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 [email protected] 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.
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.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
.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.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/metrics/precision_at_top_k