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

  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': 256, '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           	0.03743     	0.94829     	0.11244     	0           
  10          	0.03328     	0.96162     	0.10019     	0           
  100         	0.00718     	0.99902     	0.01798     	1           
  300         	0.00330     	0.99969     	0.00935     	3           
  epoch 418 reach nan state
---> try to load best state
              	0.00330     	0.99971     	0.00885     	4                eval state : test_on_restore;
              	0.00490     	0.99956     	0.00951     	4                eval state : test_on_last_epoch;
---> final result              	0.00330     	0.99971     	0.00885     	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': 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           	0.03387     	0.43379     	0.16844     	0           
  10          	0.03292     	0.53119     	0.16194     	0           
  100         	0.04102     	-1.37562    	0.18826     	0           
  300         	0.04023     	-1.11153    	0.19059     	0           
  500         	0.03692     	0.01646     	0.18680     	1           
  1000        	0.03622     	0.48001     	0.18877     	2           
---> try to load best state
              	0.03622     	0.48001     	0.18877     	2                eval state : test_on_restore;
              	0.03622     	0.48001     	0.18877     	2                eval state : test_on_last_epoch;
---> final result
              	0.03622     	0.48001     	0.18877     	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           	0.03257     	0.59368     	0.15568     	0           
  10          	0.03150     	0.54536     	0.14078     	0           
  100         	0.03827     	-0.93892    	0.16823     	0           
  300         	0.04127     	-1.97765    	0.18511     	0           
  500         	0.03499     	0.10059     	0.16961     	1           
  1000        	0.03357     	0.49813     	0.16934     	2           
---> try to load best state
              	0.03357     	0.49813     	0.16934     	2                eval state : test_on_restore;
              	0.03357     	0.49813     	0.16934     	2                eval state : test_on_last_epoch;
---> final result
              	0.03357     	0.49813     	0.16934     	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           	0.03512     	0.44390     	0.17081     	0           
  10          	0.04423     	-0.36624    	0.21730     	0           
  100         	0.04434     	0.36077     	0.22911     	0           
  300         	0.05016     	-0.69011    	0.25733     	0           
  500         	0.04954     	-0.19230    	0.25758     	1           
  1000        	0.04921     	0.43168     	0.26414     	2           
---> try to load best state
              	0.04921     	0.43168     	0.26414     	2                eval state : test_on_restore;
              	0.04921     	0.43168     	0.26414     	2                eval state : test_on_last_epoch;
---> final result
              	0.04921     	0.43168     	0.26414     	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           	0.03920     	0.34388     	0.16046     	0           
  10          	0.03449     	0.71521     	0.13970     	0           
  100         	0.04133     	0.15597     	0.17449     	0           
  300         	0.04091     	0.33798     	0.17806     	0           
  500         	0.04481     	-0.14525    	0.22289     	1           
  1000        	0.04463     	-0.02421    	0.22004     	2           
---> try to load best state
              	0.04463     	-0.02421    	0.22004     	2                eval state : test_on_restore;
              	0.04463     	-0.02421    	0.22004     	2                eval state : test_on_last_epoch;
---> final result
              	0.04463     	-0.02421    	0.22004     	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': 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           	0.04283     	0.73908     	0.14350     	0           
  10          	0.04025     	0.77879     	0.13290     	0           
  100         	0.02895     	0.71573     	0.11751     	0           
  300         	0.03334     	0.69253     	0.12232     	1           
  500         	0.04352     	0.71852     	0.13250     	1           
  1000        	0.04381     	0.77164     	0.12427     	3           
---> try to load best state
              	0.04381     	0.77164     	0.12427     	3                eval state : test_on_restore;
              	0.04381     	0.77164     	0.12427     	3                eval state : test_on_last_epoch;
---> final result
              	0.04381     	0.77164     	0.12427     	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           	0.03659     	0.68270     	0.13904     	0           
  10          	0.03380     	0.75010     	0.12346     	0           
  100         	0.02246     	0.70817     	0.10773     	1           
  300         	0.02638     	0.66903     	0.11432     	2           
  500         	0.02998     	0.68147     	0.11748     	3           
  1000        	0.02816     	0.74303     	0.11175     	5           
