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

  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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03743     	0.94829     	0.11244     	0           
  10          	0.03435     	0.95469     	0.10295     	0           
  100         	0.00910     	0.99629     	0.02185     	0           
  300         	0.00268     	0.99957     	0.00797     	3           
  500         	0.00275     	0.99962     	0.00787     	5           
  1000        	0.00317     	0.99962     	0.00855     	10          
---> try to load best state
              	0.00317     	0.99962     	0.00855     	10               eval state : test_on_restore;
              	0.00317     	0.99962     	0.00855     	10               eval state : test_on_last_epoch;
---> final result              	0.00317     	0.99962     	0.00855     	10               eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03389     	0.44739     	0.16818     	0           
  10          	0.03292     	0.47401     	0.15907     	0           
  100         	0.03128     	0.50276     	0.14474     	0           
  300         	0.03361     	0.49496     	0.16990     	0           
  500         	0.03524     	0.49885     	0.18233     	1           
  1000        	0.03622     	0.50177     	0.18842     	1           
---> try to load best state
              	0.03622     	0.50177     	0.18842     	1                eval state : test_on_restore;
              	0.03622     	0.50177     	0.18842     	1                eval state : test_on_last_epoch;
---> final result              	0.03622     	0.50177     	0.18842     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03258     	0.62683     	0.15449     	0           
  10          	0.03127     	0.60140     	0.13920     	0           
  100         	0.02964     	0.58180     	0.13360     	0           
  300         	0.02727     	0.56261     	0.12933     	0           
  500         	0.03044     	0.55646     	0.13927     	1           
  1000        	0.03318     	0.52821     	0.16631     	2           
---> try to load best state
              	0.03318     	0.52821     	0.16631     	2                eval state : test_on_restore;
              	0.03318     	0.52821     	0.16631     	2                eval state : test_on_last_epoch;
---> final result              	0.03318     	0.52821     	0.16631     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03507     	0.45516     	0.17068     	0           
  10          	0.03960     	0.49714     	0.20374     	0           
  100         	0.04283     	0.46026     	0.21962     	0           
  300         	0.04750     	0.44818     	0.25130     	0           
  500         	0.04927     	0.45259     	0.26316     	1           
  1000        	0.05049     	0.45038     	0.27134     	2           
---> try to load best state
              	0.05049     	0.45038     	0.27134     	2                eval state : test_on_restore;
              	0.05049     	0.45038     	0.27134     	2                eval state : test_on_last_epoch;
---> final result              	0.05049     	0.45038     	0.27134     	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]', '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           	0.03797     	0.38659     	0.16034     	0           
  10          	0.03749     	0.16602     	0.16425     	0           
  100         	0.04144     	0.13714     	0.18173     	0           
  300         	0.04145     	0.08191     	0.18881     	0           
  500         	0.04457     	0.02855     	0.21847     	1           
  1000        	0.04460     	0.04754     	0.21580     	2           
---> try to load best state
              	0.04460     	0.04754     	0.21580     	2                eval state : test_on_restore;
              	0.04460     	0.04754     	0.21580     	2                eval state : test_on_last_epoch;
---> final result              	0.04460     	0.04754     	0.21580     	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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03833     	0.76898     	0.13843     	0           
  10          	0.03778     	0.76999     	0.13360     	0           
  100         	0.02541     	0.73442     	0.12101     	0           
  300         	0.04727     	0.70798     	0.19659     	1           
  500         	0.04188     	0.71705     	0.21587     	1           
  epoch 508 reach nan state
---> try to load best state
              	0.04247     	0.71628     	0.21761     	1                eval state : test_on_restore;
              	0.04188     	0.71705     	0.21587     	1                eval state : test_on_last_epoch;
---> final result              	0.04188     	0.71705     	0.21587     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03467     	0.72937     	0.13337     	0           
  10          	0.03251     	0.74942     	0.12333     	0           
  100         	0.01938     	0.71532     	0.10699     	0           
  300         	0.03107     	0.69735     	0.15201     	1           
  500         	0.03045     	0.69806     	0.14991     	2           
  epoch 897 reach nan state
---> try to load best state
