nn_avg_pool1d: Applies a 1D average pooling over an input signal composed of...

In torch: Tensors and Neural Networks with 'GPU' Acceleration

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

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 documentation built on June 7, 2023, 6:19 p.m.