Computes average precision@k of predictions with respect to sparse labels.

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.

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