Computes average precision@k of predictions with respect to sparse labels.
tf.contrib.metrics.streaming_sparse_average_precision_at_top_k( top_k_predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None )
streaming_sparse_average_precision_at_top_k creates two local variables,
are used to compute the frequency. This frequency is ultimately returned as
average_precision_at_<k>: an idempotent operation that simply divides
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>. Set operations applied to
the true positives and false positives weighted by
false_positive_at_<k> using these
None, weights default to 1. Use weights of 0 to mask values.
Tensorwith shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and
predictions_idxhas shape [batch size, k]. The final dimension must be set and contains the top
kpredicted class indices. [D1, ... DN] must match
labels. Values should be in range [0, num_classes).
SparseTensorwith 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
labelshas shape [batch_size, num_labels]. [D1, ... DN] must match
top_k_predictions. Values should be in range [0, num_classes).
Tensorwhose 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
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.
Tensorwith the mean average precision values.
Operationthat increments variables appropriately, and whose value matches
ValueError: if the last dimension of top_k_predictions is not set.