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
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.
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In terminal, change to current directory.
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Install dependencies: "pip install -r requirements.txt"
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Install locally the demo package: "pip install -e ."
Demo scripts are inside the folder "example/".
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"demo_lgm.py" is the demo script for LGM models
Run "python demo_lgm.py -h" for possible arguments
Examples:
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Run Conv model with TRW and FashionMNIST. Use cuda:
"python demo_lgm.py -m conv -i trw -d FashionMNIST -g"
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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"
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"demo_nn.py" is the demo script for NN baselines
Run "python demo_nn.py -h" for possible arguments
Examples:
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Run Conv model with FashionMNIST and sigmoid activation. Use cuda:
"python demo_nn.py -m conv -a sigmoid -d FashionMNIST -g"
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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"
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