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Setting the solver to weights=True can improve accuracy by avoiding the use of DecodeNeurons, especially in the context of recurrent connections. I can add some examples in #224 to demonstrate.
One reason this is necessary is because weights=True can't be set on a passthrough connection. e.g., if you have x -> passthrough -> x as in the docs/examples/integrator_multi_d.ipynb example. Even though the passthrough ends up getting removed, there's no way to signal to the backend that I'd like to have the new connection use weights=True.
This cannot be done at the config level because if the network contains a node then you get the error:
nengo.exceptions.ValidationError: Connection.solver: weight solvers only work for connections from ensembles (got 'Node')
The text was updated successfully, but these errors were encountered:
Continuation of #74.
Setting the solver to
weights=True
can improve accuracy by avoiding the use ofDecodeNeurons
, especially in the context of recurrent connections. I can add some examples in #224 to demonstrate.One reason this is necessary is because
weights=True
can't be set on a passthrough connection. e.g., if you havex -> passthrough -> x
as in thedocs/examples/integrator_multi_d.ipynb
example. Even though thepassthrough
ends up getting removed, there's no way to signal to the backend that I'd like to have the new connection useweights=True
.This cannot be done at the config level because if the network contains a node then you get the error:
The text was updated successfully, but these errors were encountered: