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Codes and experiments for paper "Automated Spectral Kernel Learning". Preprint.
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core_functions
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README.md
run_exp1.py
run_parameter_tune.py
run_startup.py

README.md

Automated Spectral Kernel Learning

Intro

This repository provides the code used to run the experiments of the paper "Automated Spectral Kernel Learning" (https://arxiv.org/abs/1909.04894).

Environments

  • Python 3.7.4
  • Pytorch 1.2.0
  • CUDA 10.1.168
  • cuDnn 7.6.0
  • GPU: Tesla P100 16GB

Core functions

  • auto_kernel_learning.py implements the algorithm to construct an one-layer neural network, including initialization of trainable weights and untrainable biases as well as feature mapping (cosine as activation).
  • utils.py implements useful tools including load svmlight style dataset and classic datasets used in Pytorch but also various loss functions are introduced.
  • optimal_parameters.py records optimal parameters for the proposed algorithm.

Experiments

  1. Download datasets for multi-class classification (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/).
  2. Run the script to tune parameters and record them in optimal_parameters.py.
python run_parameter_tune.py
  1. Run the script to obtain results in Experiment section
python run_exp1.py
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