### `tf.contrib.metrics.streaming_sparse_recall_at_k(predictions, labels, k, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)` {#streaming_sparse_recall_at_k}

Computes recall@k of the predictions with respect to sparse labels.

If `class_id` is not specified, we'll calculate recall as the ratio of true
    positives (i.e., correct predictions, items in the top `k` highest
    `predictions` that are found in the corresponding row in `labels`) to
    actual positives (the full `labels` row).
If `class_id` is specified, we calculate recall by considering only the rows
    in the batch for which `class_id` is in `labels`, and computing the
    fraction of them for which `class_id` is in the corresponding row in
    `labels`.

`streaming_sparse_recall_at_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>`. Internally, a `top_k` operation computes a `Tensor`
indicating the top `k` `predictions`. 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:


*  <b>`predictions`</b>: Float `Tensor` with shape [D1, ... DN, num_classes] where
    N >= 1. Commonly, N=1 and predictions has shape [batch size, num_classes].
    The final dimension contains the logit values for each class. [D1, ... DN]
    must match `labels`.
*  <b>`labels`</b>: `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 `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>`.
*  <b>`k`</b>: Integer, k for @k metric.
*  <b>`class_id`</b>: 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.
*  <b>`weights`</b>: `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).
*  <b>`metrics_collections`</b>: An optional list of collections that values should
    be added to.
*  <b>`updates_collections`</b>: An optional list of collections that updates should
    be added to.
*  <b>`name`</b>: Name of new update operation, and namespace for other dependent ops.

##### Returns:


*  <b>`recall`</b>: Scalar `float64` `Tensor` with the value of `true_positives` divided
    by the sum of `true_positives` and `false_negatives`.
*  <b>`update_op`</b>: `Operation` that increments `true_positives` and
    `false_negatives` variables appropriately, and whose value matches
    `recall`.

##### Raises:


*  <b>`ValueError`</b>: 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.

