How can you implement a custom generator and discriminator architecture using PyTorch

Question

With the help of code, can you tell me how you can implement a custom generator and discriminator architecture using PyTorch?

Answer 1

You can implement a custom generator and discriminator architecture for a GAN using PyTorch by defining them as subclasses of torch.nn.Module. Here is the code snippet you can refer to below:

In the above code, we are using the following key points:

• Generator:

  • Takes a random noise vector (z) as input.
  • Produces an image-like output normalized between -1 and 1 using Tanh.

• Discriminator:

  • Takes an image as input.
  • Outputs a probability (Sigmoid) indicating if the image is real or fake.

• Modular Design: Easy to extend with more layers, regularization (e.g., Dropout), or activation functions.

Hence, by referring, you implement a custom generator and discriminator architecture using PyTorch.