code: def value(self):
          
          if self.normalized:
              conf = self.conf.astype(np.float32)
              return conf / conf.sum(1).clip(min=1e-12)[:, None]
          else:
              return self.conf
docs: Returns:
              Confustion matrix of K rows and K columns, where rows corresponds
              to ground-truth targets and columns corresponds to predicted
              targets.