Hands-On D L for Images with TF: Build intelligent computer vision app using TF

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Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features • Discover image processing for machine vision • Build an effective image classification system using the power of CNNs • Leverage TensorFlow's capabilities to perform efficient deep learning Book Descripti
Hands-On Deep Learning for Images with TensorFlow Copyright C 2018 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: Joshua Nadar Content Development Editor: Dinesh Pawar Technical Editor: Suwarna patil Copy Editor: SAFIS Project Coordinator: Nidhi Josh Proofreader: safis Indexer: Pratik Shirodkar Graphics: Jisha chirayil Production Coordinator: Shantanu Zagat First published: July 2018 Production reference: 1300718 Published by Packt Publishing Ltd L 35 Livery street Birmingham B 3 2PB, UK ISBN978-1-78953-867-0 www.pAcktpub.com Mapt map Mapt is an online digital library that gives you full access to over 5,000 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 e books 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 Mapt is fully searchable Copy and paste print, and bookmark content PacktPub, com Did you know that Packt offers eBook versions of every book published, with PDF and epubfilesavailableYoucanupgradetotheebookversionatwww.Packtpub.comandasa print book customer, you are entitled to a discount on the e book copy. Get in touch with us at service@packtpub com for more details Atwww.packtpub.comyoucanalsoreadacollectionoffreetechnicalarticlessignupfora range of free newsletters and receive exclusive discounts and offers on packt books and eBooKs Contributors About the author Will Ballard is the chief technology officer at glG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL. com, NPR, The Washington post, and whole foods. he has also held leadership roles in software developmentatNetsolve(nOwCisco),Netspend,andWorks.com(nowBankofAmerica Packt is searching for authors like you Ifyou'reinterestedinbecominganauthorforPackt,pleasevisitauthorspacktpub.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 Chapter 1: Machine Learning Toolkit stalling Docker The machine learning docker file 559 Sharing data Machine learning rest service 17 Summary 22 Chapter 2: Image data 23 MNIST digits 23 Tensors -multidimensional arrays 26 Turning images into tensors 28 Turning categories into tensors Summary 32 Chapter 3: Classical Neural Network 33 Comparison between classical dense neural networks 33 Activation and nonlinearity 35 Softmax 38 Training and testing data 40 Dropout and Flatten 41 Solvers 44 Hyperparameters 46 Grid searches 47 Summary 51 Chapter 4: A Convolutional Neural Network 52 Convolutions 52 Pooling 55 Building a convolutional neural network 58 Deep neural network 61 Summary 66 Chapter 5: An Image Classification Server 67 REST API definition 67 Trained models in docker containers 72 Making predictions 77 Summary 80 Table of Contents Other Books You May Enjoy 81 Index 84 [i] Preface TensorFlow is Google's popular offering for machine learning and deep learning. It has quIc kly become a popular choice of tool for performing fast, efficient, and accurate deep learning tasks This book shows you practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient deep learning. In this book, you will be acquainted with the different paradigms of performing deep learning, such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how the ey can be im1 plemented using TensorFloy This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, and fraud detection By the end of this book, you will have mastered all the concepts of deep learning and their implementations with Tensor Flow and Keras Who this book is for This book is for application developers, data scientists, and machine learning practitioners looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and the basics of deep learning is required to get the most out of this book What this book covers Chapter 1, Machine learning Toolkit, looks into installing Docker, setting up a machine learning Docker file, sharing data back with your host computer, and running a rEST service to provide the environment Chapter 2, Image Data, teaches MNiST digits, how to acquire them, how tensors are really just multidimensional arrays and how we can encode image data and categorical or classification data as a tensor. Then we have a quick review and a cookbook approach to consider dimensions and tensors, in order to get data prepared for machine learning Preface Chapter 3, Classical Neural Network covers an awful lot of material! We see the structure of the classical, or dense, neural network. We learn about activation, nonlinearity, and softmax. We then set up testing and training data and learn how to construct the network with Dropout and Flatten. We also learn all about solvers or how machine actually learns. We then explore hyperparameters, and finally, we fine-tune our model by means of grid sear Chapter 4, A Convolutional Neural Network teaches you convolutions, which are a loosely connected way of moving over an image to extract features. Then we learn about pooling which summarizes the most important features. We will build a convolutional neural network using these techniques and we combine many layers of convolution and pooling in order to generate a deep neural network Chapter 5, An Image Classification Server, uses a Swagger API definition to create a REsT API model, which then declaratively generates the python framework in order for us to serve that API. Then we create a docker container that captures not only our running code (that is, our service) but also our pre-trained machine learning model. This then forms a package so that we are able to deploy and use our container. Finally, we use this container to serve and make predictions To get the most out of this book Youll need. Experience with command-line shell Experience with Python scripting or application development Download the example code files You can download the example code files for this book from your account at www.pacKtpub.com.Ifyoupurchasedthisbookelsewhereyoucanvisit www.packtpub.com/supportandregistertohavethefilesemaileddirectlytoyou You can download the code files by following these steps oginorregisteratwww.packtpub.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 2] Preface Once the file is downloaded please make sure that you unzip or extract the folder using the latest version of Winrar/7-Zip for Windows ipeg/iZip/UnRarX for Mac 7-Zip/PeaZip for linux The code bundle for the book is also hosted on github nsorFlow. In case there's an update to the code, it will be updated on the existing GitHub o athttps://github.com/packtpublishiNg/hands-on-deep-learnIng-for-images-with-t repository We also have other code bundles from our rich catalog of books and videos available athttps://github.com/packtpublisHing/.Checkthemout! 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: You just have to type docker --help to make sure that everything is installed Any command-line input or output is written as follows C:\11519>docker build -t keras Bold: Indicates a new term, an important word, or words that you see onscreen For example, words in menus or dialog boxes appear in the text like this. Here is an exampl Were going to select and copy the test command we'll be using later, and click on Apply Warnings or important notes appear like this ips and tricks appear like this TIP [3]

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