hardlims

(To be removed) Symmetric hard-limit transfer function

hardlims will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Graph and Symbol

Syntax

A = hardlims(N,FP)


Description

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

A = hardlims(N,FP) takes N and optional function parameters,

N	S-by-Q matrix of net input (column) vectors
FP	Struct of function parameters (ignored)

and returns A, the S-by-Q +1/–1 matrix with +1s where N ≥ 0.

info = hardlims('code') returns information according to the code string specified:

hardlims('name') returns the name of this function.

hardlims('output',FP) returns the [min max] output range.

hardlims('active',FP) returns the [min max] active input range.

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

hardlims('fpnames') returns the names of the function parameters.

hardlims('fpdefaults') returns the default function parameters.

Examples

Here is how to create a plot of the hardlims transfer function.

n = -5:0.1:5;
a = hardlims(n);
plot(n,a)

Assign this transfer function to layer i of a network.

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

Algorithms

hardlims(n) = 1 if n ≥ 0, –1 otherwise.

Version History

Introduced before R2006a

R2026a: hardlims will be removed

hardlims will be removed in a future release.

To learn more about using activation and transfer functions in neural networks, see these examples and topics:

• Build Networks with Deep Network Designer

• List of Deep Learning Layers

• List of Functions with dlarray Support

• Compare Activation Layers

• Example Deep Learning Network Architectures

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.