mindspore.nn.SoftShrink

class mindspore.nn.SoftShrink(lambd=0.5)[source]

Applies the SoftShrink function element-wise.

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

SoftShrink Activation Function Graph:

Parameters: lambd (float) – the \(\lambda\) must be no less than zero for the SoftShrink formulation. Default: 0.5 .

Inputs:

input_x (Tensor) - The input of SoftShrink with data type of float16 or float32. Any number of additional dimensions.

Outputs:

Tensor, has the same shape and data type as input_x.

Raises

TypeError – If lambd is not a float.
TypeError – If input_x is not a Tensor.
TypeError – If dtype of input_x is neither float16 nor float32.
ValueError – If lambd is less than 0.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> input_x = Tensor(np.array([[ 0.5297,  0.7871,  1.1754], [ 0.7836,  0.6218, -1.1542]]), mindspore.float16)
>>> softshrink = nn.SoftShrink()
>>> output = softshrink(input_x)
>>> print(output)
[[ 0.02979  0.287    0.676  ]
 [ 0.2837   0.1216  -0.6543 ]]