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TensorFlow 2.0 Quick Start Guide

You're reading from  TensorFlow 2.0 Quick Start Guide

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789530759
Pages 196 pages
Edition 1st Edition
Languages
Author (1):
Tony Holdroyd Tony Holdroyd
Profile icon Tony Holdroyd

Table of Contents (15) Chapters

Preface Section 1: Introduction to TensorFlow 2.00 Alpha
Introducing TensorFlow 2 Keras, a High-Level API for TensorFlow 2 ANN Technologies Using TensorFlow 2 Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
Supervised Machine Learning Using TensorFlow 2 Unsupervised Learning Using TensorFlow 2 Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
Recognizing Images with TensorFlow 2 Neural Style Transfer Using TensorFlow 2 Recurrent Neural Networks Using TensorFlow 2 TensorFlow Estimators and TensorFlow Hub Converting from tf1.12 to tf2
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Using the VGG19 architecture

The best way to understand the next snippet is to have a look at the VGG19 architecture. Here is a good place: https://github.com/fchollet/deep-learning-models/blob/master/vgg19.py (about half way down the page).

Here, you will see that VGG19 is a fairly straightforward architecture, consisting of blocks of convolutional layers with a max pooling layer at the end of each block.

For the content layer, we use the second convolutional layer in block5. This highest block is used because the earlier blocks have feature maps more representative of individual pixels; higher layers in the network capture the high-level content in terms of objects and their arrangement in the input image, but do not constrain the actual exact pixel values of the reconstruction (see Gatys et al, 2015, https://arxiv.org/abs/1508.06576, cited previously).

For the style layers...

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