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fixed loss logging

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marvin
2026-10-03 14:47:38 +02:00
parent 0715c66a64
commit 35a3e03a10
1 changed files with 2 additions and 3 deletions
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api/src/common/training.py
+2 -3
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@@ -40,7 +40,7 @@ def _run_epoch(model, loss_fn, loader, device, prepare_batch, track_accuracy, op
loss.backward() loss.backward()
optimizer.step() optimizer.step()
loss_sum += loss.item() loss_sum += loss.item() * inputs.size(0)
count += inputs.size(0) count += inputs.size(0)
if track_accuracy: if track_accuracy:
correct += (preds.argmax(dim=1) == targets).sum().item() correct += (preds.argmax(dim=1) == targets).sum().item()
@@ -112,11 +112,10 @@ def _fit_epochs(model, loss_fn, optimizer, train_loader, val_loader, device, epo
run.log_epoch(epoch, train, val, lr=optimizer.param_groups[0]["lr"], run.log_epoch(epoch, train, val, lr=optimizer.param_groups[0]["lr"],
seconds=time.perf_counter() - start, improved=improved) seconds=time.perf_counter() - start, improved=improved)
# Loss is the summed batch loss per sample, shown x1000.
parts = [f"epoch: {epoch}"] parts = [f"epoch: {epoch}"]
if track_accuracy: if track_accuracy:
parts.append(f"train acc: {train['acc'] * 100:.1f}% | val acc: {val['acc'] * 100:.1f}%") parts.append(f"train acc: {train['acc'] * 100:.1f}% | val acc: {val['acc'] * 100:.1f}%")
parts.append(f"train loss: {train['loss'] * 1000:.2f} | val loss: {val['loss'] * 1000:.2f}") parts.append(f"train loss: {train['loss']:.4f} | val loss: {val['loss']:.4f}")
if improved and save: if improved and save:
parts.append("saved") parts.append("saved")
print(" | ".join(parts)) print(" | ".join(parts))