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.
Args |
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.
|
Returns |
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 .
|
Raises |
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.
|