tensorflow - machine learning framework

	Commit message (Collapse)	Author	Age
*	[tf.data] Adding `tf.data.Options()`, `tf.data.Dataset.options()`, and ↵	Jiri Simsa	2018-10-01
\| \| \| \| \| \|	`tf.data.Dataset.with_options()` to make it possible to respectively represent, get, and set options, such as optimization configuration, of a tf.data input pipeline. PiperOrigin-RevId: 215310764
*	[tf.data Introducing tf.data.Dataset.reduce() which reduces elements of a ↵	Jiri Simsa	2018-09-27
\| \| \| \| \| \|	(finite) dataset to a single element. PiperOrigin-RevId: 214852364
*	[tf.data] Introducing `tf.data.Dataset.window(size, shift, stride, ↵	Jiri Simsa	2018-09-17
\| \| \| \| \| \| \| \|	drop_remainder)`, which can be used for combining elements of input dataset into "windows". A window is itself a finite dataset and, among other things, can be used for generalized batching (see https://github.com/tensorflow/community/pull/5 for details). PiperOrigin-RevId: 213360134
*	[tf.data] Adding support for `num_parallel_calls` to ↵	Jiri Simsa	2018-09-06
\| \| \| \| \| \| \| \|	`tf.data.Dataset.interleave`. Unlike the `tf.data.contrib.parallel_interleave` whose parallelism is tied to the `cycle_length` argument, the newly introduced `num_parallel_calls` argument of `tf.data.Dataset.interleave` is decoupled from the `cycle_length` argument and identifies the degree of parallelism to use for fetching output elements. PiperOrigin-RevId: 211886816
*	Keep 2 sets of API Goldens (goldens/v1 and goldens/v2).	Anna R	2018-08-07
	PiperOrigin-RevId: 207781405