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Practical Convolutional Neural Networks

You're reading from   Practical Convolutional Neural Networks Implement advanced deep learning models using Python

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788392303
Length 218 pages
Edition 1st Edition
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Authors (3):
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Mohit Sewak Mohit Sewak
Author Profile Icon Mohit Sewak
Mohit Sewak
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Pradeep Pujari Pradeep Pujari
Author Profile Icon Pradeep Pujari
Pradeep Pujari
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Table of Contents (11) Chapters Close

Preface 1. Deep Neural Networks – Overview FREE CHAPTER 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

VGGNet architecture

The runner-up in the 2014 ImageNet challenge was VGGNet from the visual geometric group at Oxford University. This convolutional neural network is a simple and elegant architecture with a 7.3% error rate. It has two versions: VGG16 and VGG19.

VGG16 is a 16-layer neural network, not counting the max pooling layer and the softmax layer. Hence, it is known as VGG16. VGG19 consists of 19 layers. A pre-trained model is available in Keras for both Theano and TensorFlow backends.

The key design consideration here is depth. Increases in the depth of the network were achieved by adding more convolution layers, and it was done due to the small 3 x 3 convolution filters in all the layers. The default input size of an image for this model is 224 x 224 x 3. The image is passed through a stack of convolution layers with a stride of 1 pixel and padding of 1. It uses 3 x 3...

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