learnh (Neural Network Toolbox)

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learnh

Examples See Also

Hebb weight learning rule

Syntax

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

info = learnh(code)

Description

learnh is the Hebb weight learning function.

learnh(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 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 learnh's learning parameter, shown here with its default value.

LP.lr - 0.01 - Learning rate.

learnh(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 and output A for a layer with a 2-element input and 3 neurons. We also define the learning rate LR.

p = rand(2,1);
a = rand(3,1);
lp.lr = 0.5;

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

dW = learnh([],p,[],[],a,[],[],[],[],[],lp,[])

Network Use

To prepare the weights and the bias of layer i of a custom network to learn with learnh:

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

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

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

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

1.: Set net.trainParam (net.adaptParam) properties to desired values.

2.: Call train (adapt).

Algorithm

learnh calculates the weight change dW for a given neuron from the neuron's input P, output A, and learning rate LR according to the Hebb learning rule:

dw =  lr*a*p'