AdaNet v0.4.0

• 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.