View source on GitHub |
Adds a cosine-distance loss to the training procedure. (deprecated arguments)
tf.compat.v1.losses.cosine_distance(
labels,
predictions,
axis=None,
weights=1.0,
scope=None,
loss_collection=ops.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
dim=None
)
Note that the function assumes that predictions
and labels
are already
unit-normalized.
Returns | |
---|---|
Weighted loss float Tensor . If reduction is NONE , this has the same
shape as labels ; otherwise, it is scalar.
|
Raises | |
---|---|
ValueError
|
If predictions shape doesn't match labels shape, or
axis , labels , predictions or weights is None .
|
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
.