Computes recall@k of top-k predictions with respect to sparse labels.
tf.compat.v1.metrics.recall_at_top_k(
labels, predictions_idx, k=None, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
)
Differs from recall_at_k in that predictions must be in the form of top k
class indices, whereas recall_at_k expects logits. Refer to recall_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 always count
towards false_negative_at_<k>.
|
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 |
|---|
recall
|
Scalar float64 Tensor with the value of true_positives divided
by the sum of true_positives and false_negatives.
|
update_op
|
Operation that increments true_positives and
false_negatives variables appropriately, and whose value matches
recall.
|
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.
|
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