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

Residual connections


While very deep architectures (with many layers) perform better, they are harder to train, because the input signal decreases through the layers. Some have tried training the deep networks in multiple stages.

An alternative to this layer-wise training is to add a supplementary connection to shortcut a block of layers, named the identity connection, passing the signal without modification, in addition to the classic convolutional layers, named the residuals, forming a residual block, as shown in the following image:

Such a residual block is composed of six layers.

A residual network is a network composed of multiple residual blocks. Input is processed by a first convolution, followed by batch normalization and non-linearity:

For example, for a residual net composed of two residual blocks, and eight featuremaps in the first convolution on an input image of size 28x28, the layer output shapes will be the following:

InputLayer                       (None, 1, 28, 28)
Conv2DDNNLayer...