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

Time series forecasting with LSTM

In this recipe, we will learn how to use the LSTM implementation of Keras to predict sales based on a historical dataset. We will use the same dataset we used earlier for predicting shampoo sales. 

Getting ready

The dataset is in the sales-of-shampoo-over-a-three-ye.csv file:

"Month","Sales of shampoo over a three year period"

First, we need to import the relevant classes as follows:

from pandas import read_csv
from matplotlib import pyplot
from pandas import datetime

 Load the dataset

  1. First, we define a parser to convert YY to YYYY:
def parser(x):
    return datetime.strptime('200' + x, '%Y-%m')


  1. Next, call the read_csv function of pandas to load a .csv into a DataFrame as follows:
series = read_csv('sales-of-shampoo-over-a-three-ye.csv', header=0, parse_dates=[0], index_col=0, squeeze=True,
  1. Summarize the first few rows using the following code: