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
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

Inference with NMT


Inferencing is slightly different from the training process for NMT (Figure 10.11). As we do not have a target sentence at the inference time, we need a way to trigger the decoder at the end of the encoding phase. This shares similarities with the image captioning exercise we did in Chapter 9, Applications of LSTM – Image Caption Generation. In that exercise, we appended the <SOS> token to the beginning of the captions to denote the start of the caption and <EOS> to denote the end.

We can simply do this by giving <s> as the first input to the decoder, then by getting the prediction as the output, and by feeding in the last prediction as the next input to the NMT:

  1. Preprocess xs as explained previously

  2. Feed xs into and calculate v conditioned on xs
  3. Initialize with v
  4. For the initial prediction step, predict by conditioning the prediction on and v
  5. For subsequent time steps, while , predict by conditioning the prediction on and v

    Figure 10.11: Inferring...

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 €18.99/month. Cancel anytime