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Noisy Natural Gradient as Variational Inference

PyTorch implementation of Noisy Natural Gradient as Variational Inference.

Requirements

• Python 3
• Pytorch
• visdom

Comments

• This paper is about how to optimize bayesian neural network which has matrix variate gaussian distribution.
• This implementation contains Noisy Adam optimizer which is for Fully Factorized Gaussian(FFG) distribution, and Noisy KFAC optimizer which is for Matrix Variate Gaussian(MVG) distribution.
• These optimizers only work with bayesian network which has specific structure that I will mention below.
• Currently only linear layer is available.

Experimental comments

• I addded a lr scheduler to noisy KFAC because loss is exploded during training. I guess this happens because of slight approximation.
• For MNIST training noisy KFAC is 15-20x slower than noisy Adam, as mentioned in paper.
• I guess the noisy KFAC needs more epochs to train simple neural network structure like 2 linear layers.

Usage

Currently only MNIST dataset are currently supported, and only fully connected layer is implemented.

Options

• model : Fully Factorized Gaussian(FFG) or Matrix Variate Gaussian(MVG)
• n : total train dataset size. need this value for optimizer.
• eps : parameter for optimizer. Default to 1e-8.
• initial_size : initial input tensor size. Default to 784, size of MNIST data.
• label_size : label size. Default to 10, size of MNIST label.

More details in option_parser.py

Train

$ python train.py --model=FFG --batch_size=100 --lr=1e-3 --dataset=MNIST
$ python train.py --model=MVG --batch_size=100 --lr=1e-2 --dataset=MNIST --n=60000

Visualize

• To visualize intermediate results and loss plots, run python -m visdom.server and go to the URL http://localhost:8097

Test

$ python test.py --epoch=20

Training Graphs

1. MNIST

• network is consist of 2 linear layers.
• FFG optimized by noisy Adam : epoch 20, lr 1e-3

• MVG optimized by noisy KFAC : epoch 100, lr 1e-2, decay 0.1 for every 30 epochs
• Need to tune learning rate.

Implementation detail

• Optimizing parameter procedure is consists of 2 steps, Calculating gradient and Applying to bayeisan parameters.
• Before forward, network samples parameters with means & variances.
• Usually calling step function updates parameters, but not this case. After calling step function, you have to update bayesian parameters. Look at the ffg_model.py

TODOs

• More benchmark cases
• Supports bayesian convolution
• Implement Block Tridiagonal Covariance, which is dependent between layers.

Code reference

Visualization code(visualizer.py, utils.py) references to pytorch-CycleGAN-and-pix2pix(https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) by Jun-Yan Zhu

Author

Tony Kim