Adanet

* Officially support AdaNet on TPU using `adanet.TPUEstimator` with `adanet.Estimator` feature parity.
* Support dictionary candidate pools in `adanet.AutoEnsembleEstimator` constructor to specify human-readable candidate names.
* Improve AutoEnsembleEstimator ability to handling custom `tf.estimator.Estimator` subclasses.
* Introduce `adanet.ensemble` which contains interfaces and examples of ways to learn ensembles using AdaNet. Users can now extend AdaNet to use custom ensemble-learning methods.
* Record TensorBoard `scalar`, `image`, `histogram`, and `audio` summaries on TPU during training.
* Add debug mode to help detect NaNs and Infs during training.
* Improve subnetwork `tf.train.SessionRunHook` support to handle more edge cases.
* ~~Maintain compatibility with TensorFlow versions 1.9 thru 1.13~~ Only works for TensorFlow version >=1.13.
* Improve documentation including adding 'Getting Started' documentation to [adanet.readthedocs.io](http://adanet.readthedocs.io).
* **BREAKING CHANGE**: Importing the `adanet.subnetwork` package using `from adanet.core import subnetwork` will no longer work, because the package was moved to the `adanet/subnetwork` directory. Most users should already be using `adanet.subnetwork` or `from adanet import subnetwork`, and should not be affected.

* Support training on TPU using `adanet.TPUEstimator`.
* Allow subnetworks to specify `tf.train.SessionRunHook` instances for training with `adanet.subnetwork.TrainOpSpec`.
* Add API documentation generation with Sphinx.
* Fix bug preventing subnetworks with Resource variables from working beyond the first iteration.

* Add `shared` field to `adanet.Subnetwork` to deprecate, replace, and be more flexible than `persisted_tensors`.
* Officially support multi-head learning with or without dict labels.
* Rebuild the ensemble across iterations in Python without a frozen graph. This allows users to share more than `Tensors` between iterations including Python primitives, objects, and lambdas for greater flexibility. Eliminating reliance on a `MetaGraphDef` proto also eliminates I/O allowing for faster training, and better future-proofing.
* Allow users to pass custom eval metrics when constructing an `adanet.Estimator`.
* Add `adanet.AutoEnsembleEstimator` for learning to ensemble `tf.estimator.Estimator` instances.
* Pass labels to `adanet.subnetwork.Builder`'s `build_subnetwork` method.
* The TRAINABLE_VARIABLES collection will only contain variables relevant to the current `adanet.subnetwork.Builder`, so not passing `var_list` to the `optimizer.minimize` will lead to the same behavior as passing it in by default.
* Using `tf.summary` inside `adanet.subnetwork.Builder` is now equivalent to using the `adanet.Summary` object.
* Accessing the `global_step` from within an `adanet.subnetwork.Builder` will return the `iteration_step` variable instead, so that the step starts at zero at the beginning of each iteration. One subnetwork incrementing the step will not affect other subnetworks.
* Summaries will automatically scope themselves to the current subnetwork's scope. Similar summaries will now be correctly grouped together correctly across subnetworks in TensorBoard. This eliminates the need for the `tf.name_scope("")` hack.
* Provide an override to force the AdaNet ensemble to grow at the end of each iteration.
* Correctly seed TensorFlow graph between iterations. This breaks some tests that check the outputs of `adanet.Estimator` models.

* Add official support for `tf.keras.layers`.
* Fix bug that incorrectly pruned colocation constraints between iterations.

* Estimator no longer creates eval metric ops in train mode.
* Freezer no longer converts Variables to constants, allowing AdaNet to handle Variables larger than 2GB.
* Fixes some errors with Python 3.

* Initial AdaNet release.

Requirements
* tf-nightly>=1.9.0.dev20180601 || tensorflow>=1.9.0rc0