Training interface #766
Description
In order to give more flexibility, we could expose the entire training loop to the user:
from sbi.inference import nre_loss, ratio_estimator_based_potential, MCMCPosterior
net = classifier_nn("mdn")
data_loader = ...
for e in range(epochs):
optim.zero_grad()
loss = nre_loss(net, theta, x)
loss.backward()
optim.step()
potential = ratio_estimator_based_potential(net, prior, x_o)
posterior = MCMCPosterior(potential, proposal=prior)This would require two things:
- a good way to deal with z-scoring
- separating the loss functions from the rest