---> try to load best state
              	0.02816     	0.74303     	0.11175     	5                eval state : test_on_restore;
              	0.02816     	0.74303     	0.11175     	5                eval state : test_on_last_epoch;
---> final result
              	0.02816     	0.74303     	0.11175     	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 256, '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           	0.03458     	0.87846     	0.11909     	0           
  10          	0.03072     	0.91051     	0.10357     	0           
  100         	0.01971     	0.93706     	0.07594     	1           
  300         	0.02214     	0.93031     	0.08103     	3           
  500         	0.02507     	0.93395     	0.08548     	5           
  1000        	0.02579     	0.93939     	0.08515     	12          
---> try to load best state
              	0.02579     	0.93939     	0.08515     	12               eval state : test_on_restore;
              	0.02579     	0.93939     	0.08515     	12               eval state : test_on_last_epoch;
---> final result
              	0.02579     	0.93939     	0.08515     	12               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': 256, '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           	0.03202     	0.94754     	0.12795     	0           
  10          	0.02958     	0.95965     	0.11446     	0           
  100         	0.00512     	0.99926     	0.01393     	1           
  300         	0.00283     	0.99934     	0.01496     	3           
  500         	0.00323     	0.99916     	0.01803     	4           
---> try to load best state
              	0.01001     	0.99823     	0.02484     	7                eval state : test_on_restore;
              	0.06441     	0.84343     	0.17766     	7                eval state : test_on_last_epoch;
---> final result              	0.01001     	0.99823     	0.02484     	7                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           	0.03789     	0.40057     	0.16964     	0           
  10          	0.04042     	0.25231     	0.19066     	0           
  100         	0.04091     	0.37280     	0.20746     	0           
  300         	0.03869     	0.68249     	0.18859     	0           
  500         	0.03798     	0.66987     	0.17818     	1           
  1000        	0.03797     	0.64446     	0.17705     	2           
---> try to load best state
              	0.03797     	0.64446     	0.17705     	2                eval state : test_on_restore;
              	0.03797     	0.64446     	0.17705     	2                eval state : test_on_last_epoch;
---> final result
              	0.03797     	0.64446     	0.17705     	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           	0.03936     	0.58551     	0.15754     	0           
  10          	0.03612     	0.85685     	0.12862     	0           
  100         	0.03381     	0.76043     	0.13414     	0           
  300         	0.03710     	0.59919     	0.14829     	0           
  500         	0.03711     	0.68196     	0.14582     	1           
  1000        	0.03722     	0.69209     	0.14598     	2           
---> try to load best state
              	0.03722     	0.69209     	0.14598     	2                eval state : test_on_restore;
              	0.03722     	0.69209     	0.14598     	2                eval state : test_on_last_epoch;
---> final result
              	0.03722     	0.69209     	0.14598     	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           	0.03813     	0.48274     	0.15894     	0           
  10          	0.03708     	0.30775     	0.15612     	0           
  100         	0.03516     	0.33768     	0.14863     	0           
  300         	0.03793     	-0.47467    	0.17870     	0           
  500         	0.03603     	-0.00971    	0.16064     	1           
  1000        	0.03529     	0.39700     	0.14636     	2           
---> try to load best state
              	0.03529     	0.39700     	0.14636     	2                eval state : test_on_restore;
              	0.03529     	0.39700     	0.14636     	2                eval state : test_on_last_epoch;
---> final result
              	0.03529     	0.39700     	0.14636     	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           	0.03981     	0.42555     	0.16947     	0           
  10          	0.04103     	0.07655     	0.17973     	0           
  100         	0.03164     	0.60704     	0.12431     	0           
  300         	0.03532     	0.74778     	0.13258     	0           
  500         	0.03610     	0.75241     	0.13721     	1           
  1000        	0.03616     	0.75388     	0.13766     	2           
---> try to load best state
              	0.03616     	0.75388     	0.13766     	2                eval state : test_on_restore;
              	0.03616     	0.75388     	0.13766     	2                eval state : test_on_last_epoch;
---> final result
              	0.03616     	0.75388     	0.13766     	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           	0.03766     	0.69242     	0.16243     	0           
  10          	0.03436     	0.83896     	0.13566     	0           
