Book Image

Hands-On Generative Adversarial Networks with Keras

By : Rafael Valle
Book Image

Hands-On Generative Adversarial Networks with Keras

By: Rafael Valle

Overview of this book

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step toward understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that enable you to control characteristics of GAN output. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have developed the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA
Table of Contents (14 chapters)
Free Chapter
1
Section 1: Introduction and Environment Setup
4
Section 2: Training GANs
8
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

To get the most out of this book

This book will give you an overview of data analysis in Python. This will take you through the main libraries of Python's data science stack. It will explain how to use various Python tools to analyze, visualize, and process data effectively and you will learn about the importance of using GPUs in deep learning. The reader must have software and hardware experience in Python development.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at www.packt.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the onscreen instructions.

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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Generative-Adversarial-Networks-with-Keras. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789538205_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We are going to update all Anaconda packages using the conda command."

A block of code is set as follows:

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils
from keras.optimizers import SGD
from keras.layers.core import Dense,Activation

Any command-line input or output is written as follows:

pip install keras
Warnings or important notes appear like this.
Tips and tricks appear like this.