tf.contrib.metrics.streaming_concat

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.

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 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 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.

Returns:

• value: A Tensor representing the concatenated values.
• update_op: An operation that concatenates the next values.

Raises:

• 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.