learnlv1

LVQ1 weight learning function

Syntax

[dW,LS] = learnlv1(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
info = learnlv1('code')

Description

learnlv1 is the LVQ1 weight learning function.

[dW,LS] = learnlv1(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,

W

S-by-R weight matrix (or S-by-1 bias vector)

P

R-by-Q input vectors (or ones(1,Q))

Z

S-by-Q weighted input vectors

N

S-by-Q net input vectors

A

S-by-Q output vectors

T

S-by-Q layer target vectors

E

S-by-Q layer error vectors

gW

S-by-R gradient with respect to performance

gA

S-by-Q output gradient with respect to performance

D

S-by-S neuron distances

LP

Learning parameters, none, LP = []

LS

Learning state, initially should be = []

and returns

dW

S-by-R weight (or bias) change matrix

LS

New learning state

Learning occurs according to learnlv1’s learning parameter, shown here with its default value.

LP.lr - 0.01

Learning rate

info = learnlv1('code') returns useful information for each code character vector:

'pnames'

Names of learning parameters

'pdefaults'

Default learning parameters

'needg'

Returns 1 if this function uses gW or gA

Examples

Here you define a random input P, output A, weight matrix W, and output gradient gA for a layer with a two-element input and three neurons. Also define the learning rate LR.

p = rand(2,1);
w = rand(3,2);
a = compet(negdist(w,p));
gA = [-1;1; 1];
lp.lr = 0.5;

Because learnlv1 only needs these values to calculate a weight change (see “Algorithm” below), use them to do so.

dW = learnlv1(w,p,[],[],a,[],[],[],gA,[],lp,[])

Network Use

You can create a standard network that uses learnlv1 with lvqnet. To prepare the weights of layer i of a custom network to learn with learnlv1,

  1. Set net.trainFcn to 'trainr'. (net.trainParam automatically becomes trainr’s default parameters.)

  2. Set net.adaptFcn to 'trains'. (net.adaptParam automatically becomes trains’s default parameters.)

  3. Set each net.inputWeights{i,j}.learnFcn to 'learnlv1'.

  4. Set each net.layerWeights{i,j}.learnFcn to 'learnlv1'. (Each weight learning parameter property is automatically set to learnlv1’s default parameters.)

To train the network (or enable it to adapt),

  1. Set net.trainParam (or net.adaptParam) properties as desired.

  2. Call train (or adapt).

Algorithms

learnlv1 calculates the weight change dW for a given neuron from the neuron’s input P, output A, output gradient gA, and learning rate LR, according to the LVQ1 rule, given i, the index of the neuron whose output a(i) is 1:

dw(i,:) = +lr*(p-w(i,:)) if gA(i) = 0;= -lr*(p-w(i,:)) if gA(i) = -1

Version History

Introduced before R2006a

See Also

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