Main Content

softmax

Soft max transfer function

Description

example

Tip

To use a softmax activation for deep learning, use softmaxLayer or the dlarray method softmax.

A = softmax(N) takes a S-by-Q matrix of net input (column) vectors, N, and returns the S-by-Q matrix, A, of the softmax competitive function applied to each column of N.

softmax is a neural transfer function. Transfer functions calculate a layer’s output from its net input.

info = softmax(code) returns information about this function. For more information, see the code argument description.

Examples

collapse all

This example shows how to calculate and plot the softmax transfer function of an input matrix.

Create the input matrix, n. Then call the softmax function and plot the results.

n = [0; 1; -0.5; 0.5];
a = softmax(n);
subplot(2,1,1), bar(n), ylabel('n')
subplot(2,1,2), bar(a), ylabel('a')

Assign this transfer function to layer i of a network.

net.layers{i}.transferFcn = 'softmax';

Input Arguments

collapse all

Net input column vectors, specified as an S-by-Q matrix.

Information you want to retrieve from the function, specified as one of the following:

  • 'name' returns the name of this function.

  • 'output' returns the [min max] output range.

  • 'active' returns the [min max] active input range.

  • 'fullderiv' returns 1 or 0, depending on whether dA_dN is S-by-S-by-Q or S-by-Q.

  • 'fpnames' returns the names of the function parameters.

  • 'fpdefaults' returns the default function parameters.

Output Arguments

collapse all

Output matrix, returned as an S-by-Q matrix of the softmax competitive function applied to each column of N.

Specific information about the function, according to the option specified in the code argument, returned as either a string, a vector, or a scalar.

Algorithms

a = softmax(n) = exp(n)/sum(exp(n))

See Also

|

Introduced before R2006a