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

  Demo sGAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	103.13474   	-0.21354    	142.59787   	0           
  10          	93.43330    	0.12922     	131.16420   	0           
  100         	26.84498    	0.95784     	40.67867    	3           
  300         	13.26678    	0.99074     	20.40741    	10          
  500         	9.67460     	0.99526     	14.89304    	17          
  1000        	5.37317     	0.99864     	8.20057     	36          
---> try to load best state
              	5.37317     	0.99864     	8.20057     	36               eval state : test_on_restore;
              	5.37317     	0.99864     	8.20057     	36               eval state : test_on_last_epoch;
---> final result              	5.37317     	0.99864     	8.20057     	36               eval state : final;
---> end


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

---> module config
  dataset: {'name': 'poisson_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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	193.42258   	0.55690     	258.61453   	0           
  10          	71.87094    	0.64485     	94.09754    	0           
----------------------------------------

  Demo sGAR 
  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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00386     	0.65471     	0.00755     	0           
  10          	0.00428     	0.63135     	0.00803     	0           
  100         	0.00671     	0.40949     	0.01525     	4           
  300         	0.01073     	0.03110     	0.02448     	12          
  500         	0.01082     	0.01265     	0.02488     	20          
  1000        	0.00989     	0.15338     	0.02244     	41          
---> try to load best state
              	0.00989     	0.15338     	0.02244     	41               eval state : test_on_restore;
              	0.00989     	0.15338     	0.02244     	41               eval state : test_on_last_epoch;
---> final result              	0.00989     	0.15338     	0.02244     	41               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02043     	0.32261     	0.02838     	0           
  10          	0.01544     	0.42899     	0.02386     	0           
  100         	0.01415     	0.57876     	0.02603     	1           
  300         	0.01769     	-0.32174    	0.02784     	2           
  500         	0.02575     	-7.10600    	0.03276     	4           
  1000        	0.04397     	-66.99724   	0.05100     	9           
---> try to load best state
              	0.04397     	-66.99724   	0.05100     	9                eval state : test_on_restore;
              	0.04397     	-66.99724   	0.05100     	9                eval state : test_on_last_epoch;
---> final result              	0.04397     	-66.99724   	0.05100     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01634     	0.02322     	0.02612     	0           
  10          	0.01086     	0.30440     	0.01873     	0           
  100         	0.01226     	0.61149     	0.02514     	1           
  300         	0.01475     	-1.18657    	0.02600     	4           
  500         	0.01374     	0.32644     	0.02610     	6           
  1000        	0.01306     	0.06644     	0.02531     	13          
---> try to load best state
              	0.01306     	0.06644     	0.02531     	13               eval state : test_on_restore;
              	0.01306     	0.06644     	0.02531     	13               eval state : test_on_last_epoch;
---> final result              	0.01306     	0.06644     	0.02531     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01011     	0.35380     	0.01681     	0           
  10          	0.00658     	0.47696     	0.01294     	0           
  100         	0.01004     	0.65334     	0.01955     	2           
  300         	0.01056     	0.58545     	0.01965     	6           
  500         	0.01481     	-0.29388    	0.02343     	10          
  1000        	0.03501     	-77.96252   	0.04201     	20          
---> try to load best state
              	0.03501     	-77.96252   	0.04201     	20               eval state : test_on_restore;
              	0.03501     	-77.96252   	0.04201     	20               eval state : test_on_last_epoch;
---> final result              	0.03501     	-77.96252   	0.04201     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00859     	0.37212     	0.01601     	0           
  10          	0.00573     	0.55011     	0.01137     	0           
  100         	0.00869     	0.59884     	0.01688     	3           
  300         	0.01055     	-0.24043    	0.01875     	10          
  500         	0.01105     	0.44313     	0.01910     	17          
  1000        	0.00964     	0.54814     	0.01823     	33          
---> try to load best state
              	0.00964     	0.54814     	0.01823     	33               eval state : test_on_restore;
              	0.00964     	0.54814     	0.01823     	33               eval state : test_on_last_epoch;
---> final result              	0.00964     	0.54814     	0.01823     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00300     	0.59334     	0.00648     	0           
