Akida versions compatibility

This document is for users who need backwards compatibility across different versions of Akida either for code or models.

Upgrading models with legacy quantizers

The WeightQuantizer and TrainableWeightQuantizer objects from the CNN2SNN tool have been renamed to StdWeightQuantizer and TrainableStdWeightQuantizer in version 1.8.10.


TrainableStdWeightQuantizer is deprecated since version 2.1.0.

This introduces an incompatibility as models containing object with deprecated names will not be recognised anymore and thus cannot be loaded using cnn2snn.load_quantized_model.

In fact, loading such models would lead to the following error:

from cnn2snn import load_quantized_model
model = load_quantized_model('model_with_legacy_quantizers.h5')

ValueError: Unknown object: WeightQuantizer

Upgrading legacy models is done thru the CNN2SNN CLI upgrade action:

cnn2snn upgrade -m 'model_with_legacy_quantizers.h5' -o 'upgraded_model.h5'


The upgrade CLI and associated API as been deprecated in version 2.1.0. If still using legacy models, you should use intermediate versions to upgrade them.

This command will save an upgraded_model.h5 file that can be loaded with latest Akida tools.


While two model formats can be upgraded: h5/hdf5 Keras format and tf/pb format ; all information (compile, training, …) is preserved when upgrading h5/hdf5 models but only configuration and weights are preserved when handling tf/pb models.