DCGAN becomes unstable when I don't directly use Binary Cross Entropy for loss calculation.

I am trying DCGAN for mnist.

When I use the BCE for loss calculation for both generator and discriminator, it works fine.

But when I switch to -log(D(x)) - log(1-D(G(x)) for discriminator and log(1-D(G(x)) for generator, it works fine upto epoch 12. It can generate images, but after that everything goes nan.

What could be the reason for such behavior?

It doesn't matter which loss function I use. The values must be same right?

binary dcgan deeplearning entropy generate generator images loss mnist