GenAIDevTOProd/Noisy-Gaussian-NN

Noisy Gaussian NN – Robustness to Label Noise

Overview

This project explores how a simple 1-hidden-layer neural network handles increasing label noise when fitting a Gaussian curve.
We test three noise levels (σ = 0.05, 0.1, 0.2) to see when the network smooths effectively and when it starts to underfit.

Dataset

• Synthetic dataset: Gaussian curve (y = exp(-x^2))
• Noise added directly to labels using torch.normal
• 200 evenly spaced x points in [-2, 2]

Model

• Architecture: 1 hidden layer, 50 neurons, ReLU activation
• Loss: MSELoss
• Optimizer: Adam (lr=0.01)
• Training: 2000 epochs

Results

• Low noise: NN fits curve smoothly.
• Medium noise: Slight underfitting.
• High noise: Curve shape lost, noise dominates.

Key Insight

More noise ≠ better regularization.
Too much noise can destroy the signal beyond recovery.

Files

• GaussianApproximation.ipynb – Full experiment, plots, and analysis
• README.md – This file

License

MIT License – free to use, modify, and distribute with attribution.