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
.
See the guide: Metrics (contrib) > Metric Ops
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.
predictions
: A Tensor
of the same shape as labels
.labels
: A Tensor
of arbitrary shape.dim
: The dimension along which the cosine distance is computed.weights
: An optional Tensor
whose shape is broadcastable to predictions
, and whose dimension dim
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.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.ValueError
: If predictions
and labels
have mismatched shapes, or if weights
is not None
and its shape doesn't match predictions
, or if either metrics_collections
or updates_collections
are not a list or tuple.
© 2018 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/contrib/metrics/streaming_mean_cosine_distance