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

  Demo sGAR 
  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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04624     	0.90468     	0.13017     	0           
  10          	0.04425     	0.90883     	0.12145     	0           
  100         	0.01760     	0.98842     	0.05167     	0           
  300         	0.00842     	0.99727     	0.02539     	0           
  500         	0.00922     	0.99714     	0.02653     	1           
  1000        	0.01075     	0.99674     	0.02924     	3           
---> try to load best state
              	0.01075     	0.99674     	0.02924     	3                eval state : test_on_restore;
              	0.01075     	0.99674     	0.02924     	3                eval state : test_on_last_epoch;
---> final result              	0.01075     	0.99674     	0.02924     	3                eval state : final;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03328     	0.46570     	0.15787     	0           
  10          	0.03274     	0.49099     	0.14998     	0           
  100         	0.03136     	0.51747     	0.13774     	0           
  300         	0.03294     	0.51167     	0.15879     	0           
  500         	0.03455     	0.51530     	0.16984     	0           
  1000        	0.03563     	0.51799     	0.17533     	1           
---> try to load best state
              	0.03563     	0.51799     	0.17533     	1                eval state : test_on_restore;
              	0.03563     	0.51799     	0.17533     	1                eval state : test_on_last_epoch;
---> final result              	0.03563     	0.51799     	0.17533     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03251     	0.63428     	0.14520     	0           
  10          	0.03275     	0.60845     	0.13420     	0           
  100         	0.03166     	0.58664     	0.13439     	0           
  300         	0.02916     	0.56954     	0.12946     	0           
  500         	0.03050     	0.56751     	0.13257     	1           
  1000        	0.03251     	0.54216     	0.15538     	2           
---> try to load best state
              	0.03251     	0.54216     	0.15538     	2                eval state : test_on_restore;
              	0.03251     	0.54216     	0.15538     	2                eval state : test_on_last_epoch;
---> final result              	0.03251     	0.54216     	0.15538     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03363     	0.47347     	0.15940     	0           
  10          	0.03859     	0.51298     	0.18927     	0           
  100         	0.04165     	0.47984     	0.20432     	0           
  300         	0.04584     	0.46798     	0.23382     	0           
  500         	0.04748     	0.47172     	0.24496     	1           
  1000        	0.04862     	0.46962     	0.25268     	1           
---> try to load best state
              	0.04862     	0.46962     	0.25268     	1                eval state : test_on_restore;
              	0.04862     	0.46962     	0.25268     	1                eval state : test_on_last_epoch;
---> final result              	0.04862     	0.46962     	0.25268     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03949     	0.40252     	0.15898     	0           
  10          	0.03896     	0.20065     	0.16515     	0           
  100         	0.04435     	0.17451     	0.18557     	0           
  300         	0.04411     	0.13048     	0.19146     	0           
  500         	0.04796     	0.07499     	0.22032     	0           
  1000        	0.04786     	0.09092     	0.21774     	1           
---> try to load best state
              	0.04786     	0.09092     	0.21774     	1                eval state : test_on_restore;
              	0.04786     	0.09092     	0.21774     	1                eval state : test_on_last_epoch;
---> final result              	0.04786     	0.09092     	0.21774     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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]', '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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03734     	0.77575     	0.12625     	0           
  10          	0.03631     	0.77639     	0.12242     	0           
  100         	0.02891     	0.72655     	0.12577     	0           
  300         	0.04613     	0.70511     	0.19378     	0           
  500         	0.04465     	0.71297     	0.21433     	1           
  epoch 508 reach nan state
---> try to load best state
              	0.04522     	0.71220     	0.21606     	1                eval state : test_on_restore;
              	0.04465     	0.71297     	0.21433     	1                eval state : test_on_last_epoch;
---> final result              	0.04465     	0.71297     	0.21433     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03494     	0.77307     	0.24482     	0           
  10          	0.03244     	0.78613     	0.24079     	0           
  100         	0.02728     	0.83043     	0.22510     	0           
  300         	0.02661     	0.84986     	0.21500     	0           
  500         	0.02654     	0.85996     	0.20904     	1           
  1000        	0.02659     	0.87228     	0.20124     	2           
---> try to load best state
              	0.02659     	0.87228     	0.20124     	2                eval state : test_on_restore;
              	0.02659     	0.87228     	0.20124     	2                eval state : test_on_last_epoch;
---> final result              	0.02659     	0.87228     	0.20124     	2                eval state : final;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02956     	0.55107     	0.14932     	0           
  10          	0.03081     	0.56700     	0.14154     	0           
  100         	0.03283     	0.42903     	0.14587     	0           
  300         	0.03176     	0.47842     	0.14424     	0           
  500         	0.03072     	0.51416     	0.14347     	0           
