A demonstration of how you can use TensorFlow to implement a standard L2-regularized support vector machine (SVM) in primal form.

linear_svm.py optimizes the following SVM cost using gradient descent:

where

The first part of the cost function, i.e. the regularization part, is implemented by the regularization_loss expression, and the second part is implemented by the hinge_loss expression in the code.

Run the code using

python linear_svm.py --train linearly_separable_data.csv --svmC 1 --verbose True --num_epochs 10

On a linearly separable, 2D data, the code gives the following decision boundary:

The code here is inspired by the repository try-tf.