Softmax or sigmoid for multiclass problem
In general cases, if you are dealing with multi-class clasification problems, you should use a Softmax because you are guaranted that the sum of probabilities of all clases will sum 1, by weighting them individually and computing the join distribution, whereas with a Sigmoid, you'd be predicting the probability of each class individually, but not necesarilly weighted. If not careful and aware of the difference you can run into some issues with your output.
I am using VGG16 model and fine tuned them on my data. I am predicting ethnicity of images (faces) .i have 5 output classes like white, black,Asian, Sub-continent and others. Should i use softmax or sigmoid. And why??