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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

Differs from sparse_precision_at_k in that predictions must be in the form of top k class indices, whereas sparse_precision_at_k expects logits. Refer to sparse_precision_at_k for more details.


  • labels: int64 Tensor or 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 predictions. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions. Values outside this range are ignored.
  • predictions_idx: Integer 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 labels.
  • 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.
  • weights: Tensor whose 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 labels dimension).
  • 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.


  • precision: Scalar float64 Tensor with the value of true_positives divided by the sum of true_positives and false_positives.
  • update_op: Operation that increments true_positives and false_positives variables appropriately, and whose value matches precision.


  • ValueError: 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.