Applies a 1D power-average pooling over an input signal composed of several input planes.
On each window, the function computed is:
f(X) = √[p]{∑_{x \in X} x^{p}}
nn_lp_pool1d(norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
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 |
ceil_mode |
when TRUE, will use |
At p = ∞, one gets Max Pooling
At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)
Input: (N, C, L_{in})
Output: (N, C, L_{out}), where
L_{out} = ≤ft\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
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.
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)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.