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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_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           	0.00175     	0.74600     	0.00406     	0           
  10          	0.00194     	0.73732     	0.00451     	0           
  100         	0.00411     	0.65733     	0.00862     	0           
  300         	0.00443     	0.68749     	0.00928     	1           
  500         	0.00440     	0.69377     	0.00921     	2           
  1000        	0.00434     	0.69580     	0.00907     	4           
---> try to load best state
              	0.00434     	0.69580     	0.00907     	4                eval state : test_on_restore;
              	0.00434     	0.69580     	0.00907     	4                eval state : test_on_last_epoch;
---> final result              	0.00434     	0.69580     	0.00907     	4                eval state : final;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 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           	0.00655     	0.68225     	0.01583     	0           
  10          	0.00513     	0.73393     	0.01157     	0           
  100         	0.00423     	0.77241     	0.00902     	0           
  300         	0.00416     	0.78814     	0.00889     	1           
  500         	0.00431     	0.74357     	0.00894     	2           
  1000        	0.00423     	0.70715     	0.00895     	4           
---> try to load best state
              	0.00423     	0.70715     	0.00895     	4                eval state : test_on_restore;
              	0.00423     	0.70715     	0.00895     	4                eval state : test_on_last_epoch;
---> final result              	0.00423     	0.70715     	0.00895     	4                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 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           	0.00380     	0.80316     	0.00822     	0           
  10          	0.00400     	0.79404     	0.00848     	0           
  100         	0.00410     	0.78423     	0.00892     	0           
  300         	0.00399     	0.77696     	0.00893     	2           
  500         	0.00419     	0.76087     	0.00896     	3           
  1000        	0.00414     	0.75583     	0.00897     	6           
---> try to load best state
              	0.00414     	0.75583     	0.00897     	6                eval state : test_on_restore;
              	0.00414     	0.75583     	0.00897     	6                eval state : test_on_last_epoch;
---> final result              	0.00414     	0.75583     	0.00897     	6                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 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           	0.00352     	0.81445     	0.00750     	0           
  10          	0.00401     	0.78949     	0.00844     	0           
  100         	0.00406     	0.78728     	0.00890     	0           
  300         	0.00404     	0.77990     	0.00892     	2           
  500         	0.00410     	0.77136     	0.00894     	4           
  1000        	0.00411     	0.75295     	0.00896     	8           
---> try to load best state
              	0.00411     	0.75295     	0.00896     	8                eval state : test_on_restore;
              	0.00411     	0.75295     	0.00896     	8                eval state : test_on_last_epoch;
---> final result              	0.00411     	0.75295     	0.00896     	8                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', '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           	0.00351     	0.81250     	0.00751     	0           
  10          	0.00407     	0.78467     	0.00851     	0           
  100         	0.00406     	0.78544     	0.00892     	1           
  300         	0.00402     	0.77143     	0.00892     	3           
  500         	0.00404     	0.78234     	0.00892     	6           
  1000        	0.00407     	0.77549     	0.00894     	13          
---> try to load best state
              	0.00407     	0.77549     	0.00894     	13               eval state : test_on_restore;
              	0.00407     	0.77549     	0.00894     	13               eval state : test_on_last_epoch;
---> final result              	0.00407     	0.77549     	0.00894     	13               eval state : final;module_name : SGAR;cp_record_file : True;
