Book Image

TensorFlow Machine Learning Cookbook

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook

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 (19 chapters)
TensorFlow Machine Learning Cookbook
About the Author
About the Reviewer
Customer Feedback

Working with Nearest Neighbors

We start this chapter by implementing nearest neighbors to predict housing values. This is a great way to start with nearest neighbors because we will be dealing with numerical features and continuous targets.

Getting ready

To illustrate how making predictions with nearest neighbors works in TensorFlow, we will use the Boston housing dataset. Here we will be predicting the median neighborhood housing value as a function of several features.

Since we consider the training set the trained model, we will find the k-NNs to the prediction points and do a weighted average of the target value.

How to do it…

  1. First, we will start by loading the required libraries and starting a graph session. We will use the requests module to load the necessary Boston housing data from the UCI machine learning repository:

    import matplotlib.pyplot as plt
    import numpy as np
    import tensorflow as tf
    import requests
    sess = tf.Session()
  2. Next, we will load the data using the requests module: