What is the intuition behind setting parameters in s_span? about torchdyn

[mrpositron]

We perform the same number of function evaluations. Thus, I don't get how x affects the training.

[massastrello]

@mrpositron Although it is not possible to answer your question in a concise manner, I would say that the phenomenon you are experiencing is related to the following models choice (which you did not specify):

1. Vector field type if your vector field explicitly depends on time (i.e. f=f(s, z))), then different s_span in training should likely lead to very different vector fields and thus different training dynamics.
2. ODE solver and backprop sensitivity

[mrpositron]

Thanks for your reply!

I did not specify abot my model choise. I am sorry.
My model consists of GRU encoder and NODE decoder. (i.e. RNN-NODE). Here is a chunk of my code that defines Neural ODE:

self.ode_solve = NeuralDE(self.func, sensitivity = 'autograd', solver = 'rk4', s_span = self.span)

During forward pass I get values from the trajectory obtained from the self.ode_solve (trajectory = self.ode_solve.trajectory(hidden, self.span)) and pass it to fc layer.