TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

We proceed with the recipe as follows:

1. We will start by loading the required libraries and starting a graph session. We will use the requests module to load the necessary Boston housing data from the UCI machine learning repository:

import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
import requests 
 
sess = tf.Session() 

2. Next, we will load the data using the requests module:

housing_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data' housing_header = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] cols_used = ['CRIM', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'TAX', 'PTRATIO', 'B', 'LSTAT'] num_features = len(cols_used) ...