def testDirectories(self): self.assertTrue(os.path.exists(cifarDirectories.base())) self.assertTrue(os.path.exists(cifarDirectories.code())) self.assertTrue(os.path.exists(cifarDirectories.tests())) self.assertTrue(os.path.exists(cifarDirectories.data())) self.assertTrue(os.path.exists(cifarDirectories.cifarKaggle())) self.assertTrue(os.path.exists(cifarDirectories.demos())) self.assertTrue(os.path.exists(cifarDirectories.DeepLearningTutorials())) self.assertTrue(os.path.exists(cifarDirectories.DeepLearningTutorialsCode()))
def testDirectories(self): self.assertTrue(os.path.exists(cifarDirectories.base())) self.assertTrue(os.path.exists(cifarDirectories.code())) self.assertTrue(os.path.exists(cifarDirectories.tests())) self.assertTrue(os.path.exists(cifarDirectories.data())) self.assertTrue(os.path.exists(cifarDirectories.cifarKaggle())) self.assertTrue(os.path.exists(cifarDirectories.demos())) self.assertTrue( os.path.exists(cifarDirectories.DeepLearningTutorials())) self.assertTrue( os.path.exists(cifarDirectories.DeepLearningTutorialsCode()))
def __init__(self): class Subdata(object): def __init__(self): self.x = [] self.y = [] def append(self, x, y): self.x.append(x) self.y.append(y) def array(self): return numpy.array(self.x, dtype=numpy.float32), numpy.array(self.y) def irisType(iris): if iris == 'Iris-setosa': return 0 if iris == 'Iris-versicolor': return 1 if iris == 'Iris-virginica': return 2 return 0 maxSepalLength = 7.9 maxSepalWidth = 4.4 maxPetalLength = 6.9 maxPetalWidth = 2.5 train = Subdata() test = Subdata() valid = Subdata() randomState = random.getstate() random.seed(42) irisPath = os.path.join(cifarDirectories.data(), 'iris.data') f = open(irisPath, 'r') reader = csv.reader(f, delimiter=',') for row in reader: if len(row) > 0: sepalLength = float(row[0]) / maxSepalLength sepalWidth = float(row[1]) / maxSepalWidth petalLength = float(row[2]) / maxPetalLength petalWidth = float(row[3]) / maxPetalWidth x = [sepalLength, sepalWidth, petalLength, petalWidth] y = irisType(row[4]) r = random.random() if r < 0.8: train.append(x, y) elif r < 0.9: test.append(x, y) else: valid.append(x, y) f.close() random.setstate(randomState) Dataset.__init__(self, train.array(), valid.array(), test.array())
def getData(filename): path = os.path.join(cifarDirectories.data(), 'cifar-10-python.tar.gz') f = tarfile.open(path, 'r:gz') data = cPickle.load(f.extractfile(filename)) f.close() return data
def mnist(): path = os.path.join(cifarDirectories.data(), 'mnist.pkl.gz') f = gzip.open(path, 'rb') data = cPickle.load(f) f.close() return data