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

  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]'], '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           	0.04055     	0.78131     	0.07185     	0           
  10          	0.03834     	0.78129     	0.06944     	0           
  100         	0.03501     	0.69567     	0.06590     	0           
  300         	0.03502     	0.70911     	0.06598     	1           
  500         	0.03500     	0.70982     	0.06596     	2           
  1000        	0.03496     	0.71091     	0.06592     	4           
---> try to load best state
              	0.03496     	0.71091     	0.06592     	4                eval state : test_on_restore;
              	0.03496     	0.71091     	0.06592     	4                eval state : test_on_last_epoch;
---> final result              	0.03496     	0.71091     	0.06592     	4                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           	0.19336     	-0.34974    	0.26381     	0           
  10          	0.20101     	-0.27057    	0.27119     	0           
  100         	0.23965     	-0.16619    	0.32373     	0           
  300         	0.31357     	-0.08541    	0.42838     	1           
  500         	0.34700     	-0.11919    	0.47953     	2           
  1000        	0.40703     	-0.08030    	0.58137     	4           
---> try to load best state
              	0.40703     	-0.08030    	0.58137     	4                eval state : test_on_restore;
              	0.40703     	-0.08030    	0.58137     	4                eval state : test_on_last_epoch;
---> final result              	0.40703     	-0.08030    	0.58137     	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           	0.17363     	-0.12542    	0.25182     	0           
  10          	0.17766     	-0.23411    	0.25678     	0           
  100         	0.17831     	-0.05120    	0.25375     	0           
  300         	0.21073     	-0.02104    	0.29124     	1           
  500         	0.22363     	-0.03416    	0.30958     	2           
  1000        	0.24523     	-0.03599    	0.33950     	5           
---> try to load best state
              	0.24523     	-0.03599    	0.33950     	5                eval state : test_on_restore;
              	0.24523     	-0.03599    	0.33950     	5                eval state : test_on_last_epoch;
---> final result              	0.24523     	-0.03599    	0.33950     	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           	0.14821     	0.03765     	0.22962     	0           
  10          	0.14946     	-0.17857    	0.23035     	0           
  100         	0.14695     	-0.10438    	0.22025     	0           
  300         	0.15772     	-0.02552    	0.23131     	2           
  500         	0.16385     	-0.03915    	0.23817     	4           
  1000        	0.17504     	-0.06806    	0.25185     	9           
---> try to load best state
              	0.17504     	-0.06806    	0.25185     	9                eval state : test_on_restore;
              	0.17504     	-0.06806    	0.25185     	9                eval state : test_on_last_epoch;
---> final result              	0.17504     	-0.06806    	0.25185     	9                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           	0.14021     	0.01482     	0.23055     	0           
  10          	0.13635     	-0.07103    	0.22496     	0           
  100         	0.12677     	0.13083     	0.20387     	1           
  300         	0.12765     	0.11733     	0.20418     	4           
  500         	0.12781     	0.09652     	0.20423     	7           
  1000        	0.13082     	0.11867     	0.20651     	14          
---> try to load best state
              	0.13082     	0.11867     	0.20651     	14               eval state : test_on_restore;
              	0.13082     	0.11867     	0.20651     	14               eval state : test_on_last_epoch;
---> final result              	0.13082     	0.11867     	0.20651     	14               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]'], '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           	0.05164     	0.80804     	0.08467     	0           
  10          	0.04907     	0.82919     	0.08020     	0           
  100         	0.04400     	0.87295     	0.07000     	0           
  300         	0.04507     	0.86420     	0.07200     	1           
  500         	0.04509     	0.86411     	0.07204     	1           
  1000        	0.04511     	0.86391     	0.07211     	3           
---> try to load best state
              	0.04511     	0.86391     	0.07211     	3                eval state : test_on_restore;
              	0.04511     	0.86391     	0.07211     	3                eval state : test_on_last_epoch;
