Hands-On Deep Learning Algorithms with Python

Deconvolutional generator

We know that the role of the generator is to generate a new image by learning the real data distribution. In DCGAN, a generator is composed of convolutional transpose and batch norm layers with ReLU activations.

Note that convolutional transpose operation is also known as deconvolution operation or fractionally strided convolutions.

The input to the generator is the noise, , which we draw from a standard normal distribution, and it outputs an image of the same size as the images in the training data, say, 64 x 64 x 3.

The architecture of the generator is shown in the following diagram:

First, we convert the noise ...