              	0.03260     	0.69856     	0.15826     	4                eval state : test_on_restore;
              	0.03045     	0.69806     	0.14991     	4                eval state : test_on_last_epoch;
---> final result              	0.03045     	0.69806     	0.14991     	4                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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03385     	0.89888     	0.11311     	0           
  10          	0.02976     	0.92389     	0.09973     	0           
  100         	0.01830     	0.93439     	0.07653     	1           
  300         	0.02615     	0.79547     	0.11802     	3           
  500         	0.02627     	0.78936     	0.11955     	6           
  1000        	0.02567     	0.79430     	0.11927     	10          
---> try to load best state
              	0.02567     	0.79430     	0.11927     	10               eval state : test_on_restore;
              	0.02567     	0.79430     	0.11927     	10               eval state : test_on_last_epoch;
---> final result              	0.02567     	0.79430     	0.11927     	10               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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03202     	0.94754     	0.12795     	0           
  10          	0.03021     	0.95454     	0.11749     	0           
  100         	0.00745     	0.99307     	0.02012     	0           
  300         	0.00226     	0.99768     	0.00973     	2           
  500         	0.00255     	0.99794     	0.01145     	3           
  1000        	0.00346     	0.99813     	0.01796     	7           
---> try to load best state
              	0.00346     	0.99813     	0.01796     	7                eval state : test_on_restore;
              	0.00346     	0.99813     	0.01796     	7                eval state : test_on_last_epoch;
---> final result              	0.00346     	0.99813     	0.01796     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03462     	0.50179     	0.17380     	0           
  10          	0.03822     	0.49753     	0.20416     	0           
  100         	0.04231     	0.14092     	0.22452     	0           
  300         	0.04221     	0.23285     	0.23206     	0           
  500         	0.04027     	0.32889     	0.22324     	0           
  1000        	0.03566     	0.62037     	0.19587     	1           
---> try to load best state
              	0.03566     	0.62037     	0.19587     	1                eval state : test_on_restore;
              	0.03566     	0.62037     	0.19587     	1                eval state : test_on_last_epoch;
---> final result              	0.03566     	0.62037     	0.19587     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03469     	0.67132     	0.15585     	0           
  10          	0.03307     	0.67954     	0.13835     	0           
  100         	0.03016     	0.75463     	0.12731     	0           
  300         	0.02938     	0.74807     	0.12817     	0           
  500         	0.02910     	0.74690     	0.12747     	0           
  1000        	0.02892     	0.72259     	0.12684     	1           
---> try to load best state
              	0.02892     	0.72259     	0.12684     	1                eval state : test_on_restore;
              	0.02892     	0.72259     	0.12684     	1                eval state : test_on_last_epoch;
---> final result              	0.02892     	0.72259     	0.12684     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03416     	0.51533     	0.16014     	0           
  10          	0.03334     	0.34765     	0.15321     	0           
  100         	0.03154     	0.45550     	0.14241     	0           
  300         	0.02909     	0.28416     	0.14301     	0           
  500         	0.02929     	0.41228     	0.13721     	0           
  1000        	0.02987     	0.55569     	0.13539     	1           
---> try to load best state
              	0.02987     	0.55569     	0.13539     	1                eval state : test_on_restore;
              	0.02987     	0.55569     	0.13539     	1                eval state : test_on_last_epoch;
---> final result              	0.02987     	0.55569     	0.13539     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03607     	0.48177     	0.17468     	0           
  10          	0.03735     	0.15149     	0.18360     	0           
  100         	0.02673     	0.76225     	0.11131     	0           
  300         	0.02749     	0.80978     	0.11345     	0           
  500         	0.02878     	0.79866     	0.11716     	0           
  1000        	0.02897     	0.79431     	0.11694     	1           
---> try to load best state
              	0.02897     	0.79431     	0.11694     	1                eval state : test_on_restore;
              	0.02897     	0.79431     	0.11694     	1                eval state : test_on_last_epoch;
---> final result              	0.02897     	0.79431     	0.11694     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03374     	0.71236     	0.16962     	0           
  10          	0.03027     	0.85358     	0.14283     	0           
  100         	0.01853     	0.96148     	0.07078     	0           
