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Practical Convolutional Neural Networks

You're reading from  Practical Convolutional Neural Networks

Product type Book
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Pages 218 pages
Edition 1st Edition
Languages
Authors (3):
Mohit Sewak Mohit Sewak
Profile icon Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Profile icon Pradeep Pujari
View More author details

Table of Contents (11) Chapters

Preface 1. Deep Neural Networks – Overview 2. Introduction to Convolutional Neural Networks 3. Build Your First CNN and Performance Optimization 4. Popular CNN Model Architectures 5. Transfer Learning 6. Autoencoders for CNN 7. Object Detection and Instance Segmentation with CNN 8. GAN: Generating New Images with CNN 9. Attention Mechanism for CNN and Visual Models 10. Other Books You May Enjoy

Transfer learning example

In this example, we will take a pre-trained VGGNet and use transfer learning to train a CNN classifier that predicts dog breeds, given a dog image. Keras contains many pre-trained models, along with the code that loads and visualizes them. Another is a flower dataset that can be downloaded here. The Dog breed dataset has 133 dog breed categories and 8,351 dog images. Download the Dog breed dataset here and copy it to your folder. VGGNet has 16 convolutional with pooling layers from beginning to end and three fully connected layers followed by a softmax function. Its main objective was to show how the depth of the network gives the best performance. It came from Visual Geometric Group (VGG) at Oxford. Their best performing network is VGG16. The Dog breed dataset is relatively small and has a little overlap with the imageNet dataset. So, we can remove...

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