Hello Hüseyin!



 Huseyin:

I dont want to have n binary classifiers.


Some clarifying comments:
A multi-label, multi-class classifier should be thought of as n binary
classifiers that all run together in a single network in single pass.
The predicted output is (logits / probabilities) predictions for a class-“0”
binary classifier, yes vs. no, class-“1”, yes vs. no, and so on.


Only one Classifier … which will be capable of computing single and multilabel classification.


Not commenting specifically on LSTM:
Single-label and multi-label classifiers are somewhat different.  How
do you propose to build one that can do both?  How would you tell
it that you want a single-label vs. multi-label output?
(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.)
One thing you can do that does make sense is have the upstream
“backbone” of your network be “pre-trained” to find features that are
relevant for your classes, and then build two separate networks for
each of which you train a couple of downstream layers, once set for
the single-label case, and a second set for the multi-label case.
Best.
K. Frank