# Train the generator optimizer_g.zero_grad() fake_logits = discriminator(generator(torch.randn(100))) loss_g = criterion(fake_logits, torch.ones_like(fake_logits)) loss_g.backward() optimizer_g.step() Note that this is a simplified example, and in practice, you may need to modify the architecture and training process of the GAN to achieve good results.
For those interested in implementing GANs, there are several resources available online. One popular resource is the PDF, which provides a comprehensive overview of GANs, including their architecture, training process, and applications. gans in action pdf github
Another popular resource is the , which provides a wide range of pre-trained GAN models and code implementations. # Train the generator optimizer_g
def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) return x Another popular resource is the , which provides
GANs are a powerful class of deep learning models that have achieved impressive results in various applications. While there are still several challenges and limitations that need to be addressed, GANs have the potential to revolutionize the field of deep learning. With the availability of resources such as the PDF and GitHub repository, it is now easier than ever to get started with implementing GANs.