# torch.nn.functional.avg_pool1d¶

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 $(\text{minibatch} , \text{in\_channels} , iW)$

• 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.]]])