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

  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]'], '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           	0.03813     	0.93242     	0.13297     	0           
  10          	0.03623     	0.93760     	0.12279     	0           
  100         	0.01179     	0.99501     	0.03262     	0           
  300         	0.00404     	0.99929     	0.01289     	1           
  500         	0.00445     	0.99930     	0.01309     	1           
  1000        	0.00557     	0.99917     	0.01474     	3           
---> try to load best state
              	0.00557     	0.99917     	0.01474     	3                eval state : test_on_restore;
              	0.00557     	0.99917     	0.01474     	3                eval state : test_on_last_epoch;
---> final result              	0.00557     	0.99917     	0.01474     	3                eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03298     	0.44706     	0.16018     	0           
  10          	0.03216     	0.47339     	0.15191     	0           
  100         	0.03063     	0.50117     	0.13924     	0           
  300         	0.03278     	0.49470     	0.16160     	0           
  500         	0.03422     	0.49871     	0.17306     	1           
  1000        	0.03519     	0.50166     	0.17874     	2           
---> try to load best state
              	0.03519     	0.50166     	0.17874     	2                eval state : test_on_restore;
              	0.03519     	0.50166     	0.17874     	2                eval state : test_on_last_epoch;
---> final result              	0.03519     	0.50166     	0.17874     	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': 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           	0.03205     	0.62438     	0.14700     	0           
  10          	0.03139     	0.59794     	0.13453     	0           
  100         	0.02981     	0.57611     	0.13347     	0           
  300         	0.02732     	0.55728     	0.12925     	0           
----------------------------------------

  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]'], '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           	0.03813     	0.93242     	0.13297     	0           
  10          	0.03623     	0.93760     	0.12279     	0           
  100         	0.01179     	0.99501     	0.03262     	0           
  300         	0.00404     	0.99929     	0.01289     	1           
  500         	0.00445     	0.99930     	0.01309     	1           
  1000        	0.00557     	0.99917     	0.01474     	3           
---> try to load best state
              	0.00557     	0.99917     	0.01474     	3                eval state : test_on_restore;
              	0.00557     	0.99917     	0.01474     	3                eval state : test_on_last_epoch;
---> final result              	0.00557     	0.99917     	0.01474     	3                eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03298     	0.44706     	0.16018     	0           
  10          	0.03216     	0.47339     	0.15191     	0           
  100         	0.03063     	0.50117     	0.13924     	0           
  300         	0.03278     	0.49470     	0.16160     	0           
  500         	0.03422     	0.49871     	0.17306     	0           
  1000        	0.03519     	0.50166     	0.17874     	1           
---> try to load best state
              	0.03519     	0.50166     	0.17874     	1                eval state : test_on_restore;
              	0.03519     	0.50166     	0.17874     	1                eval state : test_on_last_epoch;
---> final result              	0.03519     	0.50166     	0.17874     	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           	0.03205     	0.62438     	0.14700     	0           
  10          	0.03139     	0.59794     	0.13453     	0           
  100         	0.02981     	0.57611     	0.13347     	0           
  300         	0.02732     	0.55728     	0.12925     	0           
  500         	0.02982     	0.55389     	0.13413     	0           
  1000        	0.03234     	0.52711     	0.15817     	1           
---> try to load best state
              	0.03234     	0.52711     	0.15817     	1                eval state : test_on_restore;
              	0.03234     	0.52711     	0.15817     	1                eval state : test_on_last_epoch;
---> final result              	0.03234     	0.52711     	0.15817     	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': 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           	0.03379     	0.45496     	0.16240     	0           
  10          	0.03836     	0.49685     	0.19327     	0           
  100         	0.04151     	0.46133     	0.20855     	0           
  300         	0.04607     	0.44961     	0.23856     	0           
  500         	0.04781     	0.45392     	0.24987     	0           
  1000        	0.04902     	0.45182     	0.25771     	1           
