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

  Demo GAR 
  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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06940     	0.91897     	0.17700     	0           
  10          	0.06112     	0.93890     	0.16816     	0           
  100         	0.03402     	0.96914     	0.14813     	0           
  300         	0.03317     	0.97120     	0.14198     	0           
  500         	0.03244     	0.97282     	0.13746     	1           
  1000        	0.03178     	0.97505     	0.13098     	3           
---> try to load best state
              	0.03178     	0.97505     	0.13098     	3                eval state : test_on_restore;
              	0.03178     	0.97505     	0.13098     	3                eval state : test_on_last_epoch;
---> final result              	0.03178     	0.97505     	0.13098     	3                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh 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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.07903     	-2.95754    	0.28953     	0           
  10          	0.13788     	0.65265     	0.62588     	0           
  epoch 55 reach nan state
---> try to load best state
              	0.19796     	0.45289     	1.03430     	0                eval state : test_on_restore;
              	0.13788     	0.65265     	0.62588     	0                eval state : test_on_last_epoch;
---> final result
              	0.13788     	0.65265     	0.62588     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh 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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.07535     	-3.05384    	0.28988     	0           
  10          	0.12489     	0.76184     	0.53458     	0           
  epoch 33 reach nan state
---> try to load best state
              	0.12489     	0.76184     	0.53458     	0                eval state : test_on_restore;
              	0.12489     	0.76184     	0.53458     	0                eval state : test_on_last_epoch;
---> final result
              	0.12489     	0.76184     	0.53458     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh 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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.06194     	-1.33592    	0.26096     	0           
  10          	0.05460     	-1.21833    	0.21933     	0           
  epoch 38 reach nan state
---> try to load best state
              	0.05460     	-1.21833    	0.21933     	0                eval state : test_on_restore;
              	0.05460     	-1.21833    	0.21933     	0                eval state : test_on_last_epoch;
---> final result
              	0.05460     	-1.21833    	0.21933     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh 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': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.02845     	0.85159     	0.09711     	0           
  10          	0.02265     	0.87167     	0.09180     	0           
  epoch 25 reach nan state
---> try to load best state
              	0.02265     	0.87167     	0.09180     	0                eval state : test_on_restore;
              	0.02265     	0.87167     	0.09180     	0                eval state : test_on_last_epoch;
---> final result
              	0.02265     	0.87167     	0.09180     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05083     	0.21543     	0.34045     	0           
  10          	0.04893     	0.25034     	0.33785     	0           
  100         	0.05343     	-0.35381    	0.39272     	0           
  300         	0.04740     	0.06846     	0.36152     	1           
  500         	0.04402     	0.27269     	0.34096     	1           
  1000        	0.03979     	0.49381     	0.31152     	3           
---> try to load best state
              	0.03979     	0.49381     	0.31152     	3                eval state : test_on_restore;
              	0.03979     	0.49381     	0.31152     	3                eval state : test_on_last_epoch;
---> final result              	0.03979     	0.49381     	0.31152     	3                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.06019     	-3.16178    	0.25497     	0           
  10          	0.04955     	-0.33202    	0.24570     	0           
  epoch 39 reach nan state
---> try to load best state
              	0.04955     	-0.33202    	0.24570     	0                eval state : test_on_restore;
              	0.04955     	-0.33202    	0.24570     	0                eval state : test_on_last_epoch;
---> final result
              	0.04955     	-0.33202    	0.24570     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05250     	0.09428     	0.27356     	0           
  10          	0.05006     	-0.45942    	0.25152     	0           
  epoch 46 reach nan state
---> try to load best state
              	0.05006     	-0.45942    	0.25152     	0                eval state : test_on_restore;
              	0.05006     	-0.45942    	0.25152     	0                eval state : test_on_last_epoch;
---> final result
              	0.05006     	-0.45942    	0.25152     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05558     	-1.83984    	0.25191     	0           
  10          	0.04867     	-0.08640    	0.24057     	0           
  epoch 72 reach nan state
---> try to load best state
              	0.05071     	-0.33049    	0.23689     	0                eval state : test_on_restore;
              	0.04867     	-0.08640    	0.24057     	0                eval state : test_on_last_epoch;
---> final result
              	0.05071     	-0.33049    	0.23689     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05969     	-0.86888    	0.31509     	0           
  10          	0.06508     	-1.48718    	0.33036     	0           
  100         	0.05731     	-0.68409    	0.29790     	0           
  300         	0.05809     	-0.74781    	0.29909     	0           
  500         	0.05788     	-0.78316    	0.29740     	1           
  1000        	0.05800     	-0.71624    	0.29909     	2           
---> try to load best state
              	0.05800     	-0.71624    	0.29909     	2                eval state : test_on_restore;
