mindspore.mint.nn.SiLU

class mindspore.mint.nn.SiLU(inplace=False)[source]

Calculate the SiLU activation function element-wise. It is also sometimes referred to as Swish function.

The SiLU function is defined as follows:

\[\text{SiLU}(x) = x * \sigma(x),\]

where \(x_i\) is an element of the input, \(\sigma(x)\) is Sigmoid function.

\[\text{sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)},\]

SiLU Activation Function Graph:

Warning

This is an experimental API that is subject to change or deletion.

Parameters: inplace (bool, optional) – If it is True, enable the in-place update function. Default: False.

Inputs:

input (Tensor) - The input tensor.

Outputs:

Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> input = mindspore.tensor([-1, 2, -3, 2, -1], mindspore.float16)
>>> silu = mindspore.mint.nn.SiLU(inplace=False)
>>> output = silu(input)
>>> print(output)
[-0.269  1.762  -0.1423  1.762  -0.269]