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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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': 0.1, '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           	154.78062   	-12.25370   	314.89493   	0           
  10          	97.85018    	-8.21072    	227.00179   	0           
  100         	57.15165    	-3.01459    	127.20757   	0           
  300         	55.58719    	-2.57913    	123.31052   	1           
  500         	55.40596    	-2.56647    	122.98919   	3           
  1000        	54.99532    	-2.54684    	122.29178   	6           
  2500        	54.14532    	-2.59465    	120.99375   	13          
---> try to load best state
              	55.82233    	-2.90040    	123.34670   	15               eval state : test_on_restore;
              	70.37205    	-6.03005    	144.02652   	15               eval state : test_on_last_epoch;
---> final result              	55.82233    	-2.90040    	123.34670   	15               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	64.11096    	-7.56709    	130.87781   	0           
  10          	57.39872    	-4.87060    	123.74926   	0           
  100         	59.51458    	-5.07730    	128.57388   	0           
  300         	56.56416    	-4.81557    	124.31715   	1           
  500         	56.87139    	-3.49966    	124.88580   	2           
  1000        	61.13569    	-3.76108    	132.27260   	4           
  epoch 1048 reach nan state
---> try to load best state
              	60.92174    	-3.65044    	132.19867   	4                eval state : test_on_restore;
              	61.13569    	-3.76108    	132.27260   	4                eval state : test_on_last_epoch;
---> final result
              	60.92174    	-3.65044    	132.19867   	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	62.65143    	-5.79361    	130.23248   	0           
  10          	58.24180    	-3.70700    	126.64041   	0           
  100         	56.80944    	-3.38666    	126.51056   	0           
  300         	56.30308    	-3.05873    	126.92922   	1           
  500         	56.59935    	-2.75010    	128.89598   	2           
----------------------------------------

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 500.0
  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           	151.78786   	-9.84420    	314.77780   	0           
  10          	143.33528   	-8.81750    	304.26422   	0           
  100         	106.89402   	-7.25396    	246.40483   	0           
  300         	88.73324    	-7.59724    	210.55589   	1           
  500         	85.41497    	-7.62967    	203.51305   	2           
  1000        	84.85626    	-7.64038    	202.30801   	6           
  2500        	84.94678    	-7.63984    	202.55403   	14          
---> try to load best state
              	85.50303    	-7.67711    	203.63191   	16               eval state : test_on_restore;
              	83.79198    	-7.37411    	201.46935   	16               eval state : test_on_last_epoch;
---> final result              	83.79198    	-7.37411    	201.46935   	16               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	92.31381    	-9.18256    	207.85425   	0           
  10          	88.21387    	-8.07866    	203.46217   	0           
  100         	89.98916    	-9.41376    	206.66269   	0           
  300         	89.06116    	-8.11404    	206.29333   	1           
  500         	90.55178    	-7.57693    	209.19212   	2           
  1000        	97.58075    	-7.17101    	222.34787   	4           
  epoch 1048 reach nan state
---> try to load best state
              	97.39669    	-7.10804    	222.31461   	4                eval state : test_on_restore;
              	97.58075    	-7.17101    	222.34787   	4                eval state : test_on_last_epoch;
---> final result
              	97.39669    	-7.10804    	222.31461   	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	90.85305    	-8.21338    	207.28685   	0           
  10          	87.78552    	-7.77641    	204.90230   	0           
  100         	86.18275    	-7.93721    	203.84615   	0           
----------------------------------------

