tf.losses.cosine_distance(
labels,
predictions,
axis=None,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
dim=None
)
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Adds a cosine-distance loss to the training procedure. (deprecated arguments)
Note that the function assumes that predictions
and labels
are already
unit-normalized.
Args:
labels
:Tensor
whose shape matches 'predictions'predictions
: An arbitrary matrix.axis
: 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.dim
: The old (deprecated) name foraxis
.
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, oraxis
,labels
,predictions
orweights
isNone
.
Eager Compatibility
The loss_collection
argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a tf.keras.Model
.