The discriminator architecture determines whether the image is real or fake. In this case, we are focused solely on the neural network that we are going to create- this doesn't involve the training step that we'll cover in the training recipe in this chapter:
The basic components of the discriminator architecture
The discriminator is typically a simple Convolution Neural Network (CNN) in simple architectures. In our first few examples, this is the type of neural network we'll be using.
Here are a few steps to illustrate how we would build a discriminator:
- First, we'll create a convolutional neural network to classify real or fake (binary classification)
- We'll create a dataset of real data and we'll use our generator to create fake dataset
- We train the discriminator model on the real and fake data
- We'll learn to balance training of the discriminator with the generator training - if the discriminator is too good, the generator will diverge