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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	222.72083   	-67437.86909	394.61365   	0           
  10          	222.37529   	-18577.74925	394.24933   	0           
  100         	220.60341   	-2093.36040 	392.36145   	0           
  300         	220.71321   	-2251.81490 	392.47971   	1           
  500         	220.77832   	-2353.52565 	392.54926   	2           
  1000        	220.92418   	-2612.76686 	392.70486   	5           
---> try to load best state
              	220.92418   	-2612.76686 	392.70486   	5                eval state : test_on_restore;
              	220.92418   	-2612.76686 	392.70486   	5                eval state : test_on_last_epoch;
---> final result              	220.92418   	-2612.76686 	392.70486   	5                eval state : final;
---> end


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

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

  Demo sGAR 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	238.32483   	-62011.65526	401.27856   	0           
  10          	237.97296   	-17590.29681	400.95721   	0           
  100         	236.26973   	-2321.76336 	399.29037   	0           
  300         	236.08842   	-2071.38437 	399.10507   	1           
  500         	236.11873   	-2110.32725 	399.13541   	3           
  1000        	236.18695   	-2202.44029 	399.20377   	6           
---> try to load best state
              	236.18695   	-2202.44029 	399.20377   	6                eval state : test_on_restore;
              	236.18695   	-2202.44029 	399.20377   	6                eval state : test_on_last_epoch;
---> final result              	236.18695   	-2202.44029 	399.20377   	6                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  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           	241.54469   	-6875.41648 	408.00058   	0           
  10          	241.79895   	-25400.11386	408.90768   	0           
  100         	251.86653   	-49749.82377	424.75635   	0           
  300         	255.94298   	-18888.35976	432.35138   	0           
  500         	258.27853   	-14665.04579	436.47861   	1           
  1000        	261.61990   	-8673.93804 	442.69299   	2           
---> try to load best state
              	261.61990   	-8673.93804 	442.69299   	2                eval state : test_on_restore;
              	261.61990   	-8673.93804 	442.69299   	2                eval state : test_on_last_epoch;
---> final result              	261.61990   	-8673.93804 	442.69299   	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  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           	241.44873   	-6879.64745 	407.99628   	0           
  10          	243.57483   	-29939.12881	410.45917   	0           
  100         	254.01306   	-17647.55542	423.61508   	0           
  300         	251.88391   	-32782.73769	422.55356   	0           
  500         	253.33234   	-20853.65921	424.96848   	1           
  1000        	257.95987   	-27136.69548	431.65723   	2           
---> try to load best state
              	257.95987   	-27136.69548	431.65723   	2                eval state : test_on_restore;
              	257.95987   	-27136.69548	431.65723   	2                eval state : test_on_last_epoch;
---> final result              	257.95987   	-27136.69548	431.65723   	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  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           	240.72693   	-6838.91797 	407.97144   	0           
  10          	243.02779   	-28676.84440	411.06888   	0           
  100         	316.66373   	-9283.71660 	510.52045   	0           
  300         	372.06137   	-2224.44723 	586.18787   	1           
  500         	268.13568   	-2829.95181 	450.70496   	1           
  1000        	164.49658   	-3104.04235 	279.91830   	3           
---> try to load best state
              	164.49658   	-3104.04235 	279.91830   	3                eval state : test_on_restore;
              	164.49658   	-3104.04235 	279.91830   	3                eval state : test_on_last_epoch;
---> final result              	164.49658   	-3104.04235 	279.91830   	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  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           	228.03162   	-6205.09165 	394.02652   	0           
  10          	213.52483   	-7595.36691 	368.64996   	0           
  100         	245.87627   	-1354.69748 	417.45514   	0           
  300         	241.04811   	-1662.79766 	409.00146   	1           
  500         	241.50468   	-1618.77727 	408.52835   	1           
  1000        	246.77757   	-1565.90140 	415.44830   	3           
---> try to load best state
              	246.77757   	-1565.90140 	415.44830   	3                eval state : test_on_restore;
              	246.77757   	-1565.90140 	415.44830   	3                eval state : test_on_last_epoch;
---> final result              	246.77757   	-1565.90140 	415.44830   	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	153.94591   	-10.53408   	315.62720   	0           
  10          	145.76637   	-10.43866   	305.10443   	0           
  100         	110.48694   	-7.96469    	248.39557   	0           
  300         	91.96136    	-8.27337    	211.93037   	1           
  500         	87.59489    	-8.25699    	202.95291   	2           
  1000        	87.46348    	-8.13847    	202.89038   	5           
---> try to load best state
              	87.46348    	-8.13847    	202.89038   	5                eval state : test_on_restore;
