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hussamalafandi/
GAN_MNIST

Name: GAN_MNIST
Availability: InStock

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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:

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

References

License

This project is licensed under the MIT License.

hussamalafandi/GAN_MNIST