Table Of ContentJava Deep Learning Cookbook
Train neural networks for classification, NLP, and
reinforcement learning using Deeplearning4j
Rahul Raj
BIRMINGHAM - MUMBAI
Java Deep Learning Cookbook
Copyright © 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form
or by any means, without the prior written permission of the publisher, except in the case of brief quotations
embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented.
However, the information contained in this book is sold without warranty, either express or implied. Neither the
author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to
have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products
mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy
of this information.
Commissioning Editor: Sunith Shetty
Acquisition Editor: Meeta Rajani
Content Development Editor: Ronn Kurien
Senior Editor: Rahul Dsouza
Technical Editor: Dinesh Pawar
Copy Editor: Safis Editing
Project Coordinator: Vaidehi Sawant
Proofreader: Safis Editing
Indexer: Rekha Nair
Production Designer: Arvindkumar Gupta
First published: November 2019
Production reference: 1081119
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78899-520-7
www.packt.com
To my wife, Sharanya, for being my loving partner throughout our joint life journey.
To my mother, Soubhagyalekshmi, and my father, Rajasekharan, for their love, continuous
support, sacrifices, and inspiration.
– Rahul Raj
Packt.com
Subscribe to our online digital library for full access to over 7,000 books and videos, as well
as industry leading tools to help you plan your personal development and advance your
career. For more information, please visit our website.
Why subscribe?
Spend less time learning and more time coding with practical eBooks and Videos
from over 4,000 industry professionals
Improve your learning with Skill Plans built especially for you
Get a free eBook or video every month
Fully searchable for easy access to vital information
Copy and paste, print, and bookmark content
Did you know that Packt offers eBook versions of every book published, with PDF and
ePub files available? You can upgrade to the eBook version at www.packt.com and as a print
book customer, you are entitled to a discount on the eBook copy. Get in touch with us at
[email protected] for more details.
At www.packt.com, you can also read a collection of free technical articles, sign up for a
range of free newsletters, and receive exclusive discounts and offers on Packt books and
eBooks.
Contributors
About the author
Rahul Raj has more than 7 years of IT industry experience in software development,
business analysis, client communication, and consulting on medium-/large-scale projects in
multiple domains. Currently, he works as a lead software engineer in a top software
development firm. He has extensive experience in development activities comprising
requirement analysis, design, coding, implementation, code review, testing, user training,
and enhancements. He has written a number of articles about neural networks in Java and
they are featured by DL4J/ official Java community channels. He is also a certified machine
learning professional, certified by Vskills, the largest government certification body in
India.
I want to thank the people who have been close to me and have supported me, especially my
wife, Sharanya, and my parents.
About the reviewers
Cristian Stancalau has an MSc and BSc in computer science and engineering from Babes-
Bolyai University, where he has worked as an assistant lecturer since 2018. Currently, he
works as chief software architect, focused on enterprise code review. Previously, he
cofounded and led a video technology start-up as technical director. Cristian has proven
mentoring and teaching expertise in both the commercial and academic sectors, advising on
Java technologies and product architecture.
I would like to thank Packt for the opportunity to perform the technical review for Java
Deep Learning Cookbook. Reading it was a real pleasure for me and I am sure it will also
be for its readers.
Aristides Villarreal Bravo is a Java developer, a member of the NetBeans Dream Team,
and a Java User Groups leader. He lives in Panama. He has organized and participated in
various conferences and seminars related to Java, Java EE, NetBeans, the NetBeans
platform, free software, and mobile devices. He is the author of the jmoordb framework,
and tutorials and blogs about Java, NetBeans, and web development. He has participated in
several interviews about topics such as NetBeans, NetBeans DZone, and JavaHispano. He is
a developer of plugins for NetBeans.
I want to thank my parents and brothers for their unconditional support (Nivia, Aristides,
Secundino, and Victor).
Packt is searching for authors like you
If you're interested in becoming an author for Packt, please visit authors.packtpub.com
and apply today. We have worked with thousands of developers and tech professionals,
just like you, to help them share their insight with the global tech community. You can
make a general application, apply for a specific hot topic that we are recruiting an author
for, or submit your own idea.
Table of Contents
Preface 1
Chapter 1: Introduction to Deep Learning in Java 7
Technical requirements 7
Deep learning intuition 8
Backpropagation 9
Multilayer Perceptron (MLP) 10
Convolutional Neural Network (CNN) 10
Recurrent Neural Network (RNN) 10
Why is DL4J important for deep learning? 11
Determining the right network type to solve deep learning problems 11
How to do it... 11
How it works... 12
There's more... 15
Determining the right activation function 18
How to do it... 18
How it works... 19
There's more... 19
Combating overfitting problems 20
How to do it... 20
How it works... 20
There's more... 21
Determining the right batch size and learning rates 22
How to do it... 22
How it works... 22
There's more... 24
Configuring Maven for DL4J 24
Getting ready 24
How to do it... 25
How it works... 26
Configuring DL4J for a GPU-accelerated environment 26
Getting ready 26
How to do it... 27
How it works... 28
There's more... 29
Troubleshooting installation issues 29
Getting ready 30
How to do it... 30
How it works... 31
There's more... 32
Table of Contents
Chapter 2: Data Extraction, Transformation, and Loading 34
Technical requirements 35
Reading and iterating through data 35
Getting ready 35
How to do it... 36
How it works... 40
There's more... 46
Performing schema transformations 46
How to do it... 46
How it works... 47
There's more... 48
Building a transformation process 49
How to do it... 49
How it works... 50
There's more... 50
Serializing transforms 52
How to do it... 52
How it works... 53
Executing a transform process 53
How to do it... 53
How it works... 54
There's more... 55
Normalizing data for network efficiency 56
How to do it... 56
How it works... 56
There's more... 57
Chapter 3: Building Deep Neural Networks for Binary Classification 59
Technical requirements 60
Extracting data from CSV input 60
How to do it... 61
How it works... 61
Removing anomalies from the data 62
How to do it... 62
How it works... 63
There's more... 65
Applying transformations to the data 66
How to do it... 66
How it works... 68
Designing input layers for the neural network model 70
Getting ready 70
How to do it... 71
How it works... 71
Designing hidden layers for the neural network model 71
How to do it... 72
[ ii ]
Table of Contents
How it works... 72
Designing output layers for the neural network model 72
How to do it... 73
How it works... 73
Training and evaluating the neural network model for CSV data 74
How to do it... 74
How it works... 75
There's more... 81
Deploying the neural network model and using it as an API 83
Getting ready 83
How to do it... 84
How it works... 87
Chapter 4: Building Convolutional Neural Networks 90
Technical requirements 91
Extracting images from disk 92
How to do it... 92
How it works... 92
Creating image variations for training data 94
How to do it... 94
How it works... 95
There's more... 97
Image preprocessing and the design of input layers 97
How to do it... 97
How it works... 98
Constructing hidden layers for a CNN 99
How to do it... 100
How it works... 100
Constructing output layers for output classification 100
How to do it... 101
How it works... 101
Training images and evaluating CNN output 101
How to do it... 102
How it works... 103
There's more... 104
Creating an API endpoint for the image classifier 105
How to do it... 106
How it works... 110
Chapter 5: Implementing Natural Language Processing 112
Technical requirements 113
Data requirements 114
Reading and loading text data 114
Getting ready 114
How to do it... 115
[ iii ]
