torch / nn / torch.nn
AvgPool1d¶
-
class
torch.nn.
AvgPool1d
(kernel_size: Union[T, Tuple[T]], stride: Union[T, Tuple[T]] = None, padding: Union[T, Tuple[T]] = 0, ceil_mode: bool = False, count_include_pad: bool = 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.The parameters
kernel_size
,stride
,padding
can each be anint
or a one-element tuple.- Parameters
kernel_size – the size of the window
stride – the stride of the window. Default value is
kernel_size
padding – implicit zero padding to be added on both sides
ceil_mode – when True, will use ceil instead of floor to compute the output shape
count_include_pad – when True, will include the zero-padding in the averaging calculation
- Shape:
Input:
Output: , 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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