BNNS.ActivationFunction.hardSwish(alpha:beta:)
An activation function that returns the hard swish function of its input.
Declaration
case hardSwish(alpha: Float, beta: Float)Discussion
This constant defines an activation function that returns values using the following operation:
HardSwish(x) = x * (ReLU6(x + 3.0) * 1.0/6.0)The following illustrates the output that the activation function generates from inputs in the range -10...10:
[Image]
See Also
Related Documentation
Activation Functions
BNNS.ActivationFunction.absBNNS.ActivationFunction.celu(alpha:)BNNS.ActivationFunction.clamp(bounds:)BNNS.ActivationFunction.clampedLeakyRectifiedLinear(alpha:beta:)BNNS.ActivationFunction.elu(alpha:)BNNS.ActivationFunction.geluApproximation(alpha:beta:)BNNS.ActivationFunction.geluApproximation2(alpha:beta:)BNNS.ActivationFunction.gumbel(alpha:beta:)BNNS.ActivationFunction.gumbelMax(alpha:beta:)BNNS.ActivationFunction.hardShrink(alpha:)BNNS.ActivationFunction.hardSigmoid(alpha:beta:)BNNS.ActivationFunction.identityBNNS.ActivationFunction.leakyRectifiedLinear(alpha:)BNNS.ActivationFunction.linear(alpha:)BNNS.ActivationFunction.linearWithBias(alpha:beta:)