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)