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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.02135     	0.94414     	0.04551     	0           
  10          	0.02066     	0.94566     	0.04440     	0           
  100         	0.02125     	0.94268     	0.04462     	0           
  300         	0.02217     	0.94091     	0.04609     	2           
  500         	0.02239     	0.94054     	0.04640     	4           
  1000        	0.02255     	0.94028     	0.04666     	9           
---> try to load best state
              	0.02255     	0.94028     	0.04666     	9                eval state : test_on_restore;
              	0.02255     	0.94028     	0.04666     	9                eval state : test_on_last_epoch;
---> final result              	0.02255     	0.94028     	0.04666     	9                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.18692     	-0.18453    	0.25936     	0           
  10          	0.18380     	0.01450     	0.25477     	0           
  100         	0.19579     	0.05759     	0.27242     	0           
  300         	0.19577     	0.05150     	0.27016     	2           
  500         	0.19597     	0.04640     	0.26986     	3           
  1000        	0.19723     	0.02082     	0.27075     	7           
---> try to load best state
              	0.19723     	0.02082     	0.27075     	7                eval state : test_on_restore;
              	0.19723     	0.02082     	0.27075     	7                eval state : test_on_last_epoch;
---> final result
              	0.19723     	0.02082     	0.27075     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.17129     	0.03213     	0.24887     	0           
  10          	0.17020     	0.11045     	0.24684     	0           
  100         	0.16986     	0.18831     	0.24266     	1           
  300         	0.17417     	0.19543     	0.24217     	2           
  500         	0.17536     	0.18946     	0.24271     	4           
  1000        	0.17644     	0.17964     	0.24334     	7           
---> try to load best state
              	0.17644     	0.17964     	0.24334     	7                eval state : test_on_restore;
              	0.17644     	0.17964     	0.24334     	7                eval state : test_on_last_epoch;
---> final result
              	0.17644     	0.17964     	0.24334     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.17615     	0.16367     	0.26604     	0           
  10          	0.18416     	0.14506     	0.27588     	0           
  100         	0.16938     	0.21980     	0.23649     	1           
  300         	0.17417     	0.19847     	0.23820     	2           
  500         	0.17544     	0.19333     	0.23907     	4           
  1000        	0.17615     	0.19214     	0.23946     	9           
---> try to load best state
              	0.17615     	0.19214     	0.23946     	9                eval state : test_on_restore;
              	0.17615     	0.19214     	0.23946     	9                eval state : test_on_last_epoch;
---> final result
              	0.17615     	0.19214     	0.23946     	9                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16060     	0.31051     	0.25220     	0           
  10          	0.15916     	0.30661     	0.24913     	0           
  100         	0.13769     	0.38731     	0.20281     	1           
  300         	0.14107     	0.37619     	0.20375     	4           
  500         	0.14257     	0.36976     	0.20466     	7           
  1000        	0.14372     	0.36455     	0.20545     	14          
---> try to load best state
              	0.14372     	0.36455     	0.20545     	14               eval state : test_on_restore;
              	0.14372     	0.36455     	0.20545     	14               eval state : test_on_last_epoch;
---> final result
              	0.14372     	0.36455     	0.20545     	14               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.15745     	0.49462     	0.26108     	0           
  10          	0.15222     	0.49196     	0.25183     	0           
  100         	0.11026     	0.57539     	0.16930     	2           
  300         	0.11502     	0.55912     	0.17284     	8           
  500         	0.11705     	0.54714     	0.17479     	14          
  epoch 881 reach nan state
---> try to load best state
              	0.11847     	0.53847     	0.17620     	26               eval state : test_on_restore;
              	0.11705     	0.54714     	0.17479     	26               eval state : test_on_last_epoch;
---> final result
              	0.11705     	0.54714     	0.17479     	26               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.10977     	0.55223     	0.19028     	0           
  10          	0.11897     	0.54430     	0.20433     	0           
  100         	0.09412     	0.59864     	0.15901     	7           
  300         	0.08820     	0.65656     	0.14637     	22          
  500         	0.08962     	0.65646     	0.14709     	36          
  1000        	0.09035     	0.65383     	0.14749     	72          
---> try to load best state
              	0.09035     	0.65383     	0.14749     	72               eval state : test_on_restore;
              	0.09035     	0.65383     	0.14749     	72               eval state : test_on_last_epoch;
---> final result
