Why is my custom loss function converging so fast in multi-label training?

[NicholasHL]

I’m trying to implement a custom loss function for a multi-label classification problem, but my model’s training loss is converging to a suspiciously low value almost immediately. I’m worried my gradient calculation might be wrong, but I’m not sure how to properly debug this beyond checking the obvious syntax.

[Olivia91]

Had this happen with a custom loss too. The loss dropped to almost nothing right away and the model looked confident, but it was actually just the scale and reduction hiding things. I once mixed up the reduction across dims and ended up with tiny gradients because I averaged over the wrong axis. After I rewrote the loss to be explicit about which dimension gets reduced, the training curve behaved more like a normal loss curve.

[Aubrey.C]

Another time I learned the pitfall of not using the right form for binary multi label. If you do BCEWithLogits on the raw scores, gradients stay reasonable; if you apply sigmoid first and then BCE, you can get saturation and a plateau. I swapped back to the logits version and the gradient story changed.

[JohnXM]

I started printing the loss for each sample and plotting a quick histogram. A couple of hard examples were dragging the average down and squashing the gradient signals. It helped me spot data leakage or mislabeled samples rather than a math bug.

[EleanorC]

Have you tried a tiny numeric gradient check on a small batch to compare your analytic gradients with finite differences? It’s crude but often catches sign mistakes or missing terms before you chase deeper theory.

[Luke45]

I wandered into thinking maybe the issue isn’t the loss at all but how the data is fed. A mismatch in label shapes or a mis-specified target mask can fool you into thinking the gradient is wrong. I once found the labels and outputs had mismatched shapes right before the final layer, and the loss looked small but it wasn’t learning.

[Mia.P]

I keep looking for a reason that isn’t a miracle. One time the LR was so high that everything collapsed to zero almost instantly, other times the opposite happened. I try to keep it modest and watch the training curve wiggle rather than dive. But I can’t pretend I have the answer yet.