mindspore.nn.SoftShrink

class mindspore.nn.SoftShrink(lambd=0.5)

Applies the SoftShrink function element-wise.

$$\begin{array}{r}\text{SoftShrink}(x)=\{\begin{array}{ll}x-\lambda ,& \text{ if }x>\lambda \\ x+\lambda ,& \text{ if }x<-\lambda \\ 0,& \text{ otherwise }\end{array}\end{array}$$

SoftShrink Activation Function Graph:

Parameters

lambd (number, optional) – The threshold $\lambda$ defined by the Soft Shrink formula. It should be greater than or equal to 0, default: 0.5 .

Inputs:

• input (Tensor) - The input of Soft Shrink. Supported dtypes:

  • Ascend: float16, float32, bfloat16.

  • CPU/GPU: float16, float32.

Outputs:

Tensor, the same shape and data type as the input.

Raises

• TypeError – If lambd is not a float, int or bool.

• TypeError – If input is not a tensor.

• TypeError – If dtype of input is not float16, float32 or bfloat16.

Supported Platforms:

Ascend GPU CPU

Examples

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