eval_model.py

torch.manual_seed(101)

# Make predictions
model.eval()
with torch.inference_mode():
    y_logits = model(X_test).to(device)
    y_preds = torch.softmax(y_logits, dim=1).argmax(dim=1)

accuracy = Accuracy(task="multiclass", num_classes=model.output_features).to(device)
confusion = ConfusionMatrix(task="multiclass", num_classes=model.output_features).to(device)

print("Accuracy: %.2f%%" % (accuracy(y_test, y_preds).item()*100))
print(confusion(y_test, y_preds))
print(classification_report(y_test.cpu(), y_preds.cpu()))