Now we will see the architecture of a discriminator in DCGAN. As we know, the discriminator takes the image and it tells us whether the image is a real image or a fake image. Thus, it is basically a binary classifier. The discriminator is composed of a series of convolutional and batch norm layers with leaky ReLU activations.
The architecture of the discriminator is shown in the following diagram:
As you can see, it takes the input image of the 64 x 64 x 3 shape and performs a series of convolutional operations with a leaky ReLU activation function. We apply batch normalization at all layers except at the input layer.
In a nutshell, we enhance the vanilla GAN by replacing the feedforward network in the generator and the discriminator with the convolutional network.