| library_name: pytorch | |
| datasets: | |
| - ylecun/mnist | |
| tags: | |
| - gan | |
| - conditional-gan | |
| - mnist | |
| - pytorch | |
| # Conditional MNIST GAN | |
| Label-conditioned generator for 28x28 MNIST digits. | |
| This repository contains **generator weights only**, as required by the assignment. The model uses a 100-dimensional standard-normal latent vector. See `model.py` for the exact architecture and `training-metrics.json` for the complete loss history. | |
| ## Training | |
| - Dataset: `ylecun/mnist` | |
| - Epochs: 25 | |
| - Batch size: 256 | |
| - Seed: 42 | |
| - Fixed-sample pixel standard deviation: 0.6101 | |
| ## Conditional evaluation | |
| - Independent classifier accuracy on real MNIST test data: 98.51% | |
| - Requested-label agreement on 2000 generated samples: 99.55% | |
| Weights are stored in `safetensors` format. The model generates synthetic images and can produce malformed or ambiguous samples; it is intended for coursework and experimentation. | |