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Home > Data > Deep Learning > Apache Spark Deep Learning Cookbook
Apache Spark Deep Learning Cookbook
Apache Spark Deep Learning Cookbook

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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Apache Spark Deep Learning Cookbook

Creating a Neural Network in Spark

In this chapter, the following recipes will be covered:

  • Creating a dataframe in PySpark
  • Manipulating columns in a PySpark dataframe
  • Converting a PySpark dataframe into an array
  • Visualizing the array in a scatterplot
  • Setting up weights and biases for input into the neural network
  • Normalizing the input data for the neural network
  • Validating array for optimal neural network performance
  • Setting up the activation function with sigmoid
  • Creating the sigmoid derivative function
  • Calculating the cost function in a neural network
  • Predicting gender based on height and weight
  • Visualizing prediction scores

Introduction

Much of this book will focus on building deep learning algorithms with libraries in Python, such as TensorFlow and Keras. While these libraries are helpful to build deep neural networks without getting deep into the calculus and linear algebra of deep learning, this chapter will do a deep dive into building a simple neural network in PySpark to make a gender prediction based on height and weight. One of the best ways to understand the foundation of neural networks is to build a model from scratch, without any of the popular deep learning libraries. Once the foundation for a neural network framework is established, understanding and utilizing some of the more popular deep neural network libraries will become much simpler.

Creating a dataframe in PySpark

dataframes will serve as the framework for any and all data that will be used in building deep learning models. Similar to the pandas library with Python, PySpark has its own built-in functionality to create a dataframe.

Getting ready

There are several ways to create a dataframe in Spark. One common way is by importing a .txt, .csv, or .json file. Another method is to manually enter fields and rows of data into the PySpark dataframe, and while the process can be a bit tedious, it is helpful, especially when dealing with a small dataset. To predict gender based on height and weight, this chapter will build a dataframe manually in PySpark. The dataset used is as follows:

While the dataset...

Manipulating columns in a PySpark dataframe

The dataframe is almost complete; however, there is one issue that requires addressing before building the neural network. Rather than keeping the gender value as a string, it is better to convert the value to a numeric integer for calculation purposes, which will become more evident as this chapter progresses.

Getting ready

This section will require  importing the following:

  • from pyspark.sql import functions

How to do it...

This section walks through the steps for the string conversion to a numeric value...

Converting a PySpark dataframe to an array

In order to form the building blocks of the neural network, the PySpark dataframe must be converted into an array. Python has a very powerful library, numpy, that makes working with arrays simple.

Getting ready

The numpy library should be already available with the installation of the anaconda3 Python package. However, if for some reason the numpy library is not available, it can be installed using the following command at the terminal:

pip install or sudo pip install will confirm whether the requirements are already satisfied by using the requested library:

import numpy as np
...

Visualizing an array in a scatterplot

The goal of the neural network that will be developed in this chapter is to predict the gender of an individual if the height and weight are known. A powerful method for understanding the relationship between height, weight, and gender is by visualizing the data points feeding the neural network. This can be done with the popular Python visualization library matplotlib.

Getting ready

As was the case with numpy, matplotlib should be available with the installation of the anaconda3 Python package. However, if for some reason matplotlib is not available, it can be installed using the following command at the terminal:

pip install or sudo pip install will confirm...

Setting up weights and biases for input into the neural network

The framework in PySpark and the data are now complete. It is time to move on to building the neural network. Regardless of the complexity of the neural network, the development follows a similar path:

  1. Input data
  2. Add the weights and biases
  3. Sum the product of the data and weights
  1. Apply an activation function
  2. Evaluate the output and compare it to the desired outcome

This section will focus on setting the weights that create the input which feeds into the activation function.

Getting ready

A cursory understanding of the building blocks of a simple neural network is helpful in understanding this section and the rest of the chapter.  Each neural network has...

Normalizing the input data for the neural network

Neural networks work more efficiently when the inputs are normalized. This minimizes the magnitude of a particular input affecting the overall outcome over other potential inputs that have lower values of magnitude. This section will normalize the height and weight inputs of the current individuals.

Getting ready

The normalization of input values requires obtaining the mean and standard deviation of those values for the final calculation.

How to do it...

This section walks through the steps to normalize the height and weight...

