I am using python and pybrain for Neural Networks. Unfortunately, my sample is realy big and when the program print the errors on the training, my memory get full before the programm completed.
Is there anyway to not print the errors from the functions?
!!!! It's not a python error. It's pybrain feature. It's print the difference of the prediction and the real sample. For example "error: 0.00424".
Each time it makes a prediction, it print this string.
Here is my code
ds = SupervisedDataSet(1, 1)
ds.addSample(x,y) <--- in a "for" to add all my sample
net = FeedForwardNetwork()
inp = LinearLayer(1)
h1 = SigmoidLayer(1)
outp = LinearLayer(1)
net.addOutputModule(outp)
net.addInputModule(inp)
net.addModule(h1)
net.addConnection(FullConnection(inp, h1))
net.addConnection(FullConnection(h1, outp))
net.sortModules()
trainer = BackpropTrainer(net, ds)
trainer.trainOnDataset(ds) ###
trainer.testOnData(verbose=True)### Here is where the function print the errors
net.activate((ind,))
You could use try/except, like this:
or you could find the source of the error. Could you add the error you get to your question?