def get_mnist_loader():
    MNIST_MEAN = np.array([0.1307])
    MNIST_STD = np.array([0.3081])
    normTransform = transforms.Normalize(MNIST_MEAN, MNIST_STD)
    trainTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    testTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    trainset = torchvision.datasets.MNIST(root='../data', train=True, download=True, transform=trainTransform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
    testset = torchvision.datasets.MNIST(root='../data', train=False, download=True, transform=testTransform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
    return (trainloader, testloader)
def get_mnist_loader():
    MNIST_MEAN = np.array([0.1307])
    MNIST_STD = np.array([0.3081])
    normTransform = transforms.Normalize(MNIST_MEAN, MNIST_STD)
    trainTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    testTransform = transforms.Compose([transforms.ToTensor(), normTransform])
    trainset = torchvision.datasets.MNIST(root='../data', train=True, download=True, transform=trainTransform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
    testset = torchvision.datasets.MNIST(root='../data', train=False, download=True, transform=testTransform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
    return (trainloader, testloader)