Hi Nathan,

The code in the demo notebook is used only for delay inference, not for training. In this code, we load a model that we trained using the RouteNet implementation in "routenet_with_link_cap.py". Then, we load samples from our datasets (generated with our packet-level simulator), make the inference with the RouteNet model and finally compare RouteNet's predictions with the values of our ground truth.

In this case it is not necessary to normalize the output parameters (i.e., delay) since we are not using them for training. We only normalize the input parameters of RouteNet (traffic and link capacities). Note that we then denormalize RouteNet's predictions to compare them with the real (denormalized) delay values of the ground truth (variable "label_Delay"):

predictions = 0.54*preds + 0.37

Regards,

José

Hi

I have noted that the normalisation applied in the demo notebook here:

https://github.com/knowledgedefinednetworking/demo-routenet/blob/master/demo_notebooks/demo.ipynb

Does not apply the same normalisation as the code here:

https://github.com/knowledgedefinednetworking/demo-routenet/blob/master/code/routenet_with_link_cap.py#L85

Specifically, delay is not normalised in the demo notebook.

The demo notebook loads a checkpoint from here:

https://github.com/knowledgedefinednetworking/demo-routenet/tree/master/trained_models

This model, then, was created without normalising the delay also. That implies that the code that was used to train that model is not the same code that is in the routenet_with_link_cap.py code at the link above.

In simpler terms, the demo notebook prediction does not work if the delay is normalised as at routenet_with_link_cap.py#L85. So, the code for training given in this repository is not compatible with the demo notebook and the trained model used as an example.

Regards

Nathan

_______________________________________________
Kdn-users mailing list
Kdn-users@knowledgedefinednetworking.org
https://mail.n3cat.upc.edu/cgi-bin/mailman/listinfo/kdn-users