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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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781789130331

Length 272 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Computer Vision

Authors (5):

Araujo

Zafar

Tzanidou

Burton

Patel

+1 more

View More author details

Table of Contents (17) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

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

1. References

2. Other Books You May Enjoy

Leave a review - let other readers know what you think

Index

MobileNets

We will finish this chapter with a new family of CNN that not only has good accuracy, but is lighter and works faster on mobile devices.

Created by Google, MobileNet's key feature is that it uses a different "sandwich" form of convolution block. Instead of the usual (CONV, BATCH_NORM,RELU), it splits 3x3 convolutions up into a 3x3 depthwise convolution, followed by a 1x1 Pointwise CONV. They call this block a depthwise separable convolution.

This factorization reduces the computation and the model size:

Depthwise separable convolution

This new convolution block (tf.layers.separable_conv2d) consists of two main parts: a depthwise convolution layer, followed by a 1x1 pointwise convolution layer. This block differs from the normal convolution in a couple of ways:

In the normal convolution layer, each filter F will be applied to all channels on the input channel at the same time (F is applied to each channel and then summed)
This new convolution F is applied on each channel separately...

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Authors (5)

Araujo

Araujo

Leonardo Araujo is just the regular, Brazilian, curious engineer, who has worked in the industry for the past 19 years (yes, in Brazil, people work before graduation), doing HW/SW development and research on the topics of control engineering and computer vision. For the past 6 years, he has focused more on Machine Learning methods. His passions are too many to put on the book.

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Zafar

Zafar

Iffat Zafar was born in Pakistan. She received her Ph.D. from the Loughborough University in Computer Vision and Machine Learning in 2008. After her Ph.D. in 2008, she worked as research associate at the Department of Computer Science, Loughborough University, for about 4 years. She currently works in the industry as an AI engineer, researching and developing algorithms using Machine Learning and Deep Learning for object detection and general Deep Learning tasks for edge and cloud-based applications.

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Tzanidou

Tzanidou

Giounona Tzanidou is a PhD in computer vision from Loughborough University, UK, where she developed algorithms for runtime surveillance video analytics. Then, she worked as a research fellow at Kingston University, London, on a project aiming at prediction detection and understanding of terrorist interest through intelligent video surveillance. She was also engaged in teaching computer vision and embedded systems modules at Loughborough University. Now an engineer, she investigates the application of deep learning techniques for object detection and recognition in videos.

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Burton

Burton

Richard Burton graduated from the University of Leicester with a master's degree in mathematics. After graduating, he worked as a research engineer at the University of Leicester for a number of years, where he developed deep learning object detection models for their industrial partners. Now, he is working as a software engineer in the industry, where he continues to research the applications of deep learning in computer vision.

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Patel

Patel

Nimesh Patel graduated from the University of Leicester with an MSc in applied computation and numerical modeling. During this time, a project collaboration with one of University of Leicesters partners was undertaken, dealing with Machine Learning for Hand Gesture recognition. Since then, he has worked in the industry, researching Machine Learning for Computer Vision related tasks, such as Depth Estimation.

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