Does the score output of a classification model has a global meaning? The scores-output layer contains the class scores that the model generated for the current sample and it is passed thru the softmax layer to get the final output in the form of a probabilities vector.

My question is whether these scores have any global meaning, when compared with scores of other samples from the dataset?


  The way I see it, a standard classification model is trained on the
  output of the softmax layer, so the model only "cares" about the
  relative scores within each sample. So it has no mechanism for
  decreasing the global scores of a sample - a scores vector of
  [101,102,103] gets the same treatment as [1,2,3]. Am I correct in my
  understanding?
The output of the final layer, before softmax activation, is the log odds of the classes. Softmax isn't a linear function, so [10,20,30] going into softmax won't give the same answer as [1,2,3].