text: import itertools
  import numpy as np
  import matplotlib.pyplot as plt
  
  
  # 绘制混淆矩阵
  def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Reds):
      """
      This function prints and plots the confusion matrix.
      Normalization can be applied by setting `normalize=True`.
      Input
      - cm : 计算出的混淆矩阵的值
      - classes : 混淆矩阵中每一行每一列对应的列
      - normalize : True:显示百分比, False:显示个数
      """
      if normalize:
          cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
          print("Normalized confusion matrix")
      else:
          print('Confusion matrix, without normalization')
      print(cm)
      plt.imshow(cm, interpolation='nearest', cmap=cmap)
      plt.title(title)
      plt.colorbar()
      tick_marks = np.arange(len(classes))
      plt.xticks(tick_marks, classes, rotation=45)
      plt.yticks(tick_marks, classes)
      fmt = '.2f' if normalize else 'd'
      thresh = cm.max() / 3.
      for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
          plt.text(j, i, format(cm[i, j], fmt),
                   verticalalignment="center",
                   horizontalalignment="center",
                   color="white" if cm[i, j] > thresh else "black")
      plt.tight_layout()
      plt.ylabel('Pred label')
      plt.xlabel('Gold label')
      plt.show()
  