---> try to load best state
              	0.04902     	0.45182     	0.25771     	1                eval state : test_on_restore;
              	0.04902     	0.45182     	0.25771     	1                eval state : test_on_last_epoch;
---> final result              	0.04902     	0.45182     	0.25771     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03757     	0.38404     	0.15682     	0           
  10          	0.03711     	0.16950     	0.16302     	0           
  100         	0.04255     	0.13903     	0.18408     	0           
  300         	0.04265     	0.08697     	0.19122     	0           
  500         	0.04594     	0.03140     	0.22046     	0           
  1000        	0.04592     	0.04925     	0.21788     	1           
---> try to load best state
              	0.04592     	0.04925     	0.21788     	1                eval state : test_on_restore;
              	0.04592     	0.04925     	0.21788     	1                eval state : test_on_last_epoch;
---> final result              	0.04592     	0.04925     	0.21788     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03743     	0.76942     	0.13160     	0           
  10          	0.03674     	0.76999     	0.12814     	0           
  100         	0.02584     	0.73125     	0.12131     	0           
  300         	0.04698     	0.70267     	0.19481     	0           
  500         	0.04309     	0.71188     	0.21293     	1           
  epoch 508 reach nan state
---> try to load best state
              	0.04368     	0.71113     	0.21467     	1                eval state : test_on_restore;
              	0.04309     	0.71188     	0.21293     	1                eval state : test_on_last_epoch;
---> final result              	0.04309     	0.71188     	0.21293     	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           	0.03394     	0.72760     	0.12700     	0           
  10          	0.03187     	0.74531     	0.11840     	0           
  100         	0.01981     	0.70969     	0.10703     	0           
  300         	0.03113     	0.68930     	0.15101     	0           
  500         	0.03080     	0.69265     	0.14764     	1           
  epoch 897 reach nan state
---> try to load best state
              	0.03328     	0.69227     	0.15606     	2                eval state : test_on_restore;
              	0.03080     	0.69265     	0.14764     	2                eval state : test_on_last_epoch;
---> final result              	0.03080     	0.69265     	0.14764     	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': 128, '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           	0.03042     	0.87878     	0.19811     	0           
  10          	0.02816     	0.88758     	0.19232     	0           
  100         	0.01825     	0.95692     	0.12000     	0           
  300         	0.01018     	0.97781     	0.09154     	0           
  500         	0.00926     	0.98350     	0.07590     	1           
  1000        	0.00771     	0.98964     	0.05136     	3           
---> try to load best state
              	0.00771     	0.98964     	0.05136     	3                eval state : test_on_restore;
              	0.00771     	0.98964     	0.05136     	3                eval state : test_on_last_epoch;
---> final result              	0.00771     	0.98964     	0.05136     	3                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           	0.02923     	0.60165     	0.12995     	0           
  10          	0.03297     	0.59915     	0.15203     	0           
  100         	0.03643     	0.35552     	0.16768     	0           
  300         	0.03602     	0.42455     	0.16965     	0           
  500         	0.03414     	0.49026     	0.16246     	1           
  1000        	0.02980     	0.69071     	0.14111     	2           
---> try to load best state
              	0.02980     	0.69071     	0.14111     	2                eval state : test_on_restore;
              	0.02980     	0.69071     	0.14111     	2                eval state : test_on_last_epoch;
---> final result              	0.02980     	0.69071     	0.14111     	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': 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           	0.02956     	0.73350     	0.11704     	0           
  10          	0.02832     	0.73227     	0.11734     	0           
  100         	0.03019     	0.77065     	0.12517     	0           
  300         	0.03197     	0.75540     	0.13516     	0           
  500         	0.03173     	0.75682     	0.13378     	1           
  1000        	0.03106     	0.74615     	0.12870     	2           
---> try to load best state
              	0.03106     	0.74615     	0.12870     	2                eval state : test_on_restore;
              	0.03106     	0.74615     	0.12870     	2                eval state : test_on_last_epoch;
