# Softmax in multi-class in deep NNs

I don't understand how to use the cross-entropy loss function and softmax activation function for multi-class classification in NNs.

Say I have a 2 layer NN (1 output layer and 1 hidden layer). For the hidden-layer, I apply sigmoid function to the pre-activation from the input layer. Now, I am not sure when I feed the pre-activation from hidden layer to output layer what activation function should I use? Should I first employ the sigmoid function to output-layer's pre-activation, then the softmax function? But that means using 2 activation functions for the same layer.