Adaptively Preconditioned Stochastic Gradient Langevin Dynamics
In this project, we try to implementent a Noisy Stochastic Gradient Descent based approach for non-convex optimization. The key intution is to precondition the noise in Stochastic Gradient Langevin Dynamics with a running average of momentum and variance of first order gradients.
The paper was accepted in International Confenrence on Machine Learning (ICML 2019) Workshop on Understanding and Improving Genrealization in Deep Learning (https://arxiv.org/abs/1906.04324).
Examples on CIFAR-10
In this example, we test ASGLD (Adaptively Preconditioned Stochastic Gradient Langevin Dynamics) on the standard CIFAR-10 image classification dataset, comparing with several baseline methods including: SGD, AdaGrad, Adam, AMSGrad, ADABound, and AMSBound.
The implementation is highly based on this project and this project.
Tested with PyTorch 1.0.0.
Visualization
The results can be viewed in a visual format in visualization.ipynb The project can be cloned to run in local machine.
Settings
Best parameters for CIFAR10 ResNet-34:
| optimizer | lr | momentum | beta1 | beta2 | final lr | gamma | noise |
|---|---|---|---|---|---|---|---|
| SGD | 0.1 | 0.9 | |||||
| AdaGrad | 0.01 | ||||||
| Adam | 0.001 | 0.99 | 0.999 | ||||
| AMSGrad | 0.001 | 0.99 | 0.999 | ||||
| AdaBound | 0.001 | 0.9 | 0.999 | 0.1 | 0.001 | ||
| AMSBound | 0.001 | 0.9 | 0.999 | 0.1 | 0.001 | ||
| GGDO2 | 0.1 | 0.9 | 0.01 | ||||
| GGDO4 | 0.1 | 0.9 |
We apply a weight decay of 5e-4 to all the optimizers.
GGDO2 algorithm reflects the ASGLD algorithm proposed in the paper.
Training on local machine
For training on local machine using parameters for GGDO2, please run the following line.
python main3.py --model=resnet --optim=ggdo2 --lr=0.1 --momentum=0.9 --noise=0.01The checkpoints will be saved in the checkpoint folder and the data points of the learning curve
will be save in the curve folder.