- All Known Implementing Classes:
- ActivationBiPolar, ActivationCompetitive, ActivationGaussian, ActivationLinear, ActivationLOG, ActivationRamp, ActivationSigmoid, ActivationSIN, ActivationSoftMax, ActivationStep, ActivationTANH
public interface ActivationFunctionextends Serializable, CloneableThis interface allows various activation functions to be used with the neural network. Activation functions are applied to the output from each layer of a neural network. Activation functions scale the output into the desired range. Methods are provided both to process the activation function, as well as the derivative of the function. Some training algorithms, particularly back propagation, require that it be possible to take the derivative of the activation function. Not all activation functions support derivatives. If you implement an activation function that is not derivable then an exception should be thrown inside of the derivativeFunction method implementation. Non-derivable activation functions are perfectly valid, they simply cannot be used with every training algorithm.