Validating array for optimal neural network performance

A little bit of validation goes a long way in ensuring that our array is normalized for optimal performance within our upcoming neural network.  

Getting ready

This section will require a bit of numpy magic using the numpy.stack() function.

How to do it...

The following steps walk through validating that our array has been normalized.

  1. Execute the following step to print the mean and standard deviation of array inputs:
print('standard deviation')
print(round(X[:,0].std(axis=0),0))
print('mean...

Introduction

Much of this book will focus on building deep learning algorithms with libraries in Python, such as TensorFlow and Keras. While these libraries are helpful to build deep neural networks without getting deep into the calculus and linear algebra of deep learning, this chapter will do a deep dive into building a simple neural network in PySpark to make a gender prediction based on height and weight. One of the best ways to understand the foundation of neural networks is to build a model from scratch, without any of the popular deep learning libraries. Once the foundation for a neural network framework is established, understanding and utilizing some of the more popular deep neural network libraries will become much simpler.

Creating a dataframe in PySpark

dataframes will serve as the framework for any and all data that will be used in building deep learning models. Similar to the pandas library with Python, PySpark has its own built-in functionality to create a dataframe.

Getting ready

There are several ways to create a dataframe in Spark. One common way is by importing a .txt, .csv, or .json file. Another method is to manually enter fields and rows of data into the PySpark dataframe, and while the process can be a bit tedious, it is helpful, especially when dealing with a small dataset. To predict gender based on height and weight, this chapter will build a dataframe manually in PySpark. The dataset used is as follows:

While the dataset will be manually added to PySpark in this chapter, the dataset can also be viewed and downloaded from the following link:

https://github.com/asherif844/ApacheSparkDeepLearningCookbook/blob/master/CH02/data/HeightAndWeight.txt

Finally, we will begin this chapter and future chapters...

Manipulating columns in a PySpark dataframe

The dataframe is almost complete; however, there is one issue that requires addressing before building the neural network. Rather than keeping the gender value as a string, it is better to convert the value to a numeric integer for calculation purposes, which will become more evident as this chapter progresses.

Getting ready

This section will require  importing the following:

  • from pyspark.sql import functions

How to do it...

This section walks through the steps for the string conversion to a numeric value in the dataframe:

  • Female --> 0 
  • Male --> 1
  1. Convert a column value inside of a dataframe requires importing functions:
from pyspark.sql import functions
  1. Next, modify the gender column to a numeric value using the following script:
df = df.withColumn('gender',functions.when(df['gender']=='Female',0).otherwise(1))
  1. Finally, reorder the columns so that gender is the last column in the dataframe using the following script:
df = df.select('height', 'weight...

Converting a PySpark dataframe to an array

In order to form the building blocks of the neural network, the PySpark dataframe must be converted into an array. Python has a very powerful library, numpy, that makes working with arrays simple.

Getting ready

The numpy library should be already available with the installation of the anaconda3 Python package. However, if for some reason the numpy library is not available, it can be installed using the following command at the terminal:

pip install or sudo pip install will confirm whether the requirements are already satisfied by using the requested library:

import numpy as np

How to do it...

This section walks through the steps to convert the dataframe into an array:

  1. View the data collected from the dataframe using the following script:
df.select("height", "weight", "gender").collect()
  1. Store the values from the collection into an array called data_array using the following script:
data_array =  np.array(df.select("height", "weight", "gender").collect())
  1. Execute...

Visualizing an array in a scatterplot

The goal of the neural network that will be developed in this chapter is to predict the gender of an individual if the height and weight are known. A powerful method for understanding the relationship between height, weight, and gender is by visualizing the data points feeding the neural network. This can be done with the popular Python visualization library matplotlib.

Getting ready

As was the case with numpy, matplotlib should be available with the installation of the anaconda3 Python package. However, if for some reasonmatplotlib is not available, it can be installed using the following command at the terminal:

pip install orsudo pip install will confirm the requirements are already satisfied by using the requested library.

How to do it...

This section walks through the steps to visualize an array through a scatterplot.

  1. Import the matplotlib library and configure the library to visualize plots inside of the Jupyter notebook using the following script:
 import...
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Key benefits

  • Train distributed complex neural networks on Apache Spark
  • Use TensorFlow and Keras to train and deploy deep learning models
  • Explore practical tips to enhance performance

Description

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.

