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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01851     	0.94615     	0.03751     	0           
  10          	0.01811     	0.94764     	0.03699     	0           
  100         	0.01914     	0.94174     	0.03795     	1           
  300         	0.01861     	0.94289     	0.03804     	3           
  500         	0.01859     	0.94292     	0.03802     	5           
  1000        	0.01853     	0.94298     	0.03798     	9           
---> try to load best state
              	0.01853     	0.94298     	0.03798     	9                eval state : test_on_restore;
              	0.01853     	0.94298     	0.03798     	9                eval state : test_on_last_epoch;
---> final result              	0.01853     	0.94298     	0.03798     	9                eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.18835     	-0.18255    	0.26042     	0           
  10          	0.19893     	-0.03649    	0.26901     	0           
  100         	0.24017     	-0.00692    	0.32396     	0           
  300         	0.31428     	-0.01636    	0.43076     	1           
  500         	0.35190     	-0.05420    	0.49051     	2           
  1000        	0.42380     	0.00802     	0.61792     	4           
---> try to load best state
              	0.42380     	0.00802     	0.61792     	4                eval state : test_on_restore;
              	0.42380     	0.00802     	0.61792     	4                eval state : test_on_last_epoch;
---> final result              	0.42380     	0.00802     	0.61792     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.16934     	0.03468     	0.24728     	0           
  10          	0.17436     	-0.00635    	0.25094     	0           
  100         	0.17604     	0.11137     	0.24883     	0           
  300         	0.21532     	0.08266     	0.29914     	1           
  500         	0.23340     	0.08135     	0.32848     	3           
  1000        	0.26184     	0.08435     	0.37799     	5           
---> try to load best state
              	0.26184     	0.08435     	0.37799     	5                eval state : test_on_restore;
              	0.26184     	0.08435     	0.37799     	5                eval state : test_on_last_epoch;
---> final result              	0.26184     	0.08435     	0.37799     	5                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.14647     	0.23823     	0.22472     	0           
  10          	0.14790     	0.12707     	0.22419     	0           
  100         	0.14434     	0.16131     	0.21367     	0           
  300         	0.15751     	0.20899     	0.22824     	2           
  500         	0.16766     	0.20185     	0.24095     	3           
  1000        	0.18635     	0.14883     	0.26699     	7           
---> try to load best state
              	0.18635     	0.14883     	0.26699     	7                eval state : test_on_restore;
              	0.18635     	0.14883     	0.26699     	7                eval state : test_on_last_epoch;
---> final result              	0.18635     	0.14883     	0.26699     	7                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, '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           	0.13861     	0.32364     	0.22456     	0           
  10          	0.13507     	0.29379     	0.21746     	0           
  100         	0.12754     	0.35400     	0.19801     	1           
  300         	0.13023     	0.34124     	0.19998     	3           
  500         	0.13260     	0.32815     	0.20255     	6           
  1000        	0.13994     	0.30217     	0.21208     	11          
---> try to load best state
              	0.13994     	0.30217     	0.21208     	11               eval state : test_on_restore;
              	0.13994     	0.30217     	0.21208     	11               eval state : test_on_last_epoch;
---> final result              	0.13994     	0.30217     	0.21208     	12               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, '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           	0.12168     	0.40913     	0.19611     	0           
  10          	0.12157     	0.39912     	0.19615     	0           
  100         	0.10453     	0.47655     	0.16991     	2           
  300         	0.10639     	0.46720     	0.16919     	7           
  500         	0.10754     	0.47046     	0.17024     	12          
  1000        	0.10864     	0.47307     	0.17145     	24          
---> try to load best state
              	0.10864     	0.47307     	0.17145     	24               eval state : test_on_restore;
              	0.10864     	0.47307     	0.17145     	24               eval state : test_on_last_epoch;
---> final result              	0.10864     	0.47307     	0.17145     	24               eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', 'interp_data': False, 'seed': None, '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           	0.09312     	0.31674     	0.15741     	0           
