Type StatsAggregator
Namespace tensorflow.data.experimental
Parent PythonObjectContainer
Interfaces IStatsAggregator
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:
To associate a `StatsAggregator` with a tf.data.Dataset object, use
the following pattern:
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.
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.
Show Example
dataset =...
dataset = dataset.apply(tf.data.experimental.latency_stats("total_bytes"))
Methods
Properties
Public instance methods
Tensor 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.
object get_summary_dyn()
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
-
object - A scalar string
tf.Tensorthat summarizes the aggregated statistics.