Load the dataset and split it to train/valid data

    :param dataset_name: string

    Returns:
    train_data: int array
        training dataset
    val_data: int array
        valid dataset

def get_dataset(dataset_name):
    """Load the dataset and split it to train/valid data

    :param dataset_name: string

    Returns:
    train_data: int array
        training dataset
    val_data: int array
        valid dataset
    """
    # mnist
    if dataset == "mnist":
        train_data = gluon.data.DataLoader(
            gluon.data.vision.MNIST('./data', train=True, transform=transformer),
            batch_size, shuffle=True, last_batch='discard')

        val_data = gluon.data.DataLoader(
            gluon.data.vision.MNIST('./data', train=False, transform=transformer),
            batch_size, shuffle=False)
    # cifar10
    elif dataset == "cifar10":
        train_data = gluon.data.DataLoader(
            gluon.data.vision.CIFAR10('./data', train=True, transform=transformer),
            batch_size, shuffle=True, last_batch='discard')

        val_data = gluon.data.DataLoader(
            gluon.data.vision.CIFAR10('./data', train=False, transform=transformer),
            batch_size, shuffle=False)

    return train_data, val_data