  300         	0.02240     	0.91260     	0.08448     	0           
  500         	0.02723     	0.81063     	0.14359     	1           
  1000        	0.02717     	0.81148     	0.14346     	1           
---> try to load best state
              	0.02717     	0.81148     	0.14346     	1                eval state : test_on_restore;
              	0.02717     	0.81148     	0.14346     	1                eval state : test_on_last_epoch;
---> final result              	0.02717     	0.81148     	0.14346     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03909     	0.81543     	0.20983     	0           
  10          	0.04001     	0.87577     	0.22822     	0           
  100         	0.04884     	0.87963     	0.35883     	0           
  300         	0.05370     	0.87175     	0.37819     	0           
  500         	0.05395     	0.87434     	0.36936     	1           
  1000        	0.05479     	0.88052     	0.34763     	2           
---> try to load best state
              	0.05479     	0.88052     	0.34763     	2                eval state : test_on_restore;
              	0.05479     	0.88052     	0.34763     	2                eval state : test_on_last_epoch;
---> final result              	0.05479     	0.88052     	0.34763     	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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03219     	0.88246     	0.12521     	0           
  10          	0.02894     	0.91898     	0.10739     	0           
  100         	0.01867     	0.90008     	0.08544     	0           
  300         	0.02695     	0.82101     	0.12977     	2           
  epoch 493 reach nan state
---> try to load best state
              	0.03378     	0.84888     	0.16965     	3                eval state : test_on_restore;
              	0.02695     	0.82101     	0.12977     	3                eval state : test_on_last_epoch;
---> final result              	0.02695     	0.82101     	0.12977     	3                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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03947     	0.95852     	0.10577     	0           
  10          	0.03603     	0.96460     	0.09676     	0           
  100         	0.01018     	0.99463     	0.02504     	0           
  epoch 136 reach nan state
---> try to load best state
              	0.03222     	0.97250     	0.13504     	0                eval state : test_on_restore;
              	0.03332     	0.97120     	0.13873     	0                eval state : test_on_last_epoch;
---> final result              	0.03222     	0.97250     	0.13504     	0                eval state : final;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05543     	0.59139     	0.16390     	0           
  10          	0.05347     	0.63014     	0.13923     	0           
  100         	0.08311     	0.43603     	0.22153     	0           
  300         	0.06717     	0.49154     	0.21267     	0           
  500         	0.06640     	0.50880     	0.20827     	0           
  1000        	0.06701     	0.49975     	0.21053     	1           
---> try to load best state
              	0.06701     	0.49975     	0.21053     	1                eval state : test_on_restore;
              	0.06701     	0.49975     	0.21053     	1                eval state : test_on_last_epoch;
---> final result              	0.06701     	0.49975     	0.21053     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04023     	0.69364     	0.15150     	0           
  10          	0.03058     	0.68407     	0.12142     	0           
  100         	0.04546     	0.55272     	0.19673     	0           
  300         	0.03214     	0.67620     	0.14548     	0           
  500         	0.04124     	0.59926     	0.18863     	0           
  1000        	0.04701     	0.50909     	0.21698     	1           
---> try to load best state
              	0.04701     	0.50909     	0.21698     	1                eval state : test_on_restore;
              	0.04701     	0.50909     	0.21698     	1                eval state : test_on_last_epoch;
---> final result              	0.04701     	0.50909     	0.21698     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.05575     	0.56158     	0.22642     	0           
  10          	0.06111     	0.62257     	0.25227     	0           
  100         	0.05157     	0.72453     	0.19984     	0           
  300         	0.02913     	0.76107     	0.11803     	0           
  500         	0.03666     	0.71462     	0.16259     	0           
  1000        	0.04958     	0.51275     	0.23788     	1           
---> try to load best state
              	0.04958     	0.51275     	0.23788     	1                eval state : test_on_restore;
              	0.04958     	0.51275     	0.23788     	1                eval state : test_on_last_epoch;
---> final result              	0.04958     	0.51275     	0.23788     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04149     	0.74524     	0.12343     	0           
  10          	0.04125     	0.79251     	0.12212     	0           
  100         	0.02807     	0.80160     	0.11469     	0           
  300         	0.02939     	0.78380     	0.11468     	0           
