tf.contrib.metrics.auc_with_confidence_intervals( labels, predictions, weights=None, alpha=0.95, logit_transformation=True, metrics_collections=(), updates_collections=(), name=None )
Computes the AUC and asymptotic normally distributed confidence interval.
USAGE NOTE: this approach requires storing all of the predictions and labels for a single evaluation in memory, so it may not be usable when the evaluation batch size and/or the number of evaluation steps is very large.
Computes the area under the ROC curve and its confidence interval using placement values. This has the advantage of being resilient to the distribution of predictions by aggregating across batches, accumulating labels and predictions and performing the final calculation using all of the concatenated values.
Tensorof ground truth labels with the same shape as
labelsand with values of 0 or 1 whose values are castable to
Tensorof predictions whose values are castable to
float64. Will be flattened into a 1-D
Tensorwhose rank is either 0, or the same rank as
alpha: Confidence interval level desired.
logit_transformation: A boolean value indicating whether the estimate should be logit transformed prior to calculating the confidence interval. Doing so enforces the restriction that the AUC should never be outside the interval [0,1].
metrics_collections: An optional iterable of collections that
aucshould be added to.
updates_collections: An optional iterable of collections that
update_opshould be added to.
name: An optional name for the variable_scope that contains the metric variables.
auc: A 1-D
Tensorcontaining the current area-under-curve, lower, and upper confidence interval values.
update_op: An operation that concatenates the input labels and predictions to the accumulated values.
weightshave mismatched shapes or if
alphaisn't in the range (0,1).
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.