forgeNN 1.0.0

A From Scratch Neural Network Framework with Educational Purposes

forgeNN

A From Scratch Neural Network Framework with Educational Purposes

Overview

forgeNN is a modern neural network framework that is developed by a solo developer learning about ML. Features vectorized operations for high-speed training.

Key Features

• ** Vectorized Operations**: NumPy-powered batch processing (100x+ speedup)
• ** Dynamic Computation Graphs**: Automatic differentiation with gradient tracking
• ** Complete Neural Networks**: From simple neurons to complex architectures
• ** Production Loss Functions**: Cross-entropy, MSE with numerical stability

Quick Start

High-Performance Training

import forgeNN
from sklearn.datasets import make_classification

# Generate dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=3)

# Create vectorized model  
model = forgeNN.VectorizedMLP(20, [64, 32], 3)
optimizer = forgeNN.VectorizedOptimizer(model.parameters(), lr=0.01)

# Fast batch training
for epoch in range(10):
    # Convert to tensors
    x_batch = forgeNN.Tensor(X)
    
    # Forward pass
    logits = model(x_batch)
    loss = forgeNN.cross_entropy_loss(logits, y)
    
    # Backward pass
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    
    acc = forgeNN.accuracy(logits, y)
    print(f"Epoch {epoch}: Loss = {loss.data:.4f}, Acc = {acc*100:.1f}%")

Legacy Examples

# For learning automatic differentiation
from forgeNN.legacy import Value, MLP

x = Value(2.0)
y = x**2 + 3*x + 1
y.backward()
print(f"dy/dx = {x.grad}")  # 7.0

# Simple neural network
model = MLP(2, [4, 1])
output = model([Value(1.0), Value(2.0)])

Architecture

• Main API: forgeNN.Tensor, forgeNN.VectorizedMLP (production use)
• Legacy API: forgeNN.legacy.* (backward compatible)
• Functions: Complete activation and loss function library
• Examples: example.py - Complete MNIST classification demo

Performance

Implementation	Speed
Vectorized	38,000+ samples/sec

MNIST Results: 93%+ accuracy in under 2 seconds!

Complete Example

See example.py for a full MNIST classification demo achieving professional results.

Contributing

I am not currently accepting contributions, but I'm always open to suggestions and feedback!

📦 PyPI Deployment

This package is ready for PyPI deployment! All deployment files are organized in the deployment/ directory to keep the main project clean.

# Build and deploy to PyPI
cd deployment
python build_package.py
python upload_package.py

For detailed deployment instructions, see deployment/DEPLOY.md.

🌟 Acknowledgments

• Inspired by educational automatic differentiation tutorials
• Built for both learning and production use
• Optimized with modern NumPy practices

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