---> final result              	0.04511     	0.86391     	0.07211     	3                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           	0.19788     	-0.34618    	0.26676     	0           
  10          	0.19664     	-0.12573    	0.26621     	0           
  100         	0.22570     	-0.09071    	0.30634     	0           
  300         	0.28835     	-0.08844    	0.38678     	1           
  500         	0.33222     	-0.10831    	0.45184     	2           
  1000        	0.39540     	-0.11312    	0.53837     	4           
---> try to load best state
              	0.39540     	-0.11312    	0.53837     	4                eval state : test_on_restore;
              	0.39540     	-0.11312    	0.53837     	4                eval state : test_on_last_epoch;
---> final result              	0.39540     	-0.11312    	0.53837     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': '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           	0.18036     	-0.09217    	0.25557     	0           
  10          	0.17979     	-0.00925    	0.25326     	0           
  100         	0.18655     	0.11431     	0.25912     	0           
  300         	0.19977     	-0.00246    	0.27564     	1           
  500         	0.20628     	-0.07558    	0.28399     	3           
  1000        	0.22721     	-0.21893    	0.32024     	5           
---> try to load best state
              	0.22721     	-0.21893    	0.32024     	5                eval state : test_on_restore;
              	0.22721     	-0.21893    	0.32024     	5                eval state : test_on_last_epoch;
---> final result              	0.22721     	-0.21893    	0.32024     	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           	0.17529     	0.05845     	0.25961     	0           
  10          	0.17285     	0.04962     	0.25328     	0           
  100         	0.17546     	0.11484     	0.24662     	1           
  300         	0.18668     	0.06799     	0.26151     	2           
  500         	0.18988     	0.05665     	0.26591     	4           
  1000        	0.19867     	0.02606     	0.27773     	9           
---> try to load best state
              	0.19867     	0.02606     	0.27773     	9                eval state : test_on_restore;
              	0.19867     	0.02606     	0.27773     	9                eval state : test_on_last_epoch;
---> final result              	0.19867     	0.02606     	0.27773     	9                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           	0.15256     	0.17047     	0.23978     	0           
  10          	0.14977     	0.19849     	0.23623     	0           
  100         	0.14292     	0.26914     	0.21699     	1           
  300         	0.14378     	0.26788     	0.21790     	4           
  500         	0.14496     	0.27208     	0.21854     	7           
  1000        	0.14741     	0.26683     	0.22057     	15          
---> try to load best state
              	0.14741     	0.26683     	0.22057     	15               eval state : test_on_restore;
              	0.14741     	0.26683     	0.22057     	15               eval state : test_on_last_epoch;
---> final result              	0.14741     	0.26683     	0.22057     	15               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]'], '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           	0.05143     	0.80382     	0.07772     	0           
  10          	0.04853     	0.82350     	0.07355     	0           
  100         	0.04212     	0.86123     	0.06569     	0           
  300         	0.04348     	0.85114     	0.06719     	1           
  500         	0.04355     	0.85073     	0.06728     	1           
  1000        	0.04370     	0.84976     	0.06748     	3           
---> try to load best state
              	0.04370     	0.84976     	0.06748     	3                eval state : test_on_restore;
              	0.04370     	0.84976     	0.06748     	3                eval state : test_on_last_epoch;
---> final result              	0.04370     	0.84976     	0.06748     	3                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           	0.19987     	-0.45402    	0.26587     	0           
  10          	0.19981     	-0.04817    	0.26878     	0           
  100         	0.25283     	0.02496     	0.34129     	0           
  300         	0.28409     	-0.04086    	0.38173     	1           
  epoch 379 reach nan state
---> try to load best state
              	0.28919     	-0.03877    	0.38975     	1                eval state : test_on_restore;
              	0.28409     	-0.04086    	0.38173     	1                eval state : test_on_last_epoch;
