# Package org.encog.engine.network.activation

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

- Class Summary
  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.
  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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