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 50.0
  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           	158.46243   	-12.09436   	320.66510   	0           
  10          	151.96330   	-10.88117   	312.43628   	0           
  100         	115.00816   	-10.09621   	251.66890   	0           
  300         	83.87258    	-13.65190   	185.53282   	2           
  500         	72.69434    	-8.60052    	160.00340   	2           
  1000        	63.31709    	-4.13648    	138.60550   	5           
  2500        	59.09246    	-3.04094    	129.73055   	12          
---> try to load best state
              	58.54207    	-2.94226    	128.65857   	14               eval state : test_on_restore;
              	59.57964    	-3.10244    	130.10898   	14               eval state : test_on_last_epoch;
---> final result              	58.54207    	-2.94226    	128.65857   	14               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	66.39827    	-7.50862    	135.70895   	0           
  10          	59.69719    	-4.85360    	128.53099   	0           
  100         	61.27077    	-5.30108    	132.29918   	0           
  300         	58.16095    	-4.58427    	127.65333   	1           
  500         	58.34023    	-3.44634    	127.92043   	2           
  1000        	62.36132    	-3.86510    	134.51158   	4           
  epoch 1048 reach nan state
---> try to load best state
              	62.18008    	-3.78768    	134.43913   	4                eval state : test_on_restore;
              	62.36132    	-3.86510    	134.51158   	4                eval state : test_on_last_epoch;
---> final result
              	62.18008    	-3.78768    	134.43913   	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	65.02194    	-5.74650    	135.16090   	0           
  10          	60.69696    	-3.89738    	131.62723   	0           
  100         	58.98030    	-3.93024    	130.82237   	0           
  300         	58.15156    	-3.69436    	130.29518   	1           
  500         	58.17512    	-3.33409    	131.56140   	2           
  1000        	57.16644    	-2.79871    	129.71410   	4           
  epoch 1066 reach nan state
---> try to load best state
              	57.88901    	-3.42868    	129.85585   	5                eval state : test_on_restore;
              	57.16644    	-2.79871    	129.71410   	5                eval state : test_on_last_epoch;
---> final result
              	57.16644    	-2.79871    	129.71410   	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	66.76576    	-5.90203    	137.25031   	0           
  10          	75.55596    	-9.94525    	151.26018   	0           
  100         	63.29833    	-4.63785    	136.55815   	0           
  300         	59.62833    	-2.57152    	133.72020   	1           
----------------------------------------

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 70.0
  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           	157.25008   	-11.68492   	319.59259   	0           
  10          	150.51775   	-10.56620   	311.08722   	0           
  100         	112.44798   	-9.43309    	248.85124   	0           
  300         	80.74841    	-12.56235   	181.00478   	1           
  500         	69.50021    	-7.79304    	154.71892   	3           
  1000        	60.25029    	-3.50624    	133.02608   	4           
  2500        	56.49575    	-2.66127    	124.91750   	10          
---> try to load best state
              	56.09343    	-2.62765    	124.16914   	12               eval state : test_on_restore;
              	57.03353    	-2.76266    	125.51658   	12               eval state : test_on_last_epoch;
---> final result              	56.09343    	-2.62765    	124.16914   	12               eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	64.22083    	-7.35169    	131.43878   	0           
  10          	57.52568    	-4.67883    	124.24049   	0           
  100         	59.30481    	-4.85932    	128.49263   	0           
  300         	56.26385    	-4.52980    	124.01472   	1           
  500         	56.51847    	-3.17177    	124.42577   	2           
  1000        	60.68850    	-3.50294    	131.42467   	4           
  epoch 1048 reach nan state
---> try to load best state
              	60.50465    	-3.41958    	131.35219   	4                eval state : test_on_restore;
              	60.68850    	-3.50294    	131.42467   	4                eval state : test_on_last_epoch;
---> final result
              	60.50465    	-3.41958    	131.35219   	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	62.82253    	-5.52659    	130.84712   	0           
  10          	58.42660    	-3.64131    	127.24631   	0           
  100         	56.74771    	-3.38754    	126.75568   	0           
  300         	56.11369    	-3.15613    	126.75675   	1           
  500         	56.29370    	-2.82799    	128.43327   	2           
  1000        	55.29614    	-2.32069    	126.49395   	4           
  epoch 1066 reach nan state
---> try to load best state
              	56.05655    	-3.06201    	126.62797   	5                eval state : test_on_restore;
              	55.29614    	-2.32069    	126.49395   	5                eval state : test_on_last_epoch;
---> final result
              	55.29614    	-2.32069    	126.49395   	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  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           	64.56877    	-5.47394    	132.96913   	0           
  10          	73.70728    	-9.33865    	147.55571   	0           
  100         	61.70158    	-4.09869    	133.50536   	0           
  300         	58.28130    	-2.24724    	131.37950   	1           
  500         	58.05128    	-2.17147    	133.35411   	2           
  1000        	56.61199    	-2.17662    	130.73514   	5           
  epoch 1085 reach nan state
---> try to load best state
              	58.06648    	-3.34373    	131.56052   	5                eval state : test_on_restore;
              	56.61199    	-2.17662    	130.73514   	5                eval state : test_on_last_epoch;
---> final result
              	56.61199    	-2.17662    	130.73514   	5                eval state : final;module_name : GAR;cp_record_file : True;
