Remove final loss in training.

	# print loss info

	print("iter {} loss {:.3f} delta loss {:.3f} %".format(i, loss.item(), 100*(loss_new/loss-1.0).item()))

# compute loss of final iteration

with torch.no_grad():

	loss = loss_fn(args.train_samples, None)

results["train"]["loss"].append(loss.item())

# record and print training time

results["train"]["time"] = time.time()-time_0

print("Took {:.3f} seconds".format(results["train"]["time"]))