learnos

(To be removed) Outstar weight learning function

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


Description

learnos is the outstar weight learning function.

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

LP.lr - 0.01

Learning rate

info = learnos('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, output A, and weight matrix W for a layer with a two-element input and three neurons. Also define the learning rate LR.

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

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

dW = learnos(w,p,[],[],a,[],[],[],[],[],lp,[])

Network Use

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

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 'learnos'.

4. Set each net.layerWeights{i,j}.learnFcn to 'learnos'. (Each weight learning parameter property is automatically set to learnos’s 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 (adapt).

Algorithms

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

dw = lr*(a-w)*p'

References

Grossberg, S., Studies of the Mind and Brain, Drodrecht, Holland, Reidel Press, 1982

Version History

Introduced before R2006a

R2026a: learnos will be removed

learnos 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.