parameters: (labels,
                             top_k_predictions,
                             class_id=None,
                             weights=None,
                             metrics_collections=None,
                             updates_collections=None,
                             name=None)
docstring: Computes recall@k of top-k predictions with respect to sparse labels.
  
    If `class_id` is specified, we calculate recall by considering only the
        entries in the batch for which `class_id` is in the label, and computing
        the fraction of them for which `class_id` is in the top-k `predictions`.
    If `class_id` is not specified, we'll calculate recall as how often on
        average a class among the labels of a batch entry is in the top-k
        `predictions`.
  
    `sparse_recall_at_top_k` creates two local variables, `true_positive_at_<k>`
    and `false_negative_at_<k>`, that are used to compute the recall_at_k
    frequency. This frequency is ultimately returned as `recall_at_<k>`: an
    idempotent operation that simply divides `true_positive_at_<k>` by total
    (`true_positive_at_<k>` + `false_negative_at_<k>`).
  
    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_at_<k>`. Set operations applied to `top_k` and `labels` calculate the
    true positives and false negatives weighted by `weights`. Then `update_op`
    increments `true_positive_at_<k>` and `false_negative_at_<k>` using these
    values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      labels: `int64` `Tensor` or `SparseTensor` with shape
        [D1, ... DN, num_labels], where 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
        `top_k_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 `false_negative_at_<k>`.
      top_k_predictions: Integer `Tensor` with shape [D1, ... DN, k] where
        N >= 1. Commonly, N=1 and top_k_predictions has shape [batch size, k].
        The final dimension contains the indices of top-k labels. [D1, ... DN]
        must match `labels`.
      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.
  
    Returns:
      recall: Scalar `float64` `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 `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.
docstring_summary: Computes recall@k of top-k predictions with respect to sparse labels.
docstring_tokens: Computes recall@k of top - k predictions with respect to sparse labels .
function: def sparse_recall_at_top_k(labels,
                             top_k_predictions,
                             class_id=None,
                             weights=None,
                             metrics_collections=None,
                             updates_collections=None,
                             name=None):
    """Computes recall@k of top-k predictions with respect to sparse labels.
  
    If `class_id` is specified, we calculate recall by considering only the
        entries in the batch for which `class_id` is in the label, and computing
        the fraction of them for which `class_id` is in the top-k `predictions`.
    If `class_id` is not specified, we'll calculate recall as how often on
        average a class among the labels of a batch entry is in the top-k
        `predictions`.
  
    `sparse_recall_at_top_k` creates two local variables, `true_positive_at_<k>`
    and `false_negative_at_<k>`, that are used to compute the recall_at_k
    frequency. This frequency is ultimately returned as `recall_at_<k>`: an
    idempotent operation that simply divides `true_positive_at_<k>` by total
    (`true_positive_at_<k>` + `false_negative_at_<k>`).
  
    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_at_<k>`. Set operations applied to `top_k` and `labels` calculate the
    true positives and false negatives weighted by `weights`. Then `update_op`
    increments `true_positive_at_<k>` and `false_negative_at_<k>` using these
    values.
  
    If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
  
    Args:
      labels: `int64` `Tensor` or `SparseTensor` with shape
        [D1, ... DN, num_labels], where 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
        `top_k_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 `false_negative_at_<k>`.
      top_k_predictions: Integer `Tensor` with shape [D1, ... DN, k] where
        N >= 1. Commonly, N=1 and top_k_predictions has shape [batch size, k].
        The final dimension contains the indices of top-k labels. [D1, ... DN]
        must match `labels`.
      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.
  
    Returns:
      recall: Scalar `float64` `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 `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.
    """
    default_name = _at_k_name('recall', class_id=class_id)
    with ops.name_scope(name, default_name,
                        (top_k_predictions, labels, weights)) as name_scope:
      return metrics_impl._sparse_recall_at_top_k(  # pylint: disable=protected-access
          labels=labels,
          predictions_idx=top_k_predictions,
          class_id=class_id,
          weights=weights,
          metrics_collections=metrics_collections,
          updates_collections=updates_collections,
          name=name_scope)
function_tokens: def sparse_recall_at_top_k ( labels , top_k_predictions , class_id = None , weights = None , metrics_collections = None , updates_collections = None , name = None ) : default_name = _at_k_name ( 'recall' , class_id = class_id ) with ops . name_scope ( name , default_name , ( top_k_predictions , labels , weights ) ) as name_scope : return metrics_impl . _sparse_recall_at_top_k ( # pylint: disable=protected-access labels = labels , predictions_idx = top_k_predictions , class_id = class_id , weights = weights , metrics_collections = metrics_collections , updates_collections = updates_collections , name = name_scope )