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Deep Learning with Theano

You're reading from Deep Learning with Theano Perform large-scale numerical and scientific computations efficiently

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786465825

Length 300 pages

Edition 1st Edition

Tools

Theano

Concepts

Deep Learning

Author (1):

Christopher Bourez

View More author details

Table of Contents (15) Chapters

Preface

1. Theano Basics

2. Classifying Handwritten Digits with a Feedforward Network FREE CHAPTER

3. Encoding Word into Vector

4. Generating Text with a Recurrent Neural Net

5. Analyzing Sentiment with a Bidirectional LSTM

6. Locating with Spatial Transformer Networks

7. Classifying Images with Residual Networks

8. Translating and Explaining with Encoding – decoding Networks

9. Selecting Relevant Inputs or Memories with the Mechanism of Attention

10. Predicting Times Sequences with Advanced RNN

11. Learning from the Environment with Reinforcement

12. Learning Features with Unsupervised Generative Networks

13. Extending Deep Learning with Theano

Index

A localization network

In Spatial Transformer Networks (STN), instead of applying the network directly to the input image signal, the idea is to add a module to preprocess the image and crop it, rotate it, and scale it to fit the object, to assist in classification:

Spatial Transformer Networks

For that purpose, STNs use a localization network to predict the affine transformation parameters and process the input:

Spatial transformer networks

In Theano, differentiation through the affine transformation is automatic, we simply have to connect the localization net with the input of the classification net through the affine transformation.

First, we create a localization network not very far from the MNIST CNN model, to predict six parameters of the affine transformation:

l_in = lasagne.layers.InputLayer((None, dim, dim))
l_dim = lasagne.layers.DimshuffleLayer(l_in, (0, 'x', 1, 2))
l_pool0_loc = lasagne.layers.MaxPool2DLayer(l_dim, pool_size=(2, 2))
l_dense_loc = mnist_cnn.model(l_pool0_loc, input_dim...

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Authors (1)

Bourez

Bourez

Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA). For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase. While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website. On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.

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