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-Change log
-==========
-
-
-1.0.2b Series
-----------
-
-Minor
-
-1. Added option for residual networks in coupling blocks using a ``residual=True`` in the coupling layer config.
-
-1.0.3b Series
-----------
-
-Major (Breaking)
-
-1. Coupling layers were refactored to ensure consistency. Old checkpoints may no longer load.
-
-Minor (Features)
-
-1. Added ``attention.py`` module containing helper networks for building transformers
-2. Added ``SetTransformer`` class in ``summary_networks.py`` as a viable alternative to ``DeepSet`` summary networks.
-3. Added ``TimeSeriesTransformer`` class in ``summary_networks.py`` as a viable alternative to ``SequentialNetworks`` summary networks.
-4. Added ``plot_z_score_contraction()`` diagnostic in ``diagnostics.py`` for gauging global inferential adequacy
-5. Added ``Orthogonal`` in ``helper_networks.py`` for learnable generalized permutations.
-
-1.1 Series
-----------
-
-Major (Breaking)
-
-1. Coupling layers have been refactored to ensure easy interoperability between spline flows and affine coupling flows
-2. New internal classes and layers have been added! Saving and loading of old models will not work! However, the interface
-remains consistent.
-3. Model comparison now works for both hierarchical and non-hierarchical Bayesian models. Classes have been generalized
-and semantics go beyond the ``EvidentialNetwork``
-4. Default settings have been changed to reflect recent insights into better hyperparameter settings.
-
-Minor
-
-Features:
-1. Added option for ``permutation='learnable'`` when creating an ``InvertibleNetwork``
-2. Added option for ``coupling_design in ["affine", "spline", "interleaved"]`` when creating an ``InvertibleNetwork``
-3. Simplified passing additional settings to the internal networks. For instance, you
-can now simply do
-``inference_network = InvertibleNetwork(num_params=20, coupling_net_settings={'mc_dropout': True})``
-to get a Bayesian neural network.
-4. ``PMPNetwork`` has been added for model comparison according to findings in https://arxiv.org/abs/2301.11873
-5. ``HierarchicalNetwork`` wrapper has been added to act as a summary network for hierarchical Bayesian models according to
-https://arxiv.org/abs/2301.11873
-6. Publication-ready calibration diagnostic for expected calibration error (ECE) in a model comparison setting has been
-added to ``diagnostics.py`` and is accessible as ``plot_calibration_curves()``
-7. A new module ``experimental`` has been added currently containing ``rectifiers.py``.
-8. Default settings for transformer-based architectures.
-9. Numerical calibration error using ``posterior_calibration_error()``
-
-General Improvements:
-1. Improved docstrings and consistent use of keyword arguments vs. configuration dictionaries
-2. Increased focus on transformer-based architectures as summary networks
-3. Figures resulting ``diagnostics.py`` have been improved and prettified
-4. Added a module ``sensitivity.py`` for testing the sensitivity of neural approximators to model misspecification
-5. Multiple bugfixes, including a major bug affecting the saving and loading of learnable permutations
-
-1.1.2 Series
-----------
-
-1. Bugfix in ``SetTransformer`` affecting saving and loading when using the version with inducing points.
-2. Bugfix in ``SetTransformer`` when using ``train_offline`` and batches result in unequal shapes.
-3. Improved documentation with examples
-
-1.1.3 Series
-----------
-
-1. Bugfix in ``SimulationMemory`` affecting the use of empty folders for initializing a ``Trainer``
-2. Bugfix in ``Trainer.train_from_presimulation()`` for model comparison tasks
-3. Added a classifier two-sample test function ``c2st`` in ``computational_utilities``
-
-
-1.1.4 Series
-----------
-
-1. Add ``bidirectional`` flag to ``SequentialNetwork`` and ``TimeSeriesTransformer`` for potential to improve
-performance.
-2. Deprecate name ``SequentialNetwork`` and use ``SequenceNetwork`` instead to avoid confusion with ``tf.keras.Sequential``.
-3. Change default to ``use_layer_norm=False`` of ``SetTransformer`` due to superior performance on relevant exchangeable models.
-
-
-1.1.5 Series
-----------
-1. Fix bug failing to propagate global context variables for model comparison.
-2. Major revamp of tutorials.
-3. Update dependencies and continuous integration.