Hands-On Generative Adversarial Networks with Keras

The deep learning environment test

We are going to verify our deep learning environment installation by building and training a simple fully-connected neural network to perform classification on images of handwritten digits from the MNIST dataset. MNIST is an introductory dataset that contains 70,000 images, thus enabling us to quickly train a small model on a CPU and extremely fast on the GPU. In this simple example, we are only interested in testing our deep learning setup.

We start by using the keras built-in function to download and load the train and test datasets associated with MNIST:

import numpy as npfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.utils import np_utilsfrom keras.optimizers import SGD ...