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

  Demo GAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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_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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00205     	0.80146     	0.00500     	0           
  10          	0.00216     	0.79633     	0.00522     	0           
  100         	0.00328     	0.74287     	0.00607     	4           
  300         	0.00274     	0.84868     	0.00591     	12          
  500         	0.00263     	0.82252     	0.00549     	21          
  epoch 935 reach nan state
---> try to load best state
              	0.00366     	0.30110     	0.00681     	35               eval state : test_on_restore;
              	0.06502     	-40.97869   	0.07730     	35               eval state : test_on_last_epoch;
---> final result              	0.00366     	0.30110     	0.00681     	35               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 30, '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           	0.01716     	0.09404     	0.02631     	0           
  10          	0.01478     	0.21830     	0.02401     	0           
  100         	0.01461     	-1.01703    	0.01949     	1           
  300         	0.01176     	-4.38356    	0.01700     	3           
  500         	0.00620     	0.14594     	0.00874     	5           
  1000        	0.00478     	0.49541     	0.00677     	10          
---> try to load best state
              	0.00478     	0.49541     	0.00677     	10               eval state : test_on_restore;
              	0.00478     	0.49541     	0.00677     	10               eval state : test_on_last_epoch;
---> final result
              	0.00478     	0.49541     	0.00677     	10               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 28, '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           	0.00677     	0.59408     	0.01129     	0           
  10          	0.00858     	0.12952     	0.01203     	0           
  100         	0.01167     	-1.78228    	0.01608     	1           
  300         	0.00290     	0.75204     	0.00473     	5           
  epoch 442 reach nan state
---> try to load best state
              	0.07121     	-79.69109   	0.09479     	7                eval state : test_on_restore;
              	0.00290     	0.75204     	0.00473     	7                eval state : test_on_last_epoch;
---> final result
              	0.00290     	0.75204     	0.00473     	7                eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 24, '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           	0.00329     	0.68488     	0.00569     	0           
  10          	0.00630     	-0.08344    	0.00868     	0           
  100         	0.00646     	-1.25423    	0.00915     	2           
  300         	0.00299     	-0.15344    	0.00471     	6           
  500         	0.00419     	0.34363     	0.00678     	11          
  epoch 581 reach nan state
---> try to load best state
              	0.00360     	0.51031     	0.00597     	13               eval state : test_on_restore;
              	0.00419     	0.34363     	0.00678     	13               eval state : test_on_last_epoch;
---> final result
              	0.00360     	0.51031     	0.00597     	13               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 16, '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           	0.00246     	0.73699     	0.00492     	0           
  10          	0.00500     	0.21218     	0.00745     	0           
  100         	0.00582     	0.09070     	0.00961     	4           
  300         	0.00207     	0.78491     	0.00371     	12          
  500         	0.00223     	0.73798     	0.00388     	20          
  epoch 830 reach nan state
---> try to load best state
              	0.05321     	-24.21220   	0.08355     	33               eval state : test_on_restore;
              	0.00223     	0.73798     	0.00388     	33               eval state : test_on_last_epoch;
---> final result
              	0.00223     	0.73798     	0.00388     	33               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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_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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00162     	0.80942     	0.00368     	0           
  10          	0.00179     	0.79769     	0.00396     	0           
  100         	0.00283     	0.71119     	0.00610     	3           
  300         	0.00263     	0.81892     	0.00566     	10          
  500         	0.00393     	-0.82127    	0.00703     	16          
  epoch 810 reach nan state
---> try to load best state
              	0.00489     	-4.05113    	0.00697     	28               eval state : test_on_restore;
              	0.04472     	-54.55484   	0.05317     	28               eval state : test_on_last_epoch;
---> final result              	0.00489     	-4.05113    	0.00697     	28               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 30, '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           	0.02273     	-0.11086    	0.03915     	0           
  10          	0.02425     	-0.46608    	0.04003     	0           
