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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):

Richard Burton

Giounona Tzanidou

Iffat Zafar

Leonardo Araujo

Nimesh Patel

+1 more

View More author details

Table of Contents (12) Chapters

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

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Code Structure best Practice

In previous chapters we encapsulated our tensorflow graph into a class without discussing it any further. This idea itself is already good coding practice. Having a class responsible to build your graph and exposing only things that are useful for using the model (that is, it’s inputs/outputs) is a good programming practice that will save you lots of time.

Singleton Pattern

It is also common practice to use design patterns in order to solve some software design problems. One of the simplest and most useful design patterns in python is the singleton one. It is used when you want to force the instantiation of a class to only one object, so even if you instantiate this class multiple times in several different places in your project, you will be referencing the same object. In our case if we ask TensorFlow to create multiple nodes or graphs with the same name, it will throw an error. Therefore, we use the singleton pattern while creating the graph to avoid building...

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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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