Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Implementing Deep Convolutional GANs (DCGANs) 

In the previous recipe, our was able to generate realistic examples after a few epochs. The MNIST dataset has a low translation invariance, so it's easier for our network to generate these examples. In the early days of GANs, the networks were very unstable and small changes could mess up the output. In 2016, DCGANs were introduced. In DCGANs, both the discriminator and the generator are fully convolutional, and the output of DCGANs has proven to be more stable. In our next recipe, we will increase the complexity of our dataset by using the Fashion-MNIST dataset and demonstrate how to implement DCGANs in PyTorch.

How to do it...

  1. We start by importing the libraries, as follows:
import matplotlib.pyplotasplt