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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 2. Deep Feedforward Networks FREE CHAPTER 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

An overview and the intuition of CNN


CNN consists of multiple layers of convolutions, polling and finally fully connected layers. This is much more efficient than pure feedforward networks we discussed in Chapter 2, Deep Feedforward Networks.

The preceding diagram takes images through Convolution Layer | Max Pooling | Convolution | Max Pooling | Fully Connected Layers this is an CNN architecture

Single Conv Layer Computation

Let's first discuss what the conv layer computes intuitively. The Conv layer's parameters consist of a set of learnable filters (also called tensors). Each filter is small spatially (depth, width, and height), but extends through the full depth of the input volume (image). A filter on the first layer of a ConvNet typically has a size of 5 x 5 x 3 (that is, five pixels width and height, and three for depth, because images have three depths for color channels). During the forward pass, filters slide (or convolve) across the width and height of the input volume and compute...

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