Neural Network Toolbox

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.

learnlv1(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 R 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

learnlv1(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 random input P, output A, weight matrix W, and output gradient gA for a layer with a two-element input and three neurons.

We 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;
  

Since learnlv1 only needs these values to calculate a weight change (see algorithm below), we will use them to do so.

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

Network Use

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

1. Set net.trainFcn to `trainr'. (net.trainParam will automatically become trainr's default parameters.)
2. Set net.adaptFcn to 'trains'. (net.adaptParam will automatically become trains's default parameters.)
3. Set each net.inputWeights{i,j}.learnFcn to 'learnlv1'. Set each net.layerWeights{i,j}.learnFcn to 'learnlv1'. (Each weight learning parameter property will automatically be 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).

Algorithm

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

See Also

learnlv2, adapt, train

learnk

learnlv2

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