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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06940     	0.91897     	0.17700     	0           
  10          	0.06337     	0.92812     	0.17044     	0           
  100         	0.03539     	0.95887     	0.14991     	0           
  300         	0.03553     	0.96251     	0.14907     	0           
  500         	0.03532     	0.96465     	0.14683     	1           
  1000        	0.03472     	0.96718     	0.14186     	2           
---> try to load best state
              	0.03472     	0.96718     	0.14186     	2                eval state : test_on_restore;
              	0.03472     	0.96718     	0.14186     	2                eval state : test_on_last_epoch;
---> final result              	0.03472     	0.96718     	0.14186     	2                eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, '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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.06177     	0.31761     	0.22157     	0           
  10          	0.08530     	0.37919     	0.33684     	0           
  100         	0.15317     	0.14814     	0.73332     	0           
  300         	0.19126     	0.15836     	0.95931     	0           
  epoch 457 reach nan state
---> try to load best state
              	0.19663     	0.09010     	0.98689     	1                eval state : test_on_restore;
              	0.19126     	0.15836     	0.95931     	1                eval state : test_on_last_epoch;
---> final result              	0.19126     	0.15836     	0.95931     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, '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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05766     	0.31646     	0.22082     	0           
  10          	0.08198     	0.35375     	0.33836     	0           
  100         	0.13093     	0.24882     	0.59403     	0           
  300         	0.19675     	0.10297     	1.00068     	1           
  500         	0.22496     	0.05000     	1.16447     	2           
  1000        	0.22746     	0.05236     	1.17909     	3           
---> try to load best state
              	0.22746     	0.05236     	1.17909     	3                eval state : test_on_restore;
              	0.22746     	0.05236     	1.17909     	3                eval state : test_on_last_epoch;
---> final result              	0.22746     	0.05236     	1.17909     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, '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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05374     	0.06514     	0.21893     	0           
  10          	0.06096     	-0.73728    	0.25364     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.06096     	-0.73728    	0.25364     	0                eval state : test_on_restore;
              	0.06096     	-0.73728    	0.25364     	0                eval state : test_on_last_epoch;
---> final result              	0.06096     	-0.73728    	0.25364     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': None, '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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.03004     	0.86007     	0.09797     	0           
  10          	0.02304     	0.87489     	0.08988     	0           
  100         	0.02400     	0.86138     	0.09379     	0           
  300         	0.02220     	0.86929     	0.09083     	0           
  500         	0.02223     	0.86904     	0.09070     	1           
  1000        	0.02246     	0.86876     	0.09057     	2           
---> try to load best state
              	0.02246     	0.86876     	0.09057     	2                eval state : test_on_restore;
              	0.02246     	0.86876     	0.09057     	2                eval state : test_on_last_epoch;
---> final result              	0.02246     	0.86876     	0.09057     	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': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05083     	0.21543     	0.34045     	0           
  10          	0.04953     	0.22619     	0.34077     	0           
  100         	0.06204     	-1.02197    	0.42920     	0           
  300         	0.05565     	-0.52136    	0.40538     	1           
  500         	0.05176     	-0.25406    	0.38914     	1           
  1000        	0.04695     	0.06713     	0.36429     	3           
---> try to load best state
              	0.04695     	0.06713     	0.36429     	3                eval state : test_on_restore;
              	0.04695     	0.06713     	0.36429     	3                eval state : test_on_last_epoch;
---> final result              	0.04695     	0.06713     	0.36429     	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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05832     	-0.99457    	0.30619     	0           
  10          	0.05795     	-0.87915    	0.30932     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05795     	-0.87915    	0.30932     	0                eval state : test_on_restore;
              	0.05795     	-0.87915    	0.30932     	0                eval state : test_on_last_epoch;
---> final result              	0.05795     	-0.87915    	0.30932     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05767     	-0.54042    	0.30988     	0           
  10          	0.05970     	-0.81061    	0.32127     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05970     	-0.81061    	0.32127     	0                eval state : test_on_restore;
              	0.05970     	-0.81061    	0.32127     	0                eval state : test_on_last_epoch;
---> final result              	0.05970     	-0.81061    	0.32127     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05917     	-0.88943    	0.30435     	0           
  10          	0.05768     	-0.60936    	0.28807     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05768     	-0.60936    	0.28807     	0                eval state : test_on_restore;
              	0.05768     	-0.60936    	0.28807     	0                eval state : test_on_last_epoch;
---> final result              	0.05768     	-0.60936    	0.28807     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.06867     	-1.87241    	0.36208     	0           
  10          	0.06873     	-1.91789    	0.36167     	0           
  100         	0.06610     	-1.56065    	0.34590     	0           
  300         	0.06345     	-1.34162    	0.33227     	0           
  500         	0.06333     	-1.31981    	0.33082     	1           
  1000        	0.06365     	-1.32786    	0.33142     	2           
---> try to load best state
              	0.06365     	-1.32786    	0.33142     	2                eval state : test_on_restore;
              	0.06365     	-1.32786    	0.33142     	2                eval state : test_on_last_epoch;
