25 Sep
2019
25 Sep
'19
11:06 a.m.
Hello Nathan,
The normalization may be considered a configurable parameter in
RouteNet. You may use the normalization function that is more convenient
depending on your training data. This means that it is not necessary to
apply always a linear function to normalize the data. The only condition
is that during the inference you must apply the opposite transformation
to the output.
In the case of RouteNet with forwarding nodes, this is a work in
progress where we have considered to use a logarithmic function to
normalize the delay data and assess if the model fits better.
Regards,
José
El 22/09/2019 a las 17:27, Nathan Sowatskey escribió:
Hi
In the demo code, for example in:
https://github.com/knowledgedefinednetworking/demo-routenet/blob/master/dem…
A form of normalisation is applied:
predictions = 0.54*preds + 0.37
This is consistent with the parse() function here:
https://github.com/knowledgedefinednetworking/demo-routenet/blob/master/cod…
if k == 'delay':
features[k] = (features[k] - 0.37) / 0.54
if k == 'traffic':
features[k] = (features[k] - 0.17) / 0.13
if k == 'link_capacity':
features[k] = (features[k] - 25.0) / 40.0
There is another case here:
https://github.com/knowledgedefinednetworking/network-modeling-GNN/blob/mas…
if k == 'delay':
features[k] = (tf.math.log(features[k]) + 1.78) / 0.93
if k == 'traffic':
features[k] = (features[k] - 0.28) / 0.15
if k == 'jitter':
features[k] = (features[k] - 1.5) / 1.5
if k == 'link_capacity':
features[k] = (features[k] - 27.0) / 14.86
if k == 'queue_sizes':
features[k] = (features[k] - 16.5) / 15.5
What is the basis for this normalisation and those specific constants please?
Why does the normalisation in routenet_with_link_cap and routenet_with_forwarding_nodes
differ?
Many thanks
Nathan
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