| Interface | Description |
|---|---|
| ActivationFunction |
This interface allows various activation functions to be used with the neural
network.
|
| Class | Description |
|---|---|
| ActivationBiPolar |
BiPolar activation function.
|
| ActivationBipolarSteepenedSigmoid |
The bipolar sigmoid activation function is like the regular sigmoid activation function,
except Bipolar sigmoid activation function.
|
| ActivationClippedLinear |
Linear activation function that bounds the output to [-1,+1].
|
| ActivationCompetitive |
An activation function that only allows a specified number, usually one, of
the out-bound connection to win.
|
| ActivationElliott |
Computationally efficient alternative to ActivationSigmoid.
|
| ActivationElliottSymmetric |
Computationally efficient alternative to ActivationTANH.
|
| ActivationGaussian |
An activation function based on the Gaussian function.
|
| ActivationLinear |
The Linear layer is really not an activation function at all.
|
| ActivationLOG |
An activation function based on the logarithm function.
|
| ActivationRamp |
A ramp activation function.
|
| ActivationReLU |
A ramp activation function.
|
| ActivationSigmoid |
The sigmoid activation function takes on a sigmoidal shape.
|
| ActivationSIN |
An activation function based on the sin function, with a double period.
|
| ActivationSoftMax |
The softmax activation function.
|
| ActivationSteepenedSigmoid |
The Steepened Sigmoid is an activation function typically used with NEAT.
|
| ActivationStep |
The step activation function is a very simple activation function.
|
| ActivationTANH |
The hyperbolic tangent activation function takes the curved shape of the
hyperbolic tangent.
|