learnlv2

(To be removed) LVQ2.1 weight learning function

learnlv2 will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

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.

[dW,LS] = learnlv2(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 weight 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 learnlv2’s learning parameter, shown here with its default value.

LP.lr - 0.01	Learning rate
LP.window - 0.25	Window size (0 to 1, typically 0.2 to 0.3)

info = learnlv2('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 sample 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);
n = negdist(w,p);
a = compet(n);
gA = [-1;1; 1];
lp.lr = 0.5;

Because learnlv2 only needs these values to calculate a weight change (see “Algorithm” below), 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 lvqnet.

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

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 'learnlv2'.

4. Set each net.layerWeights{i,j}.learnFcn to 'learnlv2'. (Each weight learning parameter property is automatically 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).

Algorithms

learnlv2 implements Learning Vector Quantization 2.1, which works as follows:

For each presentation, if the winning neuron i should not have won, and the runnerup j should have, and the distance di between the winning neuron and the input p is roughly equal to the distance dj from the runnerup neuron to the input p according to the given window,

min(di/dj, dj/di) > (1-window)/(1+window)

then move the winning neuron i weights away from the input vector, and move the runnerup neuron j weights toward the input according to

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

Version History

Introduced before R2006a

R2026a: learnlv2 will be removed

learnlv2 will be removed in a future release.

To learn more about customizing training algorithms, see these examples and topics:

• Custom Training Loops

• Define Custom Learning Rate Schedule

• Train Network Using Custom Training Loop

• Compare Custom Solvers Using Custom Training Loop

• Speed Up Deep Neural Network Training

• Accelerate Custom Training Loop Functions

• Evaluate Deep Learning Experiments by Using Metric Functions

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.