hussamalafandi/GAN_MNIST

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

Usage

Loading the Model

To load the trained model, use the following code snippet:

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

References

• Generative AI Course Repository
• Weights & Biases Training Logs

License

This project is licensed under the MIT License.