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)
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 and output can be precisely described as:
padding is non-zero, then the input is implicitly padded with negative infinity on both sides for
padding number of points.
dilation is the stride between the elements within the sliding window. This link has a nice visualization of the pooling parameters.
True, will return the argmax along with the max values. Useful for
True, will use
floorto compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.
Output: , where
>>> # pool of size=3, stride=2 >>> m = nn.MaxPool1d(3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input)
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