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# MaxPool1d¶

class torch.nn.MaxPool1d(kernel_size: Union[T, Tuple[T, ...]], stride: Optional[Union[T, Tuple[T, ...]]] = None, padding: Union[T, Tuple[T, ...]] = 0, dilation: Union[T, Tuple[T, ...]] = 1, return_indices: bool = False, ceil_mode: bool = False)[source]

Applies a 1D max pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size $(N, C, L)$ and output $(N, C, L_{out})$ can be precisely described as:

$out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1} input(N_i, C_j, stride \times k + m)$

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.

Parameters
• kernel_size – the size of the window to take a max over

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

• dilation – a parameter that controls the stride of elements in the window

• return_indices – if True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool1d later

• ceil_mode – when True, will use ceil instead of floor to compute the output shape

Shape:
• Input: $(N, C, L_{in})$

• Output: $(N, C, L_{out})$ , where

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

Examples:

>>> # pool of size=3, stride=2
>>> m = nn.MaxPool1d(3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input) ## Docs

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