[TensorFlow 2] A Simple Baseline for Bayesian Uncertainty in Deep Learning: SWA-Gaussian (SWAG)
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
Concept
Results
The red color and the blue color represent the initial state and current state respectively.
Variable | MNIST | CIFAR10 |
Performance
MNIST
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Final Epoch | 0.99230 | 0.99231 | 0.99222 | 0.99226 |
Best Loss | 0.99350 | 0.99350 | 0.99338 | 0.99344 |
SWAG (S = 30) | 0.99310 | 0.99305 | 0.99299 | 0.99302 |
SWAG (Last Momentum) | 0.99340 | 0.99340 | 0.99330 | 0.99335 |
CIFAR10
Method | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Final Epoch | 0.73130 | 0.73349 | 0.73130 | 0.73147 |
Best Loss | 0.73240 | 0.73205 | 0.73240 | 0.73099 |
SWAG (S = 30) | 0.74100 | 0.74622 | 0.74100 | 0.74260 |
SWAG (Last Momentum) | 0.73490 | 0.73888 | 0.73490 | 0.73561 |
Requirements
- Python 3.7.6
- Tensorflow 2.3.0
- Numpy 1.18.15
- whiteboxlayer 0.1.15
Reference
[1] Wesley Maddox et al. (2019). A Simple Baseline for Bayesian Uncertainty in Deep Learning. arXiv preprint arXiv:1902.02476.