---> final result              	0.28409     	-0.04086    	0.38173     	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           	0.17236     	-0.02596    	0.24170     	0           
  10          	0.17761     	0.03006     	0.24839     	0           
  100         	0.18467     	0.07026     	0.24994     	0           
  300         	0.21950     	0.01087     	0.30157     	1           
  500         	0.24033     	-0.02120    	0.33713     	2           
  1000        	0.26593     	-0.04508    	0.37841     	5           
---> try to load best state
              	0.26593     	-0.04508    	0.37841     	5                eval state : test_on_restore;
              	0.26593     	-0.04508    	0.37841     	5                eval state : test_on_last_epoch;
---> final result              	0.26593     	-0.04508    	0.37841     	5                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           	0.16142     	0.09889     	0.23690     	0           
  10          	0.16282     	0.08312     	0.23773     	0           
  100         	0.16939     	0.15343     	0.23532     	0           
  300         	0.18902     	0.11339     	0.25829     	2           
  500         	0.19966     	0.07658     	0.27339     	4           
  1000        	0.20622     	0.04253     	0.28573     	9           
---> try to load best state
              	0.20622     	0.04253     	0.28573     	9                eval state : test_on_restore;
              	0.20622     	0.04253     	0.28573     	9                eval state : test_on_last_epoch;
---> final result              	0.20622     	0.04253     	0.28573     	9                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           	0.15474     	0.26750     	0.23992     	0           
  10          	0.15102     	0.27168     	0.23366     	0           
  100         	0.14252     	0.26374     	0.20664     	1           
  300         	0.14303     	0.26035     	0.20765     	4           
  500         	0.14350     	0.26318     	0.20806     	7           
  1000        	0.14479     	0.25935     	0.20911     	14          
---> try to load best state
              	0.14479     	0.25935     	0.20911     	14               eval state : test_on_restore;
              	0.14479     	0.25935     	0.20911     	14               eval state : test_on_last_epoch;
---> final result              	0.14479     	0.25935     	0.20911     	14               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]'], '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           	0.05055     	0.75243     	0.09103     	0           
  10          	0.04702     	0.78309     	0.08733     	0           
  100         	0.04324     	0.81335     	0.08578     	0           
  300         	0.04296     	0.81399     	0.08490     	1           
  500         	0.04294     	0.81417     	0.08488     	2           
  1000        	0.04289     	0.81452     	0.08485     	4           
---> try to load best state
              	0.04289     	0.81452     	0.08485     	4                eval state : test_on_restore;
              	0.04289     	0.81452     	0.08485     	4                eval state : test_on_last_epoch;
---> final result              	0.04289     	0.81452     	0.08485     	4                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           	0.21064     	-0.77858    	0.28703     	0           
  10          	0.21745     	-0.61134    	0.29984     	0           
  100         	0.26966     	-0.48043    	0.37087     	0           
  300         	0.30514     	-0.41824    	0.41643     	1           
  500         	0.32039     	-0.49392    	0.43725     	2           
  epoch 765 reach nan state
---> try to load best state
              	0.33134     	-0.53935    	0.45054     	3                eval state : test_on_restore;
              	0.32039     	-0.49392    	0.43725     	3                eval state : test_on_last_epoch;
---> final result              	0.32039     	-0.49392    	0.43725     	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': '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           	0.19248     	-0.35808    	0.27641     	0           
  10          	0.19657     	-0.26742    	0.28319     	0           
  100         	0.22353     	-0.16903    	0.32359     	0           
  300         	0.25324     	-0.19311    	0.36661     	1           
  500         	0.27246     	-0.22655    	0.40157     	2           
  1000        	0.28818     	-0.25549    	0.42607     	5           
---> try to load best state
              	0.28818     	-0.25549    	0.42607     	5                eval state : test_on_restore;
              	0.28818     	-0.25549    	0.42607     	5                eval state : test_on_last_epoch;
