KFrank:

(One possibility is that your network has separate outputs for both
the single-label and multi-label prediction, and you train on a loss
that is a sum of a single-label and multi-label loss function.)


Hi Frank,
Thank you for your proposals.
(One possibility is that your network has separate outputs for both
the single-label and multi-label prediction, and you train on a loss
that is a sum of a single-label and multi-label loss function.)
This means that instead of computing n binary classification, I should have two branches (two output layer), where one computes the multilabel output loss and the other branch computes the single binary output loss. Then combining the both loss will get me the total loss. Just for approval 
Thanks