Module juice::layers::activation::sigmoid

Applies the nonlinear Log-Sigmoid function.

Non-linearity activation function: y = (1 + e^(-x))^(-1)

A classic choice in neural networks. But you might consider using ReLu as an alternative.

ReLu, compared to Sigmoid

• reduces the likelyhood of vanishing gradients
• increases the likelyhood of a more beneficial sparse representation
• can be computed faster
• is therefore the most popular activation function in DNNs as of this writing (2015).

Structs

• Sigmoid
  Sigmoid Activation Layer