TensorFlow 1 version
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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)