tf.metrics.mean_cosine_distance( labels, predictions, dim, weights=None, metrics_collections=None, updates_collections=None, name=None )
Computes the cosine distance between the labels and predictions.
mean_cosine_distance function creates two local variables,
count that are used to compute the average cosine distance between
labels. This average is weighted by
weights, and it is ultimately returned as
mean_distance, which is an idempotent operation that simply divides
For estimation of the metric over a stream of data, the function creates an
update_op operation that updates these variables and returns the
None, weights default to 1. Use weights of 0 to mask values.
Tensorof arbitrary shape.
Tensorof the same shape as
dim: The dimension along which the cosine distance is computed.
Tensorwhose rank is either 0, or the same rank as
labels, and must be broadcastable to
labels(i.e., all dimensions must be either
1, or the same as the corresponding
labelsdimension). Also, dimension
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.
Tensorrepresenting the current mean, the value of
update_op: An operation that increments the
labelshave mismatched shapes, or if
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
RuntimeError: If eager execution is enabled.
© 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.