tf.contrib.metrics.streaming_mean_cosine_distance(
predictions,
labels,
dim,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
Computes the cosine distance between the labels and predictions.
The streaming_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.
Args:
predictions
: ATensor
of the same shape aslabels
.labels
: ATensor
of arbitrary shape.dim
: The dimension along which the cosine distance is computed.weights
: An optionalTensor
whose shape is broadcastable topredictions
, and whose dimensiondim
is 1.metrics_collections
: An optional list of collections that the metric value variable should be added to.updates_collections
: An optional list of collections that the metric update ops should be added to.name
: An optional variable_scope name.
Returns:
mean_distance
: ATensor
representing the current mean, the value oftotal
divided bycount
.update_op
: An operation that increments thetotal
andcount
variables appropriately.
Raises:
ValueError
: Ifpredictions
andlabels
have mismatched shapes, or ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.