  100         	0.02917     	0.89030     	0.10693     	0           
  300         	0.02867     	0.79225     	0.12732     	0           
  500         	0.02931     	0.78062     	0.13541     	1           
  1000        	0.02931     	0.78095     	0.13534     	2           
---> try to load best state
              	0.02931     	0.78095     	0.13534     	2                eval state : test_on_restore;
              	0.02931     	0.78095     	0.13534     	2                eval state : test_on_last_epoch;
---> final result
              	0.02931     	0.78095     	0.13534     	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': 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           	0.03976     	0.82610     	0.19543     	0           
  10          	0.04074     	0.88352     	0.21142     	0           
  100         	0.05206     	0.89377     	0.30516     	0           
  300         	0.05165     	0.89368     	0.30404     	0           
  500         	0.05226     	0.89533     	0.29340     	1           
  1000        	0.05420     	0.88938     	0.31438     	3           
---> try to load best state
              	0.05420     	0.88938     	0.31438     	3                eval state : test_on_restore;
              	0.05420     	0.88938     	0.31438     	3                eval state : test_on_last_epoch;
---> final result
              	0.05420     	0.88938     	0.31438     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 256, '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           	0.03640     	0.82963     	0.13153     	0           
  10          	0.03320     	0.88239     	0.11190     	0           
  100         	0.01846     	0.89050     	0.08101     	0           
  300         	0.02099     	0.87651     	0.08635     	2           
  500         	0.02530     	0.87123     	0.09141     	4           
  1000        	0.02900     	0.88054     	0.09191     	9           
---> try to load best state
              	0.02900     	0.88054     	0.09191     	9                eval state : test_on_restore;
              	0.02900     	0.88054     	0.09191     	9                eval state : test_on_last_epoch;
---> final result
              	0.02900     	0.88054     	0.09191     	9                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': 256, '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           	0.03947     	0.95852     	0.10577     	0           
  10          	0.03502     	0.97018     	0.09410     	0           
  100         	0.00794     	0.99874     	0.02041     	0           
  300         	0.00442     	0.99942     	0.01444     	2           
  500         	0.00455     	0.99939     	0.01496     	3           
---> try to load best state
              	0.00549     	0.99956     	0.01117     	6                eval state : test_on_restore;
              	0.02971     	0.98314     	0.06649     	6                eval state : test_on_last_epoch;
---> final result              	0.00549     	0.99956     	0.01117     	6                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           	0.06097     	0.49191     	0.17345     	0           
  10          	0.07081     	0.84542     	0.14974     	0           
  100         	0.09712     	0.05302     	0.25080     	0           
  300         	0.08062     	-0.09437    	0.28241     	0           
  500         	0.07774     	-0.09386    	0.28413     	1           
  1000        	0.07641     	-0.08797    	0.28469     	2           
---> try to load best state
              	0.07641     	-0.08797    	0.28469     	2                eval state : test_on_restore;
              	0.07641     	-0.08797    	0.28469     	2                eval state : test_on_last_epoch;
---> final result
              	0.07641     	-0.08797    	0.28469     	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           	0.04054     	0.46560     	0.15554     	0           
  10          	0.03605     	0.82541     	0.12643     	0           
  100         	0.04768     	-0.63524    	0.19094     	0           
  300         	0.03606     	-0.16628    	0.17113     	0           
  500         	0.04043     	-0.62983    	0.19465     	1           
  1000        	0.04814     	-1.13009    	0.23349     	3           
---> try to load best state
              	0.04814     	-1.13009    	0.23349     	3                eval state : test_on_restore;
              	0.04814     	-1.13009    	0.23349     	3                eval state : test_on_last_epoch;
---> final result
              	0.04814     	-1.13009    	0.23349     	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': 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           	0.03700     	0.55264     	0.17923     	0           
  10          	0.04149     	0.14772     	0.21402     	0           
  100         	0.03487     	0.62888     	0.15660     	0           
  300         	0.03204     	-0.30116    	0.15979     	0           
  500         	0.04825     	0.35129     	0.26599     	1           
  1000        	0.04825     	0.34986     	0.26522     	2           
---> try to load best state
              	0.04825     	0.34986     	0.26522     	2                eval state : test_on_restore;
