Implementing RNN for spam prediction

To start, we will apply the standard RNN unit to predict a singular numerical output, a probability of being spam.

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 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 required 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...