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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, '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           	53.46056    	136.22884   	0.86429     	0           
  10          	53.06068    	135.07552   	0.86717     	0           
  100         	49.48153    	125.29778   	0.88946     	0           
  300         	44.43283    	112.62782   	0.91407     	2           
  500         	41.76944    	106.38201   	0.92481     	4           
  1000        	35.98731    	92.58617    	0.94495     	7           
---> try to load best state
              	35.98731    	92.58617    	0.94495     	7                eval state : test_on_restore;
              	35.98731    	92.58617    	0.94495     	7                eval state : test_on_last_epoch;
---> final result
              	35.98731    	92.58617    	0.94495     	7                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	89.86427    	191.14076   	0.33277     	0           
  10          	84.28270    	183.34630   	0.33074     	0           
  100         	100.42963   	239.03333   	-0.22429    	0           
---> try to load best state
              	100.42963   	239.03333   	-0.22429    	0                eval state : test_on_restore;
              	100.42963   	239.03333   	-0.22429    	0                eval state : test_on_last_epoch;
---> final result
              	100.42963   	239.03333   	-0.22429    	0                eval state : final;
              	100.42963   	239.03333   	-0.22429    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	71.48440    	156.80684   	0.65293     	0           
  10          	66.22835    	148.22882   	0.67636     	0           
  100         	57.29956    	135.11766   	0.81064     	0           
---> try to load best state
              	57.29956    	135.11766   	0.81064     	0                eval state : test_on_restore;
              	57.29956    	135.11766   	0.81064     	0                eval state : test_on_last_epoch;
---> final result
              	57.29956    	135.11766   	0.81064     	0                eval state : final;
              	57.29956    	135.11766   	0.81064     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	53.28131    	121.11686   	0.80778     	0           
  10          	50.06927    	117.20850   	0.82736     	0           
  100         	33.26881    	83.34254    	0.94681     	0           
---> try to load best state
              	33.26881    	83.34254    	0.94681     	0                eval state : test_on_restore;
              	33.26881    	83.34254    	0.94681     	0                eval state : test_on_last_epoch;
---> final result
              	33.26881    	83.34254    	0.94681     	0                eval state : final;
              	33.26881    	83.34254    	0.94681     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	41.58414    	99.95311    	0.87383     	0           
  10          	41.42903    	101.03555   	0.88094     	0           
  100         	30.22439    	77.24165    	0.95543     	0           
---> try to load best state
              	30.22439    	77.24165    	0.95543     	0                eval state : test_on_restore;
              	30.22439    	77.24165    	0.95543     	0                eval state : test_on_last_epoch;
---> final result
              	30.22439    	77.24165    	0.95543     	0                eval state : final;
              	30.22439    	77.24165    	0.95543     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: None 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	39.28809    	95.88187    	0.89301     	0           
  10          	38.37289    	94.71289    	0.90327     	0           
  100         	28.45866    	72.58528    	0.95945     	0           
---> try to load best state
              	28.45866    	72.58528    	0.95945     	1                eval state : test_on_restore;
              	28.45866    	72.58528    	0.95945     	1                eval state : test_on_last_epoch;
---> final result
              	28.45866    	72.58528    	0.95945     	1                eval state : final;
              	28.45866    	72.58528    	0.95945     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, '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           	52.61123    	133.19896   	0.87289     	0           
  10          	52.22468    	132.01955   	0.87583     	0           
  100         	49.00703    	122.34389   	0.89751     	0           
  300         	44.56427    	110.44833   	0.91919     	2           
  500         	41.87788    	104.76004   	0.92818     	3           
  1000        	36.03321    	91.71634    	0.94647     	6           
---> try to load best state
              	36.03321    	91.71634    	0.94647     	6                eval state : test_on_restore;
              	36.03321    	91.71634    	0.94647     	6                eval state : test_on_last_epoch;
---> final result
              	36.03321    	91.71634    	0.94647     	6                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	84.00290    	175.72600   	0.51756     	0           
