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Computes the sensitivity at a given specificity.
tf.contrib.metrics.streaming_sensitivity_at_specificity(
predictions, labels, specificity, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None
)
The streaming_sensitivity_at_specificity
function creates four local
variables, true_positives
, true_negatives
, false_positives
and
false_negatives
that are used to compute the sensitivity at the given
specificity value. The threshold for the given specificity value is computed
and used to evaluate the corresponding sensitivity.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
sensitivity
. 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
Args | |
---|---|
predictions
|
A floating point Tensor of arbitrary shape and whose values
are in the range [0, 1] .
|
labels
|
A bool Tensor whose shape matches predictions .
|
specificity
|
A scalar value in range [0, 1] .
|
weights
|
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 specificity. |
metrics_collections
|
An optional list of collections that sensitivity
should be added to.
|
updates_collections
|
An optional list of collections that update_op should
be added to.
|
name
|
An optional variable_scope name. |
Returns | |
---|---|
sensitivity
|
A scalar Tensor representing the sensitivity 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 sensitivity .
|
Raises | |
---|---|
ValueError
|
If predictions and labels have mismatched shapes, if
weights is not None and its shape doesn't match predictions , or if
specificity is not between 0 and 1, or if either metrics_collections
or updates_collections are not a list or tuple.
|