tf.metrics.precision_at_thresholds( labels, predictions, thresholds, weights=None, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes precision values for different thresholds
on predictions
.
The precision_at_thresholds
function creates four local variables, true_positives
, true_negatives
, false_positives
and false_negatives
for various values of thresholds. precision[i]
is defined as the total weight of values in predictions
above thresholds[i]
whose corresponding entry in labels
is True
, divided by the total weight of values in predictions
above thresholds[i]
(true_positives[i] / (true_positives[i] + false_positives[i])
).
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
.
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
: A floating point Tensor
of arbitrary shape and whose values are in the range [0, 1]
.thresholds
: A python list or tuple of float thresholds in [0, 1]
.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 auc
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
: A float Tensor
of shape [len(thresholds)]
.update_op
: An operation that increments the true_positives
, true_negatives
, false_positives
and false_negatives
variables that are used in the computation of 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_at_thresholds