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# AvgPool1d¶

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 $(N, C, L)$, output $(N, C, L_{out})$ and kernel_size $k$ can be precisely described as:

$\text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} \text{input}(N_i, C_j, \text{stride} \times l + m)$

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding 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 an int or a one-element tuple.

Parameters
• kernel_size – the size of the window

• stride – the stride of the window. Default value is kernel_size

• 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: $(N, C, L_{in})$ or $(C, L_{in})$.

• Output: $(N, C, L_{out})$ or $(C, L_{out})$, where

$L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor$

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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