core.py 文件源码

def get_cifar(nb_classes=10):
    # input image dimensions
    # img_rows, img_cols = 32, 32
    # # The CIFAR10 images are RGB.
    # img_channels = 3

    # The data, shuffled and split between train and test sets:
    (X_train, y_train), (X_test, y_test) = cifar10.load_data()

    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
    print('X_train shape:', X_train.shape)
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')

    # Convert class vectors to binary class matrices.
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)

    return X_train, Y_train, X_test, Y_test