  10          	0.00345     	0.56268     	0.00733     	0           
  100         	0.00654     	0.30622     	0.01611     	4           
  300         	0.01080     	-0.04486    	0.02453     	12          
  500         	0.01085     	-0.05429    	0.02476     	21          
  1000        	0.00990     	0.07351     	0.02259     	41          
---> try to load best state
              	0.00990     	0.07351     	0.02259     	41               eval state : test_on_restore;
              	0.00990     	0.07351     	0.02259     	41               eval state : test_on_last_epoch;
---> final result              	0.00990     	0.07351     	0.02259     	41               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03363     	-0.72181    	0.05203     	0           
  10          	0.02010     	-0.99877    	0.03445     	0           
  100         	0.01367     	0.42361     	0.02889     	1           
  300         	0.01731     	-0.28194    	0.03147     	3           
  500         	0.01390     	0.35756     	0.02876     	4           
  1000        	0.01910     	-4.20429    	0.03730     	9           
---> try to load best state
              	0.01910     	-4.20429    	0.03730     	9                eval state : test_on_restore;
              	0.01910     	-4.20429    	0.03730     	9                eval state : test_on_last_epoch;
---> final result              	0.01910     	-4.20429    	0.03730     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02015     	-0.38630    	0.03001     	0           
  10          	0.01022     	-0.01766    	0.01886     	0           
  100         	0.00917     	0.46342     	0.01956     	1           
  300         	0.00986     	0.46715     	0.01949     	4           
  500         	0.00955     	0.35708     	0.01963     	6           
  1000        	0.01086     	-0.49865    	0.02078     	13          
---> try to load best state
              	0.01086     	-0.49865    	0.02078     	13               eval state : test_on_restore;
              	0.01086     	-0.49865    	0.02078     	13               eval state : test_on_last_epoch;
---> final result              	0.01086     	-0.49865    	0.02078     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01015     	0.23504     	0.01953     	0           
  10          	0.00719     	0.19276     	0.01590     	0           
  100         	0.00833     	0.41408     	0.01924     	2           
  300         	0.00868     	0.33868     	0.01953     	5           
  500         	0.00881     	0.26297     	0.01972     	10          
  1000        	0.00868     	0.35087     	0.01910     	20          
---> try to load best state
              	0.00868     	0.35087     	0.01910     	20               eval state : test_on_restore;
              	0.00868     	0.35087     	0.01910     	20               eval state : test_on_last_epoch;
---> final result              	0.00868     	0.35087     	0.01910     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00782     	0.23125     	0.01691     	0           
  10          	0.00602     	0.21949     	0.01545     	0           
  100         	0.00761     	0.23612     	0.01967     	3           
  300         	0.00864     	0.11682     	0.02074     	10          
  500         	0.00887     	0.14385     	0.02110     	17          
  1000        	0.01088     	-15.65537   	0.02658     	33          
---> try to load best state
              	0.01088     	-15.65537   	0.02658     	33               eval state : test_on_restore;
              	0.01088     	-15.65537   	0.02658     	33               eval state : test_on_last_epoch;
---> final result              	0.01088     	-15.65537   	0.02658     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00313     	0.60875     	0.00681     	0           
  10          	0.00349     	0.59636     	0.00740     	0           
  100         	0.00703     	0.46228     	0.01516     	4           
  300         	0.01000     	0.27496     	0.02069     	12          
  500         	0.00981     	0.29743     	0.01994     	21          
  1000        	0.00912     	0.34699     	0.01863     	41          
---> try to load best state
              	0.00912     	0.34699     	0.01863     	41               eval state : test_on_restore;
              	0.00912     	0.34699     	0.01863     	41               eval state : test_on_last_epoch;
---> final result              	0.00912     	0.34699     	0.01863     	41               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.12572     	-0.92125    	0.22977     	0           
  10          	0.06132     	-0.80158    	0.09960     	0           
  100         	0.01177     	-0.49254    	0.02904     	1           
  300         	0.02340     	-1.98676    	0.04734     	2           
  500         	0.01805     	-0.93391    	0.04333     	4           
  1000        	0.07737     	-83.50390   	0.10963     	9           
---> try to load best state
              	0.07737     	-83.50390   	0.10963     	9                eval state : test_on_restore;
              	0.07737     	-83.50390   	0.10963     	9                eval state : test_on_last_epoch;
