Hands-On Generative Adversarial Networks with PyTorch 1.x

The architecture of a discriminator

The discriminator network of a DCGAN consists of 4 hidden layers (again, we treat the input layer as the 1^st hidden layer) and 1 output layer. Convolution layers are used in all layers, which are followed by batch normalization layers except that the first layer does not have batch normalization. LeakyReLU activation functions are used in the hidden layers and Sigmoid is used for the output layer. The architecture of the discriminator is shown in the following:

Discriminator architecture in DCGAN

The input channel can be either 1 or 3, depending on whether you are dealing with grayscale images or color images. ...