PReLU

class torch.nn.modules.activation.PReLU(num_parameters=1, init=0.25, device=None, dtype=None)

Applies the element-wise PReLU function.

$$\text{PReLU}(x) = \max(0,x) + a * \min(0,x)$$

or

$$\text{PReLU}(x) = \begin{cases} x, & \text{ if } x \ge 0 \\ ax, & \text{ otherwise } \end{cases}$$

Here $a$ is a learnable parameter. When called without arguments, nn.PReLU() uses a single parameter $a$ across all input channels. If called with nn.PReLU(nChannels), a separate $a$ is used for each input channel.

Note

weight decay should not be used when learning $a$ for good performance.

Note

Channel dim is the 2nd dim of input. When input has dims < 2, then there is no channel dim and the number of channels = 1.

Parameters

• num_parameters (int) – number of $a$ to learn. Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input. Default: 1

• init (float) – the initial value of $a$. Default: 0.25

Shape:

• Input: $( *)$ where * means, any number of additional dimensions.

• Output: $(*)$, same shape as the input.

Variables

weight (Tensor) – the learnable weights of shape (num_parameters).

Examples:

>>> m = nn.PReLU()
>>> input = torch.randn(2)
>>> output = m(input)

extra_repr()

Return the extra representation of the module.

Return type

str

forward(input)

Runs the forward pass.

Return type

Tensor

reset_parameters()

Resets parameters based on their initialization used in __init__.