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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

Retraining using MobileNet models

The stripped and quantized model generated in the previous section is still over 20 MB in size. This is because the pre-built Inception v3 model used for retraining is a large-scale deep learning model, with over 25 million parameters, and Inception v3 was not created with a mobile-first goal.

In June 2017, Google released MobileNets v1, a total of 16 mobile-first deep learning models for TensorFlow. These models are only a few MB in size, with 0.47 million to 4.24 million parameters, still achieving decent accuracy (just a bit lower than Inception v3). See its README for more information: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md.

The retrain.py script discussed in the previous section also supports retraining based on MobileNet models. Simply run a command like the following:

python tensorflow/examples...
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