Book Image

Apache Spark Deep Learning Cookbook

By : Ahmed Sherif, Amrith Ravindra
Book Image

Apache Spark Deep Learning Cookbook

By: Ahmed Sherif, Amrith Ravindra

Overview of this book

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark. Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning. By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Index

Evaluating the model


Here's the moment of truth: we are going to see if our model is able to give us a good prediction for the AAPL stock in 2017 and 2018.

Getting ready

We will perform a model evaluation using the mean squared error. Therefore, we will need to import the following library:

import sklearn.metrics as metrics

How to do it...

This section walks through visualizing and calculating the predicted vs. actual stock quotes for Apple in 2017 and 2018.

  1. Plot a side by side comparison of Actual versus Predicted stock to compare trends using the following script:
plt.figure(figsize=(16,6))
plt.plot(combined_array[:,0],color='red', label='actual')
plt.plot(combined_array[:,1],color='blue', label='predicted')
plt.legend(loc = 'lower right')
plt.title('2017 Actual vs. Predicted APPL Stock')
plt.xlabel('Days')
plt.ylabel('Scaled Quotes')
plt.show()
  1. Calculate the mean squared error between the actual ytest versus predicted stock using the following script:
import sklearn.metrics as metrics
np.sqrt...