---> final result              	0.03106     	0.74615     	0.12870     	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': 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           	0.02907     	0.61003     	0.12368     	0           
  10          	0.02843     	0.48307     	0.12859     	0           
  100         	0.02724     	0.57109     	0.12375     	0           
  300         	0.02926     	0.48996     	0.13347     	0           
  500         	0.02815     	0.56006     	0.12722     	1           
  1000        	0.02599     	0.64263     	0.11842     	2           
---> try to load best state
              	0.02599     	0.64263     	0.11842     	2                eval state : test_on_restore;
              	0.02599     	0.64263     	0.11842     	2                eval state : test_on_last_epoch;
---> final result              	0.02599     	0.64263     	0.11842     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', '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           	0.03082     	0.58388     	0.13074     	0           
  10          	0.03201     	0.34086     	0.14164     	0           
  100         	0.02447     	0.75069     	0.10555     	0           
  300         	0.02895     	0.78485     	0.12048     	0           
  500         	0.03012     	0.77816     	0.12335     	1           
  1000        	0.03033     	0.77574     	0.12322     	2           
---> try to load best state
              	0.03033     	0.77574     	0.12322     	2                eval state : test_on_restore;
              	0.03033     	0.77574     	0.12322     	2                eval state : test_on_last_epoch;
---> final result              	0.03033     	0.77574     	0.12322     	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': 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           	0.02871     	0.77065     	0.12256     	0           
  10          	0.02616     	0.86407     	0.10437     	0           
  100         	0.02008     	0.93380     	0.08615     	0           
  300         	0.02034     	0.90306     	0.07625     	0           
  500         	0.02272     	0.83237     	0.10256     	1           
  1000        	0.02270     	0.83299     	0.10246     	2           
---> try to load best state
              	0.02270     	0.83299     	0.10246     	2                eval state : test_on_restore;
              	0.02270     	0.83299     	0.10246     	2                eval state : test_on_last_epoch;
---> final result              	0.02270     	0.83299     	0.10246     	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': 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           	0.03421     	0.85999     	0.15372     	0           
  10          	0.03518     	0.89681     	0.16635     	0           
  100         	0.04279     	0.90949     	0.25987     	0           
  300         	0.04583     	0.90211     	0.26165     	1           
  500         	0.04549     	0.90371     	0.24846     	1           
  1000        	0.04542     	0.90513     	0.22179     	3           
---> try to load best state
              	0.04542     	0.90513     	0.22179     	3                eval state : test_on_restore;
              	0.04542     	0.90513     	0.22179     	3                eval state : test_on_last_epoch;
---> final result              	0.04542     	0.90513     	0.22179     	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': 128, '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           	0.04551     	0.94823     	0.11817     	0           
  10          	0.04072     	0.95503     	0.10867     	0           
  100         	0.01566     	0.98925     	0.04092     	0           
  300         	0.00604     	0.99841     	0.01736     	0           
  500         	0.00639     	0.99847     	0.01820     	1           
  1000        	0.00725     	0.99843     	0.01978     	3           
---> try to load best state
              	0.00725     	0.99843     	0.01978     	3                eval state : test_on_restore;
              	0.00725     	0.99843     	0.01978     	3                eval state : test_on_last_epoch;
---> final result              	0.00725     	0.99843     	0.01978     	3                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           	0.05736     	0.51667     	0.17304     	0           
  10          	0.06273     	0.54884     	0.16344     	0           
  100         	0.09103     	0.23068     	0.24864     	0           
  300         	0.07418     	0.31589     	0.23789     	0           
  500         	0.07364     	0.33762     	0.23423     	0           
  1000        	0.07416     	0.32566     	0.23622     	2           
---> try to load best state
              	0.07416     	0.32566     	0.23622     	2                eval state : test_on_restore;
              	0.07416     	0.32566     	0.23622     	2                eval state : test_on_last_epoch;
---> final result              	0.07416     	0.32566     	0.23622     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03763     	0.64221     	0.14584     	0           
