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torcheval_multiclass_accuracy_metric_quickstart.py
pythonThis quickstart demonstrates how to initialize, update, and compute a Multicla
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torcheval_multiclass_accuracy_metric_quickstart.py
1import torch
2from torcheval.metrics import MulticlassAccuracy
3
4# Initialize the metric
5metric = MulticlassAccuracy()
6
7# Generate some dummy data
8# 5 samples, 4 classes
9input = torch.tensor([[0.1, 0.5, 0.2, 0.2], [0.5, 0.1, 0.2, 0.2], [0.2, 0.2, 0.5, 0.1], [0.1, 0.1, 0.1, 0.7], [0.1, 0.1, 0.8, 0.0]])
10target = torch.tensor([1, 0, 2, 3, 2])
11
12# Update the metric with the data
13metric.update(input, target)
14
15# Compute the metric
16result = metric.compute()
17print(f"Accuracy: {result}")
18
19# Reset the metric for the next batch/epoch
20metric.reset()