  500         	0.02748     	0.77966     	0.11248     	0           
  1000        	0.02784     	0.78020     	0.11290     	1           
---> try to load best state
              	0.02784     	0.78020     	0.11290     	1                eval state : test_on_restore;
              	0.02784     	0.78020     	0.11290     	1                eval state : test_on_last_epoch;
---> final result              	0.02784     	0.78020     	0.11290     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03867     	0.90261     	0.11370     	0           
  10          	0.03592     	0.91756     	0.10941     	0           
  100         	0.03106     	0.93017     	0.09508     	0           
  300         	0.03085     	0.92186     	0.09856     	0           
  500         	0.03114     	0.91905     	0.10027     	0           
  1000        	0.03194     	0.91884     	0.10056     	1           
---> try to load best state
              	0.03194     	0.91884     	0.10056     	1                eval state : test_on_restore;
              	0.03194     	0.91884     	0.10056     	1                eval state : test_on_last_epoch;
---> final result              	0.03194     	0.91884     	0.10056     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.04083     	0.93032     	0.12004     	0           
  10          	0.03810     	0.93665     	0.11723     	0           
  100         	0.03326     	0.94388     	0.10033     	0           
  300         	0.03321     	0.94405     	0.10139     	0           
  500         	0.03403     	0.94099     	0.10496     	1           
  1000        	0.03337     	0.93994     	0.10804     	2           
---> try to load best state
              	0.03337     	0.93994     	0.10804     	2                eval state : test_on_restore;
              	0.03337     	0.93994     	0.10804     	2                eval state : test_on_last_epoch;
---> final result              	0.03337     	0.93994     	0.10804     	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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03809     	0.92877     	0.11705     	0           
  10          	0.03568     	0.93565     	0.11352     	0           
  100         	0.03055     	0.94565     	0.09406     	0           
  300         	0.03033     	0.94863     	0.09086     	1           
  500         	0.03053     	0.94758     	0.09196     	3           
  1000        	0.03065     	0.94410     	0.09570     	6           
---> try to load best state
              	0.03065     	0.94410     	0.09570     	6                eval state : test_on_restore;
              	0.03065     	0.94410     	0.09570     	6                eval state : test_on_last_epoch;
---> final result              	0.03065     	0.94410     	0.09570     	6                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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03838     	0.95057     	0.10787     	0           
  10          	0.03529     	0.95602     	0.09847     	0           
  100         	0.00962     	0.99502     	0.02350     	0           
  300         	0.00299     	0.99934     	0.00942     	2           
  500         	0.00311     	0.99940     	0.00984     	3           
  1000        	0.00366     	0.99940     	0.01154     	7           
---> try to load best state
              	0.00366     	0.99940     	0.01154     	7                eval state : test_on_restore;
              	0.00366     	0.99940     	0.01154     	7                eval state : test_on_last_epoch;
---> final result              	0.00366     	0.99940     	0.01154     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04387     	0.36567     	0.16301     	0           
  10          	0.03456     	0.17866     	0.15480     	0           
  100         	0.03438     	0.06626     	0.15362     	0           
  300         	0.03385     	0.06018     	0.15245     	0           
  500         	0.03372     	0.07829     	0.15170     	0           
  1000        	0.03360     	0.09375     	0.15113     	1           
---> try to load best state
              	0.03360     	0.09375     	0.15113     	1                eval state : test_on_restore;
              	0.03360     	0.09375     	0.15113     	1                eval state : test_on_last_epoch;
---> final result              	0.03360     	0.09375     	0.15113     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03781     	0.37055     	0.16111     	0           
  10          	0.03436     	0.13952     	0.15548     	0           
  100         	0.03428     	0.11868     	0.15454     	0           
  300         	0.03293     	-0.04132    	0.15345     	0           
  500         	0.03293     	-0.01690    	0.15287     	0           
  1000        	0.03322     	0.07349     	0.15138     	2           
---> try to load best state
              	0.03322     	0.07349     	0.15138     	2                eval state : test_on_restore;
              	0.03322     	0.07349     	0.15138     	2                eval state : test_on_last_epoch;
---> final result              	0.03322     	0.07349     	0.15138     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03572     	0.34137     	0.16046     	0           
