divideblock

Divide targets into three sets using blocks of indices

Syntax

[trainInd,valInd,testInd] = divideblock(Q,trainRatio,valRatio,testRatio)

Description

[trainInd,valInd,testInd] = divideblock(Q,trainRatio,valRatio,testRatio) separates targets into three sets: training, validation, and testing. It takes the following inputs:

Q

Number of targets to divide up.

trainRatio

Ratio of targets for training. Default = 0.7.

valRatio

Ratio of targets for validation. Default = 0.15.

testRatio

Ratio of targets for testing. Default = 0.15.

and returns

trainInd

Training indices

valInd

Validation indices

testInd

Test indices

Examples

[trainInd,valInd,testInd] = divideblock(3000,0.6,0.2,0.2);

Network Use

Here are the network properties that define which data division function to use, what its parameters are, and what aspects of targets are divided up, when train is called.

net.divideFcn
net.divideParam
net.divideMode

See Also

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Introduced in R2008a

Deep Learning Toolbox Documentation

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