Hands-On Deep Learning Algorithms with Python

Discriminator loss

The discriminator loss is given as follows:

First, we will implement the first term, .

The first term, , implies the expectations of the log likelihood of images sampled from the real data distribution being real.

It is basically the binary cross-entropy loss. We can implement binary cross-entropy loss with the tf.nn.sigmoid_cross_entropy_with_logits() TensorFlow function. It takes two parameters as inputs, logits and labels ...