mindspore.mint.nn.functional.relu

mindspore.mint.nn.functional.relu(input, inplace=False)

Computes ReLU (Rectified Linear Unit activation function) of input tensors element-wise.

It returns $max(input,0)$ element-wise. Specially, the neurons with the negative output will be suppressed and the active neurons will stay the same.

$$ReLU(input)=(input{)}^{+}=max(0,input)$$

ReLU Activation Function Graph:

Parameters

• input (Tensor) – The input Tensor.

• inplace (bool, optional) – Whether to use inplace mode, Defaults to False.

Returns

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

Raises

• TypeError – If dtype of input is not Number type.

• 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([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> output = mint.nn.functional.relu(input)
>>> print(output)
[[0. 4. 0.]
 [2. 0. 9.]]