Write code to implement a custom loss function for a GAN in PyTorch

Question

Can you write a code to implement a custom loss function for a GAN in PyTorch?

Answer 1

Here is the code below that you can refer to implement a custom loss function for a GAN in PyTorch:

In the above code, we are using Discriminator Loss, which uses BCE loss to distinguish real from fake data, Generator Loss, which encourages the generator to fool the discriminator and Flexibility, which modifies the loss function for specific GAN variations (e.g., Wasserstein GAN).

Hence, this approach is customizable and can be used for various GAN architectures.