Intelligent Mobile Projects with TensorFlow

By : Jeff Tang

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

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

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

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

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

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

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

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

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

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

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

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

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

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Setting up Android Studio

Android Studio is the best tool for Android app development, and TensorFlow has great support for using it. Unlike Xcode, you can install and run Android Studio on Mac, Windows, or Linux. For detailed system requirements, see the Android Studio website (https://developer.android.com/studio/index.html). Here, we'll just cover how to set up Android Studio 3.0 or 3.0.1 on Mac – all the apps in the book have been tested on both versions.

First, download the Android Studio 3.0.1, or the latest version if it's newer than 3.0.1 and if you don't mind fixing possible minor issues, from the preceding link. You can also download 3.0.1 or 3.0 from its archives at https://developer.android.com/studio/archive.html.

Then, double-click the downloaded file and drag and drop the Android Studio.app icon to Applications. If you have a previously installed Android Studio, you'll get a prompt asking you if you want to replace it with the newer one. You can just select Replace.

Next, open Android Studio and you need to provide the path to the Android SDK, which by default is in ~/Library/Android/sdk if you have a previous version of Android Studio installed, or you can select Open an existing Android Studio project, then go to the TensorFlow 1.4 source directory created in the section Setting up TensorFlow on MacOS, and open the tensorflow/examples/android folder. After that, you can download the Android SDK by either clicking the link to an Install Build Tools message or going to Android Studio's Tools | Android | SDK Manager, as shown in the following screenshot. From the SDK Tools tab there, you can check the box next to a specific version of Android SDK Tools and click the OK button to install that version:

Fig 1.4 Android SDK Manager to install SDK tools and NDK

Finally, as TensorFlow Android apps use the native TensorFlow library in C++ to load and run TensorFlow models, you need to install the Android Native Development Kit (NDK), which you can do either from the Android SDK Manager shown in the preceding screenshot, or by downloading NDK directly from https://developer.android.com/ndk/downloads/index.html. Both the NDK version r16b and r15c have been tested to run the Android apps in the book. If you download the NDK directly, you may also need to set the Android NDK location after opening your project and selecting Android Studio's File | Project Structure, as shown in the following screenshot:

Fig 1.5 Setting project-level Android NDK location

With both Android SDK and NDK installed and set up, you're ready to test run sample TensorFlow Android apps.

Intelligent Mobile Projects with TensorFlow

By : Jeff Tang

Intelligent Mobile Projects with TensorFlow

By: Jeff Tang

Overview of this book

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