Working with bag of words

We start by showing how to work with a bag of words embedding in TensorFlow. This mapping is what we introduced in the introduction. Here we show how to use this type of embedding to do spam prediction.

Getting ready

To illustrate how to use bag of words with a text dataset, we will use a spam-ham phone text database from the UCI machine learning data repository (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection). This is a collection of phone text messages that are spam or not-spam (ham). We will download this data, store it for future use, and then proceed with the bag of words method to predict whether a text is spam or not. The model that will operate on the bag of words will be a logistic model with no hidden layers. We will use stochastic training, with batch size of one, and compute the accuracy on a held-out test set at the end.

How to do it…

For this example, we will start by getting the data, normalizing and splitting the text, running it through...