Python Matplotlib绘制准确率与损失率折线图及混淆矩阵实战

import matplotlib.pyplot as plt
 
epochs = [0,1,2,3]
acc = [4,8,6,5]
loss = [3,2,1,4]
 
plt.plot(epochs,acc,color='r',label='acc') # r表示红色
plt.plot(epochs,loss,color=(0,0,0),label='loss') #也可以用RGB值表示颜色
 
#####非必须内容#########
plt.xlabel('epochs') #x轴表示
plt.ylabel('y label') #y轴表示
plt.title("chart") #图标标题表示
plt.legend()  #每条折线的label显示
#######################
plt.savefig('test.jpg') #保存图片，路径名为test.jpg
plt.show()  #显示图片

可视化混淆矩阵

#!/usr/bin/python3.5
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['FangSong'] #可显示中文字符
plt.rcParams['axes.unicode_minus']=False
 
classes = ['a','b','c','d','e','f','g']
confusion_matrix = np.array([(99,1,2,2,0,0,6),(1,98,7,6,2,1,1),(0,0,86,0,0,2,0),(0,0,0,86,1,0,0),(0,0,0,1,94,1,0),(0,1,5,1,0,96,8),(0,0,0,4,3,0,85)],dtype=np.float64)
 
plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Oranges) #按照像素显示出矩阵
plt.title('混淆矩阵')
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=-45)
plt.yticks(tick_marks, classes)
 
thresh = confusion_matrix.max() / 2.
#iters = [[i,j] for i in range(len(classes)) for j in range((classes))]
#ij配对，遍历矩阵迭代器
iters = np.reshape([[[i,j] for j in range(7)] for i in range(7)],(confusion_matrix.size,2))
for i, j in iters:
 plt.text(j, i, format(confusion_matrix[i, j]),fontsize=7) #显示对应的数字
 
plt.ylabel('真实类别')
plt.xlabel('预测类别')
plt.tight_layout()
plt.show()

绘制多类别正确率曲线

  fig ,ax= plt.subplots()
  plt.plot(np.arange(iterations), fig_acc，'b')
  plt.plot(np.arange(iterations), fig_realacc, 'r')
  ax.set_xlabel('迭代次数')
  ax.set_ylabel('正确率（%）')
 
  labels = ["训练正确率", "测试正确率"]
  # labels = [l.get_label() for l in lns]
  plt.legend( labels, loc=7)
  plt.show()