  10          	0.09371     	0.39149     	0.15981     	0           
  100         	0.08626     	0.50027     	0.14913     	6           
  300         	0.08059     	0.58888     	0.13806     	19          
  500         	0.08090     	0.58423     	0.13825     	32          
  1000        	0.08123     	0.58601     	0.13826     	65          
---> try to load best state
              	0.08123     	0.58601     	0.13826     	65               eval state : test_on_restore;
              	0.08123     	0.58601     	0.13826     	65               eval state : test_on_last_epoch;
---> final result              	0.08123     	0.58601     	0.13826     	65               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': 'TopOP_mfGent_v5', '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02135     	0.94414     	0.04551     	0           
  10          	0.02069     	0.94560     	0.04452     	0           
  100         	0.02160     	0.94243     	0.04312     	0           
  300         	0.02091     	0.94432     	0.04360     	2           
  500         	0.02090     	0.94431     	0.04362     	4           
  1000        	0.02086     	0.94429     	0.04367     	8           
---> try to load best state
              	0.02086     	0.94429     	0.04367     	8                eval state : test_on_restore;
              	0.02086     	0.94429     	0.04367     	8                eval state : test_on_last_epoch;
---> final result              	0.02086     	0.94429     	0.04367     	8                eval state : final;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.18627     	-0.19808    	0.25831     	0           
  10          	0.19230     	-0.03552    	0.26216     	0           
  100         	0.22259     	-0.12927    	0.30619     	0           
  300         	0.29208     	-0.09712    	0.39353     	1           
  500         	0.34010     	-0.11507    	0.46742     	2           
  1000        	0.39352     	-0.11975    	0.55536     	5           
---> try to load best state
              	0.39352     	-0.11975    	0.55536     	5                eval state : test_on_restore;
              	0.39352     	-0.11975    	0.55536     	5                eval state : test_on_last_epoch;
---> final result              	0.39352     	-0.11975    	0.55536     	5                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': 'TopOP_mfGent_v5', '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           	0.17022     	0.02428     	0.24736     	0           
  10          	0.17344     	0.09662     	0.24645     	0           
  100         	0.17869     	0.16423     	0.25199     	0           
  300         	0.19250     	0.07281     	0.26677     	1           
  500         	0.19819     	0.03549     	0.27314     	2           
  1000        	0.21053     	-0.08193    	0.29109     	5           
---> try to load best state
              	0.21053     	-0.08193    	0.29109     	5                eval state : test_on_restore;
              	0.21053     	-0.08193    	0.29109     	5                eval state : test_on_last_epoch;
---> final result              	0.21053     	-0.08193    	0.29109     	5                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': 'TopOP_mfGent_v5', '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           	0.16619     	0.16966     	0.25150     	0           
  10          	0.16739     	0.17811     	0.24456     	0           
  100         	0.16853     	0.21925     	0.23467     	0           
  300         	0.18111     	0.19042     	0.25063     	2           
  500         	0.18334     	0.18798     	0.25347     	3           
  1000        	0.18600     	0.17425     	0.25614     	7           
---> try to load best state
              	0.18600     	0.17425     	0.25614     	7                eval state : test_on_restore;
              	0.18600     	0.17425     	0.25614     	7                eval state : test_on_last_epoch;
---> final result              	0.18600     	0.17425     	0.25614     	7                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': 'TopOP_mfGent_v5', '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           	0.14592     	0.31534     	0.23054     	0           
  10          	0.14339     	0.33435     	0.22472     	0           
  100         	0.13640     	0.39657     	0.20355     	1           
  300         	0.14041     	0.39257     	0.20731     	3           
  500         	0.14160     	0.39350     	0.20819     	5           
  1000        	0.14179     	0.39712     	0.20804     	13          
---> try to load best state
              	0.14179     	0.39712     	0.20804     	13               eval state : test_on_restore;
              	0.14179     	0.39712     	0.20804     	13               eval state : test_on_last_epoch;
---> final result              	0.14179     	0.39712     	0.20804     	13               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': 'TopOP_mfGent_v5', '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           	0.12239     	0.54698     	0.20241     	0           
