contrib.metrics.streaming_sparse_precision_at_top_k
tf.contrib.metrics.streaming_sparse_precision_at_top_k
tf.contrib.metrics.streaming_sparse_precision_at_top_k
streaming_sparse_precision_at_top_k( top_k_predictions, labels, 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 precision@k of top-k 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 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
: IntegerTensor
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 matchlabels
. -
labels
:int64
Tensor
orSparseTensor
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 andlabels
has shape [batch_size, num_labels]. [D1, ... DN] must matchtop_k_predictions
. Values should be in range [0, num_classes), where num_classes is the last dimension ofpredictions
. 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 ofpredictions
. Ifclass_id
is outside this range, the method returns NAN. -
weights
:Tensor
whose rank is either 0, or n-1, where n is the rank oflabels
. If the latter, it must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglabels
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
: Scalarfloat64
Tensor
with the value oftrue_positives
divided by the sum oftrue_positives
andfalse_positives
. -
update_op
:Operation
that incrementstrue_positives
andfalse_positives
variables appropriately, and whose value matchesprecision
.
Raises:
-
ValueError
: Ifweights
is notNone
and its shape doesn't matchpredictions
, or if eithermetrics_collections
orupdates_collections
are not a list or tuple. -
ValueError
: Iftop_k_predictions
has rank < 2.
© 2017 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_top_k