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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Bring magic to your mobile apps using TensorFlow Lite and Core ML
Key Features
- Explore machine learning using classification, analytics, and detection tasks.
- Work with image, text and video datasets to delve into real-world tasks
- Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite
Book Description
Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so.
The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google's ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN.
By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.
What you will learn
- Demystify the machine learning landscape on mobile
- Age and gender detection using TensorFlow Lite and Core ML
- Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning
- Create a digit classifier using adversarial learning
- Build a cross-platform application with face filters using OpenCV
- Classify food using deep CNNs and TensorFlow Lite on iOS
Who this book is for
Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 217
Veröffentlichungsjahr: 2018
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Machine Learning Projects for Mobile Applications
Copyright © 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:Dayne CastelinoContent Development Editor:Rhea HenriquesTechnical Editor: Sayli NikaljeCopy Editor: Safis EditingProject Coordinator: Manthan PatelProofreader: Safis EditingIndexer: Mariammal ChettiyarGraphics:Jisha ChirayilProduction Coordinator:Aparna Bhagat
First published: October 2018
Production reference: 1311018
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78899-459-0
www.packtpub.com
To my wife, Nanthana, for putting up with me during the course of this book. I know it must not have been easy.
To my parents, for their constant support.
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Contributors
About the author
Karthikeyan NG is the Head of Engineering and Technology at the Indian lifestyle and fashion retail brand. He served as a software engineer at Symantec Corporation and has worked with two US-based startups as an early employee and has built various products. He has 9+ years of experience in various scalable products using Web, Mobile, ML, AR, and VR technologies. He is an aspiring entrepreneur and technology evangelist. His interests lie in exploring new technologies and innovative ideas to resolve a problem. He has also bagged prizes from more than 15 hackathons, is a TEDx speaker and a speaker at technology conferences and meetups as well as guest lecturer at a Bengaluru University. When not at work, he is found trekking.
About the reviewer
Mayur Ravindra Narkhedehas a good blend of experience in data science and industrial domain. He is a researcher with a B.Tech in computer science and an M.Tech in CSE with a specialization in Artificial Intelligence.
A data scientist whose core experience lies in building automated end-to-end solutions, he is proficient at applying technology, AI, ML, data mining, and design thinking to better understand and predict improvements in business functions and desirable requirements with growth profitability.
He has worked on multiple advanced solutions, such as ML and predictive model development for the oil and gas industry, financial services, road traffic and transport, life sciences, and the big data platform for asset-intensive industries.
Packt is searching for authors like you
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Table of Contents
Title Page
Copyright and Credits
Machine Learning Projects for Mobile Applications
Dedication
Packt Upsell
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the code
Download the color images
Conventions used
Get in touch
Reviews
Mobile Landscapes in Machine Learning
Machine learning basics
Supervised learning
Unsupervised learning
Linear regression - supervised learning
TensorFlow Lite and Core ML
TensorFlow Lite
Supported platforms
TensorFlow Lite memory usage and performance 
Hands-on with TensorFlow Lite 
Converting SavedModel into TensorFlow Lite format
Strategies
TensorFlow Lite on Android
Downloading the APK binary
TensorFlow Lite on Android Studio
Building the TensorFlow Lite demo app from the source
Installing Bazel
Installing using Homebrew
Installing Android NDK and SDK
TensorFlow Lite on iOS
Prerequisites
Building the iOS demo app
Core ML
Core ML model conversion
Converting your own model into a Core ML model
Core ML on an iOS app
Summary
CNN Based Age and Gender Identification Using Core ML
Age, gender, and emotion prediction
Age prediction
Gender prediction
Convolutional Neural Networks 
Finding patterns
Finding features from an image
Pooling layer
Rectified linear units
Local response normalization layer
Dropout layer
Fully connected layer
CNNs for age and gender prediction
Architecture
Training the network
Initializing the dataset
The implementation on iOS using Core ML
Summary
Applying Neural Style Transfer on Photos
Artistic neural style transfer
Background
VGG network
Layers in the VGG network
Building the applications
TensorFlow-to-Core ML conversion
iOS application
Android application
Setting up the model
Training your own model
Building the application
Setting up the camera and an image picker 
Summary
References
Deep Diving into the ML Kit with Firebase
