Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Natural Language Processing with TensorFlow

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

Arrow left icon
Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
Arrow right icon
View More author details
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

Visualizing word embeddings with TensorBoard

When we wanted to visualize word embedding in Chapter 3, Word2vec – Learning Word Embeddings, we manually implemented the visualization with the t-SNE algorithm. However, you also could use TensorBoard for visualizing word embeddings. TensorBoard is a visualization tool provided with TensorFlow. You can use TensorBoard to visualize the TensorFlow variables in your program. This allows you to see how various variables behave over time (for example, model loss/accuracy), so you can identify potential issues in your model.

TensorBoard enables you to visualize scalar values and vectors as histograms. Apart from this, TensorBoard also allows you to visualize word embeddings. Therefore, it takes all the required code implementation away from you, if you need to analyze what the embeddings look like. Next we will see how we can use TensorBoard to visualize word embeddings. The code for this exercise is provided in tensorboard_word_embeddings.ipynb...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image