In this section, we will see how to implement an RNN in TensorFlow to predict spam/ham from texts.
The popular spam dataset from the UCI ML repository will be used, which can be downloaded from http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smssp amcollection.zip.
The dataset contains texts from several emails, some of which were marked as spam. Here we will train a model that will learn to distinguish between spam and non-spam emails using only the text of the email. Let's get started by importing the required libraries and model:
import os import re import io import requests import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from zipfile ...