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
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 FREE CHAPTER 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 A. Mathematical Foundations and Advanced TensorFlow Index

Comparing LSTMs to LSTMs with peephole connections and GRUs


Now we will compare LSTMs to LSTMs with peepholes and GRUs in the text generation task. This will help us to compare how well different models (LSTMs with peepholes and GRUs) perform in terms of perplexity as well as the quality of the generated text. This is available as an exercise in lstm_extensions.ipynb located in the ch8 folder.

Standard LSTM

First, we will reiterate the components of a standard LSTM. We will not repeat the code for standard LSTMs as it is identical to what we discussed previously. Finally, we will see some text generated by an LSTM.

Review

Here we will revisit what a standard LSTM looks like. As we already mentioned, an LSTM consists of the following:

  • Input gate: This decides how much of the current input is written to the cell state

  • Forget gate: This decides how much of the previous cell state is written to the current cell state

  • Output gate: This decides how much information from the cell state is exposed to...

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