mindspore.mint.nn.functional.softplus
- mindspore.mint.nn.functional.softplus(input, beta=1, threshold=20)[source]
Applies softplus function to input element-wise.
The softplus function is shown as follows, x is the element of input :
\[\text{output} = \frac{1}{beta}\log(1 + \exp(\text{beta * x}))\]where \(input * beta > threshold\), the implementation converts to the linear function to ensure numerical stability.
- Parameters
input (Tensor) –
Tensor of any dimension. Supported dtypes:
Ascend: float16, float32, bfloat16.
beta (number.Number, optional) – Scaling parameters in the softplus function. Default:
1
.threshold (number.Number, optional) – For numerical stability, the softplus function is converted to a threshold parameter of a linear function. Default:
20
.
- Returns
Tensor, with the same type and shape as the input.
- Raises
- Supported Platforms:
Ascend
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, mint >>> input = Tensor(np.array([0.1, 0.2, 30, 25]), mindspore.float32) >>> output = mint.nn.functional.softplus(input) >>> print(output) [0.74439657 0.7981388 30. 25.]