  1000        	0.02833     	0.62707     	0.14086     	1           
---> try to load best state
              	0.02833     	0.62707     	0.14086     	1                eval state : test_on_restore;
              	0.02833     	0.62707     	0.14086     	1                eval state : test_on_last_epoch;
---> final result              	0.02833     	0.62707     	0.14086     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03260     	0.64453     	0.15563     	0           
  10          	0.03590     	0.60293     	0.17810     	0           
  100         	0.03848     	0.57838     	0.19585     	0           
  300         	0.03847     	0.56748     	0.19743     	0           
  500         	0.03823     	0.57293     	0.19643     	0           
  1000        	0.03787     	0.57595     	0.19316     	1           
---> try to load best state
              	0.03787     	0.57595     	0.19316     	1                eval state : test_on_restore;
              	0.03787     	0.57595     	0.19316     	1                eval state : test_on_last_epoch;
---> final result              	0.03787     	0.57595     	0.19316     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03247     	0.53541     	0.16383     	0           
  10          	0.03481     	0.44552     	0.17760     	0           
  100         	0.03427     	0.51405     	0.17552     	0           
  300         	0.03442     	0.53171     	0.17594     	0           
  500         	0.03421     	0.54226     	0.17504     	0           
  1000        	0.03302     	0.55853     	0.17151     	1           
---> try to load best state
              	0.03302     	0.55853     	0.17151     	1                eval state : test_on_restore;
              	0.03302     	0.55853     	0.17151     	1                eval state : test_on_last_epoch;
---> final result              	0.03302     	0.55853     	0.17151     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03144     	0.53305     	0.14913     	0           
  10          	0.03204     	0.40725     	0.15476     	0           
  100         	0.03107     	0.56303     	0.15949     	0           
  300         	0.03268     	0.58938     	0.16688     	0           
  500         	0.03348     	0.59065     	0.16811     	0           
  1000        	0.03380     	0.59095     	0.16851     	1           
---> try to load best state
              	0.03380     	0.59095     	0.16851     	1                eval state : test_on_restore;
              	0.03380     	0.59095     	0.16851     	1                eval state : test_on_last_epoch;
---> final result              	0.03380     	0.59095     	0.16851     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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]', '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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02787     	0.71594     	0.13135     	0           
  10          	0.02681     	0.74202     	0.12919     	0           
  100         	0.02379     	0.76868     	0.13744     	0           
  300         	0.02882     	0.76273     	0.13940     	0           
  500         	0.02829     	0.71969     	0.14051     	1           
  1000        	0.02831     	0.72008     	0.14052     	2           
---> try to load best state
              	0.02831     	0.72008     	0.14052     	2                eval state : test_on_restore;
              	0.02831     	0.72008     	0.14052     	2                eval state : test_on_last_epoch;
---> final result              	0.02831     	0.72008     	0.14052     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05481     	0.93730     	0.13597     	0           
  10          	0.04902     	0.94333     	0.12652     	0           
  100         	0.02307     	0.97989     	0.06555     	0           
  300         	0.00833     	0.99533     	0.02310     	0           
  500         	0.00887     	0.99537     	0.02369     	1           
  1000        	0.01022     	0.99505     	0.02574     	2           
---> try to load best state
              	0.01022     	0.99505     	0.02574     	2                eval state : test_on_restore;
              	0.01022     	0.99505     	0.02574     	2                eval state : test_on_last_epoch;
---> final result              	0.01022     	0.99505     	0.02574     	2                eval state : final;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05716     	0.54047     	0.17083     	0           
  10          	0.06255     	0.57243     	0.16038     	0           
  100         	0.09134     	0.26051     	0.24628     	0           
  300         	0.07456     	0.34309     	0.23573     	0           
  500         	0.07400     	0.36457     	0.23202     	0           
  1000        	0.07453     	0.35278     	0.23403     	1           
---> try to load best state
              	0.07453     	0.35278     	0.23403     	1                eval state : test_on_restore;
              	0.07453     	0.35278     	0.23403     	1                eval state : test_on_last_epoch;
---> final result              	0.07453     	0.35278     	0.23403     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03763     	0.66203     	0.14429     	0           
  10          	0.03632     	0.64084     	0.13521     	0           
  100         	0.05423     	0.42787     	0.22157     	0           
  300         	0.04074     	0.61750     	0.16332     	0           
  500         	0.04896     	0.52425     	0.20425     	0           
  1000        	0.05405     	0.42644     	0.23072     	1           
---> try to load best state
              	0.05405     	0.42644     	0.23072     	1                eval state : test_on_restore;
              	0.05405     	0.42644     	0.23072     	1                eval state : test_on_last_epoch;
---> final result              	0.05405     	0.42644     	0.23072     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04024     	0.53479     	0.18499     	0           
  10          	0.04339     	0.60178     	0.20550     	0           
