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, aTensorwhose dimensions must matchpredictions. Will be cast tobool.predictions: The predicted values, aTensorof arbitrary dimensions. Will be cast tobool.weights: OptionalTensorwhose 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 correspondinglabelsdimension).metrics_collections: An optional list of collections thatprecisionshould be added to.updates_collections: An optional list of collections thatupdate_opshould be added to.name: An optional variable_scope name.
Returns:
precision: Scalar floatTensorwith the value oftrue_positivesdivided by the sum oftrue_positivesandfalse_positives.update_op:Operationthat incrementstrue_positivesandfalse_positivesvariables appropriately and whose value matchesprecision.
Raises:
ValueError: Ifpredictionsandlabelshave mismatched shapes, or ifweightsis notNoneand its shape doesn't matchpredictions, or if eithermetrics_collectionsorupdates_collectionsare not a list or tuple.RuntimeError: If eager execution is enabled.