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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	114.93838   	0.06737     	206.29156   	0           
  10          	103.54828   	0.14803     	186.15903   	1           
  100         	62.38847    	0.32047     	116.31811   	10          
  300         	40.79322    	0.29123     	81.34348    	28          
  500         	19.82156    	0.42599     	40.46910    	46          
---> try to load best state
              	37.30575    	-351.81944  	52.42180    	73               eval state : test_on_restore;
              	309.18329   	-299.47260  	393.32550   	73               eval state : test_on_last_epoch;
---> final result              	37.30575    	-351.81944  	52.42180    	73               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	500.78671   	-15.94966   	669.29071   	0           
  10          	167.51135   	-11.38345   	234.09782   	0           
  100         	38.68481    	-64.89284   	52.87584    	1           
  300         	41.36773    	-164.67555  	58.05957    	4           
  500         	37.32100    	-60.48043   	51.32175    	7           
  epoch 941 reach nan state
---> try to load best state
              	201.34723   	-1.24673    	291.93103   	14               eval state : test_on_restore;
              	37.32100    	-60.48043   	51.32175    	14               eval state : test_on_last_epoch;
---> final result
              	37.32100    	-60.48043   	51.32175    	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	393.66919   	-7.56545    	575.11133   	0           
  10          	148.34277   	-6.76741    	223.99738   	0           
  100         	45.05199    	-75.88403   	67.08595    	2           
  300         	40.35973    	-126.11111  	57.14257    	6           
  500         	38.73767    	-42.39193   	54.21101    	10          
  epoch 796 reach nan state
---> try to load best state
              	101.36665   	-0.07573    	170.18068   	16               eval state : test_on_restore;
              	38.73767    	-42.39193   	54.21101    	16               eval state : test_on_last_epoch;
---> final result
              	38.73767    	-42.39193   	54.21101    	16               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	235.36211   	-5.03874    	408.21646   	0           
  10          	96.09033    	-7.55148    	153.43378   	0           
  100         	40.33378    	-91.52908   	57.05168    	3           
  300         	40.06750    	-85.24840   	56.79112    	8           
  500         	37.16108    	-78.62208   	53.17194    	14          
  epoch 846 reach nan state
---> try to load best state
              	78.80654    	0.30982     	153.89810   	24               eval state : test_on_restore;
              	37.16108    	-78.62208   	53.17194    	24               eval state : test_on_last_epoch;
---> final result
              	37.16108    	-78.62208   	53.17194    	24               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	223.16040   	-3.40073    	378.12976   	0           
  10          	97.40421    	-3.05354    	158.27344   	0           
  100         	40.27097    	-54.58746   	57.13610    	5           
  300         	37.59578    	-42.96594   	53.20189    	15          
  500         	31.81646    	-17.84876   	48.25140    	24          
  epoch 650 reach nan state
---> try to load best state
              	22.27994    	-0.24200    	43.00895    	32               eval state : test_on_restore;
              	31.81646    	-17.84876   	48.25140    	32               eval state : test_on_last_epoch;
---> final result
              	22.27994    	-0.24200    	43.00895    	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	124.70927   	-1.27217    	204.31000   	0           
  10          	75.90099    	-3.86883    	120.07141   	1           
  100         	39.49883    	-50.07496   	56.25434    	10          
  300         	37.41374    	-39.49854   	52.91765    	29          
  500         	29.75813    	-13.64662   	46.66650    	48          
  epoch 677 reach nan state
---> try to load best state
              	12.03288    	0.43492     	23.00179    	64               eval state : test_on_restore;
              	29.75813    	-13.64662   	46.66650    	65               eval state : test_on_last_epoch;
---> final result
              	12.03288    	0.43492     	23.00179    	65               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.77345   	-0.65835    	198.01315   	0           
  10          	76.58149    	-4.83015    	113.49920   	1           
  100         	39.78477    	-50.44038   	57.63647    	12          
  300         	36.28978    	-26.90110   	52.65095    	35          
  500         	28.47877    	-8.51554    	43.41020    	58          
  epoch 698 reach nan state
---> try to load best state
              	11.96070    	0.36667     	25.15685    	80               eval state : test_on_restore;
              	28.47877    	-8.51554    	43.41020    	80               eval state : test_on_last_epoch;
---> final result
              	11.96070    	0.36667     	25.15685    	80               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	114.93848   	0.06737     	206.29181   	0           
  10          	103.54825   	0.14802     	186.15891   	1           
  100         	62.39741    	0.32039     	116.33362   	9           
  300         	40.82234    	0.29052     	81.42229    	28          
  500         	19.82797    	0.42600     	40.47740    	46          
---> try to load best state
              	37.33402    	-351.48791  	52.45672    	73               eval state : test_on_restore;
              	309.32837   	-299.47503  	393.41257   	73               eval state : test_on_last_epoch;
---> final result              	37.33402    	-351.48791  	52.45672    	73               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	526.23712   	-16.63651   	761.19458   	0           
  10          	168.65021   	-12.74536   	245.19936   	0           
  100         	39.24211    	-162.38724  	57.96686    	1           
  300         	37.60213    	-223.21035  	53.87998    	4           
  500         	40.01645    	-128.58231  	58.06548    	7           
  1000        	207.89963   	-1.47179    	396.22971   	15          
---> try to load best state
              	207.89963   	-1.47179    	396.22971   	15               eval state : test_on_restore;
              	207.89963   	-1.47179    	396.22971   	15               eval state : test_on_last_epoch;
---> final result
              	207.89963   	-1.47179    	396.22971   	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	493.36780   	-12.75107   	749.60181   	0           
  10          	144.61659   	-6.87282    	217.25829   	0           
  100         	39.49553    	-141.08750  	57.17523    	2           
  300         	36.74754    	-46.35498   	52.98634    	6           
  500         	37.39806    	-86.99300   	54.02760    	10          
  epoch 884 reach nan state
---> try to load best state
              	57.53934    	0.18039     	137.15875   	17               eval state : test_on_restore;
              	37.39806    	-86.99300   	54.02760    	17               eval state : test_on_last_epoch;
---> final result
              	37.39806    	-86.99300   	54.02760    	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	293.47989   	-4.54967    	457.29218   	0           
  10          	109.03763   	-8.05965    	163.65416   	0           
  100         	40.90007    	-72.10136   	58.38899    	3           
  300         	38.06016    	-55.59809   	53.67051    	8           
  500         	36.86975    	-69.86253   	52.58537    	14          
  epoch 774 reach nan state
---> try to load best state
              	29.56324    	0.30173     	59.71973    	22               eval state : test_on_restore;
              	36.86975    	-69.86253   	52.58537    	22               eval state : test_on_last_epoch;
---> final result
              	36.86975    	-69.86253   	52.58537    	22               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	199.52232   	-2.36718    	326.34351   	0           
  10          	90.76063    	-5.50526    	137.67068   	0           
  100         	40.79753    	-53.01610   	58.28847    	5           
  300         	38.60218    	-33.82104   	55.09108    	14          
  500         	35.12914    	-26.92480   	52.64425    	24          
  epoch 737 reach nan state
---> try to load best state
              	17.22031    	0.12289     	36.14173    	35               eval state : test_on_restore;
              	35.12914    	-26.92480   	52.64425    	35               eval state : test_on_last_epoch;
---> final result
              	17.22031    	0.12289     	36.14173    	35               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	137.24432   	-1.16643    	232.12807   	0           
  10          	84.68239    	-4.90853    	129.79327   	1           
  100         	39.89983    	-52.39614   	57.55672    	9           
  300         	37.33823    	-30.34180   	53.65427    	29          
  500         	31.15123    	-11.07599   	52.69312    	48          
  epoch 917 reach nan state
---> try to load best state
              	46.11957    	0.33063     	89.09126    	87               eval state : test_on_restore;
              	31.15123    	-11.07599   	52.69312    	87               eval state : test_on_last_epoch;
---> final result
              	31.15123    	-11.07599   	52.69312    	87               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.77636   	-0.65836    	198.01884   	0           
  10          	76.56942    	-4.82372    	113.47724   	1           
  100         	40.37621    	-57.54189   	58.23205    	11          
  300         	37.59924    	-43.96905   	52.62335    	35          
  500         	32.68243    	-44.71723   	47.69973    	57          
  epoch 731 reach nan state
---> try to load best state
              	13.36175    	0.42035     	26.59177    	84               eval state : test_on_restore;
              	32.68243    	-44.71723   	47.69973    	84               eval state : test_on_last_epoch;
---> final result
              	13.36175    	0.42035     	26.59177    	84               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	114.93845   	0.06737     	206.29181   	0           
  10          	103.54855   	0.14802     	186.15982   	1           