---> final result              	0.07737     	-83.50390   	0.10963     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01678     	0.02649     	0.03145     	0           
  10          	0.01013     	0.17262     	0.01871     	0           
  100         	0.00903     	0.38992     	0.01816     	1           
  300         	0.01046     	0.24771     	0.01874     	4           
  500         	0.02983     	-19.22617   	0.03981     	6           
  1000        	0.01918     	-18.92168   	0.02562     	13          
---> try to load best state
              	0.01918     	-18.92168   	0.02562     	13               eval state : test_on_restore;
              	0.01918     	-18.92168   	0.02562     	13               eval state : test_on_last_epoch;
---> final result              	0.01918     	-18.92168   	0.02562     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01439     	0.14111     	0.02951     	0           
  10          	0.00848     	0.19030     	0.01687     	0           
  100         	0.00828     	0.43319     	0.01785     	2           
  300         	0.00957     	0.33605     	0.01810     	5           
  500         	0.01019     	-0.71403    	0.02016     	10          
  1000        	0.01100     	-0.30462    	0.02185     	20          
---> try to load best state
              	0.01100     	-0.30462    	0.02185     	20               eval state : test_on_restore;
              	0.01100     	-0.30462    	0.02185     	20               eval state : test_on_last_epoch;
---> final result              	0.01100     	-0.30462    	0.02185     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00595     	0.48675     	0.01128     	0           
  10          	0.00568     	0.32875     	0.01452     	0           
  100         	0.00836     	0.28770     	0.01883     	3           
  300         	0.00977     	0.28808     	0.01894     	10          
  500         	0.00934     	0.06257     	0.01918     	17          
  1000        	0.00862     	0.17566     	0.01910     	33          
---> try to load best state
              	0.00862     	0.17566     	0.01910     	33               eval state : test_on_restore;
              	0.00862     	0.17566     	0.01910     	33               eval state : test_on_last_epoch;
---> final result              	0.00862     	0.17566     	0.01910     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00292     	0.66005     	0.00621     	0           
  10          	0.00335     	0.64047     	0.00693     	0           
  100         	0.00656     	0.45089     	0.01445     	4           
  300         	0.00960     	0.27277     	0.02018     	13          
  500         	0.00947     	0.30114     	0.01959     	21          
  1000        	0.00889     	0.35211     	0.01843     	41          
---> try to load best state
              	0.00889     	0.35211     	0.01843     	41               eval state : test_on_restore;
              	0.00889     	0.35211     	0.01843     	41               eval state : test_on_last_epoch;
---> final result              	0.00889     	0.35211     	0.01843     	41               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.02175     	-3.59125    	0.03268     	0           
  10          	0.01126     	-1.96069    	0.02194     	0           
  100         	0.01115     	-0.39501    	0.02121     	1           
  300         	0.01106     	-2.71035    	0.02095     	3           
  500         	0.01166     	-0.40068    	0.02139     	4           
  1000        	0.01173     	-0.53127    	0.02145     	9           
---> try to load best state
              	0.01173     	-0.53127    	0.02145     	9                eval state : test_on_restore;
              	0.01173     	-0.53127    	0.02145     	9                eval state : test_on_last_epoch;
---> final result              	0.01173     	-0.53127    	0.02145     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01424     	0.28228     	0.02275     	0           
  10          	0.01137     	0.06250     	0.01980     	0           
  100         	0.01165     	0.27084     	0.02063     	1           
  300         	0.01303     	-0.56342    	0.02308     	4           
  500         	0.04218     	-90.14965   	0.05125     	6           
  1000        	0.04904     	-124.45529  	0.05957     	13          
---> try to load best state
              	0.04904     	-124.45529  	0.05957     	13               eval state : test_on_restore;
              	0.04904     	-124.45529  	0.05957     	13               eval state : test_on_last_epoch;
---> final result              	0.04904     	-124.45529  	0.05957     	13               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00747     	0.49728     	0.01311     	0           
  10          	0.00633     	0.30263     	0.01320     	0           
  100         	0.00957     	0.38657     	0.01831     	2           
  300         	0.01125     	0.11371     	0.01939     	5           
  500         	0.01066     	0.36735     	0.01881     	10          
  1000        	0.01343     	-0.30171    	0.02162     	20          
---> try to load best state
              	0.01343     	-0.30171    	0.02162     	20               eval state : test_on_restore;
              	0.01343     	-0.30171    	0.02162     	20               eval state : test_on_last_epoch;
