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Natural Language Processing with TensorFlow

You're reading from  Natural Language Processing with TensorFlow

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Pages 472 pages
Edition 1st Edition
Languages
Authors (2):
Motaz Saad Motaz Saad
Thushan Ganegedara Thushan Ganegedara
Profile icon Thushan Ganegedara
View More author details

Table of Contents (16) Chapters

Natural Language Processing with TensorFlow
Contributors
Preface
1. Introduction to Natural Language Processing 2. Understanding TensorFlow 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 Mathematical Foundations and Advanced TensorFlow Index

Captions generated for test images


Let's see what sort of captions are generated for the test images.

After 100 steps, the only thing that our model has learned is that the caption starts with an SOS token, and there are some words followed by a bunch of EOS tokens (see Figure 9.11):

Figure 9.11: Captions generated after 100 steps

After 1,000 steps, our model knows to generate slightly semantic phrases and recognizes objects in some images correctly (for example, a man holding a tennis racket, shown in Figure 9.12). However, the text seems to be short and vague, and in addition, several images are described incorrectly:

Figure 9.12: Captions generated after 1,000 steps

After 2,000 steps, our model has become quite good at generating expressive phrases composed of proper grammar (see Figure 9.13). Images are not described with small and vague phrases as we saw in step 1,000 before:

Figure 9.13: Captions generated after 2,000 steps

After 5,000 steps, our model now recognizes most of the images correctly...

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