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:
Maximum number of epochs to train
Epochs between displays (NaN for no displays)
Generate command-line output
Show training GUI
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:
To train the network:
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).