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AvgPool1d
class torch.nn.AvgPool1d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)
[source]-
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 , output and
kernel_size
can be precisely described as:If
padding
is non-zero, then the input is implicitly zero-padded on both sides forpadding
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 anint
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 offloor
to compute the output shape - count_include_pad (bool) – when True, will include the zero-padding in the averaging calculation
- Shape:
-
- Input: or .
Output: or , where
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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