  10          	0.03652     	0.61925     	0.13814     	0           
  100         	0.05390     	0.40154     	0.22415     	0           
  300         	0.03881     	0.59712     	0.16465     	0           
  500         	0.04625     	0.50273     	0.20546     	1           
  1000        	0.05094     	0.40375     	0.23192     	2           
---> try to load best state
              	0.05094     	0.40375     	0.23192     	2                eval state : test_on_restore;
              	0.05094     	0.40375     	0.23192     	2                eval state : test_on_last_epoch;
---> final result              	0.05094     	0.40375     	0.23192     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.03859     	0.51256     	0.18460     	0           
  10          	0.04163     	0.58239     	0.20430     	0           
  100         	0.03418     	0.69865     	0.15662     	0           
  300         	0.03018     	0.71789     	0.12097     	0           
  500         	0.04095     	0.64891     	0.17757     	1           
  1000        	0.05207     	0.44034     	0.25197     	2           
---> try to load best state
              	0.05207     	0.44034     	0.25197     	2                eval state : test_on_restore;
              	0.05207     	0.44034     	0.25197     	2                eval state : test_on_last_epoch;
---> final result              	0.05207     	0.44034     	0.25197     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.04322     	0.68797     	0.13882     	0           
  10          	0.04094     	0.73249     	0.12873     	0           
  100         	0.02853     	0.75001     	0.11101     	0           
  300         	0.03033     	0.74174     	0.11370     	0           
  500         	0.02854     	0.74094     	0.11318     	1           
  1000        	0.02946     	0.73954     	0.11352     	2           
---> try to load best state
              	0.02946     	0.73954     	0.11352     	2                eval state : test_on_restore;
              	0.02946     	0.73954     	0.11352     	2                eval state : test_on_last_epoch;
---> final result              	0.02946     	0.73954     	0.11352     	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', '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           	0.03584     	0.89397     	0.11186     	0           
  10          	0.03027     	0.92155     	0.09694     	0           
  100         	0.02322     	0.94736     	0.07143     	0           
  300         	0.02278     	0.94077     	0.07693     	0           
  500         	0.02299     	0.93978     	0.07735     	1           
  1000        	0.02408     	0.93933     	0.07808     	2           
---> try to load best state
              	0.02408     	0.93933     	0.07808     	2                eval state : test_on_restore;
              	0.02408     	0.93933     	0.07808     	2                eval state : test_on_last_epoch;
---> final result              	0.02408     	0.93933     	0.07808     	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': 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           	0.03594     	0.93699     	0.09948     	0           
  10          	0.03109     	0.95622     	0.08532     	0           
  100         	0.01999     	0.97782     	0.05883     	0           
  300         	0.01995     	0.97731     	0.05890     	0           
  500         	0.02083     	0.97484     	0.06132     	1           
  1000        	0.01952     	0.97375     	0.06021     	2           
---> try to load best state
              	0.01952     	0.97375     	0.06021     	2                eval state : test_on_restore;
              	0.01952     	0.97375     	0.06021     	2                eval state : test_on_last_epoch;
---> final result              	0.01952     	0.97375     	0.06021     	3                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]'], '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           	0.03998     	0.93731     	0.13096     	0           
  10          	0.03633     	0.94115     	0.12125     	0           
  100         	0.01710     	0.97991     	0.04791     	0           
  300         	0.00515     	0.99530     	0.01524     	0           
  500         	0.00583     	0.99522     	0.01645     	1           
  1000        	0.00722     	0.99473     	0.02316     	3           
---> try to load best state
              	0.00722     	0.99473     	0.02316     	3                eval state : test_on_restore;
              	0.00722     	0.99473     	0.02316     	3                eval state : test_on_last_epoch;
---> final result              	0.00722     	0.99473     	0.02316     	3                eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.04449     	0.47328     	0.16323     	0           
  10          	0.03554     	0.30903     	0.15240     	0           
  100         	0.03534     	0.22012     	0.15062     	0           
  300         	0.03478     	0.21552     	0.14928     	0           
