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。