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

Feature standardization of image data

In this recipe, we will look at how Keras can be used for feature standardization of image data. 

Getting ready

Make sure the Keras installation and Jupyter Notebook installation have been completed.

How to do it...

We will be using the mnist dataset. First, let's plot the mnist images without standardization:

from keras.datasets import mnist
from matplotlib import pyplot

(X_train, y_train), (X_test, y_test) = mnist.load_data()
# create a grid of 3x3 images
for i in range(0, 9):
    ax = pyplot.subplot(330 + 1 + i)
    ax.tick_params(axis='x', colors='white')
    ax.tick_params(axis='y', colors='white')
 pyplot.imshow(X_train[i], cmap=pyplot.get_cmap('gray'))
# show the plot

The output plot will be similar to the following screenshot:

For feature standardization, we are planning to use ImageDataGenerator.

Initializing ImageDataGenerator

Use keras.preprocessing.image.ImageDataGenerator(featurewise_center=False, samplewise_center...