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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

By : Nick McClure
3.7 (18)
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TensorFlow Machine Learning Cookbook

TensorFlow Machine Learning Cookbook

3.7 (18)
By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (13 chapters)
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12
Index

Parallelizing TensorFlow


To extend our reach for parallelizing TensorFlow, we can also perform separate operations of our graph on entirely different machines in a distributed manner. This recipe will show us how that is achieved.

Getting ready

A few months after the release of TensorFlow, Google released TensorFlow Distributed. This was a big upgrade to the TensorFlow ecosystem, allowing a TensorFlow cluster to be set up (separate worker machines), to share the computational task of training and evaluating models. Using TensorFlow Distributed is as easy as setting up some parameters for workers and then assigning different jobs to different workers.

In this recipe, we will set up two local workers and assign them different jobs.

How to do it…

  1. To start, we load TensorFlow and define our two local workers with a configuration dictionary file (ports 2222 and 2223):

    import tensorflow as tf
    # Cluster for 2 local workers (tasks 0 and 1):
    cluster = tf.train.ClusterSpec({'local': ['localhost:2222', ...

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