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Machine Learning with TensorFlow 1.x

You're reading from Machine Learning with TensorFlow 1.x Second generation machine learning with Google's brainchild - TensorFlow 1.x

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781786462961

Length 304 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (3):

Hua

Ahmed

Ul Azeem

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with TensorFlow FREE CHAPTER

2. Your First Classifier

3. The TensorFlow Toolbox

4. Cats and Dogs

5. Sequence to Sequence Models-Parlez-vous Français?

6. Finding Meaning

7. Making Money with Machine Learning

8. The Doctor Will See You Now

9. Cruise Control - Automation

10. Go Live and Go Big

11. Going Further - 21 Problems

12. Advanced Installation

Caffe to TensorFlow

In this section, we will show you how to take advantage of many pre-trained models from Caffe Model Zoo (https://github.com/BVLC/caffe/wiki/Model-Zoo). There are lots of Caffe models for different tasks with all kinds of architectures. After converting these models to TensorFlow, you can use it as a part of your architectures or you can fine-tune our model for different tasks. Using these pre-trained models as initial weights is an effective approach for training instead of training from scratch. We will show you how to use a caffe-to-tensorflow approach from Saumitro Dasgupta at https://github.com/ethereon/caffe-tensorflow.

However, there are lots of differences between Caffe and TensorFlow. This technique only supports a subset of layer types from Caffe. Even though there are some Caffe architectures that are verified by the author of this project such as ResNet, VGG, and GoogLeNet.

First, we need to clone the caffe-tensorflow repository using the git clone command:

ubuntu...

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Authors (3)

Hua

Hua

Quan Hua is a Computer Vision and Machine Learning Engineer at BodiData, a data platform for body measurements, where he focuses on developing computer vision and machine learning applications for a handheld technology capable of acquiring a body avatar while a person is fully clothed. He earned a bachelor of science degree from the University of Science, Vietnam, specializing in Computer Vision. He has been working in the field of computer vision and machine learning for about 3 years at start-ups. Quan has been writing for Packt since 2015 for a Computer Vision book, OpenCV 3 Blueprints.

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Ahmed

Ahmed

Saif Ahmed is an accomplished quantitative analyst and data scientist with 15 years of industry experience. His career started in management consulting at Accenture and lead him to quantitative and senior management roles at Goldman Sachs and AIG Investments. Most recently, he co-founded and runs a start-up focused on applying Deep Learning to automating medical imaging. He obtained his bachelor's degree in computer science from Cornell University and is currently pursuing a graduate degree in data science at U.C. Berkeley.

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Ul Azeem

Ul Azeem

Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, hes pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.

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