  10          	0.03690     	-0.34890    	0.16906     	0           
  100         	0.03341     	0.45472     	0.14395     	0           
  300         	0.03349     	0.86921     	0.15011     	0           
  500         	0.03443     	0.64474     	0.15429     	1           
  1000        	0.03275     	0.30147     	0.14677     	2           
---> try to load best state
              	0.03275     	0.30147     	0.14677     	2                eval state : test_on_restore;
              	0.03275     	0.30147     	0.14677     	2                eval state : test_on_last_epoch;
---> final result              	0.03275     	0.30147     	0.14677     	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]', '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           	0.04896     	0.91186     	0.13294     	0           
  10          	0.04032     	0.93555     	0.11697     	0           
  100         	0.03470     	0.96007     	0.10542     	0           
  300         	0.03070     	0.96399     	0.10347     	0           
  500         	0.03235     	0.96333     	0.10398     	1           
  1000        	0.03408     	0.96286     	0.10443     	2           
---> try to load best state
              	0.03408     	0.96286     	0.10443     	2                eval state : test_on_restore;
              	0.03408     	0.96286     	0.10443     	2                eval state : test_on_last_epoch;
---> final result              	0.03408     	0.96286     	0.10443     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', '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           	0.04612     	0.90858     	0.15793     	0           
  10          	0.04270     	0.93158     	0.14608     	0           
  100         	0.03682     	0.96016     	0.12665     	0           
  300         	0.03771     	0.95984     	0.12613     	0           
  500         	0.03864     	0.95968     	0.12533     	1           
  1000        	0.03889     	0.95882     	0.12564     	2           
---> try to load best state
              	0.03889     	0.95882     	0.12564     	2                eval state : test_on_restore;
              	0.03889     	0.95882     	0.12564     	2                eval state : test_on_last_epoch;
---> final result              	0.03889     	0.95882     	0.12564     	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': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03713     	0.92595     	0.14049     	0           
  10          	0.03220     	0.94853     	0.12701     	0           
  100         	0.02899     	0.96802     	0.11018     	0           
  300         	0.02963     	0.96844     	0.10787     	0           
  500         	0.03049     	0.96887     	0.10592     	1           
  1000        	0.03279     	0.96854     	0.10496     	2           
---> try to load best state
              	0.03279     	0.96854     	0.10496     	2                eval state : test_on_restore;
              	0.03279     	0.96854     	0.10496     	2                eval state : test_on_last_epoch;
---> final result              	0.03279     	0.96854     	0.10496     	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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03642     	0.93924     	0.10778     	0           
  10          	0.03126     	0.95799     	0.09407     	0           
  100         	0.01847     	0.97875     	0.06568     	0           
  300         	0.01793     	0.97713     	0.06796     	1           
  500         	0.01836     	0.97711     	0.06872     	3           
  1000        	0.02007     	0.97681     	0.07135     	6           
---> try to load best state
              	0.02007     	0.97681     	0.07135     	6                eval state : test_on_restore;
              	0.02007     	0.97681     	0.07135     	6                eval state : test_on_last_epoch;
---> final result              	0.02007     	0.97681     	0.07135     	6                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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04092     	0.95149     	0.10498     	0           
  10          	0.03774     	0.95827     	0.09546     	0           
  100         	0.00945     	0.99605     	0.02207     	0           
  300         	0.00333     	0.99935     	0.01051     	2           
  500         	0.00335     	0.99940     	0.01085     	4           
  1000        	0.00378     	0.99936     	0.01256     	9           
---> try to load best state
              	0.00378     	0.99936     	0.01256     	9                eval state : test_on_restore;
              	0.00378     	0.99936     	0.01256     	9                eval state : test_on_last_epoch;
---> final result              	0.00378     	0.99936     	0.01256     	9                eval state : final;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03270     	0.61357     	0.15973     	0           
  10          	0.03206     	0.59372     	0.15141     	0           
  100         	0.03144     	0.60866     	0.14237     	0           
  300         	0.03311     	0.56155     	0.16491     	0           
  500         	0.03314     	0.56607     	0.16554     	1           
