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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

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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