Difference of implementation between tensorflow softmax_cross_entropy_with_logits and sigmoid_cross_entropy_with_logits
the major difference between sigmoid and softmax is that softmax function return result in terms of probability which is kind of more inline with the ML philosophy. Sum of all outputs from softmax result to 1. This is turn tells you how confident the network is about the answer.
Whereas, sigmoid outputs are discreet. Its either correct or incorrect. You would have to write code to calculate the probability yourself.
As far as performance of the network goes. Softmax generally gives better accuracy than sigmoid. But, it also highly dependent on other hyper parameters also.
I recently came across tensorflow softmax_cross_entropy_with_logits, but I can not figure out what the difference on the implementation is compared to sigmoid_cross_entropy_with_logits.