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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Burget_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           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	5           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	5                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'Burget_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           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	4           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	4                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	4                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', '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           	0.00176     	0.74277     	0.00406     	0           
  10          	0.00233     	0.57212     	0.00464     	0           
  100         	0.00584     	-0.14724    	0.00877     	0           
  300         	0.00435     	0.68437     	0.00891     	1           
  500         	0.00438     	0.69572     	0.00915     	2           
  1000        	0.00434     	0.69583     	0.00908     	5           
---> try to load best state
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_restore;
              	0.00434     	0.69583     	0.00908     	5                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69583     	0.00908     	5                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 256, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], '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           	0.00656     	0.68219     	0.01583     	0           
  10          	0.00514     	0.73337     	0.01160     	0           
  epoch 84 reach nan state
---> try to load best state
              	0.00435     	0.75958     	0.00961     	0                eval state : test_on_restore;
              	0.00514     	0.73337     	0.01160     	0                eval state : test_on_last_epoch;
---> final result              	0.00435     	0.75958     	0.00961     	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 256, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], '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           	0.00380     	0.80318     	0.00822     	0           
  10          	0.00400     	0.79404     	0.00848     	0           
  epoch 85 reach nan state
---> try to load best state
              	0.00409     	0.78599     	0.00893     	0                eval state : test_on_restore;
              	0.00400     	0.79404     	0.00848     	0                eval state : test_on_last_epoch;
---> final result              	0.00400     	0.79404     	0.00848     	0                eval state : final;module_name : SGAR;cp_record_file : True;
