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

Visualizing further 


This section will describe how to squash the dimensionality of all the trained words and put it all into one giant matrix for visualization purposes. Since each word is a 300-dimensional vector, it needs to be brought down to a lower dimension for us to visualize it in a 2D space.

Getting ready

Once the model is saved and checkpointed after training, begin by loading it into memory, as you did in the previous section. The libraries and modules that will be utilized in this section are: 

  • tSNE
  • pandas
  • Seaborn
  • numpy

How to do it...

The steps are as follows:

  1. Squash the dimensionality of the 300-dimensional word vectors by using the following command:
 tsne = sklearn.manifold.TSNE(n_components=2, random_state=0)
  1. Put all the word vectors into one giant matrix (named all_word_vectors_matrix), and view it using the following commands:
 all_word_vectors_matrix = got2vec.wv.syn0
 print (all_word_vectors_matrix)
  1. Use the tsne technique to fit all the learned representations into a two- dimensional...