  100         	0.01842     	0.25272     	0.03365     	1           
  300         	0.01620     	0.29611     	0.03076     	4           
  500         	0.01384     	0.23754     	0.02546     	7           
  1000        	0.01267     	0.47276     	0.02650     	13          
---> try to load best state
              	0.01267     	0.47276     	0.02650     	14               eval state : test_on_restore;
              	0.01267     	0.47276     	0.02650     	14               eval state : test_on_last_epoch;
---> final result
              	0.01267     	0.47276     	0.02650     	14               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 28, '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           	0.00555     	0.48774     	0.01080     	0           
  10          	0.00890     	-0.59203    	0.01308     	0           
  100         	0.00588     	0.11250     	0.01056     	2           
  300         	0.00452     	0.45442     	0.00905     	5           
  500         	0.00285     	0.73656     	0.00623     	8           
  1000        	0.00253     	0.73866     	0.00577     	16          
---> try to load best state
              	0.00253     	0.73866     	0.00577     	16               eval state : test_on_restore;
              	0.00253     	0.73866     	0.00577     	16               eval state : test_on_last_epoch;
---> final result
              	0.00253     	0.73866     	0.00577     	16               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 24, '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           	0.00265     	0.53225     	0.00715     	0           
  10          	0.00575     	-1.30155    	0.00989     	0           
  100         	0.00433     	0.07281     	0.01067     	2           
  300         	0.00477     	-1.89132    	0.00921     	8           
  500         	0.03940     	-9.40931    	0.05729     	13          
  1000        	0.00328     	0.58597     	0.00831     	25          
---> try to load best state
              	0.00328     	0.58597     	0.00831     	25               eval state : test_on_restore;
              	0.00328     	0.58597     	0.00831     	25               eval state : test_on_last_epoch;
---> final result
              	0.00328     	0.58597     	0.00831     	25               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 16, '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           	0.00230     	0.47752     	0.00671     	0           
  10          	0.00442     	-0.04790    	0.00864     	0           
  100         	0.00402     	0.26304     	0.01208     	4           
  300         	0.00588     	-0.75640    	0.01289     	13          
  500         	0.00349     	0.38011     	0.01100     	21          
  epoch 531 reach nan state
---> try to load best state
              	0.00349     	0.37994     	0.01100     	23               eval state : test_on_restore;
              	0.00349     	0.38011     	0.01100     	23               eval state : test_on_last_epoch;
---> final result
              	0.00349     	0.37994     	0.01100     	23               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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_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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00158     	0.79228     	0.00386     	0           
  10          	0.00169     	0.78289     	0.00433     	0           
  100         	0.00306     	0.65507     	0.00651     	3           
  300         	0.00266     	0.78239     	0.00631     	12          
  500         	0.00270     	0.62782     	0.00604     	19          
  epoch 746 reach nan state
---> try to load best state
              	0.00601     	0.41541     	0.01653     	27               eval state : test_on_restore;
              	0.10942     	-105.76227  	0.12985     	27               eval state : test_on_last_epoch;
---> final result              	0.00601     	0.41541     	0.01653     	27               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 30, '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           	0.10694     	-0.78454    	0.18241     	0           
  10          	0.11295     	-0.68847    	0.19528     	0           
  100         	0.06567     	-0.73859    	0.11372     	1           
  300         	0.05215     	-1.34449    	0.09106     	3           
  500         	0.03910     	-0.79078    	0.06859     	5           
  1000        	0.03720     	-0.54293    	0.07808     	11          
---> try to load best state
              	0.03720     	-0.54293    	0.07808     	11               eval state : test_on_restore;
              	0.03720     	-0.54293    	0.07808     	11               eval state : test_on_last_epoch;
---> final result
              	0.03720     	-0.54293    	0.07808     	11               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 28, '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           	0.00754     	0.50304     	0.01335     	0           
  10          	0.01035     	-0.04089    	0.01521     	0           
  100         	0.00792     	-0.29536    	0.01248     	1           
  300         	0.00473     	0.39511     	0.00856     	4           
  500         	0.00467     	-0.21095    	0.00868     	7           
  1000        	0.00328     	0.23434     	0.00660     	15          
