Set-processing

The implementation and code for the paper:

L. Maziarka, M. Smieja, A. Nowak, J. Tabor, L. Struski, P. Spurek, Set aggregation network for structured data processing, arXiv preprint: https://arxiv.org/abs/1810.01868, 2018

The reposiory consists of

• README.md - this file
• src - the main code directory, divided into:
  • data handling - the function helpers for loading and preprocessing the data
  • layer - the SAN implementation
  • metrics - code for gathering and reporting the results
  • model - the implementation of the used in experiments models
  • train - all train related logic and main programs.

Running the code:

An example of running the code for small-scale convolutional network for the CIFAR-10 experiment:

python src/train/image_main.py --folder path/to/output/folder --head-hidden-dim 256 --head-layers 1 --shuffle --print-params --l2 0.0 --batch-size 256 --learning-rate 1e-3 --epochs 15 --body-type cnn-avg --dropout 0.0

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Where:

• --folder - the path to the folder with outputs
• --head-hidden-dim - the hidden dimension of the predictor head
• --head-layers - number of hidden layers in the predictor head
• --shuffle - shuffle the input dataset
• --print-params - print the passed command line arguments
• --l2 - the used l2 regularization scale
• --batch-size - the size of one batch
• --learning-rate - the used learning rate
• --epochs - the number of epochs
• --body-type - the type of used body model and the corresponding aggregation method. (see --help)
• --dropout - the dropout rate

For more options and their description please run:

python src/train/image_main.py --help

Please note that the default values of hyper-parameters in the code are not necessarily equal to the values used in the paper. For the hyper-parameter setting please refer to the experiments description in the paper.

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