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

Related Content you might be interested in

Current Title:

Small book image
Keras Deep Learning Cookbook
Rajdeep Dua | Sujit Pal | Manpreet Singh Ghotra
Oct, 2018 | 252 pages
Small book image
Deep Learning with Keras
Sujit Pal | Antonio Gulli
Apr, 2017 | 318 pages
Small book image
Deep Learning Quick Reference
Michael Bernico
Mar, 2018 | 272 pages
Small book image
Generative Adversarial Networks Cookbook
Josh Kalin
Dec, 2018 | 268 pages
Table of Contents (17 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
Keras Installation
Keras Installation
Introduction
Installing Keras on Ubuntu 16.04
Installing Keras with Jupyter Notebook in a Docker image
Installing Keras on Ubuntu 16.04 with GPU enabled
2
Working with Keras Datasets and Models
Working with Keras Datasets and Models
Introduction
CIFAR-10 dataset
CIFAR-100 dataset
MNIST dataset
Load data from a CSV file
Models in Keras – getting started
Sequential models
Shared layer models
Keras functional APIs
Keras functional APIs – linking the layers
Image classification using Keras functional APIs
3
Data Preprocessing, Optimization, and Visualization
Data Preprocessing, Optimization, and Visualization
Feature standardization of image data
Sequence padding
Model visualization
Optimization 
Common code for samples
Optimization with stochastic gradient descent
Optimization with Adam
Optimization with AdaDelta
Optimization with RMSProp
4
Classification Using Different Keras Layers
Classification Using Different Keras Layers
Introduction
Classification for breast cancer
Classification for spam detection
5
Implementing Convolutional Neural Networks
Implementing Convolutional Neural Networks
Introduction
Cervical cancer classification
Digit recognition
6
Generative Adversarial Networks
Generative Adversarial Networks
Introduction
Basic GAN
Boundary seeking GAN
DCGAN
7
Recurrent Neural Networks
Recurrent Neural Networks
Introduction
Simple RNNs for time series data
LSTM networks for time series data
Time series forecasting with LSTM
Sequence to sequence learning for the same length output with LSTM
8
Natural Language Processing Using Keras Models
Natural Language Processing Using Keras Models
Introduction
Word embedding
Sentiment analysis
9
Text Summarization Using Keras Models
Text Summarization Using Keras Models
Introduction
Text summarization for reviews
10
Reinforcement Learning
Reinforcement Learning
Introduction
The CartPole game with Keras
Dueling DQN to play Cartpole 
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

CIFAR-10 dataset

Load the CIFAR-10 small images classification dataset from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz. 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:

Reference: https://www.cs.toronto.edu/~kriz/cifar.html.

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...
Previous Section
End of Section 3
Next Section