tensorflow - machine learning framework

	Commit message (Collapse)	Author	Age
*	Automated rollback of commit 950cf87104bfee28e2165fe368f66337b8a1336d	A. Unique TensorFlower	2018-10-10
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*	[tf.data vectorization] Add vectorizer for `Add` op	Rachel Lim	2018-10-09
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*	[tf.data vectorization] Handle captured inputs in MapVectorization optimization	Rachel Lim	2018-10-09
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*	Add ability to vectorize nodes that do not derive from function arguments. ↵	Rachel Lim	2018-10-04
\| \| \| \| \| \|	(This indirectly handles "Const" outputs automagically, since they are always unstacked.) PiperOrigin-RevId: 215749824
*	[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] Deprecate `tf.contrib.data` and introduce `tf.data.experimental` ↵	Derek Murray	2018-10-01
	to replace it. This change prepares `tf.data` for TensorFlow 2.0, where `tf.contrib` will no longer exist. It retains the pre-existing endpoints in `tf.contrib.data` with deprecation warnings. Note there are some exceptions to the move: * Deprecated symbols in `tf.contrib.data` have not been moved to `tf.data.experimental`, because replacements already exist. * `tf.contrib.data.LMDBDataset` has not been moved, because we plan to move it to a SIG-maintained repository. * `tf.contrib.data.assert_element_shape()` has not yet been moved, because it depends on functionality in `tf.contrib`, and it will move in a later change. * `tf.contrib.data.AUTOTUNE` has not yet been moved, because we have not yet determined how to `tf_export()` a Python integer. * The stats-related API endpoints have not yet appeared in a released version of TensorFlow, so these are moved to `tf.data.experimental` without retaining an endpoint in `tf.contrib.data`. In addition, this change includes some build rule and ApiDef refactoring: * Some of the "//third_party/tensorflow/python:training" dependencies had to be split in order to avoid a circular dependency. * The `tf.contrib.stateless` ops now have a private core library for the generated wrappers (and accordingly are hidden in their ApiDef) so that `tf.data.experimental.sample_from_datasets()` can depend on them. PiperOrigin-RevId: 215304249