              	0.05800     	-0.71624    	0.29909     	2                eval state : test_on_last_epoch;
---> final result
              	0.05800     	-0.71624    	0.29909     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04721     	0.41646     	0.31487     	0           
  10          	0.04470     	0.45694     	0.30993     	0           
  100         	0.04237     	0.33181     	0.33378     	0           
  300         	0.04068     	0.40756     	0.32299     	0           
  500         	0.03877     	0.46938     	0.31323     	1           
  1000        	0.03762     	0.56128     	0.29630     	3           
---> try to load best state
              	0.03762     	0.56128     	0.29630     	3                eval state : test_on_restore;
              	0.03762     	0.56128     	0.29630     	3                eval state : test_on_last_epoch;
---> final result              	0.03762     	0.56128     	0.29630     	3                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05292     	-0.76357    	0.22692     	0           
  10          	0.04552     	0.21318     	0.22837     	0           
  epoch 39 reach nan state
---> try to load best state
              	0.04552     	0.21318     	0.22837     	0                eval state : test_on_restore;
              	0.04552     	0.21318     	0.22837     	0                eval state : test_on_last_epoch;
---> final result
              	0.04552     	0.21318     	0.22837     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05229     	0.35413     	0.26151     	0           
  10          	0.05208     	0.21343     	0.26298     	0           
  epoch 45 reach nan state
---> try to load best state
              	0.05208     	0.21343     	0.26298     	0                eval state : test_on_restore;
              	0.05208     	0.21343     	0.26298     	0                eval state : test_on_last_epoch;
---> final result
              	0.05208     	0.21343     	0.26298     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04872     	0.28469     	0.25937     	0           
  10          	0.04694     	0.33091     	0.24515     	0           
  epoch 33 reach nan state
---> try to load best state
              	0.04694     	0.33091     	0.24515     	0                eval state : test_on_restore;
              	0.04694     	0.33091     	0.24515     	0                eval state : test_on_last_epoch;
---> final result
              	0.04694     	0.33091     	0.24515     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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     	0.19959     	0.23108     	0           
  10          	0.05288     	0.31023     	0.23473     	0           
  epoch 100 reach nan state
---> try to load best state
              	0.04422     	0.40963     	0.22965     	0                eval state : test_on_restore;
              	0.05288     	0.31023     	0.23473     	0                eval state : test_on_last_epoch;
---> final result
              	0.04422     	0.40963     	0.22965     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06946     	0.88362     	0.15315     	0           
  10          	0.06386     	0.90811     	0.13847     	0           
  100         	0.01246     	0.99741     	0.03377     	0           
  300         	0.01613     	0.99520     	0.04884     	0           
  500         	0.01784     	0.99404     	0.05394     	1           
  1000        	0.01978     	0.99234     	0.05824     	2           
---> try to load best state
              	0.01978     	0.99234     	0.05824     	2                eval state : test_on_restore;
              	0.01978     	0.99234     	0.05824     	2                eval state : test_on_last_epoch;
---> final result              	0.01978     	0.99234     	0.05824     	2                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.08388     	0.84214     	0.16750     	0           
  10          	0.13897     	-0.40640    	0.28796     	0           
  epoch 34 reach nan state
---> try to load best state
              	0.13897     	-0.40640    	0.28796     	0                eval state : test_on_restore;
              	0.13897     	-0.40640    	0.28796     	0                eval state : test_on_last_epoch;
---> final result
              	0.13897     	-0.40640    	0.28796     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04683     	0.85340     	0.13151     	0           
  10          	0.08209     	-5.77690    	0.30704     	0           
  100         	0.04593     	-0.17608    	0.18190     	0           
  300         	0.05275     	0.35102     	0.23926     	0           
  500         	0.05371     	0.31199     	0.24530     	1           
  1000        	0.05468     	0.33220     	0.25191     	2           
---> try to load best state
              	0.05468     	0.33220     	0.25191     	2                eval state : test_on_restore;
              	0.05468     	0.33220     	0.25191     	2                eval state : test_on_last_epoch;
---> final result
              	0.05468     	0.33220     	0.25191     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05665     	-0.09746    	0.23117     	0           
  10          	0.04951     	0.44337     	0.15149     	0           
  100         	0.04867     	0.51387     	0.22444     	0           
  300         	0.05425     	0.40837     	0.26136     	0           
  500         	0.05497     	0.39119     	0.26564     	1           
  1000        	0.05459     	0.38904     	0.26621     	2           
---> try to load best state
              	0.05459     	0.38904     	0.26621     	2                eval state : test_on_restore;
              	0.05459     	0.38904     	0.26621     	2                eval state : test_on_last_epoch;
---> final result
              	0.05459     	0.38904     	0.26621     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04258     	0.69124     	0.13324     	0           
  10          	0.03452     	0.74557     	0.11273     	0           
  epoch 21 reach nan state
---> try to load best state
              	0.03452     	0.74557     	0.11273     	0                eval state : test_on_restore;
              	0.03452     	0.74557     	0.11273     	0                eval state : test_on_last_epoch;
---> final result
