Class StatsAggregator
Defined in tensorflow/python/data/experimental/ops/stats_aggregator.py.
A stateful resource that aggregates statistics from one or more iterators.
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 = dataset_ops.Options()
options.experimental_stats = tf.data.experimental.StatsOptions(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.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.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.