Why do we need to apply softmax after getting the logit values? I know it says that it would help to normalise the scores and get a probabilistic interpretation. But is it not that that the utility of logits/softmax scores is to determine which value is bigger and then infer the label.
For example, if you get a logits score of [-4.2095,  4.6053], where -4.2095 refers to label0  and 4.6053 refers to label1. Then as 4.6053 > -4.2095, I would keep label1 as my prediction. If instead I apply softmax to the logits score, I get [1.4850e-04, 9.9985e-01]. With this softmax score I will still infer that the predicted label is label1.