convwf

Convolution weight function

Syntax

Z = convwf(W,P)
dim = convwf('size',S,R,FP)
dw = convwf('dw',W,P,Z,FP)
info = convwf('code')


Description

Weight functions apply weights to an input to get weighted inputs.

Z = convwf(W,P) returns the convolution of a weight matrix W and an input P.

dim = convwf('size',S,R,FP) takes the layer dimension S, input dimension R, and function parameters, and returns the weight size.

dw = convwf('dw',W,P,Z,FP) returns the derivative of Z with respect to W.

info = convwf('code') returns information about this function. The following codes are defined:

'deriv'	Name of derivative function
'fullderiv'	Reduced derivative = 2, full derivative = 1, linear derivative = 0
'pfullderiv'	Input: reduced derivative = 2, full derivative = 1, linear derivative = 0
'wfullderiv'	Weight: reduced derivative = 2, full derivative = 1, linear derivative = 0
'name'	Full name
'fpnames'	Returns names of function parameters
'fpdefaults'	Returns default function parameters

Examples

Here you define a random weight matrix W and input vector P and calculate the corresponding weighted input Z.

W = rand(4,1);
P = rand(8,1);
Z = convwf(W,P)

Network Use

To change a network so an input weight uses convwf, set net.inputWeights{i,j}.weightFcn to 'convwf'. For a layer weight, set net.layerWeights{i,j}.weightFcn to 'convwf'.

In either case, call sim to simulate the network with convwf.

Version History

Introduced in R2006a