---> try to load best state
              	0.00328     	0.23434     	0.00660     	15               eval state : test_on_restore;
              	0.00328     	0.23434     	0.00660     	15               eval state : test_on_last_epoch;
---> final result
              	0.00328     	0.23434     	0.00660     	15               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 24, '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           	0.00493     	0.52110     	0.01130     	0           
  10          	0.00693     	0.34182     	0.01194     	0           
  100         	0.00462     	0.43905     	0.00882     	2           
  300         	0.00351     	0.61217     	0.00741     	7           
  500         	0.00272     	0.68744     	0.00625     	12          
  epoch 959 reach nan state
---> try to load best state
              	0.06518     	-80.75367   	0.09061     	22               eval state : test_on_restore;
              	0.00272     	0.68744     	0.00625     	22               eval state : test_on_last_epoch;
---> final result
              	0.00272     	0.68744     	0.00625     	22               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 16, '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           	0.00210     	0.72436     	0.00450     	0           
  10          	0.00512     	-0.15685    	0.00777     	0           
  100         	0.00477     	-0.63682    	0.00781     	5           
  300         	0.00179     	0.80224     	0.00358     	17          
  500         	0.00200     	0.71411     	0.00368     	25          
  1000        	0.00202     	0.79275     	0.00353     	44          
---> try to load best state
              	0.00202     	0.79275     	0.00353     	44               eval state : test_on_restore;
              	0.00202     	0.79275     	0.00353     	44               eval state : test_on_last_epoch;
---> final result
              	0.00202     	0.79275     	0.00353     	44               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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_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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00143     	0.82551     	0.00352     	0           
  10          	0.00160     	0.81422     	0.00396     	0           
  100         	0.00285     	0.73932     	0.00592     	3           
  300         	0.00246     	0.81049     	0.00565     	10          
  500         	0.00245     	0.76058     	0.00551     	17          
  1000        	0.00232     	0.76989     	0.00485     	33          
---> try to load best state
              	0.00232     	0.76989     	0.00485     	33               eval state : test_on_restore;
              	0.00232     	0.76989     	0.00485     	33               eval state : test_on_last_epoch;
---> final result              	0.00232     	0.76989     	0.00485     	33               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 30, '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           	0.01335     	-0.37280    	0.02074     	0           
  10          	0.01772     	-1.56568    	0.02409     	0           
  100         	0.01215     	-8.04154    	0.01608     	1           
  300         	0.01098     	-1.25842    	0.01459     	3           
  500         	0.00488     	0.23961     	0.00704     	5           
  1000        	0.00325     	0.48393     	0.00502     	10          
---> try to load best state
              	0.00325     	0.48393     	0.00502     	10               eval state : test_on_restore;
              	0.00325     	0.48393     	0.00502     	10               eval state : test_on_last_epoch;
---> final result
              	0.00325     	0.48393     	0.00502     	10               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 28, '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           	0.01304     	0.27103     	0.01778     	0           
  10          	0.00999     	-0.64569    	0.01406     	0           
  100         	0.00737     	-0.19711    	0.01169     	1           
  300         	0.00606     	-1.82301    	0.00889     	4           
  500         	0.00410     	0.53527     	0.00622     	7           
  1000        	0.00375     	-0.19812    	0.00529     	14          
---> try to load best state
              	0.00375     	-0.19812    	0.00529     	14               eval state : test_on_restore;
              	0.00375     	-0.19812    	0.00529     	14               eval state : test_on_last_epoch;
---> final result
              	0.00375     	-0.19812    	0.00529     	15               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 24, '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           	0.00489     	0.66843     	0.00725     	0           
  10          	0.00501     	0.04865     	0.00721     	0           
  100         	0.00404     	-3.36590    	0.00597     	2           
  300         	0.00214     	0.72215     	0.00342     	7           
  500         	0.00229     	0.78190     	0.00371     	11          
  1000        	0.00308     	-0.15618    	0.00509     	22          
---> try to load best state
              	0.00308     	-0.15618    	0.00509     	22               eval state : test_on_restore;
