Working with a linear SVM

For this example, we will create a linear separator from the iris dataset. We know from prior chapters that the sepal length and petal width create a linear separable binary dataset for predicting whether a flower is I. setosa or not.

Getting ready

To implement a soft separable SVM in TensorFlow, we will implement the specific loss function, as follows:

Here, A is the vector of partial slopes, b is the intercept, 

 is a vector of inputs,

 is the actual class, (-1 or 1), and 

 is the soft separability regularization parameter.

How to do it...

We proceed with the recipe as follows:

1. We start by loading the necessary libraries. This will include the scikit-learn dataset library for access to the iris dataset. Use the following code:

import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
from sklearn import datasets 

Note

To set up scikit-learn for this exercise, we just need to type $pip install -U scikit-learn. Note that it also comes installed with Anaconda...