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Apache Spark Deep Learning Cookbook

You're reading from   Apache Spark Deep Learning Cookbook Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788474221
Length 474 pages
Edition 1st Edition
Languages
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Authors (2):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
Amrith Ravindra Amrith Ravindra
Author Profile Icon Amrith Ravindra
Amrith Ravindra
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Toc

Table of Contents (15) Chapters Close

Preface 1. Setting Up Spark for Deep Learning Development FREE CHAPTER 2. Creating a Neural Network in Spark 3. Pain Points of Convolutional Neural Networks 4. Pain Points of Recurrent Neural Networks 5. Predicting Fire Department Calls with Spark ML 6. Using LSTMs in Generative Networks 7. Natural Language Processing with TF-IDF 8. Real Estate Value Prediction Using XGBoost 9. Predicting Apple Stock Market Cost with LSTM 10. Face Recognition Using Deep Convolutional Networks 11. Creating and Visualizing Word Vectors Using Word2Vec 12. Creating a Movie Recommendation Engine with Keras 13. Image Classification with TensorFlow on Spark 14. Other Books You May Enjoy

Visualizing word counts in the dataset


A picture is worth a thousand words and this section will set out to prove that. Unfortunately, Spark does not have any inherent plotting capabilities as of version 2.2. In order to plot values in a dataframe, we must convert to pandas. 

Getting ready

This section will require importing matplotlib for plotting:

import matplotlib.pyplot as plt
%matplotlib inline

How to do it...

This section walks through the steps to convert the Spark dataframe into a visualization that can be seen in the Jupyter notebook. 

  1. Convert Spark dataframe to a pandas dataframe using the following script:
df_plot = df.select('id', 'word_count').toPandas()
  1. Plot the dataframe using the following script:
import matplotlib.pyplot as plt
%matplotlib inline

df_plot.set_index('id', inplace=True)
df_plot.plot(kind='bar', figsize=(16, 6))
plt.ylabel('Word Count')
plt.title('Word Count distribution')
plt.show()

How it works...

This section explains how the Spark dataframe is converted to pandas...

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