tf.metrics.precision(
labels,
predictions,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes the precision of the predictions with respect to the labels.
The precision
function creates two local variables,
true_positives
and false_positives
, that are used to compute the
precision. This value is ultimately returned as precision
, an idempotent
operation that simply divides true_positives
by the sum of true_positives
and false_positives
.
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
. 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:
labels
: The ground truth values, aTensor
whose dimensions must matchpredictions
. Will be cast tobool
.predictions
: The predicted values, aTensor
of arbitrary dimensions. 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 thatprecision
should be added to.updates_collections
: An optional list of collections thatupdate_op
should be added to.name
: An optional variable_scope name.
Returns:
precision
: Scalar floatTensor
with the value oftrue_positives
divided by the sum oftrue_positives
andfalse_positives
.update_op
:Operation
that incrementstrue_positives
andfalse_positives
variables appropriately and whose value matchesprecision
.
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.RuntimeError
: If eager execution is enabled.