Nets

A Lightweight Neural Network Framework (NumPy-Based)

Nets is a from-scratch deep learning framework built using NumPy, designed to be educational, modular, and extensible, while also supporting real-world experimentation workflows such as training, evaluation, and visualization.

🔥 Highlights

•	🧠 Custom autograd engine (Tensor-based)

•	⚙️ Modular neural network API (like PyTorch)

•	📦 Layers: Linear, RNN, (Conv2D in progress)

•	⚡ Optimizers: SGD, Adam

•	📉 Losses: MSE, CrossEntropy

•	📊 Advanced Dashboard (W&B-like)

•	🧪 Experiment tracking system

•	📁 Save / Load run logs

•	🎯 Supports MLP, RNN, and basic CNN workflows

🧠 Core Concepts

Tensor

•	Wraps NumPy arrays

•	Supports automatic differentiation

•	Builds computational graph dynamically

from nets.tensor.tensor import Tensor x = Tensor([1,2,3], requires_grad=True)

Model Definition

from nets.nn import Sequential, Linear, ReLU

model = Sequential( Linear(784, 256), ReLU(), Linear(256, 10) )

Training

from nets.optim.adam import Adam from nets.losses.cross_entropy import cross_entropy

optimizer = Adam(model.parameters(), lr=0.001)

logits = model(x) loss = cross_entropy(logits, y)

loss.backward() optimizer.step() optimizer.zero_grad()

📊 Dashboard (W&B-like)

NETS includes a real-time experiment dashboard powered by Dash + Plotly.

Features: • 📈 Live Loss & Accuracy plots • 📊 Precision / Recall / F1 (auto-detected) • 🔥 Confusion Matrix • 🔁 Run comparison • 🧾 Experiment metadata • 🗑 Run deletion • 🎯 Task-aware visualization

Run Dashboard

from nets.visualization.dashboard import Dashboard Dashboard(logger).run()

📊 Metrics Supported

Classification • Accuracy • Precision • Recall • F1 Score • Confusion Matrix

Regression (planned) • MAE • RMSE • R²

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🧩 Features (Detailed)

Autograd Engine • Reverse-mode differentiation • Dynamic graph construction • Supports broadcasting

Layers • Linear • ReLU / Sigmoid / Tanh • RNN • Conv2D (in progress)

Optimizers • SGD (with momentum) • Adam

Data • Dataset abstraction • DataLoader (mini-batch support)

Visualization • Real-time metrics • Interactive plots • Task-aware rendering

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⚠️ Limitations

•	No GPU acceleration (NumPy backend only)
•	Conv2D still being optimized
•	No Transformer yet
•	Limited NLP support (no embeddings)

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🚀 Future Work

•	🔥 Transformer (Encoder + Decoder)
•	⚡ GPU support (CuPy backend)
•	📦 Model saving / loading
•	🧠 Automatic Trainer (Lightning-like)
•	🌐 Deployment utilities

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🤝 Contribution

Feel free to fork and improve: • Add layers (Conv, Attention) • Improve performance • Enhance dashboard UI

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📸 Dashboard Preview

Below are real snapshots of the NETS Dashboard during training:

🔹 RNN Training View

🔹 MLP MNIST Training View

These demonstrate: • Real-time metric tracking • Adaptive visualization (task-aware) • Clean UI with multiple metric panels

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🏁 Conclusion

NETS demonstrates that:

You can build a functional deep learning framework from scratch, understand every component deeply, and still run meaningful experiments.

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