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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

CNNs in action

Taking as an example the 5×5 input matrix shown earlier, a CNN is made up of an input layer consisting of 25 neurons (5×5) that has the task of acquiring the input value corresponding to each pixel and transferring it to the next layer.

In a multilayer network, the output from all of the neurons in the input layer would be connected to each neuron in the hidden layer (the fully connected layer). In CNN networks, however, the connection scheme that defines the convolutional layer that we are going to describe is significantly different. As you may be able to guess, this is the main type of layer: the use of one or more of these layers in a CNN is indispensable.

In a convolutional layer, each neuron is connected to a certain region of the input area called the receptive field. For example, using a 3×3 kernel filter, each neuron will have a bias and 9 weights (3×3) connected to a single receptive field. To effectively recognize an image, we need various different...

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