# tf.compat.v1.metrics.recall_at_top_k

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/metrics_impl.py#L2756-L2837) |

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

    tf.compat.v1.metrics.recall_at_top_k(
        labels,
        predictions_idx,
        k=None,
        class_id=None,
        weights=None,
        metrics_collections=None,
        updates_collections=None,
        name=None
    )

Differs from `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.

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| ## Args ||
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| `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 always count towards `false_negative_at_<k>`. |
| `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. |

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| ## Returns ||
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| `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`. |

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| ## Raises ||
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| `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. |

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