  10          	0.12557     	0.53063     	0.20699     	0           
  100         	0.10789     	0.58945     	0.17249     	2           
  300         	0.11070     	0.56699     	0.16994     	8           
  500         	0.11172     	0.56401     	0.17085     	13          
  1000        	0.11269     	0.55872     	0.17187     	24          
---> try to load best state
              	0.11269     	0.55872     	0.17187     	24               eval state : test_on_restore;
              	0.11269     	0.55872     	0.17187     	24               eval state : test_on_last_epoch;
---> final result              	0.11269     	0.55872     	0.17187     	24               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': 'TopOP_mfGent_v5', '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           	0.09481     	0.58029     	0.16142     	0           
  10          	0.09632     	0.59423     	0.16573     	0           
  100         	0.09305     	0.61386     	0.15973     	6           
  300         	0.08467     	0.63020     	0.14532     	20          
  500         	0.08508     	0.62933     	0.14547     	33          
  1000        	0.08542     	0.63323     	0.14555     	64          
---> try to load best state
              	0.08542     	0.63323     	0.14555     	64               eval state : test_on_restore;
              	0.08542     	0.63323     	0.14555     	64               eval state : test_on_last_epoch;
---> final result              	0.08542     	0.63323     	0.14555     	64               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': 'TopOP_mfGent_v5', '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02250     	0.94015     	0.04476     	0           
  10          	0.02179     	0.94338     	0.04343     	0           
  100         	0.02280     	0.93907     	0.04306     	0           
  300         	0.02200     	0.94165     	0.04325     	2           
  500         	0.02200     	0.94150     	0.04331     	3           
  1000        	0.02203     	0.94113     	0.04345     	7           
---> try to load best state
              	0.02203     	0.94113     	0.04345     	7                eval state : test_on_restore;
              	0.02203     	0.94113     	0.04345     	7                eval state : test_on_last_epoch;
---> final result              	0.02203     	0.94113     	0.04345     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.19112     	-0.29346    	0.25953     	0           
  10          	0.19217     	0.06841     	0.26033     	0           
  100         	0.25378     	0.05596     	0.34631     	0           
  300         	0.28601     	-0.03859    	0.38954     	1           
  epoch 379 reach nan state
---> try to load best state
              	0.29207     	-0.03578    	0.39870     	1                eval state : test_on_restore;
              	0.28601     	-0.03859    	0.38954     	1                eval state : test_on_last_epoch;
---> final result              	0.28601     	-0.03859    	0.38954     	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': 'TopOP_mfGent_v5', '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           	0.16303     	0.08456     	0.23545     	0           
  10          	0.16990     	0.16619     	0.23932     	0           
  100         	0.17834     	0.18956     	0.24285     	0           
  300         	0.21858     	0.13992     	0.29623     	1           
  500         	0.24211     	0.11604     	0.33238     	2           
  1000        	0.26828     	0.07991     	0.37021     	4           
---> try to load best state
              	0.26828     	0.07991     	0.37021     	4                eval state : test_on_restore;
              	0.26828     	0.07991     	0.37021     	4                eval state : test_on_last_epoch;
---> final result              	0.26828     	0.07991     	0.37021     	4                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': 'TopOP_mfGent_v5', '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           	0.15387     	0.21166     	0.23049     	0           
  10          	0.15666     	0.23791     	0.22984     	0           
  100         	0.16232     	0.29410     	0.22676     	0           
  300         	0.18523     	0.23618     	0.25456     	2           
  500         	0.19686     	0.21069     	0.27007     	3           
  1000        	0.20825     	0.17880     	0.28564     	7           
---> try to load best state
              	0.20825     	0.17880     	0.28564     	7                eval state : test_on_restore;
              	0.20825     	0.17880     	0.28564     	7                eval state : test_on_last_epoch;
---> final result              	0.20825     	0.17880     	0.28564     	7                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': 'TopOP_mfGent_v5', '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           	0.14886     	0.34207     	0.23332     	0           
  10          	0.14640     	0.37210     	0.22611     	0           
  100         	0.13312     	0.41456     	0.19451     	1           
  300         	0.13500     	0.39167     	0.19623     	3           
  500         	0.13613     	0.38549     	0.19752     	5           
  1000        	0.14015     	0.37921     	0.20227     	11          
---> try to load best state
              	0.14015     	0.37921     	0.20227     	11               eval state : test_on_restore;
              	0.14015     	0.37921     	0.20227     	11               eval state : test_on_last_epoch;
---> final result              	0.14015     	0.37921     	0.20227     	11               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': 'TopOP_mfGent_v5', '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           	0.13708     	0.39978     	0.21539     	0           
  10          	0.13719     	0.39501     	0.21529     	0           
  100         	0.11684     	0.48503     	0.18211     	2           
  300         	0.11941     	0.48177     	0.17996     	6           
  500         	0.12017     	0.48467     	0.18032     	11          
  1000        	0.12134     	0.48330     	0.18122     	22          
---> try to load best state
              	0.12134     	0.48330     	0.18122     	22               eval state : test_on_restore;
              	0.12134     	0.48330     	0.18122     	22               eval state : test_on_last_epoch;
---> final result              	0.12134     	0.48330     	0.18122     	22               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': 'TopOP_mfGent_v5', '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           	0.10122     	0.27643     	0.16647     	0           
  10          	0.10275     	0.34473     	0.16917     	0           
  100         	0.09374     	0.58389     	0.15839     	6           
  300         	0.08901     	0.64394     	0.14671     	19          
  500         	0.08958     	0.64308     	0.14699     	31          
  1000        	0.09019     	0.64121     	0.14735     	63          
---> try to load best state
              	0.09019     	0.64121     	0.14735     	63               eval state : test_on_restore;
              	0.09019     	0.64121     	0.14735     	63               eval state : test_on_last_epoch;
---> final result              	0.09019     	0.64121     	0.14735     	63               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': 'TopOP_mfGent_v5', '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02422     	0.93715     	0.04867     	0           
  10          	0.02387     	0.93950     	0.04729     	0           
  100         	0.02512     	0.93730     	0.04559     	0           
  300         	0.02380     	0.94016     	0.04571     	2           
  500         	0.02378     	0.94008     	0.04575     	3           
  1000        	0.02373     	0.93990     	0.04587     	7           
---> try to load best state
              	0.02373     	0.93990     	0.04587     	7                eval state : test_on_restore;
              	0.02373     	0.93990     	0.04587     	7                eval state : test_on_last_epoch;
---> final result              	0.02373     	0.93990     	0.04587     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.19990     	-0.42754    	0.27546     	0           
  10          	0.20633     	-0.22616    	0.28187     	0           
  100         	0.25720     	-0.17024    	0.35304     	0           
  300         	0.29485     	-0.17584    	0.39728     	1           
  500         	0.30818     	-0.21015    	0.41526     	1           
  epoch 765 reach nan state
---> try to load best state
              	0.31999     	-0.20382    	0.43365     	2                eval state : test_on_restore;
              	0.30818     	-0.21015    	0.41526     	2                eval state : test_on_last_epoch;
---> final result              	0.30818     	-0.21015    	0.41526     	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': 'TopOP_mfGent_v5', '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           	0.18274     	-0.11110    	0.26508     	0           
  10          	0.18596     	0.03363     	0.26565     	0           
  100         	0.20416     	0.10453     	0.27868     	0           
  300         	0.24948     	0.01979     	0.33537     	2           
  500         	0.26725     	-0.02386    	0.36268     	3           
  1000        	0.28757     	-0.07089    	0.39281     	6           
---> try to load best state
              	0.28757     	-0.07089    	0.39281     	6                eval state : test_on_restore;
              	0.28757     	-0.07089    	0.39281     	6                eval state : test_on_last_epoch;
---> final result              	0.28757     	-0.07089    	0.39281     	6                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': 'TopOP_mfGent_v5', '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           	0.16492     	0.19037     	0.24818     	0           
  10          	0.16372     	0.23743     	0.24100     	0           
