# pairwise_distance¶

paddle.nn.functional. pairwise_distance ( x, y, p=2.0, epsilon=1e-06, keepdim=False, name=None ) [source]

It computes the pairwise distance between two vectors. The distance is calculated by p-order norm:

$\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}.$
Parameters
• x (Tensor) – Tensor, shape is $$[N, D]$$ or $$[D]$$, where $$N$$ is batch size, $$D$$ is the dimension of vector. Available dtype is float16, float32, float64.

• y (Tensor) – Tensor, shape is $$[N, D]$$ or $$[D]$$, where $$N$$ is batch size, $$D$$ is the dimension of vector. Available dtype is float16, float32, float64.

• p (float, optional) – The order of norm. Default: $$2.0$$.

• epsilon (float, optional) – Add small value to avoid division by zero. Default: $$1e-6$$.

• keepdim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result tensor is one dimension less than the result of |x-y| unless keepdim is True. Default: False.

• name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

Tensor, the dtype is same as input tensor.

• If keepdim is True, the output shape is $$[N, 1]$$ or $$[1]$$, depending on whether the input has data shaped as $$[N, D]$$.

• If keepdim is False, the output shape is $$[N]$$ or $$[]$$, depending on whether the input has data shaped as $$[N, D]$$.

Examples

>>> import paddle
>>> x = paddle.to_tensor([[1., 3.], [3., 5.]], dtype=paddle.float64)
>>> y = paddle.to_tensor([[5., 6.], [7., 8.]], dtype=paddle.float64)
>>> distance = paddle.nn.functional.pairwise_distance(x, y)
>>> print(distance)
Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True,
[4.99999860, 4.99999860])