chainer-graph-cnn
This is a Chainer implementation ofDefferrard et al., "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering", NIPS 2016.(https://arxiv.org/abs/1606.09375)
Disclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details.
This is not the original author's implementation. This implementation was based on https://github.com/mdeff/cnn_graph.
# Trains a GraphCNN on MNIST $ python tools/train.py -c configs/default.json -o results -e 100 -g 0
pip install -r requirements.txt
This implementation has been tested with the following versions.
python 2.7.6 chainer (1.19.0) nose (1.3.7) numpy (1.11.3) scikit-learn (0.18.1) scipy (0.18.1)
It may work with other versions; not tested.
Using ADAM alpha=1e-4 epoch iteration main/loss main/accuracy validation/main/loss validation/main/accuracy 1 600 0.515395 0.854901 0.193552 0.9453 2 1200 0.195267 0.942567 0.122769 0.9652 3 1800 0.139023 0.95875 0.0955012 0.9726 4 2400 0.110456 0.9676 0.0769727 0.9762 5 3000 0.0932845 0.972033 0.0643796 0.9812 6 3600 0.0811693 0.975149 0.0603944 0.9824 7 4200 0.074127 0.978266 0.0556359 0.9831 8 4800 0.0670138 0.980266 0.0509385 0.9839 9 5400 0.0625065 0.980933 0.0496262 0.9839 10 6000 0.0585658 0.982366 0.0493765 0.9838 11 6600 0.0547269 0.983082 0.0444783 0.9859 12 7200 0.050334 0.984582 0.0413585 0.9866 13 7800 0.0493707 0.985032 0.0416611 0.9873 14 8400 0.0459602 0.985999 0.0437013 0.9859 15 9000 0.044378 0.986715 0.0406627 0.987 16 9600 0.0430196 0.986815 0.0394637 0.9866 17 10200 0.0404675 0.988182 0.0385143 0.9877 18 10800 0.0398833 0.988265 0.0366019 0.989 19 11400 0.0371923 0.988998 0.0348309 0.9875 20 12000 0.0361765 0.989215 0.0402662 0.9858 -- snip -- 100 60000 0.0157423 0.995832 0.0292472 0.9901
Using ADAM alpha=1e-3 epoch iteration main/loss main/accuracy validation/main/loss validation/main/accuracy 1 600 0.225126 0.930017 0.0767015 0.9768 2 1200 0.0977682 0.969899 0.0606019 0.9801 3 1800 0.0770546 0.976016 0.0513997 0.9838 4 2400 0.0666313 0.979532 0.0424098 0.9866 5 3000 0.06334 0.980782 0.051125 0.9841 6 3600 0.0578026 0.982532 0.0457874 0.985 7 4200 0.0541042 0.983982 0.0405522 0.9875 8 4800 0.0514735 0.984432 0.0443701 0.9867 9 5400 0.0503822 0.984448 0.0557598 0.9812 10 6000 0.0465654 0.985432 0.035589 0.9897 11 6600 0.0455079 0.985932 0.03442 0.988 12 7200 0.0425339 0.986882 0.038998 0.9868 13 7800 0.0427513 0.986899 0.0395496 0.9873 14 8400 0.0431217 0.986815 0.0372915 0.9877 15 9000 0.0420674 0.987432 0.0401286 0.9864 16 9600 0.0408353 0.987482 0.0404751 0.9876 17 10200 0.0401931 0.987515 0.0372056 0.9879 18 10800 0.0388781 0.988315 0.0389307 0.9889 19 11400 0.0391798 0.988198 0.0406604 0.9872 20 12000 0.0380889 0.988298 0.039208 0.9867 -- snip -- 100 60000 0.0320832 0.990331 0.0345484 0.9887
MIT License. Please see the LICENSE file for details.
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