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Deep Learning with Keras

You're reading from   Deep Learning with Keras Implementing deep learning models and neural networks with the power of Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787128422
Length 318 pages
Edition 1st Edition
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Authors (2):
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Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
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Table of Contents (10) Chapters Close

Preface 1. Neural Networks Foundations FREE CHAPTER 2. Keras Installation and API 3. Deep Learning with ConvNets 4. Generative Adversarial Networks and WaveNet 5. Word Embeddings 6. Recurrent Neural Network — RNN 7. Additional Deep Learning Models 8. AI Game Playing 9. Conclusion

Keras adversarial GANs for forging CIFAR

Now we can use a GAN approach to learn how to forge CIFAR-10 and create synthetic images that look real. Let's see the open source code (https://github.com/bstriner/keras-adversarial/blob/master/examples/example_gan_cifar10.py).  Again, note that it uses the syntax of Keras 1.x, but it also runs on the top of Keras 2.x thanks to a convenient set of utility functions contained in legacy.py (https://github.com/bstriner/keras-adversarial/blob/master/keras_adversarial/legacy.py). First, the open source example imports a number of packages:

import matplotlib as mpl
# This line allows mpl to run with no DISPLAY defined
mpl.use('Agg')
import pandas as pd
import numpy as np
import os
from keras.layers import Dense, Reshape, Flatten, Dropout, LeakyReLU,
Activation, BatchNormalization, SpatialDropout2D
from keras.layers.convolutional import Convolution2D, UpSampling2D...
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