              	0.09035     	0.65383     	0.14749     	72               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': 'TopOP_mfGent_v5', '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: 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           	0.02250     	0.94015     	0.04476     	0           
  10          	0.02172     	0.94346     	0.04331     	0           
  100         	0.02294     	0.93324     	0.04596     	0           
  300         	0.02355     	0.92959     	0.04737     	2           
  500         	0.02372     	0.92865     	0.04764     	4           
  1000        	0.02386     	0.92800     	0.04785     	8           
---> try to load best state
              	0.02386     	0.92800     	0.04785     	8                eval state : test_on_restore;
              	0.02386     	0.92800     	0.04785     	8                eval state : test_on_last_epoch;
---> final result              	0.02386     	0.92800     	0.04785     	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.19142     	-0.29382    	0.25970     	0           
  10          	0.18682     	-0.05120    	0.25508     	0           
  100         	0.21758     	0.18947     	0.29621     	0           
  300         	0.22786     	0.12570     	0.30975     	2           
  500         	0.22816     	0.12603     	0.30969     	3           
  1000        	0.22915     	0.11644     	0.30990     	6           
---> try to load best state
              	0.22915     	0.11644     	0.30990     	6                eval state : test_on_restore;
              	0.22915     	0.11644     	0.30990     	6                eval state : test_on_last_epoch;
---> final result
              	0.22915     	0.11644     	0.30990     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16379     	0.08089     	0.23611     	0           
  10          	0.16368     	0.15518     	0.23543     	0           
  100         	0.17048     	0.23218     	0.23742     	0           
  300         	0.17266     	0.20437     	0.23427     	2           
  500         	0.17275     	0.19187     	0.23409     	4           
  1000        	0.17198     	0.17383     	0.23317     	8           
---> try to load best state
              	0.17198     	0.17383     	0.23317     	8                eval state : test_on_restore;
              	0.17198     	0.17383     	0.23317     	8                eval state : test_on_last_epoch;
---> final result
              	0.17198     	0.17383     	0.23317     	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.15756     	0.20175     	0.23624     	0           
  10          	0.16239     	0.23829     	0.24225     	0           
  100         	0.15824     	0.27762     	0.22551     	1           
  300         	0.17268     	0.22325     	0.23136     	3           
  epoch 449 reach nan state
---> try to load best state
              	0.17741     	0.18741     	0.23511     	5                eval state : test_on_restore;
              	0.17268     	0.22325     	0.23136     	5                eval state : test_on_last_epoch;
---> final result
              	0.17268     	0.22325     	0.23136     	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16372     	0.31152     	0.25557     	0           
  10          	0.16109     	0.31855     	0.25008     	0           
  100         	0.13741     	0.38407     	0.19852     	1           
  300         	0.14291     	0.35830     	0.20131     	5           
  500         	0.14493     	0.34321     	0.20285     	9           
  1000        	0.14708     	0.32579     	0.20460     	18          
---> try to load best state
              	0.14708     	0.32579     	0.20460     	18               eval state : test_on_restore;
              	0.14708     	0.32579     	0.20460     	18               eval state : test_on_last_epoch;
---> final result
              	0.14708     	0.32579     	0.20460     	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16979     	0.34152     	0.26705     	0           
  10          	0.16592     	0.33390     	0.26059     	0           
  100         	0.12527     	0.43689     	0.18656     	3           
  300         	0.12644     	0.43751     	0.18529     	9           
  500         	0.12716     	0.43216     	0.18568     	16          
  1000        	0.12727     	0.42872     	0.18576     	32          
---> try to load best state
              	0.12727     	0.42872     	0.18576     	32               eval state : test_on_restore;
              	0.12727     	0.42872     	0.18576     	32               eval state : test_on_last_epoch;
---> final result
              	0.12727     	0.42872     	0.18576     	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.12065     	0.43451     	0.19885     	0           
  10          	0.12976     	0.34693     	0.21629     	0           
  100         	0.09822     	0.55357     	0.16346     	7           
  300         	0.09560     	0.61706     	0.15162     	22          
  500         	0.09724     	0.61312     	0.15268     	36          
  1000        	0.09815     	0.60619     	0.15327     	69          
---> try to load best state
              	0.09815     	0.60619     	0.15327     	69               eval state : test_on_restore;
              	0.09815     	0.60619     	0.15327     	69               eval state : test_on_last_epoch;
---> final result
              	0.09815     	0.60619     	0.15327     	69               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': 'TopOP_mfGent_v5', '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: 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           	0.02422     	0.93715     	0.04867     	0           
