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

  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': 128, '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           	50.23768    	129.70575   	0.87994     	0           
  10          	49.83587    	128.59578   	0.88239     	0           
  100         	46.42620    	119.34004   	0.90125     	1           
  300         	42.27537    	108.25125   	0.92082     	3           
  500         	40.56353    	103.67871   	0.92820     	5           
  1000        	35.95199    	92.36927    	0.94459     	10          
---> try to load best state
              	35.95199    	92.36927    	0.94459     	10               eval state : test_on_restore;
              	35.95199    	92.36927    	0.94459     	10               eval state : test_on_last_epoch;
---> final result
              	35.95199    	92.36927    	0.94459     	10               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           	90.00611    	190.98315   	0.32719     	0           
  10          	84.57294    	183.51300   	0.32437     	0           
  100         	100.84126   	239.57323   	-0.23608    	0           
---> try to load best state
              	100.84126   	239.57323   	-0.23608    	0                eval state : test_on_restore;
              	100.84126   	239.57323   	-0.23608    	0                eval state : test_on_last_epoch;
---> final result
              	100.84126   	239.57323   	-0.23608    	0                eval state : final;
              	100.84126   	239.57323   	-0.23608    	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           	71.54523    	156.73241   	0.65009     	0           
  10          	66.41690    	148.36153   	0.67331     	0           
  100         	57.47395    	135.32323   	0.80907     	0           
---> try to load best state
              	57.47395    	135.32323   	0.80907     	0                eval state : test_on_restore;
              	57.47395    	135.32323   	0.80907     	0                eval state : test_on_last_epoch;
---> final result
              	57.47395    	135.32323   	0.80907     	0                eval state : final;
              	57.47395    	135.32323   	0.80907     	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           	53.32683    	121.09668   	0.80602     	0           
  10          	50.17552    	117.22123   	0.82564     	0           
  100         	33.39368    	83.45210    	0.94618     	0           
---> try to load best state
              	33.39368    	83.45210    	0.94618     	0                eval state : test_on_restore;
              	33.39368    	83.45210    	0.94618     	0                eval state : test_on_last_epoch;
---> final result
              	33.39368    	83.45210    	0.94618     	0                eval state : final;
              	33.39368    	83.45210    	0.94618     	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           	41.68097    	99.90434    	0.87187     	0           
  10          	41.51555    	100.89031   	0.87900     	0           
  100         	30.23467    	77.13947    	0.95507     	0           
---> try to load best state
              	30.23467    	77.13947    	0.95507     	0                eval state : test_on_restore;
              	30.23467    	77.13947    	0.95507     	0                eval state : test_on_last_epoch;
---> final result
              	30.23467    	77.13947    	0.95507     	0                eval state : final;
              	30.23467    	77.13947    	0.95507     	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           	39.31891    	95.81493    	0.89103     	0           
  10          	38.40854    	94.60721    	0.90142     	0           
  100         	28.49298    	72.46058    	0.95906     	1           
---> try to load best state
              	28.49298    	72.46058    	0.95906     	1                eval state : test_on_restore;
              	28.49298    	72.46058    	0.95906     	1                eval state : test_on_last_epoch;
---> final result
              	28.49298    	72.46058    	0.95906     	1                eval state : final;
              	28.49298    	72.46058    	0.95906     	1                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': 128, '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           	38.91088    	95.40720    	0.89250     	0           
  10          	37.78946    	93.78214    	0.90341     	0           
  100         	28.26156    	71.58024    	0.95953     	1           
---> try to load best state
              	28.26156    	71.58024    	0.95953     	1                eval state : test_on_restore;
              	28.26156    	71.58024    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26156    	71.58024    	0.95953     	1                eval state : final;
              	28.26156    	71.58024    	0.95953     	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': 128, '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           	50.23768    	129.70575   	0.87994     	0           
  10          	49.83587    	128.59576   	0.88239     	0           
  100         	46.42620    	119.34003   	0.90125     	0           
  300         	42.27537    	108.25124   	0.92082     	2           
  500         	40.56353    	103.67870   	0.92820     	4           
  1000        	35.95199    	92.36926    	0.94459     	9           
---> try to load best state
              	35.95199    	92.36926    	0.94459     	9                eval state : test_on_restore;
              	35.95199    	92.36926    	0.94459     	9                eval state : test_on_last_epoch;
---> final result
              	35.95199    	92.36926    	0.94459     	9                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           	82.87539    	174.60478   	0.51187     	0           
  10          	79.72122    	168.59320   	0.53673     	0           
  100         	117.26005   	251.73912   	-0.05266    	0           
---> try to load best state
              	117.26005   	251.73912   	-0.05266    	0                eval state : test_on_restore;
              	117.26005   	251.73912   	-0.05266    	0                eval state : test_on_last_epoch;
---> final result
              	117.26005   	251.73912   	-0.05266    	0                eval state : final;
              	117.26005   	251.73912   	-0.05266    	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           	72.63741    	156.26234   	0.64667     	0           
  10          	67.56960    	149.57466   	0.67084     	0           
  100         	71.94465    	167.58679   	0.72291     	0           
---> try to load best state
              	71.94465    	167.58679   	0.72291     	0                eval state : test_on_restore;
              	71.94465    	167.58679   	0.72291     	0                eval state : test_on_last_epoch;
---> final result
              	71.94465    	167.58679   	0.72291     	0                eval state : final;
              	71.94465    	167.58679   	0.72291     	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           	55.06699    	127.84450   	0.80331     	0           
  10          	52.98183    	125.46621   	0.81555     	0           
  100         	39.39622    	98.57442    	0.92135     	0           
---> try to load best state
              	39.39622    	98.57442    	0.92135     	0                eval state : test_on_restore;
              	39.39622    	98.57442    	0.92135     	0                eval state : test_on_last_epoch;
---> final result
              	39.39622    	98.57442    	0.92135     	0                eval state : final;
              	39.39622    	98.57442    	0.92135     	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           	50.55025    	118.26347   	0.82409     	0           
  10          	47.77849    	115.36433   	0.84086     	0           
  100         	32.89292    	84.69028    	0.94613     	0           
---> try to load best state
              	32.89292    	84.69028    	0.94613     	0                eval state : test_on_restore;
              	32.89292    	84.69028    	0.94613     	0                eval state : test_on_last_epoch;
---> final result
              	32.89292    	84.69028    	0.94613     	0                eval state : final;
              	32.89292    	84.69028    	0.94613     	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           	39.54314    	95.87396    	0.88726     	0           
  10          	38.57780    	94.30830    	0.89499     	0           
  100         	28.49856    	72.15532    	0.95912     	1           
---> try to load best state
              	28.49856    	72.15532    	0.95912     	1                eval state : test_on_restore;
              	28.49856    	72.15532    	0.95912     	1                eval state : test_on_last_epoch;
---> final result
              	28.49856    	72.15532    	0.95912     	1                eval state : final;
              	28.49856    	72.15532    	0.95912     	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': 128, '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           	38.91087    	95.40718    	0.89250     	0           
  10          	37.78945    	93.78213    	0.90341     	0           
  100         	28.26157    	71.58025    	0.95953     	1           
---> try to load best state
              	28.26157    	71.58025    	0.95953     	1                eval state : test_on_restore;
              	28.26157    	71.58025    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26157    	71.58025    	0.95953     	1                eval state : final;
              	28.26157    	71.58025    	0.95953     	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': 128, '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           	50.23769    	129.70576   	0.87994     	0           
  10          	49.83586    	128.59578   	0.88239     	0           
  100         	46.42620    	119.34004   	0.90125     	0           
  300         	42.27536    	108.25123   	0.92082     	2           
  500         	40.56353    	103.67870   	0.92820     	4           
  1000        	35.95199    	92.36925    	0.94459     	8           
---> try to load best state
              	35.95199    	92.36925    	0.94459     	8                eval state : test_on_restore;
              	35.95199    	92.36925    	0.94459     	8                eval state : test_on_last_epoch;
---> final result
              	35.95199    	92.36925    	0.94459     	9                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           	86.11145    	187.18347   	0.39659     	0           
  10          	80.71252    	179.59555   	0.40628     	0           
  100         	109.13796   	246.67613   	-0.05484    	0           
---> try to load best state
              	109.13796   	246.67613   	-0.05484    	0                eval state : test_on_restore;
              	109.13796   	246.67613   	-0.05484    	0                eval state : test_on_last_epoch;
---> final result
              	109.13796   	246.67613   	-0.05484    	0                eval state : final;
              	109.13796   	246.67613   	-0.05484    	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           	83.20456    	185.75764   	0.42500     	0           
  10          	78.56949    	179.58302   	0.41632     	0           
  100         	85.74557    	223.61661   	0.18492     	0           
---> try to load best state
              	85.74557    	223.61661   	0.18492     	0                eval state : test_on_restore;
