class NetworkUtil(object): ''' Class to train and test a neural network ''' def __init__(self, nInputs=None, nHiddenLayers=None, bias=True, new=True, fileName=""): self.params = {} self.config = ConfigProvider().getNNTrainConfig() if new: self.params["nInputs"] = nInputs self.params["nHiddenLayers"] = nHiddenLayers self.params["bias"] = bias self.nn = NeuralNetwork().createNew(nInputs, nHiddenLayers, N_OUTPUT, bias) else: self.nn = NeuralNetwork().load(fileName) def train(self, trainData, convergence=False): start = time.time() print "start Training at " + str(datetime.fromtimestamp(start)) if convergence: self.nn.trainConvergence(trainData, **self.config) else: self.nn.train(trainData, **self.config) print "Training Done in %.2fs" % (time.time() - start) def test(self, testData=None): self.nn.test(testData) print "Testing Done" def activate(self, testData): total = (len(testData) / 2) / 100.0 matrix = np.array([[0, 0, 0], [0, 0, 0]]) resArr = [] for inpt, target in testData: res = self.nn.activate(inpt) clazz = self.nn._clazz(res) resArr.append([res, clazz, target]) matrix[target[0]][clazz] += 1 matrix[0, 2] = matrix[0, 0] / total matrix[1, 2] = matrix[1, 1] / total return matrix, np.array(resArr) def save(self, name): self.nn.save(name) def __str__(self): self.params.update(self.config) return str(self.nn) + "\n" + str(self.params)
class TestNeuralNetwork(unittest.TestCase): def setUp(self): self.nn = NeuralNetwork() self.config = { "learningrate": 0.01, "momentum": 0.1, "maxEpochs": 500, "continueEpochs": 15, "validationProportion": 0.25 } self.nn.createNew(2, 4, 1, bias=True) def _createData(self, nInput, nTarget, values): ds = SupervisedDataSet(nInput, nTarget) for inp, target in values: ds.addSample(inp, target) return ds def createORData(self): values = [((0, 0), (0,)), ((0, 1), (1,)), ((1, 0), (1,)), ((1, 1), (1,))]*2 return self._createData(2, 1, values) def createXORData(self): values = [((0, 0), (0,)), ((0, 1), (1,)), ((1, 0), (1,)), ((1, 1), (0,))]*2 return self._createData(2, 1, values) def createANDData(self): values = [((0, 0), (0,)), ((0, 1), (0,)), ((1, 0), (0,)), ((1, 1), (1,))]*2 return self._createData(2, 1, values) def removeFile(self): try: os.remove(self.nn.path + name + ".nn") except OSError as e: print e.message def test_xor(self): ds = self.createXORData() self.nn.train(ds, **self.config) #TODO may fail with delta 0.2 for inpt, target in ds: self.assertAlmostEqual(self.nn.activate(inpt), target, delta=0.2) def test_and(self): ds = self.createANDData() self.nn.train(ds, **self.config) #TODO may fail with delta 0.2 for inpt, target in ds: self.assertEqual(self.nn._clazz(self.nn.activate(inpt)), target) def test_saveAndLoad(self): ds = self.createORData() self.nn.train(ds) self.nn.save(name) nn2 = NeuralNetwork() nn2.load(name) self.assertNotEqual(self.nn, nn2) self.assertTrue(sameEntries(self.nn.net.params, nn2.net.params)) for inpt, _ in ds: self.assertEqual(self.nn.activate(inpt), nn2.activate(inpt)) self.removeFile() def test_test(self): ds = self.createORData() self.nn.train(ds) self.nn.test(ds, False) def test_testBeforeTrain(self): with self.assertRaises(ValueError): self.nn.test(None, False)