  100         	62.39831    	0.32032     	116.32999   	9           
  300         	40.86606    	0.29210     	81.52343    	28          
  500         	19.82175    	0.42664     	40.47031    	46          
---> try to load best state
              	37.44952    	-354.47189  	52.58094    	73               eval state : test_on_restore;
              	309.01166   	-300.57084  	393.12183   	73               eval state : test_on_last_epoch;
---> final result              	37.44952    	-354.47189  	52.58094    	73               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	652.98889   	-120.14795  	847.24420   	0           
  10          	182.40013   	-47.70455   	250.35350   	0           
  100         	40.82159    	-115.68961  	62.64697    	1           
  300         	43.42305    	-92.82989   	62.83425    	4           
  500         	45.52986    	-13.73811   	68.13230    	7           
  epoch 877 reach nan state
---> try to load best state
              	243.57222   	-1.42711    	395.40320   	13               eval state : test_on_restore;
              	45.52986    	-13.73811   	68.13230    	13               eval state : test_on_last_epoch;
---> final result
              	45.52986    	-13.73811   	68.13230    	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	487.08139   	-10.03985   	760.66418   	0           
  10          	136.96152   	-4.64643    	204.49118   	0           
  100         	40.68986    	-145.92671  	59.59654    	2           
  300         	36.65215    	-39.70052   	53.85711    	5           
  500         	37.27080    	-60.11968   	54.57745    	9           
  epoch 891 reach nan state
---> try to load best state
              	206.79182   	-0.67157    	397.50403   	17               eval state : test_on_restore;
              	37.27080    	-60.11968   	54.57745    	17               eval state : test_on_last_epoch;
---> final result
              	37.27080    	-60.11968   	54.57745    	17               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	380.18936   	-3.34523    	591.90723   	0           
  10          	119.32756   	-2.53982    	176.02820   	0           
  100         	40.88565    	-94.27239   	61.79515    	3           
  300         	39.83580    	-62.51329   	58.94956    	8           
  500         	38.45890    	-50.31035   	57.53967    	14          
  epoch 767 reach nan state
---> try to load best state
              	34.41835    	-0.89990    	81.74464    	21               eval state : test_on_restore;
              	38.45890    	-50.31035   	57.53967    	22               eval state : test_on_last_epoch;
---> final result
              	38.45890    	-50.31035   	57.53967    	22               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	251.33372   	-1.72543    	405.32489   	0           
  10          	104.40331   	-3.43178    	153.98840   	0           
  100         	41.55882    	-82.91239   	60.69980    	5           
  300         	39.08935    	-43.30346   	55.81426    	15          
  500         	33.46529    	-15.97746   	48.79096    	24          
  epoch 732 reach nan state
---> try to load best state
              	21.13116    	0.06866     	43.31425    	35               eval state : test_on_restore;
              	33.46529    	-15.97746   	48.79096    	35               eval state : test_on_last_epoch;
---> final result
              	21.13116    	0.06866     	43.31425    	35               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	148.02147   	-0.97656    	257.73962   	0           
  10          	86.84933    	-4.95391    	131.64468   	1           
  100         	39.31306    	-50.81557   	56.90227    	10          
  300         	36.89314    	-28.80873   	52.04501    	28          
  500         	31.51487    	-23.84440   	47.17386    	45          
  epoch 722 reach nan state
---> try to load best state
              	18.32997    	0.19775     	39.23451    	65               eval state : test_on_restore;
              	31.51487    	-23.84440   	47.17386    	65               eval state : test_on_last_epoch;
---> final result
              	18.32997    	0.19775     	39.23451    	65               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.78275   	-0.65840    	198.02309   	0           
  10          	76.64255    	-4.83745    	113.55833   	1           
  100         	40.85325    	-49.99361   	59.28372    	11          
  300         	35.30932    	-19.59336   	51.90105    	33          
  500         	32.60683    	-32.33242   	47.67730    	55          
  epoch 638 reach nan state
---> try to load best state
              	11.61504    	-0.11441    	22.41470    	69               eval state : test_on_restore;
              	32.60683    	-32.33242   	47.67730    	69               eval state : test_on_last_epoch;
---> final result
              	11.61504    	-0.11441    	22.41470    	70               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	114.93842   	0.06737     	206.29158   	0           
  10          	103.54831   	0.14802     	186.15901   	1           
  100         	62.40319    	0.32041     	116.34779   	10          
  300         	40.91904    	0.29022     	81.65702    	29          
