Do we need to do multiple times deep learning and average ROC? Do we need to do multiple times deep learning and average ROC(AUC)?
Since we've might get different ROC(AUC) every round we train and test (by KERAS)
Is it necessary to average ROC(AUC) with multiple times training and testing?
(or just choose the best round?)
No, you don't need to do that. Do probability calibration (temperature scaling) on your trained network. Here a link to do that.