data=dict( train=dict( type='NewPaviaLoader', params=dict( training=True, num_workers=0, image_mat_path='PU_gyh.mat', gt_mat_path='PU_gt.mat', num_train_samples_per_class=30, sub_minibatch=20 ) ), test=dict( type='NewPaviaLoader', params=dict( training=False, num_workers=0, image_mat_path='PU_gyh.mat', gt_mat_path='PU_gt.mat', num_train_samples_per_class=30, sub_minibatch=20 ) ) ),
When I try to adjust the sub_minibatch parameter to test the effect of "GS2 sampling strategy", I found that Houston can also converge and get 83% OA without GS2 sampling strategy.
And I found that when I adjust the "sub_minibatch" in train dict, the effect on accuracy is small, but the "sub_minibatch" in test dict have great effect on accuracy.
Could you please tell me what's the function of "sub_minibatch" in test dict? Cuase I think there is no need to set minibatch in the test process.
Thank you very much!!!
data=dict( train=dict( type='NewPaviaLoader', params=dict( training=True, num_workers=0, image_mat_path='PU_gyh.mat', gt_mat_path='PU_gt.mat', num_train_samples_per_class=30, sub_minibatch=20 ) ), test=dict( type='NewPaviaLoader', params=dict( training=False, num_workers=0, image_mat_path='PU_gyh.mat', gt_mat_path='PU_gt.mat', num_train_samples_per_class=30, sub_minibatch=20 ) ) ),When I try to adjust the sub_minibatch parameter to test the effect of "GS2 sampling strategy", I found that Houston can also converge and get 83% OA without GS2 sampling strategy. And I found that when I adjust the "sub_minibatch" in train dict, the effect on accuracy is small, but the "sub_minibatch" in test dict have great effect on accuracy. Could you please tell me what's the function of "sub_minibatch" in test dict? Cuase I think there is no need to set minibatch in the test process. Thank you very much!!!