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.
