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# torch.nn.functional.avg_pool1d

torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) → Tensor

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

  See `AvgPool1d` for details and output shape.

  Parameters:

    - input – input tensor of shape (minibatch,in_channels,i W)(\text{minibatch} , \text{in\_channels} , iW)(minibatch,in_channels,iW)

    - kernel_size – the size of the window. Can be a single number or a tuple (kW,)

    - stride – the stride of the window. Can be a single number or a tuple (sW,). Default: `kernel_size`

    - padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0

    - ceil_mode – when True, will use ceil instead of floor to compute the output shape. Default: `False`

    - count_include_pad – when True, will include the zero-padding in the averaging calculation. Default: `True`

  Examples:

      >>> # pool of square window of size=3, stride=2
      >>> input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
      >>> F.avg_pool1d(input, kernel_size=3, stride=2)
      tensor([[[ 2.,  4.,  6.]]])

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