标签:oss string ali lin end highlight label ring for
得到每个epoch的loss和predict精度后,就可以愉快地画图直观地看出训练结果和收敛性了。
# coding:utf-8 import matplotlib.pyplot as plt data_dir = "D:\\result.txt" Train_Loss_list = [] Train_Accuracy_list = [] Valid_Loss_list = [] Valid_Accuracy_list = [] f1 = open(data_dir,‘r‘) data = [] #把训练结果输出到result.txt里,比较笨的办法,按字节位数去取数字结果 for line in f1: if(line.find(‘train‘)>=0): # if (string.find(line, ‘train‘) != -1): Train_Loss_list.append(line[12:18]) Train_Accuracy_list.append(line[24:30]) if (line.find(‘valid‘) >= 0): # if (string.find(line, ‘valid‘) != -1): Valid_Loss_list.append(line[12:18]) Valid_Accuracy_list.append(line[24:30]) f1.close() #迭代了30次,所以x的取值范围为(0,30),然后再将每次相对应的准确率以及损失率附在x上 x1 = range(0, 30) x2 = range(0, 30) y1 = Train_Accuracy_list y2 = Train_Loss_list y3 = Valid_Accuracy_list y4 = Valid_Loss_list plt.subplot(2, 1, 1) # plt.plot(x1, y1, ‘o-‘,color=‘r‘) plt.plot(x1, y1, ‘o-‘,label="Train_Accuracy") plt.plot(x1, y3, ‘o-‘,label="Valid_Accuracy") plt.title(‘Test accuracy vs. epoches‘) plt.ylabel(‘Test accuracy‘) plt.legend(loc=‘best‘) plt.subplot(2, 1, 2) plt.plot(x2, y2, ‘.-‘,label="Train_Loss") plt.plot(x2, y4, ‘.-‘,label="Valid_Loss") plt.xlabel(‘Test loss vs. epoches‘) plt.ylabel(‘Test loss‘) plt.legend(loc=‘best‘) plt.show()
标签:oss string ali lin end highlight label ring for
原文地址:https://www.cnblogs.com/marszhw/p/10962903.html