TensorFlow Machine Learning Cookbook - Second Edition

How to do it...

We proceed with the recipe as follows:

1. First, we load the libraries we need and start a graph, as follows:

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

2. Next, we will load the iris dataset and split apart the targets for each class. We will only be using sepal length and petal width to illustrate because we want to be able to plot the outputs. We also separate the x and y values for each class for plotting purposes at the end. Use the following code:

iris = datasets.load_iris() x_vals = np.array([[x[0], x[3]] for x in iris.data]) y_vals1 = np.array([1 if y==0 else -1 for y in iris.target]) y_vals2 = np.array([1 if y==1 else -1 for y in iris.target]) ...