  100         	0.16769     	0.29401     	0.23437     	0           
  300         	0.18618     	0.24728     	0.25816     	2           
  500         	0.20217     	0.22352     	0.28093     	4           
  1000        	0.22249     	0.20266     	0.31200     	9           
---> try to load best state
              	0.22249     	0.20266     	0.31200     	9                eval state : test_on_restore;
              	0.22249     	0.20266     	0.31200     	9                eval state : test_on_last_epoch;
---> final result              	0.22249     	0.20266     	0.31200     	9                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': 'TopOP_mfGent_v5', '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           	0.15048     	0.39117     	0.23684     	0           
  10          	0.14621     	0.42003     	0.22542     	0           
  100         	0.13883     	0.41709     	0.20789     	1           
  300         	0.14308     	0.40647     	0.21355     	3           
  500         	0.14570     	0.39907     	0.21578     	6           
  1000        	0.14839     	0.39612     	0.21861     	11          
---> try to load best state
              	0.14839     	0.39612     	0.21861     	11               eval state : test_on_restore;
              	0.14839     	0.39612     	0.21861     	11               eval state : test_on_last_epoch;
---> final result              	0.14839     	0.39612     	0.21861     	11               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': 'TopOP_mfGent_v5', '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           	0.12836     	0.54644     	0.20487     	0           
  10          	0.12777     	0.54495     	0.20409     	0           
  100         	0.11449     	0.55234     	0.18329     	2           
  300         	0.11731     	0.53182     	0.18447     	6           
  500         	0.11828     	0.52868     	0.18557     	11          
  1000        	0.11915     	0.52491     	0.18642     	22          
---> try to load best state
              	0.11915     	0.52491     	0.18642     	22               eval state : test_on_restore;
              	0.11915     	0.52491     	0.18642     	22               eval state : test_on_last_epoch;
---> final result              	0.11915     	0.52491     	0.18642     	22               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': 'TopOP_mfGent_v5', '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           	0.10087     	0.60130     	0.16640     	0           
  10          	0.10207     	0.60189     	0.16993     	0           
  100         	0.09625     	0.60881     	0.15837     	7           
  300         	0.08945     	0.61788     	0.14724     	20          
  500         	0.09000     	0.61641     	0.14766     	32          
  1000        	0.09065     	0.61468     	0.14808     	64          
---> try to load best state
              	0.09065     	0.61468     	0.14808     	64               eval state : test_on_restore;
              	0.09065     	0.61468     	0.14808     	65               eval state : test_on_last_epoch;
---> final result              	0.09065     	0.61468     	0.14808     	65               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': 'TopOP_mfGent_v5', '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02279     	0.94325     	0.04634     	0           
  10          	0.02212     	0.94452     	0.04542     	0           
  100         	0.02278     	0.94179     	0.04390     	0           
  300         	0.02208     	0.94342     	0.04472     	2           
  500         	0.02208     	0.94335     	0.04476     	3           
  1000        	0.02210     	0.94320     	0.04486     	7           
---> try to load best state
              	0.02210     	0.94320     	0.04486     	7                eval state : test_on_restore;
              	0.02210     	0.94320     	0.04486     	7                eval state : test_on_last_epoch;
---> final result              	0.02210     	0.94320     	0.04486     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.21348     	-0.55494    	0.28630     	0           
  10          	0.22451     	-0.26596    	0.29622     	0           
  100         	0.24556     	-0.35461    	0.32159     	0           
  300         	0.31498     	-0.51913    	0.40958     	1           
  500         	0.31772     	-0.42356    	0.41342     	2           
  1000        	0.34163     	-0.31184    	0.44968     	3           
---> try to load best state
              	0.34163     	-0.31184    	0.44968     	3                eval state : test_on_restore;
              	0.34163     	-0.31184    	0.44968     	3                eval state : test_on_last_epoch;
---> final result              	0.34163     	-0.31184    	0.44968     	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': 'TopOP_mfGent_v5', '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           	0.18975     	-0.18066    	0.26554     	0           
  10          	0.19593     	0.07266     	0.26397     	0           
