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

  Demo GAR 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, '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']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  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           	102.63653   	-0.18402    	142.05165   	0           
  10          	92.96957    	0.15149     	130.56879   	0           
  100         	31.32136    	0.94371     	46.79470    	4           
  300         	11.81661    	0.99274     	18.25283    	12          
  500         	8.04813     	0.99673     	12.46513    	20          
  1000        	5.13114     	0.99875     	7.81910     	40          
---> try to load best state
              	5.13114     	0.99875     	7.81910     	40               eval state : test_on_restore;
              	5.13114     	0.99875     	7.81910     	41               eval state : test_on_last_epoch;
---> final result              	5.13114     	0.99875     	7.81910     	41               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 30, '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: False
  input_normalize: 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           	184.94458   	0.51181     	244.65782   	0           
  10          	174.47928   	-13.93039   	224.73209   	0           
  100         	153.07765   	-3.97309    	196.45819   	1           
  300         	159.50844   	-8.08901    	198.82930   	4           
  500         	164.65138   	-9.33011    	204.15938   	7           
  1000        	155.29732   	-5.17792    	194.41579   	13          
---> try to load best state
              	155.29732   	-5.17792    	194.41579   	13               eval state : test_on_restore;
              	155.29732   	-5.17792    	194.41579   	13               eval state : test_on_last_epoch;
---> final result
              	155.29732   	-5.17792    	194.41579   	13               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 28, '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: False
  input_normalize: 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           	194.65892   	0.58152     	252.36098   	0           
  10          	164.24147   	-7.74635    	214.31441   	0           
  100         	74.20886    	0.47208     	101.73700   	2           
  300         	81.51443    	0.33441     	113.47575   	6           
  500         	80.85603    	0.34318     	112.59260   	9           
  1000        	73.22106    	0.49478     	103.12837   	19          
---> try to load best state
              	73.22106    	0.49478     	103.12837   	19               eval state : test_on_restore;
              	73.22106    	0.49478     	103.12837   	19               eval state : test_on_last_epoch;
---> final result
              	73.22106    	0.49478     	103.12837   	19               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 24, '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: False
  input_normalize: 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           	110.39195   	0.62143     	158.61853   	0           
  10          	122.73619   	-1.51050    	164.94232   	0           
  100         	46.53829    	0.87441     	65.86879    	3           
  300         	19.56681    	0.98194     	28.47099    	8           
  500         	16.45685    	0.98766     	23.89109    	14          
  1000        	10.08585    	0.99555     	14.67202    	26          
---> try to load best state
              	10.08585    	0.99555     	14.67202    	27               eval state : test_on_restore;
              	10.08585    	0.99555     	14.67202    	27               eval state : test_on_last_epoch;
---> final result
              	10.08585    	0.99555     	14.67202    	27               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 16, '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: False
  input_normalize: 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           	105.89560   	0.61156     	157.86494   	0           
  10          	95.20000    	-0.27665    	137.03563   	0           
  100         	41.53449    	0.89821     	59.13605    	5           
  300         	15.81044    	0.98756     	23.23445    	15          
  500         	9.44611     	0.99579     	13.91987    	23          
  epoch 892 reach nan state
---> try to load best state
              	6.02805     	0.99835     	8.90440     	38               eval state : test_on_restore;
              	9.44611     	0.99579     	13.91987    	38               eval state : test_on_last_epoch;
---> final result
              	6.02805     	0.99835     	8.90440     	38               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, '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_normalize: True
  output_normalize: True
  noise_init: 100.0
  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           	114.59908   	-0.85171    	155.61711   	0           
  10          	104.38416   	-0.27859    	143.84470   	0           
  100         	38.95272    	0.90823     	58.75074    	3           
  300         	15.93553    	0.98667     	24.51546    	10          
  500         	10.07255    	0.99491     	15.47637    	16          
  epoch 697 reach nan state
---> try to load best state
              	11.04782    	0.99503     	14.39966    	23               eval state : test_on_restore;
              	46.37880    	0.88466     	59.54482    	23               eval state : test_on_last_epoch;
---> final result              	11.04782    	0.99503     	14.39966    	23               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 30, '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: False
  input_normalize: 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           	189.95917   	-0.26308    	250.14720   	0           
  10          	165.82990   	-8.21203    	215.21262   	0           
  100         	180.17700   	-33.19287   	218.88002   	1           
  300         	184.79767   	-88.26688   	222.10448   	3           
  500         	149.57726   	-10.49167   	181.52567   	5           
  1000        	189.66638   	-128.39329  	227.06619   	11          
---> try to load best state
              	189.66638   	-128.39329  	227.06619   	11               eval state : test_on_restore;
              	189.66638   	-128.39329  	227.06619   	11               eval state : test_on_last_epoch;
---> final result
              	189.66638   	-128.39329  	227.06619   	11               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 28, '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: False
  input_normalize: 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           	157.22787   	0.49528     	209.89131   	0           
  10          	139.24213   	-3.08549    	187.49701   	0           
  100         	53.41382    	0.83339     	75.33325    	2           
  300         	58.70733    	0.76366     	81.15501    	6           
  500         	67.22745    	0.64507     	93.26101    	10          
  1000        	60.73266    	0.72606     	84.44392    	18          
---> try to load best state
              	60.73266    	0.72606     	84.44392    	18               eval state : test_on_restore;
              	60.73266    	0.72606     	84.44392    	18               eval state : test_on_last_epoch;
---> final result
              	60.73266    	0.72606     	84.44392    	18               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 24, '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: False
  input_normalize: 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           	132.42085   	0.72786     	187.37740   	0           
  10          	113.65401   	-1.03375    	158.70802   	0           
  100         	47.47382    	0.86229     	67.48799    	3           
  300         	15.19774    	0.98982     	21.92619    	8           
  500         	13.46082    	0.99189     	19.30366    	13          
  1000        	9.30699     	0.99626     	13.27948    	27          
---> try to load best state
              	9.30699     	0.99626     	13.27948    	27               eval state : test_on_restore;
              	9.30699     	0.99626     	13.27948    	27               eval state : test_on_last_epoch;
---> final result
              	9.30699     	0.99626     	13.27948    	27               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 16, '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: False
  input_normalize: 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           	187.99278   	0.62551     	274.85367   	0           
  10          	97.94595    	-0.12019    	149.57138   	0           
  100         	40.87627    	0.90179     	60.59107    	5           
  300         	19.21012    	0.98097     	28.84583    	14          
  500         	12.74390    	0.99192     	19.18397    	24          
  1000        	7.00423     	0.99767     	10.56426    	46          
---> try to load best state
              	7.00423     	0.99767     	10.56426    	46               eval state : test_on_restore;
              	7.00423     	0.99767     	10.56426    	46               eval state : test_on_last_epoch;
---> final result
              	7.00423     	0.99767     	10.56426    	46               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, '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_normalize: True
  output_normalize: True
  noise_init: 100.0
  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           	104.64944   	-0.51081    	144.70587   	0           
  10          	94.74729    	-0.06936    	133.54449   	0           
  100         	34.73892    	0.91804     	54.20252    	3           
  300         	12.42046    	0.99072     	20.25590    	11          
  500         	7.63927     	0.99664     	12.49692    	19          
  epoch 682 reach nan state
---> try to load best state
              	8.49091     	0.99680     	11.92641    	26               eval state : test_on_restore;
              	38.22908    	0.94264     	47.49974    	26               eval state : test_on_last_epoch;
---> final result              	8.49091     	0.99680     	11.92641    	26               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 30, '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: False
  input_normalize: 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           	156.40811   	-14.19269   	197.99243   	0           
  10          	185.44646   	-38.76786   	231.08722   	0           
  100         	187.55977   	-111.01357  	225.90999   	1           
  300         	195.05534   	-178.57177  	234.20757   	4           
  500         	182.71774   	-102.67442  	219.10233   	7           
  1000        	185.28328   	-203.88493  	222.12979   	14          
---> try to load best state
              	185.28328   	-203.88493  	222.12979   	14               eval state : test_on_restore;
              	185.28328   	-203.88493  	222.12979   	14               eval state : test_on_last_epoch;
---> final result
              	185.28328   	-203.88493  	222.12979   	14               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 28, '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: False
  input_normalize: 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           	296.34622   	0.56826     	373.08691   	0           
  10          	186.47148   	-4.76407    	240.41626   	0           
  100         	46.68943    	0.88484     	62.35497    	1           
  300         	57.60385    	0.81203     	75.98112    	4           
  500         	45.67040    	0.89168     	60.42080    	8           