  500         	19.82655    	0.42527     	40.47184    	47          
---> try to load best state
              	37.15964    	-347.70785  	52.27311    	75               eval state : test_on_restore;
              	308.21902   	-300.72272  	392.08041   	75               eval state : test_on_last_epoch;
---> final result              	37.15964    	-347.70785  	52.27311    	75               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	465.15976   	-38.04871   	721.49030   	0           
  10          	148.75066   	-15.43663   	227.55078   	0           
  100         	38.66674    	-89.84761   	55.10967    	1           
  300         	39.28516    	-72.87387   	56.58269    	4           
  500         	35.88070    	-27.77376   	51.35564    	6           
  epoch 986 reach nan state
---> try to load best state
              	449.46811   	-9.43613    	717.86182   	13               eval state : test_on_restore;
              	35.88070    	-27.77376   	51.35564    	13               eval state : test_on_last_epoch;
---> final result
              	35.88070    	-27.77376   	51.35564    	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	400.66626   	-15.95814   	631.47107   	0           
  10          	133.49596   	-11.10653   	206.87721   	0           
  100         	48.29070    	-33.51917   	70.85911    	2           
  300         	40.06667    	-93.47569   	56.98577    	6           
  500         	35.35690    	-34.79648   	50.45028    	9           
  epoch 750 reach nan state
---> try to load best state
              	90.25697    	-0.03542    	177.90982   	15               eval state : test_on_restore;
              	35.35690    	-34.79648   	50.45028    	15               eval state : test_on_last_epoch;
---> final result
              	35.35690    	-34.79648   	50.45028    	15               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	296.54514   	-3.92928    	482.71988   	0           
  10          	110.98491   	-4.04586    	181.94438   	0           
  100         	44.03651    	-25.17176   	63.97083    	3           
  300         	38.43307    	-52.39808   	54.42865    	8           
  500         	35.75225    	-47.94358   	50.30119    	14          
  epoch 811 reach nan state
---> try to load best state
              	74.52250    	0.16817     	159.68776   	23               eval state : test_on_restore;
              	35.75225    	-47.94358   	50.30119    	23               eval state : test_on_last_epoch;
---> final result
              	35.75225    	-47.94358   	50.30119    	23               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	211.51958   	-2.79299    	367.29266   	0           
  10          	92.59748    	-2.30649    	153.22800   	0           
  100         	43.92614    	-47.27510   	61.98840    	5           
  300         	38.39189    	-45.04387   	54.47504    	15          
  500         	31.18872    	-5.86951    	48.27186    	24          
  epoch 788 reach nan state
---> try to load best state
              	23.92510    	0.17319     	52.08183    	38               eval state : test_on_restore;
              	31.18872    	-5.86951    	48.27186    	38               eval state : test_on_last_epoch;
---> final result
              	31.18872    	-5.86951    	48.27186    	38               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	125.71793   	-1.62265    	211.04309   	0           
  10          	75.91545    	-3.15051    	121.73888   	1           
  100         	38.97416    	-52.61430   	55.35971    	9           
  300         	37.84400    	-37.07041   	53.30037    	29          
  500         	27.41763    	-3.48582    	47.09958    	47          
  epoch 742 reach nan state
---> try to load best state
              	15.69369    	0.39981     	29.97914    	70               eval state : test_on_restore;
              	27.41763    	-3.48582    	47.09958    	70               eval state : test_on_last_epoch;
---> final result
              	15.69369    	0.39981     	29.97914    	71               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.75836   	-0.65827    	197.99373   	0           
  10          	76.51514    	-4.81449    	113.42786   	1           
  100         	40.02720    	-51.45584   	58.09089    	12          
  300         	36.71199    	-33.35009   	53.52843    	34          
  500         	31.36608    	-19.87131   	47.47261    	57          
  epoch 834 reach nan state
---> try to load best state
              	16.52004    	0.39099     	31.70747    	93               eval state : test_on_restore;
              	31.36608    	-19.87131   	47.47261    	93               eval state : test_on_last_epoch;
---> final result
              	16.52004    	0.39099     	31.70747    	93               eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	114.93845   	0.06737     	206.29166   	0           
  10          	103.54855   	0.14802     	186.15971   	0           
  100         	62.39212    	0.32041     	116.31940   	8           
  300         	40.93347    	0.29018     	81.71949    	26          
  500         	19.82598    	0.42620     	40.47697    	43          
---> try to load best state
