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

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02819     	0.88066     	0.05604     	0.34234            	0           
  10          	0.02718     	0.88719     	0.05454     	0.23163            	0           
  100         	0.02701     	0.88911     	0.05366     	-0.10283           	0           
  300         	0.02752     	0.88171     	0.05434     	-0.02400           	1           
  500         	0.02774     	0.87923     	0.05459     	0.05832            	2           
  1000        	0.02792     	0.87743     	0.05479     	0.15117            	5           
---> try to load best state
              	0.02792     	0.87743     	0.05479     	0.15117            	5                eval state : test_on_restore;
              	0.02792     	0.87743     	0.05479     	0.15117            	5                eval state : test_on_last_epoch;
---> final result              	0.02792     	0.87743     	0.05479     	0.15117            	5                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19017     	-0.24851    	0.26120     	0.14370            	0           
  10          	0.19070     	-0.06836    	0.26061     	0.06123            	0           
  100         	0.20381     	-0.01991    	0.27612     	-0.60431           	0           
  300         	0.21705     	-0.10669    	0.29048     	-0.59161           	2           
  epoch 327 reach nan state
---> try to load best state
              	0.21708     	-0.10703    	0.29051     	-0.59135           	2                eval state : test_on_restore;
              	0.21705     	-0.10669    	0.29048     	-0.59161           	2                eval state : test_on_last_epoch;
---> final result
              	0.21705     	-0.10669    	0.29048     	-0.59161           	2                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17205     	-0.03921    	0.24931     	0.20653            	0           
  10          	0.17366     	-0.02131    	0.25229     	0.12423            	0           
  100         	0.17106     	-0.01358    	0.24743     	-0.53534           	0           
  300         	0.17167     	-0.00323    	0.24001     	-0.59990           	2           
  epoch 423 reach nan state
---> try to load best state
              	0.17458     	-0.03206    	0.24156     	-0.57735           	3                eval state : test_on_restore;
              	0.17167     	-0.00323    	0.24001     	-0.59990           	3                eval state : test_on_last_epoch;
---> final result
              	0.17167     	-0.00323    	0.24001     	-0.59990           	3                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15033     	0.13433     	0.23035     	0.28372            	0           
  10          	0.15596     	0.06856     	0.23715     	0.18951            	0           
  100         	0.14165     	0.18002     	0.21464     	-0.58514           	0           
  300         	0.14730     	0.14399     	0.21279     	-0.58942           	2           
  500         	0.15057     	0.11780     	0.21441     	-0.53492           	4           
  1000        	0.15465     	0.07842     	0.21685     	-0.47543           	9           
---> try to load best state
              	0.15465     	0.07842     	0.21685     	-0.47543           	9                eval state : test_on_restore;
              	0.15465     	0.07842     	0.21685     	-0.47543           	9                eval state : test_on_last_epoch;
---> final result
              	0.15465     	0.07842     	0.21685     	-0.47543           	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.14803     	0.22259     	0.24008     	0.33237            	0           
  10          	0.15192     	0.20117     	0.24644     	0.22955            	0           
  100         	0.12525     	0.29376     	0.19594     	-0.62619           	1           
  300         	0.12800     	0.26923     	0.19459     	-0.58040           	4           
  500         	0.12953     	0.26241     	0.19485     	-0.52391           	7           
  1000        	0.13057     	0.25480     	0.19516     	-0.45768           	15          
---> try to load best state
              	0.13057     	0.25480     	0.19516     	-0.45768           	15               eval state : test_on_restore;
              	0.13057     	0.25480     	0.19516     	-0.45768           	15               eval state : test_on_last_epoch;
---> final result
              	0.13057     	0.25480     	0.19516     	-0.45768           	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.14064     	0.19405     	0.22813     	0.34717            	0           
  10          	0.13502     	0.24889     	0.21879     	0.22533            	0           
  100         	0.10341     	0.44698     	0.16887     	-0.64261           	3           
  300         	0.10492     	0.43863     	0.16860     	-0.57411           	10          
  500         	0.10579     	0.43558     	0.16877     	-0.51465           	17          
  1000        	0.10637     	0.43268     	0.16893     	-0.44508           	34          
---> try to load best state
              	0.10637     	0.43268     	0.16893     	-0.44508           	34               eval state : test_on_restore;
              	0.10637     	0.43268     	0.16893     	-0.44508           	34               eval state : test_on_last_epoch;
---> final result
              	0.10637     	0.43268     	0.16893     	-0.44508           	35               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.02866     	0.93185     	0.05013     	0.34632            	0           
  10          	0.02700     	0.93761     	0.04806     	0.24443            	0           
  100         	0.02650     	0.94148     	0.04673     	-0.03588           	0           
  300         	0.02650     	0.94138     	0.04650     	0.05047            	1           
  500         	0.02653     	0.94127     	0.04649     	0.12943            	2           
  1000        	0.02657     	0.94108     	0.04654     	0.22034            	5           
---> try to load best state
              	0.02657     	0.94108     	0.04654     	0.22034            	5                eval state : test_on_restore;
              	0.02657     	0.94108     	0.04654     	0.22034            	5                eval state : test_on_last_epoch;
---> final result              	0.02657     	0.94108     	0.04654     	0.22034            	5                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18740     	-0.24256    	0.25697     	0.11267            	0           
  10          	0.18210     	-0.02773    	0.25114     	0.02887            	0           
  100         	0.19582     	0.02277     	0.27002     	-0.61938           	0           
  300         	0.19583     	0.02283     	0.26746     	-0.60152           	2           
  500         	0.19568     	0.01924     	0.26677     	-0.54949           	3           
  1000        	0.19652     	-0.00826    	0.26727     	-0.49234           	6           
---> try to load best state
              	0.19652     	-0.00826    	0.26727     	-0.49234           	6                eval state : test_on_restore;
              	0.19652     	-0.00826    	0.26727     	-0.49234           	6                eval state : test_on_last_epoch;
---> final result
              	0.19652     	-0.00826    	0.26727     	-0.49234           	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17049     	0.00136     	0.24603     	0.17889            	0           
  10          	0.16847     	0.08805     	0.24373     	0.09634            	0           
  100         	0.16820     	0.16492     	0.23938     	-0.59157           	0           
  300         	0.17328     	0.16356     	0.23875     	-0.61719           	2           
  500         	0.17451     	0.15636     	0.23946     	-0.56562           	4           
  1000        	0.17571     	0.14558     	0.24053     	-0.49675           	8           
---> try to load best state
              	0.17571     	0.14558     	0.24053     	-0.49675           	8                eval state : test_on_restore;
              	0.17571     	0.14558     	0.24053     	-0.49675           	8                eval state : test_on_last_epoch;
---> final result
              	0.17571     	0.14558     	0.24053     	-0.49675           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17516     	0.14481     	0.26374     	0.23609            	0           
  10          	0.18191     	0.13774     	0.27335     	0.15781            	0           
  100         	0.16829     	0.18584     	0.23413     	-0.65219           	0           
  300         	0.17270     	0.15645     	0.23533     	-0.60851           	2           
  500         	0.17393     	0.15231     	0.23629     	-0.55342           	5           
  1000        	0.17472     	0.15177     	0.23695     	-0.48704           	9           
---> try to load best state
              	0.17472     	0.15177     	0.23695     	-0.48704           	9                eval state : test_on_restore;
              	0.17472     	0.15177     	0.23695     	-0.48704           	9                eval state : test_on_last_epoch;
---> final result
              	0.17472     	0.15177     	0.23695     	-0.48704           	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15985     	0.23206     	0.25357     	0.32148            	0           
  10          	0.15855     	0.23151     	0.25115     	0.21391            	0           
  100         	0.13683     	0.35146     	0.20410     	-0.64776           	1           
  300         	0.14022     	0.34141     	0.20476     	-0.59503           	4           
  500         	0.14167     	0.33586     	0.20552     	-0.53461           	7           
  1000        	0.14275     	0.33169     	0.20618     	-0.46457           	14          
---> try to load best state
              	0.14275     	0.33169     	0.20618     	-0.46457           	14               eval state : test_on_restore;
              	0.14275     	0.33169     	0.20618     	-0.46457           	14               eval state : test_on_last_epoch;
---> final result
              	0.14275     	0.33169     	0.20618     	-0.46457           	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.15466     	0.33189     	0.26004     	0.37839            	0           
  10          	0.14934     	0.34572     	0.25098     	0.30725            	0           
  100         	0.10746     	0.55669     	0.16758     	-0.64915           	3           
  300         	0.11278     	0.54362     	0.17108     	-0.59449           	10          
  500         	0.11491     	0.53269     	0.17292     	-0.53433           	17          
  epoch 876 reach nan state
---> try to load best state
              	0.11637     	0.52390     	0.17427     	-0.47539           	31               eval state : test_on_restore;
              	0.11491     	0.53269     	0.17292     	-0.53433           	31               eval state : test_on_last_epoch;
---> final result
              	0.11491     	0.53269     	0.17292     	-0.53433           	31               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.03870     	0.88605     	0.06376     	0.34700            	0           
  10          	0.03679     	0.89560     	0.06136     	0.25058            	0           
  100         	0.03916     	0.87777     	0.06423     	-0.12581           	0           
  300         	0.04134     	0.87009     	0.06658     	-0.01275           	1           
  500         	0.04175     	0.86933     	0.06692     	0.08203            	2           
  1000        	0.04197     	0.86923     	0.06710     	0.17419            	5           
---> try to load best state
              	0.04197     	0.86923     	0.06710     	0.17419            	5                eval state : test_on_restore;
              	0.04197     	0.86923     	0.06710     	0.17419            	5                eval state : test_on_last_epoch;
---> final result              	0.04197     	0.86923     	0.06710     	0.17419            	5                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.19283     	-0.26629    	0.26107     	0.11372            	0           
  10          	0.18852     	-0.03220    	0.25636     	0.03041            	0           
  100         	0.20112     	0.02723     	0.27509     	-0.61196           	0           
  300         	0.20031     	0.03350     	0.27213     	-0.59527           	2           
  500         	0.20029     	0.03351     	0.27148     	-0.54413           	3           
  1000        	0.20107     	0.01343     	0.27164     	-0.48809           	7           
---> try to load best state
              	0.20107     	0.01343     	0.27164     	-0.48809           	7                eval state : test_on_restore;
              	0.20107     	0.01343     	0.27164     	-0.48809           	7                eval state : test_on_last_epoch;
---> final result
              	0.20107     	0.01343     	0.27164     	-0.48809           	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.17742     	-0.03282    	0.25069     	0.17988            	0           
  10          	0.17637     	0.05170     	0.24906     	0.09763            	0           
  100         	0.17684     	0.13949     	0.24662     	-0.58364           	0           
  300         	0.18110     	0.15890     	0.24620     	-0.60867           	2           
  500         	0.18220     	0.15659     	0.24663     	-0.55847           	4           
  1000        	0.18305     	0.14938     	0.24702     	-0.49128           	8           
---> try to load best state
              	0.18305     	0.14938     	0.24702     	-0.49128           	8                eval state : test_on_restore;
              	0.18305     	0.14938     	0.24702     	-0.49128           	8                eval state : test_on_last_epoch;
---> final result
              	0.18305     	0.14938     	0.24702     	-0.49128           	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.18275     	0.10858     	0.26797     	0.23701            	0           
  10          	0.19092     	0.09696     	0.27895     	0.15928            	0           
  100         	0.17625     	0.16785     	0.24123     	-0.64354           	1           
  300         	0.18116     	0.15675     	0.24340     	-0.59986           	3           
  500         	0.18245     	0.15422     	0.24434     	-0.54589           	5           
  1000        	0.18326     	0.15290     	0.24488     	-0.48074           	10          
---> try to load best state
              	0.18326     	0.15290     	0.24488     	-0.48074           	10               eval state : test_on_restore;
              	0.18326     	0.15290     	0.24488     	-0.48074           	10               eval state : test_on_last_epoch;
---> final result
              	0.18326     	0.15290     	0.24488     	-0.48074           	10               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16738     	0.26451     	0.25611     	0.32191            	0           
  10          	0.16642     	0.26223     	0.25375     	0.21443            	0           
  100         	0.14756     	0.33715     	0.21119     	-0.64098           	1           
  300         	0.15105     	0.32707     	0.21257     	-0.58853           	4           
  500         	0.15254     	0.32044     	0.21347     	-0.52887           	7           
  1000        	0.15371     	0.31493     	0.21422     	-0.45962           	15          
---> try to load best state
              	0.15371     	0.31493     	0.21422     	-0.45962           	15               eval state : test_on_restore;
              	0.15371     	0.31493     	0.21422     	-0.45962           	15               eval state : test_on_last_epoch;
---> final result
              	0.15371     	0.31493     	0.21422     	-0.45962           	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
GAR for 64 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'TopOP_mfGent_v5_64', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 64, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.16404     	0.46541     	0.26458     	0.37897            	0           
  10          	0.15948     	0.46231     	0.25622     	0.30801            	0           
  100         	0.12252     	0.52644     	0.17891     	-0.64090           	3           
  300         	0.12764     	0.50857     	0.18299     	-0.58665           	10          
  500         	0.12963     	0.49849     	0.18484     	-0.52737           	18          
  epoch 876 reach nan state
---> try to load best state
              	0.13094     	0.49013     	0.18609     	-0.46928           	32               eval state : test_on_restore;
              	0.12963     	0.49849     	0.18484     	-0.52737           	32               eval state : test_on_last_epoch;
---> final result
              	0.12963     	0.49849     	0.18484     	-0.52737           	32               eval state : final;module_name : GAR;cp_record_file : True;
