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15.14. Hidden Layers of Neurons

Applies to: NeuralTools, all releases

How many "hidden layers" of neurons exist within the software?

Generally speaking, too many layers result in an over-parameterized model (over-fitting), and too few result in a poorly fitted model. Therefore, does the software have the ability to determine the optimal number of hidden layers?

By default, NeuralTools will train a Probabilistic Neural Net (PN net); these are considered better as classifiers, and also provide probabilities of predictions. PN nets are not prone to over-fitting.

Using an MLF net is also an option. This is the standard type of neural net, called "Multiple-layer feedforward/MLF nets" in NeuralTools. With an MLF net, it's not so much the number of hidden layers that is the issue. NeuralTools allows up to 2 layers, but few applications require more than one layer. The question is the number of nodes in that one layer. NeuralTools will suggest a number of nodes based on the data, but that is not necessarily the optimal number. The way to find the optimal number is to run Best Net Search: the best configuration is found based on results on the data set aside for testing.

Last edited: 2015-09-03

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