  100         	0.19639     	0.12422     	0.26312     	0           
  300         	0.21086     	0.13046     	0.27607     	1           
  500         	0.22420     	0.08305     	0.29250     	2           
  1000        	0.24767     	0.04430     	0.32382     	4           
---> try to load best state
              	0.24767     	0.04430     	0.32382     	4                eval state : test_on_restore;
              	0.24767     	0.04430     	0.32382     	4                eval state : test_on_last_epoch;
---> final result              	0.24767     	0.04430     	0.32382     	4                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': 'TopOP_mfGent_v5', '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           	0.16473     	0.21571     	0.24160     	0           
  10          	0.16552     	0.32332     	0.23817     	0           
  100         	0.15427     	0.38073     	0.21905     	0           
  300         	0.16703     	0.35259     	0.23109     	2           
  500         	0.17492     	0.33825     	0.24068     	3           
  1000        	0.18477     	0.31717     	0.25615     	7           
---> try to load best state
              	0.18477     	0.31717     	0.25615     	7                eval state : test_on_restore;
              	0.18477     	0.31717     	0.25615     	7                eval state : test_on_last_epoch;
---> final result              	0.18477     	0.31717     	0.25615     	7                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': 'TopOP_mfGent_v5', '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           	0.15178     	0.39273     	0.23271     	0           
  10          	0.14545     	0.40433     	0.22200     	0           
  100         	0.13088     	0.45269     	0.19635     	1           
  300         	0.13391     	0.44435     	0.19838     	3           
  500         	0.13655     	0.43635     	0.20072     	6           
  1000        	0.14035     	0.42890     	0.20572     	12          
---> try to load best state
              	0.14035     	0.42890     	0.20572     	12               eval state : test_on_restore;
              	0.14035     	0.42890     	0.20572     	12               eval state : test_on_last_epoch;
---> final result              	0.14035     	0.42890     	0.20572     	12               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': 'TopOP_mfGent_v5', '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           	0.12026     	0.56470     	0.19666     	0           
  10          	0.12182     	0.52665     	0.19886     	0           
  100         	0.10672     	0.58408     	0.17296     	2           
  300         	0.10746     	0.57680     	0.17103     	6           
  500         	0.10837     	0.57616     	0.17171     	11          
  1000        	0.11077     	0.57311     	0.17402     	24          
---> try to load best state
              	0.11077     	0.57311     	0.17402     	24               eval state : test_on_restore;
              	0.11077     	0.57311     	0.17402     	24               eval state : test_on_last_epoch;
---> final result              	0.11077     	0.57311     	0.17402     	24               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': 'TopOP_mfGent_v5', '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           	0.08966     	0.41788     	0.15273     	0           
  10          	0.09017     	0.47365     	0.15542     	0           
  100         	0.08366     	0.58778     	0.14607     	6           
  300         	0.07888     	0.64836     	0.13496     	18          
  500         	0.07952     	0.64348     	0.13540     	31          
  1000        	0.07994     	0.64276     	0.13570     	63          
---> try to load best state
              	0.07994     	0.64276     	0.13570     	63               eval state : test_on_restore;
              	0.07994     	0.64276     	0.13570     	63               eval state : test_on_last_epoch;
---> final result              	0.07994     	0.64276     	0.13570     	63               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': 'TopOP_mfGent_v5', '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01862     	0.97083     	0.03314     	0           
  10          	0.01821     	0.97152     	0.03278     	0           
  100         	0.01917     	0.96727     	0.03329     	0           
  300         	0.01874     	0.96883     	0.03305     	2           
  500         	0.01872     	0.96887     	0.03303     	3           
  1000        	0.01867     	0.96896     	0.03300     	7           
---> try to load best state
              	0.01867     	0.96896     	0.03300     	7                eval state : test_on_restore;
              	0.01867     	0.96896     	0.03300     	7                eval state : test_on_last_epoch;
---> final result              	0.01867     	0.96896     	0.03300     	7                eval state : final;
---> end

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

