pyspark/bigdl/models/lenet/utils.py
def preprocess_mnist(sc, options):
    """
    Preprocess mnist dataset.
    Normalize and transform into Sample of RDDs.
    """
    train_data = get_mnist(sc, "train", options.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TRAIN_MEAN, mnist.TRAIN_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    test_data = get_mnist(sc, "test", options.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TEST_MEAN, mnist.TEST_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    return train_data, test_data
def preprocess_mnist(sc, options):
    """
    Preprocess mnist dataset.
    Normalize and transform into Sample of RDDs.
    """
    train_data = get_mnist(sc, "train", options.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TRAIN_MEAN, mnist.TRAIN_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    test_data = get_mnist(sc, "test", options.dataPath)\
        .map(lambda rec_tuple: (normalizer(rec_tuple[0], mnist.TEST_MEAN, mnist.TEST_STD),
                                rec_tuple[1]))\
        .map(lambda t: Sample.from_ndarray(t[0], t[1]))
    return train_data, test_data
Preprocess mnist dataset.
    Normalize and transform into Sample of RDDs.