Who is this book for?

If you’re looking for a practical resource for implementing efficiently distributed deep learning models with Apache Spark, then this book is for you. Knowledge of core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the most out of this book. Some knowledge of Python programming will also be useful.

What you will learn

  • Set up a fully functional Spark environment
  • Understand practical machine learning and deep learning concepts
  • Employ built-in machine learning libraries within Spark
  • Discover libraries that are compatible with TensorFlow and Keras
  • Explore NLP models such as word2vec and TF-IDF on Spark
  • Organize DataFrames for deep learning evaluation
  • Apply testing and training modeling to ensure accuracy
  • Access readily available code that can be reused
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Publication date : Jul 13, 2018
Length: 474 pages
Edition : 1st
Language : English
ISBN-13 : 9781788474221
Category :
Data
Languages :
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Concepts :
Deep Learning
Tools :
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Product Details

Publication date : Jul 13, 2018
Length: 474 pages
Edition : 1st
Language : English
ISBN-13 : 9781788474221
Category :
Data
Languages :
Scala
Concepts :
Deep Learning
Tools :
Apache Spark

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Table of Contents

14 Chapters
Setting Up Spark for Deep Learning Development Chevron down icon Chevron up icon
Setting Up Spark for Deep Learning Development
Introduction
Downloading an Ubuntu Desktop image
Installing and configuring Ubuntu with VMWare Fusion on macOS
Installing and configuring Ubuntu with Oracle VirtualBox on Windows
Installing and configuring Ubuntu Desktop for Google Cloud Platform
Installing and configuring Spark and prerequisites on Ubuntu Desktop
Integrating Jupyter notebooks with Spark
Starting and configuring a Spark cluster
Stopping a Spark cluster
Creating a Neural Network in Spark Chevron down icon Chevron up icon
Creating a Neural Network in Spark
Introduction
Creating a dataframe in PySpark
Manipulating columns in a PySpark dataframe
Converting a PySpark dataframe to an array
Visualizing an array in a scatterplot
Setting up weights and biases for input into the neural network
Normalizing the input data for the neural network
Validating array for optimal neural network performance
Setting up the activation function with sigmoid
Creating the sigmoid derivative function
Calculating the cost function in a neural network
Predicting gender based on height and weight
Visualizing prediction scores
Pain Points of Convolutional Neural Networks Chevron down icon Chevron up icon
Pain Points of Convolutional Neural Networks
Introduction
Pain Point #1: Importing MNIST images
Pain Point #2: Visualizing MNIST images
Pain Point #3: Exporting MNIST images as files
Pain Point #4: Augmenting MNIST images
Pain Point #5: Utilizing alternate sources for trained images
Pain Point #6: Prioritizing high-level libraries for CNNs
Pain Points of Recurrent Neural Networks Chevron down icon Chevron up icon
Pain Points of Recurrent Neural Networks
Introduction
Introduction to feedforward networks
Sequential workings of RNNs
Pain point #1 – The vanishing gradient problem
Pain point #2 – The exploding gradient problem
Sequential working of LSTMs
Predicting Fire Department Calls with Spark ML Chevron down icon Chevron up icon
Predicting Fire Department Calls with Spark ML
Introduction
Downloading the San Francisco fire department calls dataset
Identifying the target variable of the logistic regression model
Preparing feature variables for the logistic regression model
Applying the logistic regression model
Evaluating the accuracy of the logistic regression model
Using LSTMs in Generative Networks Chevron down icon Chevron up icon
Using LSTMs in Generative Networks
Introduction
Downloading novels/books that will be used as input text
Preparing and cleansing data
Tokenizing sentences
Training and saving the LSTM model
Generating similar text using the model
Natural Language Processing with TF-IDF Chevron down icon Chevron up icon
Natural Language Processing with TF-IDF
Introduction
Downloading the therapy bot session text dataset
Analyzing the therapy bot session dataset
Visualizing word counts in the dataset
Calculating sentiment analysis of text
Removing stop words from the text
Training the TF-IDF model
Evaluating TF-IDF model performance
Comparing model performance to a baseline score
Real Estate Value Prediction Using XGBoost Chevron down icon Chevron up icon
Real Estate Value Prediction Using XGBoost
Downloading the King County House sales dataset
Performing exploratory analysis and visualization
Plotting correlation between price and other features
Predicting the price of a house
Predicting Apple Stock Market Cost with LSTM Chevron down icon Chevron up icon
Predicting Apple Stock Market Cost with LSTM
Downloading stock market data for Apple
Exploring and visualizing stock market data for Apple
Preparing stock data for model performance
Building the LSTM model
Evaluating the model
Face Recognition Using Deep Convolutional Networks Chevron down icon Chevron up icon
Face Recognition Using Deep Convolutional Networks
Introduction
Downloading and loading the MIT-CBCL dataset into the memory
Plotting and visualizing images from the directory
Preprocessing images
Model building, training, and analysis
Creating and Visualizing Word Vectors Using Word2Vec Chevron down icon Chevron up icon