              	37.15793    	-347.72603  	52.26685    	69               eval state : test_on_restore;
              	308.14749   	-300.83328  	391.98492   	69               eval state : test_on_last_epoch;
---> final result              	37.15793    	-347.72603  	52.26685    	69               eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	605.14117   	-164.28630  	858.60699   	0           
  10          	176.28018   	-43.40279   	255.71452   	0           
  100         	46.76706    	-259.09428  	79.62263    	1           
  300         	53.24379    	-56.83105   	85.06668    	3           
  500         	45.75892    	-43.29724   	76.17524    	6           
  epoch 901 reach nan state
---> try to load best state
              	501.61816   	-12.20943   	804.92010   	11               eval state : test_on_restore;
              	45.75892    	-43.29724   	76.17524    	11               eval state : test_on_last_epoch;
---> final result
              	45.75892    	-43.29724   	76.17524    	11               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	340.40479   	-12.68279   	522.88116   	0           
  10          	140.67940   	-7.69563    	216.22195   	0           
  100         	45.68633    	-38.14898   	68.07004    	2           
  300         	38.10582    	-78.80939   	55.60587    	6           
  500         	35.91929    	-45.36626   	52.04595    	10          
  epoch 929 reach nan state
---> try to load best state
              	112.44057   	-0.12781    	210.86737   	19               eval state : test_on_restore;
              	35.91929    	-45.36626   	52.04595    	19               eval state : test_on_last_epoch;
---> final result
              	35.91929    	-45.36626   	52.04595    	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	254.31544   	-4.89419    	421.69360   	0           
  10          	104.97469   	-8.24048    	163.11110   	0           
  100         	41.80579    	-70.50948   	59.32091    	3           
  300         	37.83244    	-63.28256   	53.46554    	8           
  500         	35.05014    	-26.16229   	49.63968    	14          
  epoch 838 reach nan state
---> try to load best state
              	74.66579    	0.29255     	147.06348   	23               eval state : test_on_restore;
              	35.05014    	-26.16229   	49.63968    	23               eval state : test_on_last_epoch;
---> final result
              	35.05014    	-26.16229   	49.63968    	23               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	168.71555   	-1.24411    	269.98938   	0           
  10          	86.19247    	-3.39483    	128.73740   	0           
  100         	39.93621    	-72.98371   	57.77365    	5           
  300         	38.14238    	-57.37975   	54.92519    	15          
  500         	29.97183    	-22.31343   	47.80773    	24          
  epoch 792 reach nan state
---> try to load best state
              	31.47793    	-1.85412    	55.90154    	38               eval state : test_on_restore;
              	29.97183    	-22.31343   	47.80773    	38               eval state : test_on_last_epoch;
---> final result
              	29.97183    	-22.31343   	47.80773    	38               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 64 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	123.61240   	-1.24610    	198.20879   	0           
  10          	80.48277    	-5.31835    	121.00895   	1           
  100         	39.47187    	-51.12607   	56.34284    	9           
  300         	36.28289    	-33.35131   	51.80457    	29          
  500         	29.95286    	-20.67038   	48.15699    	47          
  epoch 848 reach nan state
---> try to load best state
              	17.14061    	0.41445     	32.98919    	80               eval state : test_on_restore;
              	29.95286    	-20.67038   	48.15699    	80               eval state : test_on_last_epoch;
---> final result
              	17.14061    	0.41445     	32.98919    	80               eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 128 samples
---> Training x,yl -> yh part

---> 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': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: True
  input_normalize: True
  output_normalize: True
  noise_init: 1.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True, 'squeeze_to_01': False}
  input_normalzie: True
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	119.75838   	-0.65828    	197.99326   	0           
  10          	76.51729    	-4.81066    	113.43349   	1           
  100         	40.16790    	-58.68786   	58.71384    	12          
  300         	36.70517    	-39.92645   	52.50615    	36          
  500         	26.64207    	-17.13384   	41.99090    	58          
  epoch 631 reach nan state
---> try to load best state
              	24.23726    	-7.47685    	37.41542    	73               eval state : test_on_restore;
              	26.64207    	-17.13384   	41.99090    	73               eval state : test_on_last_epoch;
---> final result
              	24.23726    	-7.47685    	37.41542    	73               eval state : final;module_name : GAR;cp_record_file : True;
