LPPool1d#

classtorch.nn.modules.pooling.LPPool1d(norm_type,kernel_size,stride=None,ceil_mode=False)[source]#

Applies a 1D power-average pooling over an input signal composed of several input planes.

On each window, the function computed is:

$$f(X)=\sqrt[p]{\sum _{x\in X}{x}^p}$$f(X)=p​x∈X∑​xp​

• At p =$\mathrm{\infty }$∞, one gets Max Pooling

• At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)

Note

If the sum to the power ofp is zero, the gradient of this function isnot defined. This implementation will set the gradient to zero in this case.

Parameters

• kernel_size (Union[int,tuple[int]]) – a single int, the size of the window

• stride (Union[int,tuple[int]]) – a single int, the stride of the window. Default value iskernel_size

• ceil_mode (bool) – when True, will useceil instead offloor to compute the output shape

Shape:

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

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

  $$L_{out}=\lfloor \frac{L_{in}-\text{kernel\_size}}{\text{stride}}+1\rfloor$$Lout​=⌊strideLin​−kernel_size​+1⌋

Examples::

>>># power-2 pool of window of length 3, with stride 2.>>>m=nn.LPPool1d(2,3,stride=2)>>>input=torch.randn(20,16,50)>>>output=m(input)

forward(input)[source]#

Runs the forward pass.

Return type

Tensor