#ML, #softmax
I've asked my self why Softmax uses exponent. Here's a good response:

- Monotonically increasing — To ensure that larger inputs are mapped to larger outputs.
- Non-negative outputs — Because probability values must be non-negative.
- The outputs should sum to one — This can be achieved by simply dividing each element of the output by the sum of all elements of the output.

https://www.quora.com/Why-is-exponential-function-used-in-softmax-function-in-machine-learning