  500         	0.03464     	0.23088     	0.14853     	0           
  1000        	0.03453     	0.24393     	0.14794     	1           
---> try to load best state
              	0.03453     	0.24393     	0.14794     	1                eval state : test_on_restore;
              	0.03453     	0.24393     	0.14794     	1                eval state : test_on_last_epoch;
---> final result              	0.03453     	0.24393     	0.14794     	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           	0.03882     	0.47678     	0.16099     	0           
  10          	0.03531     	0.27283     	0.15296     	0           
  100         	0.03521     	0.26150     	0.15163     	0           
  300         	0.03387     	0.13167     	0.14966     	0           
  500         	0.03386     	0.15222     	0.14917     	0           
  1000        	0.03410     	0.22696     	0.14811     	1           
---> try to load best state
              	0.03410     	0.22696     	0.14811     	1                eval state : test_on_restore;
              	0.03410     	0.22696     	0.14811     	1                eval state : test_on_last_epoch;
---> final result              	0.03410     	0.22696     	0.14811     	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': 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           	0.03662     	0.45119     	0.15985     	0           
  10          	0.03753     	-0.17829    	0.16649     	0           
  100         	0.03412     	0.53066     	0.14197     	0           
  300         	0.03444     	0.88137     	0.15096     	0           
  500         	0.03540     	0.69590     	0.15379     	0           
  1000        	0.03385     	0.41756     	0.14440     	1           
---> try to load best state
              	0.03385     	0.41756     	0.14440     	1                eval state : test_on_restore;
              	0.03385     	0.41756     	0.14440     	1                eval state : test_on_last_epoch;
---> final result              	0.03385     	0.41756     	0.14440     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04896     	0.91753     	0.13534     	0           
  10          	0.04012     	0.94027     	0.11895     	0           
  100         	0.03672     	0.95743     	0.11261     	0           
  300         	0.03222     	0.96068     	0.11028     	0           
  500         	0.03381     	0.95996     	0.11060     	0           
  1000        	0.03548     	0.95955     	0.11096     	1           
---> try to load best state
              	0.03548     	0.95955     	0.11096     	1                eval state : test_on_restore;
              	0.03548     	0.95955     	0.11096     	1                eval state : test_on_last_epoch;
---> final result              	0.03548     	0.95955     	0.11096     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.04669     	0.91343     	0.16157     	0           
  10          	0.04305     	0.93531     	0.14906     	0           
  100         	0.03812     	0.95701     	0.12820     	0           
  300         	0.03799     	0.95864     	0.12377     	0           
  500         	0.03767     	0.95903     	0.12085     	1           
  1000        	0.04057     	0.95191     	0.13257     	1           
---> try to load best state
              	0.04057     	0.95191     	0.13257     	1                eval state : test_on_restore;
              	0.04057     	0.95191     	0.13257     	1                eval state : test_on_last_epoch;
---> final result              	0.04057     	0.95191     	0.13257     	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': 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           	0.03758     	0.93377     	0.14366     	0           
  10          	0.03245     	0.95424     	0.12949     	0           
  100         	0.03085     	0.96313     	0.11275     	0           
  300         	0.03063     	0.96473     	0.10473     	0           
  500         	0.03170     	0.96320     	0.10071     	1           
  1000        	0.03497     	0.95502     	0.10510     	2           
---> try to load best state
              	0.03497     	0.95502     	0.10510     	2                eval state : test_on_restore;
              	0.03497     	0.95502     	0.10510     	2                eval state : test_on_last_epoch;
---> final result              	0.03497     	0.95502     	0.10510     	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]'], '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           	0.04620     	0.93977     	0.11369     	0           
  10          	0.04169     	0.94624     	0.10418     	0           
  100         	0.01303     	0.99372     	0.03127     	0           
  300         	0.00577     	0.99851     	0.01564     	0           
  500         	0.00621     	0.99853     	0.01627     	1           
  1000        	0.00716     	0.99854     	0.01815     	3           
---> try to load best state
              	0.00716     	0.99854     	0.01815     	3                eval state : test_on_restore;
