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.
labels
: The ground truth values, a Tensor
whose dimensions must match predictions
. Will be cast to bool
.predictions
: The predicted values, a Tensor
of arbitrary dimensions. Will be cast to bool
.weights
: Optional Tensor
whose rank is either 0, or the same rank as labels
, and must be broadcastable to labels
(i.e., all dimensions must be either 1
, or the same as the corresponding labels
dimension).metrics_collections
: An optional list of collections that precision
should be added to.updates_collections
: An optional list of collections that update_op
should be added to.name
: An optional variable_scope name.precision
: Scalar float Tensor
with the value of true_positives
divided by the sum of true_positives
and false_positives
.update_op
: Operation
that increments true_positives
and false_positives
variables appropriately and whose value matches precision
.ValueError
: If predictions
and labels
have mismatched shapes, or if weights
is not None
and its shape doesn't match predictions
, or if either metrics_collections
or updates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.
© 2018 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