              	0.00308     	-0.15618    	0.00509     	22               eval state : test_on_last_epoch;
---> final result
              	0.00308     	-0.15618    	0.00509     	22               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 16, '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           	0.00283     	0.75836     	0.00467     	0           
  10          	0.00350     	0.46664     	0.00520     	0           
  100         	0.00350     	0.59393     	0.00542     	4           
  300         	0.00254     	0.76584     	0.00379     	12          
  500         	0.00264     	0.78561     	0.00415     	20          
  1000        	0.00231     	0.79205     	0.00349     	40          
---> try to load best state
              	0.00231     	0.79205     	0.00349     	40               eval state : test_on_restore;
              	0.00231     	0.79205     	0.00349     	40               eval state : test_on_last_epoch;
---> final result
              	0.00231     	0.79205     	0.00349     	40               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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_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}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00143     	0.75100     	0.00369     	0           
  10          	0.00154     	0.73499     	0.00419     	0           
  100         	0.00285     	0.71534     	0.00749     	3           
  300         	0.00295     	0.77142     	0.00754     	10          
  500         	0.00267     	0.68792     	0.00650     	16          
  epoch 704 reach nan state
---> try to load best state
              	0.00503     	0.12299     	0.01268     	22               eval state : test_on_restore;
              	0.09265     	-60.84474   	0.10979     	22               eval state : test_on_last_epoch;
---> final result              	0.00503     	0.12299     	0.01268     	22               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 30, '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           	0.01713     	-0.40023    	0.03532     	0           
  10          	0.02297     	-1.65792    	0.04622     	0           
  100         	0.01432     	-6.40622    	0.04015     	1           
  300         	0.01409     	-7.16645    	0.03604     	3           
  500         	0.01429     	-7.95799    	0.03635     	5           
  epoch 991 reach nan state
---> try to load best state
              	0.07018     	-35.60169   	0.09635     	10               eval state : test_on_restore;
              	0.01429     	-7.95799    	0.03635     	10               eval state : test_on_last_epoch;
---> final result
              	0.01429     	-7.95799    	0.03635     	10               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 28, '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           	0.00622     	0.09671     	0.01282     	0           
  10          	0.01081     	-0.64194    	0.01999     	0           
  100         	0.00813     	-1.61900    	0.01945     	1           
  300         	0.00564     	-0.43231    	0.01555     	4           
  500         	0.00576     	-0.68038    	0.01530     	7           
  1000        	0.00471     	-1.15545    	0.01200     	14          
---> try to load best state
              	0.00471     	-1.15545    	0.01200     	14               eval state : test_on_restore;
              	0.00471     	-1.15545    	0.01200     	14               eval state : test_on_last_epoch;
---> final result
              	0.00471     	-1.15545    	0.01200     	14               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 24, '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           	0.00249     	0.70226     	0.00493     	0           
  10          	0.00537     	0.12553     	0.00844     	0           
  100         	0.00334     	0.11756     	0.00599     	2           
  300         	0.00226     	0.62987     	0.00436     	7           
  500         	0.00289     	0.52987     	0.00512     	11          
  1000        	0.00399     	0.48721     	0.00771     	22          
---> try to load best state
              	0.00399     	0.48721     	0.00771     	22               eval state : test_on_restore;
              	0.00399     	0.48721     	0.00771     	22               eval state : test_on_last_epoch;
---> final result
              	0.00399     	0.48721     	0.00771     	22               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 16, '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           	0.00209     	0.74900     	0.00461     	0           
  10          	0.00351     	0.42393     	0.00642     	0           
  100         	0.00322     	0.31629     	0.00610     	4           
  300         	0.00192     	0.69120     	0.00434     	12          
  500         	0.00315     	0.32577     	0.00561     	20          
  1000        	0.00235     	0.70952     	0.00535     	38          
---> try to load best state
              	0.00235     	0.70952     	0.00535     	38               eval state : test_on_restore;
              	0.00235     	0.70952     	0.00535     	39               eval state : test_on_last_epoch;
---> final result
              	0.00235     	0.70952     	0.00535     	39               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
