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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

Using the retrained models in the sample iOS app

The iOS simple example we see in Chapter 1, Getting Started with Mobile TensorFlow, uses the Inception v1 model. To make the app use our retrained Inception v3 model and MobileNet model to do better dog breed recognition, we need to make a few changes to the app. Let's first see what it takes to use the retrained quantized_stripped_dogs_retrained.pb in the iOS simple app:

  1. Double-click the tf_simple_example.xcworkspace file in tensorflow/examples/ios/simple to open the app in Xcode
  2. Drag the quantized_stripped_dogs_retrained.pb model file, the dog_retrained_labels.txt label file, and the lab1.jpg image file we used to test the label_image script, and drop to the project's data folder, making sure both Copy items if needed and Add to targets are checked, as shown in the following screenshot:
Figure 2.5 Adding the retrained...
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