  Demo sGAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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           	0.20160     	-0.42500    	0.27826     	0           
  10          	0.20806     	-0.15644    	0.28707     	0           
  100         	0.24489     	-0.12210    	0.33005     	0           
  epoch 265 reach nan state
---> try to load best state
              	0.38042     	-0.17501    	0.52093     	1                eval state : test_on_restore;
              	0.24489     	-0.12210    	0.33005     	1                eval state : test_on_last_epoch;
---> final result              	0.24489     	-0.12210    	0.33005     	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': 'TopOP_mfGent_v5', '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           	0.17982     	-0.17644    	0.25847     	0           
  10          	0.18186     	-0.01923    	0.25575     	0           
  100         	0.19735     	0.05923     	0.26602     	0           
  300         	0.21932     	0.13773     	0.29629     	1           
  500         	0.25486     	0.13488     	0.34955     	2           
  1000        	0.28062     	0.11868     	0.39639     	4           
---> try to load best state
              	0.28062     	0.11868     	0.39639     	4                eval state : test_on_restore;
              	0.28062     	0.11868     	0.39639     	4                eval state : test_on_last_epoch;
---> final result              	0.28062     	0.11868     	0.39639     	4                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': 'TopOP_mfGent_v5', '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           	0.15613     	0.21226     	0.23625     	0           
  10          	0.15746     	0.22449     	0.23475     	0           
  100         	0.15285     	0.25863     	0.21900     	0           
  300         	0.16266     	0.24656     	0.23011     	2           
  500         	0.16733     	0.25280     	0.23504     	3           
  1000        	0.17555     	0.24229     	0.24583     	7           
---> try to load best state
              	0.17555     	0.24229     	0.24583     	7                eval state : test_on_restore;
              	0.17555     	0.24229     	0.24583     	7                eval state : test_on_last_epoch;
---> final result              	0.17555     	0.24229     	0.24583     	7                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': 'TopOP_mfGent_v5', '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           	0.14360     	0.38740     	0.22498     	0           
  10          	0.13908     	0.39914     	0.21729     	0           
  100         	0.12662     	0.40032     	0.19297     	1           
  300         	0.13001     	0.40266     	0.19504     	3           
  500         	0.13375     	0.39531     	0.19787     	5           
  1000        	0.14144     	0.37280     	0.20634     	11          
---> try to load best state
              	0.14144     	0.37280     	0.20634     	11               eval state : test_on_restore;
              	0.14144     	0.37280     	0.20634     	11               eval state : test_on_last_epoch;
---> final result              	0.14144     	0.37280     	0.20634     	11               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': 'TopOP_mfGent_v5', '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           	0.13361     	0.18316     	0.21480     	0           
  10          	0.13538     	0.36900     	0.21583     	0           
  100         	0.11518     	0.46905     	0.18329     	2           
  300         	0.11723     	0.46066     	0.17671     	6           
  500         	0.11883     	0.45899     	0.17830     	11          
  1000        	0.12014     	0.45975     	0.17940     	22          
---> try to load best state
              	0.12014     	0.45975     	0.17940     	22               eval state : test_on_restore;
              	0.12014     	0.45975     	0.17940     	22               eval state : test_on_last_epoch;
---> final result              	0.12014     	0.45975     	0.17940     	22               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': 'TopOP_mfGent_v5', '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           	0.09981     	0.39912     	0.16534     	0           
  10          	0.10217     	0.44912     	0.17046     	0           
  100         	0.09843     	0.52828     	0.16602     	6           
  300         	0.09037     	0.55956     	0.14890     	18          
  500         	0.09093     	0.56645     	0.14913     	31          
  1000        	0.09152     	0.57691     	0.14928     	63          
---> try to load best state
              	0.09152     	0.57691     	0.14928     	63               eval state : test_on_restore;
              	0.09152     	0.57691     	0.14928     	63               eval state : test_on_last_epoch;
---> final result              	0.09152     	0.57691     	0.14928     	63               eval state : final;module_name : SGAR;cp_record_file : True;
