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 \((N, C, L_{out})\) 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

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.

Shape

• Input: \((N, C, L_{in})\)

• Output: \((N, C, L_{out})\), where

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

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,.,.) = 
#>   0.4693 -0.1130 -0.5694
#> [ CPUFloatType{1,1,3} ]