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Class StatsAggregator
A stateful resource that aggregates statistics from one or more iterators.
Aliases:
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
tf.data.experimental.StatsAggregator.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.