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

In the simplest case, the output value of the layer with input size $(N, C, L)$ , output $(N, C, L_{o u t})$ 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

Details

$out (N_{i}, C_{j}, l) = \frac{1}{k} \sum_{m = 0}^{k - 1} input (N_{i}, C_{j}, stride \times l + m)$

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.

Shape

Input: $(N, C, L_{i n})$
Output: $(N, C, L_{o u t})$ , where

$L_{o u t} = ⌊ \frac{L_{i n} + 2 \times padding - kernel\_size}{stride} + 1 ⌋$

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))
}
#> torch_tensor
#> (1,.,.) = 
#>  -0.4294 -0.6027  0.4520
#> [ CPUFloatType{1,1,3} ]