tf.metrics.precision
tf.metrics.precision
tf.metrics.precision
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.
© 2017 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/precision