Machine Learning with TensorFlow 1.x: Second generation machine learning with Google's brainchild - TensorFlow 1.x

296 Pages·2017·14.361 MB·English

by  Quan Hua, Shams Ul Azeem, Saif Ahmed

Machine Learning with TensorFlow 1.x Q M Quan Hua, Shams Ul Azeem, Saif Ahmed
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Firstly, you’ll cover the basic installation • Learn how to build deep neural h
procedure and explore the capabilities networks using TensorFlow 1.x
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Learning with
of TensorFlow 1.x. This is followed by e
training and running the fi rst classifi er, n
and coverage of the unique features of the s
• Cover key tasks such as clustering, o
library including data fl ow graphs, training,
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and the visualization of performance with sentiment analysis, and regression F
TensorBoard—all within an example-rich analysis using TensorFlow 1.x l
o
context using problems from multiple w TensorFlow 1.x
industries. You’ll be able to further explore

1
text and image analysis, and be introduced
• Find out how to write clean and .x
to CNN models and their setup in
elegant Python code that will optimize
TensorFlow 1.x. Next, you’ll implement a
the strength of your algorithms
complete real-life production system from
training to serving a deep learning model. As
you advance you’ll learn about Amazon Web
Services (AWS) and create a deep neural
• Discover how to embed your machine
network to solve a video action recognition
learning model in a web application
problem. Lastly, you’ll convert the Caffe
for increased accessibility
model to TensorFlow and be introduced Second generation machine learning with Google’s
to the high-level TensorFlow library,
brainchild – TensorFlow 1.x
TensorFlow-Slim.
• Learn how to use multiple GPUs for
By the end of this book, you will be geared
faster training using AWS
up to take on any challenges of implementing
TensorFlow 1.x in your machine learning
environment.
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Machine Learning with
TensorFlow 1.x
Second generation machine learning with Google's brainchild
- TensorFlow 1.x
Quan Hua
Shams Ul Azeem
Saif Ahmed
BIRMINGHAM - MUMBAI
Machine Learning with TensorFlow 1.x
Copyright © 2017 Packt Publishing
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First published: November 2017
Production reference: 1171117
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ISBN 978-1-78646-296-1
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Credits
Authors Copy Editor
Quan Hua
Zainab Bootwala
Shams Ul Azeem
Saif Ahmed
Reviewer Project Coordinator
Nathan Lintz Prajakta Naik
Commissioning Editor Proofreader
Kunal Parikh Safis Editing
Acquisition Editor Indexer
Tushar Gupta Rekha Nair
Content Development Editor Graphics
Siddhi Chavan Jason Monteiro
Technical Editor Production Coordinator
Mehul Singh Deepika Naik
About the Authors
Quan Hua is a Computer Vision and Machine Learning Engineer at BodiData, a data
platform for body measurements, where he focuses on developing computer vision and
machine learning applications for a handheld technology capable of acquiring a body
avatar while a person is fully clothed. He earned a bachelor of science degree from the
University of Science, Vietnam, specializing in Computer Vision. He has been working in
the field of computer vision and machine learning for about 3 years at start-ups.
Quan has been writing for Packt since 2015 for a Computer Vision book, OpenCV 3
Blueprints.
I wish to thank everyone who has encouraged me on the way while writing this book.
I want to express my sincere gratitude to my co-authors, editors, and reviewers for their
advice and assistance.
I would like to thank the members of my family and my wife, Kim Ngoc, who supported
and encouraged me in spite of all the time it took me away from them. They all kept me
going, and this book would not have been possible without them.
I would also like to thank my teachers who gave me knowledge of Computer Vision and
Machine Learning.
Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad,
Pakistan. He has a great interest in the computer science field, and he started his journey
with Android development. Now, he’s pursuing his career in Machine Learning,
particularly in deep learning, by doing medical-related freelancing projects with different
companies.
He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE
International Conference, ROBIO as a co-author of Designing of motions for humanoid
goalkeeper robots.
Saif Ahmed is an accomplished quantitative analyst and data scientist with 15 years of
industry experience. His career started in management consulting at Accenture and lead
him to quantitative and senior management roles at Goldman Sachs and AIG Investments.
Most recently, he co-founded and runs a start-up focused on applying Deep Learning to
automating medical imaging. He obtained his bachelor's degree in computer science from
Cornell University and is currently pursuing a graduate degree in data science at U.C.
Berkeley.
About the Reviewer
Nathan Lintz is a Machine Learning researcher, focusing on text classification. When he
began with Machine Learning, he primarily used Theano but quickly switched to
TensorFlow when it was released. TensorFlow has greatly reduced the time it takes to build
Machine Learning systems thanks to its intuitive and powerful neural network utilities.
I want to thank my family and professors for all the help they have given me. Without
them, I would have never been able to pursue my passion for software engineering and
Machine Learning.
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Table of Contents
Preface
1
Chapter 1: Getting Started with TensorFlow
6
Current use 7
Installing TensorFlow 7
Ubuntu installation 8
macOS installation 9
Windows installation 11
Virtual machine setup 16
Testing the installation 24
Summary 26
Chapter 2: Your First Classifier
27
The key parts 27
Obtaining training data 28
Downloading training data 28
Understanding classes 29
Automating the training data setup 29
Additional setup 31
Converting images to matrices 32
Logical stopping points 37
The machine learning briefcase 37
Training day 41
Saving the model for ongoing use 45
Why hide the test set? 45
Using the classifier 46
Deep diving into the network 46
Skills learned 47
Summary 48
Chapter 3: The TensorFlow Toolbox
49
A quick preview 50
Installing TensorBoard 54
Incorporating hooks into our code 55
Handwritten digits 55
AlexNet 60