Contents

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.inputWeight{i,j}.weightFcn to 'convwf'. For a layer weight, set net.layerWeight{i,j}.weightFcn to 'convwf'.

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

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