tf.metrics.recall_at_thresholds

Computes various recall values for different thresholds on predictions.

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tf.compat.v1.metrics.recall_at_thresholds

tf.metrics.recall_at_thresholds(
    labels, predictions, thresholds, weights=None, metrics_collections=None,
    updates_collections=None, name=None
)

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.
RuntimeError	If eager execution is enabled.