torch (version 0.8.1)

nn_avg_pool1d: Applies a 1D 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 \((N, C, L)\), output \((N, C, L_{out})\) and kernel_size \(k\) can be precisely described as:

Usage

nn_avg_pool1d(
  kernel_size,
  stride = NULL,
  padding = 0,
  ceil_mode = FALSE,
  count_include_pad = TRUE
)

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

Shape

• Input: \((N, C, L_{in})\)

• Output: \((N, C, L_{out})\), where

$$ L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor $$

Details

$$ \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} \mbox{input}(N_i, C_j, \mbox{stride} \times l + m) $$

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 each be an int or a one-element tuple.

Examples

if (torch_is_installed()) {

# pool with window of size=3, stride=2
m <- nn_avg_pool1d(3, stride = 2)
m(torch_randn(1, 1, 8))
}