tf.contrib.metrics.precision_at_recall(
labels,
predictions,
target_recall,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
Computes the precision at a given recall.
This function creates variables to track the true positives, false positives,
true negatives, and false negatives at a set of thresholds. Among those
thresholds where recall is at least target_recall
, precision is computed
at the threshold where recall is closest to target_recall
.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
precision at target_recall
. update_op
increments the counts of true
positives, false positives, true negatives, and false negatives 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 precision and recall, see http://en.wikipedia.org/wiki/Precision_and_recall
Args:
labels
: The ground truth values, aTensor
whose dimensions must matchpredictions
. Will be cast tobool
.predictions
: A floating pointTensor
of arbitrary shape and whose values are in the range[0, 1]
.target_recall
: A scalar value in range[0, 1]
.weights
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglabels
dimension).num_thresholds
: The number of thresholds to use for matching the given recall.metrics_collections
: An optional list of collections to whichprecision
should be added.updates_collections
: An optional list of collections to whichupdate_op
should be added.name
: An optional variable_scope name.
Returns:
precision
: A scalarTensor
representing the precision at the giventarget_recall
value.update_op
: An operation that increments the variables for tracking the true positives, false positives, true negatives, and false negatives and whose value matchesprecision
.
Raises:
ValueError
: Ifpredictions
andlabels
have mismatched shapes, ifweights
is notNone
and its shape doesn't matchpredictions
, or iftarget_recall
is not between 0 and 1, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.