分類器平均準確率計算：

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())

分類器各個子類準確率計算：

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)

以上這篇Pytorch 實現計算分類器準確率(總分類及子分類)就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。