xor.py

#learn XOR with a nerual network with saving of the learned paramaters

import pybrain
from pybrain.datasets import *
from pybrain.tools.shortcuts import buildNetwork
from pybrain.supervised.trainers import BackpropTrainer
import pickle

if __name__ == "__main__":
	ds = SupervisedDataSet(2, 1)
	ds.addSample( (0,0) , (0,))
	ds.addSample( (0,1) , (1,))
	ds.addSample( (1,0) , (1,))
	ds.addSample( (1,1) , (0,))

	net = buildNetwork(2, 4, 1, bias=True)

	# try:
        #         f = open('_learned', 'r')
	# 	net = pickle.load(f)
	# 	f.close()
	# except:
        trainer = BackpropTrainer(net, learningrate = 0.01, momentum = 0.99)
        trainer.trainOnDataset(ds, 3000)
        trainer.testOnData()
        # f = open('_learned', 'w')
        # pickle.dump(net, f)
        # f.close()
	

	print net.activate((1,1))
        print net.activate((1,0))
        print net.activate((0,1))
        print net.activate((0,0))