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Intelligent Mobile Projects with TensorFlow

You're reading from  Intelligent Mobile Projects with TensorFlow

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788834544
Pages 404 pages
Edition 1st Edition
Languages
Author (1):
Jeff Tang Jeff Tang
Profile icon Jeff Tang
Toc

Table of Contents (14) Chapters close

Preface 1. Getting Started with Mobile TensorFlow 2. Classifying Images with Transfer Learning 3. Detecting Objects and Their Locations 4. Transforming Pictures with Amazing Art Styles 5. Understanding Simple Speech Commands 6. Describing Images in Natural Language 7. Recognizing Drawing with CNN and LSTM 8. Predicting Stock Price with RNN 9. Generating and Enhancing Images with GAN 10. Building an AlphaZero-like Mobile Game App 11. Using TensorFlow Lite and Core ML on Mobile 12. Developing TensorFlow Apps on Raspberry Pi 13. Other Books You May Enjoy

Running the TensorFlow and Keras models on Android

It turns out it's like walking on the beach using the models on Android - we don't even need to use the custom TensorFlow Android library as we did in the last chapter, although we have to use the custom TensorFlow library (not the TensorFlow pod as of February 2018) on iOS. The TensorFlow Android library built with compile 'org.tensorflow:tensorflow-android:+' in the build.gradle file must have more complete data type support for the Less Op than the TensorFlow pod for iOS does.

To test the models in Android, create a new Android app StockPrice and add the two model files to its assets folder. Then add a couple of buttons and a TextView in the layout and define some fields and constants in the MainActivity.java:

    private static final String TF_MODEL_FILENAME = "file:///android_asset/amzn_tf_frozen...
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