# nn_avg_pool2d: Applies a 2D average pooling over an input signal composed of... In torch: Tensors and Neural Networks with 'GPU' Acceleration

 nn_avg_pool2d R Documentation

## Applies a 2D average pooling over an input signal composed of several input planes.

### Description

In the simplest case, the output value of the layer with input size (N, C, H, W), output (N, C, H_{out}, W_{out}) and kernel_size (kH, kW) can be precisely described as:

### Usage

nn_avg_pool2d(
kernel_size,
stride = NULL,
ceil_mode = FALSE,
divisor_override = NULL
)


### Arguments

 kernel_size the size of the window stride the stride of the window. Default value is kernel_size padding implicit zero padding to be added on both sides ceil_mode when TRUE, will use ceil instead of floor to compute the output shape count_include_pad when TRUE, will include the zero-padding in the averaging calculation divisor_override if specified, it will be used as divisor, otherwise kernel_size will be used

### Details

out(N_i, C_j, h, w) = \frac{1}{kH * kW} ∑_{m=0}^{kH-1} ∑_{n=0}^{kW-1} input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

The parameters kernel_size, stride, padding 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} + 2 \times \mbox{padding}[0] - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor

W_{out} = ≤ft\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor

### Examples

if (torch_is_installed()) {

# pool of square window of size=3, stride=2
m <- nn_avg_pool2d(3, stride = 2)
# pool of non-square window
m <- nn_avg_pool2d(c(3, 2), stride = c(2, 1))
input <- torch_randn(20, 16, 50, 32)
output <- m(input)
}


torch documentation built on Jan. 24, 2023, 1:05 a.m.