TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

We proceed with the recipe as follows: 

1. First, we load the libraries needed and initialize the computational graph. Note that we also load matplotlib here, because we would like to plot the resultant line afterward:

import matplotlib.pyplot as plt 
import numpy as np 
from sklearn import datasets 
import tensorflow as tf 
sess = tf.Session() 

2. Next, we load the iris data. We will also need to transform the target data to be just 1 or 0, whether the target is setosa or not. Since the iris dataset marks setosa as a 0, we will change all targets with the value 0 to 1, and the other values all to 0. We will also only use two features, petal length and petal width. These two features are the third and fourth entry in each x-value ...