Neural Network Toolbox™

Cyclical order weight/bias training

Syntax

• [net,TR] = trainc(net,TR,trainV,valV,testV)
  info = trainc('info')
  

Description

trainc is not called directly. Instead it is called by train for networks whose net.trainFcn property is set to 'trainc'.

trainc trains a network with weight and bias learning rules with incremental updates after each presentation of an input. Inputs are presented in cyclic order.

trainc(net,TR,trainV,valV,testV) takes these inputs,

net	Neural network
TR	Initial training record created by train
trainV	Training data created by train
valV	Validation data created by train
testV	Test data created by train

and returns

net	Trained network
TR	Training record of various values over each epoch:

Each argument trainV, valV and testV is a structure of these fields:

X	N x TS cell array of inputs for N inputs and TS time steps. X{i,ts} is an Ri x Q matrix for the ith input and TS time step.
Xi	N x Nid cell array of input delay states for N inputs and Nid delays. Xi{i,j} is an Ri x Q matrix for the ith input and jth state.
Pd	N x S x Nid cell array of delayed input states.
T	NoxTS cell array of targets for No outputs and TS time steps. T{i,ts} is an Si x Q matrix for the ith output and TS time step.
Tl	Nl x TS cell array of targets for Nl layers and TS time steps. Tl{i,ts} is an Si x Q matrix for the ith layer and TS time step.
Ai	Nl x TS cell array of layer delays states for Nl layers, TS time steps. Ai{i,j} is an Si x Q matrix of delayed outputs for layer i, delay j.

Training occurs according to trainc's training parameters, shown here with their default values:

net.trainParam.epochs	100		Maximum number of epochs to train
net.trainParam.goal	0		Performance goal
net.trainParam.max_fail	5		Maximum validation failures
net.trainParam.show	25		Epochs between displays (NaN for no 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

trainc('info') returns useful information about this function.

Network Use

You can create a standard network that uses trainc by calling newp. To prepare a custom network to be trained with trainc,

1. Set net.trainFcn to 'trainc'. This sets net.trainParam to trainc's default parameters.
2. 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,

1. Set net.trainParam properties to desired values.
2. Set weight and bias learning parameters to desired values.
3. Call train.

See newp for training examples.

Algorithm

For each epoch, each vector (or sequence) is presented in order to the network, with the 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.

See Also

newp, newlin, train

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