pyFDN.model_response#

pyFDN.model_response(model, excitation=None)[source]#

Run model on an impulse and wrap the result in a Response.

Pass excitation to reuse a tensor across steps; impulse_excitation() builds one.

Gradients flow through the returned response, so this is what the trainer calls each step – and, detached, what you can call to inspect a model.