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A wrapper to compute the pairwise distance between sources
and targets
.
nsl.lib.pairwise_distance_wrapper(
sources, targets, weights=1.0, distance_config=None
)
distances = weights * distance_config.distance_type(sources, targets)
This wrapper calculates the weighted distance between (sources, targets)
pairs, and provides an option to return the distance as the sum over the
difference along the given axis, when vector based distance is needed.
For the usage of weights
and reduction
, please refer to tf.losses
. For
the usage of sum_over_axis
, see the following examples:
Given target tensors with shape [batch_size, features]
, the reduction set to
tf.compat.v1.losses.Reduction.MEAN
, and sum_over_axis
set to the last
dimension, the weighted average distance of sample pairs will be returned.
For example: With a distance_config('L2', sum_over_axis=1), the distance
between [[1, 1], [2, 2], [0, 2], [5, 5]] and [[1, 1], [0, 2], [4, 4], [1, 4]]
will be {(0+0) + (4+0) + (16+4) + (16+1)}/4 = 10.25
If sum_over_axis
is None
, the weighted average distance of feature pairs
(instead of sample pairs) will be returned. For example: With a
distance_config('L2'), the distance between
[[1, 1], [2, 2], [0, 2], [5, 5]] and [[1, 1], [0, 2], [4, 4], [1, 4]] will be
{(0+0) + (4+0) + (16+4) + (16+1)}/8 = 5.125
If transform_fn
is not None
, the transform function is applied to both
sources
and targets
before computing the distance. For example:
distance_config('KL_DIVERGENCE', sum_over_axis=1, transform_fn='SOFTMAX')
treats sources
and targets
as logits, and computes the KLdivergence
between the two probability distributions.
Args  

sources

Tensor of type float32 or float64 .

targets

Tensor of the same type and shape as sources .

weights

(optional) Tensor whose rank is either 0, or the same as that of
targets , and must be broadcastable to targets (i.e., all dimensions
must be either 1 , or the same as the corresponding distance dimension).

distance_config

An instance of nsl.configs.DistanceConfig that contains
the following configuration (or hyperparameters) for computing distances:
(a) distance_type : Type of distance function to apply.
(b) reduction : Type of distance reduction. See tf.losses.Reduction .
(c) sum_over_axis : (optional) The distance is the sum over the
difference along the specified axis. Note that if sum_over_axis is not
None and the rank of weights is nonzero, then the size of weights
along sum_over_axis must be 1.
(d) transform_fn : (optional) If set, both sources and targets will
be transformed before calculating the distance. If set to 'SOFTMAX', it
will be performed on the axis specified by 'sum_over_axis', or 1 if the
axis is not specified. If None , the default distance config will be
used.

Returns  

Weighted distance scalar Tensor . If reduction is
tf.compat.v1.losses.Reduction.MEAN , this has the same shape as
targets .

Raises  

ValueError

If the shape of targets doesn't match that of sources, or if the shape of weights is invalid. 
TypeError

If the distance function gets an unexpected keyword argument. 