Table Of Contents
Table Of Contents

mx.symbol.SVMOutput

Description

Computes support vector machine based transformation of the input.

This tutorial demonstrates using SVM as output layer for classification instead of softmax: https://github.com/dmlc/mxnet/tree/master/example/svm_mnist.

Usage

mx.symbol.SVMOutput(...)

Arguments

Argument

Description

data

NDArray-or-Symbol.

Input data for SVM transformation.

label

NDArray-or-Symbol.

Class label for the input data.

margin

float, optional, default=1.

The loss function penalizes outputs that lie outside this margin. Default margin is 1.

regularization.coefficient

float, optional, default=1.

Regularization parameter for the SVM. This balances the tradeoff between coefficient size and error.

use.linear

boolean, optional, default=0.

Whether to use L1-SVM objective. L2-SVM objective is used by default.

name

string, optional.

Name of the resulting symbol.

Value

out The result mx.symbol