              	0.04825     	0.34986     	0.26522     	2                eval state : test_on_last_epoch;
---> final result
              	0.04825     	0.34986     	0.26522     	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           	0.04835     	0.59683     	0.15555     	0           
  10          	0.04285     	0.66501     	0.14133     	0           
  100         	0.03281     	0.72243     	0.11842     	0           
  300         	0.02976     	0.72357     	0.11798     	0           
  500         	0.02902     	0.72484     	0.11561     	1           
  1000        	0.02873     	0.72221     	0.11570     	2           
---> try to load best state
              	0.02873     	0.72221     	0.11570     	2                eval state : test_on_restore;
              	0.02873     	0.72221     	0.11570     	2                eval state : test_on_last_epoch;
---> final result
              	0.02873     	0.72221     	0.11570     	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           	0.04094     	0.85521     	0.12793     	0           
  10          	0.03283     	0.89508     	0.10993     	0           
  100         	0.02568     	0.93303     	0.08110     	0           
  300         	0.02503     	0.93350     	0.08193     	1           
  500         	0.02477     	0.93412     	0.08016     	1           
  1000        	0.02483     	0.93477     	0.08032     	3           
---> try to load best state
              	0.02483     	0.93477     	0.08032     	3                eval state : test_on_restore;
              	0.02483     	0.93477     	0.08032     	3                eval state : test_on_last_epoch;
---> final result
              	0.02483     	0.93477     	0.08032     	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': 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           	0.03893     	0.91303     	0.11424     	0           
  10          	0.03393     	0.94071     	0.09609     	0           
  100         	0.02310     	0.97247     	0.06328     	0           
  300         	0.02462     	0.96946     	0.06811     	1           
  500         	0.02514     	0.96979     	0.06778     	1           
  1000        	0.02428     	0.96988     	0.06864     	3           
---> try to load best state
              	0.02428     	0.96988     	0.06864     	3                eval state : test_on_restore;
              	0.02428     	0.96988     	0.06864     	3                eval state : test_on_last_epoch;
---> final result
              	0.02428     	0.96988     	0.06864     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 256, '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           	0.03755     	0.90600     	0.11169     	0           
  10          	0.03295     	0.93296     	0.09534     	0           
  100         	0.02093     	0.96500     	0.06287     	0           
  300         	0.02194     	0.96305     	0.06426     	2           
  500         	0.02395     	0.96172     	0.06705     	3           
  1000        	0.02404     	0.96102     	0.06902     	6           
---> try to load best state
              	0.02404     	0.96102     	0.06902     	6                eval state : test_on_restore;
              	0.02404     	0.96102     	0.06902     	6                eval state : test_on_last_epoch;
---> final result
              	0.02404     	0.96102     	0.06902     	6                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': 256, '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           	0.03838     	0.95057     	0.10787     	0           
  10          	0.03423     	0.96315     	0.09569     	0           
  100         	0.00755     	0.99884     	0.01932     	1           
  300         	0.00367     	0.99955     	0.01205     	2           
  500         	0.00373     	0.99954     	0.01236     	4           
---> try to load best state
              	0.00463     	0.99959     	0.01033     	7                eval state : test_on_restore;
              	0.02385     	0.98920     	0.05387     	7                eval state : test_on_last_epoch;
---> final result              	0.00463     	0.99959     	0.01033     	7                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           	0.04698     	0.46105     	0.15828     	0           
  10          	0.03734     	0.30276     	0.15144     	0           
  100         	0.03449     	0.13777     	0.15106     	0           
  300         	0.03461     	0.05195     	0.15289     	0           
  500         	0.03426     	0.09126     	0.15190     	1           
  1000        	0.03442     	0.04198     	0.15309     	2           
---> try to load best state
              	0.03442     	0.04198     	0.15309     	2                eval state : test_on_restore;
              	0.03442     	0.04198     	0.15309     	2                eval state : test_on_last_epoch;
---> final result
              	0.03442     	0.04198     	0.15309     	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           	0.03782     	0.45352     	0.15665     	0           
  10          	0.03476     	0.26952     	0.15164     	0           
  100         	0.03324     	0.29735     	0.14675     	0           
