mindspore.mint.nn.functional.elu

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mindspore.mint.nn.functional.elu(input, alpha=1.0, inplace=False)[source]

Exponential Linear Unit activation function

Applies the exponential linear unit function element-wise. The activation function is defined as:

\[ELU_{i} = \begin{cases} x_i, &\text{if } x_i \geq 0; \cr \alpha * (\exp(x_i) - 1), &\text{otherwise.} \end{cases}\]

where \(x_i\) represents the element of the input and \(\alpha\) represents the alpha parameter, and alpha represents the smoothness of the ELU.

ELU Activation Function Graph:

../../_images/ELU.png

Warning

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

Parameters

  • input (Tensor) – The input of ELU is a Tensor of any dimension.

  • alpha (float, optional) – The alpha value of ELU, the data type is float. Default: 1.0.

  • inplace (bool, optional) – Whether to use inplace mode, the data type is bool. Default: False.

Returns

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

Raises

  • RuntimeError – If the dtype of input is not float16, float32 or bfloat16.

  • TypeError – If the dtype of alpha is not float.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> input = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float32)
>>> output = mint.nn.functional.elu(input)
>>> print(output)
[-0.63212055  -0.86466473  0.  2.  1.]