GAN Implementation - MNIST Digit Generation

🔥 Features

• Complete GAN Implementation: Both standard and optimized versions
• MNIST Digit Generation: Generate realistic handwritten digits
• Multiple Training Modes: Standard and lite modes for different performance needs
• Comprehensive Logging: Detailed training logs and progress tracking
• GPU Support: MPS (Apple Silicon), CUDA, and CPU support
• Visualization: Real-time training progress and generated samples

📊 Results

• Generator Parameters: 576K (lite) / 3.5M (standard)
• Discriminator Parameters: 533K (lite) / 2.7M (standard)
• Training Time: ~5 minutes (lite mode) / ~30 minutes (standard)

🚀 Quick Start

Installation

Usage

1. Open the Jupyter Notebook:
   jupyter notebook Gan.ipynb
2. Run the cells to train the GAN and generate digits
3. Choose your mode:
   • Standard Mode: Full implementation with detailed logging
   • Lite Mode: Optimized for faster training and lower resource usage

📁 Project Structure

GAN_Implementation/
├── Gan.ipynb                           # Main implementation notebook
├── requirements.txt                    # Python dependencies
├── README.md                          # This file
├── Generative Adversarial Networks (GANs).md  # Theory and documentation
├── gan_training.log                   # Training logs (standard mode)
├── gan_training_lite.log             # Training logs (lite mode)
├── generator_lite.pth                # Saved model weights
└── data/                             # MNIST dataset
    └── MNIST/
        └── raw/                      # Raw MNIST data files

🧠 Implementation Details

Architecture

• Input: Random noise vector (100D standard / 64D lite)
• Hidden layers with ReLU/BatchNorm activation
• Output: 784D vector (28x28 MNIST image)
• Activation: Tanh (output range [-1, 1])

• Input: 784D image vector
• Hidden layers with LeakyReLU/Dropout
• Output: Single probability (real vs fake)
• Activation: Sigmoid

Training Process

1. Data Preparation: MNIST dataset normalized to [-1, 1]
2. Adversarial Training:
   • Discriminator learns to distinguish real vs fake images
   • Generator learns to fool the discriminator
3. Loss Function: Binary Cross-Entropy Loss
4. Optimization: Adam optimizer with β₁=0.5, β₂=0.999

📈 Training Modes

Standard Mode

• Latent Dimension: 100
• Epochs: 50-100
• Batch Size: 64-128
• Dataset: Full MNIST (60K samples)
• Best for: High-quality results

Lite Mode

• Latent Dimension: 64
• Epochs: 50
• Batch Size: 64
• Dataset: Subset (10K samples)
• Best for: Quick experimentation and testing

🔧 Technical Features

• Device Auto-Detection: Automatically uses MPS, CUDA, or CPU
• Memory Optimization: Efficient memory usage with cache clearing
• Progress Tracking: Real-time loss monitoring and sample generation
• Model Persistence: Save/load trained models
• Comprehensive Logging: Detailed training metrics and timing

📊 Performance Metrics

🎯 Use Cases

• Educational: Learn GAN fundamentals with working code
• Research: Baseline for GAN experiments
• Prototyping: Quick testing of GAN modifications
• Production: Scalable digit generation system

🔗 Links & Resources

• GitHub Repository: https://github.com/GruheshKurra/GAN_Implementation
• Hugging Face: https://huggingface.co/karthik-2905/GAN_Implementation
• Blog Post: [Coming Soon on daily.dev]
• Theory Documentation: See Generative Adversarial Networks (GANs).md

🛠️ Requirements

• Python 3.7+
• PyTorch 2.0+
• torchvision 0.15+
• matplotlib 3.5+
• numpy 1.21+
• jupyter 1.0+

📝 License

🤝 Contributing

📞 Contact

• Author: Karthik
• GitHub: @GruheshKurra

🙏 Acknowledgments

• Original GAN paper by Ian Goodfellow et al.
• PyTorch team for the excellent deep learning framework
• MNIST dataset creators