Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell


CNNs for a GAN had been unsuccessful for some time until authors of the paper() came up with the following approach.

Here are the architecture guidelines for stable deep convolutional GANs:

  • Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator)
  • Use batch norm in both the generator and the discriminator
  • Remove fully connected hidden layers for deeper architectures
  • Use ReLU activation in the generator for all layers except for the output, which uses tanh
  • Use LeakyReLU activation in the discriminator for all layers

To build this architecture, we are going to use the same Fashion-MNIST dataset. 

Getting ready

Make relevant imports and initialize the DCGAN class, as shown in the following code:

from __future__ import print_function, division

from keras.datasets import fashion_mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations...