# tf-svm

**Repository Path**: wu_yangyang_admin/tf-svm

## Basic Information

- **Project Name**: tf-svm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-23
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Tensorflow Linear SVM
===

A demonstration of how you can use [TensorFlow](http://www.tensorflow.org/) to
implement a standard L2-regularized support vector machine (SVM) in primal form. 

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

![](figures/L.png)

where 

![](figures/D.png)


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: 


<img src="figures/result.png" width="500px"/>

The code here is inspired by the repository
[try-tf](https://github.com/jasonbaldridge/try-tf).