Release history¶

0.4.2 (May 17, 2022)¶

Compatible with TensorFlow 2.1 - 2.9

Added

Compatible with TensorFlow 2.1 - 2.8

Added

Compatible with TensorFlow 2.1 - 2.7

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

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)

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)

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_initializer``parameters 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)

Initial release of KerasLMU 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!