  300         	0.03480     	-0.11427    	0.15668     	0           
  500         	0.03509     	-0.47469    	0.16529     	1           
  1000        	0.03503     	-0.45575    	0.16491     	2           
---> try to load best state
              	0.03503     	-0.45575    	0.16491     	2                eval state : test_on_restore;
              	0.03503     	-0.45575    	0.16491     	2                eval state : test_on_last_epoch;
---> final result
              	0.03503     	-0.45575    	0.16491     	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           	0.03772     	0.23894     	0.16259     	0           
  10          	0.03646     	0.02757     	0.15845     	0           
  100         	0.03275     	0.68922     	0.13345     	0           
  300         	0.03684     	0.93920     	0.17508     	0           
  500         	0.03725     	0.93919     	0.17933     	1           
  1000        	0.03672     	0.93973     	0.17448     	2           
---> try to load best state
              	0.03672     	0.93973     	0.17448     	2                eval state : test_on_restore;
              	0.03672     	0.93973     	0.17448     	2                eval state : test_on_last_epoch;
---> final result
              	0.03672     	0.93973     	0.17448     	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           	0.05144     	0.90514     	0.13855     	0           
  10          	0.04192     	0.93250     	0.11902     	0           
  100         	0.03596     	0.96176     	0.10455     	0           
  300         	0.03051     	0.96494     	0.10126     	0           
  500         	0.03205     	0.96471     	0.10128     	1           
  1000        	0.03341     	0.96419     	0.10163     	3           
---> try to load best state
              	0.03341     	0.96419     	0.10163     	3                eval state : test_on_restore;
              	0.03341     	0.96419     	0.10163     	3                eval state : test_on_last_epoch;
---> final result
              	0.03341     	0.96419     	0.10163     	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           	0.04567     	0.90077     	0.15849     	0           
  10          	0.04159     	0.92602     	0.14478     	0           
  100         	0.03950     	0.95852     	0.12748     	0           
  300         	0.03649     	0.95346     	0.13086     	0           
  500         	0.03846     	0.95623     	0.13055     	1           
  1000        	0.04187     	0.95706     	0.13129     	3           
---> try to load best state
              	0.04187     	0.95706     	0.13129     	3                eval state : test_on_restore;
              	0.04187     	0.95706     	0.13129     	3                eval state : test_on_last_epoch;
---> final result
              	0.04187     	0.95706     	0.13129     	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           	0.03800     	0.91769     	0.14289     	0           
  10          	0.03288     	0.94444     	0.12740     	0           
  100         	0.02923     	0.96829     	0.11115     	0           
  300         	0.03053     	0.96361     	0.11821     	1           
  500         	0.03034     	0.96390     	0.11843     	2           
  1000        	0.03266     	0.96321     	0.11926     	4           
---> try to load best state
              	0.03266     	0.96321     	0.11926     	4                eval state : test_on_restore;
              	0.03266     	0.96321     	0.11926     	4                eval state : test_on_last_epoch;
---> final result
              	0.03266     	0.96321     	0.11926     	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 256, '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           	0.03815     	0.92312     	0.11395     	0           
  10          	0.03304     	0.94798     	0.09747     	0           
  100         	0.02091     	0.97730     	0.06567     	1           
  300         	0.02139     	0.97527     	0.06718     	3           
  500         	0.02264     	0.97498     	0.06924     	5           
  1000        	0.02547     	0.97358     	0.07421     	12          
---> try to load best state
              	0.02547     	0.97358     	0.07421     	12               eval state : test_on_restore;
              	0.02547     	0.97358     	0.07421     	12               eval state : test_on_last_epoch;
---> final result
              	0.02547     	0.97358     	0.07421     	12               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': 256, '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           	0.04092     	0.95149     	0.10498     	0           
  10          	0.03663     	0.96493     	0.09315     	0           
  epoch 64 reach nan state
---> try to load best state
              	0.04717     	0.95487     	0.19413     	0                eval state : test_on_restore;
              	0.22876     	0.72376     	0.65771     	0                eval state : test_on_last_epoch;
---> final result              	0.04717     	0.95487     	0.19413     	0                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           	0.06268     	0.67610     	0.25658     	0           
  10          	0.05918     	0.58275     	0.24424     	0           