              	85.74557    	223.61661   	0.18492     	0                eval state : test_on_last_epoch;
---> final result
              	85.74557    	223.61661   	0.18492     	0                eval state : final;
              	85.74557    	223.61661   	0.18492     	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           	56.52799    	130.24721   	0.78876     	0           
  10          	53.11472    	125.14867   	0.80617     	0           
  100         	38.07396    	97.41238    	0.92017     	0           
---> try to load best state
              	38.07396    	97.41238    	0.92017     	0                eval state : test_on_restore;
              	38.07396    	97.41238    	0.92017     	0                eval state : test_on_last_epoch;
---> final result
              	38.07396    	97.41238    	0.92017     	0                eval state : final;
              	38.07396    	97.41238    	0.92017     	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           	45.33019    	109.14801   	0.85578     	0           
  10          	42.47932    	105.85886   	0.87056     	0           
  100         	30.13411    	79.29982    	0.94986     	0           
---> try to load best state
              	30.13411    	79.29982    	0.94986     	0                eval state : test_on_restore;
              	30.13411    	79.29982    	0.94986     	0                eval state : test_on_last_epoch;
---> final result
              	30.13411    	79.29982    	0.94986     	0                eval state : final;
              	30.13411    	79.29982    	0.94986     	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           	40.43437    	98.25620    	0.88829     	0           
  10          	39.13135    	96.57240    	0.89967     	0           
  100         	29.09321    	75.18224    	0.95665     	1           
---> try to load best state
              	29.09321    	75.18224    	0.95665     	1                eval state : test_on_restore;
              	29.09321    	75.18224    	0.95665     	1                eval state : test_on_last_epoch;
---> final result
              	29.09321    	75.18224    	0.95665     	1                eval state : final;
              	29.09321    	75.18224    	0.95665     	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': 128, '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           	38.91088    	95.40718    	0.89250     	0           
  10          	37.78945    	93.78212    	0.90341     	0           
  100         	28.26156    	71.58022    	0.95953     	1           
---> try to load best state
              	28.26156    	71.58022    	0.95953     	1                eval state : test_on_restore;
              	28.26156    	71.58022    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26156    	71.58022    	0.95953     	1                eval state : final;
              	28.26156    	71.58022    	0.95953     	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': 128, '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           	50.23770    	129.70576   	0.87994     	0           
  10          	49.83587    	128.59576   	0.88239     	0           
  100         	46.42619    	119.34001   	0.90125     	0           
  300         	42.27538    	108.25124   	0.92082     	2           
  500         	40.56352    	103.67870   	0.92820     	4           
  1000        	35.95200    	92.36928    	0.94459     	9           
---> try to load best state
              	35.95200    	92.36928    	0.94459     	9                eval state : test_on_restore;
              	35.95200    	92.36928    	0.94459     	9                eval state : test_on_last_epoch;
---> final result
              	35.95200    	92.36928    	0.94459     	9                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           	101.69480   	209.53590   	-0.02789    	0           
  10          	97.74767    	202.86240   	-0.04438    	0           
  100         	134.60680   	291.66306   	-2.88462    	0           
---> try to load best state
              	134.60680   	291.66306   	-2.88462    	0                eval state : test_on_restore;
              	134.60680   	291.66306   	-2.88462    	0                eval state : test_on_last_epoch;
---> final result
              	134.60680   	291.66306   	-2.88462    	0                eval state : final;
              	134.60680   	291.66306   	-2.88462    	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           	86.18044    	187.86143   	0.43490     	0           
  10          	81.20628    	181.26169   	0.44192     	0           
  100         	65.89046    	175.77220   	0.69845     	0           
---> try to load best state
              	65.89046    	175.77220   	0.69845     	0                eval state : test_on_restore;
              	65.89046    	175.77220   	0.69845     	0                eval state : test_on_last_epoch;
---> final result
              	65.89046    	175.77220   	0.69845     	0                eval state : final;
              	65.89046    	175.77220   	0.69845     	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           	68.35884    	153.78784   	0.69145     	0           
  10          	63.28780    	146.55579   	0.71988     	0           
  100         	41.48548    	116.66759   	0.89774     	0           
---> try to load best state
              	41.48548    	116.66759   	0.89774     	0                eval state : test_on_restore;
              	41.48548    	116.66759   	0.89774     	0                eval state : test_on_last_epoch;
---> final result
              	41.48548    	116.66759   	0.89774     	0                eval state : final;
              	41.48548    	116.66759   	0.89774     	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           	51.54998    	120.53768   	0.81581     	0           
