Search icon CANCEL
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
Deep Learning for Natural Language Processing

You're reading from   Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

Arrow left icon
Product type Paperback
Published in Jun 2019
Publisher
ISBN-13 9781838550295
Length 372 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (4):
Arrow left icon
Karthiek Reddy Bokka Karthiek Reddy Bokka
Author Profile Icon Karthiek Reddy Bokka
Karthiek Reddy Bokka
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
Tanuj Jain Tanuj Jain
Author Profile Icon Tanuj Jain
Tanuj Jain
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

About the Book 1. Introduction to Natural Language Processing FREE CHAPTER 2. Applications of Natural Language Processing 3. Introduction to Neural Networks 4. Foundations of Convolutional Neural Network 5. Recurrent Neural Networks 6. Gated Recurrent Units (GRUs) 7. Long Short-Term Memory (LSTM) 8. State-of-the-Art Natural Language Processing 9. A Practical NLP Project Workflow in an Organization 1. Appendix

Output Gate and Current Activation

Note that all we have done is update the cell state until now. We need to generate the activation for the current state as well; that is, (h[t]). This is done using an output gate that is calculated as given:

Figure 7.20: Expression for output gate.

The input at timestep t is multiplied by a new set of weights, W_o, with the dimensions (n_h, n_x). The activation from the previous timestep (h[t-1]) is multiplied by another new set of weights, U_o, with the dimensions (n_h, n_h). Note that the multiplications are matrix multiplications. These two terms are then added and passed through a sigmoid function to squish the output, o[t], within a range of [0,1]. The output has the same number of dimensions as there are in cell state vector h (n_h, 1).

The output gate is responsible for regulating the amount by which the current cell state is allowed to affect the activation value for the timestep. In our example sentence, it is worth propagating the...

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