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# tf.contrib.metrics.streaming_sparse_average_precision_at_k

```tf.contrib.metrics.streaming_sparse_average_precision_at_k(
predictions,
labels,
k,
weights=None,
metrics_collections=None,
name=None
)
```

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

Computes average [email protected] of predictions with respect to sparse labels.

See `sparse_average_precision_at_k` for details on formula. `weights` are applied to the result of `sparse_average_precision_at_k`

`streaming_sparse_average_precision_at_k` creates two local variables, `average_precision_at_<k>/total` and `average_precision_at_<k>/max`, that are used to compute the frequency. This frequency is ultimately returned as `average_precision_at_<k>`: an idempotent operation that simply divides `average_precision_at_<k>/total` by `average_precision_at_<k>/max`.

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, 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 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:

• `predictions`: 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`.
• `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 `predictions_`. Values should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. Values outside this range are ignored.
• `k`: Integer, k for @k metric. This will calculate an average precision for range `[1,k]`, as documented above.
• `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:

• `mean_average_precision`: Scalar `float64` `Tensor` with the mean average precision values.
• `update`: `Operation` that increments variables appropriately, and whose value matches `metric`.