Creating and Visualizing Word Vectors Using Word2Vec
Introduction
Acquiring data
Importing the necessary libraries
Preparing the data
Building and training the model
Visualizing further 
Analyzing further
Creating a Movie Recommendation Engine with Keras Chevron down icon Chevron up icon
Creating a Movie Recommendation Engine with Keras
Introduction
Downloading MovieLens datasets
Manipulating and merging the MovieLens datasets
Exploring the MovieLens datasets
Preparing dataset for the deep learning pipeline
Applying the deep learning model with Keras
Evaluating the recommendation engine's accuracy
Image Classification with TensorFlow on Spark Chevron down icon Chevron up icon
Image Classification with TensorFlow on Spark
Introduction
Downloading 30 images each of Messi and Ronaldo
Configuring PySpark installation with deep learning packages
Loading images on to PySpark dataframes
Understanding transfer learning
Creating a pipeline for image classification training
Evaluating model performance
Fine-tuning model parameters
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Adnan Masood, PhD Aug 16, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The tremendous impact of Artificial Intelligence and Machine learning, and the uncanny effectiveness of deep neural networks are hard to escape in both academia and industry. Meanwhile implementation grade material outlining the deep learning using Spark are not always easy to find. The manuscript you are holding in your hands (or in your e-reader) is an problem-solution oriented approach which not only shows Spark’s capabilities but also the art of possible around various machine learning and deep learning problems.Full disclosure, I am the technical reviewer of the book and wrote the foreword. It was a pleasure reading and reviewing the Ahmed Sherif and Amrith Ravindra’s work which I hope you as a reader will also find very compelling.The authors begin with helping to set up Spark for Deep Learning development by providing clear and concisely written recipes. The initial setup is naturally followed by creating a neural network, elaborating on pain points of Convolutional Neural Networks, and Recurrent Neural Networks. Later on, authors provided practical (yet simplified) use cases of predicting fire department calls with SparkML, real estate value prediction using XGBoost, predicting the stock market cost of Apple with LSTM, and creating a movie recommendation Engine with Keras. The book covers pertinent and highly relevant technologies with operational use cases like LSTMs in Generative Networks, natural language processing with TF-IDF, face recognition using deep convolutional networks, creating and visualizing word vectors using Word2Vec and image classification with TensorFlow on Spark. Beside crisp and focused writing, this wide array of highly relevant machine learning and deep learning topics give the book its core strength.I hope this book will help you leverage Apache Spark to tackle business and technology problems. Highly recommended reading.
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Todd Sep 06, 2023
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$55 bucks… not worth it. There’s this website called google
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Bethany Sep 06, 2023
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An absolutely disgrace from cover to cover. I’d rather eat the book than use the recipes.
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Amanda Sep 02, 2023
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Wouldn’t recommend
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Patrick Sullivan Sep 06, 2023
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Total Garbage. Not worth it
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Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
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May 2023 286 pages
Full star icon 4.2 (34)
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Generative AI with LangChain
Generative AI with LangChain
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Dec 2023 368 pages
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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
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May 2023 456 pages
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About the authors

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Profile icon Ahmed Sherif
Ahmed Sherif
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Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.
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Profile icon Ravindra
Ravindra
Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his masters, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.
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Shipping Details

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Economy: Delivery to most addresses in the US within 10-15 business days

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UK:

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Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
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Rest of the World:

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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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  2. American Samoa
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  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela