class NeuralNetworkTest(unittest.TestCase): def setUp(self): self.neuralNetwork = NeuralNetwork(learning_rate=0.15,n_hidden=2,momentum=0.95,activation='tanh') self.acceptanceEpsilon = 0.05 self.seed = 1 self.maxIterations = 11000 def tearDown(self): del self.neuralNetwork del self.acceptanceEpsilon del self.seed del self.maxIterations def retrieveEstimationError(self,x,target): #setting number of inputs and number of outputs in the neural network _ , xColumns = x.shape _ , targetColumns = target.shape self.neuralNetwork.n_in = xColumns self.neuralNetwork.n_out = targetColumns self.neuralNetwork.initialize_weights() self.neuralNetwork.backpropagation(x,target,maxIterations=self.maxIterations) # Network result after training estimation = self.neuralNetwork.feed_forward(x) estimationError = EstimationError(estimatedValues=estimation,targetValues=target) estimationError.computeErrors() totalError = estimationError.getTotalError() return totalError def testXOR(self): numpy.random.seed(seed=self.seed) x = numpy.array([[0,0], [0,1], [1,0], [1,1]]) target = numpy.array([[0] ,[1] ,[1] ,[0]]) totalError = self.retrieveEstimationError(x,target) print 'Error XOR:',totalError self.assertTrue(totalError<=self.acceptanceEpsilon) def testOR(self): numpy.random.seed(seed=self.seed) x = numpy.array([[0,0], [0,1], [1,0], [1,1]]) target = numpy.array([[0] ,[1] ,[1] ,[1]]) totalError = self.retrieveEstimationError(x,target) print 'Error OR:',totalError self.assertTrue(totalError<=self.acceptanceEpsilon) def testAND(self): numpy.random.seed(seed=self.seed) x = numpy.array([[0,0], [0,1], [1,0], [1,1]]) target = numpy.array([[0] ,[0] ,[0] ,[1]]) totalError = self.retrieveEstimationError(x,target) print 'Error AND:',totalError self.assertTrue(totalError<=self.acceptanceEpsilon) #test (x1 or x2) and x3 def testORAND(self): numpy.random.seed(seed=self.seed) x = numpy.array([[0,0,0], [0,0,1], [0,1,0], [1,0,0], [0,1,1], [1,1,0], [1,0,1], [1,1,1]]) target = numpy.array([[0] ,[0] ,[0] ,[0] ,[1] ,[0] ,[1] ,[1]]) totalError = self.retrieveEstimationError(x,target) print 'Error ORAND:',totalError self.assertTrue(totalError<=self.acceptanceEpsilon) #test (x1 and x2) or x3 def testANDOR(self): numpy.random.seed(seed=self.seed) x = numpy.array([[0,0,0], [0,0,1], [0,1,0], [1,0,0], [0,1,1], [1,1,0], [1,0,1], [1,1,1]]) target = numpy.array([[0] ,[1] ,[0] ,[0] ,[1] ,[1] ,[1] ,[1]]) totalError = self.retrieveEstimationError(x,target) print 'Error ANDOR:',totalError self.assertTrue(totalError<=self.acceptanceEpsilon)
data = dataset.readlines()
for entry in data:
(x,y,area) = entry.split()
x = float(x)
y = float(y)
area = int(area)
if area==-1: area = 0
x_input.append([x,y])
target.append([area])
x_input = numpy.array(x_input)
target = numpy.array(target)
numpy.random.seed(seed=1) #using fixed seed for testing purposes
_ , xColumns = x_input.shape
_ , targetColumns = target.shape
n_hidden = 8
momentum = 0
neuralNetwork = NeuralNetwork(learning_rate=0.01,n_in=xColumns,n_hidden=n_hidden,n_out=targetColumns, momentum = momentum)
neuralNetwork.initialize_weights()
results_file = ''.join(['results_lr',str(neuralNetwork.learning_rate),'_m',str(momentum),'_',str(n_hidden),"hidden",file_name.rsplit('.', 1)[0]]+['.out'])
neuralNetwork.backpropagation(x_input,target,maxIterations=10000, batch= False,file_name=results_file)
network_file = ''.join(['trained_lr',str(neuralNetwork.learning_rate),'_m',str(momentum),'_',str(n_hidden),"hidden",file_name.rsplit('.', 1)[0]]+['.nn'])
pickle.dump(neuralNetwork, file(network_file,'wb'))
print neuralNetwork.feed_forward(x_input)
nn2 = pickle.load(file(network_file,'rb'))
print network_file
print nn2.feed_forward(x_input)
