def load_data()

in ec2-spot-deep-learning-training/download_cifar10.py [0:0]

• 25 lines of code
• 5 McCabe index (conditional complexity)

def load_data(download_path=os.getcwd()):
    """Loads CIFAR10 dataset.

    # Returns
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
    """
    dirname = 'cifar-10-batches-py'
    origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'

    if not os.path.exists(os.path.join(download_path, dirname)):
        if not os.path.exists(download_path):
            os.mkdir(download_path)
        path = get_file(dirname, origin=origin, untar=True, cache_dir=download_path, cache_subdir='')

    else:
        path = os.path.join(download_path, dirname)
        print("Dataset already exists at: {}".format(path))

    num_train_samples = 50000

    x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')
    y_train = np.empty((num_train_samples,), dtype='uint8')

    for i in range(1, 6):
        fpath = os.path.join(path, 'data_batch_' + str(i))
        (x_train[(i - 1) * 10000: i * 10000, :, :, :],
         y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)

    fpath = os.path.join(path, 'test_batch')
    x_test, y_test = load_batch(fpath)

    y_train = np.reshape(y_train, (len(y_train), 1))
    y_test = np.reshape(y_test, (len(y_test), 1))

    if K.image_data_format() == 'channels_last':
        x_train = x_train.transpose(0, 2, 3, 1)
        x_test = x_test.transpose(0, 2, 3, 1)

    return (x_train, y_train), (x_test, y_test)