# summarize performance (accuracy, mean, sd)
def performance_summary(scores, params):
    print(scores, params)
    for score in range(len(scores)):
        m, s = mean(scores[score]), std(scores[score])
        print('Filter=%d: %.3f%% (+/-%.3f)' % (params[score], m, s))
    # boxplot of scores
    plt.boxplot(scores, labels=params)
    plt.savefig('cnn_filters.png')