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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

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- Minor inconsistencies between the UI and descriptions.

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mindspore.mint.sigmoid

mindspore.mint.sigmoid(input)

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:

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.sigmoid(input)
>>> print(output)
[0.7310586  0.880797   0.95257413 0.98201376 0.9933072 ]