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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 a simple speech recognition model in iOS with Swift

We created a Swift-based iOS app that uses the TensorFlow pod in Chapter 2, Classifying Images with Transfer Learning. Let's now create a new Swift app that uses the TensorFlow iOS libraries we manually built in the last section and use the speech commands model in our Swift app:

  1. Create a new Single View iOS project from Xcode, and set up the project in the same way as steps 1 and 2 in the previous section, except set the Language as Swift.
  2. Select Xcode File | New | File ... and select Objective-C File. Enter the name RunInference. You'll see a message box asking you "Would you like to configure an Objective-C bridging header?" Click the Create Bridging Header. Rename the file RunInference.m to RunInfence.mm as we'll mix our C, C++, and Objective-C code to do post-recording audio processing and...
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