Release 0.7.0

Compatible with TensorFlow 2.4 - 2.13

Changed

• Minimum supported Python version is now 3.8 (3.7 reached end of life in June 2023). (#54)

Release 0.6.0

Compatible with TensorFlow 2.4 - 2.11

Changed

• LMUFeedforward can now be used with unknown sequence lengths, and LMU will use LMUFeedforward for unknown sequence lengths (as long as the other conditions are met, as before). (#52)
• Allow input_to_hidden=True with hidden_cell=None. This will act as a skip connection. (#52)
• Changed order of LMU states so that the LMU memory state always comes first, and any states from the hidden cell come afterwards. (#52)

Fixed

• Fixed errors when setting non-default dtype on LMU layers. (#52)

Release 0.5.0

Compatible with TensorFlow 2.4 - 2.11

Added

• Layers are registered with the Keras serialization system (no longer need to be passed as custom_objects). (#49)

Release 0.4.2

Compatible with TensorFlow 2.1 - 2.9

Added

• Added support for TensorFlow 2.9. (#48)

Release 0.4.1

Compatible with TensorFlow 2.1 - 2.8

Added

• Added support for TensorFlow 2.8. (#46)

Release 0.4.0

Added

• Setting kernel_initializer=None now removes the dense input kernel. (#40)
• The keras_lmu.LMUFFT layer now supports memory_d > 1. keras_lmu.LMU now uses this implementation for all values of memory_d when feedforward conditions are satisfied (no hidden-to-memory or memory-to-memory connections, and the sequence length is not None). (#40)
• Added trainable_theta option, which will allow the theta parameter to be learned during training. (#41)
• Added discretizer option, which controls the method used to solve for the A and B LMU matrices. This is mainly useful in combination with trainable_theta=True, where setting discretizer="euler" may improve the training speed (possibly at the cost of some accuracy). (#41)
• The keras_lmu.LMUFFT layer can now use raw convolution internally (as opposed to FFT-based convolution). The new conv_mode option exposes this. The new truncate_ir option allows truncating the impulse response when running with a raw convolution mode, for efficiency. Whether FFT-based or raw convolution is faster depends on the specific model, hardware, and amount of truncation. (#42)

Changed

• The A and B matrices are now stored as constants instead of non-trainable variables. This can improve the training/inference speed, but it means that saved weights from previous versions will be incompatible. (#41)
• Renamed keras_lmu.LMUFFT to keras_lmu.LMUFeedforward. (#42)

Fixed

• Fixed dropout support in TensorFlow 2.6. (#42)

Release 0.3.1

Changed

• Raise a validation error if hidden_to_memory or input_to_hidden are True when hidden_cell=None. (#26)

Fixed

• Fixed a bug with the autoswapping in keras_lmu.LMU during training. (#28)
• Fixed a bug where dropout mask was not being reset properly in the hidden cell. (#29)

Release 0.3.0

Changed

• Renamed module from lmu to keras_lmu (so it will now be imported via import keras_lmu), renamed package from lmu to keras-lmu (so it will now be installed via pip install keras-lmu), and changed any references to "NengoLMU" to "KerasLMU" (since this implementation is based in the Keras framework rather than Nengo). In the future the lmu namespace will be used as a meta-package to encapsulate LMU implementations in different frameworks. (#24)

Release 0.2.0

Added

• Added documentation for package description, installation, usage, API, examples, and project information. (#20)
• Added LMU FFT cell variant and auto-switching LMU class. (#21)
• LMUs can now be used with any Keras RNN cell (e.g. LSTMs or GRUs) through the hidden_cell parameter. This can take an RNN cell (like tf.keras.layers.SimpleRNNCell or tf.keras.layers.LSTMCell) or a feedforward layer (like tf.keras.layers.Dense) or None (to create a memory-only LMU). The output of the LMU memory component will be fed to the hidden_cell. (#22)
• Added hidden_to_memory, memory_to_memory, and input_to_hidden parameters to LMUCell, which can be used to enable/disable connections between components of the LMU. They default to disabled. (#22)
• LMUs can now be used with multi-dimensional memory components. This is controlled through a new memory_d parameter of LMUCell. (#22)
• Added dropout parameter to LMUCell (which applies dropout to the input) and recurrent_dropout (which applies dropout to the memory_to_memory connection, if it is enabled). Note that dropout can be added in the hidden component through the hidden_cell object. (#22)

Changed

• Renamed lmu.lmu module to lmu.layers. (#22)
• Combined the *_encoders_initializerparameters of LMUCell into a single kernel_initializer parameter. (#22)
• Combined the *_kernel_initializer parameters of LMUCell into a single recurrent_kernel_initializer parameter. (#22)

Removed

• Removed Legendre, InputScaled, LMUCellODE, and LMUCellGating classes. (#22)
• Removed the method, realizer, and factory arguments from LMUCell (they will take on the same default values as before, they just cannot be changed). (#22)
• Removed the trainable_* arguments from LMUCell. This functionality is largely redundant with the new functionality added for enabling/disabling internal LMU connections. These were primarily used previously for e.g. setting a connection to zero and then disabling learning, which can now be done more efficiently by disabling the connection entirely. (#22)
• Removed the units and hidden_activation parameters of LMUCell (these are now specified directly in the hidden_cell. (#22)
• Removed the dependency on nengolib. (#22)
• Dropped support for Python 3.5, which reached its end of life in September 2020. (#22)

0.1.0 release

Initial release of LMU 0.1.0! Supports Python 3.5+.

The API is considered unstable; parts are likely to change in the future.

Thanks to all of the contributors for making this possible!