jsmastl.py 文件源码

def data_stl10():
    """
        Preprocess STL dataset
        :return:
        """

    # These values are specific to CIFAR10
    img_rows = 96
    img_cols = 96
    nb_classes = 10

    # the data, shuffled and split between train and test sets
    #(X_train, y_train), (X_test, y_test) = cifar10.load_data()
    X_train = np.load('x_stl10_train.npy')
    y_train = np.load('y_stl10_train.npy') - 1
    X_test = np.load('x_stl10_test.npy')
    y_test = np.load('y_stl10_test.npy') - 1
    if keras.backend.image_dim_ordering() == 'th':
        X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)
        X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)
    else:
        X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
        X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255
    X_test /= 255
    # np.save("cifar10_legitimate.npy",X_test)
    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


#getting the grid visualization