Implementing RNN for Spam Prediction

To start we will apply the standard RNN unit to predict a singular numerical output.

Getting ready

In this recipe, we will implement a standard RNN in TensorFlow to predict whether or not a text message is spam or ham. We will use the SMS spam-collection dataset from the ML repository at UCI.The architecture we will use for prediction will be an input RNN sequence from the embedded text, and we will take the last RNN output as a prediction of spam or ham (1 or 0).

How to do it…

1. We start by loading the libraries necessary for this script:

   import os
   import re
   import io
   import requests
   import numpy as np
   import matplotlib.pyplot as plt
   import tensorflow as tf
   from zipfile import ZipFile

2. Next we start a graph session and set the RNN model parameters. We will run the data through 20 epochs, in batch sizes of 250.The maximum length of each text we will consider is 25 words; we will cut longer texts to 25 or zero pad shorter texts.The RNN will be of size 10 units...