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TensorFlow Machine Learning Cookbook

You're reading from   TensorFlow Machine Learning Cookbook Over 60 practical recipes to help you master Google's TensorFlow machine learning library

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786462169
Length 370 pages
Edition 1st Edition
Languages
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Author (1):
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Nick McClure Nick McClure
Author Profile Icon Nick McClure
Nick McClure
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with TensorFlow FREE CHAPTER 2. The TensorFlow Way 3. Linear Regression 4. Support Vector Machines 5. Nearest Neighbor Methods 6. Neural Networks 7. Natural Language Processing 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Taking TensorFlow to Production 11. More with TensorFlow Index

Using a Multilayer Neural Network


We will now apply our knowledge of different layers to real data with using a multilayer neural network on the Low Birthweight dataset.

Getting ready

Now that we know how to create neural networks and work with layers, we will apply this methodology towards predicting the birthweight in the low birthweight dataset. We'll create a neural network with three hidden layers. The low- birthweight dataset includes the actual birthweight and an indicator variable if the birthweight is above or below 2,500 grams. In this example, we'll make the target the actual birthweight (regression) and then see what the accuracy is on the classification at the end, and let's see if our model can identify if the birthweight will be <2,500 grams.

How to do it…

  1. First we'll start by loading the libraries and initializing our computational graph:

    import tensorflow as tf
    import matplotlib.pyplot as plt
    import requests
    import numpy as np
    sess = tf.Session()
  2. Now we'll load the data from...

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