PairwiseDistance¶
- class paddle.nn. PairwiseDistance ( p=2.0, epsilon=1e-06, keepdim=False, name=None ) [source]
-
This operator 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) – The order of norm. The default value is 2.
epsilon (float, optional) – Add small value to avoid division by zero, default value is 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'unlesskeepdimis True, default value is False.name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
- Shape:
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- x: \([N, D]\) where D is the dimension of vector, available dtype
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is float32, float64.
y: \([N, D]\), y have the same shape and dtype as x. out: \([N]\). If
keepdimisTrue, the out shape is \([N, 1]\).The same dtype as input tensor.
Examples
import paddle import numpy as np paddle.disable_static() x_np = np.array([[1., 3.], [3., 5.]]).astype(np.float64) y_np = np.array([[5., 6.], [7., 8.]]).astype(np.float64) x = paddle.to_tensor(x_np) y = paddle.to_tensor(y_np) dist = paddle.nn.PairwiseDistance() distance = dist(x, y) print(distance.numpy()) # [5. 5.]-
forward
(
x,
y
)
forward¶
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Defines the computation performed at every call. Should be overridden by all subclasses.
- Parameters
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*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments
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extra_repr
(
)
extra_repr¶
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Extra representation of this layer, you can have custom implementation of your own layer.