ML Kit basics
Basic feature set
Building the application
Adding Firebase to our application
Face detection
Face orientation tracking
Landmarks
Classification
Implementing face detection
Face detector configuration
Running the face detector
Step one: creating a FirebaseVisionImage from the input
Using a bitmap
From media.Image
From a ByteBuffer
From a ByteArray
From a file
Step two: creating an instance of FirebaseVisionFaceDetector object
Step three: image detection
Retrieving information from detected faces
Barcode scanner
Step one: creating a FirebaseVisionImage object
From bitmap
From media.Image
From ByteBuffer
From ByteArray
From file
Step two: creating a FirebaseVisionBarcodeDetector object
Step three: barcode detection
Text recognition
On-device text recognition
Detecting text on a device
Cloud-based text recognition
Configuring the detector
Summary
A Snapchat-Like AR Filter on Android
MobileNet models
Building the dataset
Retraining of images 
Model conversion from GraphDef to TFLite
Gender model
Emotion model
Comparison of MobileNet versions
Building the Android application
References
Questions
Summary
Handwritten Digit Classifier Using Adversarial Learning
Generative Adversarial Networks
Generative versus discriminative algorithms
Steps in GAN
Understanding the MNIST database
Building the TensorFlow model
Training the neural network
Building the Android application
FreeHandView for writing
Digit classifier
Summary
Face-Swapping with Your Friends Using OpenCV
Understanding face-swapping
Steps in face-swapping
Facial key point detection
Identifying the convex hull
Delaunay triangulation and Voronoi diagrams
Affine warp triangles
Seamless cloning
Building the Android application
Building a native face-swapper library
Android.mk
Application.mk
Applying face-swapping logic
Building the application
Summary
References
Questions
Classifying Food Using Transfer Learning
Transfer learning
Approaches in transfer learning
Training our own TensorFlow model 
Installing TensorFlow
Training the images
Retraining with own images
Training steps parameter
Architecture
Distortions
Hyperparameters
Running the training script
Model conversion
Building the iOS application
Summary
What's Next?
What you have learned so far
Where to start when developing an ML application
IBM Watson services
Microsoft Azure Cognitive Services
Amazon ML
Google Cloud ML
Building your own model
Limitations of building your own model
Personalized user experience
Better search results
Targeting the right user
Summary
Further reading
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Machine learning is a growing technique that focuses on the development of computer programs that can be changed or modified when exposed to new data. It has made significant advances that have enabled practical applications of machine learning (ML) and, by extension, the overall field of Artificial Intelligence (AI).
This book presents the implementation of seven practical, real-world projects that will teach you how to leverage TensorFlow Lite and Core ML to perform efficient machine learning. We will be learning about the recent advancements in TensorFlow and its extensions, such as TensorFlow Lite, to design intelligent apps that learn from complex/large datasets. We will delve into advancements such as deep learning by building apps using deep neural network architecture such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), transfer learning, and much more.
By the end of this book, you will not only have mastered all the concepts of and learned how to implement machine learning and deep learning, but you will also have learned how to solve the problems and challenges faced while building powerful apps on mobile using TensorFlow Lite and Core ML.
Who this book is for
Machine Learning Projects for Mobile Applications is for you if you are a data scientist, ML expert, deep learning, or AI enthusiast who fancies mastering ML and deep learning implementation with practical examples using TensorFlow and Keras. Basic knowledge of Python programming language would be an added advantage.
What this book covers
Chapter 1, Mobile Landscapes in Machine Learning, makes us familiar with the basic ideas behind TensorFlow Lite and Core ML.
Chapter 2, CNN Based Age and Gender Identification Using Core ML, teaches us how to build an iOS application to detect the age, gender, and emotion of a person from a camera feed or from the user's photo gallery using the existing data models that were built for the same purpose.
Chapter 3, Applying Neural Style Transfer on Photos, teaches us how to build a complete iOS and Android application in which image transformations are applied to our own images in a fashion similar to the Instagram app.
Chapter 4, Deep Diving into the ML Kit with Firebase, explores the Google Firebase-based ML Kit platform for mobile applications.
Chapter 5, A Snapchat-Like AR Filter on Android, takes us on a journey where we will build an AR filter that is used on applications such as Snapchat and Instagram using TensorFlow Lite.
Chapter 6, Handwritten Digit Classifier Using Adversarial Learning, explains how to build an Android application that identifies handwritten digits.
Chapter 7, Face-Swapping with Your Friends Using OpenCV, takes a close look at building an application where a face in an image is replaced by another face.
