tf.contrib.metrics.streaming_sparse_average_precision_at_k( predictions, labels, k, weights=None, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
See the guide: Metrics (contrib) > Metric Ops
Computes average [email protected] of predictions with respect to sparse labels.
See sparse_average_precision_at_k
for details on formula. weights
are applied to the result of sparse_average_precision_at_k
streaming_sparse_average_precision_at_k
creates two local variables, average_precision_at_<k>/total
and average_precision_at_<k>/max
, that are used to compute the frequency. This frequency is ultimately returned as average_precision_at_<k>
: an idempotent operation that simply divides average_precision_at_<k>/total
by average_precision_at_<k>/max
.
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, a top_k
operation computes a Tensor
indicating the top k
predictions
. Set operations applied to top_k
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.
predictions
: Float Tensor
with shape [D1, ... DN, num_classes] where N >= 1. Commonly, N=1 and predictions
has shape [batch size, num_classes]. The final dimension contains the logit values for each class. [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 predictions_
. Values should be in range [0, num_classes), where num_classes is the last dimension of predictions
. Values outside this range are ignored.k
: Integer, k for @k metric. This will calculate an average precision for range [1,k]
, as documented above.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.mean_average_precision
: Scalar float64
Tensor
with the mean average precision values.update
: Operation
that increments variables appropriately, and whose value matches metric
.
© 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/contrib/metrics/streaming_sparse_average_precision_at_k