TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

For this recipe, we will create several helper functions. These functions will load the data, normalize the text, generate the vocabulary, and generate data batches. Only after all this will we then start training our word embeddings. To be clear, we are not predicting any target variables, but we will be fitting word embeddings instead:

1. First, we will load the necessary libraries and start a graph session:

import tensorflow as tf 
import matplotlib.pyplot as plt 
import numpy as np 
import random 
import os 
import string 
import requests 
import collections 
import io 
import tarfile 
import urllib.request 
from nltk.corpus import stopwords 
sess = tf.Session() 

2. Then we declare some model parameters. We will look at 50 pairs of word ...