  10          	0.02381     	0.93966     	0.04712     	0           
  100         	0.02407     	0.93486     	0.04823     	0           
  300         	0.02489     	0.93132     	0.05040     	1           
  500         	0.02521     	0.93021     	0.05104     	3           
  1000        	0.02546     	0.92934     	0.05158     	6           
---> try to load best state
              	0.02546     	0.92934     	0.05158     	6                eval state : test_on_restore;
              	0.02546     	0.92934     	0.05158     	6                eval state : test_on_last_epoch;
---> final result              	0.02546     	0.92934     	0.05158     	6                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.20161     	-0.42396    	0.27742     	0           
  10          	0.19774     	-0.30105    	0.27381     	0           
  100         	0.21451     	-0.15487    	0.29611     	0           
  300         	0.23785     	-0.17847    	0.32601     	1           
  500         	0.24223     	-0.18685    	0.33175     	2           
  1000        	0.24740     	-0.23921    	0.33885     	5           
---> try to load best state
              	0.24740     	-0.23921    	0.33885     	5                eval state : test_on_restore;
              	0.24740     	-0.23921    	0.33885     	5                eval state : test_on_last_epoch;
---> final result
              	0.24740     	-0.23921    	0.33885     	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.18413     	-0.10270    	0.26720     	0           
  10          	0.18303     	-0.03338    	0.26570     	0           
  100         	0.18173     	0.08796     	0.26164     	0           
  300         	0.18750     	0.07462     	0.25958     	1           
  500         	0.19129     	0.05944     	0.26096     	3           
  1000        	0.19972     	0.00516     	0.26574     	6           
---> try to load best state
              	0.19972     	0.00516     	0.26574     	6                eval state : test_on_restore;
              	0.19972     	0.00516     	0.26574     	6                eval state : test_on_last_epoch;
---> final result
              	0.19972     	0.00516     	0.26574     	6                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16966     	0.18420     	0.25495     	0           
  10          	0.17650     	0.16379     	0.26291     	0           
  100         	0.16057     	0.28612     	0.23217     	1           
  300         	0.17370     	0.24869     	0.23875     	3           
  epoch 413 reach nan state
---> try to load best state
              	0.17739     	0.22583     	0.24154     	4                eval state : test_on_restore;
              	0.17370     	0.24869     	0.23875     	4                eval state : test_on_last_epoch;
---> final result
              	0.17370     	0.24869     	0.23875     	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16536     	0.36838     	0.25787     	0           
  10          	0.16809     	0.37353     	0.26054     	0           
  100         	0.14089     	0.41760     	0.20733     	2           
  300         	0.14530     	0.38834     	0.21066     	6           
  500         	0.14785     	0.37162     	0.21277     	9           
  1000        	0.14959     	0.35992     	0.21441     	19          
---> try to load best state
              	0.14959     	0.35992     	0.21441     	19               eval state : test_on_restore;
              	0.14959     	0.35992     	0.21441     	19               eval state : test_on_last_epoch;
---> final result
              	0.14959     	0.35992     	0.21441     	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.15027     	0.50298     	0.23898     	0           
  10          	0.14347     	0.52199     	0.22770     	0           
  100         	0.11657     	0.54771     	0.18206     	3           
  300         	0.12050     	0.52095     	0.18577     	10          
  500         	0.12232     	0.50842     	0.18734     	16          
  1000        	0.12329     	0.50072     	0.18824     	32          
---> try to load best state
              	0.12329     	0.50072     	0.18824     	32               eval state : test_on_restore;
              	0.12329     	0.50072     	0.18824     	32               eval state : test_on_last_epoch;
---> final result
              	0.12329     	0.50072     	0.18824     	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.11848     	0.57878     	0.20302     	0           
  10          	0.12589     	0.54418     	0.21709     	0           
  100         	0.09659     	0.60850     	0.15619     	7           
  300         	0.09457     	0.62809     	0.15156     	22          
  500         	0.09593     	0.62618     	0.15290     	37          
  1000        	0.09656     	0.62429     	0.15353     	73          
---> try to load best state
              	0.09656     	0.62429     	0.15353     	73               eval state : test_on_restore;
              	0.09656     	0.62429     	0.15353     	73               eval state : test_on_last_epoch;
---> final result
              	0.09656     	0.62429     	0.15353     	73               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': 'TopOP_mfGent_v5', '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: 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           	0.02279     	0.94325     	0.04634     	0           