Chapter 8, Classifying Food Using Transfer Learning, explains how to classify food items using transfer learning.
Chapter 9, What's Next?, gives us a glimpse into all the applications built throughout the book and their relevance in the future.
To get the most out of this book
If you have prior knowledge of building mobile apps, that will help greatly. If not, it is advisable to learn the basics of Java or Kotlin for Android, or Swift for iOS.
If you have basic knowledge of Python, that will help you build your own data model, but Python skill is not mandatory.
The applications in the book are built using a MacBook Pro. Most of the command-line operations are shown with the assumption that you have a bash shell installed on your machine. They may not work in a Windows development environment.
Download the code
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Mobile Landscapes in Machine Learning
Computers are improving by the day, and device form factors are changing tremendously. In the past, we would only see computers at offices, but now we see them on our home desks, on our laps, in our pockets, and on our wrists. The market is becoming increasingly varied as machines are being equipped with more and more intelligence.
Almost every adult currently carries a device around with them, and it is estimated that we look at our smartphones at least 50 times a day, whether there is a need to or not. These machines affect our daily decision-making processes. Devices are now equipped with applications such as Siri, Google Assistant, Alexa, or Cortana, features that are designed to mimic human intelligence. The ability to answer any query thrown at them presents these types of technology as master humans. On the backend, these systems improve using the collective intelligence acquired from all users. The more you interact with virtual assistants, the better are the results they give out.
Despite these advancements, how much closer are we to creating a human brain through a machine? We are in 2018 now. If science discovers a way to control the neurons of our brain, this may be possible in the near future. Machines that mimic the capabilities of a human are helping to solve complex textual, visual, and audio problems. They resemble the tasks carried out by a human brain on a daily basis—on average, the human brain makes approximately 35,000 decisions in a day.
While we will be able to mimic the human brain in the future, it will come at a cost. We don't have a cheaper solution for it at the moment. The magnitude of power consumption of a human brain simulation program limits it in comparison to a human brain. The human brain consumes about 20 W of power, while a simulation program consumes about 1 MW of power or more. Neurons in the human brain operate at a speed of 200 Hz, while a typical microprocessor operates at a speed of 2 GHz, which is 10 million times more than that.
While we are still far from cloning a human brain, we can implement an algorithm that makes conscious decisions based on previous data as well as data from similar devices. This is where the subset of Artificial Intelligence (AI) comes in handy. With predefined algorithms that identify patterns from the complex data we have, these types of intelligence can then give us useful information.
When the computer starts making decisions without being instructed explicitly every time, we achieve machine learning (ML) capability. ML is used everywhere right now, including through features such as identifying email spam, recommending the best product to buy on an e-commerce website, tagging your face automatically on a social media photograph, and so on. All of these are done using the patterns identified in historical data, and also through algorithms that reduce unnecessary noise from the data and produce quality output. When the data accumulates more and more, the computers can make better decisions.
Since we have wider access to mobile devices and the amount of time we spend on those devices is rapidly increasing, it makes sense to run ML models on the mobile phone itself. In the mobile phone market, Android and iOS platforms take the lead to cover the whole smartphone spectrum. We will explore how TensorFlow Lite and Core ML works on these mobile platforms.
The topics that will be covered in this chapter are as follows:
ML basics (with an example)
TensorFlow and Core ML basics
Machine learning basics
ML is a concept that describes the process of a set of generic algorithms analyzing your data, and providing you with interesting data without writing any specific codes for your problem.
Alternatively, we can look at ML as a black box how cutting edge scientists are using it to do something crazy like detecting epilepsy or cancer disease, yet your simple email inbox is using it to filter spam every day.
On a larger level, ML can be classified into the following two categories:
Supervised learning
Unsupervised learning
Unsupervised learning
In this case, you only have input data (x) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about it.
In unsupervised learning, you may not have any data in the beginning. Say for example on the same scenario discussed above in supervised learning, you have a basket full of fruits and you are asked to group them into similar groups. But you don't have any previous data or there are no training or labeling is done earlier. In that case, you need to understand the domain first because you have no idea whether the input is a fruit or not. In that case, you need to first understand all the characteristics of every input and then to try to match with every new input. May be at the final step you might have classified all the red color fruits into one baskets and the green color fruits into another basket. But not an accurate classification. This is called as unsupervised learning.
