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

Generating similar text using the model


Now that you have a trained language model, it can be used. In this case, you can use it to generate new sequences of text that have the same statistical properties as the source text. This is not practical, at least not for this example, but it gives a concrete example of what the language model has learned.

Getting ready

  1. Begin by loading the training sequences again. You may do so by using the load_document() function, which we developed initially. This is done by using the following code:
def load_document(name):
    file = open(name, 'r')
    text = file.read()
    file.close()
    return text

# load sequences of cleaned text
input_filename = 'junglebook_sequences.txt'
doc = load_document(input_filename)
lines = doc.split('\n')

The output of the preceding code is illustrated in the following screenshot:

  1. Note that the input filename is now 'junglebook_sequences.txt', which will load the saved training sequences into the memory. We need the text so that...