04237     	-1.12046    	0.17859     	0           
  500         	0.03912     	-0.05240    	0.16879     	1           
  1000        	0.03749     	0.42201     	0.16551     	3           
---> try to load best state
              	0.03749     	0.42201     	0.16551     	3                eval state : test_on_restore;
              	0.03749     	0.42201     	0.16551     	3                eval state : test_on_last_epoch;
---> final result
              	0.03749     	0.42201     	0.16551     	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': 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           	0.03676     	0.53744     	0.14404     	0           
  10          	0.03653     	0.47357     	0.13972     	0           
  100         	0.04246     	-1.01420    	0.17095     	0           
  300         	0.04409     	-1.93584    	0.18061     	1           
  500         	0.03802     	0.02797     	0.15714     	2           
  1000        	0.03661     	0.43845     	0.15156     	4           
---> try to load best state
              	0.03661     	0.43845     	0.15156     	4                eval state : test_on_restore;
              	0.03661     	0.43845     	0.15156     	4                eval state : test_on_last_epoch;
---> final result
              	0.03661     	0.43845     	0.15156     	4                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           	0.03722     	0.37944     	0.15393     	0           
  10          	0.04315     	-0.44508    	0.19199     	0           
  100         	0.04245     	0.30764     	0.19739     	0           
  300         	0.04774     	-0.67752    	0.22579     	1           
  500         	0.04693     	-0.23079    	0.22412     	1           
  1000        	0.04614     	0.37688     	0.22653     	3           
---> try to load best state
              	0.04614     	0.37688     	0.22653     	3                eval state : test_on_restore;
              	0.04614     	0.37688     	0.22653     	3                eval state : test_on_last_epoch;
---> final result
              	0.04614     	0.37688     	0.22653     	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': 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           	0.04627     	0.22031     	0.16916     	0           
  10          	0.04303     	0.62214     	0.15668     	0           
  100         	0.04866     	0.03127     	0.19181     	0           
  300         	0.04915     	0.21002     	0.19447     	1           
  500         	0.05429     	-0.24526    	0.23363     	1           
  1000        	0.05379     	-0.13693    	0.23122     	3           
---> try to load best state
              	0.05379     	-0.13693    	0.23122     	3                eval state : test_on_restore;
              	0.05379     	-0.13693    	0.23122     	3                eval state : test_on_last_epoch;
---> final result
              	0.05379     	-0.13693    	0.23122     	3                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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03494     	0.77307     	0.24482     	0           
  10          	0.03178     	0.79260     	0.23856     	0           
  100         	0.02603     	0.84969     	0.21605     	0           
  300         	0.02596     	0.86633     	0.20639     	0           
  500         	0.02610     	0.87374     	0.20135     	1           
---> try to load best state
              	0.04695     	0.74499     	0.23612     	2                eval state : test_on_restore;
              	0.21659     	-20.40632   	0.59095     	2                eval state : test_on_last_epoch;
---> final result              	0.04695     	0.74499     	0.23612     	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': 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           	0.04700     	0.19633     	0.18180     	0           
  10          	0.04430     	0.10059     	0.16994     	0           
  100         	0.04125     	0.30160     	0.16038     	0           
  300         	0.04047     	0.47143     	0.16029     	0           
  500         	0.04246     	0.42534     	0.16694     	1           
  1000        	0.04303     	0.39980     	0.16869     	2           
---> try to load best state
              	0.04303     	0.39980     	0.16869     	2                eval state : test_on_restore;
              	0.04303     	0.39980     	0.16869     	2                eval state : test_on_last_epoch;
---> final result
              	0.04303     	0.39980     	0.16869     	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           	0.05552     	0.31128     	0.19943     	0           
  10          	0.05684     	0.50185     	0.21346     	0           
  100         	0.05983     	0.19626     	0.23687     	0           
  300         	0.06124     	0.17047     	0.23849     	0           
  500         	0.06131     	0.18415     	0.23957     	1           
  1000        	0.06135     	0.18687     	0.23982     	1           
---> try to load best state
              	0.06135     	0.18687     	0.23982     	1                eval state : test_on_restore;
              	0.06135     	0.18687     	0.23982     	1                eval state : test_on_last_epoch;
---> final result
              	0.06135     	0.18687     	0.23982     	1                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           	0.05425     	0.13562     	0.20652     	0           
  10          	0.05386     	0.07053     	0.21537     	0           
  100         	0.05457     	0.12031     	0.21553     	0           
  300         	0.05788     	0.05343     	0.21787     	0           
  500         	0.05636     	0.10743     	0.21534     	1           
  1000        	0.05502     	0.18154     	0.21192     	2           
---> try to load best state
              	0.05502     	0.18154     	0.21192     	2                eval state : test_on_restore;
              	0.05502     	0.18154     	0.21192     	2                eval state : test_on_last_epoch;
---> final result
              	0.05502     	0.18154     	0.21192     	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           	0.05103     	0.22142     	0.18598     	0           
  10          	0.05083     	0.09368     	0.18958     	0           
  100         	0.05292     	0.22904     	0.20046     	0           
  300         	0.05502     	0.24541     	0.20816     	0           
  500         	0.05522     	0.24556     	0.20915     	1           
  1000        	0.05521     	0.24748     	0.20932     	2           
---> try to load best state
              	0.05521     	0.24748     	0.20932     	2                eval state : test_on_restore;
              	0.05521     	0.24748     	0.20932     	2                eval state : test_on_last_epoch;
---> final result
              	0.05521     	0.24748     	0.20932     	2                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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05481     	0.93730     	0.13597     	0           
  10          	0.04718     	0.95189     	0.12355     	0           
  100         	0.01808     	0.99338     	0.05491     	0           
  300         	0.00875     	0.99833     	0.02389     	0           
  500         	0.00891     	0.99830     	0.02444     	1           
---> try to load best state
              	0.00973     	0.99863     	0.01850     	2                eval state : test_on_restore;
              	0.04029     	0.96970     	0.08497     	2                eval state : test_on_last_epoch;
---> final result              	0.00973     	0.99863     	0.01850     	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': 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           	0.05901     	0.49696     	0.16917     	0           
  10          	0.06904     	0.84943     	0.14523     	0           
  100         	0.09727     	0.05840     	0.24972     	0           
  300         	0.08142     	-0.08969    	0.28189     	0           
  500         	0.07846     	-0.08955    	0.28368     	1           
  1000        	0.07706     	-0.08385    	0.28428     	2           
---> try to load best state
              	0.07706     	-0.08385    	0.28428     	2                eval state : test_on_restore;
              	0.07706     	-0.08385    	0.28428     	2                eval state : test_on_last_epoch;
---> final result
              	0.07706     	-0.08385    	0.28428     	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': 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           	0.03997     	0.47012     	0.15195     	0           
  10          	0.03545     	0.82823     	0.12223     	0           
  100         	0.04715     	-0.62716    	0.18924     	0           
  300         	0.03804     	-0.16166    	0.16950     	0           
  500         	0.04243     	-0.63071    	0.19332     	1           
  1000        	0.04982     	-1.14412    	0.23242     	2           
---> try to load best state
              	0.04982     	-1.14412    	0.23242     	2                eval state : test_on_restore;
              	0.04982     	-1.14412    	0.23242     	2                eval state : test_on_last_epoch;
---> final result
              	0.04982     	-1.14412    	0.23242     	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           	0.03924     	0.55594     	0.17687     	0           
  10          	0.04411     	0.15231     	0.21186     	0           
  100         	0.03674     	0.63148     	0.15476     	0           
  300         	0.03442     	-0.29831    	0.15945     	0           
  500         	0.05304     	0.34723     	0.26693     	1           
  1000        	0.05304     	0.34590     	0.26616     	2           
---> try to load best state
              	0.05304     	0.34590     	0.26616     	2                eval state : test_on_restore;
              	0.05304     	0.34590     	0.26616     	2                eval state : test_on_last_epoch;
---> final result
              	0.05304     	0.34590     	0.26616     	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           	0.04822     	0.60242     	0.15092     	0           
  10          	0.04378     	0.66889     	0.13744     	0           
  100         	0.03292     	0.72434     	0.11662     	0           
  300         	0.03078     	0.72313     	0.11768     	0           
  500         	0.03085     	0.72421     	0.11626     	1           
  1000        	0.03016     	0.72161     	0.11637     	3           
---> try to load best state
              	0.03016     	0.72161     	0.11637     	3                eval state : test_on_restore;
              	0.03016     	0.72161     	0.11637     	3                eval state : test_on_last_epoch;
---> final result
              	0.03016     	0.72161     	0.11637     	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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05316     	0.91978     	0.15360     	0           
  10          	0.04538     	0.93497     	0.13812     	0           
  100         	0.01906     	0.99160     	0.05659     	0           
  300         	0.01174     	0.99599     	0.03308     	0           
  500         	0.01331     	0.99534     	0.03834     	1           
---> try to load best state
              	0.04145     	0.96505     	0.10186     	2                eval state : test_on_restore;
              	0.18076     	-1.86386    	0.51307     	2                eval state : test_on_last_epoch;
---> final result              	0.04145     	0.96505     	0.10186     	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': 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           	0.06217     	0.22076     	0.17231     	0           
  10          	0.05254     	0.05551     	0.17294     	0           
  100         	0.05057     	-0.10485    	0.17616     	0           
  300         	0.05095     	-0.18933    	0.17796     	0           
  500         	0.05083     	-0.14728    	0.17757     	1           
  1000        	0.05110     	-0.19633    	0.17844     	2           
---> try to load best state
              	0.05110     	-0.19633    	0.17844     	2                eval state : test_on_restore;
              	0.05110     	-0.19633    	0.17844     	2                eval state : test_on_last_epoch;
---> final result
              	0.05110     	-0.19633    	0.17844     	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': 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           	0.05224     	0.21012     	0.17179     	0           
  10          	0.05052     	0.02256     	0.17415     	0           
  100         	0.04915     	0.05784     	0.17232     	0           