  10          	48.30638    	117.19286   	0.83352     	0           
  100         	32.90710    	87.97353    	0.94266     	0           
---> try to load best state
              	32.90710    	87.97353    	0.94266     	0                eval state : test_on_restore;
              	32.90710    	87.97353    	0.94266     	0                eval state : test_on_last_epoch;
---> final result
              	32.90710    	87.97353    	0.94266     	0                eval state : final;
              	32.90710    	87.97353    	0.94266     	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           	41.19207    	100.59058   	0.88092     	0           
  10          	40.29038    	100.77290   	0.89150     	0           
  100         	30.13816    	79.90749    	0.95250     	1           
---> try to load best state
              	30.13816    	79.90749    	0.95250     	1                eval state : test_on_restore;
              	30.13816    	79.90749    	0.95250     	1                eval state : test_on_last_epoch;
---> final result
              	30.13816    	79.90749    	0.95250     	1                eval state : final;
              	30.13816    	79.90749    	0.95250     	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': 128, '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           	38.91088    	95.40720    	0.89250     	0           
  10          	37.78946    	93.78214    	0.90341     	0           
  100         	28.26157    	71.58025    	0.95953     	1           
---> try to load best state
              	28.26157    	71.58025    	0.95953     	1                eval state : test_on_restore;
              	28.26157    	71.58025    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26157    	71.58025    	0.95953     	1                eval state : final;
              	28.26157    	71.58025    	0.95953     	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': 128, '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           	50.23769    	129.70575   	0.87994     	0           
  10          	49.83586    	128.59576   	0.88239     	0           
  100         	46.42621    	119.34005   	0.90125     	1           
  300         	42.27538    	108.25125   	0.92082     	2           
  500         	40.56353    	103.67871   	0.92820     	4           
  1000        	35.95198    	92.36924    	0.94459     	9           
---> try to load best state
              	35.95198    	92.36924    	0.94459     	9                eval state : test_on_restore;
              	35.95198    	92.36924    	0.94459     	9                eval state : test_on_last_epoch;
---> final result
              	35.95198    	92.36924    	0.94459     	9                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           	96.79455    	204.04453   	-0.01573    	0           
  10          	93.88351    	199.85458   	-0.12524    	0           
  100         	141.39191   	318.12607   	-5.68106    	0           
---> try to load best state
              	141.39191   	318.12607   	-5.68106    	0                eval state : test_on_restore;
              	141.39191   	318.12607   	-5.68106    	0                eval state : test_on_last_epoch;
---> final result
              	141.39191   	318.12607   	-5.68106    	0                eval state : final;
              	141.39191   	318.12607   	-5.68106    	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           	78.46953    	172.38901   	0.45767     	0           
  10          	72.24507    	163.55264   	0.49845     	0           
  100         	74.29261    	177.76837   	0.59923     	0           
---> try to load best state
              	74.29261    	177.76837   	0.59923     	0                eval state : test_on_restore;
              	74.29261    	177.76837   	0.59923     	0                eval state : test_on_last_epoch;
---> final result
              	74.29261    	177.76837   	0.59923     	0                eval state : final;
              	74.29261    	177.76837   	0.59923     	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           	56.99706    	132.01125   	0.76099     	0           
  10          	52.75231    	126.91456   	0.78396     	0           
  100         	42.65650    	108.77442   	0.88805     	0           
---> try to load best state
              	42.65650    	108.77442   	0.88805     	0                eval state : test_on_restore;
              	42.65650    	108.77442   	0.88805     	0                eval state : test_on_last_epoch;
---> final result
              	42.65650    	108.77442   	0.88805     	0                eval state : final;
              	42.65650    	108.77442   	0.88805     	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           	47.11768    	112.66009   	0.84606     	0           
  10          	44.95417    	108.86504   	0.86216     	0           
  100         	32.04551    	81.10135    	0.95002     	0           
---> try to load best state
              	32.04551    	81.10135    	0.95002     	0                eval state : test_on_restore;
              	32.04551    	81.10135    	0.95002     	0                eval state : test_on_last_epoch;
---> final result
              	32.04551    	81.10135    	0.95002     	0                eval state : final;
              	32.04551    	81.10135    	0.95002     	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           	39.57779    	96.73005    	0.89047     	0           
  10          	39.11144    	96.22871    	0.89992     	0           
  100         	29.17222    	73.69179    	0.95813     	1           
---> try to load best state
              	29.17222    	73.69179    	0.95813     	1                eval state : test_on_restore;
              	29.17222    	73.69179    	0.95813     	1                eval state : test_on_last_epoch;
