tf.contrib.metrics.streaming_sparse_precision_at_k( predictions, labels, k, class_id=None, 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 [email protected] of the predictions with respect to sparse labels.
If class_id
is not specified, we calculate precision as the ratio of true positives (i.e., correct predictions, items in the top k
highest 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_k
creates two local variables, true_positive_at_<k>
and false_positive_at_<k>
, that are used to compute the [email protected] 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, 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.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.
© 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_precision_at_k