BaşlayınÜcretsiz Başlayın

Multi-class model evaluation

Let's evaluate our cloud classifier with precision and recall to see how well it can classify the seven cloud types. In this multi-class classification task it is important how you average the scores over classes. Recall that there are four approaches:

  • Not averaging, and analyzing the results per class;
  • Micro-averaging, ignoring the classes and computing the metrics globally;
  • Macro-averaging, computing metrics per class and averaging them;
  • Weighted-averaging, just like macro but with the average weighted by class size.

Both Precision and Recall are already imported from torchmetrics. It's time to see how well our model is doing!

Bu egzersiz

Intermediate Deep Learning with PyTorch

kursunun bir parçasıdır
Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define metrics
metric_precision = Precision(task=____, num_classes=____, average=____)
metric_recall = ____

net.eval()
with torch.no_grad():
    for images, labels in dataloader_test:
        outputs = net(images)
        _, preds = torch.max(outputs, 1)
        metric_precision(preds, labels)
        metric_recall(preds, labels)

precision = metric_precision.compute()
recall = metric_recall.compute()
print(f"Precision: {precision}")
print(f"Recall: {recall}")
Kodu Düzenle ve Çalıştır