  1000        	41.11991    	0.91690     	54.30053    	15          
---> try to load best state
              	41.11991    	0.91690     	54.30053    	15               eval state : test_on_restore;
              	41.11991    	0.91690     	54.30053    	15               eval state : test_on_last_epoch;
---> final result
              	41.11991    	0.91690     	54.30053    	15               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 24, '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: False
  input_normalize: 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           	232.03516   	0.60947     	304.07205   	0           
  10          	153.29526   	-4.03885    	203.35484   	0           
  100         	45.74271    	0.88884     	62.49329    	2           
  300         	27.42944    	0.96442     	38.32848    	6           
  epoch 417 reach nan state
---> try to load best state
              	20.60865    	0.98051     	28.89932    	9                eval state : test_on_restore;
              	27.42944    	0.96442     	38.32848    	10               eval state : test_on_last_epoch;
---> final result
              	20.60865    	0.98051     	28.89932    	10               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 16, '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: False
  input_normalize: 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           	155.43683   	0.71978     	216.04430   	0           
  10          	95.08880    	-0.04815    	138.01450   	0           
  100         	29.30640    	0.95513     	42.92659    	6           
  300         	13.85181    	0.99155     	19.93666    	15          
  epoch 469 reach nan state
---> try to load best state
              	8.47480     	0.99684     	12.45740    	22               eval state : test_on_restore;
              	13.85181    	0.99155     	19.93666    	22               eval state : test_on_last_epoch;
---> final result
              	8.47480     	0.99684     	12.45740    	22               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, '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_normalize: True
  output_normalize: True
  noise_init: 100.0
  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           	101.12260   	-0.28375    	137.60913   	0           
  10          	89.00589    	0.17681     	123.01244   	0           
  100         	27.32581    	0.96226     	39.61827    	3           
  300         	9.48331     	0.99586     	14.12891    	10          
  500         	6.04769     	0.99837     	9.00857     	17          
  epoch 790 reach nan state
---> try to load best state
              	7.33936     	0.99822     	9.15904     	26               eval state : test_on_restore;
              	56.34794    	0.89605     	67.61312    	26               eval state : test_on_last_epoch;
---> final result              	7.33936     	0.99822     	9.15904     	26               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 30, '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: False
  input_normalize: 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           	209.94247   	0.31108     	273.30249   	0           
  10          	183.89192   	-41.43185   	225.70695   	0           
  100         	111.08714   	-0.89703    	142.28067   	1           
  300         	129.89502   	-3.27827    	161.63045   	3           
  500         	95.03110    	0.05062     	120.05132   	5           
  1000        	124.51353   	-3.86637    	155.16638   	10          
---> try to load best state
              	124.51353   	-3.86637    	155.16638   	10               eval state : test_on_restore;
              	124.51353   	-3.86637    	155.16638   	10               eval state : test_on_last_epoch;
---> final result
              	124.51353   	-3.86637    	155.16638   	10               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 28, '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: False
  input_normalize: 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           	198.33272   	-0.00151    	265.03647   	0           
  10          	156.93933   	-6.71849    	202.86935   	0           
  100         	86.35361    	0.41152     	113.63348   	1           
  300         	72.34792    	0.65512     	97.24471    	4           
  500         	70.53576    	0.67894     	94.42391    	7           
  1000        	64.63311    	0.74584     	87.05550    	15          
---> try to load best state
              	64.63311    	0.74584     	87.05550    	15               eval state : test_on_restore;
              	64.63311    	0.74584     	87.05550    	15               eval state : test_on_last_epoch;
---> final result
              	64.63311    	0.74584     	87.05550    	15               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 24, '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: False
  input_normalize: 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           	209.76003   	0.63411     	283.67419   	0           
  10          	129.15131   	-1.89204    	181.52966   	0           
  100         	45.66158    	0.88520     	63.70998    	2           
  300         	16.92310    	0.98833     	23.25052    	6           
  500         	13.75618    	0.99178     	19.86589    	11          
  1000        	10.46719    	0.99563     	14.89192    	25          
---> try to load best state
              	10.46719    	0.99563     	14.89192    	25               eval state : test_on_restore;
              	10.46719    	0.99563     	14.89192    	25               eval state : test_on_last_epoch;
---> final result
              	10.46719    	0.99563     	14.89192    	25               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 16, '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: False
