Hardshrink

Hardshrink#

class brainpy.dnn.Hardshrink(lambd=0.5)[source]#

Applies the Hard Shrinkage (Hardshrink) function element-wise.

Hardshrink is defined as:

\[\begin{split}\text{HardShrink}(x) = \begin{cases} x, & \text{ if } x > \lambda \\ x, & \text{ if } x < -\lambda \\ 0, & \text{ otherwise } \end{cases}\end{split}\]
Parameters:

lambd (float) – the \(\lambda\) value for the Hardshrink formulation. Default: 0.5

Shape:
  • Input: \((*)\), where \(*\) means any number of dimensions.

  • Output: \((*)\), same shape as the input.

Examples:

>>> import brainpy as bp
>>> import brainpy.math as bm
>>> m = bp.dnn.Hardshrink()
>>> input = bm.random.randn(2)
>>> output = m(input)
update(input)[source]#

The function to specify the updating rule.

Return type:

TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)