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

  Demo GAR 
  seed: None 
  interp_data: False 

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---> 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_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           	0.00175     	0.74600     	0.00406     	0           
  10          	0.00211     	0.73458     	0.00480     	0           
  100         	0.00414     	0.55883     	0.00761     	0           
  300         	0.00474     	0.49722     	0.00956     	1           
  500         	0.00469     	0.51331     	0.00958     	2           
---> try to load best state
              	0.00466     	0.51932     	0.00952     	3                eval state : test_on_restore;
              	0.00527     	-0.43237    	0.00981     	3                eval state : test_on_last_epoch;
---> final result              	0.00466     	0.51932     	0.00952     	3                eval state : final;
----------> finish x-yl training


---------->
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': 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           	0.00631     	0.62471     	0.01438     	0           
  10          	0.00565     	0.65579     	0.01116     	0           
  100         	0.00550     	-0.40430    	0.00917     	0           
  300         	0.00565     	-1.45594    	0.00904     	2           
  500         	0.00896     	0.43436     	0.01614     	3           
  epoch 566 reach nan state
---> try to load best state
              	0.01334     	0.41121     	0.02486     	4                eval state : test_on_restore;
              	0.00896     	0.43436     	0.01614     	4                eval state : test_on_last_epoch;
---> final result
              	0.00896     	0.43436     	0.01614     	4                eval state : final;module_name : GAR;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': 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           	0.00358     	0.74029     	0.00669     	0           
  10          	0.00362     	0.73895     	0.00665     	0           
  100         	0.00551     	-1.55261    	0.00823     	1           
  300         	0.00532     	-1.70834    	0.00839     	3           
  500         	0.00427     	0.66800     	0.00730     	5           
  epoch 649 reach nan state
---> try to load best state
              	0.00365     	0.72663     	0.00666     	6                eval state : test_on_restore;
              	0.00427     	0.66800     	0.00730     	6                eval state : test_on_last_epoch;
---> final result
              	0.00365     	0.72663     	0.00666     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', '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           	0.00322     	0.76745     	0.00608     	0           
  10          	0.00341     	0.77402     	0.00644     	0           
  100         	0.00495     	-0.76313    	0.00769     	1           
  300         	0.00526     	-0.55070    	0.00831     	4           
  500         	0.00300     	0.74921     	0.00554     	6           
  epoch 969 reach nan state
---> try to load best state
              	0.00245     	0.67727     	0.00431     	13               eval state : test_on_restore;
              	0.00300     	0.74921     	0.00554     	13               eval state : test_on_last_epoch;
---> final result
              	0.00245     	0.67727     	0.00431     	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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]', '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           	0.00315     	0.77911     	0.00606     	0           
  10          	0.00335     	0.78181     	0.00642     	0           
  100         	0.00470     	-0.16144    	0.00750     	2           
  300         	0.00427     	0.26136     	0.00723     	5           
  500         	0.00408     	0.44594     	0.00727     	9           
  epoch 782 reach nan state
---> try to load best state
              	0.00273     	0.61436     	0.00411     	14               eval state : test_on_restore;
              	0.00408     	0.44594     	0.00727     	15               eval state : test_on_last_epoch;
---> final result
              	0.00273     	0.61436     	0.00411     	15               eval state : final;module_name : GAR;cp_record_file : True;
