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

Deep approaches for RNN


The core principle of deep learning to improve the representative power of a network is to add more layers. For RNN, two approaches to increase the number of layers are possible:

  • The first one is known as stacking or stacked recurrent network, where the output of the hidden layer of a first recurrent net is used as input to a second recurrent net, and so on, with as many recurrent networks on top of each other:

For a depth d and T time steps, the maximum number of connections between input and output is d + T – 1:

  • The second approach is the deep transition network, consisting of adding more layers to the recurrent connection:

    Figure 2

In this case, the maximum number of connections between input and output is d x T, which has been proved to be a lot more powerful.

Both approaches provide better results.

However, in the second approach, as the number of layers increases by a factor, the training becomes much more complicated and unstable since the signal fades or explodes...