# nn_max_pool1d: MaxPool1D module In torch: Tensors and Neural Networks with 'GPU' Acceleration

## Description

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

## Usage

 1 2 3 4 5 6 7 8 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, …, \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} = ≤ft\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation} \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor

## Examples

 1 2 3 4 5 6 7 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 Oct. 7, 2021, 9:22 a.m.