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