Softshrink

Softshrink#

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

Applies the soft shrinkage function elementwise:

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

lambd (float) – the \(\lambda\) (must be no less than zero) value for the Softshrink 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.Softshrink()
>>> 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)

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