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Computes precision@k of top-k predictions with respect to sparse labels.
tf.contrib.metrics.streaming_sparse_precision_at_top_k(
top_k_predictions, labels, class_id=None, weights=None,
metrics_collections=None, updates_collections=None, name=None
)
If class_id
is not specified, we calculate precision as the ratio of
true positives (i.e., correct predictions, items in top_k_predictions
that are found in the corresponding row in labels
) to positives (all
top_k_predictions
).
If class_id
is specified, we calculate precision by considering only the
rows in the batch for which class_id
is in the top k
highest
predictions
, and computing the fraction of them for which class_id
is
in the corresponding row in labels
.
We expect precision to decrease as k
increases.
streaming_sparse_precision_at_top_k
creates two local variables,
true_positive_at_k
and false_positive_at_k
, that are used to compute
the precision@k frequency. This frequency is ultimately returned as
precision_at_k
: an idempotent operation that simply divides
true_positive_at_k
by total (true_positive_at_k
+ false_positive_at_k
).
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, set operations applied to top_k_predictions
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 | |
---|---|
top_k_predictions
|
Integer Tensor with shape [D1, ... DN, k] where N >= 1.
Commonly, N=1 and top_k_predictions has shape [batch size, k]. The final
dimension contains the indices of top-k labels. [D1, ... DN] must match
labels .
|
labels
|
int64 Tensor or SparseTensor with shape [D1, ... DN,
num_labels], where 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 top_k_predictions .
Values should be in range [0, num_classes), where num_classes is the last
dimension of predictions . Values outside this range are ignored.
|
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.
|
ValueError
|
If top_k_predictions has rank < 2.
|