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Interface Summary | |
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ActivationFunction | This interface allows various activation functions to be used with the neural network. |
Class Summary | |
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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. |
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. |
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