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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.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

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mindspore.ops.rrelu

mindspore.ops.rrelu(input, lower=1.0 / 8, upper=1.0 / 3)

Randomized Leaky ReLU activation function.

The activation function is defined as:

$$\text{rrelu}(inpu{t}_{ji})=\{\begin{array}{ll}inpu{t}_{ji},& \text{if }inpu{t}_{ji}\ge 0;\\ {\alpha }_{ji}\ast inpu{t}_{ji},& \text{otherwise.}\end{array}$$

where ${\alpha }_{ji}$ ~ $U(l,u)$, $l\le u$.

Applies the rrelu function elementally, as described in the paper: Empirical Evaluation of Rectified Activations in Convolution Network .

Parameters

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

• lower (Union[int, float]) – Slope of the activation function at x < 0. Default: 1.0 / 8 .

• upper (Union[int, float]) – Slope of the activation function at x < 0. Default: 1.0 / 3 .

Returns

Tensor, after rrelu, has the same type and shape as the input.

Raises

• TypeError – If lower is not a float or an int.

• TypeError – If upper is not a float or an int.

• TypeError – If input is not a Tensor.

• TypeError – If input is not a Tensor of mindspore.float16 or mindspore.float32.

• ValueError – If lower is greater than upper.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[-1.0, 4.0], [2.0, 0]]), mindspore.float32)
>>> output = ops.rrelu(x)
>>> print(output)
[[-0.31465699  4.        ]
 [ 2.          0.        ]]