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trainr

Random order incremental training with learning functions

Syntax

net.trainFcn = 'trainr'
[net,tr] = train(net,...)

Description

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'

[net,tr] = train(net,...)

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's training parameters, shown here with their default values:

net.trainParam.epochs100

Maximum number of epochs to train

net.trainParam.goal0

Performance goal

net.trainParam.show25

Epochs between displays (NaN for no displays)

net.trainParam.showCommandLine0

Generate command-line output

net.trainParam.showWindow1

Show training GUI

net.trainParam.timeinf

Maximum time to train in seconds

Network Use

You can create a standard network that uses trainr by calling competlayer or selforgmap. To prepare a custom network to be trained with trainr,

  1. Set net.trainFcn to 'trainr'. This sets net.trainParam to trainr's default parameters.

  2. Set each net.inputWeights{i,j}.learnFcn to a learning function.

  3. Set each net.layerWeights{i,j}.learnFcn to a learning function.

  4. 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 help competlayer and help selforgmap for training examples.

More About

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Algorithms

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.

See Also

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