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

Last update: May 04, 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

You might also like...

Neural Ensemble Search for Performant and Calibrated Predictions

An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

zeus is a Python implementation of the Ensemble Slice Sampling method.

Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning

This Jupyter notebook shows one way to implement a simple first-order low-pass filter on sampled data in discrete time.

A fast Evolution Strategy implementation in Python

Code for the paper Task Agnostic Morphology Evolution.

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Releases(v0.1.0)

v0.1.0(Jan 2, 2022)

Owner

Sayak Paul

Hysterese plugin with two temperature offset areas

For IBM Quantum Challenge 2021 (May 20 - 26)

End-to-End Speech Processing Toolkit

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Hypersearch weight debugging and losses tutorial

A PyTorch implementation of the Transformer model in "Attention is All You Need".

A Python package for generating concise, high-quality summaries of a probability distribution

Code for "Learning Graph Cellular Automata"

FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware.

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

Code & Data for Enhancing Photorealism Enhancement

A CNN implementation using only numpy. Supports multidimensional images, stride, etc.

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

A multi-scale unsupervised learning for deformable image registration

A Python library for unevenly-spaced time series analysis

A command line simple note taking app

Leaf: Multiple-Choice Question Generation

Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

EMNLP 2020 - Summarizing Text on Any Aspects

Underwater image enhancement