              	0.00716     	0.99854     	0.01815     	3                eval state : test_on_last_epoch;
---> final result              	0.00716     	0.99854     	0.01815     	3                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           	0.03315     	0.61847     	0.15760     	0           
  10          	0.03262     	0.59887     	0.14939     	0           
  100         	0.03222     	0.61361     	0.14051     	0           
  300         	0.03354     	0.56704     	0.16277     	0           
  500         	0.03358     	0.57152     	0.16340     	0           
  1000        	0.03365     	0.56213     	0.16411     	1           
---> try to load best state
              	0.03365     	0.56213     	0.16411     	1                eval state : test_on_restore;
              	0.03365     	0.56213     	0.16411     	1                eval state : test_on_last_epoch;
---> final result              	0.03365     	0.56213     	0.16411     	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.06000     	0.42231     	0.18829     	0           
  10          	0.06091     	0.41286     	0.20090     	0           
  100         	0.05639     	0.33175     	0.16231     	0           
  300         	0.03847     	0.29819     	0.15575     	0           
  500         	0.03437     	0.32383     	0.14540     	1           
  1000        	0.03569     	0.35621     	0.13982     	2           
---> try to load best state
              	0.03569     	0.35621     	0.13982     	2                eval state : test_on_restore;
              	0.03569     	0.35621     	0.13982     	2                eval state : test_on_last_epoch;
---> final result              	0.03569     	0.35621     	0.13982     	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': 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           	0.06948     	0.55462     	0.20050     	0           
  10          	0.06807     	0.50348     	0.21517     	0           
  100         	0.05927     	-0.42724    	0.20824     	0           
  300         	0.04582     	-0.35233    	0.20269     	0           
  500         	0.06185     	-0.94726    	0.29473     	1           
  1000        	0.09379     	-1.89085    	0.44471     	2           
---> try to load best state
              	0.09379     	-1.89085    	0.44471     	2                eval state : test_on_restore;
              	0.09379     	-1.89085    	0.44471     	2                eval state : test_on_last_epoch;
---> final result              	0.09379     	-1.89085    	0.44471     	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           	0.04108     	0.88952     	0.12877     	0           
  10          	0.03641     	0.93172     	0.10960     	0           
  100         	0.03337     	0.97266     	0.07926     	0           
  300         	0.03225     	0.97413     	0.07463     	0           
  500         	0.03256     	0.97401     	0.07491     	1           
  1000        	0.03274     	0.97390     	0.07528     	2           
---> try to load best state
              	0.03274     	0.97390     	0.07528     	2                eval state : test_on_restore;
              	0.03274     	0.97390     	0.07528     	2                eval state : test_on_last_epoch;
---> final result              	0.03274     	0.97390     	0.07528     	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           	0.03676     	0.88894     	0.13025     	0           
  10          	0.03400     	0.93498     	0.11013     	0           
  100         	0.02689     	0.97735     	0.07145     	0           
  300         	0.02884     	0.97407     	0.07391     	0           
  500         	0.02914     	0.97401     	0.07424     	1           
  1000        	0.02916     	0.97401     	0.07423     	2           
---> try to load best state
              	0.02916     	0.97401     	0.07423     	2                eval state : test_on_restore;
              	0.02916     	0.97401     	0.07423     	2                eval state : test_on_last_epoch;
---> final result              	0.02916     	0.97401     	0.07423     	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           	0.03715     	0.93665     	0.10217     	0           
  10          	0.03230     	0.95647     	0.08733     	0           
  100         	0.02159     	0.97740     	0.06011     	0           
  300         	0.02321     	0.97455     	0.06462     	0           
  500         	0.02434     	0.97191     	0.06785     	1           
  1000        	0.02464     	0.97147     	0.06866     	2           
---> try to load best state
              	0.02464     	0.97147     	0.06866     	2                eval state : test_on_restore;
              	0.02464     	0.97147     	0.06866     	2                eval state : test_on_last_epoch;
---> final result              	0.02464     	0.97147     	0.06866     	2                eval state : final;module_name : SGAR;cp_record_file : True;