  100         	0.03602     	0.71337     	0.15855     	0           
  300         	0.03279     	0.73239     	0.12132     	0           
  500         	0.04391     	0.66454     	0.17693     	0           
  1000        	0.05611     	0.45737     	0.25073     	1           
---> try to load best state
              	0.05611     	0.45737     	0.25073     	1                eval state : test_on_restore;
              	0.05611     	0.45737     	0.25073     	1                eval state : test_on_last_epoch;
---> final result              	0.05611     	0.45737     	0.25073     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04396     	0.70802     	0.13553     	0           
  10          	0.04157     	0.75355     	0.12526     	0           
  100         	0.02864     	0.76970     	0.10782     	0           
  300         	0.03096     	0.75418     	0.11242     	0           
  500         	0.03049     	0.74950     	0.11447     	0           
  1000        	0.03137     	0.74959     	0.11453     	1           
---> try to load best state
              	0.03137     	0.74959     	0.11453     	1                eval state : test_on_restore;
              	0.03137     	0.74959     	0.11453     	1                eval state : test_on_last_epoch;
---> final result              	0.03137     	0.74959     	0.11453     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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]', '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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03586     	0.90484     	0.10815     	0           
  10          	0.03017     	0.93175     	0.09269     	0           
  100         	0.02303     	0.95531     	0.06758     	0           
  300         	0.02399     	0.94396     	0.07868     	0           
  500         	0.02450     	0.94080     	0.08076     	1           
  1000        	0.02530     	0.94070     	0.08090     	2           
---> try to load best state
              	0.02530     	0.94070     	0.08090     	2                eval state : test_on_restore;
              	0.02530     	0.94070     	0.08090     	2                eval state : test_on_last_epoch;
---> final result              	0.02530     	0.94070     	0.08090     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05316     	0.91978     	0.15360     	0           
  10          	0.04733     	0.92359     	0.14228     	0           
  100         	0.02422     	0.97197     	0.06678     	0           
  300         	0.01251     	0.98726     	0.03201     	0           
  500         	0.01325     	0.98593     	0.03359     	1           
  1000        	0.01619     	0.98397     	0.04180     	2           
---> try to load best state
              	0.01619     	0.98397     	0.04180     	2                eval state : test_on_restore;
              	0.01619     	0.98397     	0.04180     	2                eval state : test_on_last_epoch;
---> final result              	0.01619     	0.98397     	0.04180     	2                eval state : final;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04316     	0.48699     	0.15474     	0           
  10          	0.03502     	0.32647     	0.14447     	0           
  100         	0.03595     	0.24185     	0.14362     	0           
  300         	0.03566     	0.23736     	0.14259     	0           
  500         	0.03553     	0.25218     	0.14188     	1           
  1000        	0.03543     	0.26481     	0.14133     	1           
---> try to load best state
              	0.03543     	0.26481     	0.14133     	1                eval state : test_on_restore;
              	0.03543     	0.26481     	0.14133     	1                eval state : test_on_last_epoch;
---> final result              	0.03543     	0.26481     	0.14133     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03831     	0.49014     	0.15251     	0           
  10          	0.03478     	0.29059     	0.14510     	0           
  100         	0.03510     	0.28127     	0.14401     	0           
  300         	0.03541     	0.15687     	0.14358     	0           
  500         	0.03531     	0.17664     	0.14303     	1           
  1000        	0.03504     	0.24843     	0.14156     	2           
---> try to load best state
              	0.03504     	0.24843     	0.14156     	2                eval state : test_on_restore;
              	0.03504     	0.24843     	0.14156     	2                eval state : test_on_last_epoch;
---> final result              	0.03504     	0.24843     	0.14156     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03567     	0.46520     	0.15107     	0           
  10          	0.03660     	-0.15754    	0.15880     	0           
  100         	0.03251     	0.53875     	0.13376     	0           
  300         	0.02967     	0.88039     	0.14034     	0           
  500         	0.03227     	0.70100     	0.14386     	1           
  1000        	0.03393     	0.43359     	0.13696     	1           
---> try to load best state
              	0.03393     	0.43359     	0.13696     	1                eval state : test_on_restore;
              	0.03393     	0.43359     	0.13696     	1                eval state : test_on_last_epoch;
---> final result              	0.03393     	0.43359     	0.13696     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04688     	0.92120     	0.12603     	0           
  10          	0.03733     	0.94161     	0.11047     	0           
  100         	0.03394     	0.95690     	0.10519     	0           
  300         	0.02968     	0.95680     	0.10380     	0           
  500         	0.03088     	0.95651     	0.10383     	0           
  1000        	0.03209     	0.95685     	0.10383     	1           
---> try to load best state
              	0.03209     	0.95685     	0.10383     	1                eval state : test_on_restore;
              	0.03209     	0.95685     	0.10383     	1                eval state : test_on_last_epoch;
