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 , output and
kernel_sizecan be precisely described as:
paddingis non-zero, then the input is implicitly zero-padded on both sides for
paddingnumber of points.
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.
paddingcan each be an
intor a one-element tuple.
kernel_size – the size of the window
stride – the stride of the window. Default value is
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
Output: , where
>>> # 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.]]])