---> final result              	0.06365     	-1.32786    	0.33142     	2                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]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04721     	0.41646     	0.31487     	0           
  10          	0.04541     	0.44087     	0.31220     	0           
  100         	0.04428     	0.18892     	0.35157     	0           
  300         	0.04322     	0.27337     	0.34147     	0           
  500         	0.04238     	0.32938     	0.33416     	1           
  1000        	0.04046     	0.41610     	0.32174     	2           
---> try to load best state
              	0.04046     	0.41610     	0.32174     	2                eval state : test_on_restore;
              	0.04046     	0.41610     	0.32174     	2                eval state : test_on_last_epoch;
---> final result              	0.04046     	0.41610     	0.32174     	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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04945     	0.00153     	0.25747     	0           
  10          	0.04855     	0.08488     	0.24742     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.04855     	0.08488     	0.24742     	0                eval state : test_on_restore;
              	0.04855     	0.08488     	0.24742     	0                eval state : test_on_last_epoch;
---> final result              	0.04855     	0.08488     	0.24742     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05432     	0.08136     	0.27429     	0           
  10          	0.05849     	-0.20543    	0.30288     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05849     	-0.20543    	0.30288     	0                eval state : test_on_restore;
              	0.05849     	-0.20543    	0.30288     	0                eval state : test_on_last_epoch;
---> final result              	0.05849     	-0.20543    	0.30288     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05275     	-0.08582    	0.28023     	0           
  10          	0.05312     	-0.11272    	0.28477     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05312     	-0.11272    	0.28477     	0                eval state : test_on_restore;
              	0.05312     	-0.11272    	0.28477     	0                eval state : test_on_last_epoch;
---> final result              	0.05312     	-0.11272    	0.28477     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.05127     	0.01915     	0.25999     	0           
  10          	0.05091     	0.01035     	0.26110     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05091     	0.01035     	0.26110     	0                eval state : test_on_restore;
              	0.05091     	0.01035     	0.26110     	0                eval state : test_on_last_epoch;
---> final result              	0.05091     	0.01035     	0.26110     	0                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]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06946     	0.88362     	0.15315     	0           
  10          	0.06547     	0.88663     	0.14162     	0           
  100         	0.00899     	0.99486     	0.01959     	0           
  300         	0.01091     	0.99082     	0.02517     	0           
  500         	0.01242     	0.98866     	0.02808     	1           
  1000        	0.01512     	0.98835     	0.03542     	2           
---> try to load best state
              	0.01512     	0.98835     	0.03542     	2                eval state : test_on_restore;
              	0.01512     	0.98835     	0.03542     	2                eval state : test_on_last_epoch;
---> final result              	0.01512     	0.98835     	0.03542     	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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.06703     	0.49249     	0.18061     	0           
  10          	0.07770     	0.53423     	0.17597     	0           
  100         	0.07579     	0.38753     	0.22942     	0           
  300         	0.07749     	0.35146     	0.23577     	1           
  500         	0.07795     	0.33947     	0.23770     	1           
  1000        	0.07886     	0.31337     	0.24169     	2           
---> try to load best state
              	0.07886     	0.31337     	0.24169     	2                eval state : test_on_restore;
              	0.07886     	0.31337     	0.24169     	2                eval state : test_on_last_epoch;
---> final result              	0.07886     	0.31337     	0.24169     	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': 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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04510     	0.70922     	0.14207     	0           
  10          	0.04361     	0.64678     	0.14944     	0           
  100         	0.03589     	0.69682     	0.12999     	0           
  300         	0.04355     	0.61206     	0.16926     	0           
  500         	0.05041     	0.55485     	0.19536     	0           
  1000        	0.05615     	0.47185     	0.22272     	2           
---> try to load best state
              	0.05615     	0.47185     	0.22272     	2                eval state : test_on_restore;
              	0.05615     	0.47185     	0.22272     	2                eval state : test_on_last_epoch;
---> final result              	0.05615     	0.47185     	0.22272     	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': 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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04549     	0.49671     	0.18613     	0           
  10          	0.05212     	0.60469     	0.22663     	0           
  100         	0.03437     	0.68210     	0.12799     	0           
  300         	0.04457     	0.68766     	0.15927     	0           
  500         	0.05798     	0.54701     	0.22574     	1           
  1000        	0.06405     	0.46446     	0.25447     	2           
---> try to load best state
              	0.06405     	0.46446     	0.25447     	2                eval state : test_on_restore;
              	0.06405     	0.46446     	0.25447     	2                eval state : test_on_last_epoch;
---> final result              	0.06405     	0.46446     	0.25447     	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': 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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04423     	0.71511     	0.13739     	0           
  10          	0.03867     	0.76612     	0.12058     	0           
  100         	0.03045     	0.75904     	0.11247     	0           
  300         	0.03287     	0.74107     	0.11918     	1           
  500         	0.03358     	0.74084     	0.11935     	1           
  1000        	0.03294     	0.74049     	0.11932     	2           
---> try to load best state
