Computes precision@k of the predictions with respect to sparse labels.
labels, predictions_idx, k=None, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
sparse_precision_at_k in that predictions must be in the form
k class indices, whereas
sparse_precision_at_k expects logits.
sparse_precision_at_k for more details.
SparseTensor with 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
labels has shape
[batch_size, num_labels]. [D1, ... DN] must match
should be in range [0, num_classes), where num_classes is the last
predictions. Values outside this range are ignored.
Tensor with shape [D1, ... DN, k] where
N >= 1. Commonly, N=1 and predictions has shape [batch size, k].
The final dimension contains the top
k predicted class indices.
[D1, ... DN] must match
Integer, k for @k metric. Only used for the default op name.
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
class_id is outside this range, the method returns
Tensor whose rank is either 0, or n-1, where n is the rank of
labels. If the latter, i