  10          	81.01308    	169.41833   	0.54159     	0           
  100         	117.36517   	251.59668   	-0.04748    	0           
---> try to load best state
              	117.36517   	251.59668   	-0.04748    	0                eval state : test_on_restore;
              	117.36517   	251.59668   	-0.04748    	0                eval state : test_on_last_epoch;
---> final result
              	117.36517   	251.59668   	-0.04748    	0                eval state : final;
              	117.36517   	251.59668   	-0.04748    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	73.75578    	157.30879   	0.64923     	0           
  10          	68.68331    	149.98428   	0.67352     	0           
  100         	71.76875    	167.03595   	0.72693     	0           
---> try to load best state
              	71.76875    	167.03595   	0.72693     	0                eval state : test_on_restore;
              	71.76875    	167.03595   	0.72693     	0                eval state : test_on_last_epoch;
---> final result
              	71.76875    	167.03595   	0.72693     	0                eval state : final;
              	71.76875    	167.03595   	0.72693     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	55.80994    	128.49295   	0.80445     	0           
  10          	53.35769    	125.21273   	0.81751     	0           
  100         	38.71542    	97.06092    	0.92374     	0           
---> try to load best state
              	38.71542    	97.06092    	0.92374     	0                eval state : test_on_restore;
              	38.71542    	97.06092    	0.92374     	0                eval state : test_on_last_epoch;
---> final result
              	38.71542    	97.06092    	0.92374     	0                eval state : final;
              	38.71542    	97.06092    	0.92374     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	51.06225    	118.44648   	0.82548     	0           
  10          	47.87090    	114.75855   	0.84286     	0           
  100         	32.48816    	83.74785    	0.94793     	0           
---> try to load best state
              	32.48816    	83.74785    	0.94793     	0                eval state : test_on_restore;
              	32.48816    	83.74785    	0.94793     	0                eval state : test_on_last_epoch;
---> final result
              	32.48816    	83.74785    	0.94793     	0                eval state : final;
              	32.48816    	83.74785    	0.94793     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 0 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	39.84789    	95.57629    	0.89005     	0           
  10          	38.79136    	94.01506    	0.89863     	0           
  100         	28.94471    	72.28964    	0.96004     	1           
---> try to load best state
              	28.94471    	72.28964    	0.96004     	1                eval state : test_on_restore;
              	28.94471    	72.28964    	0.96004     	1                eval state : test_on_last_epoch;
---> final result
              	28.94471    	72.28964    	0.96004     	1                eval state : final;
              	28.94471    	72.28964    	0.96004     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, '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           	58.51264    	150.86597   	0.81905     	0           
  10          	58.01769    	149.51288   	0.82324     	0           
  100         	53.82416    	138.03043   	0.85542     	0           
  300         	48.32969    	123.44357   	0.88883     	1           
  500         	45.59203    	116.82271   	0.90171     	3           
  1000        	39.54088    	102.15575   	0.92729     	6           
---> try to load best state
              	39.54088    	102.15575   	0.92729     	6                eval state : test_on_restore;
              	39.54088    	102.15575   	0.92729     	6                eval state : test_on_last_epoch;
---> final result
              	39.54088    	102.15575   	0.92729     	6                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	87.66941    	190.10077   	0.36184     	0           
  10          	83.40730    	183.94403   	0.36714     	0           
  100         	111.18575   	251.22914   	-0.11788    	0           
---> try to load best state
              	111.18575   	251.22914   	-0.11788    	0                eval state : test_on_restore;
              	111.18575   	251.22914   	-0.11788    	0                eval state : test_on_last_epoch;
---> final result
              	111.18575   	251.22914   	-0.11788    	0                eval state : final;
              	111.18575   	251.22914   	-0.11788    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	84.46211    	188.43892   	0.39360     	0           
  10          	80.87537    	183.52426   	0.37973     	0           
  100         	87.45351    	227.10492   	0.13866     	0           
---> try to load best state
              	87.45351    	227.10492   	0.13866     	0                eval state : test_on_restore;
