mindspore.nn.PReLU

class mindspore.nn.PReLU(channel=1, w=0.25)

Applies PReLU activation function element-wise.

PReLU is defined as:

$$PReLU(x_i)=max(0,x_i)+w\ast min(0,x_i),$$

where $x_i$ is an element of an channel of the input.

Here $w$ is a learnable parameter with a default initial value 0.25. Parameter $w$ has dimensionality of the argument channel. If called without argument channel, a single parameter $w$ will be shared across all channels.

PReLU Activation Function Graph:

Parameters

• channel (int) – The elements number of parameter $w$. It could be an int, and the value is 1 or the channels number of input tensor x. Default: 1 .

• w (Union[float, list, Tensor]) – The initial value of parameter. It could be a float, a float list or a tensor has the same dtype as the input tensor x. Default: 0.25 .

Inputs:

• x (Tensor) - The input of PReLU with data type of float16 or float32. The shape is $(N,\ast )$ where $\ast$ means, any number of additional dimensions.

Outputs:

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

Raises

• TypeError – If channel is not an int.

• TypeError – If w is not one of a float, a float list, a float Tensor.

• TypeError – If dtype of x is neither float16 nor float32.

• ValueError – If the x is a 0-D or 1-D Tensor on Ascend.

• ValueError – If channel is less than 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> x = Tensor(np.array([[[[0.1, 0.6], [0.9, 0.9]]]]), mindspore.float32)
>>> prelu = nn.PReLU()
>>> output = prelu(x)
>>> print(output)
[[[[0.1 0.6]
   [0.9 0.9]]]]