  100         	0.05872     	-0.67589    	0.23194     	0           
  300         	0.06557     	0.61055     	0.26695     	1           
  500         	0.06599     	0.62039     	0.26831     	2           
  1000        	0.06592     	0.62516     	0.26800     	3           
---> try to load best state
              	0.06592     	0.62516     	0.26800     	3                eval state : test_on_restore;
              	0.06592     	0.62516     	0.26800     	3                eval state : test_on_last_epoch;
---> final result
              	0.06592     	0.62516     	0.26800     	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           	0.07331     	0.53981     	0.25457     	0           
  10          	0.07677     	0.51027     	0.27410     	0           
  100         	0.06304     	0.83917     	0.18849     	0           
  300         	0.04267     	0.31271     	0.15321     	0           
  500         	0.06952     	-1.04202    	0.37150     	1           
  1000        	0.06757     	-0.86244    	0.35767     	2           
---> try to load best state
              	0.06757     	-0.86244    	0.35767     	2                eval state : test_on_restore;
              	0.06757     	-0.86244    	0.35767     	2                eval state : test_on_last_epoch;
---> final result
              	0.06757     	-0.86244    	0.35767     	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           	0.08986     	0.62675     	0.27866     	0           
  10          	0.09522     	0.32547     	0.31069     	0           
  100         	0.06502     	0.78339     	0.21089     	0           
  300         	0.05364     	-3.20979    	0.25963     	0           
  500         	0.08947     	-0.94197    	0.40809     	1           
  1000        	0.12297     	-0.68919    	0.52608     	3           
---> try to load best state
              	0.12297     	-0.68919    	0.52608     	3                eval state : test_on_restore;
              	0.12297     	-0.68919    	0.52608     	3                eval state : test_on_last_epoch;
---> final result
              	0.12297     	-0.68919    	0.52608     	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           	0.06377     	0.85904     	0.23227     	0           
  10          	0.06042     	0.89530     	0.20820     	0           
  100         	0.05876     	0.90716     	0.18216     	0           
  300         	0.06054     	0.90672     	0.17798     	0           
  500         	0.06125     	0.90671     	0.17783     	1           
  1000        	0.06210     	0.90643     	0.17861     	3           
---> try to load best state
              	0.06210     	0.90643     	0.17861     	3                eval state : test_on_restore;
              	0.06210     	0.90643     	0.17861     	3                eval state : test_on_last_epoch;
---> final result
              	0.06210     	0.90643     	0.17861     	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           	0.06317     	0.85802     	0.23427     	0           
  10          	0.06028     	0.89665     	0.20911     	0           
  100         	0.05408     	0.91333     	0.16614     	0           
  300         	0.05621     	0.90801     	0.17490     	1           
  500         	0.05651     	0.90780     	0.17537     	1           
  1000        	0.05651     	0.90780     	0.17536     	3           
---> try to load best state
              	0.05651     	0.90780     	0.17536     	3                eval state : test_on_restore;
              	0.05651     	0.90780     	0.17536     	3                eval state : test_on_last_epoch;
---> final result
              	0.05651     	0.90780     	0.17536     	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           	0.05997     	0.90114     	0.18785     	0           
  10          	0.05485     	0.91522     	0.17545     	0           
  100         	0.04422     	0.92897     	0.13475     	0           
  300         	0.04958     	0.91942     	0.15292     	1           
  500         	0.05093     	0.91809     	0.15475     	1           
  1000        	0.05216     	0.91817     	0.15512     	3           
---> try to load best state
              	0.05216     	0.91817     	0.15512     	3                eval state : test_on_restore;
              	0.05216     	0.91817     	0.15512     	3                eval state : test_on_last_epoch;
---> final result
              	0.05216     	0.91817     	0.15512     	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 256 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 256, '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           	0.06023     	0.89288     	0.19618     	0           
  10          	0.05534     	0.90943     	0.18243     	0           
  100         	0.03956     	0.92933     	0.12980     	0           
  epoch 158 reach nan state
---> try to load best state
              	0.03633     	0.93414     	0.12160     	1                eval state : test_on_restore;
              	0.03956     	0.92933     	0.12980     	1                eval state : test_on_last_epoch;
---> final result
              	0.03633     	0.93414     	0.12160     	1                eval state : final;module_name : GAR;cp_record_file : True;
