Dev #634
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Dev #634
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* Add weight normalization * vmap over axes instead of for-loop to norm over multiple axes, docs for weight_norm * Removed defaulted arguments, simplified `_norm_except_axis`, silenced `beartype` warning. * Remove `self.v` from `WeightNorm`, use `self.layer.weight` directly --------- Co-authored-by: boris <[email protected]> Co-authored-by: Patrick Kidger <[email protected]>
This is to fix a crash on TPUs, see #628.
This is to be more in line with the abstract/final design pattern, that we generally try to subscribe to: https://docs.kidger.site/equinox/pattern/ This advocates against using `super()`. In practice we don't use it absolutely everywhere, for backward compatibility: there are two cases where we can't use it: - `Conv`, `ConvTranspose`, `Pool`, `AdaptivePool` are concrete classes which are subclassed. As such their subclasses (e.g. `Conv1d`) aren't strict. - The name of `StatefulLayer` does not start with `"Abstract"`. This means that we can't make any downstream layers (`BatchNorm`, `SpectralNorm`) strict either.
…ed in both Equinox and Optimistix
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