tf.contrib.metrics.streaming_recall_at_thresholds( predictions, labels, thresholds, weights=None, metrics_collections=None, updates_collections=None, name=None )
See the guide: Metrics (contrib) > Metric
Computes various recall values for different
THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.metrics.recall_at_thresholds. Note that the order of the labels and predictions arguments has been switched.
streaming_recall_at_thresholds function creates four local variables,
false_negatives for various values of thresholds.
recall[i] is defined as the total weight of values in
thresholds[i] whose corresponding entry in
True, divided by the total weight of
True values in
true_positives[i] / (true_positives[i] + false_negatives[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
None, weights default to 1. Use weights of 0 to mask values.
predictions: A floating point
Tensorof arbitrary shape and whose values are in the range
Tensorwhose shape matches
thresholds: A python list or tuple of float thresholds in
Tensorwhose 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
metrics_collections: An optional list of collections that
recallshould be added to.
updates_collections: An optional list of collections that
update_opshould be added to.
name: An optional variable_scope name.
recall: A float
update_op: An operation that increments the
false_negativesvariables that are used in the computation of
labelshave mismatched shapes, or if
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.