def load_data():
    'Loads CIFAR10 dataset.\n\n  Returns:\n      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n  '
    dirname = 'cifar-10-batches-py'
    origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
    path = get_file(dirname, origin=origin, untar=True)
    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))
Loads CIFAR10 dataset.

Returns:
    Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
def load_data():
    'Loads CIFAR10 dataset.\n\n  Returns:\n      Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n  '
    dirname = 'cifar-10-batches-py'
    origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
    path = get_file(dirname, origin=origin, untar=True)
    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))