tf.contrib.metrics.streaming_concat(values, axis=0, max_size=None, metrics_collections=None, updates_collections=None, name=None)
See the guide: Metrics (contrib) > Metric Ops
Concatenate values along an axis across batches.
The function streaming_concat
creates two local variables, array
and size
, that are used to store concatenated values. Internally, array
is used as storage for a dynamic array (if maxsize
is None
), which ensures that updates can be run in amortized constant time.
For estimation of the metric over a stream of data, the function creates an update_op
operation that appends the values of a tensor and returns the value
of the concatenated tensors.
This op allows for evaluating metrics that cannot be updated incrementally using the same framework as other streaming metrics.
values
: Tensor
to concatenate. Rank and the shape along all axes other than the axis to concatenate along must be statically known.axis
: optional integer axis to concatenate along.max_size
: optional integer maximum size of value
along the given axis. Once the maximum size is reached, further updates are no-ops. By default, there is no maximum size: the array is resized as necessary.metrics_collections
: An optional list of collections that value
should be added to.updates_collections
: An optional list of collections update_op
should be added to.name
: An optional variable_scope name.value
: A Tensor
representing the concatenated values.update_op
: An operation that concatenates the next values.ValueError
: if values
does not have a statically known rank, axis
is not in the valid range or the size of values
is not statically known along any axis other than axis
.Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
© 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_concat