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

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

Computes the recall of the predictions with respect to the labels.

The recall function creates two local variables, true_positives and false_negatives, that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides true_positives by the sum of true_positives and false_negatives.

For estimation of the metric over a stream of data, the function creates an update_op that updates these variables and returns the recall. update_op weights each prediction by the corresponding value in weights.

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: The predicted values, a Tensor of arbitrary dimensions. Will be cast to bool.
• 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: Scalar float Tensor with the value of true_positives divided by the sum of true_positives and false_negatives.
• update_op: Operation that increments true_positives and false_negatives variables appropriately and whose value matches 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.

Defined in tensorflow/python/ops/metrics_impl.py.