torch (version 0.8.1)

nn_max_pool1d: MaxPool1D module

Description

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

Usage

nn_max_pool1d(
  kernel_size,
  stride = NULL,
  padding = 0,
  dilation = 1,
  return_indices = FALSE,
  ceil_mode = FALSE
)

Arguments

kernel_size

the size of the window to take a max over

stride

the stride of the window. Default value is kernel_size

padding

implicit zero padding to be added on both sides

dilation

a parameter that controls the stride of elements in the window

return_indices

if TRUE, will return the max indices along with the outputs. Useful for nn_max_unpool1d() later.

ceil_mode

when TRUE, will use ceil instead of floor to compute the output shape

Shape

• Input: \((N, C, L_{in})\)

• Output: \((N, C, L_{out})\), where

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

Details

In the simplest case, the output value of the layer with input size \((N, C, L)\) and output \((N, C, L_{out})\) can be precisely described as:

$$ out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1} input(N_i, C_j, stride \times k + m) $$

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.

Examples

if (torch_is_installed()) {
# pool of size=3, stride=2
m <- nn_max_pool1d(3, stride = 2)
input <- torch_randn(20, 16, 50)
output <- m(input)
}