Hands-On Deep Learning Algorithms with Python

Generator

We know that the goal of the generator is to generate a fake image by learning the real data distribution. First, we sample noise from a Gaussian distribution and create z. Then we concatenate z with our conditional variable, , and feed this as an input to the generator which outputs a basic version of the image.

The loss function of the generator is given as follows:

Let's examine this formula:

• implies we sample z from the fake data ...