On each window, the function computed is:

## Usage

nn_lp_pool2d(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

the size of the window

stride

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)

The parameters kernel_size, stride can either be:

• a single int -- in which case the same value is used for the height and width dimension

• a tuple of two ints -- in which case, the first int is used for the height dimension, and the second int for the width dimension

## 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: $$(N, C, H_{in}, W_{in})$$

• Output: $$(N, C, H_{out}, W_{out})$$, where

$$H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor$$ $$W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor$$

## Examples

if (torch_is_installed()) {

# power-2 pool of square window of size=3, stride=2
m <- nn_lp_pool2d(2, 3, stride = 2)
# pool of non-square window of power 1.2
m <- nn_lp_pool2d(1.2, c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}