  1000        	0.03322     	0.55657     	0.16625     	2           
---> try to load best state
              	0.03322     	0.55657     	0.16625     	3                eval state : test_on_restore;
              	0.03322     	0.55657     	0.16625     	3                eval state : test_on_last_epoch;
---> final result              	0.03322     	0.55657     	0.16625     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06000     	0.41563     	0.19005     	0           
  10          	0.06090     	0.40606     	0.20261     	0           
  100         	0.05615     	0.32331     	0.16347     	0           
  300         	0.03763     	0.28970     	0.15631     	0           
  500         	0.03381     	0.31554     	0.14611     	1           
  1000        	0.03507     	0.34816     	0.14114     	3           
---> try to load best state
              	0.03507     	0.34816     	0.14114     	3                eval state : test_on_restore;
              	0.03507     	0.34816     	0.14114     	3                eval state : test_on_last_epoch;
---> final result              	0.03507     	0.34816     	0.14114     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06989     	0.54909     	0.20248     	0           
  10          	0.06822     	0.49665     	0.21712     	0           
  100         	0.05866     	-0.45218    	0.21000     	0           
  300         	0.04361     	-0.37640    	0.20383     	0           
  500         	0.05827     	-0.97711    	0.29649     	1           
  1000        	0.08976     	-1.92101    	0.44764     	2           
---> try to load best state
              	0.08976     	-1.92101    	0.44764     	2                eval state : test_on_restore;
              	0.08976     	-1.92101    	0.44764     	2                eval state : test_on_last_epoch;
---> final result              	0.08976     	-1.92101    	0.44764     	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': 0.01, '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           	0.04251     	0.88639     	0.13151     	0           
  10          	0.03760     	0.92988     	0.11207     	0           
  100         	0.03347     	0.97332     	0.08057     	0           
  300         	0.03255     	0.97564     	0.07481     	0           
  500         	0.03285     	0.97548     	0.07522     	1           
  1000        	0.03309     	0.97533     	0.07573     	2           
---> try to load best state
              	0.03309     	0.97533     	0.07573     	2                eval state : test_on_restore;
              	0.03309     	0.97533     	0.07573     	2                eval state : test_on_last_epoch;
---> final result              	0.03309     	0.97533     	0.07573     	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': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03730     	0.88597     	0.13266     	0           
  10          	0.03422     	0.93336     	0.11222     	0           
  100         	0.02665     	0.97801     	0.07202     	0           
  300         	0.02928     	0.97545     	0.07405     	0           
  500         	0.02955     	0.97536     	0.07457     	1           
  1000        	0.02957     	0.97537     	0.07456     	2           
---> try to load best state
              	0.02957     	0.97537     	0.07456     	2                eval state : test_on_restore;
              	0.02957     	0.97537     	0.07456     	2                eval state : test_on_last_epoch;
---> final result              	0.02957     	0.97537     	0.07456     	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': 128, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03832     	0.93382     	0.10577     	0           
  10          	0.03290     	0.95478     	0.09049     	0           
  100         	0.02151     	0.97780     	0.06074     	0           
  300         	0.02300     	0.97547     	0.06499     	0           
  500         	0.02413     	0.97342     	0.06763     	1           
  1000        	0.02470     	0.97294     	0.06891     	2           
---> try to load best state
              	0.02470     	0.97294     	0.06891     	2                eval state : test_on_restore;
              	0.02470     	0.97294     	0.06891     	2                eval state : test_on_last_epoch;
---> final result              	0.02470     	0.97294     	0.06891     	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': 256, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	0.03648     	0.92874     	0.10388     	0           
  10          	0.03146     	0.94846     	0.09003     	0           
  100         	0.01664     	0.96129     	0.06440     	0           
  300         	0.02415     	0.91877     	0.10219     	2           
  500         	0.02518     	0.91634     	0.10617     	3           
  epoch 893 reach nan state
---> try to load best state
              	0.03203     	0.95091     	0.13335     	7                eval state : test_on_restore;
              	0.02518     	0.91634     	0.10617     	7                eval state : test_on_last_epoch;
---> final result              	0.02518     	0.91634     	0.10617     	7                eval state : final;module_name : SGAR;cp_record_file : True;
