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:
paddingis non-zero, then the input is implicitly zero-padded on both sides for
paddingnumber of points.
dilationcontrols the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what
kernel_size – the size of the window to take a max over
stride – the stride of the window. Default value is
padding – implicit zero padding to be added on both sides
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
ceil_mode – when True, will use ceil instead of floor to compute the output shape
Output: , where
>>> # pool of size=3, stride=2 >>> m = nn.MaxPool1d(3, stride=2) >>> input = torch.randn(20, 16, 50) >>> output = m(input)