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

  Demo sGAR 
  seed: 7 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 7, '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           	0.08251     	0.88160     	0.16986     	0           
  10          	0.07772     	0.89746     	0.16705     	0           
  100         	0.03954     	0.95321     	0.12314     	0           
  300         	0.03659     	0.96424     	0.12297     	0           
  500         	0.03741     	0.96170     	0.12561     	1           
  1000        	0.03819     	0.95919     	0.12665     	3           
---> try to load best state
              	0.03819     	0.95919     	0.12665     	3                eval state : test_on_restore;
              	0.03819     	0.95919     	0.12665     	3                eval state : test_on_last_epoch;
---> final result              	0.03819     	0.95919     	0.12665     	3                eval state : final;
---> end

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

  Demo sGAR 
  seed: 7 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 7, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07176     	0.45324     	0.22486     	0           
  10          	0.08444     	0.00013     	0.27661     	0           
  100         	0.12677     	-0.47790    	0.44920     	0           
  epoch 138 reach nan state
---> try to load best state
              	0.12605     	-0.42251    	0.44438     	0                eval state : test_on_restore;
              	0.12677     	-0.47790    	0.44920     	0                eval state : test_on_last_epoch;
---> final result              	0.12605     	-0.42251    	0.44438     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 7 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 7, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07124     	0.59738     	0.22046     	0           
  10          	0.07196     	0.58783     	0.22293     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.07416     	0.57523     	0.23204     	0                eval state : test_on_restore;
              	0.07196     	0.58783     	0.22293     	0                eval state : test_on_last_epoch;
---> final result              	0.07196     	0.58783     	0.22293     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 7 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 7, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06263     	0.59433     	0.20870     	0           
  10          	0.06591     	0.62957     	0.22369     	0           
  100         	0.05660     	0.35280     	0.18770     	0           
  300         	0.05885     	0.35881     	0.19617     	0           
  500         	0.05977     	0.38512     	0.20004     	1           
  epoch 899 reach nan state
---> try to load best state
              	0.05541     	0.42959     	0.18484     	3                eval state : test_on_restore;
              	0.05977     	0.38512     	0.20004     	3                eval state : test_on_last_epoch;
---> final result              	0.05541     	0.42959     	0.18484     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 7 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 7, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.07738     	0.84183     	0.21123     	0           
  10          	0.06683     	0.86577     	0.19255     	0           
  100         	0.04860     	0.90043     	0.13344     	0           
  300         	0.05185     	0.89111     	0.13731     	0           
  500         	0.05032     	0.89094     	0.13676     	1           
  1000        	0.05027     	0.89048     	0.13658     	2           
---> try to load best state
              	0.05027     	0.89048     	0.13658     	2                eval state : test_on_restore;
              	0.05027     	0.89048     	0.13658     	2                eval state : test_on_last_epoch;
---> final result              	0.05027     	0.89048     	0.13658     	2                eval state : final;module_name : SGAR;cp_record_file : True;
