tf.contrib.metrics.precision_recall_at_equal_thresholds( labels, predictions, weights=None, num_thresholds=None, use_locking=None, name=None )
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
A helper method for creating metrics related to precision-recall curves.
These values are true positives, false negatives, true negatives, false positives, precision, and recall. This function returns a data structure that contains ops within it.
Unlike _streaming_confusion_matrix_at_thresholds (which exhibits O(T * N) space and run time), this op exhibits O(T + N) space and run time, where T is the number of thresholds and N is the size of the predictions tensor. Hence, it may be advantageous to use this function when predictions
is big.
For instance, prefer this method for per-pixel classification tasks, for which the predictions tensor may be very large.
Each number in predictions
, a float in [0, 1]
, is compared with its corresponding label in labels
, and counts as a single tp/fp/tn/fn value at each threshold. This is then multiplied with weights
which can be used to reweight certain values, or more commonly used for masking values.
labels
: A bool Tensor
whose shape matches predictions
.predictions
: A floating point Tensor
of arbitrary shape and whose values are in the range [0, 1]
.weights
: Optional; If provided, a Tensor
that has the same dtype as, and broadcastable to, predictions
. This tensor is multiplied by counts.num_thresholds
: Optional; Number of thresholds, evenly distributed in [0, 1]
. Should be >= 2
. Defaults to 201. Note that the number of bins is 1 less than num_thresholds
. Using an even num_thresholds
value instead of an odd one may yield unfriendly edges for bins.use_locking
: Optional; If True, the op will be protected by a lock. Otherwise, the behavior is undefined, but may exhibit less contention. Defaults to True.name
: Optional; variable_scope name. If not provided, the string 'precision_recall_at_equal_threshold' is used.result
: A named tuple (See PrecisionRecallData within the implementation of this function) with properties that are variables of shape [num_thresholds]
. The names of the properties are tp, fp, tn, fn, precision, recall, thresholds.update_op
: An op that accumulates values.ValueError
: If predictions
and labels
have mismatched shapes, or if weights
is not None
and its shape doesn't match predictions
, or if includes
contains invalid keys.
© 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/contrib/metrics/precision_recall_at_equal_thresholds