tf.metrics.average_precision_at_k(
labels,
predictions,
k,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/python/ops/metrics_impl.py
.
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.
Args:
labels
:int64
Tensor
orSparseTensor
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 andlabels
has shape [batch_size, num_labels]. [D1, ... DN] must matchpredictions
. Values should be in range [0, num_classes), where num_classes is the last dimension ofpredictions
. Values outside this range are ignored.predictions
: FloatTensor
with shape [D1, ... DN, num_classes] where N >= 1. Commonly, N=1 andpredictions
has shape [batch size, num_classes]. The final dimension contains the logit values for each class. [D1, ... DN] must matchlabels
.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 oflabels
. If the latter, it must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglabels
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
: Scalarfloat64
Tensor
with the mean average precision values.update
:Operation
that increments variables appropriately, and whose value matchesmetric
.
Raises:
ValueError
: if k is invalid.RuntimeError
: If eager execution is enabled.