by Yuesong Shen

This repository contains the demo code (as a python package) for the paper:

"Probabilistic Discriminative Learning with Layered Graphical Models" by Yuesong Shen, Tao Wu, Csaba Domokos and Daniel Cremers

Link: arXiv:1902.00057 [cs.LG]

If you find this code useful for your research, please consider citing the above paper.

Bibtex:

@ARTICLE{2019arXiv190200057S,
       author = {{Shen}, Yuesong and {Wu}, Tao and {Domokos}, Csaba and {Cremers}, Daniel},
        title = "{Probabilistic Discriminative Learning with Layered Graphical Models}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
         year = 2019,
        month = Jan,
          eid = {arXiv:1902.00057},
        pages = {arXiv:1902.00057},
archivePrefix = {arXiv},
       eprint = {1902.00057},
 primaryClass = {cs.LG}
}

The code is released under GPL v3 or later. For any questions please contact: [email protected]

Tested environment: Ubuntu 16.04; Python 3.6; gcc 5.4.0.

Required dependencies: Python 3.5+ along with pip; ABI compatible C++ compiler.

• In terminal, change to current directory.

• Install dependencies: "pip install -r requirements.txt"

• Install locally the demo package: "pip install -e ."

Demo scripts are inside the folder "example/".

• "demo_lgm.py" is the demo script for LGM models

  Run "python demo_lgm.py -h" for possible arguments

  Examples:

  • Run Local model with sequential TRW and FashionMNIST. Use cuda:

    "python demo_lgm.py -m local -i seqtrw -d FashionMNIST -g"

  • run Dense model with LBP (2 inference iterations) and MNIST for 10 epochs. Use cpu only:

    "python demo_lgm.py -m dense -i loopy -n 2 -d MNIST -e 10"

• "demo_nn.py" is the demo script for NN baselines

  Run "python demo_nn.py -h" for possible arguments

  Examples:

  • Run Local model with FashionMNIST and sigmoid activation. Use cuda:

    "python demo_nn.py -m local -a sigmoid -d FashionMNIST -g"

  • run Dense model with relu and MNIST for 10 epochs. Use cpu only:

    "python demo_nn.py -m dense -a relu -d MNIST -e 10"