Add kan and hyperparams #182
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This change prevents division by zero. This appears to be the reason for the initial instability of inference, so now we can explore various KAN applications. Note: We should convert this clamp into an argparse param and config for experimentation.
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The wrapped nn.linear uses a different initialization, would it be possible to have this match the previous nn.Linear initialization values? |
Sounds good, will look into whether we can forward the initialization values from model.py |
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I tested too, and seems actually like it should be fine, will make some direct edits and merge in. |
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Merged in the latest repo changes and created a new PR with suggested adjustments: |
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These combine the latest PRs towards adding ability to experiment with KAN as a replacement for linear layers and for the MLP, while retaining backwards compatibility with prior features.
A small change was needed for the current KAN implementation, requiring a clamp to prevent div by zero.
After implementing the above, inference with the sample.py file became much more stable.