git clone https://github.com/yourusername/gan-in-action.git cd gan-in-action pip install -r requirements.txt python train.py --epochs 100 --batch-size 128
You can copy this Markdown into your editor, generate the PDF, and push the source to GitHub. # GANs in Action: From Theory to Implementation A Practical Guide to Generative Adversarial Networks gans in action pdf github
# Train Discriminator noise = torch.randn(batch_size, latent_dim, 1, 1, device=device) fake_imgs = generator(noise) loss_D = (criterion(discriminator(real_imgs), real_labels) + criterion(discriminator(fake_imgs.detach()), fake_labels)) / 2 opt_D.zero_grad() loss_D.backward() opt_D.step() git clone https://github
gan-in-action/ ├── README.md ├── requirements.txt ├── paper.pdf ├── train.py ├── models/ │ ├── generator.py │ └── discriminator.py ├── utils/ │ └── metrics.py └── images/ └── generated_samples.png We presented a self-contained guide to GANs, from the minimax game formulation to a working DCGAN in PyTorch. The implementation trains on CIFAR-10 and includes practical advice for avoiding common pitfalls. GANs remain an active research area, with extensions to conditional generation, text-to-image, and 3D synthesis. GANs remain an active research area, with extensions