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# MaxPool1d

class torch.nn.MaxPool1d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)

  Applies a 1D max 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) and output (N,C,L o u t)(N, C, L_{out})(N,C,L o u t​) can be precisely described as:

  o u t(N i,C j,k)=max⁡m=0,…,kernel_size−1 i n p u t(N i,C j,s t r i d e×k+m)out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1} input(N_i, C_j, stride \times k + m) o u t(N i​,C j​,k)=m=0,…,kernel_size−1 max​in p u t(N i​,C j​,s t r i d e×k+m)

  If `padding` is non-zero, then the input is implicitly padded with negative infinity on both sides for `padding` number of points. `dilation` is the stride between the elements within the sliding window. This link has a nice visualization of the pooling parameters.

  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.

  Parameters:

    - kernel_size (Union[int, Tuple[int]]) – The size of the sliding window, must be > 0.

    - stride (Union[int, Tuple[int]]) – The stride of the sliding window, must be > 0. Default value is `kernel_size`.

    - padding (Union[int, Tuple[int]]) – Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2.

    - dilation (Union[int, Tuple[int]]) – The stride between elements within a sliding window, must be > 0.

    - return_indices (bool) – If `True`, will return the argmax along with the max values. Useful for `torch.nn.MaxUnpool1d` later

    - ceil_mode (bool) – If `True`, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

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

  Examples:

      >>> # pool of size=3, stride=2
      >>> m = nn.MaxPool1d(3, stride=2)
      >>> input = torch.randn(20, 16, 50)
      >>> output = m(input)

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