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RELEASE.md

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Current version (0.7.0.dev)

  • Under development.
  • TODO: Add official Keras Model support, including Keras layers, Sequential, and Model subclasses for defining subnetworks.

Release 0.6.1

  • Maintain compatibility with TensorFlow versions >=1.9.

Release 0.6.0

  • 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.
  • 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.

Release 0.5.0

  • 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.

Release 0.4.0

  • 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.

Release 0.3.0

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

Release 0.2.0

  • 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.

Release 0.1.0

  • Initial AdaNet release.

Requirements

  • tf-nightly>=1.9.0.dev20180601 || tensorflow>=1.9.0rc0