Neural network test: Binary

nn-binary.py

from pybrain.tools.shortcuts import buildNetwork
from pybrain.structure import FeedForwardNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer
from pybrain.structure import LinearLayer, SigmoidLayer, TanhLayer
from pybrain.structure import FullConnection

import random

def int2bin(a):
	arr = [int(x) for x in bin(a)[2:]]
	return [0]*(4-len(arr)) + arr


# Network
net = FeedForwardNetwork()

# Layers
inLayer = LinearLayer(1)
hiddenLayer = SigmoidLayer(16)
outLayer = LinearLayer(4)

net.addInputModule(inLayer)
net.addModule(hiddenLayer)
net.addOutputModule(outLayer)

# Connection
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)

net.addConnection(in_to_hidden)
net.addConnection(hidden_to_out)

# init network
net.sortModules()


# Data set
ds = SupervisedDataSet(1, 4)

#numbers = []
#for num in range(0,10):
#	numbers += [int(random.randint(0, 15))]
numbers = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,15]
for num in numbers:
	ds.addSample((num,), tuple(int2bin(num)))
#print numbers
print ds


# Traning
trainer = BackpropTrainer(net, ds)
if True:
	i = 0
	while i < 1000:
		err = trainer.train()
		print "Traning error:", err
		i=i+1
else:
	err = trainer.trainUntilConvergence()



# Activation
for i in range(16):
	print i, net.activate((i,))



#trainer.trainUntilConvergence()