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Intelligent Mobile Projects with TensorFlow

You're reading from   Intelligent Mobile Projects with TensorFlow Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788834544
Length 404 pages
Edition 1st Edition
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Author (1):
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Jeff Tang Jeff Tang
Author Profile Icon Jeff Tang
Jeff Tang
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Mobile TensorFlow FREE CHAPTER 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

Using the retrained models in the sample Android app

To use our retrained Inception v3 model and MobileNet model in Android's TF Classify app is also pretty straightforward. Follow the steps here to test both retrained models:

  1. Open the sample TensorFlow Android app, located in tensorflow/examples/android, using Android Studio.
  2. Drag and drop two retrained models, quantized_stripped_dogs_retrained .pb and dog_retrained_mobilenet10_224.pb as well as the label file, dog_retrained_labels.txt to the assets folder of the android app.
  3. Open the file ClassifierActivity.java, to use the Inception v3 retrained model, and replace the following code:
private static final int INPUT_SIZE = 224; 
private static final int IMAGE_MEAN = 117; 
private static final float IMAGE_STD = 1; 
private static final String INPUT_NAME = "input"; 
private static final String OUTPUT_NAME = &quot...
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