              	87.45351    	227.10492   	0.13866     	0                eval state : test_on_last_epoch;
---> final result
              	87.45351    	227.10492   	0.13866     	0                eval state : final;
              	87.45351    	227.10492   	0.13866     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	57.91881    	134.56384   	0.77105     	0           
  10          	55.85767    	131.07910   	0.78573     	0           
  100         	41.21967    	103.50742   	0.90659     	0           
---> try to load best state
              	41.21967    	103.50742   	0.90659     	0                eval state : test_on_restore;
              	41.21967    	103.50742   	0.90659     	0                eval state : test_on_last_epoch;
---> final result
              	41.21967    	103.50742   	0.90659     	0                eval state : final;
              	41.21967    	103.50742   	0.90659     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	47.43811    	115.47021   	0.83699     	0           
  10          	45.86055    	113.67215   	0.85010     	0           
  100         	33.00824    	85.70055    	0.93837     	0           
---> try to load best state
              	33.00824    	85.70055    	0.93837     	0                eval state : test_on_restore;
              	33.00824    	85.70055    	0.93837     	0                eval state : test_on_last_epoch;
---> final result
              	33.00824    	85.70055    	0.93837     	0                eval state : final;
              	33.00824    	85.70055    	0.93837     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 1 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	43.03645    	106.22048   	0.86602     	0           
  10          	41.76457    	104.63790   	0.87764     	0           
  100         	31.10484    	80.60658    	0.94695     	1           
---> try to load best state
              	31.10484    	80.60658    	0.94695     	1                eval state : test_on_restore;
              	31.10484    	80.60658    	0.94695     	1                eval state : test_on_last_epoch;
---> final result
              	31.10484    	80.60658    	0.94695     	1                eval state : final;
              	31.10484    	80.60658    	0.94695     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, '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           	63.15240    	176.22296   	0.74257     	0           
  10          	62.63614    	174.83255   	0.74817     	0           
  100         	58.24139    	162.78555   	0.79169     	0           
  300         	52.33691    	145.70906   	0.84120     	2           
  500         	49.08754    	136.23416   	0.86413     	3           
  1000        	42.07421    	116.97044   	0.90426     	6           
---> try to load best state
              	42.07421    	116.97044   	0.90426     	6                eval state : test_on_restore;
              	42.07421    	116.97044   	0.90426     	6                eval state : test_on_last_epoch;
---> final result
              	42.07421    	116.97044   	0.90426     	6                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	106.24739   	220.14369   	-0.16275    	0           
  10          	102.84408   	215.35738   	-0.18940    	0           
  100         	137.79610   	299.69699   	-3.34896    	0           
---> try to load best state
              	137.79610   	299.69699   	-3.34896    	0                eval state : test_on_restore;
              	137.79610   	299.69699   	-3.34896    	0                eval state : test_on_last_epoch;
---> final result
              	137.79610   	299.69699   	-3.34896    	0                eval state : final;
              	137.79610   	299.69699   	-3.34896    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	90.61987    	199.68079   	0.35600     	0           
  10          	86.13479    	195.05823   	0.35710     	0           
  100         	69.17721    	185.70673   	0.65418     	0           
---> try to load best state
              	69.17721    	185.70673   	0.65418     	0                eval state : test_on_restore;
              	69.17721    	185.70673   	0.65418     	0                eval state : test_on_last_epoch;
---> final result
              	69.17721    	185.70673   	0.65418     	0                eval state : final;
              	69.17721    	185.70673   	0.65418     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	72.87968    	168.36842   	0.63672     	0           
  10          	68.10620    	162.91342   	0.66457     	0           
  100         	44.36642    	126.82284   	0.87364     	0           
---> try to load best state
              	44.36642    	126.82284   	0.87364     	0                eval state : test_on_restore;
              	44.36642    	126.82284   	0.87364     	0                eval state : test_on_last_epoch;
---> final result
              	44.36642    	126.82284   	0.87364     	0                eval state : final;
