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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

Plotting and visualizing images from the directory


This section will describe how to read and visualize the downloaded images before they are preprocessed and fed into the neural network for training. This is an important step in this chapter because the images need to be visualized to get a better understanding of the image sizes so they can be accurately cropped to omit the background and preserve only the necessary facial features.

Getting ready

Before beginning, complete the initial setup of importing the necessary libraries and functions as well as setting the path of the working directory.

How to do it...

The steps are as follows:

  1. Download the necessary libraries using the following lines of code. The output must result in a line that saysUsing TensorFlow backend, as shown in the screenshot that follows:
%matplotlib inline
from os import listdir
from os.path import isfile, join
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from keras.models import Sequential...
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