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.