Loss Functions: Paper vs Code

[Ceralu]

I am finding a difference between the loss function explained in the paper and the loss functions in the code.

For the supervised loss, in the code, I understand that minimizing loss_lab is equivalent to making T.sum(T.exp(output_before_softmax_lab)) go to 1 and also making max D(x_lab) equal to 1 for the correct label.

However, what I don't understand is the expression of loss_unl. How is it equivalent to the loss function L_unsupervised in the paper which aims to make the discriminator predict class K+1 when the data is fake and predict not K+1 when the data is unlabelled?

Edit: I accidentally clicked to submit issue before finishing writing it.
Edit: This is kind of similar to issue #14 which didn't receive any answer.

[zychen2016]

Any one could answer this issue?