Building a Simple Python-Based GAN in 5 minutes

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Abstraction: Minimal PyTorch GAN with generator-discriminator adversarial training loop

Key points:

GAN consists of a generator (noise vector → synthetic data) and a discriminator (real vs. fake classifier) trained adversarially
Both networks are 2-layer MLPs; generator uses ReLU + Tanh activations, discriminator uses ReLU + Sigmoid
Loss function is BCEWithLogitsLoss; both networks use Adam optimizer at lr=0.0002
Training alternates: update discriminator on real + fake images, then update generator to fool the discriminator
Generator receives gradient signal from discriminator's feedback; generator loss = loss_fn(d_fake, ones_like(d_fake))
GANs generate sharp synthetic data and are relatively easy to train, though mode collapse and instability are known challenges

Connections: Pytorch · Generative Adversarial Networks · Deep Learning · Generative Models