bioinf-jku/SNNs resource

Health v2 · maintenance only

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1586 stars · 196 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of tutorials and reference implementations for Self-Normalizing Networks (SNNs) from Klambauer et al., demonstrating SELU activations, LeCun normal initialization, and AlphaDropout. It provides TensorFlow/Keras scripts and PyTorch Jupyter notebooks on MNIST and CIFAR10, plus supporting notebooks and calculations.

Use cases

• learn how self-normalizing networks work
• implement SELU activation networks in Keras or PyTorch
• reproduce the SNN paper experiments on MNIST and CIFAR10
• find the correct initialization and dropout for SELU networks
• tutorial examples of AlphaDropout usage

When to choose

• you want to learn or teach the SNN/SELU technique with runnable code
• you need reference implementations in both TensorFlow/Keras and PyTorch
• you want to reproduce results from the original SNN paper

When to avoid

• you need a production-ready neural network library
• you want architectures beyond simple MLPs and CNNs on benchmark datasets
• you need a maintained framework rather than educational example code

Facets

learning-resource · maturity maintenance

machine-learning deep-learning deep-learning machine-learning tutorials python cross-platform self-normalizing-networks selu keras pytorch jupyter-notebooks mnist cifar10 gpu

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