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

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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 
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Index
Index

Introduction

In this chapter, we will learn various recipes on how to create recurrent neural networks (RNNs) using Keras. First, we will understand the need for RNN. We will start with the simple RNNs followed by long short-term memory (LSTM) RNNs (these networks remember the state over a long period of time because of special gates in the cell).

The need for RNNs

Traditional neural networks cannot remember their past interactions, and that is a significant shortcoming. RNNs address this issue. They are networks with loops in them, allowing information to persist. RNNs have loops. In the next diagram, a chunk of the neural network, A, looks at some input, xt, and outputs a value, ht. A loop in the network allows information to be passed from one step of the network to the next.

This diagram shows what a neural network looks like:

Previous Section
End of Section 2
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