              	87.46348    	-8.13847    	202.89038   	5                eval state : test_on_last_epoch;
---> final result              	87.46348    	-8.13847    	202.89038   	5                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	141.59052   	-29.34627   	232.58183   	0           
  10          	101.50043   	-12.43413   	209.30507   	0           
  epoch 82 reach nan state
---> try to load best state
              	90.33285    	-8.70843    	206.59515   	0                eval state : test_on_restore;
              	101.50043   	-12.43413   	209.30507   	0                eval state : test_on_last_epoch;
---> final result              	90.33285    	-8.70843    	206.59515   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	156.53200   	-25.11329   	247.26091   	0           
  10          	111.44019   	-15.40757   	215.49602   	0           
  epoch 97 reach nan state
---> try to load best state
              	94.85337    	-9.36544    	212.13553   	0                eval state : test_on_restore;
              	111.44019   	-15.40757   	215.49602   	0                eval state : test_on_last_epoch;
---> final result              	94.85337    	-9.36544    	212.13553   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	143.56291   	-15.91793   	239.39311   	0           
  10          	110.23707   	-13.45814   	217.94574   	0           
  100         	95.55753    	-9.83619    	210.25656   	0           
  epoch 114 reach nan state
---> try to load best state
              	95.11512    	-9.55078    	210.65077   	0                eval state : test_on_restore;
              	95.55753    	-9.83619    	210.25656   	0                eval state : test_on_last_epoch;
---> final result              	95.55753    	-9.83619    	210.25656   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	130.73378   	-10.88210   	233.85367   	0           
  10          	97.76218    	-9.68953    	202.93425   	0           
  100         	103.80268   	-9.32969    	217.02654   	0           
  300         	96.27160    	-8.95971    	213.70436   	0           
  500         	96.08529    	-8.84978    	213.36127   	1           
  1000        	96.11545    	-8.70559    	212.92822   	2           
---> try to load best state
              	96.11545    	-8.70559    	212.92822   	3                eval state : test_on_restore;
              	96.11545    	-8.70559    	212.92822   	3                eval state : test_on_last_epoch;
---> final result              	96.11545    	-8.70559    	212.92822   	3                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	155.89417   	-10.39067   	321.38535   	0           
  10          	148.71747   	-9.66568    	312.20087   	0           
  100         	116.80732   	-8.27352    	263.00192   	0           
  300         	98.74581    	-8.10650    	228.87465   	1           
  500         	93.56097    	-8.08681    	218.49590   	2           
  1000        	91.93479    	-7.99894    	215.28064   	5           
---> try to load best state
              	91.93479    	-7.99894    	215.28064   	5                eval state : test_on_restore;
              	91.93479    	-7.99894    	215.28064   	5                eval state : test_on_last_epoch;
---> final result              	91.93479    	-7.99894    	215.28064   	5                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	154.80400   	-41.97162   	248.16351   	0           
  10          	152.56734   	-60.51625   	264.30951   	0           
  epoch 82 reach nan state
---> try to load best state
              	144.51378   	-28.88493   	269.68222   	0                eval state : test_on_restore;
              	152.56734   	-60.51625   	264.30951   	0                eval state : test_on_last_epoch;
---> final result              	152.56734   	-60.51625   	264.30951   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	169.99365   	-27.89241   	263.55383   	0           
  10          	133.21252   	-27.01026   	238.73044   	0           
  100         	123.52942   	-17.22028   	243.86382   	0           
  300         	121.93838   	-11.66559   	243.99187   	0           
  500         	122.90932   	-10.51329   	245.24225   	1           
  1000        	127.00775   	-9.22884    	249.88605   	2           
---> try to load best state
              	127.00775   	-9.22884    	249.88605   	2                eval state : test_on_restore;
              	127.00775   	-9.22884    	249.88605   	2                eval state : test_on_last_epoch;
---> final result              	127.00775   	-9.22884    	249.88605   	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	140.53947   	-12.65560   	249.62103   	0           
  10          	98.97643    	-9.84535    	211.99899   	0           
  100         	102.03635   	-9.07292    	220.77795   	0           
  epoch 201 reach nan state
---> try to load best state
              	98.85490    	-8.48763    	220.59554   	0                eval state : test_on_restore;
              	102.03635   	-9.07292    	220.77795   	0                eval state : test_on_last_epoch;
---> final result              	98.85490    	-8.48763    	220.59554   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	129.54451   	-10.95294   	241.76588   	0           
  10          	93.44924    	-9.40103    	206.08904   	0           
  epoch 85 reach nan state
---> try to load best state
              	98.44381    	-11.05396   	220.95485   	0                eval state : test_on_restore;
              	93.44924    	-9.40103    	206.08904   	0                eval state : test_on_last_epoch;
---> final result              	93.44924    	-9.40103    	206.08904   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	153.56622   	-10.87567   	320.94781   	0           
