Proper hyperparameter handling #53
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Proposed changes
Until now, all parameters related to model training and inference were hardcoded in the respective scripts. Now we have yaml config files for the training. The parameters are subsequently stored in the model files itself and are automatically loaded later during inference to configure the model and dataset.
Additionally we also store the optimizer and lr_sheduler state which enables effortless restarting.
Old models were converted using a small script.
Related issues
Close #21
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