TensorFlow 2.0 Beta is available Learn more

tf.data.experimental.StatsAggregator

Class StatsAggregator

A stateful resource that aggregates statistics from one or more iterators.

Aliases:

  • Class tf.compat.v1.data.experimental.StatsAggregator
  • Class tf.data.experimental.StatsAggregator

Defined in python/data/experimental/ops/stats_aggregator.py.

To record statistics, use one of the custom transformation functions defined in this module when defining your tf.data.Dataset. All statistics will be aggregated by the StatsAggregator that is associated with a particular iterator (see below). For example, to record the latency of producing each element by iterating over a dataset:

dataset = ...
dataset = dataset.apply(tf.data.experimental.latency_stats("total_bytes"))

To associate a StatsAggregator with a tf.data.Dataset object, use the following pattern:

aggregator = tf.data.experimental.StatsAggregator()
dataset = ...

# Apply `StatsOptions` to associate `dataset` with `aggregator`.
options = tf.data.Options()
options.experimental_stats.aggregator = aggregator
dataset = dataset.with_options(options)

To get a protocol buffer summary of the currently aggregated statistics, use the StatsAggregator.get_summary() tensor. The easiest way to do this is to add the returned tensor to the tf.GraphKeys.SUMMARIES collection, so that the summaries will be included with any existing summaries.

aggregator = tf.data.experimental.StatsAggregator()
# ...
stats_summary = aggregator.get_summary()
tf.compat.v1.add_to_collection(tf.GraphKeys.SUMMARIES, stats_summary)

__init__

__init__()

Creates a StatsAggregator.

Methods

get_summary

get_summary()

Returns a string tf.Tensor that summarizes the aggregated statistics.

The returned tensor will contain a serialized tf.compat.v1.summary.Summary protocol buffer, which can be used with the standard TensorBoard logging facilities.

Returns:

A scalar string tf.Tensor that summarizes the aggregated statistics.