tf.metrics.specificity_at_sensitivity( labels, predictions, sensitivity, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None )
Computes the specificity at a given sensitivity.
specificity_at_sensitivity function creates four local variables,
false_negatives that are used to compute the specificity at the given sensitivity value. The threshold for the given sensitivity value is computed and used to evaluate the corresponding specificity.
For estimation of the metric over a stream of data, the function creates an
update_op operation that updates these variables and returns the
update_op increments the
false_negatives counts with the weight of each case found in the
None, weights default to 1. Use weights of 0 to mask values.
For additional information about specificity and sensitivity, see the following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
labels: The ground truth values, a
Tensorwhose dimensions must match
predictions. Will be cast to
predictions: A floating point
Tensorof arbitrary shape and whose values are in the range
sensitivity: A scalar value in range
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
num_thresholds: The number of thresholds to use for matching the given sensitivity.
metrics_collections: An optional list of collections that
specificityshould be added to.
updates_collections: An optional list of collections that
update_opshould be added to.
name: An optional variable_scope name.
specificity: A scalar
Tensorrepresenting the specificity at the given
update_op: An operation that increments the
false_negativesvariables appropriately and whose value matches
labelshave mismatched shapes, if
Noneand its shape doesn't match
predictions, or if
sensitivityis not between 0 and 1, 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.