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Hyperparams and resulting numbers #33

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meitarronen opened this issue Oct 1, 2021 · 0 comments
Open

Hyperparams and resulting numbers #33

meitarronen opened this issue Oct 1, 2021 · 0 comments

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@meitarronen
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Hi, thank you so much for your contribution and sharing your project! This is a great paper :)
I would highly appreciate your help with running the training process as I am not sure how to run certain parts of it in order to reproduce your results from the paper.

  1. How should we run the edgeConstruction script? Which hyperparams should we choose?
  2. Do you have available configurations for more datasets?
  3. Using the following commands I got fairly good numbers on MNIST, but lower than the reported ones.
    Can you perhaps guide me how to get closer to the reported numbers?

Script lines:
python pretraining.py --data mnist --id 1 --niter 50000 --lr 10 --step 20000
python extract_feature.py --data mnist --net checkpoint_4.pth.tar --features pretrained
python edgeConstruction.py --dataset mnist --format mat --samples 70000 --prep 'minmax' --k 10 --algo 'mknn'
python copyGraph.py --data mnist --graph pretrained.mat --features pretrained.pkl --out pretrained
python DCC.py --data mnist --net checkpoint_4.pth.tar --id 1
The results I got:
ARI: 0.830861826385 AMI: 0.7969629161498257 NMI: 0.8647221174121507 ACC: 0.8187 K: 173

Thank you so much in advance!!

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