Applies a 1D power-average pooling over an input signal composed of several input planes.

On each window, the function computed is:

Usage

nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)

Arguments

norm_type

if inf than one gets max pooling if 0 you get sum pooling ( proportional to the avg pooling)

kernel_size

a single int, the size of the window

stride

a single int, the stride of the window. Default value is kernel_size

ceil_mode

when TRUE, will use ceil instead of floor to compute the output shape

Details

$$ f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} $$

• At p = \(\infty\), one gets Max Pooling

• At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)

Note

If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case.

Shape

• Input: \((N, C, L_{in})\)

• Output: \((N, C, L_{out})\), where

$$ L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor $$

Examples

if (torch_is_installed()) {
# power-2 pool of window of length 3, with stride 2.
m <- nn_lp_pool1d(2, 3, stride = 2)
input <- torch_randn(20, 16, 50)
output <- m(input)
}