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无论是官方文档还是各位大神的论文或搭建的网络很多都是计算准确率，很少有计算误判率，
下面就说说怎么计算准确率以及误判率、召回率等指标
1.计算正确率
获取每批次的预判正确个数
train_correct = (pred == batch_y.squeeze(1)).sum()
该语句的意思是 预测的标签与实际标签相等的总数
获取训练集总的预判正确个数
train_acc += train_correct.data[0] #用来计算正确率
准确率 ： train_acc / (len(train_data))
2.误判率
举例：当你是二分类时，你需要计算 原标签为1，但预测为 0 ，以及 原标签为0，预测为1的 误判率
误判率又分为：
CTW : correct to wrong 标签为正确的，预测为错误的
WTC: wrong to correct 标签为错误的，预测为正确的
zes=Variable(torch.zeros(lasize).type(torch.LongTensor))#全0变量
ons=Variable(torch.ones(lasize).type(torch.LongTensor))#全1变量
train_correct01 = ((pred==zes)&(batch_y.squeeze(1)==ons)).sum() #原标签为1，预测为 0
的总数
train_correct10 = ((pred==ons)&(batch_y.squeeze(1)==zes)).sum() #原标签为0，预测为1 的总数
train_correct11 = ((pred_y==ons)&(batch_y.squeeze(1)==ons)).sum()
train_correct00 = ((pred_y==zes)&(batch_y.squeeze(1)==zes)).sum()
获取训练集总的误判个数
FN += train_correct01.data[0]
FP += train_correct10.data[0]
TP += train_correct11.data[0]
TN += train_correct00.data[0]
误判率 ：
(FN+FP)/(len(train_data)) #CTW+WTC
3.精准率和召回率
精准率： P = TP/ (TP+FP)
召回率： R = TP/ (TP+FN)
4.真正例率和假正例率
真正例率：TPR = TP/ (TP+FN)
假正例率：FPR =FP/ (FP+TN)
最后，当你要计算多分类的误判率时，只需在二分类的基础上类推即可
以上这篇Pytorch 计算误判率,计算准确率,计算召回率的例子就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
