PReLU module
Applies the element-wise function:
\mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
or
\mbox{PReLU}(x) = ≤ft\{ \begin{array}{ll} x, & \mbox{ if } x ≥q 0 \\ ax, & \mbox{ otherwise } \end{array} \right.
nn_prelu(num_parameters = 1, init = 0.25)
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 |
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.
Input: (N, *) where * means, any number of additional
dimensions
Output: (N, *), same shape as the input
weight (Tensor): the learnable weights of shape (num_parameters).
weight decay should not be used when learning a for good performance.
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.
if (torch_is_installed()) {
m <- nn_prelu()
input <- torch_randn(2)
output <- m(input)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.