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

  Demo GAR 
  seed: None 
  interp_data: False 

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---> 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_normalize: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01852     	0.94616     	0.03751     	0           
  10          	0.01836     	0.94779     	0.03701     	0           
  100         	0.01839     	0.94451     	0.03712     	1           
  300         	0.01903     	0.94346     	0.03767     	3           
  500         	0.01894     	0.94402     	0.03747     	6           
---> try to load best state
              	0.01893     	0.94425     	0.03745     	9                eval state : test_on_restore;
              	0.01895     	0.94446     	0.03744     	9                eval state : test_on_last_epoch;
---> final result              	0.01895     	0.94446     	0.03744     	9                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.18871     	-0.17167    	0.26102     	0           
  10          	0.19150     	0.00841     	0.26249     	0           
  100         	0.19663     	-0.00096    	0.26965     	0           
  300         	0.19359     	-0.04623    	0.26043     	2           
  500         	0.19608     	-0.03236    	0.26258     	3           
  epoch 911 reach nan state
---> try to load best state
              	0.20030     	-0.01571    	0.26660     	6                eval state : test_on_restore;
              	0.19608     	-0.03236    	0.26258     	6                eval state : test_on_last_epoch;
---> final result
              	0.19608     	-0.03236    	0.26258     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.17035     	0.03680     	0.24855     	0           
  10          	0.17263     	0.06380     	0.25182     	0           
  100         	0.17399     	0.00870     	0.25345     	1           
  300         	0.16908     	0.02329     	0.23768     	3           
  500         	0.17140     	0.03861     	0.23970     	5           
  epoch 730 reach nan state
---> try to load best state
              	0.17370     	0.03905     	0.24119     	7                eval state : test_on_restore;
              	0.17140     	0.03861     	0.23970     	7                eval state : test_on_last_epoch;
---> final result
              	0.17140     	0.03861     	0.23970     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.14944     	0.23612     	0.22842     	0           
  10          	0.15522     	0.19612     	0.23498     	0           
  100         	0.14309     	0.21094     	0.21823     	1           
  300         	0.14408     	0.18862     	0.21142     	4           
  500         	0.14742     	0.19569     	0.21267     	7           
  1000        	0.15222     	0.19839     	0.21458     	14          
---> try to load best state
              	0.15222     	0.19839     	0.21458     	15               eval state : test_on_restore;
              	0.15222     	0.19839     	0.21458     	15               eval state : test_on_last_epoch;
---> final result
              	0.15222     	0.19839     	0.21458     	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.14681     	0.31281     	0.23762     	0           
  10          	0.15106     	0.29907     	0.24383     	0           
  100         	0.12650     	0.33847     	0.20320     	2           
  300         	0.12643     	0.31372     	0.19293     	6           
  500         	0.12598     	0.33342     	0.19220     	10          
  1000        	0.12679     	0.35147     	0.19170     	21          
---> try to load best state
              	0.12679     	0.35147     	0.19170     	21               eval state : test_on_restore;
              	0.12679     	0.35147     	0.19170     	21               eval state : test_on_last_epoch;
---> final result
              	0.12679     	0.35147     	0.19170     	21               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.14144     	0.37387     	0.22687     	0           
  10          	0.13591     	0.38909     	0.21780     	0           
  100         	0.10454     	0.49403     	0.17105     	3           
  300         	0.10477     	0.48769     	0.16667     	10          
  500         	0.10452     	0.49718     	0.16635     	18          
  1000        	0.10431     	0.51155     	0.16584     	34          
---> try to load best state
              	0.10431     	0.51155     	0.16584     	34               eval state : test_on_restore;
              	0.10431     	0.51155     	0.16584     	34               eval state : test_on_last_epoch;
---> final result
              	0.10431     	0.51155     	0.16584     	34               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh 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: True
  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, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.10462     	0.25154     	0.17999     	0           
  10          	0.10941     	0.31377     	0.18922     	0           
  100         	0.09662     	0.50443     	0.16275     	7           
  300         	0.08544     	0.58041     	0.14219     	23          
  500         	0.08730     	0.57179     	0.14159     	38          
  1000        	0.08480     	0.59973     	0.13946     	75          
---> try to load best state
              	0.08480     	0.59973     	0.13946     	75               eval state : test_on_restore;
              	0.08480     	0.59973     	0.13946     	75               eval state : test_on_last_epoch;
---> final result
              	0.08480     	0.59973     	0.13946     	75               eval state : final;module_name : GAR;cp_record_file : True;
