Can SigmoidFocalCrossEntropy in Tensorflow (tf-addons) be used in Multiclass Classification? ( What is the right way)?

Question

Focal Loss given in Tensorflow is used for class imbalance. For Binary class classification, there are a lots of codes available but for Multiclass classification, a very little help is there. I ran the code with One Hot Encoded target variables of 250 classes and it gave me results without any error.

y = pd.get_dummies(df['target']) # One hot encoded target classes
model.compile(
    optimizer="adam", loss=tfa.losses.SigmoidFocalCrossEntropy(), metrics= metric
)

I just want to know whoever wrote this code or someone having enough knowledge of this code, can it be used be used for Multiclass Classification. If no then how come it did not give me errors, instead better results than CrossEntropy. Also, in other implementations like this one, the value of alpha has to be given for every class but just one value in Tensorflow's implementations.

What is the correct way to use this?

Clintonclintonia

Answer 1

Some basics first.

Categorical Crossentropy is designed to incentivize a model a model to predict 100% for the correct label. It was designed for models that predict single-label multi-class classification - like CIFAR10 or Imagenet. Usually these models finish in a Dense layer with more than one output.

Binary Crossentropy is designed to incentivize a model to predict 100% if the label is one, or, 0% is the label is zero. Usually these models finish in a Dense layer with exactly one output.

When you apply Binary Crossentropy to a single-label multi-class classification problem, you are doing something that is mathematically valid but defines a slightly different task: you are incentivizing a single-label classification model to not only get the true label correct, but also minimize the false labels.

For example, if your target is dog, and your model predict 60% dog, CCE doesn't care if your model predicts 20% cat and 20% French horn, or, 40% cat and 0% French horn. So this is aligned with a top-1 accuracy concept.

But if you take that same model and apply BCE, and your model predictions 60% dog, BCE DOES care if your models predict 20%/20% cat/frenchhorn, vs 40%/0% cat/frenchhorn. To put it in precise terminology, the former is more "calibrated" and so it has some additional measure of goodness. However, this has little correlation to top-1 accuracy.

When you use BCE, presumably you are wasting the model's energy to focus on calibration at the expense of top-1 acc. But as you might have seen, it doesn't always work out that way. Sometimes BCE gives you superior results. I don't know that there's a clear explanation of that but I'd assume that the additional signals (in the case of Imagenet, you'll literally get 1000 times more signals) somehow creates a smoother loss value that perhaps helps smooth the gradients you receive.

The alpha value of focal loss additionally penalizes very wrong predictions and lessens the penalty if your model predicts something close to the right answer - like predicting 90% cat if the ground truth is cat. This would be a shift from the original definition of CCE, based on the theory of Maximum Likelihood Estimation... which focuses on calibration... vs the normal metric most ML practitioners care about: top-1 accuracy.

Focal loss was originally designed for binary classification so the original formulation only has a single alpha value. The repo you pointed to extends the concept of Focal Loss to single-label classification and therefore there are multiple alpha values: one per class. However, by my read, it loses the additional possible smoothing effect of BCE.

Net net, for the best results, you'll want to benchmark CCE, BCE, Binary Focal Loss (out of TFA and per the original paper), and the single-label multi-class Focal Loss that you found in that repo. In general, those the discovery of those alpha values is done via guess & check, or grid search.

There's a lot of manual guessing and checking in ML unfortunately.

Hypogynous