nn_lp_pool1d: Applies a 1D power-average pooling over an input signal...

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

Applies a 1D power-average pooling over an input signal composed of several input planes.

Description

On each window, the function computed is:

Usage

nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)

Arguments

norm_type	if inf than one gets max pooling if 0 you get sum pooling ( proportional to the avg pooling)
kernel_size	a single int, the size of the window
stride	a single int, the stride of the window. Default value is kernel_size
ceil_mode	when TRUE, will use ceil instead of floor to compute the output shape

Details

f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}

• At p = \infty, one gets Max Pooling

• At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)

Shape

• Input: (N, C, L_{in})

• Output: (N, C, L_{out}), where

L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor

Note

If the sum to the power of p is zero, the gradient of this function is not defined. This implementation will set the gradient to zero in this case.

Examples

if (torch_is_installed()) {
# power-2 pool of window of length 3, with stride 2.
m <- nn_lp_pool1d(2, 3, stride = 2)
input <- torch_randn(20, 16, 50)
output <- m(input)
}

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