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tf.contrib.metrics.streaming_concat

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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.

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.