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# 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)=∑x∈X x p p f(X) = \sqrt[p]{\sum_{x \in X} x^{p}} f(X)=p​x∈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 i n)(N, C, L_{in})(N,C,L in​) or (C,L i n)(C, L_{in})(C,L in​).

    - Output: (N,C,L o u t)(N, C, L_{out})(N,C,L o u t​) or (C,L o u t)(C, L_{out})(C,L o u t​), where

      L o u t=⌊L i n−kernel_size stride+1⌋L_{out} = \left\lfloor\frac{L_{in} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor L o u t​=⌊stride L in​−kernel_size​+1⌋
  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)

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