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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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Toc

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

Image captioning – how it works

The model that won the first MSCOCO Image Captioning Challenge in 2015 is described in the paper, Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge (https://arxiv.org/pdf/1609.06647.pdf). Before we talk about the training process, which is also covered pretty well in TensorFlow's im2txt model documentation website at https://github.com/tensorflow/models/tree/master/research/im2txt, let's first get a basic understanding of how the model works. This will also help you understand training and inference code in Python, as well as the inference code in iOS and Android you'll see later in the chapter.

The winning Show and Tell model is trained using an end-to-end method, similar to the latest deep learning-based speech recognition models we covered briefly in the previous chapter. It uses the MSCOCO image...

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