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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> 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]'], '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: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	130.65968   	0.04878     	230.48550   	0           
  10          	115.67155   	0.16104     	207.17157   	0           
  100         	34.86905    	0.41580     	63.66299    	3           
----------------------------------------

  Demo GAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 10.0
  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        	gaussian_loss      	time        
  1           	0.05934     	-0.47044    	0.13908     	7.43657            	0           
  10          	0.05452     	-0.11993    	0.12545     	0.24769            	1           
  100         	0.02560     	0.55865     	0.04910     	14.70881           	7           
  300         	0.01266     	0.64301     	0.02177     	35.21103           	22          
  500         	0.01055     	0.65380     	0.01881     	46.62729           	36          
  1000        	0.00977     	0.66327     	0.01802     	67.60952           	74          
---> try to load best state
              	0.00977     	0.66327     	0.01802     	67.60952           	74               eval state : test_on_restore;
              	0.00977     	0.66327     	0.01802     	67.60952           	74               eval state : test_on_last_epoch;
---> final result              	0.00977     	0.66327     	0.01802     	67.60952           	74               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.08171     	-64.90161   	0.14845     	-0.20508           	21          
  10          	0.06438     	-4.57776    	0.11397     	-9.20805           	62          
  100         	0.04021     	0.29992     	0.06467     	-53.04290          	302         
  300         	0.03266     	0.54024     	0.05309     	-72.22675          	817         
  500         	0.03280     	0.54736     	0.05427     	-76.85657          	1323        
  1000        	0.05266     	-3.15218    	0.08755     	-63.64771          	2575        
---> try to load best state
              	0.05266     	-3.15218    	0.08755     	-63.64771          	2594             eval state : test_on_restore;
              	0.05266     	-3.15218    	0.08755     	-63.64771          	2613             eval state : test_on_last_epoch;
---> final result
              	0.05266     	-3.15218    	0.08755     	-63.64771          	2632             eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.07054     	-8.98100    	0.13071     	1.44436            	24          
  10          	0.05357     	-1.13309    	0.09662     	-6.95781           	83          
  100         	0.03025     	0.63755     	0.05228     	-41.58442          	484         
  300         	0.02576     	0.74118     	0.04502     	-55.48932          	1353        
  500         	0.02632     	0.73666     	0.04606     	-61.92773          	2225        
  1000        	0.04054     	-0.07438    	0.07181     	-57.05843          	4381        
---> try to load best state
              	0.04054     	-0.07438    	0.07181     	-57.05843          	4401             eval state : test_on_restore;
              	0.04054     	-0.07438    	0.07181     	-57.05843          	4422             eval state : test_on_last_epoch;
---> final result
              	0.04054     	-0.07438    	0.07181     	-57.05843          	4442             eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.06012     	-3.27581    	0.11455     	3.76095            	34          
  10          	0.04563     	-0.40793    	0.08425     	-4.21171           	132         
  100         	0.02725     	0.70090     	0.04770     	-33.97770          	880         
  300         	0.02499     	0.76061     	0.04388     	-43.27121          	2527        
  500         	0.02526     	0.76060     	0.04446     	-46.34115          	4161        
  1000        	0.03164     	0.56064     	0.05738     	-40.21213          	8244        
---> try to load best state
              	0.03164     	0.56064     	0.05738     	-40.21213          	8268             eval state : test_on_restore;
              	0.03164     	0.56064     	0.05738     	-40.21213          	8292             eval state : test_on_last_epoch;
---> final result
              	0.03164     	0.56064     	0.05738     	-40.21213          	8316             eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.04723     	-0.57952    	0.09383     	6.26902            	57          
  10          	0.03625     	0.28450     	0.06815     	-1.26354           	935         
  100         	0.02205     	0.77716     	0.03980     	-30.11927          	3048        
  300         	0.02128     	0.80262     	0.03943     	-38.65006          	6911        
