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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 FREE CHAPTER

2. Classifying Handwritten Digits with a Feedforward Network

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

Theano Op in C for CPU

Another inefficiency arises from the fact the Python implementation of an operator adds a significant overhead each time computations are performed, that is, for each instance of our operator in the graph. The Python code is not compiled as the rest of the graph by Theano in C and the overhead occurs when the C implementation is wrapped into Python and data is exchanged.

To remedy this, it is possible to directly write some C code that will be incorporated into the code of the rest of the graph and compiled together.

When implementing an operator directly in C, NumPy is the underlying library to manage arrays, with the the NumPy-API extending Python C-API. The Python class defining the new C operator does not have to implement the perform() method; instead, it returns the C code to incorporate in the c_code(), c_support_code() and c_support_code_apply() methods:

def c_code_cache_version(self):
    return (6, 0)

def c_support_code(self):
    c_support_code = "&quot...

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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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