How to compare several MLP layer layouts with each other

Some days ago I showed how you can run several experiments in one go.
Obviously this can be used to compare several ANN layer architectures as an alternative to the approach discussed in this (much earlier) post

There is an example configuration shipped with Nkululeko, and you simply can specify your layer specifications per experiment like this:

classifiers = [
    {'--model': 'mlp',
    '--layers': '\"{\'l1\':16,\'l2\':4}\"'},
    {'--model': 'mlp',
    '--layers': '\"{\'l1\':64,\'l2\':16}\"'},
    {'--model': 'mlp',
    '--layers': '\"{\'l1\':128,\'l2\':32}\"',
    '--learning_rate': '.0001',
    '--drop': '.3',},
    {'--model': 'xgb',
    {'--model': 'svm',

i.e in this example three MLP classifiers are specified with architectures:

  • (hidden) layer 1 with 16 neurons, and (hidden) layer 2 with 4 neurons
  • one layer with 64 and one with 16 neurons
  • and a third one with
    • one layer with 128 and a second one with 32 neurons,
    • learning rate of .0001 and
    • dropout probability of 30%

and, for comparison:

  • a XGB classifier
  • and a SVM classifier

both only need to be trained one epoch because there are no weights to be adapted.
The MLP classifiers are trained with the epoch number that is specified in the sceleton config file

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