tf.metrics.recall( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )
Computes the recall of the predictions with respect to the labels.
recall function creates two local variables,
false_negatives, that are used to compute the recall. This value is ultimately returned as
recall, an idempotent operation that simply divides
true_positives by the sum of
For estimation of the metric over a stream of data, the function creates an
update_op that updates these variables and returns the
update_op weights each prediction by the corresponding value in
None, weights default to 1. Use weights of 0 to mask values.
labels: The ground truth values, a
Tensorwhose dimensions must match
predictions. Will be cast to
predictions: The predicted values, a
Tensorof arbitrary dimensions. Will be cast to
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
metrics_collections: An optional list of collections that
recallshould be added to.
updates_collections: An optional list of collections that
update_opshould be added to.
name: An optional variable_scope name.
recall: Scalar float
Tensorwith the value of
true_positivesdivided by the sum of
false_negativesvariables appropriately and whose value matches
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.