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

Simple RNNs for time series data

In this recipe, we will learn how to use a simple RNN implementation of Keras to predict sales based on a historical dataset.


RNNs are a class of artificial neural network where connections between nodes of the network form a directed graph along a sequence. This topology allows it to exhibit dynamic temporal behavior for input of the time sequence type. Unlike feedforward neural networks, RNNs can use their internal state (also calledmemory) to process sequences of inputs. This makes them suitable for tasks such as unsegmented, connected handwriting recognition or speech recognition.

A simple RNN is implemented as part of the keras.layers.SimpleRNN class as follows:

keras.layers.SimpleRNN(units, activation='tanh',