How to do supervised deepbelief training in PyBrain?

Question

I have trouble getting the DeepBeliefTrainer to work on my data in PyBrain/Python. Since I can't find any examples other than unsupervised on how to use the deep learning in PyBrain, I hope that someone can give examples that would show a basic concept of usage.

I have tried to initialize using:

epochs = 100
layerDims = [768,100,100,1]

net = buildNetwork(*layerDims)
dataset = self.dataset
trainer = DeepBeliefTrainer(net, dataset=dataSet)
trainer.trainEpochs(epochs)

I try to use a SupervisedDataset for regression, but the training just fails. Have anyone succeded with using deeplearning trainer for supervised machine learning? And how did you do it?

Error I get:

File "/Library/Python/2.7/site-packages/PyBrain-0.3.1-py2.7.egg/pybrain/structure/networks/rbm.py", line 39, in __init__
self.con = self.net.connections[self.visible][0]
KeyError: None

Answer 1

It's because your initial network: net = buildNetwork(*layerDims) doesn't have a layer with the name of the visible layer in your deep belief network, which is 'visible'. So, in order to find it mapped in the initial network, you can do something like:

net.addInputModule(LinearLayer(input_dim, 'visible'))
[...]
trainer = DeepBeliefTrainer(net, dataset=dataSet)