nn.functional.avg_pool1d

lucid.nn.functional.avg_pool1d(input_: Tensor, kernel_size: int | tuple[int] = 1, stride: int | tuple[int] = 1, padding: int | tuple[int] = 0) → Tensor

The avg_pool1d function performs a one-dimensional average pooling operation on the input tensor.

Function Signature

def avg_pool1d(
    input_: Tensor,
    kernel_size: int | tuple[int] = 1,
    stride: int | tuple[int] = 1,
    padding: int | tuple[int] = 0,
) -> Tensor

Parameters

• input_ (Tensor):

  The input tensor of shape (N, C, L), where N is the batch size, C is the channels, and L is the length.

• kernel_size (int | tuple[int], optional):

  The size of the pooling window. Default: 1.

• stride (int | tuple[int], optional):

  The stride of the pooling operation. Default: 1.

• padding (int | tuple[int], optional):

  The amount of zero-padding added to both sides of the input. Default: 0.

Returns

• Tensor:

  The result of the 1D average pooling operation, with shape (N, C, L_out), where:

  \[L_{out} = \frac{L + 2 \cdot \text{padding} - \text{kernel size}}{\text{stride}} + 1\]

Examples

Performing a simple 1D average pooling:

>>> import lucid.nn.functional as F
>>> input_ = Tensor([[[1.0, 2.0, 3.0, 4.0]]])  # Shape: (1, 1, 4)
>>> out = F.avg_pool1d(input_, kernel_size=2, stride=2, padding=0)
>>> print(out)
Tensor([[[1.5, 3.5]]])