When people mention logits, they're usually referring to the raw output of the model before it gets transformed/normalised into a probability distribution (i.e.

When people mention logits, they're usually referring to the raw output of the model before it gets transformed/normalised into a probability distribution (i.e. sums to 1, range [0,1]). Logits can take any value. The naming might not be mathematically strict, because it assumes(?) that you're going to apply softmax (which interprets the output of the model as logits), but that's how the term is used. For example in many classification problems you get a 1D vector of logits from the final layer, you apply softmax to normalise, then argmax to extract the predicted class. It extends to other tasks like semantic segmentation (predict pixel classes) where the "logit" output is the same size as the image with a channel for each class and you apply the same process to get a single channel image with class-per-pixel. Here's a nice explanation: https://stackoverflow.com/a/66804099/395457 https://stackoverflow.com/a/66804099/395457