---> final result
              	29.17222    	73.69179    	0.95813     	1                eval state : final;
              	29.17222    	73.69179    	0.95813     	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': 128, '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           	38.91087    	95.40717    	0.89250     	0           
  10          	37.78945    	93.78212    	0.90341     	0           
  100         	28.26157    	71.58024    	0.95953     	1           
---> try to load best state
              	28.26157    	71.58024    	0.95953     	1                eval state : test_on_restore;
              	28.26157    	71.58024    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26157    	71.58024    	0.95953     	1                eval state : final;
              	28.26157    	71.58024    	0.95953     	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': 128, '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           	50.23769    	129.70576   	0.87994     	0           
  10          	49.83587    	128.59578   	0.88239     	0           
  100         	46.42621    	119.34007   	0.90125     	0           
  300         	42.27537    	108.25124   	0.92082     	2           
  500         	40.56353    	103.67871   	0.92820     	4           
  1000        	35.95199    	92.36926    	0.94459     	8           
---> try to load best state
              	35.95199    	92.36926    	0.94459     	8                eval state : test_on_restore;
              	35.95199    	92.36926    	0.94459     	8                eval state : test_on_last_epoch;
---> final result
              	35.95199    	92.36926    	0.94459     	8                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           	101.83740   	210.44812   	-0.06364    	0           
  10          	97.42031    	205.02089   	-0.15240    	0           
  100         	130.55559   	299.78592   	-2.53625    	0           
---> try to load best state
              	130.55559   	299.78592   	-2.53625    	0                eval state : test_on_restore;
              	130.55559   	299.78592   	-2.53625    	0                eval state : test_on_last_epoch;
---> final result
              	130.55559   	299.78592   	-2.53625    	0                eval state : final;
              	130.55559   	299.78592   	-2.53625    	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           	63.75284    	143.12453   	0.70278     	0           
  10          	58.49234    	136.69937   	0.73081     	0           
  100         	42.95777    	114.76462   	0.87637     	0           
---> try to load best state
              	42.95777    	114.76462   	0.87637     	0                eval state : test_on_restore;
              	42.95777    	114.76462   	0.87637     	0                eval state : test_on_last_epoch;
---> final result
              	42.95777    	114.76462   	0.87637     	0                eval state : final;
              	42.95777    	114.76462   	0.87637     	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           	54.74051    	127.95605   	0.79643     	0           
  10          	52.33275    	123.79277   	0.81066     	0           
  100         	39.93414    	96.65840    	0.91953     	0           
---> try to load best state
              	39.93414    	96.65840    	0.91953     	0                eval state : test_on_restore;
              	39.93414    	96.65840    	0.91953     	0                eval state : test_on_last_epoch;
---> final result
              	39.93414    	96.65840    	0.91953     	0                eval state : final;
              	39.93414    	96.65840    	0.91953     	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           	42.29644    	101.32949   	0.86627     	0           
  10          	41.50676    	100.33857   	0.87752     	0           
  100         	31.31241    	77.47261    	0.95559     	0           
---> try to load best state
              	31.31241    	77.47261    	0.95559     	0                eval state : test_on_restore;
              	31.31241    	77.47261    	0.95559     	0                eval state : test_on_last_epoch;
---> final result
              	31.31241    	77.47261    	0.95559     	0                eval state : final;
              	31.31241    	77.47261    	0.95559     	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           	39.43949    	95.91745    	0.89038     	0           
  10          	38.53596    	94.75350    	0.89993     	0           
  100         	28.86921    	72.32207    	0.95921     	1           
---> try to load best state
              	28.86921    	72.32207    	0.95921     	1                eval state : test_on_restore;
              	28.86921    	72.32207    	0.95921     	1                eval state : test_on_last_epoch;
---> final result
              	28.86921    	72.32207    	0.95921     	1                eval state : final;
              	28.86921    	72.32207    	0.95921     	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': 128, '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           	38.91087    	95.40719    	0.89250     	0           
  10          	37.78945    	93.78212    	0.90341     	0           
  100         	28.26155    	71.58021    	0.95953     	1           
---> try to load best state
              	28.26155    	71.58021    	0.95953     	1                eval state : test_on_restore;
              	28.26155    	71.58021    	0.95953     	1                eval state : test_on_last_epoch;
---> final result
              	28.26155    	71.58021    	0.95953     	1                eval state : final;
              	28.26155    	71.58021    	0.95953     	1                eval state : final;module_name : LarGP;cp_record_file : True;