  input_normalize: 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           	150.76511   	0.71882     	210.88158   	0           
  10          	92.11717    	0.15978     	132.17805   	0           
  100         	32.09105    	0.95367     	44.67974    	5           
  300         	12.43381    	0.99358     	17.81405    	15          
  500         	7.96488     	0.99743     	11.40542    	24          
  epoch 998 reach nan state
---> try to load best state
              	4.59099     	0.99918     	6.54495     	42               eval state : test_on_restore;
              	7.96488     	0.99743     	11.40542    	42               eval state : test_on_last_epoch;
---> final result
              	4.59099     	0.99918     	6.54495     	43               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, '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_normalize: True
  output_normalize: True
  noise_init: 100.0
  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           	101.98813   	-0.34748    	140.21355   	0           
  10          	91.18791    	0.08011     	127.43176   	0           
  100         	32.13396    	0.94141     	47.48493    	3           
  300         	12.09105    	0.99274     	18.18896    	9           
  500         	7.86163     	0.99705     	11.81056    	16          
  epoch 754 reach nan state
---> try to load best state
              	5.82320     	0.99848     	8.54487     	25               eval state : test_on_restore;
              	58.51509    	0.89958     	72.33409    	25               eval state : test_on_last_epoch;
---> final result              	5.82320     	0.99848     	8.54487     	25               eval state : final;
----------> finish x-yl training


---------->
GAR for 2.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 30, '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: False
  input_normalize: 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           	154.41541   	-0.58337    	200.28886   	0           
  10          	186.97931   	-10.10683   	238.90695   	0           
  100         	154.46593   	-4.17167    	197.20650   	1           
  300         	145.14308   	-4.64006    	181.37970   	3           
  500         	160.41397   	-7.85118    	200.63411   	5           
  1000        	162.44830   	-7.93359    	204.00433   	10          
---> try to load best state
              	162.44830   	-7.93359    	204.00433   	10               eval state : test_on_restore;
              	162.44830   	-7.93359    	204.00433   	10               eval state : test_on_last_epoch;
---> final result
              	162.44830   	-7.93359    	204.00433   	10               eval state : final;module_name : GAR;subset : 2.0;cp_record_file : True;
---> end


---------->
GAR for 4.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 28, '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: False
  input_normalize: 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           	279.89816   	0.57887     	364.75989   	0           
  10          	173.70468   	-2.74919    	234.18158   	0           
  100         	78.28077    	0.54581     	104.37822   	1           
  300         	46.21677    	0.88328     	62.79774    	4           
  500         	49.92887    	0.85149     	68.90109    	7           
  1000        	39.72458    	0.91035     	55.58058    	14          
---> try to load best state
              	39.72458    	0.91035     	55.58058    	14               eval state : test_on_restore;
              	39.72458    	0.91035     	55.58058    	14               eval state : test_on_last_epoch;
---> final result
              	39.72458    	0.91035     	55.58058    	15               eval state : final;module_name : GAR;subset : 4.0;cp_record_file : True;
---> end


---------->
GAR for 8.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 24, '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: False
  input_normalize: 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           	238.84386   	0.60409     	322.97287   	0           
  10          	135.22075   	-1.73596    	187.58421   	0           
  100         	32.99181    	0.94527     	46.89200    	2           
  300         	13.47947    	0.99228     	19.35524    	6           
  500         	12.04113    	0.99405     	17.08062    	13          
  1000        	7.49256     	0.99780     	10.46923    	25          
---> try to load best state
              	7.49256     	0.99780     	10.46923    	26               eval state : test_on_restore;
              	7.49256     	0.99780     	10.46923    	26               eval state : test_on_last_epoch;
---> final result
              	7.49256     	0.99780     	10.46923    	26               eval state : final;module_name : GAR;subset : 8.0;cp_record_file : True;
---> end


---------->
GAR for 16.0 subset samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'poisson_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 16, '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: False
  input_normalize: 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           	149.27106   	0.70893     	222.10846   	0           
  10          	85.15594    	0.22388     	129.77054   	0           
  100         	27.52242    	0.96090     	41.04432    	4           
  300         	10.22461    	0.99512     	15.45532    	14          
  500         	6.48510     	0.99813     	9.70436     	23          
  epoch 653 reach nan state
---> try to load best state
              	5.01578     	0.99889     	7.47879     	30               eval state : test_on_restore;
              	6.48510     	0.99813     	9.70436     	30               eval state : test_on_last_epoch;
---> final result
              	5.01578     	0.99889     	7.47879     	30               eval state : final;module_name : GAR;subset : 16.0;cp_record_file : True;
