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Computes the cosine distance between the labels and predictions.
tf.compat.v1.metrics.mean_cosine_distance(
labels,
predictions,
dim,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
The mean_cosine_distance
function creates two local variables,
total
and count
that are used to compute the average cosine distance
between predictions
and labels
. This average is weighted by weights
,
and it is ultimately returned as mean_distance
, which is an idempotent
operation that simply divides total
by count
.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
mean_distance
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Returns | |
---|---|
mean_distance
|
A Tensor representing the current mean, the value of
total divided by count .
|
update_op
|
An operation that increments the total and count variables
appropriately.
|