  300         	0.05293     	-0.33869    	0.18188     	0           
  500         	0.05539     	-0.63633    	0.18979     	1           
  1000        	0.05527     	-0.62021    	0.18950     	2           
---> try to load best state
              	0.05527     	-0.62021    	0.18950     	2                eval state : test_on_restore;
              	0.05527     	-0.62021    	0.18950     	2                eval state : test_on_last_epoch;
---> final result
              	0.05527     	-0.62021    	0.18950     	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': 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           	0.05443     	-0.04475    	0.17824     	0           
  10          	0.05197     	-0.21483    	0.18022     	0           
  100         	0.04530     	0.50129     	0.15895     	0           
  300         	0.04925     	0.88651     	0.16841     	0           
  500         	0.05044     	0.89060     	0.17093     	1           
  1000        	0.04960     	0.88689     	0.16832     	2           
---> try to load best state
              	0.04960     	0.88689     	0.16832     	2                eval state : test_on_restore;
              	0.04960     	0.88689     	0.16832     	2                eval state : test_on_last_epoch;
---> final result
              	0.04960     	0.88689     	0.16832     	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': 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           	0.06402     	0.88012     	0.15440     	0           
  10          	0.05325     	0.91740     	0.12723     	0           
  100         	0.04774     	0.93425     	0.11886     	0           
  300         	0.04284     	0.92860     	0.11588     	1           
  500         	0.04271     	0.92895     	0.11546     	1           
  1000        	0.04356     	0.92942     	0.11543     	3           
---> try to load best state
              	0.04356     	0.92942     	0.11543     	3                eval state : test_on_restore;
              	0.04356     	0.92942     	0.11543     	3                eval state : test_on_last_epoch;
---> final result
              	0.04356     	0.92942     	0.11543     	3                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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03956     	0.90872     	0.16698     	0           
  10          	0.03435     	0.92753     	0.15406     	0           
  100         	0.02054     	0.99008     	0.05437     	0           
  300         	0.01531     	0.99124     	0.03956     	0           
  500         	0.01457     	0.99021     	0.03601     	1           
---> try to load best state
              	0.01660     	0.98917     	0.03479     	2                eval state : test_on_restore;
              	0.04972     	0.94801     	0.10983     	2                eval state : test_on_last_epoch;
---> final result              	0.01660     	0.98917     	0.03479     	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': 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           	0.04083     	0.59533     	0.15157     	0           
  10          	0.04160     	0.48389     	0.15180     	0           
  100         	0.04929     	-0.66757    	0.17035     	0           
  300         	0.04139     	0.51995     	0.15700     	0           
  500         	0.04134     	0.53252     	0.15708     	1           
  1000        	0.04129     	0.53824     	0.15679     	2           
---> try to load best state
              	0.04129     	0.53824     	0.15679     	2                eval state : test_on_restore;
              	0.04129     	0.53824     	0.15679     	2                eval state : test_on_last_epoch;
---> final result
              	0.04129     	0.53824     	0.15679     	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': 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           	0.06954     	0.40589     	0.18747     	0           
  10          	0.07104     	0.38081     	0.19557     	0           
  100         	0.06650     	0.72099     	0.14485     	0           
  300         	0.06793     	0.15722     	0.19669     	0           
  500         	0.08213     	-0.42898    	0.34525     	1           
  1000        	0.08049     	-0.38946    	0.33606     	2           
---> try to load best state
              	0.08049     	-0.38946    	0.33606     	2                eval state : test_on_restore;
              	0.08049     	-0.38946    	0.33606     	2                eval state : test_on_last_epoch;
---> final result
              	0.08049     	-0.38946    	0.33606     	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': 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           	0.07978     	0.53820     	0.19936     	0           
  10          	0.08147     	0.33229     	0.21715     	0           
  100         	0.06341     	0.67987     	0.14915     	0           
  300         	0.06756     	-0.90103    	0.24743     	0           
  500         	0.09105     	-0.67753    	0.36041     	1           
  1000        	0.10996     	-0.58204    	0.44579     	2           
---> try to load best state
              	0.10996     	-0.58204    	0.44579     	2                eval state : test_on_restore;
              	0.10996     	-0.58204    	0.44579     	2                eval state : test_on_last_epoch;
---> final result
              	0.10996     	-0.58204    	0.44579     	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': 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           	0.04182     	0.87474     	0.12882     	0           
  10          	0.03795     	0.92054     	0.10907     	0           
  100         	0.04028     	0.96074     	0.08679     	0           
  300         	0.04090     	0.96738     	0.08215     	0           
  500         	0.04109     	0.96771     	0.08181     	1           
  1000        	0.04009     	0.96812     	0.08064     	2           
---> try to load best state
              	0.04009     	0.96812     	0.08064     	2                eval state : test_on_restore;
              	0.04009     	0.96812     	0.08064     	2                eval state : test_on_last_epoch;
---> final result
              	0.04009     	0.96812     	0.08064     	2                eval state : final;module_name : GAR;cp_record_file : True;
