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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.
The text was updated successfully, but these errors were encountered:
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.
The text was updated successfully, but these errors were encountered: