Machine Learning with TensorFlow 1.x E-Book

Quan Hua

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 249

Veröffentlichungsjahr: 2017

Beschreibung

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x.

Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a 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 network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.

By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Leseprobe

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

Published by Packt Publishing Ltd.

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ISBN 978-1-78646-296-1

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Credits

Authors

Quan Hua

Shams Ul Azeem

Saif Ahmed

Copy Editor

Zainab Bootwala

Reviewer

Nathan Lintz

Project Coordinator

Prajakta Naik

Commissioning Editor

Kunal Parikh

Proofreader

Safis Editing

Acquisition Editor

Tushar Gupta

Indexer

Rekha Nair

Content Development Editor

Siddhi Chavan

Graphics

Jason Monteiro

Technical Editor

Mehul Singh

Production Coordinator

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 NUSTIslamabad, 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

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

Getting Started with TensorFlow

Current use

Installing TensorFlow

Ubuntu installation

macOS installation

Windows installation

Virtual machine setup

Testing the installation

Summary

Your First Classifier

The key parts

Obtaining training data

Downloading training data

Understanding classes

Automating the training data setup

Additional setup

Converting images to matrices

Logical stopping points

The machine learning briefcase

Training day

Saving the model for ongoing use

Why hide the test set?

Using the classifier

Deep diving into the network

Skills learned

Summary

The TensorFlow Toolbox

A quick preview

Installing TensorBoard

Incorporating hooks into our code

Handwritten digits

AlexNet

Automating runs

Summary

Cats and Dogs

Revisiting notMNIST

Program configurations

Understanding convolutional networks

Revisiting configurations

Constructing the convolutional network

Fulfilment

Training day

Actual cats and dogs

Saving the model for ongoing use

Using the classifier

Skills learned

Summary

Sequence to Sequence Models-Parlez-vous Français?

A quick preview

Drinking from the firehose

Training day

Summary

Finding Meaning

Additional setup

Skills learned

Summary

Making Money with Machine Learning

Inputs and approaches

Getting the data

Approaching the problem

Downloading and modifying data

Viewing the data

Extracting features

Preparing for training and testing

Building the network

Training

Testing

Taking it further

Practical considerations for the individual

Skills learned

Summary

The Doctor Will See You Now

The challenge

The data

The pipeline

Understanding the pipeline

Preparing the dataset

Explaining the data preparation

Training routine

Validation routine

Visualize outputs with TensorBoard

Inception network

Going further

Other medical data challenges

The ISBI grand challenge

Reading medical data

Skills Learned

Summary

Cruise Control - Automation

An overview of the system

Setting up the project

Loading a pre-trained model to speed up the training

Testing the pre-trained model

Training the model for our dataset

Introduction to the Oxford-IIIT Pet dataset

Dataset Statistics

Downloading the dataset

Preparing the data

Setting up input pipelines for training and testing

Defining the model

Defining training operations

Performing the training process

Exporting the model for production

Serving the model in production

Setting up TensorFlow Serving

Running and testing the model

Designing the web server

Testing the system

Automatic fine-tune in production

Loading the user-labeled data

Performing a fine-tune on the model

Setting up cronjob to run every day

Summary

Go Live and Go Big

Quick look at Amazon Web Services

P2 instances

G2 instances

F1 instances

Pricing

Overview of the application

Datasets

Preparing the dataset and input pipeline

Pre-processing the video for training

Input pipeline with RandomShuffleQueue

Neural network architecture

Training routine with single GPU

Training routine with multiple GPU

Overview of Mechanical Turk

Summary

Going Further - 21 Problems

Dataset and challenges

Problem 1 - ImageNet dataset

Problem 2 - COCO dataset

Problem 3 - Open Images dataset

Problem 4 - YouTube-8M dataset

Problem 5 - AudioSet dataset

Problem 6 - LSUN challenge

Problem 7 - MegaFace dataset

Problem 8 - Data Science Bowl 2017 challenge

Problem 9 - StarCraft Game dataset

TensorFlow-based Projects

Problem 10 - Human Pose Estimation

Problem 11 - Object Detection - YOLO

Problem 12 - Object Detection - Faster RCNN

Problem 13 - Person Detection - tensorbox

Problem 14 - Magenta

Problem 15 - Wavenet

Problem 16 - Deep Speech

Interesting Projects

Problem 17 - Interactive Deep Colorization - iDeepColor

Problem 18 - Tiny face detector

Problem 19 - People search

Problem 20 - Face Recognition - MobileID

Problem 21 - Question answering - DrQA

Caffe to TensorFlow

TensorFlow-Slim

Summary

Advanced Installation

Installation

Installing Nvidia driver

Installing the CUDA toolkit

Installing cuDNN

Installing TensorFlow

Verifying TensorFlow with GPU support

Using TensorFlow with Anaconda

Summary

Preface

Machine Learning has revolutionized the modern world. Many machine learning algorithms, especially deep learning, have been used worldwide, ranging from mobile devices to cloud-based services. TensorFlow is one of the leading open source software libraries and helps you build, train, and deploy your Machine Learning system for a variety of applications. This practical book is designed to bring you the best of TensorFlow and help you build real-world Machine Learning systems.

By the end of this book, you will have a deep understanding of TensorFlow and be able to apply Machine Learning techniques to your application.

What this book covers

Chapter 1, Getting Started with TensorFlow, shows how to install Tensorflow and get started on Ubuntu, macOS, and Windows.

Chapter 2, Your First Classifier, guides you through your first journey with a handwriting recognizer.

Chapter 3, The TensorFlow Toolbox, gives you an overview of the tools that Tensorflow provides to work more effectively and easily.

Chapter 4, Cats and Dogs, teaches you how to build an image classifier using Convolutional Neural Networks in TensorFlow.

Chapter 5, Sequence to Sequence Models—Parlez-vous Français?, discusses how to build an English to French translator using sequence-to-sequence models.

Chapter 6, Finding Meaning, explores the ways to find the meaning in the text by using sentiment analysis, entity extraction, keyword extraction, and word-relation extraction.

Chapter 7, Making Money with Machine Learning, dives into an area with copious amounts of data: the financial world. You will learn how to work with the time series data to solve the financial problems.

Chapter 8, The Doctor Will See You Now, investigates ways to tackle an enterprise-grade problem—medical diagnosis—using deep neural networks.

Chapter 9, Cruise Control - Automation, teaches you how to create a production system, ranging from training to serving a model. The system can also receive user feedbacks and automatically train itself every day.

Chapter 10, Go Live and Go Big, guides you through the world of Amazon Web Services and shows you how to take advantage of a multiple GPUs system on Amazon servers.

Chapter 11, Going Further - 21 Problems, introduces 21 real-life problems that you can use in deep learning—TensorFlow to solve after reading this book.

Appendix, Advanced Installation, discusses GPUs and focuses on a step-by-step CUDA setup and a GPU-based TensorFlow installation.

What you need for this book

For software, the whole book is based on TensorFlow. You can use either Linux, Windows, or macOS.

For hardware, you will need a computer or laptop that runs Ubuntu, macOS, or Windows. As authors, we encourage you to have an NVIDIA graphics card if you want to work with deep neural networks, especially when you want to work with large-scale datasets.

Who this book is for

This book is ideal for you if you aspire to build Machine Learning systems that are smart and practical enough for real-world applications. You should be comfortable with Machine Learning concepts, Python programming, IDEs, and the command line. This book will be useful to people who program professionally as part of their job, or those who are working as scientists and engineers and need to learn about Machine Learning and TensorFlow in support of their work.

Reader feedback

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If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide atwww.packtpub.com/authors.

Customer support

Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.

Downloading the example code

You can download the example code files for this book from your account athttp://www.packtpub.com. If you purchased this book elsewhere, you can visithttp://www.packtpub.com/supportand register to have the files e-mailed directly to you.

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Downloading the color images of this book

We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. You can download this file fromhttps://www.packtpub.com/sites/default/files/downloads/MachineLearningwithTensorFlow1.x_ColorImages.pdf.

Errata

Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our books—maybea mistake in the text or the code—we would be grateful if you could report this to us. By doing so, you can save other readers from frustration and help us improve subsequent versions of this book. If you find any errata, please report them by visitinghttp://www.packtpub.com/submit-errata, selecting your book, clicking on theErrata Submission Formlink, and entering the details of your errata. Once your errata are verified, your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the Errata section of that title.

To view the previously submitted errata, go tohttps://www.packtpub.com/books/content/supportand enter the name of the book in the search field. The required information willappear undertheErratasection.

Piracy

Piracy of copyrighted material on the Internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously. If you come across any illegal copies of our works in any form on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy.

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We appreciate your help in protecting our authors and our ability to bring you valuable content.

Questions

If you have a problem with any aspect of this book, you can contact us [email protected], and we will do our best to address the problem.

Getting Started with TensorFlow

The proliferation of large public datasets, inexpensive GPUs, and open-minded developer culture has revolutionized machine learning efforts in recent years. Training data, the lifeblood of machine learning, has become widely available and easily consumable in recent years. Computing power has made the required horsepower available to small businesses and even individuals. The current decade is incredibly exciting for data scientists.

Some of the top platforms used in the industry include Caffe, Theano, and Torch. While the underlying platforms are actively developed and openly shared, usage is limited largely to machine learning practitioners due to difficult installations, non-obvious configurations, and difficulty with productionizing solutions.

Late 2015 and 2016 brought additional platforms into the landscape—TensorFlow from Google, CNTK from Microsoft, and Veles from Samsung, among other options. Google's TensorFlow is the most exciting for several reasons.

TensorFlow has one of the easiest installations of any platform, bringing machine learning capabilities squarely into the realm of casual tinkerers and novice programmers. Meanwhile, high-performance features, such as—multiGPU support, make the platform exciting for experienced data scientists and industrial use as well. TensorFlow also provides a reimagined process and multiple user-friendly utilities, such as TensorBoard, to manage machine learning efforts. Finally, the platform has significant backing and community support from the world's largest machine learning powerhouse--Google. All this is before even considering the compelling underlying technical advantages, which we'll dive into later.

In this chapter, we will cover the following topics:

macOS X

Microsoft Windows and Linux, both the core software and all the dependencies

VM setup to enable Windows installation

Current use

Although TensorFlow has been public for just two years, numerous community efforts have already successfully ported over existing machine learning projects. Some examples include handwriting recognition, language translation, animal classification, medical image triage, and sentiment analysis. The wide applicability of machine learning to so many industries and problems always intrigues people. With TensorFlow, these problems are not only feasible but easily achievable. In fact, we will tackle and solve each of the preceding problems within the course of this book!

Installing TensorFlow

TensorFlow conveniently offers several types of installation and operates on multiple operating systems. The basic installation is CPU-only, while more advanced installations unleash serious horsepower by pushing calculations onto the graphics card, or even to multiple graphics cards. We recommend starting with a basic CPU installation at first. More complex GPU and CUDA installations will be discussed in Appendix, Advanced Installation.

Even with just a basic CPU installation, TensorFlow offers multiple options, which are as follows:

A basic Python

pip

installation

A segregated Python installation via Virtualenv

A fully segregated container-based installation via Docker

We recommend a Python installation via Virtualenv, but our examples will use a basic Python pip installation to help you focus on the crux of our task, that is, getting TensorFlow up and running. Again, more advanced installation types will be covered in Appendix, Advanced Installation.

TensorFlow can fully work on Linux and macOS with both Python 2.7 and 3.5. On Windows, we can only use TensorFlow with Python 3.5.x or 3.6.x. It can also be easily used on Windows by running a Linux virtual machine (VM). With an Ubuntu virtual machine, we can use TensorFlow with Python 2.7. However, we can't use TensorFlow with GPU support in a virtual machine. As of TensorFlow 1.2, TensorFlow doesn't provide GPU support on macOS. Therefore, if you want to use macOS with GPU-enabled TensorFlow, you will have to compile from sources, which is out of the scope of this chapter. Otherwise, you can still use TensorFlow 1.0 or 1.1, which provides GPU support out of the box on macOS. Linux and Windows users can use TensorFlow with both CPU and GPU support.

Ubuntu installation

Ubuntu is one of the best Linux distributions for working with Tensorflow. We highly recommend that you use an Ubuntu machine, especially if you want to work with GPU. We will do most of our work on the Ubuntu terminal. We will begin with installing python-pip and python-dev via the following command:

sudo apt-get install python-pip python-dev

A successful installation will appear as follows:

If you find missing packages, you can correct them via the following command:

sudo apt-get update --fix-missing

Then, you can continue the python and pip installation.

We are now ready to install TensorFlow. We will do a CPU-only installation, and if you wish to do an advanced GPU-enabled installation, we will cover that in Appendix, Advanced Installation.

The CPU installation is initiated via the following command:

sudo pip install tensorflow

A successful installation will appear as follows:

macOS installation

If you use Python, you will probably already have the Python package installer, pip. However, if not, you can easily install it using the easy_install pip command. You'll note that we actually executed sudo easy_install pip—the sudo prefix was required because the installation requires administrative rights.

We will make the fair assumption that you already have the basic package installer, easy_install, available; if not, you can install it from https://pypi.python.org/pypi/setuptools. A successful installation will appear as shown in the following screenshot:

Next, we will install the six package:

sudo easy_install --upgrade six

A successful installation will appear as shown in the following screenshot:

Surprisingly, those are the only two prerequisites for TensorFlow, and we can now install the core platform. We will use the pip package installer mentioned earlier and install TensorFlow directly from Google's site. The most recent version at the time of writing this book is v1.3, but you should change this to the latest version you wish to use:

sudo pip install tensorflow

The pip installer will automatically gather all the other required dependencies. You will see each individual download and installation until the software is fully installed.

