class PairwiseDistance(Module):
r"""
Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:
-
.. math ::
\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}
-
Args:
p (real): the norm degree. Default: 2
eps (float, optional): Small value to avoid division by zero.
Default: 1e-6
- keepdim (bool, optional): Determines whether or not to keep the batch dimension.
+ keepdim (bool, optional): Determines whether or not to keep the vector dimension.
Default: False
-
Shape:
- Input1: :math:`(N, D)` where `D = vector dimension`
- Input2: :math:`(N, D)`, same shape as the Input1
- - Output: :math:`(N)`. If :attr:`keepdim` is ``False``, then :math:`(N, 1)`.
-
+ - Output: :math:`(N)`. If :attr:`keepdim` is ``True``, then :math:`(N, 1)`.
Examples::
-
>>> pdist = nn.PairwiseDistance(p=2)
>>> input1 = torch.randn(100, 128)
>>> input2 = torch.randn(100, 128)
@weak_module
class CosineSimilarity(Module):
r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim.
-
.. math ::
\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-
Args:
dim (int, optional): Dimension where cosine similarity is computed. Default: 1
eps (float, optional): Small value to avoid division by zero.
Default: 1e-8
-
Shape:
- Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`
- Input2: :math:`(\ast_1, D, \ast_2)`, same shape as the Input1
- Output: :math:`(\ast_1, \ast_2)`
-
Examples::
-
>>> input1 = torch.randn(100, 128)
>>> input2 = torch.randn(100, 128)
>>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)