  10          	0.02204     	0.94459     	0.04529     	0           
  100         	0.02263     	0.94065     	0.04607     	0           
  300         	0.02311     	0.94005     	0.04679     	2           
  500         	0.02325     	0.93986     	0.04695     	4           
  1000        	0.02334     	0.93977     	0.04708     	8           
---> try to load best state
              	0.02334     	0.93977     	0.04708     	8                eval state : test_on_restore;
              	0.02334     	0.93977     	0.04708     	8                eval state : test_on_last_epoch;
---> final result              	0.02334     	0.93977     	0.04708     	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.21410     	-0.54546    	0.28681     	0           
  10          	0.21460     	-0.38161    	0.28997     	0           
  100         	0.22900     	-0.10081    	0.30129     	1           
  300         	0.21466     	-0.05463    	0.27910     	2           
  500         	0.21382     	-0.05913    	0.27762     	3           
  1000        	0.21679     	-0.15425    	0.28023     	7           
---> try to load best state
              	0.21679     	-0.15425    	0.28023     	7                eval state : test_on_restore;
              	0.21679     	-0.15425    	0.28023     	7                eval state : test_on_last_epoch;
---> final result
              	0.21679     	-0.15425    	0.28023     	7                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.19090     	-0.17030    	0.26658     	0           
  10          	0.19070     	-0.09856    	0.26577     	0           
  100         	0.18689     	0.10638     	0.25168     	1           
  300         	0.18593     	0.13840     	0.24733     	2           
  500         	0.18653     	0.14286     	0.24770     	4           
  1000        	0.18778     	0.13878     	0.24896     	8           
---> try to load best state
              	0.18778     	0.13878     	0.24896     	8                eval state : test_on_restore;
              	0.18778     	0.13878     	0.24896     	8                eval state : test_on_last_epoch;
---> final result
              	0.18778     	0.13878     	0.24896     	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16629     	0.22284     	0.24332     	0           
  10          	0.16729     	0.25797     	0.24381     	0           
  100         	0.15707     	0.36304     	0.22511     	1           
  300         	0.15367     	0.37822     	0.21683     	3           
  500         	0.15354     	0.37933     	0.21633     	6           
  1000        	0.15341     	0.38233     	0.21624     	13          
---> try to load best state
              	0.15341     	0.38233     	0.21624     	13               eval state : test_on_restore;
              	0.15341     	0.38233     	0.21624     	13               eval state : test_on_last_epoch;
---> final result
              	0.15341     	0.38233     	0.21624     	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16411     	0.38792     	0.25029     	0           
  10          	0.16591     	0.37262     	0.25302     	0           
  100         	0.13646     	0.42114     	0.20075     	1           
  300         	0.13656     	0.42538     	0.19784     	5           
  500         	0.13813     	0.41613     	0.19845     	9           
  1000        	0.13925     	0.40674     	0.19898     	19          
---> try to load best state
              	0.13925     	0.40674     	0.19898     	19               eval state : test_on_restore;
              	0.13925     	0.40674     	0.19898     	19               eval state : test_on_last_epoch;
---> final result
              	0.13925     	0.40674     	0.19898     	19               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.14696     	0.52098     	0.23914     	0           
  10          	0.14120     	0.49925     	0.22941     	0           
  100         	0.10902     	0.57409     	0.17221     	3           
  300         	0.11132     	0.56700     	0.17228     	9           
  500         	0.11267     	0.56038     	0.17301     	16          
  1000        	0.11342     	0.55488     	0.17348     	32          
---> try to load best state
              	0.11342     	0.55488     	0.17348     	32               eval state : test_on_restore;
              	0.11342     	0.55488     	0.17348     	32               eval state : test_on_last_epoch;
---> final result
              	0.11342     	0.55488     	0.17348     	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.10327     	0.37878     	0.18011     	0           
  10          	0.11310     	0.36768     	0.20174     	0           
  100         	0.08530     	0.60787     	0.14625     	7           
  300         	0.08289     	0.67584     	0.13699     	22          
  500         	0.08390     	0.67936     	0.13747     	37          
  1000        	0.08452     	0.67984     	0.13779     	73          
---> try to load best state
              	0.08452     	0.67984     	0.13779     	73               eval state : test_on_restore;
              	0.08452     	0.67984     	0.13779     	73               eval state : test_on_last_epoch;
---> final result
              	0.08452     	0.67984     	0.13779     	73               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': 'TopOP_mfGent_v5', '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: 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           	0.01862     	0.97083     	0.03314     	0           
