Neural Network Toolbox™

Sequential order incremental training with learning functions

Syntax

• [net,TR,Ac,El] = trains(net,Pd,Tl,Ai,Q,TS)
  info = trains(code)
  

Description

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

trains trains a network with weight and bias learning rules with sequential updates. The sequence of inputs is presented to the network with updates occurring after each time step.

This incremental training algorithm is commonly used for adaptive applications.

trains takes these inputs:

net	Neural network
Pd	Delayed inputs
Tl	Layer targets
Ai	Initial input conditions
Q	Batch size
TS	Time steps

and after training the network with its weight and bias learning functions returns

net	Updated network
TR	Training record:
	TR.timesteps	Number of time steps
	TR.perf	Performance for each time step
Ac	Collective layer outputs
El	Layer errors

Training occurs according to trains's training parameter, shown here with its default value:

net.trainParam.passes

1

Number of times to present sequence

Dimensions for these variables are

Pd	Nl x Ni x TS cell array	Each Pd{i,j,ts} is a Dij x Q matrix.
Tl	Nl x TS cell array	Each Tl{i,ts} is a Ui x Q matrix or [].
Ai	Nl x LD cell array	Each Ai{i,k} is an Si x Q matrix.
Ac	Nl x (LD+TS) cell array	Each Ac{i,k} is an Si x Q matrix.
El	Nl x TS cell array	Each El{i,k} is an Si x Q matrix or [].

where

Ni	=		net.numInputs
Nl	=		net.numLayers
LD	=		net.numLayerDelays
Ri	=		net.inputs{i}.size
Si	=		net.layers{i}.size
Ui	=		net.outputs{i}.size
Dij	=		Ri * length(net.inputWeights{i,j}.delays)

trains(code) returns useful information for each code string:

'pnames'	Names of training parameters
'pdefaults'	Default training parameters

Network Use

You can create a standard network that uses trains for adapting by calling newp or newlin.

To prepare a custom network to adapt with trains,

1. Set net.adaptFcn to 'trains'. This sets net.adaptParam to trains'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 allow the network to adapt,

1. Set weight and bias learning parameters to desired values.
2. Call adapt.

See newp and newlin for adaption examples.

Algorithm

Each weight and bias is updated according to its learning function after each time step in the input sequence.

See Also

newp, newlin, train, trainb, trainc, trainr

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