tf.contrib.metrics.streaming_false_positive_rate(
predictions,
labels,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
Computes the false positive rate of predictions with respect to labels.
The false_positive_rate
function creates two local variables,
false_positives
and true_negatives
, that are used to compute the
false positive rate. This value is ultimately returned as
false_positive_rate
, an idempotent operation that simply divides
false_positives
by the sum of false_positives
and true_negatives
.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
false_positive_rate
. update_op
weights each prediction by the
corresponding value in weights
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args:
predictions
: The predicted values, aTensor
of arbitrary dimensions. Will be cast tobool
.labels
: The ground truth values, aTensor
whose dimensions must matchpredictions
. Will be cast tobool
.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).metrics_collections
: An optional list of collections thatfalse_positive_rate
should be added to.updates_collections
: An optional list of collections thatupdate_op
should be added to.name
: An optional variable_scope name.
Returns:
false_positive_rate
: Scalar floatTensor
with the value offalse_positives
divided by the sum offalse_positives
andtrue_negatives
.update_op
:Operation
that incrementsfalse_positives
andtrue_negatives
variables appropriately and whose value matchesfalse_positive_rate
.
Raises:
ValueError
: Ifpredictions
andlabels
have mismatched shapes, or ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.