---> final result              	0.01343     	-0.30171    	0.02162     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00589     	0.59521     	0.01046     	0           
  10          	0.00531     	0.53559     	0.01097     	0           
  100         	0.00886     	0.55008     	0.01722     	3           
  300         	0.00887     	0.44477     	0.01793     	10          
  500         	0.00969     	0.56468     	0.01815     	17          
  1000        	0.01337     	-4.41788    	0.02136     	33          
---> try to load best state
              	0.01337     	-4.41788    	0.02136     	33               eval state : test_on_restore;
              	0.01337     	-4.41788    	0.02136     	34               eval state : test_on_last_epoch;
---> final result              	0.01337     	-4.41788    	0.02136     	34               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo sGAR 
  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: 10.0
  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     	0.55739     	0.00744     	0           
  10          	0.00316     	0.54697     	0.00815     	0           
  100         	0.00622     	0.36325     	0.01459     	4           
  300         	0.00992     	0.13064     	0.02158     	13          
  500         	0.00986     	0.15411     	0.02136     	21          
  1000        	0.00907     	0.22019     	0.01979     	41          
---> try to load best state
              	0.00907     	0.22019     	0.01979     	41               eval state : test_on_restore;
              	0.00907     	0.22019     	0.01979     	41               eval state : test_on_last_epoch;
---> final result              	0.00907     	0.22019     	0.01979     	41               eval state : final;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.03801     	-0.60962    	0.06861     	0           
  10          	0.01737     	-0.41957    	0.03195     	0           
  100         	0.00960     	0.07332     	0.02310     	1           
  300         	0.00981     	0.23457     	0.02197     	2           
  500         	0.00966     	0.14372     	0.02287     	4           
  1000        	0.01438     	-7.67000    	0.03305     	9           
---> try to load best state
              	0.01438     	-7.67000    	0.03305     	9                eval state : test_on_restore;
              	0.01438     	-7.67000    	0.03305     	9                eval state : test_on_last_epoch;
---> final result              	0.01438     	-7.67000    	0.03305     	9                eval state : final;module_name : SGAR;subset : 2.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.01877     	-0.34613    	0.03605     	0           
  10          	0.01096     	0.12466     	0.02207     	0           
  100         	0.00904     	-0.14092    	0.02296     	1           
  300         	0.00924     	-0.39797    	0.02272     	4           
  500         	0.06682     	-93.68516   	0.08249     	6           
  1000        	0.01042     	-0.83552    	0.02411     	14          
---> try to load best state
              	0.01042     	-0.83552    	0.02411     	14               eval state : test_on_restore;
              	0.01042     	-0.83552    	0.02411     	14               eval state : test_on_last_epoch;
---> final result              	0.01042     	-0.83552    	0.02411     	14               eval state : final;module_name : SGAR;subset : 4.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00903     	0.24020     	0.01878     	0           
  10          	0.00705     	-0.39212    	0.01932     	0           
  100         	0.00841     	0.06295     	0.02072     	2           
  300         	0.00886     	-0.18276    	0.02131     	5           
  500         	0.00875     	-0.06575    	0.02098     	10          
  1000        	0.00900     	0.01599     	0.02110     	20          
---> try to load best state
              	0.00900     	0.01599     	0.02110     	20               eval state : test_on_restore;
              	0.00900     	0.01599     	0.02110     	20               eval state : test_on_last_epoch;
---> final result              	0.00900     	0.01599     	0.02110     	20               eval state : final;module_name : SGAR;subset : 8.0;cp_record_file : True;
---> end


---------->
SGAR 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: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	0.00635     	0.46286     	0.01297     	0           
  10          	0.00599     	0.32484     	0.01541     	0           
  100         	0.00808     	0.31508     	0.01903     	3           
  300         	0.00845     	0.30215     	0.01972     	10          
  500         	0.01053     	0.10217     	0.01981     	17          
  1000        	0.00841     	0.29335     	0.01923     	33          
---> try to load best state
              	0.00841     	0.29335     	0.01923     	33               eval state : test_on_restore;
              	0.00841     	0.29335     	0.01923     	33               eval state : test_on_last_epoch;
---> final result              	0.00841     	0.29335     	0.01923     	33               eval state : final;module_name : SGAR;subset : 16.0;cp_record_file : True;