---> final result              	0.03209     	0.95685     	0.10383     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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]', '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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04528     	0.91834     	0.15141     	0           
  10          	0.04115     	0.93847     	0.13982     	0           
  100         	0.03729     	0.95515     	0.12658     	0           
  300         	0.03508     	0.95610     	0.12461     	0           
  500         	0.03775     	0.95299     	0.13028     	0           
  1000        	0.06103     	0.92607     	0.18016     	1           
---> try to load best state
              	0.06103     	0.92607     	0.18016     	1                eval state : test_on_restore;
              	0.06103     	0.92607     	0.18016     	1                eval state : test_on_last_epoch;
---> final result              	0.06103     	0.92607     	0.18016     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03956     	0.90872     	0.16698     	0           
  10          	0.03534     	0.92122     	0.15732     	0           
  100         	0.02419     	0.96452     	0.06097     	0           
  300         	0.01292     	0.99221     	0.03993     	0           
  500         	0.01522     	0.98993     	0.04099     	1           
  1000        	0.01929     	0.96584     	0.04623     	2           
---> try to load best state
              	0.01929     	0.96584     	0.04623     	2                eval state : test_on_restore;
              	0.01929     	0.96584     	0.04623     	2                eval state : test_on_last_epoch;
---> final result              	0.01929     	0.96584     	0.04623     	2                eval state : final;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04210     	0.68296     	0.14971     	0           
  10          	0.04198     	0.66831     	0.14282     	0           
  100         	0.04194     	0.67947     	0.13561     	0           
  300         	0.04265     	0.64330     	0.15484     	0           
  500         	0.04268     	0.64690     	0.15539     	0           
  1000        	0.04275     	0.63961     	0.15604     	1           
---> try to load best state
              	0.04275     	0.63961     	0.15604     	1                eval state : test_on_restore;
              	0.04275     	0.63961     	0.15604     	1                eval state : test_on_last_epoch;
---> final result              	0.04275     	0.63961     	0.15604     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07204     	0.52289     	0.18912     	0           
  10          	0.07407     	0.51630     	0.20082     	0           
  100         	0.07372     	0.46078     	0.17387     	0           
  300         	0.05992     	0.43203     	0.16753     	0           
  500         	0.05307     	0.45601     	0.15321     	0           
  1000        	0.04830     	0.48500     	0.14022     	1           
---> try to load best state
              	0.04830     	0.48500     	0.14022     	1                eval state : test_on_restore;
              	0.04830     	0.48500     	0.14022     	1                eval state : test_on_last_epoch;
---> final result              	0.04830     	0.48500     	0.14022     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.08212     	0.61507     	0.20447     	0           
  10          	0.08234     	0.58398     	0.21743     	0           
  100         	0.08005     	0.01913     	0.21475     	0           
  300         	0.07412     	0.05892     	0.21042     	0           
  500         	0.09241     	-0.33757    	0.28660     	0           
  1000        	0.12571     	-1.01456    	0.41583     	1           
---> try to load best state
              	0.12571     	-1.01456    	0.41583     	1                eval state : test_on_restore;
              	0.12571     	-1.01456    	0.41583     	1                eval state : test_on_last_epoch;
---> final result              	0.12571     	-1.01456    	0.41583     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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': 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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04367     	0.91480     	0.12083     	0           
  10          	0.03817     	0.94534     	0.10154     	0           
  100         	0.04620     	0.95961     	0.09702     	0           
  300         	0.05057     	0.95520     	0.11225     	0           
  500         	0.05002     	0.95641     	0.10908     	0           
  1000        	0.04932     	0.95777     	0.10562     	1           
---> try to load best state
              	0.04932     	0.95777     	0.10562     	1                eval state : test_on_restore;
              	0.04932     	0.95777     	0.10562     	1                eval state : test_on_last_epoch;
---> final result              	0.04932     	0.95777     	0.10562     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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]', '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: 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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04013     	0.90992     	0.12125     	0           
  10          	0.03608     	0.94056     	0.10245     	0           
  100         	0.03327     	0.96540     	0.07845     	0           
  300         	0.04787     	0.95543     	0.11144     	0           
  500         	0.04687     	0.95679     	0.10794     	0           
  1000        	0.04690     	0.95677     	0.10800     	2           
---> try to load best state
              	0.04690     	0.95677     	0.10800     	2                eval state : test_on_restore;
              	0.04690     	0.95677     	0.10800     	2                eval state : test_on_last_epoch;
---> final result              	0.04690     	0.95677     	0.10800     	2                eval state : final;module_name : SGAR;cp_record_file : True;
