This 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.
For a list of all members of this type, see IActivationFunction Members .
| Type | Description |
|---|---|
| ActivationBiPolar | BiPolar activation function. This will scale the neural data into the bipolar range. Greater than zero becomes 1, less than or equal to zero becomes -1. |
| ActivationGaussian | An activation function based on the gaussian function. |
| ActivationLinear | The Linear layer is really not an activation function at all. The input is simply passed on, unmodified, to the output. This activation function is primarily theoretical and of little actual use. Usually an activation function that scales between 0 and 1 or -1 and 1 should be used. |
| ActivationLOG | An activation function based on the logarithm function. |
| ActivationSigmoid | The sigmoid activation function takes on a sigmoidal shape. Only positive numbers are generated. Do not use this activation function if negative number output is desired. |
| ActivationSIN | An activation function based on the sin function. |
| ActivationSoftMax | The softmax activation function. |
| ActivationTANH | The hyperbolic tangent activation function takes the curved shape of the hyperbolic tangent. This activation function produces both positive and negative output. Use this activation function if both negative and positive output is desired. |
| BasicActivationFunction | Holds basic functionality that all activation functions will likely have use of. Specifically it implements a name and description for the EncogPersistedObject class. |
Namespace: Encog.Neural.Activation
Assembly: encog-core-cs (in encog-core-cs.dll)
IActivationFunction Members | Encog.Neural.Activation Namespace