learnlv2 (Neural Network Toolbox)

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learnlv2

Examples See Also

LVQ2 weight learning function

Syntax

[dW,LS] = learnlv2(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)

info = learnlv2(code)

Description

learnlv2 is the LVQ2 weight learning function.

learnlv2(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,

W - S x R weight matrix (or S x 1 bias vector).

P - R x Q input vectors (or ones(1,Q)).

Z - S x Q weighted input vectors.

N - S x Q net input vectors.

A - S x Q output vectors.

T - S x Q layer target vectors.

E - S x Q layer error vectors.

gW - S x R weight gradient with respect to performance.

gA - S x Q output gradient with respect to performance.

D - S x S neuron distances.

LP - Learning parameters, none, LP = [].

LS - Learning state, initially should be = [].

and returns,

dW - S x 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.

learnlv2(code) returns useful information for each code string:

'pnames' - Names of learning parameters.

'pdefaults' - Default learning parameters.

'needg' - Returns 1 if this function uses gW or gA.

Examples

Here we define a sample input P, output A, weight matrix W, and output gradient gA for a layer with a 2-element input and 3 neurons.

We also define the learning rate LR.

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

Since learnlv2 only needs these values to calculate a weight change (see algorithm below), we will use them to do so.

dW = learnlv2(w,p,[],n,a,[],[],[],gA,[],lp,[])

Network Use

You can create a standard network that uses learnlv2 with newlvq.

To prepare the weights of layer i of a custom network to learn with learnlv2:

1.: Set net.trainFcn to 'trainwb1'. (net.trainParam will automatically become trainwb1's default parameters.)

2.: Set net.adaptFcn to 'adaptwb'. (net.adaptParam will automatically become trainwb1's default parameters.)

3.: Set each net.inputWeights{i,j}.learnFcn to 'learnlv2'. Set each net.layerWeights{i,j}.learnFcn to 'learnlv2'. (Each weight learning parameter property will automatically be set to learnlv2'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).

Algorithm

learnlv2 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 LVQ2 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; if gA(i) is -1 then the index j is found of the neuron with the greatest net input n(k), from the neurons whose gA(k) is 1. This neuron's weights are updated as follows:

dw(j,:) = +lr*(p-w(i,:))