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show.py

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+import matplotlib.pyplot as plt
+
+
+# -*- coding: UTF-8 -*-
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+# 这里导入你自己的数据
+# ......
+# ......
+# x_axix，train_pn_dis这些都是长度相同的list()
+MAE=[42.124,40.244,30.196,29.367,28.168,26.955,26.135,25.254,23.277,24.866,35.236]
+MSE = [88.121,87.788,85.822,56.258,63.689,54.101,53.936,47.517,44.022,52.201,79.654]
+x=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95,1.0]
+# MAE=[25.622,25.245,24.568,24.121,23.689,23.277,24.248,26.498,26.875,27.012,27.232]
+# MSE = [53.414,52.654,50.864,48.871,46.856,44.022,48.572,55.913,56.248,56.875,56.921]
+# x=[0.1,0.09,0.08,0.07,0.06,0.05,0.04,0.03,0.02,0.01,0]
+#plt.title('Ablation study of the loss ratio about heads')
+# 开始画图
+sub_axix = filter(lambda x: x % 200 == 0, x)
+plt.title('Ablation study of the loss ratio about backbone',fontsize=16)
+
+plt.plot(x, MAE, color='green', label='MAE')
+plt.plot(x, MSE, color='red', label='MSE')
+plt.xticks(fontsize=12)
+plt.yticks(fontsize=12)
+plt.xlabel(...,fontsize=16)
+plt.ylabel(...,fontsize=16)
+plt.legend()  # 显示图例
+
+plt.xlabel('Loss weigh of backbone')
+#plt.ylabel('rate')
+plt.show()
+# python 一个折线图绘制多个曲线
+
+# ......
+# x_axix，train_pn_dis这些都是长度相同的list()
+
+# 开始画图
+# sub_axix = filter(lambda x: x % 200 == 0, x_axix)
+# plt.title('Result Analysis')
+# plt.plot(x_axix, train_acys, color='green', label='training accuracy')
+# plt.plot(sub_axix, test_acys, color='red', label='testing accuracy')
+# plt.plot(x_axix, train_pn_dis, color='skyblue', label='PN distance')
+# plt.plot(x_axix, thresholds, color='blue', label='threshold')
+# plt.legend()  # 显示图例
+#
+# plt.xlabel('iteration times')
+# plt.ylabel('rate')
+# plt.show()
+# plt.plot(x, squares)
+# plt.show()