#add sample data set for i in range(len(traininglist)): DS.addSample(traininglist[i], traininglabels[i]) X = DS['input'] Y = DS['target'] dataTrain, dataTest = DS.splitWithProportion(0.8) xTrain, yTrain = dataTrain['input'], dataTrain['target'] xTest, yTest = dataTest['input'], dataTest['target'] #step3 # trainner use BP algorithm verbose = True trainer = BackpropTrainer(fnn, dataTrain, verbose=True, learningrate=0.5, lrdecay=0.5, momentum=0) trainer.trainUntilConvergence(DS, maxEpochs=10) NetworkWriter.writeToFile(fnn, 'networkClassifier.txt') print("#############") out = fnn.activateOnDataset(DS) print(out) # u can give an input, and use activate function to see the result. #fnn.activate(input) # Reference: http://pybrain.org/docs/tutorial/netmodcon.html#examining-a-network; https://www.zengmingxia.com/use-pybrain-to-fit-neural-networks/