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# tf.contrib.metrics.streaming_sparse_precision_at_top_k

```tf.contrib.metrics.streaming_sparse_precision_at_top_k(
top_k_predictions,
labels,
class_id=None,
weights=None,
metrics_collections=None,
name=None
)
```

See the guide: Metrics (contrib) > Metric `Ops`

Computes [email protected] of top-k predictions with respect to sparse labels.

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

We expect precision to decrease as `k` increases.

`streaming_sparse_precision_at_top_k` creates two local variables, `true_positive_at_k` and `false_positive_at_k`, that are used to compute the [email protected] frequency. This frequency is ultimately returned as `precision_at_k`: an idempotent operation that simply divides `true_positive_at_k` by total (`true_positive_at_k` + `false_positive_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 `precision_at_k`. Internally, set operations applied to `top_k_predictions` and `labels` calculate the true positives and false positives weighted by `weights`. Then `update_op` increments `true_positive_at_k` and `false_positive_at_k` using these values.

If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.

#### Args:

• `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`.
• `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 are ignored.
• `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:

• `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`.

#### 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.
• `ValueError`: If `top_k_predictions` has rank < 2.