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/xornet/
python train.py

# Or for image classification:
cd examples/cifar10/
python train_cifar10_mlp.py

Available Examples

🧠 xornet/ - Neural Network Fundamentals

• 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

👁️ cifar10/ - Real-World Computer Vision

• 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

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 → xornet/ works (Dense layers + activations)
• Module 11 → cifar10/ works with training loops

Quick Demo

Want to see TinyTorch in action? Try these:

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

# Train on real images (5 minutes, 57% accuracy):
python examples/cifar10/train_cifar10_mlp.py --epochs 10

Performance Achievements

• XORnet: 100% accuracy (perfect solution)
• CIFAR-10: 57.2% accuracy (exceeds typical course benchmarks)

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