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# AvgPool1d

class torch.nn.AvgPool1d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)

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

  In the simplest case, the output value of the layer with input size (N,C,L)(N, C, L)(N,C,L), output (N,C,L o u t)(N, C, L_{out})(N,C,L o u t​) and `kernel_size` k k k can be precisely described as:

  out(N i,C j,l)=1 k∑m=0 k−1 input(N i,C j,stride×l+m)\text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} \text{input}(N_i, C_j, \text{stride} \times l + m)out(N i​,C j​,l)=k 1​m=0∑k−1​input(N i​,C j​,stride×l+m)

  If `padding` is non-zero, then the input is implicitly zero-padded on both sides for `padding` number of points.

  Note

  When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.

  The parameters `kernel_size`, `stride`, `padding` can each be an `int` or a one-element tuple.

  Parameters:

    - kernel_size (Union[int, Tuple[int]]) – the size of the window

    - stride (Union[int, Tuple[int]]) – the stride of the window. Default value is `kernel_size`

    - padding (Union[int, Tuple[int]]) – implicit zero padding to be added on both sides

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

    - count_include_pad (bool) – when True, will include the zero-padding in the averaging calculation

  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+2×padding−kernel_size stride+1⌋L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor L o u t​=⌊stride L in​+2×padding−kernel_size​+1⌋

  Examples:

      >>> # pool with window of size=3, stride=2
      >>> m = nn.AvgPool1d(3, stride=2)
      >>> m(torch.tensor([[[1., 2, 3, 4, 5, 6, 7]]]))
      tensor([[[2., 4., 6.]]])

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