PairwiseDistance¶

class torch.nn.PairwiseDistance(p: float = 2.0, eps: float = 1e-06, keepdim: bool = False)[source]¶

Computes the batchwise pairwise distance between vectors $v_1$ , $v_2$ using the p-norm:

$$\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.$$

Parameters

• 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 vector dimension. Default: False

Shape:

• Input1: $(N, D)$ where D = vector dimension

• Input2: $(N, D)$ , same shape as the Input1

• Output: $(N)$ . If keepdim is True, then $(N, 1)$ .

Examples::

>>> pdist = nn.PairwiseDistance(p=2)
>>> input1 = torch.randn(100, 128)
>>> input2 = torch.randn(100, 128)
>>> output = pdist(input1, input2)