Parameter-ensemble-differential-evolution - Shows how to do parameter ensembling using differential evolution.

Last update: May 4, 2022

Overview

Ensembling parameters with differential evolution

This repository shows how to ensemble parameters of two trained neural networks using differential evolution. The steps followed are as follows:

Train two networks (architecturally same) on the same dataset (CIFAR-10 used here) but from two different random initializations.
Ensemble their weights using the following formulae:
```
w_t = w_o * ema + (1 - ema) * w_p
```
w_o and w_p represents the learned of a neural network.
Randomly initialize a network (same architecture as above) and populate its parameters w_t using the above formulae.

ema is usually chosen by the developer in an empirical manner. This project uses differential evolution to find it.

Below are the top-1 accuracies (on CIFAR-10 test set) of two individually trained two models along with their ensembled variant:

Model one: 63.23%
Model two: 63.42%
Ensembled: 63.35%

With the more conventional average prediction ensembling, I was able to get to 64.92%. This is way better than what I got by ensembling the parameters. Nevertheless, the purpose of this project was to just try out an idea.

Reproducing the results

Ensure the requirements.txt is satisfied. Then train two models with ensuring your working directory is at the root of this project:

$ git clone https://github.com/sayakpaul/parameter-ensemble-differential-evolution
$ cd parameter-ensemble-differential-evolution
$ pip install -qr requirements.txt
$ for i in `seq 1 2`; python train.py; done

Then just follow the ensemble-parameters.ipynb notebook. You can also use the networks I trained. Instructions are available inside the notebook.

Parameter-ensemble-differential-evolution - Shows how to do parameter ensembling using differential evolution.

Related tags

Overview

Ensembling parameters with differential evolution

Reproducing the results

References

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