# nn_lp_pool2d: Applies a 2D power-average pooling over an input signal... In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

On each window, the function computed is:

f(X) = √[p]{∑_{x \in X} x^{p}}

## Usage

 1 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

• At p = , 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

## Shape

• Input: (N, C, H_{in}, W_{in})

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

H_{out} = ≤ft\lfloor\frac{H_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor

W_{out} = ≤ft\lfloor\frac{W_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor

## 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.

## Examples

  1 2 3 4 5 6 7 8 9 10 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) } 

torch documentation built on Oct. 7, 2021, 9:22 a.m.