# How to define loss function for Discriminator in GANs?

To train the discriminator network in GANs we set the label for the true samples as $$1$$ and $$0$$ for fake ones. Then we use binary cross-entropy loss for training.

Since we set the label $$1$$ for true samples that means $$p_{data}(x) = 1$$ and now binary cross-entropy loss is: $$L_1 = \sum_{i=1}^{N} P_{data}(x_i)log(D(x)) + (1-P_{data}(x_i))log(1-D(x))$$ $$L_1 = \sum_{i=1}^{N} P_{data}(x_i)log(D(x))$$ $$L_1 = E_{x \sim P_{data}(x)}[log(D(x))]$$

For the second part, since we set the label $$0$$ for fake samples that means $$p_{z}(z) = 0$$ and now binary cross-entropy loss is: $$L_2 = \sum_{i=1}^{N} P_{z}(z_i)log(D_{G}(z)) + (1-P_{z}(z_i))log(1-D_{G}(z))$$ $$L_2 = \sum_{i=1}^{N} 1-P_{z}(z_i)log(1-D_{G}(z))$$ $$L_2 = E_{z \sim \bar{P_{z}(z)}}[log(1-D_{G}(z))]$$ Now we combine those two losses and get: $$L_D = E_{x \sim P_{data}(x)}[log(D(x))] + E_{z \sim \bar{P_{z}(z)}}[log(1-D_{G}(z))]$$ When I was reading about GANs I saw that the loss function for discriminator is defined as: $$L_D = E_{x \sim P_{data}(x)}[log(D(x))] + E_{z \sim P_{z}(z)}[log(1-D_{G}(z))]$$ Should not it be $$E_{z \sim \bar{P_{z}(z)}}$$ instead of $$E_{z \sim P_{z}(z)}$$ ?

I doubt whether your derivation is correct. Your are trying to apply binary-cross entropy for data on each label separately, which is not the correct way to do.

The procedure for calculating binary cross entropy is as follows

1. Pass the input $$x$$ whose label is $$y \in \{0, 1\}$$ to your model $$M$$.
2. Obtain $$\hat{y} \in [0, 1]$$ as output of your model $$M$$ instead of actual label $$y$$.
3. Calculate binary cross-entropy loss using the equation

$$L_{CE} = y \log \hat{y} + (1-y) \log (1 - \hat{y})$$

It is true that there are two types of inputs to a discriminator: genuine and fake. Genuine data is labelled by 1 and fake data is labelled by 0. Use the variable $$x'$$ to represent the input to the discriminator module $$D$$. If the input $$x'$$ is genuine then its label is 1 and if your input $$x'$$ is fake then its label is 0. Note that it is better to avoid the unnecessary details regarding the generator or noise vector while formulating the binary cross-entropy loss of discriminator. Just see discriminator as a module taking two classes of inputs: genuine and fake. Suppose the discriminator outputs $$\hat{y} \in [0, 1]$$ for the input $$x'$$ instead of actual label $$y \in \{0, 1\}$$ then the binary cross-entropy loss is given by

$$L_{CE} = y \log \hat{y} + (1-y) \log(1-\hat{y})$$ $$\implies L_{CE} = y \log D(x') + (1-y) \log(1 - D(x'))$$

Suppose the input $$x'$$ is a genuine one $$x$$ then $$y = 1$$ and

$$\implies L_{CE} = \log D(x)$$

Suppose the input $$x'$$ is a fake one $$G(z)$$ then $$y = 0$$ and

$$\implies L_{CE} = \log (1-D(G(z)))$$

Since the labels are clear from the input of the discriminator $$D$$, we can write the binary cross-entropy loss for $$2m$$ samples $$\{x_1, x_2, x_3, \cdots, x_m, z_1, z_2, z_3, \cdots, z_m\}$$ as

$$\implies L_{CE}^{2m} = \dfrac{1}{2m} \sum\limits_{i = 1}^{m} \log D(x_i) + \sum\limits_{i = 1}^{m} \log (1-D(G(z_i)))$$

Later, we need to perform some mathematical analysis, which I am not sure about whether it is due to law of large numbers or some other 1, 2 we equate the mean to the actual expectations on probability distribution and hence

$$L_{CE}^{2m} = \dfrac{1}{2} \sum\limits_{i = 1}^{m} \dfrac{1}{m} \log D(x_i) + \sum\limits_{i = 1}^{m} \dfrac{1}{m} \log (1-D(G(z_i)))$$

$$= \dfrac{1}{2} {\LARGE(} \mathbb{E}_{x ∼ P_{data}}[\log D(x)] + \mathbb{E}_{z ∼ p_z}[log (1 - D(G(z)))] {\LARGE)}$$

Since removing $$\dfrac{1}{2}$$ does not matter while optimizing the loss function, the final loss function is given by

$$L_{D} = \mathbb{E}_{x ∼ P_{data}}[\log D(x)] +\mathbb{E}_{z ∼ p_z}[log (1 - D(G(z)))]$$

• You are wrong about the binary cross-entropy for GANs. While this is true for the common classification problem, in the GAN we have both real and fake data at the same time. So both labels are correct and equal to 1 (true for real and true for fake). I also wrote it in my answer. Aug 3 at 8:23
• @ArayKarjauv I don't think so. Discriminator has only two labels 1 for genuine $x$ and 0 or fake $G(z)$. If you are sure enough, please point the exact step where the derivation is wrong. Aug 3 at 10:50
• Your conclusion that the loss becomes $L_{CE} = \log D(x)$ when $y = 1$ and $L_{CE} = \log (1-D(G(z)))$ when $y = 0$ is wrong. The intuition is that these 2 terms have different labels. The first one is $y_{real}$, whereas the second is $y_{fake}$, which always equal to 1, hence, the loss is always $\implies L(D, G) = \log D(x_i) + \log (1-D(G(z_i)))$. Aug 3 at 14:13
• You can think of it as if the loss were a combination of 2 cross-entropy loss functions: one for real data and one for fake. But since the labels there would be constant, we simplify it. Aug 3 at 14:17
• @ArayKarjauv I can't figure out where can it be wrong. In case of your analysis, it seems to me that there are four labels and in the analysis I did, there are only two labels. Aug 5 at 0:45