  10          	146.01137   	-10.28181   	310.82355   	0           
  100         	119.45209   	-8.00480    	268.21942   	0           
  300         	106.96527   	-7.77556    	244.72961   	1           
  500         	104.13796   	-7.73528    	239.21500   	2           
  1000        	104.00417   	-7.70373    	239.03873   	4           
---> try to load best state
              	104.00417   	-7.70373    	239.03873   	4                eval state : test_on_restore;
              	104.00417   	-7.70373    	239.03873   	4                eval state : test_on_last_epoch;
---> final result              	104.00417   	-7.70373    	239.03873   	4                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	187.03206   	-34.28063   	284.43277   	0           
  10          	171.81819   	-50.17219   	283.51920   	0           
  100         	128.92166   	-29.06377   	260.25574   	0           
  300         	123.55582   	-24.24134   	255.46359   	0           
  500         	123.57583   	-24.07742   	255.51883   	0           
  1000        	123.57595   	-24.07573   	255.51941   	2           
---> try to load best state
              	123.57595   	-24.07573   	255.51941   	2                eval state : test_on_restore;
              	123.57595   	-24.07573   	255.51941   	2                eval state : test_on_last_epoch;
---> final result              	123.57595   	-24.07573   	255.51941   	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	151.74089   	-14.43173   	267.57294   	0           
  10          	102.90955   	-10.81332   	222.99072   	0           
  100         	107.38882   	-9.62789    	240.20622   	0           
  epoch 127 reach nan state
---> try to load best state
              	107.53561   	-9.60829    	240.77664   	0                eval state : test_on_restore;
              	107.38882   	-9.62789    	240.20622   	0                eval state : test_on_last_epoch;
---> final result              	107.38882   	-9.62789    	240.20622   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	145.19521   	-12.77956   	262.78500   	0           
  10          	110.49945   	-11.52706   	237.41870   	0           
  100         	109.56197   	-9.63394    	239.96274   	0           
  300         	107.31759   	-8.48077    	242.18216   	0           
  500         	107.87661   	-8.43265    	242.84091   	1           
  1000        	108.05027   	-8.33484    	242.99525   	2           
---> try to load best state
              	108.05027   	-8.33484    	242.99525   	2                eval state : test_on_restore;
              	108.05027   	-8.33484    	242.99525   	2                eval state : test_on_last_epoch;
---> final result              	108.05027   	-8.33484    	242.99525   	2                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	135.28851   	-10.37360   	259.31351   	0           
  10          	109.38449   	-10.43057   	237.93539   	0           
  100         	112.21249   	-8.61675    	247.33084   	0           
  300         	109.87214   	-8.52606    	246.86929   	0           
  epoch 314 reach nan state
---> try to load best state
              	109.90472   	-8.50971    	246.81770   	0                eval state : test_on_restore;
              	109.87214   	-8.52606    	246.86929   	0                eval state : test_on_last_epoch;
---> final result              	109.90472   	-8.50971    	246.81770   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, '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_normalzie: True
  output_normalize: True
  noise_init: 100.0
  grid_config: {'grid_size': [-1], 'type': 'fixed', 'dimension_map': 'identity', 'auto_broadcast_grid_size': True}
---> training record
  epoch       	mae         	r2          	rmse        	time        
  1           	152.73190   	-9.24419    	320.74789   	0           
  10          	145.97603   	-8.61657    	311.51941   	0           
  100         	123.24545   	-7.99205    	275.46375   	0           
  300         	99.50481    	-8.62095    	231.07603   	1           
  500         	93.64712    	-9.03755    	219.21715   	1           
  1000        	94.84830    	-8.75835    	221.76984   	3           
---> try to load best state
              	94.84830    	-8.75835    	221.76984   	3                eval state : test_on_restore;
              	94.84830    	-8.75835    	221.76984   	3                eval state : test_on_last_epoch;
---> final result              	94.84830    	-8.75835    	221.76984   	3                eval state : final;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	177.09694   	-43.73544   	269.08160   	0           
  10          	103.25842   	-22.30726   	210.81578   	0           
  100         	103.89518   	-18.55618   	226.06404   	0           
  epoch 113 reach nan state
---> try to load best state
              	105.01708   	-19.75215   	226.98138   	0                eval state : test_on_restore;
              	103.89518   	-18.55618   	226.06404   	0                eval state : test_on_last_epoch;
---> final result              	103.89518   	-18.55618   	226.06404   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	176.73824   	-41.10387   	268.20483   	0           
  10          	109.09544   	-18.12588   	216.06827   	0           
  epoch 85 reach nan state
---> try to load best state
              	105.76249   	-21.00109   	231.01770   	0                eval state : test_on_restore;
              	109.09544   	-18.12588   	216.06827   	0                eval state : test_on_last_epoch;
---> final result              	109.09544   	-18.12588   	216.06827   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	148.75404   	-15.24099   	256.96359   	0           
  10          	98.43566    	-11.19622   	209.41399   	0           
  100         	103.05103   	-11.92311   	225.58514   	0           
  300         	98.37873    	-10.22766   	225.65723   	0           
  epoch 458 reach nan state
---> try to load best state
              	98.34927    	-9.96948    	225.82198   	0                eval state : test_on_restore;
              	98.37873    	-10.22766   	225.65723   	0                eval state : test_on_last_epoch;
---> final result              	98.37873    	-10.22766   	225.65723   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'FlowMix3D_MF', 'interp_data': False, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': False, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': True, 'slice_param': [0.6, 0.4]}
  connection_method: res_mapping
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  auto_broadcast_kernel: True
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: 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           	131.02776   	-10.86490   	246.62576   	0           
  10          	93.90830    	-9.06770    	208.36119   	0           
  100         	108.58015   	-11.58527   	233.93846   	0           
  300         	98.43781    	-9.35927    	225.39487   	0           
  500         	98.31549    	-9.35124    	225.18593   	1           
  epoch 584 reach nan state
---> try to load best state
              	98.14040    	-9.35420    	224.93770   	1                eval state : test_on_restore;
              	98.31549    	-9.35124    	225.18593   	1                eval state : test_on_last_epoch;
---> final result              	98.14040    	-9.35420    	224.93770   	1                eval state : final;module_name : SGAR;cp_record_file : True;
---> end

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

  Demo sGAR 
  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: 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           	156.04570   	-11.25692   	318.52505   	0           
  10          	148.83105   	-10.15156   	309.55743   	0           
  100         	111.56065   	-9.16404    	248.36491   	0           
  300         	82.98324    	-12.82435   	187.28706   	1           
  500         	72.80994    	-9.71590    	163.70987   	1           
  1000        	64.59368    	-5.23746    	144.70488   	3           
---> try to load best state
              	64.59368    	-5.23746    	144.70488   	3                eval state : test_on_restore;
              	64.59368    	-5.23746    	144.70488   	3                eval state : test_on_last_epoch;
---> final result              	64.59368    	-5.23746    	144.70488   	3                eval state : final;
---> end


---------->
SGAR 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': 0.01, '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           	113.83753   	-19.99777   	155.91879   	0           
  10          	84.96217    	-13.93535   	147.04376   	0           
  100         	67.90694    	-8.07497    	146.36461   	0           
  epoch 103 reach nan state
---> try to load best state
              	67.57566    	-9.00435    	144.99170   	0                eval state : test_on_restore;
              	67.90694    	-8.07497    	146.36461   	0                eval state : test_on_last_epoch;
---> final result              	67.57566    	-9.00435    	144.99170   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': 0.01, '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           	100.40693   	-5.24174    	152.86610   	0           
  10          	79.15497    	-8.17023    	149.15892   	0           
  100         	68.82996    	-5.17936    	148.78922   	0           
  epoch 146 reach nan state
---> try to load best state
              	67.53411    	-4.44802    	147.76118   	0                eval state : test_on_restore;
              	68.82996    	-5.17936    	148.78922   	0                eval state : test_on_last_epoch;
---> final result              	67.53411    	-4.44802    	147.76118   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR 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': 0.01, '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           	125.95477   	-17.52617   	189.96339   	0           
  10          	94.85037    	-15.19323   	171.27556   	0           
  epoch 85 reach nan state
---> try to load best state
              	65.45913    	-4.62619    	151.41908   	0                eval state : test_on_restore;
              	94.85037    	-15.19323   	171.27556   	0                eval state : test_on_last_epoch;
---> final result              	65.45913    	-4.62619    	151.41908   	0                eval state : final;module_name : SGAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh 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]', '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           	117.34005   	-11.78221   	193.62613   	0           
  10          	76.50596    	-6.77109    	148.31702   	0           
  100         	77.92756    	-9.39733    	159.47749   	0           
  epoch 220 reach nan state
---> try to load best state
              	68.80761    	-4.91059    	154.57684   	0                eval state : test_on_restore;
              	77.92756    	-9.39733    	159.47749   	0                eval state : test_on_last_epoch;
---> final result              	68.80761    	-4.91059    	154.57684   	0                eval state : final;module_name : SGAR;cp_record_file : True;
