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Hands-On Convolutional Neural Networks with TensorFlow

You're reading from   Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789130331
Length 272 pages
Edition 1st Edition
Languages
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Authors (5):
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Richard Burton Richard Burton
Author Profile Icon Richard Burton
Richard Burton
Giounona Tzanidou Giounona Tzanidou
Author Profile Icon Giounona Tzanidou
Giounona Tzanidou
Iffat Zafar Iffat Zafar
Author Profile Icon Iffat Zafar
Iffat Zafar
Leonardo Araujo Leonardo Araujo
Author Profile Icon Leonardo Araujo
Leonardo Araujo
Nimesh Patel Nimesh Patel
Author Profile Icon Nimesh Patel
Nimesh Patel
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Table of Contents (12) Chapters Close

Preface 1. Setup and Introduction to TensorFlow FREE CHAPTER 2. Deep Learning and Convolutional Neural Networks 3. Image Classification in TensorFlow 4. Object Detection and Segmentation 5. VGG, Inception Modules, Residuals, and MobileNets 6. Autoencoders, Variational Autoencoders, and Generative Adversarial Networks 7. Transfer Learning 8. Machine Learning Best Practices and Troubleshooting 9. Training at Scale 10. References 11. Other Books You May Enjoy

Initializing variables

Before we are able to use our variables in our graph, we must initialize them. We need to create a graph node that will do this for us. Using tf.global_variables_initializer will add an initializer node to our graph. If we run this node in a session, then all the variables in our graph will become initialized so that we are able to use them. So, for now, let's create an initializer node as follows:

initializer = tf.global_variables_initializer()

As we did not explicitly say what kind of initialization to use for our variables, TensorFlow will use a default one called the Glorot Normal Initializer, which is also known as Xavier Initialization.

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Hands-On Convolutional Neural Networks with TensorFlow
Published in: Aug 2018
Publisher: Packt
ISBN-13: 9781789130331
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