---
tags:
- gan
- mnist
- pytorch
- generative-model
- deep-learning
license: mit
datasets:
- mnist
library_name: pytorch
---

# GAN for MNIST Digit Generation

This repository contains a Generative Adversarial Network (GAN) trained on the MNIST dataset to generate realistic handwritten digits. The model was trained as part of the Generative AI course.

## Model Details

- **Model Type**: GAN
- **Dataset**: MNIST (handwritten digits)
- **Generator Input**: Latent vector of size 100
- **Output**: 28x28 grayscale images
- **Framework**: PyTorch

## Training Details

- **Optimizer**: Adam
- **Learning Rate**: 0.0002
- **Beta1**: 0.5
- **Epochs**: 50
- **Batch Size**: 64
- **Weight Decay**: 0.0001
- **Logging**: [Weights & Biases](https://wandb.ai/hussam-alafandi/GAN_MNIST/runs/6ehnzhm0?nw=nwuserhussamalafandi)

## Usage

### Loading the Model

To load the trained model, use the following code snippet:
   ```python
   from gan import Generator
   import torch

   latent_dim = 100
   generator = Generator(latent_dim)
   generator.load_state_dict(torch.load("./gan_mnist.pth"))
   generator.eval()

   # Generate samples
   z = torch.randn(16, latent_dim)
   samples = generator(z)
   ```
## Example Results
![generated images](./gan_mnist.png)

## References

- [Generative AI Course Repository](https://github.com/hussamalafandi/Generative_AI)
- [Weights & Biases Training Logs](https://wandb.ai/hussam-alafandi/GAN_MNIST/runs/6ehnzhm0?nw=nwuserhussamalafandi)

## License

This project is licensed under the MIT License.