绘制几个模型某性能指标在某个范围内的大小比较图
我们在做深度学习实验时，很多时候会需要对几个模型的性能进行对比并进行可视化，从而清楚地展示模型性能的大小关系。
下面以几个模型的精确度大小比较为例：
代码：
import matplotlib.pyplot as plt
CNNLSTM_valence_acc = [0.6885742, 0.690625, 0.69453126, 0.69277346, 0.69003904, 0.6965332, 0.6917969, 0.68652344, 0.6916992, 0.69140625, 0.6933594, 0.6972656, 0.69277346, 0.6941406, 0.69501954, 0.69257814, 0.6929687, 0.69208986, 0.6905273, 0.6930664, 0.69384766, 0.6856445, 0.6958984, 0.6929687, 0.693457, 0.69501954, 0.6893555, 0.7, 0.6904297, 0.69628906, 0.6921875, 0.6910156, 0.6988281, 0.69492185, 0.69541013, 0.69443357, 0.69257814, 0.6955078, 0.6942383, 0.69628906, 0.6933594, 0.6976563, 0.69267577, 0.6948242, 0.69277346, 0.6964844, 0.6923828, 0.6959961, 0.6993164, 0.6935547]
CNN3Conv_valence_acc = [0.53, 0.57, 0.52, 0.55, 0.57, 0.53, 0.54, 0.55, 0.5, 0.49, 0.53, 0.45, 0.55, 0.56, 0.54, 0.49, 0.54, 0.55, 0.57, 0.52, 0.49, 0.54, 0.5, 0.49, 0.56, 0.52, 0.52, 0.55, 0.55, 0.57, 0.5, 0.5, 0.52, 0.55, 0.54, 0.57, 0.53, 0.55, 0.51, 0.54, 0.54, 0.53, 0.56, 0.52, 0.53, 0.53, 0.54, 0.53, 0.5, 0.54]
CNN5Conv_valence_acc = [0.69, 0.6699999999999999, 0.6799999999999999, 0.66, 0.54, 0.57, 0.6699999999999999, 0.6699999999999999, 0.61, 0.61, 0.63, 0.62, 0.63, 0.62, 0.6699999999999999, 0.62, 0.66, 0.6699999999999999, 0.62, 0.58, 0.61, 0.66, 0.61, 0.65, 0.63, 0.62, 0.62, 0.59, 0.62, 0.63, 0.62, 0.63, 0.62, 0.62, 0.63, 0.61, 0.62, 0.62, 0.6699999999999999, 0.63, 0.64, 0.64, 0.65, 0.6699999999999999, 0.62, 0.69, 0.58, 0.59, 0.63, 0.61]
print(len(CNNLSTM_valence_acc))
print(len(CNN3Conv_valence_acc))
print(len(CNN5Conv_valence_acc))
x = range(len(CNN5Conv_valence_acc))
plt.plot(x, CNNLSTM_valence_acc, label=u'CNN-LSTM')
plt.plot(x, CNN3Conv_valence_acc, label=u'CNN3Conv')
plt.plot(x, CNN5Conv_valence_acc, label=u'CNN5Conv')
plt.legend()
plt.xlabel(u"epoch")
plt.ylabel(u"accuracy")
plt.show()
效果图：