              	0.03294     	0.74049     	0.11932     	2                eval state : test_on_restore;
              	0.03294     	0.74049     	0.11932     	2                eval state : test_on_last_epoch;
---> final result              	0.03294     	0.74049     	0.11932     	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': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07214     	0.87174     	0.16540     	0           
  10          	0.06594     	0.89128     	0.15435     	0           
  100         	0.02362     	0.97844     	0.04646     	0           
  300         	0.02100     	0.97980     	0.04497     	0           
  500         	0.02009     	0.98035     	0.04362     	1           
  1000        	0.01880     	0.98112     	0.04149     	3           
---> try to load best state
              	0.01880     	0.98112     	0.04149     	3                eval state : test_on_restore;
              	0.01880     	0.98112     	0.04149     	3                eval state : test_on_last_epoch;
---> final result              	0.01880     	0.98112     	0.04149     	3                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04845     	0.39114     	0.15059     	0           
  10          	0.04017     	0.37976     	0.13586     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.04017     	0.37976     	0.13586     	0                eval state : test_on_restore;
              	0.04017     	0.37976     	0.13586     	0                eval state : test_on_last_epoch;
---> final result              	0.04017     	0.37976     	0.13586     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04190     	0.39437     	0.14806     	0           
  10          	0.03873     	0.39625     	0.13414     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.03873     	0.39625     	0.13414     	0                eval state : test_on_restore;
              	0.03873     	0.39625     	0.13414     	0                eval state : test_on_last_epoch;
---> final result              	0.03873     	0.39625     	0.13414     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04245     	0.37017     	0.14741     	0           
  10          	0.03980     	-0.18137    	0.15308     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.03980     	-0.18137    	0.15308     	0                eval state : test_on_restore;
              	0.03980     	-0.18137    	0.15308     	0                eval state : test_on_last_epoch;
---> final result              	0.03980     	-0.18137    	0.15308     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04979     	0.92755     	0.11677     	0           
  10          	0.03855     	0.95413     	0.09678     	0           
  100         	0.03909     	0.95540     	0.10087     	0           
  300         	0.03642     	0.95338     	0.10151     	0           
  500         	0.03710     	0.95349     	0.10142     	1           
  1000        	0.03654     	0.95297     	0.10141     	2           
---> try to load best state
              	0.03654     	0.95297     	0.10141     	2                eval state : test_on_restore;
              	0.03654     	0.95297     	0.10141     	2                eval state : test_on_last_epoch;
---> final result              	0.03654     	0.95297     	0.10141     	2                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]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05062     	0.88726     	0.16660     	0           
  10          	0.04827     	0.90536     	0.15848     	0           
  100         	0.03245     	0.94391     	0.08068     	0           
  300         	0.02805     	0.95881     	0.07131     	0           
  500         	0.02600     	0.96586     	0.06583     	1           
  1000        	0.02395     	0.97051     	0.05886     	2           
---> try to load best state
              	0.02395     	0.97051     	0.05886     	2                eval state : test_on_restore;
              	0.02395     	0.97051     	0.05886     	2                eval state : test_on_last_epoch;
---> final result              	0.02395     	0.97051     	0.05886     	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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04177     	0.70284     	0.13497     	0           
  10          	0.04202     	0.66907     	0.13485     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.04202     	0.66907     	0.13485     	0                eval state : test_on_restore;
              	0.04202     	0.66907     	0.13485     	0                eval state : test_on_last_epoch;
---> final result              	0.04202     	0.66907     	0.13485     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.06687     	0.46482     	0.17357     	0           
  10          	0.06658     	0.46474     	0.18060     	0           
  epoch 53 reach nan state
---> try to load best state
              	0.04636     	0.50783     	0.13704     	0                eval state : test_on_restore;
              	0.06658     	0.46474     	0.18060     	0                eval state : test_on_last_epoch;
---> final result              	0.04636     	0.50783     	0.13704     	0                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.07674     	0.59103     	0.18312     	0           
  10          	0.07571     	0.55857     	0.20030     	0           
  100         	0.05751     	0.57068     	0.13960     	0           
  300         	0.07177     	0.17613     	0.18963     	0           
  500         	0.09870     	-0.32940    	0.28392     	1           
  1000        	0.11902     	-0.70581    	0.35460     	2           
---> try to load best state
              	0.11902     	-0.70581    	0.35460     	2                eval state : test_on_restore;
              	0.11902     	-0.70581    	0.35460     	2                eval state : test_on_last_epoch;
---> final result              	0.11902     	-0.70581    	0.35460     	2                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': 0.02, 'optional_param': 0.02, 'noise': 0.02}
  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           	0.04446     	0.90670     	0.10998     	0           
  10          	0.03991     	0.94702     	0.08813     	0           
  100         	0.04149     	0.94803     	0.09158     	0           
  300         	0.04555     	0.94718     	0.09646     	0           
  500         	0.04554     	0.94682     	0.09657     	1           
  1000        	0.04545     	0.94661     	0.09649     	2           
---> try to load best state
              	0.04545     	0.94661     	0.09649     	2                eval state : test_on_restore;
              	0.04545     	0.94661     	0.09649     	2                eval state : test_on_last_epoch;
---> final result              	0.04545     	0.94661     	0.09649     	2                eval state : final;module_name : SGAR;cp_record_file : True;
