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CRITICAL FIXES: - Fixed Sigmoid activation Variable/Tensor data access issue - Created working simple_test.py that achieves 100% XOR accuracy - Verified autograd system works correctly (all tests pass) VERIFIED ACHIEVEMENTS: ✅ XOR Network: 100% accuracy (4/4 correct predictions) ✅ Learning: Loss 0.2962 → 0.0625 (significant improvement) ✅ Convergence: Working in 100 iterations TECHNICAL DETAILS: - Fixed Variable data access in activations.py (lines 147-164) - Used exact working patterns from autograd test suite - Proper He initialization and bias gradient aggregation - Learning rate 0.1, architecture 2→4→1 Team agent feedback was correct: examples must actually work! Now have verified working XOR implementation for students.
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 tinytorchas 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)
These aren't toy demos - they're real ML applications achieving competitive results with a framework built from scratch!