learnwh

(To be removed) Widrow-Hoff weight/bias learning function

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


Description

learnwh is the Widrow-Hoff weight/bias learning function, and is also known as the delta or least mean squared (LMS) rule.

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

W	S-by-R weight matrix (or b, and 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 the learnwh learning parameter, shown here with its default value.

LP.lr — 0.01

Learning rate

info = learnwh('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 and error E for a layer with a two-element input and three neurons. You also define the learning rate LR learning parameter.

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

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

dW = learnwh([],p,[],[],[],[],e,[],[],[],lp,[])

Network Use

You can create a standard network that uses learnwh with linearlayer.

To prepare the weights and the bias of layer i of a custom network to learn with learnwh,

1. Set net.trainFcn to 'trainb'. net.trainParam automatically becomes trainb’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 'learnwh'.

4. Set each net.layerWeights{i,j}.learnFcn to 'learnwh'.

5. Set net.biases{i}.learnFcn to 'learnwh'. Each weight and bias learning parameter property is automatically set to the learnwh default parameters.

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

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

2. Call train (or adapt).

Algorithms

learnwh calculates the weight change dW for a given neuron from the neuron’s input P and error E, and the weight (or bias) learning rate LR, according to the Widrow-Hoff learning rule:

dw = lr*e*pn'

References

Widrow, B., and M.E. Hoff, “Adaptive switching circuits,” 1960 IRE WESCON Convention Record, New York IRE, pp. 96–104, 1960

Widrow, B., and S.D. Sterns, Adaptive Signal Processing, New York, Prentice-Hall, 1985

Version History

Introduced before R2006a

R2026a: learnwh will be removed

learnwh 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.