Implementing a DCGAN using Keras

In this section, we will write an implementation of a DCGAN in the Keras framework. Keras is a meta-framework that uses TensorFlow or Teano as a backend. It provides high-level APIs for working with neural networks. It also has pre-built neural network layers, optimizers, regularizers, initializers, and data-preprocessing layers for easy prototyping compared to low-level frameworks, such as TensorFlow. Let's start by writing the implementation of the generator network.

Generator

As mentioned in the Architecture of DCGAN section, the generator network consists of some 2D convolutional layers, upsampling layers, a reshape layer, and a batch normalization layer. In Keras, every operation...