tf.losses.cosine_distance
tf.losses.cosine_distance
tf.losses.cosine_distance
cosine_distance( labels, predictions, dim=None, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS )
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Adds a cosine-distance loss to the training procedure.
Note that the function assumes that predictions
and labels
are already unit-normalized.
Args:
-
labels
:Tensor
whose shape matches 'predictions' -
predictions
: An arbitrary matrix. -
dim
: The dimension along which the cosine distance is computed. -
weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglosses
dimension). -
scope
: The scope for the operations performed in computing the loss. -
loss_collection
: collection to which this loss will be added. -
reduction
: Type of reduction to apply to loss.
Returns:
Weighted loss float Tensor
. If reduction
is NONE
, this has the same shape as labels
; otherwise, it is scalar.
Raises:
-
ValueError
: Ifpredictions
shape doesn't matchlabels
shape, orweights
isNone
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/losses/cosine_distance