Random order incremental training with learning functions
net.trainFcn = 'trainr'
[net,tr] = train(net,...)
trainr is not called directly. Instead it is called by
train for networks whose net.trainFcn property is set to
'trainr', thus:
net.trainFcn = 'trainr' sets the network trainFcn
property.
[net,tr] = train(net,...) trains the network with
trainr.
trainr trains a network with weight and bias learning rules with
incremental updates after each presentation of an input. Inputs are presented in random
order.
Training occurs according to trainr training parameters, shown here with
their default values:
net.trainParam.epochs | 1000 | Maximum number of epochs to train |
net.trainParam.goal | 0 | Performance goal |
net.trainParam.max_fail | 6 | Maximum validation failures |
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 |
You can create a standard network that uses trainr by calling
competlayer or selforgmap. To prepare a custom network to
be trained with trainr,
Set net.trainFcn to 'trainr'.
This sets net.trainParam to trainr’s 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 help competlayer and help selforgmap for training
examples.
For each epoch, all training vectors (or sequences) are each presented once in a different random order, with the network and weight and bias values updated accordingly after each individual presentation.
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.