              	0.03452     	0.74557     	0.11273     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07214     	0.87174     	0.16540     	0           
  10          	0.06362     	0.91334     	0.15080     	0           
  100         	0.01691     	0.99416     	0.04157     	0           
  300         	0.01542     	0.99450     	0.03666     	0           
  500         	0.01563     	0.99444     	0.03653     	1           
  1000        	0.01638     	0.99403     	0.03709     	3           
---> try to load best state
              	0.01638     	0.99403     	0.03709     	3                eval state : test_on_restore;
              	0.01638     	0.99403     	0.03709     	3                eval state : test_on_last_epoch;
---> final result              	0.01638     	0.99403     	0.03709     	3                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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     	0.80354     	0.11722     	0           
  10          	0.04443     	0.58858     	0.13178     	0           
  epoch 43 reach nan state
---> try to load best state
              	0.04443     	0.58858     	0.13178     	0                eval state : test_on_restore;
              	0.04443     	0.58858     	0.13178     	0                eval state : test_on_last_epoch;
---> final result
              	0.04443     	0.58858     	0.13178     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.03746     	0.82119     	0.11407     	0           
  10          	0.04187     	0.59399     	0.13373     	0           
  epoch 44 reach nan state
---> try to load best state
              	0.04187     	0.59399     	0.13373     	0                eval state : test_on_restore;
              	0.04187     	0.59399     	0.13373     	0                eval state : test_on_last_epoch;
---> final result
              	0.04187     	0.59399     	0.13373     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04277     	-0.64010    	0.16924     	0           
  10          	0.04573     	0.09446     	0.15196     	0           
  epoch 41 reach nan state
---> try to load best state
              	0.04573     	0.09446     	0.15196     	0                eval state : test_on_restore;
              	0.04573     	0.09446     	0.15196     	0                eval state : test_on_last_epoch;
---> final result
              	0.04573     	0.09446     	0.15196     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04472     	0.93509     	0.11126     	0           
  10          	0.04916     	0.93970     	0.11663     	0           
  epoch 43 reach nan state
---> try to load best state
              	0.04916     	0.93970     	0.11663     	0                eval state : test_on_restore;
              	0.04916     	0.93970     	0.11663     	0                eval state : test_on_last_epoch;
---> final result
              	0.04916     	0.93970     	0.11663     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 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, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05062     	0.88726     	0.16660     	0           
  10          	0.04725     	0.91663     	0.15566     	0           
  100         	0.02497     	0.97509     	0.05872     	0           
  300         	0.02445     	0.97865     	0.05812     	1           
  500         	0.02455     	0.98041     	0.05822     	1           
  1000        	0.02520     	0.98211     	0.05979     	3           
---> try to load best state
              	0.02520     	0.98211     	0.05979     	3                eval state : test_on_restore;
              	0.02520     	0.98211     	0.05979     	3                eval state : test_on_last_epoch;
---> final result              	0.02520     	0.98211     	0.05979     	3                eval state : final;
----------> finish x-yl training


---------->
GAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.04383     	0.85133     	0.11974     	0           
  10          	0.04625     	0.52304     	0.15802     	0           
  epoch 40 reach nan state
---> try to load best state
              	0.04625     	0.52304     	0.15802     	0                eval state : test_on_restore;
              	0.04625     	0.52304     	0.15802     	0                eval state : test_on_last_epoch;
---> final result
              	0.04625     	0.52304     	0.15802     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.06427     	-0.17261    	0.20431     	0           
  10          	0.04798     	0.61067     	0.13258     	0           
  epoch 41 reach nan state
---> try to load best state
              	0.04798     	0.61067     	0.13258     	0                eval state : test_on_restore;
              	0.04798     	0.61067     	0.13258     	0                eval state : test_on_last_epoch;
---> final result
              	0.04798     	0.61067     	0.13258     	0                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.07265     	0.16108     	0.20820     	0           
  10          	0.04957     	0.66804     	0.13054     	0           
  100         	0.05994     	-0.79189    	0.20849     	0           
  300         	0.09964     	-0.70178    	0.37260     	0           
  500         	0.10871     	-0.65051    	0.40579     	1           
  1000        	0.11409     	-0.62816    	0.42534     	2           
---> try to load best state
              	0.11409     	-0.62816    	0.42534     	2                eval state : test_on_restore;
              	0.11409     	-0.62816    	0.42534     	2                eval state : test_on_last_epoch;
---> final result
              	0.11409     	-0.62816    	0.42534     	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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           	0.05085     	0.89987     	0.11333     	0           
  10          	0.04358     	0.95253     	0.08756     	0           
  epoch 13 reach nan state
---> try to load best state
              	0.04358     	0.95253     	0.08756     	0                eval state : test_on_restore;
              	0.04358     	0.95253     	0.08756     	0                eval state : test_on_last_epoch;
---> final result
              	0.04358     	0.95253     	0.08756     	0                eval state : final;module_name : GAR;cp_record_file : True;
