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

CIFAR-10 dataset

Load the CIFAR-10 small images classification dataset from The CIFAR-10 dataset is made up of 60,000 32 x 32 color images in 10 classes, and there are 6000 images per class. The dataset consists of 50,000 training images and 10,000 test images.


The dataset has been divided into five training batches and one test batch, each with 10,000 images. The test batch contains 1,000 randomly selected images from each class. The training batches contain the rest of the images in a random order; some training batches may contain more images from one class than another. The training batches contain 5,000 images from each class, such as shown in the following image:


How to do it...

Let's load this dataset using the Keras APIs and print the shape and size:

from keras.datasets import cifar10

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print("X_train shape: " + str...