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Neural Network Programming with TensorFlow

You're reading from   Neural Network Programming with TensorFlow Unleash the power of TensorFlow to train efficient neural networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788390392
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (11) Chapters Close

Preface 1. Maths for Neural Networks FREE CHAPTER 2. Deep Feedforward Networks 3. Optimization for Neural Networks 4. Convolutional Neural Networks 5. Recurrent Neural Networks 6. Generative Models 7. Deep Belief Networking 8. Autoencoders 9. Research in Neural Networks 10. Getting started with TensorFlow

Convolution operations


Convolution operations are key components of a CNN; these operations use an input tensor and a filter to compute the output. The key is deciding the parameters available to tune them.

Suppose we are tracking the location of an object. Its output is a single x(t), which is the position of the object at time t. Both x and t are real-valued, that is, we can get a different reading at any instant in time. Suppose that our measurement is noisy. To obtain a less noisy estimate of the object's position, we would like to average together measurements. More recent measurements are more relevant for us; we want this to be a weighted average giving higher weight to recent measurements. We can compute this using a weighting function w(a), where a is the age of a measurement (when the measurement was taken) If we apply a weighted average operation at every moment, we obtain a new function providing a smoothed estimate of the position of the object:

This operation is called convolution...

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