Book Image

Intelligent Mobile Projects with TensorFlow

By : Jeff Tang
Book Image

Intelligent Mobile Projects with TensorFlow

By: Jeff Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Mobile TensorFlow
Getting Started with Mobile TensorFlow
Setting up TensorFlow
Setting up Xcode
Setting up Android Studio
TensorFlow Mobile vs TensorFlow Lite
Running sample TensorFlow iOS apps
Running sample TensorFlow Android apps
Summary
2
Classifying Images with Transfer Learning
Classifying Images with Transfer Learning
Transfer learning – what and why
Retraining using the Inception v3 model
Retraining using MobileNet models
Using the retrained models in the sample iOS app
Using the retrained models in the sample Android app
Adding TensorFlow to your own iOS app
Adding TensorFlow to your own Android app
Summary
3
Detecting Objects and Their Locations
Detecting Objects and Their Locations
Object detection–a quick overview
Setting up the TensorFlow Object Detection API
Retraining SSD-MobileNet and Faster RCNN models
Using object detection models in iOS
Using YOLO2–another object-detection model
Summary
4
Transforming Pictures with Amazing Art Styles
Transforming Pictures with Amazing Art Styles
Neural Style Transfer – a quick overview
Training fast neural-style transfer models
Using fast neural-style transfer models in iOS
Using fast neural-style transfer models in Android
Using the TensorFlow Magenta multi-style model in iOS
Using the TensorFlow Magenta multi-style model in Android
Summary
5
Understanding Simple Speech Commands
Understanding Simple Speech Commands
Speech recognition – a quick overview
Training a simple commands recognition model
Using a simple speech recognition model in Android
Using a simple speech recognition model in iOS with Objective-C
Using a simple speech recognition model in iOS with Swift
Summary
6
Describing Images in Natural Language
Describing Images in Natural Language
Image captioning – how it works
Training and freezing an image captioning model
Transforming and optimizing the image captioning model
Using the image captioning model in iOS
Using the image captioning model in Android
Summary
7
Recognizing Drawing with CNN and LSTM
Recognizing Drawing with CNN and LSTM
Drawing classification – how it works
Training, predicting, and preparing the drawing classification model
Using the drawing classification model in iOS
Using the drawing classification model in Android
Summary
8
Predicting Stock Price with RNN
Predicting Stock Price with RNN
RNN and stock price prediction – what and how
Using the TensorFlow RNN API for stock price prediction
Using the Keras RNN LSTM API for stock price prediction
Running the TensorFlow and Keras models on iOS
Running the TensorFlow and Keras models on Android
Summary
9
Generating and Enhancing Images with GAN
Generating and Enhancing Images with GAN
GAN – what and why
Building and training GAN models with TensorFlow
Using the GAN models in iOS
Using the GAN models in Android
Summary
10
Building an AlphaZero-like Mobile Game App
Building an AlphaZero-like Mobile Game App
AlphaZero – how does it work?
Training and testing an AlphaZero-like model for Connect 4
Using the model in iOS to play Connect 4
Using the model in Android to play Connect 4
Summary
11
Using TensorFlow Lite and Core ML on Mobile
Using TensorFlow Lite and Core ML on Mobile
TensorFlow Lite – an overview
Using TensorFlow Lite in iOS
Using TensorFlow Lite in Android
Core ML for iOS – an overview
Using Core ML with Scikit-Learn machine learning
Using Core ML with Keras and TensorFlow
Summary
12
Developing TensorFlow Apps on Raspberry Pi
Developing TensorFlow Apps on Raspberry Pi
Setting up Raspberry Pi and making it move
Setting up TensorFlow on Raspberry Pi
Image recognition and text to speech
Audio recognition and robot movement
Reinforcement learning on Raspberry Pi
Summary
Final words
13
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Leave a review - let other readers know what you think

Setting up Xcode

Xcode is used to develop iOS apps and you need a Mac computer and a free Apple ID to download and install it. If your Mac is relatively older and with OS X El Capitan (version 10.11.6), you can download Xcode 8.2.1 at https://developer.apple.com/download/more. Or if you have macOS Sierra (version 10.12.6) or later, installed, you can download Xcode 9.2 or 9.3, the latest version as of May 2018, from the preceding link. All the iOS apps in the book have been tested in both Xcode 8.2.1, 9.2, and 9.3.

To install Xcode, simply double-click the downloaded file and follow the steps on the screen. It's pretty straightforward. You can now run apps on the iOS simulator that comes with Xcode or your own iOS device. Starting Xcode 7, you can run and debug your iOS apps on an iOS device for free, but if you want to distribute or publish your apps, you need to enroll in the Apple Developer Program for $99 a year as an individual: https://developer.apple.com/programs/enroll.

Although you can test run many apps in the book with the Xcode simulator, some apps in the book require the camera on your actual iOS device to take a picture before processing it with a deep learning model trained with TensorFlow. In addition, it's generally better to test a model on an actual device for accurate performance and memory usage: a model that runs fine in the simulator may crash or run too slow in an actual device. So it's strongly recommended or required that you test and run the iOS apps in the book on your actual iOS device at least once, if not always.

This book assumes you're comfortable with iOS programming, but if you're new to iOS development, you can learn from many excellent online tutorials such as the iOS tutorials by Ray Wenderlich (https://www.raywenderlich.com). We won't cover complicated iOS programming; we'll mainly show you how to use the TensorFlow C++ API in our iOS apps to run the TensorFlow trained models to perform all kinds of intelligent tasks. Both Objective-C and Swift code, the two official iOS programming languages from Apple, will be used to interact with the C++ code in our mobile AI apps.

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