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

Highway networks design principle


Adding more layers in the transition connections increases the vanishing or exploding gradient issue during backpropagation in long term dependency.

In the Chapter 4, Generating Text with a Recurrent Neural Net, LSTM and GRU networks have been introduced as solutions to address this issue. Second order optimization techniques also help overcome this problem.

A more general principle, based on identity connections, to improve the training in deep networks Chapter 7, Classifying Images with Residual Networks, can also be applied to deep transition networks.

Here is the principle in theory:

Given an input x to a hidden layer H with weigh :

A highway networks design consists of adding the original input information (with an identity layer) to the output of a layer or a group of layers, as a shortcut:

y = x

Two mixing gates, the transform gate and the carry gate, learn to modulate the influence of the transformation in the hidden layer, and the amount of original...