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LLPR improvements #307
LLPR improvements #307
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Please see comments about how the parameters
are supposed to be interpreted, defined, extracted, and used.
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llpr_model = LLPRUncertaintyModel(model) | ||
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print("Last layer parameters:") |
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see later comment
""" | ||
self.model = self.model.train() # we need gradients w.r.t. parameters | ||
# disable gradients for all parameters that are not in the list | ||
for parameter in self.model.parameters(): |
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The logic behind the introduction of parameters
is very unclear. What are the parameters? How does the relate to the last-layer features? How are these parameters to be defined, and then extracted for the covariance matrix to be correctly calculated? This needs to be documented.
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To me it looks good, didn't understand the shift from computing something on the energies and forces to working directly on the loss functions, but now i understand and see it now.
Thanks for the revamp, filippo, looking forward to playing with this!
Implements a set of improvements to the LLPR:
📚 Documentation preview 📚: https://metatrain--307.org.readthedocs.build/en/307/