deep-lyapunov 0.1.1

Analyze neural network training stability using Lyapunov exponents and perturbation-based trajectory analysis

deep-lyapunov

Analyze neural network training stability using Lyapunov exponents and perturbation-based trajectory analysis.

What is deep-lyapunov?

deep-lyapunov helps you understand how stable your neural network training is. It answers questions like:

• Do small changes in initialization lead to similar or different final models?
• Is my training process reproducible?
• Which architectures are more stable to train?

It uses concepts from dynamical systems theory—specifically Lyapunov exponents—to quantify training stability.

Key Concepts

Metric	What It Tells You
Convergence Ratio	Do weight trajectories come together (< 1) or spread apart (> 1)?
Lyapunov Exponent	Rate of trajectory separation. Negative = stable, Positive = chaotic
Effective Dimensionality	How many degrees of freedom in your weight dynamics?

Installation

pip install deep-lyapunov

For development:

pip install deep-lyapunov[dev]

Quick Start

import torch
import torch.nn as nn
from deep_lyapunov import StabilityAnalyzer

# Your model (any PyTorch nn.Module)
model = nn.Sequential(
    nn.Linear(784, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

# Create analyzer
analyzer = StabilityAnalyzer(
    model=model,
    perturbation_scale=0.01,  # 1% perturbation
    n_trajectories=5,          # Compare 5 perturbed copies
)

# Analyze stability during training
results = analyzer.analyze(
    train_fn=your_training_function,
    train_loader=train_loader,
    n_epochs=10,
)

# View results
print(f"Convergence Ratio: {results.convergence_ratio:.2f}x")
print(f"Lyapunov Exponent: {results.lyapunov:.4f}")
print(f"Training is: {results.behavior}")  # 'convergent' or 'divergent'

# Generate visualizations
results.plot_trajectories()
results.save_report("stability_report/")

Manual Recording Mode

If you have a custom training loop:

analyzer = StabilityAnalyzer(model)

analyzer.start_recording()
for epoch in range(10):
    train_one_epoch(model, train_loader)
    analyzer.record_checkpoint()

results = analyzer.compute_metrics()

Understanding Results

Convergent Training (ratio < 1.0)

• Different initializations converge to similar solutions
• Training is reproducible
• Good for production deployment

Divergent Training (ratio > 1.0)

• Small differences amplify during training
• Multiple distinct solutions exist
• Good for ensemble diversity

API Reference

StabilityAnalyzer

StabilityAnalyzer(
    model: nn.Module,
    perturbation_scale: float = 0.01,
    n_trajectories: int = 5,
    n_pca_components: int = 10,
    track_gradients: bool = False,
    device: str = "auto",
)

AnalysisResults

results.convergence_ratio  # float: final_spread / initial_spread
results.lyapunov           # float: average Lyapunov exponent
results.behavior           # str: 'convergent' or 'divergent'
results.spread_evolution   # np.ndarray: spread at each epoch
results.pca_trajectories   # np.ndarray: trajectories in PCA space

results.plot_trajectories()  # Visualize weight trajectories
results.plot_convergence()   # Plot spread evolution
results.save_report(path)    # Generate full report
results.to_dict()            # Export as dictionary

Examples

See the examples/ directory for:

• basic_usage.py - Simple stability analysis
• custom_training.py - Integration with custom training loops
• comparing_architectures.py - Compare stability across architectures

Theory

The analysis is based on perturbation-based trajectory analysis:

1. Start with a base model initialization
2. Create N copies with small Gaussian perturbations (scale ε)
3. Train all copies independently on the same data
4. Track how the weight trajectories evolve
5. Compute stability metrics from trajectory spread

The Lyapunov exponent λ is computed as:

λ = (1/T) × log(final_spread / initial_spread)

• λ < 0: Trajectories converge (stable)
• λ > 0: Trajectories diverge (chaotic/sensitive)
• λ ≈ 0: Neutral stability

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Citation

If you use deep-lyapunov in your research, please cite:

@software{deep_lyapunov,
  title = {deep-lyapunov: Neural Network Training Stability Analysis},
  author = {Sampathkumar, Rajesh},
  year = {2025},
  url = {https://github.com/aiexplorations/deep-lyapunov}
}

License

MIT License - see LICENSE for details.