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

  Demo res cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00305     	0.00875     	0.93956     	0           
  10          	0.00303     	0.00866     	0.94061     	0           
  100         	0.00277     	0.00779     	0.95061     	0           
  300         	0.00193     	0.00592     	0.96866     	1           
  500         	0.00152     	0.00479     	0.97734     	2           
  1000        	0.00134     	0.00415     	0.98140     	4           
---> try to load best state
              	0.00134     	0.00415     	0.98140     	4                eval state : test_on_restore;
              	0.00134     	0.00415     	0.98140     	4                eval state : test_on_last_epoch;
---> final result
              	0.00134     	0.00415     	0.98140     	4                eval state : final;
----------> finish x-yl training


---------->
res cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, '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': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, '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}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00137     	0.00426     	0.98024     	0           
  10          	0.00137     	0.00428     	0.98020     	0           
  100         	0.00148     	0.00450     	0.97971     	0           
  300         	0.00159     	0.00472     	0.97934     	1           
  500         	0.00164     	0.00480     	0.97922     	2           
  1000        	0.00167     	0.00485     	0.97918     	4           
---> try to load best state
              	0.00167     	0.00485     	0.97918     	4                eval state : test_on_restore;
              	0.00167     	0.00485     	0.97918     	4                eval state : test_on_last_epoch;
---> final result
              	0.00167     	0.00485     	0.97918     	4                eval state : final;module_name : ResGP;cp_record_file : True;
