tf.metrics.precision_at_top_k(
labels,
predictions_idx,
k=None,
class_id=None,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/python/ops/metrics_impl.py
.
Computes precision@k of the predictions with respect to sparse labels.
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.
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_idx
: IntegerTensor
with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the topk
predicted class indices. [D1, ... DN] must matchlabels
.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 ofpredictions
. Ifclass_id
is outside this range, the method returns NAN.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:
precision
: Scalarfloat64
Tensor
with the value oftrue_positives
divided by the sum oftrue_positives
andfalse_positives
.update_op
:Operation
that incrementstrue_positives
andfalse_positives
variables appropriately, and whose value matchesprecision
.
Raises:
ValueError
: Ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple.RuntimeError
: If eager execution is enabled.