A successful installation will appear as shown in the following screenshot:

That's it! If you were able to get to this point, you can start to train and run your first model. Skip to Chapter 2, Your First Classifier, to train your first model.

macOS X users wishing to completely segregate their installation can use a VM instead, as described in the Windows installation.

Windows installation

As we mentioned earlier, TensorFlow with Python 2.7 does not function natively on Windows. In this section, we will guide you through installing TensorFlow with Python 3.5 and set up a VM with Linux if you want to use TensorFlow with Python 2.7.

First, we need to install Python 3.5.x or 3.6.x 64-bit from the following links:

https://www.python.org/downloads/release/python-352/

https://www.python.org/downloads/release/python-362/

Make sure that you download the 64-bit version of Python where the name of the installation has amd64, such as python-3.6.2-amd64.exe. The Python 3.6.2 installation looks like this:

We will select Add Python 3.6 to PATH and click Install Now. The installation process will complete with the following screen:

We will click the Disable path length limit and then click Close to finish the Python installation. Now, let's open the Windows PowerShell application under the Windows menu. We will install the CPU-only version of Tensorflow with the following command:

pip3 install tensorflow

The result of the installation will look like this:

Congratulations, you can now use TensorFlow on Windows with Python 3.5.x or 3.6.x support. In the next section, we will show you how to set up a VM to use TensorFlow with Python 2.7. However, you can skip to the Test installation section of Chapter 2, Your First Classifier, if you don't need Python 2.7.

Now, we will show you how to set up a VM with Linux to use TensorFlow with Python 2.7. We recommend the free VirtualBox system available at https://www.virtualbox.org/wiki/Downloads. The latest stable version at the time of writing is v5.0.14, available at the following URL:

http://download.virtualbox.org/virtualbox/5.1.28/VirtualBox-5.1.28-117968-Win.exe

A successful installation will allow you to run the Oracle VM VirtualBox Manager dashboard, which looks like this:

Virtual machine setup

Linux comes in numerous flavors, but as the TensorFlow documentation mostly mentions Ubuntu, we'll be working with Ubuntu Linux. You are welcome to use any flavor of Linux, but you should be aware that there are subtle differences across flavors and versions of each flavor. Most differences are benign, but some may trip up the installation or even usage of TensorFlow.

Even after choosing Ubuntu, there are many versions and configurations; you can see some at http://cdimage.ubuntu.com/ubuntu-gnome/releases/14.04/release/.

We will install the most popular version, which is Ubuntu 14.04.4 LTS (make sure to download a version appropriate for your computer). Versions marked x86 are designed to run on 32-bit machines, while those marked with some variation of 64 are designed to run on 64-bit machines. Most modern machines are 64-bit, so if you are unsure, go with the latter.

Installations happen via an ISO file, which is, essentially, a file equivalent of an installation CD. The ISO for Ubuntu 14.04.4 LTS is ubuntu-gnome-14.04-desktop-amd64.iso.

Once you have downloaded the installation ISO, we will set up a VM and use the ISO file to install Ubuntu Linux on the VM.

Setting up the VM on Oracle VM VirtualBox Manager is relatively simple, but pay close attention as the default options are not sufficient for TensorFlow. You will go through the following seven screens, and at the end, it will prompt you for the installation file, which was just downloaded.

We will first set up the type of operating system and configure the random access memory (RAM) allocated to the VM:

Note that we selected a 64-bit installation as that is the image we're using; you can choose to use a 32-bit image if you need:

How much RAM you allocate depends on how much your machine has. In the following screenshot, we will allocate half our RAM(8 GB) to our VM. Remember that this is consumed only while we are running the VM, so we can be liberal with our allocations. We can allocate at least 4 GB:

Our VM will need a hard disk. We'll create a

Virtual Hard Disk

(

VHD

), as shown in the following screenshot:

Then, we will choose the type of hard drive for the VM, that is,

VDI (VirtualBox Disk Image)

, as shown in the following screenshot:

Next, we will choose how much space to allocate for the VHD. This is important to understand as we will soon work with extremely large datasets:

We will allocate 12 GB because TensorFlow and typical TensorFlow applications have an array of dependencies, such as

NumPy

,

SciPy

, and

Pandas

. Our exercises will also be downloading large datasets, which are to be used for training:

After setting up the VM, it will appear on the left side VM listing. Select it and click on

Start

. This is the equivalent of booting up the machine:

As the machine boots for the first time, provide it the installation CD (in our case, the Ubuntu ISO we downloaded earlier):