分类器平均准确率计算：
correct = torch.zeros(1).squeeze().cuda()
total = torch.zeros(1).squeeze().cuda()
for i, (images, labels) in enumerate(train_loader):
images = Variable(images.cuda())
labels = Variable(labels.cuda())
output = model(images)
prediction = torch.argmax(output, 1)
correct += (prediction == labels).sum().float()
total += len(labels)
acc_str = 'Accuracy: %f'%((correct/total).cpu().detach().data.numpy())
分类器各个子类准确率计算：
correct = list(0. for i in range(args.class_num))
total = list(0. for i in range(args.class_num))
for i, (images, labels) in enumerate(train_loader):
images = Variable(images.cuda())
labels = Variable(labels.cuda())
output = model(images)
prediction = torch.argmax(output, 1)
res = prediction == labels
for label_idx in range(len(labels)):
label_single = label[label_idx]
correct[label_single] += res[label_idx].item()
total[label_single] += 1
acc_str = 'Accuracy: %f'%(sum(correct)/sum(total))
for acc_idx in range(len(train_class_correct)):
try:
acc = correct[acc_idx]/total[acc_idx]
except:
acc = 0
finally:
acc_str += '\tclassID:%d\tacc:%f\t'%(acc_idx+1, acc)
以上这篇Pytorch 实现计算分类器准确率(总分类及子分类)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。
