LPPool1d

class torch.nn.LPPool1d(norm_type, kernel_size, stride=None, ceil_mode=False)

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

On each window, the function computed is:

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

• At p = \(\infty\), one gets Max Pooling

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

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.

Parameters:

• kernel_size (Union[int, Tuple[int]]) – a single int, the size of the window

• stride (Union[int, Tuple[int]]) – a single int, the stride of the window. Default value is kernel_size

• ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape

Shape:

• Input: \((N, C, L_{in})\) or \((C, L_{in})\).

• Output: \((N, C, L_{out})\) or \((C, L_{out})\), where

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

Examples::

>>> # power-2 pool of window of length 3, with stride 2.
>>> m = nn.LPPool1d(2, 3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input)