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

Improving the Predictions of Linear Models

In the prior recipes, we have noted that the number of parameters we are fitting far exceeds the equivalent linear models. In this recipe, we will attempt to improve our logistic model of low birthweight with using a neural network.

Getting ready

For this recipe, we will load the low birth-weight data and use a neural network with two hidden fully connected layers with sigmoid activations to fit the probability of a low birth-weight.

How to do it

  1. We start by loading the libraries and initializing our computational graph:

    import matplotlib.pyplot as plt
    import numpy as np
    import tensorflow as tf
    import requests
    sess = tf.Session()
  2. Now we will load, extract, and normalize our data just like as in the prior recipe, except that we are going to using the low birthweight indicator variable as our target instead of the actual birthweight:

    birthdata_url = '''
    birth_file = requests.get(birthdata_url)