---> final result              	0.28818     	-0.25549    	0.42607     	5                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           	0.16980     	0.00559     	0.25887     	0           
  10          	0.16815     	0.04116     	0.25560     	0           
  100         	0.17786     	0.01186     	0.25659     	1           
  300         	0.18499     	-0.01500    	0.26526     	2           
  500         	0.19045     	0.00269     	0.27288     	4           
  1000        	0.19678     	0.02039     	0.28278     	9           
---> try to load best state
              	0.19678     	0.02039     	0.28278     	9                eval state : test_on_restore;
              	0.19678     	0.02039     	0.28278     	9                eval state : test_on_last_epoch;
---> final result              	0.19678     	0.02039     	0.28278     	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           	0.15473     	0.15347     	0.24891     	0           
  10          	0.15124     	0.15078     	0.24409     	0           
  100         	0.14980     	0.12545     	0.23421     	1           
  300         	0.15038     	0.11783     	0.23471     	4           
  500         	0.15149     	0.11197     	0.23558     	7           
  1000        	0.15207     	0.13068     	0.23557     	15          
---> try to load best state
              	0.15207     	0.13068     	0.23557     	15               eval state : test_on_restore;
              	0.15207     	0.13068     	0.23557     	15               eval state : test_on_last_epoch;
---> final result              	0.15207     	0.13068     	0.23557     	15               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]'], '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           	0.05542     	0.83605     	0.08245     	0           
  10          	0.05373     	0.84685     	0.08017     	0           
  100         	0.05171     	0.85355     	0.07737     	0           
  300         	0.05317     	0.84862     	0.07905     	1           
  500         	0.05319     	0.84851     	0.07910     	2           
  1000        	0.05325     	0.84827     	0.07920     	4           
---> try to load best state
              	0.05325     	0.84827     	0.07920     	4                eval state : test_on_restore;
              	0.05325     	0.84827     	0.07920     	4                eval state : test_on_last_epoch;
---> final result              	0.05325     	0.84827     	0.07920     	4                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           	0.22173     	-0.77852    	0.29657     	0           
  10          	0.22854     	-0.53328    	0.30588     	0           
  100         	0.25623     	-0.65776    	0.33812     	0           
  300         	0.32145     	-0.67634    	0.42262     	1           
  500         	0.32224     	-0.57467    	0.42251     	2           
  1000        	0.34099     	-0.45045    	0.45069     	4           
---> try to load best state
              	0.34099     	-0.45045    	0.45069     	4                eval state : test_on_restore;
              	0.34099     	-0.45045    	0.45069     	4                eval state : test_on_last_epoch;
---> final result              	0.34099     	-0.45045    	0.45069     	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': 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           	0.19933     	-0.35539    	0.27676     	0           
  10          	0.20166     	-0.12051    	0.27592     	0           
  100         	0.20866     	-0.09302    	0.28088     	0           
  300         	0.21770     	-0.03039    	0.28838     	1           
  500         	0.22749     	-0.01851    	0.30019     	2           
  1000        	0.24513     	-0.05554    	0.32216     	5           
---> try to load best state
              	0.24513     	-0.05554    	0.32216     	5                eval state : test_on_restore;
              	0.24513     	-0.05554    	0.32216     	5                eval state : test_on_last_epoch;
---> final result              	0.24513     	-0.05554    	0.32216     	5                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           	0.17515     	0.09519     	0.25538     	0           
  10          	0.17465     	0.19030     	0.25464     	0           
  100         	0.16797     	0.19643     	0.24063     	1           
  300         	0.17419     	0.18204     	0.24663     	3           
  500         	0.18040     	0.17795     	0.25400     	4           
  1000        	0.18770     	0.17911     	0.26322     	9           
---> try to load best state
              	0.18770     	0.17911     	0.26322     	9                eval state : test_on_restore;
              	0.18770     	0.17911     	0.26322     	9                eval state : test_on_last_epoch;
---> final result              	0.18770     	0.17911     	0.26322     	9                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           	0.15933     	0.26358     	0.24555     	0           
  10          	0.15561     	0.28257     	0.23974     	0           
  100         	0.14537     	0.28805     	0.21931     	1           
  300         	0.14619     	0.28326     	0.21982     	4           
  500         	0.14689     	0.27511     	0.22029     	7           
  1000        	0.14881     	0.25744     	0.22249     	14          
---> try to load best state
              	0.14881     	0.25744     	0.22249     	14               eval state : test_on_restore;
              	0.14881     	0.25744     	0.22249     	14               eval state : test_on_last_epoch;
---> final result              	0.14881     	0.25744     	0.22249     	15               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]'], '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           	0.04873     	0.80350     	0.07883     	0           
  10          	0.04702     	0.82125     	0.07593     	0           
  100         	0.04354     	0.84306     	0.07078     	0           
  300         	0.04460     	0.83221     	0.07257     	1           
  500         	0.04467     	0.83184     	0.07267     	2           
  1000        	0.04482     	0.83100     	0.07288     	4           
---> try to load best state
              	0.04482     	0.83100     	0.07288     	4                eval state : test_on_restore;
              	0.04482     	0.83100     	0.07288     	4                eval state : test_on_last_epoch;
---> final result              	0.04482     	0.83100     	0.07288     	4                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           	0.21065     	-0.59984    	0.28573     	0           
  10          	0.21318     	-0.31569    	0.29268     	0           
  100         	0.24929     	-0.21578    	0.33383     	0           
  epoch 265 reach nan state
---> try to load best state
              	0.38366     	-0.20006    	0.51976     	1                eval state : test_on_restore;
              	0.24929     	-0.21578    	0.33383     	1                eval state : test_on_last_epoch;
---> final result              	0.24929     	-0.21578    	0.33383     	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           	0.18850     	-0.32781    	0.26615     	0           
  10          	0.18948     	-0.20435    	0.26465     	0           
  100         	0.21501     	-0.06234    	0.28996     	0           
  300         	0.24126     	0.01880     	0.32933     	1           
  500         	0.28145     	0.02391     	0.39032     	3           
  1000        	0.30939     	0.03359     	0.44584     	5           
---> try to load best state
              	0.30939     	0.03359     	0.44584     	5                eval state : test_on_restore;
              	0.30939     	0.03359     	0.44584     	5                eval state : test_on_last_epoch;
---> final result              	0.30939     	0.03359     	0.44584     	5                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           	0.16568     	0.10195     	0.24634     	0           
  10          	0.16618     	0.11045     	0.24581     	0           
  100         	0.16499     	0.11783     	0.23624     	0           
  300         	0.17328     	0.10888     	0.24668     	2           
  500         	0.17739     	0.11021     	0.25220     	4           
  1000        	0.18462     	0.09854     	0.26268     	8           
---> try to load best state
              	0.18462     	0.09854     	0.26268     	8                eval state : test_on_restore;
              	0.18462     	0.09854     	0.26268     	8                eval state : test_on_last_epoch;
---> final result              	0.18462     	0.09854     	0.26268     	8                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           	0.14924     	0.27883     	0.23374     	0           
  10          	0.14551     	0.29373     	0.22828     	0           
  100         	0.13773     	0.28078     	0.21021     	1           
  300         	0.13876     	0.27769     	0.21116     	4           
  500         	0.13980     	0.27569     	0.21165     	7           
  1000        	0.14355     	0.27309     	0.21531     	14          
---> try to load best state
              	0.14355     	0.27309     	0.21531     	14               eval state : test_on_restore;
              	0.14355     	0.27309     	0.21531     	15               eval state : test_on_last_epoch;
---> final result              	0.14355     	0.27309     	0.21531     	15               eval state : final;module_name : SGAR;cp_record_file : True;
