HardSigmoidLayer.md

CHardSigmoidLayer Class

• CHardSigmoidLayer Class
  • Settings
  • Trainable parameters
  • Inputs
  • Outputs

This class implements a layer that calculates the HardSigmoid activation function for each element of a single input.

The activation function has the following formula:

f(x) = 0     if x <= 0
f(x) = x     if 0 < x < 1
f(x) = 1     if x >= 1

Settings

There are no settings for this layer.

Trainable parameters

There are no trainable parameters for this layer.

Inputs

There is only one input, which accepts a data blob of arbitrary size.

Outputs

There is only one output; it contains a blob of the same size as the input. Each element of the output contains the value of the activation function calculated on the corresponding element of the input.