MaxPool1D module

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:

$$out(N_i,C_j,k)=\underset{m=0,\dots ,\text{kernel\_size}-1}{max}input(N_i,C_j,stride\times k+m)$$

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

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

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)
}