learnh
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.
[dW,LS] = learnh(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
takes several inputs,
W |
|
P |
|
Z |
|
N |
|
A |
|
T |
|
E |
|
gW |
|
gA |
|
D |
|
LP | Learning parameters, none, |
LS | Learning state, initially should be = |
and returns
dW |
|
LS | New learning state |
Learning occurs according to learnh
’s learning parameter, shown here
with its default value.
LP.lr - 0.01 | Learning rate |
info = learnh('
returns useful
information for each code
')code
character vector:
'pnames' | Names of learning parameters |
'pdefaults' | Default learning parameters |
'needg' | Returns 1 if this function uses |
Examples
Here you define a random input P
and output A
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); lp.lr = 0.5;
Because learnh
only needs these values to calculate a weight change (see
“Algorithm” below), 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
,
Set
net.trainFcn
to'trainr'
. (net.trainParam
automatically becomestrainr
’s default parameters.)Set
net.adaptFcn
to'trains'
. (net.adaptParam
automatically becomestrains
’s default parameters.)Set each
net.inputWeights{i,j}.learnFcn
to'learnh'
.Set each
net.layerWeights{i,j}.learnFcn
to'learnh'
. (Each weight learning parameter property is automatically set tolearnh
’s default parameters.)
To train the network (or enable it to adapt),
Set
net.trainParam
(ornet.adaptParam
) properties to desired values.Call
train
(adapt
).
Algorithms
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'
References
Hebb, D.O., The Organization of Behavior, New York, Wiley, 1949
Version History
Introduced before R2006a