Implementing a non-linear SVM

For this recipe, we will apply a non-linear kernel to split a dataset.

Getting ready

In this section, we will implement the preceding Gaussian kernel SVM on real data. We will load the iris dataset and create a classifier for I. setosa (versus Non-setosa). We will see the effect of various gamma values on the classification.

 

How to do it...

We proceed with the recipe as follows:

1. We first load the necessary libraries, which includes the scikit-learn datasets so that we can load the iris data. Then, we will start a graph session. Use the following code:

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 data, extract the sepal length and petal width, and separate the x and y values for each class (for plotting purposes later), as follows:

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