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Deep Learning with Theano

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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786465825
Length 300 pages
Edition 1st Edition
Tools
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Author (1):
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Christopher Bourez Christopher Bourez
Author Profile Icon Christopher Bourez
Christopher Bourez
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Table of Contents (15) Chapters Close

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 Python for CPU

As a mathematical compilation engine, Theano's purpose is to compile a graph of computations in an optimal way for a target platform.

The development of new operators is possible in Python or C for compilation either on the CPU or GPU.

First, we address the simplest case, in Python for CPU, which will enable you to add new operations very easily and quickly.

To fix the ideas, let's implement a simple affine operator that performs the affine transformation a * x + b, given x as the input.

The operator is defined by a class deriving from the generic theano.Op class:

import theano, numpy

class AXPBOp(theano.Op):
    """
    This creates an Op that takes x to a*x+b.
    """
    __props__ = ("a", "b")

    def __init__(self, a, b):
        self.a = a
        self.b = b
        super(AXPBOp, self).__init__()

    def make_node(self, x):
        x = theano.tensor.as_tensor_variable(x)
        return theano.Apply...
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