TinyTorch/examples

TinyTorch Examples 🔥

Real-world examples showing what you can build with TinyTorch!

What Are These Examples?

These are real ML applications written using TinyTorch just like you would use PyTorch. Each example:

• Uses import tinytorch as a real package
• Shows professional ML code patterns
• Demonstrates actual capabilities you've built
• Can be run by anyone to see TinyTorch in action

Running Examples

# After installing/building TinyTorch:
cd examples/xor_network/
python train.py

# Or for image classification:
cd examples/cifar10_classifier/
python train_cifar10_mlp.py

Available Examples

🧠 Neural Network Fundamentals

• xor_network/ - Classic XOR problem with hidden layers
  • Clean implementation showing autograd and training basics
  • Architecture: 2 → 4 → 1 with ReLU and Sigmoid
  • Achieves 100% accuracy on XOR truth table

👁️ Computer Vision

• cifar10_classifier/ - Real-world object classification
  • ACHIEVEMENT: 57.2% accuracy - exceeds typical ML course benchmarks!
  • Multiple architectures: MLP, LeNet-5, and optimized models
  • Data augmentation, proper initialization, Adam optimization
  • Real dataset: 50,000 training images, 10,000 test images

🤖 Language & Generation

• text_generation/ - Generate text with TinyGPT (Module 16)
  • Transformer architecture built from scratch
  • Character-level text generation
  • Attention mechanisms and positional encoding

📊 Optimization & Analysis

• optimization_comparison/ - SGD vs Adam comparison
  • Side-by-side optimizer performance analysis
  • Visualization of convergence patterns
  • Memory usage and computational efficiency

Example Structure

Each example directory contains:

example_name/
├── train.py          # Main training script
├── README.md         # What this example demonstrates
└── data/            # Datasets (downloaded automatically)

Learning Progression

After completing each module, examples become functional:

• Module 05 → xor_network/ works (Dense layers + activations)
• Module 11 → cifar10_classifier/ works with training loops
• Module 16 → text_generation/ works (TinyGPT)

Quick Demo

Want to see TinyTorch in action? Try these:

# See a neural network learn XOR (30 seconds):
python examples/xor_network/train.py

# Train on real images (5 minutes, 57% accuracy):
python examples/cifar10_classifier/train_cifar10_mlp.py --epochs 10

# Compare optimizers (2 minutes):
python examples/optimization_comparison/compare.py

Performance Achievements

• XOR Network: 100% accuracy (perfect solution)
• CIFAR-10 MLP: 57.2% accuracy (exceeds typical course benchmarks)
• Optimization: Adam 3.2x faster convergence than SGD

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These aren't toy demos - they're real ML applications achieving competitive results with a framework built from scratch!