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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

Running the TensorFlow and Keras models on iOS

We won't bore you by repeating the project setup step - just follow what we did before to create a new Objective-C project named StockPrice that will use the manually built TensorFlow library (see the iOS section of Chapter 7, Recognizing Drawing with CNN and LSTM, if you need detailed info). Then add the two amzn_tf_frozen.pb and amzn_keras_frozen.pb model files to the project and you should have your StockPrice project in Xcode, as in Figure 8.3:

Figure 8.3 iOS app using the TensorFlow- and Keras-trained models in Xcode

In ViewController.mm, we'll first declare some variables and one constant:

unique_ptr<tensorflow::Session> tf_session;
UITextView *_tv;
UIButton *_btn;
NSMutableArray *_closeprices;
const int SEQ_LEN = 20;

Then create a button-tap handler to let the user choose either the TensorFlow or Keras model ...

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