Batch unsupervised weight/bias training
net.trainFcn = 'trainbu'
[net,tr] = train(net,...)
trainbu trains a network with weight and bias learning rules with batch
updates. Weights and biases updates occur at the end of an entire pass through the input
data.
trainbu is not called directly. Instead the train
function calls it for networks whose NET.trainFcn property is set to
'trainbu', thus:
net.trainFcn = 'trainbu' sets the network trainFcn
property.
[net,tr] = train(net,...) trains the network with
trainbu.
Training occurs according to trainbu training parameters, shown here
with the following default values:
net.trainParam.epochs | 1000 | Maximum number of epochs to train |
net.trainParam.show | 25 | Epochs between displays ( |
net.trainParam.showCommandLine | false | Generate command-line output |
net.trainParam.showWindow | true | Show training GUI |
net.trainParam.time | inf | Maximum time to train in seconds |
Validation and test vectors have no impact on training for this function, but act as independent measures of network generalization.
You can create a standard network that uses trainbu by calling
selforgmap. To prepare a custom network to be trained with
trainbu:
Set NET.trainFcn to 'trainbu'.
(This option sets NET.trainParam to trainbu default
parameters.)
Set each NET.inputWeights{i,j}.learnFcn to a
learning function.
Set each NET.layerWeights{i,j}.learnFcn to a
learning function.
Set each NET.biases{i}.learnFcn to a learning
function. (Weight and bias learning parameters are automatically set to default values for the
given learning function.)
To train the network:
Set NET.trainParam properties to desired
values.
Set weight and bias learning parameters to desired values.
Call train.
See selforgmap for training examples.
Each weight and bias updates according to its learning function after each epoch (one pass through the entire set of input vectors).
Training stops when any of these conditions is met:
The maximum number of epochs (repetitions) is reached.
Performance is minimized to the goal.
The maximum amount of time is exceeded.
Validation performance has increased more than max_fail times since
the last time it decreased (when using validation).