mindspore.mint.nn.functional.logsigmoid

mindspore.mint.nn.functional.logsigmoid(input)

Applies logsigmoid activation element-wise. The input is a Tensor with any valid shape.

Logsigmoid is defined as:

$$\text{logsigmoid}(x_i)=\log (\frac{1}{1+\exp (-x_i)}),$$

where $x_i$ is the element of the input.

LogSigmoid Activation Function Graph:

Warning

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

Parameters

input (Tensor) – The input of LogSigmoid with data type of bfloat16, float16 or float32. The shape is $(\ast )$ where $\ast$ means, any number of additional dimensions.

Returns

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

Raises

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

• TypeError – If input is not a Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> input = Tensor([1.0, 2.0, 3.0], mindspore.float32)
>>> output = mint.nn.functional.logsigmoid(input)
>>> print(output)
[-0.31326166 -0.12692806 -0.04858734]