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

Source:`R/nn-pooling.R`

`nn_lp_pool1d.Rd`

On each window, the function computed is:

## 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)
}
```