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

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

See the guide: Metrics (contrib) > Metric Ops

Computes average precision@k 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.

Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py.