nn_max_pool1d: MaxPool1D module

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

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

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.

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

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)
}

torch documentation built on May 29, 2024, 9:54 a.m.