Book Image

Deep Learning with Theano

By : Christopher Bourez
Book Image

Deep Learning with Theano

By: Christopher Bourez

Overview of this book

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.
Table of Contents (22 chapters)
Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Seq2seq for translation


Sequence-to-sequence (Seq2seq) networks have their first application in language translation.

A translation task has been designed for the conferences of the Association for Computational Linguistics (ACL), with a dataset, WMT16, composed of translations of news in different languages. The purpose of this dataset is to evaluate new translation systems or techniques. We'll use the German-English dataset.

  1. First, preprocess the data:

    python 0-preprocess_translations.py --srcfile data/src-train.txt --targetfile data/targ-train.txt --srcvalfile data/src-val.txt --targetvalfile data/targ-val.txt --outputfile data/demo
    First pass through data to get vocab...
    Number of sentences in training: 10000
    Number of sentences in valid: 2819
    Source vocab size: Original = 24995, Pruned = 24999
    Target vocab size: Original = 35816, Pruned = 35820
    (2819, 2819)
    Saved 2819 sentences (dropped 181 due to length/unk filter)
    (10000, 10000)
    Saved 10000 sentences (dropped 0 due to length/unk filter...