Why take softmax at all at the final layer for multi-class classification problems? For example softmax of the vector [1, .5]
Is [.621, .379]
I mean if we just took the straight ratio, it'd give me [.667, .333] instead
Does that really make a difference?
Is it cause the vector can have negative numbers that we softmax things? What benefit do we get from making an odder way to give a ratio/probability to certain numbers as opposed to just taking a ratio of the numbers?