Here we'll use the notation w k to mean the vector of weights from each input x i to the output node k, and the indicator function 1{}, which evaluates to 1 if the condition in the brackets is true and to 0 otherwise. Hence the cross-entropy loss is simply the log of the output probability corresponding to the correct class, and we therefore also call this the negative log likelihood loss: