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Computes the precision of the predictions with respect to the labels.
tf.compat.v1.metrics.precision(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The precision
function creates two local variables,
true_positives
and false_positives
, that are used to compute the
precision. This value is ultimately returned as precision
, an idempotent
operation that simply divides true_positives
by the sum of true_positives
and false_positives
.
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
. update_op
weights each prediction by the corresponding value in
weights
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args | |
---|---|
labels
|
The ground truth values, a Tensor whose dimensions must match
predictions . Will be cast to bool .
|
predictions
|
The predicted values, a Tensor of arbitrary dimensions. Will
be cast to bool .
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and 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 precision should
be added to.
|
updates_collections
|
An optional list of collections that update_op should
be added to.
|
name
|
An optional variable_scope name. |
Returns | |
---|---|
precision
|
Scalar float 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 predictions and labels have mismatched shapes, or 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. |