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Hands-On Generative Adversarial Networks with Keras
Hands-On Generative Adversarial Networks with Keras

Hands-On Generative Adversarial Networks with Keras: Your guide to implementing next-generation generative adversarial networks

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Profile Icon Rafael Valle
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Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5 (2 Ratings)
Paperback May 2019 272 pages 1st Edition
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Arrow left icon
Profile Icon Rafael Valle
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Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5 (2 Ratings)
Paperback May 2019 272 pages 1st Edition
eBook
S$32.99 S$47.99
Paperback
S$59.99
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eBook
S$32.99 S$47.99
Paperback
S$59.99
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Hands-On Generative Adversarial Networks with Keras

Deep Learning Basics and Environment Setup

In this chapter, we offer you essential knowledge for building and training deep learning models, including Generative Adversarial Networks (GANs). We are going to explain the basics of deep learning, starting with a simple example of a learning algorithm based on linear regression. We will also provide instructions on how to set up a deep learning programming environment using Python and Keras. We will also talk about the importance of computing power in deep learning; we are going to describe guidelines to fully take advantage of NVIDIA GPUs by maximizing the memory footprint, enabling the CUDA Deep Neural Network library (cuDNN), and eventually using distributed training setups with multiple GPUs. Finally, in addition to installing the libraries that will be necessary for upcoming projects in this book, you will test your installation...

Deep learning basics

Deep learning is a subset of machine learning, which is a field of artificial intelligence that uses mathematics and computers to learn from data and map it from some input to some output. Loosely speaking, a map or a model is a function with parameters that maps the input to an output. Learning the map, also known as mode, occurs by updating the parameters of the map such that some expected empirical loss is minimized. The empirical loss is a measure of distance between the values predicted by the model and the target values given the empirical data.

Notice that this learning setup is extremely powerful because it does not require having an explicit understanding of the rules that define the map. An interesting aspect of this setup is that it does not guarantee that you will learn the exact map that maps the input to the output, but some other maps, as expected...

Deep learning environment setup

In this section, we will provide instructions on how to set up a Python deep learning programming environment that will be used throughout the book. We will start with Anaconda, which makes package management and deployment easy, and NVIDIA's CUDA Toolkit and cuDNN, which make training and inference in deep learning models quick. There are several compute cloud services, like Amazon Web Services (AWS), that provide ready to use deep learning environments with NVIDIA GPUs.

Installing Anaconda and Python

Anaconda is a free and open source efficient distribution that provides easy package management and deployment for the programming languages R and Python. Anaconda focuses on data science...

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 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

The training set has 60,000 samples and the test set has 10,000 samples. The dataset is balanced...

Summary

In this chapter, we covered essential knowledge for building and training deep learning models, starting with a simple example based on linear regression. We covered important topics in machine learning such as parameter estimation and backpropagation, loss functions, and diverse neural network layers. We described how to set up a deep learning programming environment that will be used throughout this book. After installing our deep learning programming environment, we trained and evaluated a simple neural network model for the classification of handwritten digits.

In the next chapter, we will cover generative models, explaining the advantages and disadvantages of each class of generative models, including GANs.

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Key benefits

  • Discover various GAN architectures using a Python and Keras library
  • Understand how GAN models function with the help of theoretical and practical examples
  • Apply your learnings to become an active contributor to open source GAN applications

Description

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

Who is this book for?

This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a mix of theory and hands-on content to implement GANs using Keras. Working knowledge of Python is expected.

What you will learn

  • Discover how GANs work and the advantages and challenges of working with them
  • Control the output of GANs with the help of conditional GANs, using embedding and space manipulation
  • Apply GANs to computer vision, natural language processing (NLP), and audio processing
  • Understand how to implement progressive growing of GANs
  • Use GANs for image synthesis and speech enhancement
  • Explore the future of GANs in visual and sonic arts
  • Implement pix2pixHD to turn semantic label maps into photorealistic images

Product Details

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Publication date : May 03, 2019
Length: 272 pages
Edition : 1st
Language : English
ISBN-13 : 9781789538205
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Publication date : May 03, 2019
Length: 272 pages
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Language : English
ISBN-13 : 9781789538205
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Table of Contents

13 Chapters
Section 1: Introduction and Environment Setup Chevron down icon Chevron up icon
Deep Learning Basics and Environment Setup Chevron down icon Chevron up icon
Introduction to Generative Models Chevron down icon Chevron up icon
Section 2: Training GANs Chevron down icon Chevron up icon
Implementing Your First GAN Chevron down icon Chevron up icon
Evaluating Your First GAN Chevron down icon Chevron up icon
Improving Your First GAN Chevron down icon Chevron up icon
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio Chevron down icon Chevron up icon
Progressive Growing of GANs Chevron down icon Chevron up icon
Generation of Discrete Sequences Using GANs Chevron down icon Chevron up icon
Text-to-Image Synthesis with GANs Chevron down icon Chevron up icon
TequilaGAN - Identifying GAN Samples Chevron down icon Chevron up icon
Whats next in GANs Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.5
(2 Ratings)
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2 star 50%
1 star 50%
Mike H. May 29, 2019
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The book holds promise but suffers from several major issues. First of all is the quality is the editorial. This author LOVES their commas. They use what seems like an average of three commas per sentence making those sentences unnecessarily long and often difficult to follow.My other complaint is that the beginning of the book states, in several places, that you only need a general, rudimentary understanding of python as a prerequisite, then launches into graduate level mathematics throughout the entire first half of the book. There is only an exceedingly brief overview of introductory NN concepts like in other books I've read, not enough for someone just starting out to grasp the concepts. This sort of negates the author's insistence in the beginning that you don't need much experience or advanced education in the field to understand the concepts presented.None of the information provided in the book is immediately applicable and it is written for the most part as if lifted straight out of an academic journal. So much of this book is written like that: terribly academic, boring to read, hard to follow, and not as practical as others in this subject (I've read over a dozen on this topic alone).There are also several misprints leading to certain formulas being unreadable and several typos I've found in the text. The code examples are not the easiest to follow compared to other books, even for an experienced developer like myself, because of the way they are presented and described (most of the time there is actually very little explanation as to what is going on in the code). For example, there is no (or very little) description as to why certain things are handled and instantiated in a certain way as presented.Lastly, the graphs and images reference colors in certain segments but they are all printed in black and white - this is a common problem with books published by Pact I've found and not necessarily the author's fault.Overall, it is a hard book to get through, even for this type of material which can be very technical. It is dry, formulaic, academic, and not nearly as practical or easy to follow as others I've read. It feels more like a graduate thesis sometimes than a practical resource for developers and people just starting out in this field. There are far better, and more practical, options available out there for those of you who haven't yet completed a Master's degree in mathematics.
Amazon Verified review Amazon
Tobsi Hausi Jun 08, 2019
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0 Stars.I expected I would learn how to build my own model.You only use the examples from GitHub, and these don't work.
Amazon Verified review Amazon
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