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Hands-On Generative Adversarial Networks with Keras

You're reading from   Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789538205
Length 272 pages
Edition 1st Edition
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Author (1):
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Rafael Valle Rafael Valle
Author Profile Icon Rafael Valle
Rafael Valle
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Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Deep Learning Basics and Environment Setup 3. Introduction to Generative Models 4. Section 2: Training GANs
5. Implementing Your First GAN 6. Evaluating Your First GAN 7. Improving Your First GAN 8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9. Progressive Growing of GANs 10. Generation of Discrete Sequences Using GANs 11. Text-to-Image Synthesis with GANs 12. TequilaGAN - Identifying GAN Samples 13. Whats next in GANs

Imports

In this section, we provide a list of libraries and methods that will be used in our first GAN implementation. The following code block is the list of libraries to be used:

import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
from math import ceil
import numpy as np
from keras.models import Sequential, Model
from keras.layers import Input, ReLU, LeakyReLU, Dense
from keras.layers.core import Activation, Reshape
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.core import Flatten
from keras.optimizers import SGD, Adam
from keras.datasets import cifar10
from keras import initializers

Lines one through four import libraries and methods that are necessary for plotting.

Line five imports numpy, which is used for overall data operations, including generation, manipulation...

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