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Generative Adversarial Networks Projects
book

Generative Adversarial Networks Projects

by Kailash Ahirwar
January 2019
Intermediate to advanced
316 pages
8h 16m
English
Packt Publishing
Content preview from Generative Adversarial Networks Projects

VGG19 network

We will use the pre-trained VGG19 network. The purpose of the VGG19 network is to extract feature maps of the generated and the real images. In this section, we will build and compile the VGG19 network with pre-trained weights in Keras:

  1. Start by specifying the input shape:
input_shape = (256, 256, 3)
  1. Next, load a pre-trained VGG19 and specify the outputs for the model:
vgg = VGG19(weights="imagenet")vgg.outputs = [vgg.layers[9].output]
  1. Next, create a symbolic input_tensor, which will be our symbolic input to the VGG19 network, as follows:
input_layer = Input(shape=input_shape)
  1. Next, use the VGG19 network to extract the features:
features = vgg(input_layer)
  1. Finally, create a Keras model and specify the inputs and outputs ...
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Publisher Resources

ISBN: 9781789136678