Multi label classifier in RMDL.

[abhilash42]

Problem Statement
The problem I am solving is to classify genres of movies. So, the output in my case would be one-hot encoded for 20 genres(array of 20 elements with 1 or 0). For this we are using loss function of binary_crossentropy and sigmoid as the last layer activation.

Describe the solution you'd like
We would like to have an option of binary_crossentropy=True in parameters, and also number_of_classes should be configurable and not np.max(y_train)+1 which would always be 2 in my case.

Describe alternatives you've considered
No alternatives have been considered for now.

[kk7nc]

Thank you for your question:
is there two option which are change "sparse_categorical=True" to sparse_categorical= False, and mack your output int as

import numpy as np
 a = np.array([[0,1,0,0],[1,0,0,0],[0,0,0,1]])
 [np.where(r==1)[0][0] for r in a]
[1, 0, 3]