One-hot vector to softmax-like distribution in tensorflow
A solution could be to keep your one-hot vector ;). 
Another one, more general, is to make a random positive vector, then compute the difference between the highest score d and the score of your true class, then add a random number between d and +infinity to the true class's score, then normalize to get a valid distribution. (note that you can force the true class's initial score to be 0, but that will probably be a tiny bit longer to code).
The choice of the distributions for the initial random vector and for the quantity to add to the true class's score will change the output distribution, but I don't know which one you want or why you want to do that...
Is there a way in tensorflow to transform a one-hot vector into a softmax-like distribution?
For example, I have the following one-hot vector:

[0 0 0 0 1 0]

I want to have a vector with probabilities where the one value is the most likely number, like:

[0.1 0.1 0.1 0.1 0.5 0.1]

This vector should always be random, but with the true class having the highest probability.
How can I reach this?