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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

The machine learning pipeline for image caption generation


Here we will look at the image caption generation pipeline at a very high level and then discuss it piece by piece until we have the full model. The image caption generation framework consists of three main components and one optional component:

  • A CNN generating encoded vectors for images

  • An embedding layer learning word vectors

  • (Optional) An adaptation layer that can transform a given embedding dimensionality to an arbitrary dimensionality (details will be discussed later)

  • An LSTM taking the encoded vectors of the images, and outputting the corresponding caption

First, let's look at the CNN generating the encoded vectors for images. We can achieve this by first training a CNN on a large classification dataset, such as ImageNet, and using that knowledge to generate compressed vectorized representations of images.

One might ask, why not input the image as it is to the LSTM? Let's go back to a simple calculation we did in the previous chapter...

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