# PairwiseDistance¶

class paddle.nn. PairwiseDistance ( 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-oreder norm:

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

Shape:
x: $$[N, D]$$ or $$[D]$$, where $$N$$ is batch size, $$D$$

is the dimension of the data. Available data type is float32, float64.

Definition list ends without a blank line; unexpected unindent.

y: $$[N, D]$$ or $$[D]$$, y have the same dtype as x. output: The same dtype as input tensor.

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• 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
distance = dist(x, y)
print(distance.numpy()) # [5. 5.]

forward ( x, y )

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

extra_repr ( )

Extra representation of this layer, you can have custom implementation of your own layer.