  500         	0.02146     	0.80426     	0.03971     	-40.94168          	10859       
  1000        	0.02476     	0.74754     	0.04593     	-36.41013          	20755       
---> try to load best state
              	0.02476     	0.74754     	0.04593     	-36.41013          	20786            eval state : test_on_restore;
              	0.02476     	0.74754     	0.04593     	-36.41013          	20818            eval state : test_on_last_epoch;
---> final result
              	0.02476     	0.74754     	0.04593     	-36.41013          	20849            eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 10.0
  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        	gaussian_loss      	time        
  1           	0.05447     	-0.23507    	0.12842     	7.54935            	0           
  10          	0.04953     	0.04061     	0.11517     	0.27778            	1           
  100         	0.02406     	0.56532     	0.04563     	11.93346           	7           
  300         	0.01295     	0.63984     	0.02180     	30.90039           	21          
  500         	0.01109     	0.65366     	0.01931     	42.38539           	35          
  1000        	0.01032     	0.66328     	0.01851     	61.49260           	71          
---> try to load best state
              	0.01032     	0.66328     	0.01851     	61.49260           	71               eval state : test_on_restore;
              	0.01032     	0.66328     	0.01851     	61.49260           	71               eval state : test_on_last_epoch;
---> final result              	0.01032     	0.66328     	0.01851     	61.49260           	71               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', '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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.08235     	-36.30819   	0.15020     	0.23541            	22          
  10          	0.06230     	-1.63716    	0.10695     	-8.66761           	70          
  100         	0.04116     	0.39237     	0.06809     	-49.54989          	340         
  300         	0.03056     	0.62312     	0.05146     	-77.80787          	858         
  500         	0.03064     	0.64166     	0.05105     	-87.53555          	1388        
  1000        	0.04197     	0.11895     	0.06774     	-83.73463          	2657        
---> try to load best state
              	0.04197     	0.11895     	0.06774     	-83.73463          	2676             eval state : test_on_restore;
              	0.04197     	0.11895     	0.06774     	-83.73463          	2695             eval state : test_on_last_epoch;
---> final result
              	0.04197     	0.11895     	0.06774     	-83.73463          	2714             eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', '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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.06784     	-6.85088    	0.12708     	2.23038            	24          
  10          	0.05066     	-0.69066    	0.09042     	-5.98273           	84          
  100         	0.03233     	0.60276     	0.05507     	-41.02615          	498         
  300         	0.02762     	0.70960     	0.04745     	-55.70329          	1398        
  500         	0.02799     	0.70252     	0.04795     	-61.45797          	2302        
  1000        	0.04029     	0.07819     	0.07001     	-56.01588          	4526        
---> try to load best state
              	0.04029     	0.07819     	0.07001     	-56.01588          	4546             eval state : test_on_restore;
              	0.04029     	0.07819     	0.07001     	-56.01588          	4567             eval state : test_on_last_epoch;
---> final result
              	0.04029     	0.07819     	0.07001     	-56.01588          	4588             eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'NavierStock_mfGent_v1_02', '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', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	0.06128     	-3.31028    	0.11766     	3.14171            	32          
  10          	0.04579     	-0.25560    	0.08404     	-4.81413           	133         
  100         	0.02700     	0.70970     	0.04673     	-37.73146          	935         
  300         	0.02484     	0.75964     	0.04405     	-50.33763          	2693        
  500         	0.02538     	0.74673     	0.04489     	-54.63386          	4447        
  1000        	0.03301     	0.46821     	0.06045     	-50.11690          	8804        
---> try to load best state
              	0.03301     	0.46821     	0.06045     	-50.11690          	8828             eval state : test_on_restore;
              	0.03301     	0.46821     	0.06045     	-50.11690          	8852             eval state : test_on_last_epoch;
---> final result
              	0.03301     	0.46821     	0.06045     	-50.11690          	8877             eval state : final;module_name : GAR;cp_record_file : True;