  10          	0.01818     	0.97156     	0.03263     	0           
  100         	0.01825     	0.97019     	0.03221     	0           
  300         	0.01846     	0.97043     	0.03221     	2           
  500         	0.01853     	0.97042     	0.03226     	4           
  1000        	0.01861     	0.97030     	0.03238     	8           
---> try to load best state
              	0.01861     	0.97030     	0.03238     	8                eval state : test_on_restore;
              	0.01861     	0.97030     	0.03238     	8                eval state : test_on_last_epoch;
---> final result              	0.01861     	0.97030     	0.03238     	8                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.20240     	-0.40948    	0.27955     	0           
  10          	0.20279     	-0.24124    	0.28284     	0           
  100         	0.22360     	-0.04076    	0.30284     	0           
  epoch 255 reach nan state
---> try to load best state
              	0.22778     	-0.10999    	0.30061     	1                eval state : test_on_restore;
              	0.22360     	-0.04076    	0.30284     	1                eval state : test_on_last_epoch;
---> final result
              	0.22778     	-0.10999    	0.30061     	1                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.18101     	-0.16276    	0.26017     	0           
  10          	0.17997     	-0.03328    	0.25751     	0           
  100         	0.18126     	0.11753     	0.25029     	0           
  300         	0.18556     	0.09663     	0.24830     	2           
  500         	0.18588     	0.08601     	0.24878     	4           
  1000        	0.18683     	0.07105     	0.24980     	8           
---> try to load best state
              	0.18683     	0.07105     	0.24980     	8                eval state : test_on_restore;
              	0.18683     	0.07105     	0.24980     	8                eval state : test_on_last_epoch;
---> final result
              	0.18683     	0.07105     	0.24980     	8                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.15913     	0.21770     	0.23998     	0           
  10          	0.16332     	0.22913     	0.24395     	0           
  100         	0.14867     	0.31729     	0.21928     	1           
  300         	0.16178     	0.22957     	0.22213     	3           
  500         	0.16548     	0.20113     	0.22501     	6           
  1000        	0.16847     	0.17527     	0.22759     	12          
---> try to load best state
              	0.16847     	0.17527     	0.22759     	12               eval state : test_on_restore;
              	0.16847     	0.17527     	0.22759     	12               eval state : test_on_last_epoch;
---> final result
              	0.16847     	0.17527     	0.22759     	13               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.15432     	0.38480     	0.23844     	0           
  10          	0.15933     	0.36918     	0.24488     	0           
  100         	0.12991     	0.40022     	0.19488     	1           
  300         	0.13427     	0.37633     	0.19566     	5           
  500         	0.13654     	0.36216     	0.19694     	9           
  1000        	0.13836     	0.34831     	0.19806     	18          
---> try to load best state
              	0.13836     	0.34831     	0.19806     	18               eval state : test_on_restore;
              	0.13836     	0.34831     	0.19806     	18               eval state : test_on_last_epoch;
---> final result
              	0.13836     	0.34831     	0.19806     	18               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.16413     	0.20573     	0.26248     	0           
  10          	0.16633     	0.27143     	0.26499     	0           
  100         	0.11963     	0.44962     	0.18205     	3           
  300         	0.12578     	0.44779     	0.18183     	10          
  500         	0.12849     	0.42825     	0.18392     	16          
  1000        	0.13064     	0.40976     	0.18579     	32          
---> try to load best state
              	0.13064     	0.40976     	0.18579     	32               eval state : test_on_restore;
              	0.13064     	0.40976     	0.18579     	32               eval state : test_on_last_epoch;
---> final result
              	0.13064     	0.40976     	0.18579     	32               eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'TopOP_mfGent_v5', '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: 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           	0.12255     	0.28564     	0.21105     	0           
  10          	0.13594     	0.35452     	0.23826     	0           
  100         	0.10681     	0.57083     	0.17735     	7           
  300         	0.09666     	0.58334     	0.15359     	22          
  500         	0.09819     	0.57820     	0.15451     	37          
  1000        	0.09918     	0.57048     	0.15518     	74          
---> try to load best state
              	0.09918     	0.57048     	0.15518     	74               eval state : test_on_restore;
              	0.09918     	0.57048     	0.15518     	74               eval state : test_on_last_epoch;
---> final result
              	0.09918     	0.57048     	0.15518     	74               eval state : final;module_name : GAR;cp_record_file : True;
