# Check Your Understanding: GAN Anatomy

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True or false: the discriminator network and generator network influence each other solely through the data produced by the generator and the labels produced by the discriminator. When it comes to backpropagation, they are separate networks. True Incorrect: during generator training, gradients propagate through the discriminator network to the generator network (although the discriminator does not update its weights during generator training). False Correct: during generator training, gradients propagate through the discriminator network to the generator network (although the discriminator does not update its weights during generator training). So the weights in the discriminator network influence the updates to the generator network. True or false: a typical GAN trains the generator and the discriminator simultaneously. True Incorrect. A typical GAN alternates between training the discriminator and training the generator. There is some \[research \](https://arxiv.org/abs/1706.04156) on training the generator and discriminator simultaneously. False Correct. A typical GAN alternates between training the discriminator and training the generator. True or false: a GAN always uses the same loss function for both discriminator and generator training. True Incorrect. While it's possible for a GAN to use the same loss for both generator and discriminator training (or the same loss differing only in sign), it's not required. In fact it's more common to use different losses for the discriminator and the generator. False Correct. While it's possible for a GAN to use the same loss for both generator and discriminator training (or the same loss differing only in sign), it's not required. In fact it's more common to use different losses for the discriminator and the generator.