tf.metrics.recall_at_top_k( labels, predictions_idx, k=None, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None )
Computes [email protected] of top-k predictions with respect to sparse labels.
recall_at_k in that predictions must be in the form of top
k class indices, whereas
recall_at_k expects logits. Refer to
recall_at_k for more details.
SparseTensorwith shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and
labelshas shape [batch_size, num_labels]. [D1, ... DN] must match
predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of
predictions. Values outside this range always count towards
Tensorwith shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the top
kpredicted class indices. [D1, ... DN] must match
k: Integer, k for @k metric. Only used for the default op name.
class_id: Integer class ID for which we want binary metrics. This should be in range [0, num_classes), where num_classes is the last dimension of
predictions. If class_id is outside this range, the method returns NAN.
Tensorwhose rank is either 0, or n-1, where n is the rank of
labels. If the latter, it 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 values should be added to.
updates_collections: An optional list of collections that updates should be added to.
name: Name of new update operation, and namespace for other dependent ops.
Tensorwith the value of
true_positivesdivided by the sum of
false_negativesvariables appropriately, and whose value matches
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
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