tf.data.experimental.StatsAggregator
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A stateful resource that aggregates statistics from one or more iterators.
tf.data.experimental.StatsAggregator()
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)
Note: This interface is experimental and expected to change. In particular,
we expect to add other implementations of StatsAggregator
that provide
different ways of exporting statistics, and add more types of statistics.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
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