mindspore.ops.sigmoid

mindspore.ops.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 can be 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 GPU CPU

Examples

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