The recall_at_thresholds function creates four local variables,
true_positives, true_negatives, false_positives and false_negatives
for various values of thresholds. recall[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 True values in labels
(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 recall.
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
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 recall should be
added to.
updates_collections
An optional list of collections that update_op should
be added to.
name
An optional variable_scope name.
Returns
recall
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 recall.
Raises
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.