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Computes various recall values for different thresholds on predictions.

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.

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.

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.

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.