mygrad.nnet.activations.soft_sign

mygrad.nnet.activations.soft_sign(x: ArrayLike, *, constant: bool | None = None) → Tensor

Returns the soft sign function x / (1 + |x|).

Parameters:

xArrayLike

Input data.

constantboolean, optional (default=False)

If True, the returned tensor is a constant (it does not back-propagate a gradient).

Returns:

mygrad.Tensor

The soft sign function applied to x elementwise.

Examples

>>> import mygrad as mg
>>> from mygrad.nnet.activations import soft_sign
>>> x = mg.arange(-5, 6)
>>> x
Tensor([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])
>>> y = soft_sign(x); y
Tensor([-0.83333333, -0.8       , -0.75      , -0.66666667, -0.5       ,
     0.        ,  0.5       ,  0.66666667,  0.75      ,  0.8       ,
     0.83333333])

(Source code, png, hires.png, pdf)