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Computes precision@k of the predictions with respect to sparse labels.
tf.compat.v1.metrics.precision_at_top_k(
labels, predictions_idx, k=None, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
)
Differs from sparse_precision_at_k
in that predictions must be in the form
of top k
class indices, whereas sparse_precision_at_k
expects logits.
Refer to sparse_precision_at_k
for more details.
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 predictions
. Values
should be in range [0, num_classes), where num_classes is the last
dimension of predictions
. Values outside this range are ignored.predictions_idx
: Integer Tensor
with shape [D1, ... DN, k] where
N >= 1. Commonly, N=1 and predictions has shape [batch size, k].
The final dimension contains the top k
predicted class indices.
[D1, ... DN] must match labels
.k
: Integer, k for @k metric. Only used for the default op name.class_id
: Integer class ID for which we want binary metrics. This should be
in range [0, num_classes], where num_classes is the last dimension of
predictions
. If class_id
is outside this range, the method returns
NAN.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.precision
: Scalar float64
Tensor
with the value of true_positives
divided by the sum of true_positives
and false_positives
.update_op
: Operation
that increments true_positives
and
false_positives
variables appropriately, and whose value matches
precision
.ValueError
: If weights
is not None
and its shape doesn't match
predictions
, or if either metrics_collections
or updates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.