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Calculating accuracy using torchmetrics

Tracking accuracy during training helps identify the best-performing epoch.

In this exercise, you'll use torchmetrics to calculate accuracy on a facemask dataset with three classes. The plot_errors function will highlight misclassified samples, helping you analyze model errors.

torchmetrics package is already imported. Model outputs are softmax probabilities, and labels are one-hot encoded vectors.

This exercise is part of the course

Introduction to Deep Learning with PyTorch

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create accuracy metric
metric = torchmetrics.____(____, ____)
for features, labels in dataloader:
    outputs = model(features)
  
    # Calculate accuracy over the batch
    metric.____(____, ____)
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