tf.contrib.metrics.streaming_recall_at_k( predictions, labels, k, weights=None, metrics_collections=None, updates_collections=None, name=None )
See the guide: Metrics (contrib) > Metric
Computes the [email protected] of the predictions with respect to dense labels. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-08. Instructions for updating: Please use
streaming_sparse_recall_at_k, and reshape labels from [batch_size] to [batch_size, 1].
streaming_recall_at_k function creates two local variables,
count, that are used to compute the [email protected] frequency. This frequency is ultimately returned as
recall_at_<k>: an idempotent operation that simply divides
For estimation of the metric over a stream of data, the function creates an
update_op operation that updates these variables and returns the
recall_at_<k>. Internally, an
in_top_k operation computes a
Tensor with shape [batch_size] whose elements indicate whether or not the corresponding label is in the top
total with the reduced sum of
True, and it increments
count with the reduced sum of
None, weights default to 1. Use weights of 0 to mask values.
predictions: A float
Tensorof dimension [batch_size, num_classes].
Tensorof dimension [batch_size] whose type is in
k: The number of top elements to look at for computing recall.
Tensorwhose 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
metrics_collections: An optional list of collections that
recall_at_kshould be added to.
updates_collections: An optional list of collections
update_opshould be added to.
name: An optional variable_scope name.
Tensorrepresenting the [email protected], the fraction of labels which fall into the top
update_op: An operation that increments the
countvariables appropriately and whose value matches
labelshave mismatched shapes, or if
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.