What is the normalization parameter for when using cross-entropy as a performance function of a patternnet?

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I'm currently investigating the possiblities of using the Matlab function patternnet to generate and train a pattern recognition ANN.
Within the performance function used here (cross-entropy), there is an option to set network.performParam.normalization = 'standard'.
My question is: What is the benefit of normalizing targets / outputs by setting this option, when the target vectors should be designed as [000010000] anyways?
So the range of the outputs of the individual output neurons and the targets is 0...1 anyway?
Thanks!

回答(1 个)

Sahithi Kanumarlapudi
performParam.normalization parameter is designed to be used with any neural network. Setting its value to ‘standard’ results in outputs and targets being normalized to (-1, +1), and therefore errors in the range (-2, +2) which is helpful in many cases.
But in case of pattern recognition networks this might not be helpful as the outputs and targets are already (0,1).

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