mindspore.mint.nn.functional.sigmoid

mindspore.mint.nn.functional.sigmoid(input)[source]

Computes Sigmoid of input element-wise. The Sigmoid function is defined as:

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

where \(x_i\) is an element of x.

Sigmoid Function Graph:

../../_images/Sigmoid.png
Parameters

input (Tensor) – input is \(x\) in the preceding formula. Tensor of any dimension, the data type is float16, float32, float64, complex64 or complex128.

Returns

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

Raises

  • TypeError – If dtype of input is not float16, float32, float64, complex64 or complex128.

  • TypeError – If input is not a Tensor.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([1, 2, 3, 4, 5]), mindspore.float32)
>>> output = mint.nn.functional.sigmoid(input)
>>> print(output)
[0.7310586  0.880797   0.95257413 0.98201376 0.9933072 ]