tf.contrib.metrics.streaming_concat(
values,
axis=0,
max_size=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
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
length of the concatenated axis.
This op allows for evaluating metrics that cannot be updated incrementally using the same framework as other streaming metrics.
Args:
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 ofvalue
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 thatvalue
should be added to.updates_collections
: An optional list of collectionsupdate_op
should be added to.name
: An optional variable_scope name.
Returns:
value
: ATensor
representing the concatenated values.update_op
: An operation that concatenates the next values.
Raises:
ValueError
: ifvalues
does not have a statically known rank,axis
is not in the valid range or the size ofvalues
is not statically known along any axis other thanaxis
.