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

Continuous Bag of Words model


The design of the neural network to predict a word given its surrounding context is shown in the following figure:

The input layer receives the context while the output layer predicts the target word. The model we'll use for the CBOW model has three layers: input layer, hidden layer (also called the projection layer or embedding layer), and output layer. In our setting, the vocabulary size is V and the hidden layer size is N. Adjacent units are fully connected.

The input and the output can be represented either by an index (an integer, 0-dimensional) or a one-hot-encoding vector (1-dimensional). Multiplying with the one-hot-encoding vector v consists simply of taking the j-th row of the embedding matrix:

Since the index representation is more efficient than the one-hot encoding representation in terms of memory usage, and Theano supports indexing symbolic variables, it is preferable to adopt the index representation as much as possible.

Therefore, input (context...