How to perform supervised training of a deep neural network when the target label has only 0 and 1?
You can try MSE loss. If you want to stick to binary cross-entropy (used in binary classification), consider using label smoothing.
I am trying to train a Deep Neural Network (DNN) with labeled data. The labels are encoded in such a way that it only contains values 0 and 1. The shape of the encoded label is 5 x 5 x 232. About 95%  of values in the label is 0and rests are 1. Currently, I am using binary_crossentroy loss function to train the network.
What is the best technique to train the DNN in such a scenario? Is the choice of binary_crossentroy
as the loss function is appropriate in this case? Any suggestion to improve the performance of the model.