              	44.36642    	126.82284   	0.87364     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	56.28967    	138.99759   	0.77008     	0           
  10          	53.07609    	136.32060   	0.78823     	0           
  100         	35.64368    	99.52451    	0.92110     	0           
---> try to load best state
              	35.64368    	99.52451    	0.92110     	0                eval state : test_on_restore;
              	35.64368    	99.52451    	0.92110     	0                eval state : test_on_last_epoch;
---> final result
              	35.64368    	99.52451    	0.92110     	0                eval state : final;
              	35.64368    	99.52451    	0.92110     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 2 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	46.43766    	122.45621   	0.83541     	0           
  10          	45.29979    	121.64574   	0.84657     	0           
  100         	32.95504    	91.49900    	0.93162     	1           
---> try to load best state
              	32.95504    	91.49900    	0.93162     	1                eval state : test_on_restore;
              	32.95504    	91.49900    	0.93162     	1                eval state : test_on_last_epoch;
---> final result
              	32.95504    	91.49900    	0.93162     	1                eval state : final;
              	32.95504    	91.49900    	0.93162     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, '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           	59.84759    	148.10597   	0.83536     	0           
  10          	59.39468    	146.89270   	0.83882     	0           
  100         	55.25026    	136.34229   	0.86603     	0           
  300         	48.98098    	121.60396   	0.89738     	1           
  500         	45.46777    	113.59148   	0.91188     	3           
  1000        	38.59393    	97.41632    	0.93721     	6           
---> try to load best state
              	38.59393    	97.41632    	0.93721     	6                eval state : test_on_restore;
              	38.59393    	97.41632    	0.93721     	6                eval state : test_on_last_epoch;
---> final result
              	38.59393    	97.41632    	0.93721     	6                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	98.57602    	206.64880   	-0.04547    	0           
  10          	95.63041    	202.85529   	-0.16115    	0           
  100         	142.11238   	319.72394   	-5.89449    	0           
---> try to load best state
              	142.11238   	319.72394   	-5.89449    	0                eval state : test_on_restore;
              	142.11238   	319.72394   	-5.89449    	0                eval state : test_on_last_epoch;
---> final result
              	142.11238   	319.72394   	-5.89449    	0                eval state : final;
              	142.11238   	319.72394   	-5.89449    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	80.34128    	175.22310   	0.44064     	0           
  10          	74.09263    	166.83025   	0.47949     	0           
  100         	74.95289    	179.76021   	0.58397     	0           
---> try to load best state
              	74.95289    	179.76021   	0.58397     	0                eval state : test_on_restore;
              	74.95289    	179.76021   	0.58397     	0                eval state : test_on_last_epoch;
---> final result
              	74.95289    	179.76021   	0.58397     	0                eval state : final;
              	74.95289    	179.76021   	0.58397     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	58.73521    	134.91054   	0.75047     	0           
  10          	54.57014    	130.31190   	0.77318     	0           
  100         	43.62146    	111.05561   	0.88277     	0           
---> try to load best state
              	43.62146    	111.05561   	0.88277     	0                eval state : test_on_restore;
              	43.62146    	111.05561   	0.88277     	0                eval state : test_on_last_epoch;
---> final result
              	43.62146    	111.05561   	0.88277     	0                eval state : final;
              	43.62146    	111.05561   	0.88277     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	48.93578    	116.22781   	0.83789     	0           
  10          	46.78725    	112.96116   	0.85390     	0           
  100         	33.23867    	84.11335    	0.94610     	0           
---> try to load best state
              	33.23867    	84.11335    	0.94610     	0                eval state : test_on_restore;
              	33.23867    	84.11335    	0.94610     	0                eval state : test_on_last_epoch;
---> final result
              	33.23867    	84.11335    	0.94610     	0                eval state : final;
              	33.23867    	84.11335    	0.94610     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 3 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	41.75930    	100.99783   	0.88212     	0           
  10          	41.20466    	100.38505   	0.89191     	0           
  100         	30.38091    	76.01820    	0.95475     	1           
---> try to load best state
              	30.38091    	76.01820    	0.95475     	1                eval state : test_on_restore;
              	30.38091    	76.01820    	0.95475     	1                eval state : test_on_last_epoch;
---> final result
              	30.38091    	76.01820    	0.95475     	1                eval state : final;
              	30.38091    	76.01820    	0.95475     	1                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, '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           	53.09381    	136.35614   	0.86075     	0           
  10          	52.68904    	135.19226   	0.86376     	0           
  100         	49.35825    	125.51224   	0.88666     	0           
  300         	44.78922    	113.32309   	0.91104     	1           
  500         	42.15400    	107.15361   	0.92186     	3           
  1000        	36.47205    	93.26900    	0.94284     	6           
---> try to load best state
              	36.47205    	93.26900    	0.94284     	6                eval state : test_on_restore;
              	36.47205    	93.26900    	0.94284     	6                eval state : test_on_last_epoch;
---> final result
              	36.47205    	93.26900    	0.94284     	6                eval state : final;
----------> finish x-yl training

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	102.91315   	211.51926   	-0.06352    	0           
  10          	98.17484    	205.62759   	-0.15168    	0           
  100         	130.54655   	299.36371   	-2.52968    	0           
---> try to load best state
              	130.54655   	299.36371   	-2.52968    	0                eval state : test_on_restore;
              	130.54655   	299.36371   	-2.52968    	0                eval state : test_on_last_epoch;
---> final result
              	130.54655   	299.36371   	-2.52968    	0                eval state : final;
              	130.54655   	299.36371   	-2.52968    	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	64.87351    	144.51523   	0.70069     	0           
  10          	59.20637    	137.62978   	0.72884     	0           
  100         	42.95546    	114.32372   	0.87586     	0           
---> try to load best state
              	42.95546    	114.32372   	0.87586     	0                eval state : test_on_restore;
              	42.95546    	114.32372   	0.87586     	0                eval state : test_on_last_epoch;
---> final result
              	42.95546    	114.32372   	0.87586     	0                eval state : final;
              	42.95546    	114.32372   	0.87586     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, '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': 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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	55.67324    	129.24870   	0.79404     	0           
  10          	52.97763    	124.73907   	0.80845     	0           
  100         	40.06961    	96.57124    	0.91887     	0           
---> try to load best state
              	40.06961    	96.57124    	0.91887     	0                eval state : test_on_restore;
              	40.06961    	96.57124    	0.91887     	0                eval state : test_on_last_epoch;
---> final result
              	40.06961    	96.57124    	0.91887     	0                eval state : final;
              	40.06961    	96.57124    	0.91887     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	43.04897    	102.52019   	0.86320     	0           
  10          	42.13961    	101.42326   	0.87471     	0           
  100         	31.65699    	77.91751    	0.95462     	0           
---> try to load best state
              	31.65699    	77.91751    	0.95462     	0                eval state : test_on_restore;
              	31.65699    	77.91751    	0.95462     	0                eval state : test_on_last_epoch;
---> final result
              	31.65699    	77.91751    	0.95462     	0                eval state : final;
              	31.65699    	77.91751    	0.95462     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end

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

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'SOFC_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 64, '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_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	40.07478    	97.06573    	0.88744     	0           
  10          	39.14770    	95.93038    	0.89711     	0           
  100         	29.29223    	73.10678    	0.95793     	1           
---> try to load best state
              	29.29223    	73.10678    	0.95793     	1                eval state : test_on_restore;
              	29.29223    	73.10678    	0.95793     	1                eval state : test_on_last_epoch;
---> final result
              	29.29223    	73.10678    	0.95793     	1                eval state : final;
              	29.29223    	73.10678    	0.95793     	1                eval state : final;module_name : LarGP;cp_record_file : True;
