tf.metrics.specificity_at_sensitivity( labels, predictions, sensitivity, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes the specificity at a given sensitivity.
The specificity_at_sensitivity
function creates four local variables, true_positives
, true_negatives
, false_positives
and 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 specificity
. update_op
increments the true_positives
, true_negatives
, false_positives
and false_negatives
counts with the weight of each case found in the predictions
and labels
.
If weights
is 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 Tensor
whose dimensions must match predictions
. Will be cast to bool
.predictions
: A floating point Tensor
of arbitrary shape and whose values are in the range [0, 1]
.sensitivity
: A scalar value in range [0, 1]
.weights
: Optional Tensor
whose 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 labels
dimension).num_thresholds
: The number of thresholds to use for matching the given sensitivity.metrics_collections
: An optional list of collections that specificity
should be added to.updates_collections
: An optional list of collections that update_op
should be added to.name
: An optional variable_scope name.specificity
: A scalar Tensor
representing the specificity at the given specificity
value.update_op
: An operation that increments the true_positives
, true_negatives
, false_positives
and false_negatives
variables appropriately and whose value matches specificity
.ValueError
: If predictions
and labels
have mismatched shapes, if weights
is not None
and its shape doesn't match predictions
, or if sensitivity
is not between 0 and 1, or if either metrics_collections
or updates_collections
are 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.
https://www.tensorflow.org/api_docs/python/tf/metrics/specificity_at_sensitivity