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

The AWS Certified SysOps Administrator

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

Covid19-Forecasting - An interactive website that tracks, models and predicts COVID-19 Cases

GenshinMapAutoMarkTools - Tools To add/delete/refresh resources mark in Genshin Impact Map

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

Open-Ended Commonsense Reasoning (NAACL 2021)

EMNLP 2021 - Frustratingly Simple Pretraining Alternatives to Masked Language Modeling

Dataset and Code for ICCV 2021 paper "Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme"

Implementation of Deep Deterministic Policy Gradiet Algorithm in Tensorflow

Memory-efficient optimum einsum using opt_einsum planning and PyTorch kernels.

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

Image classification for projects and researches

This is an official implementation for "Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation".

OptaPlanner wrappers for Python. Currently significantly slower than OptaPlanner in Java or Kotlin.

The repository for the paper "When Do You Need Billions of Words of Pretraining Data?"

GPOEO is a micro-intrusive GPU online energy optimization framework for iterative applications

Heterogeneous Deep Graph Infomax

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

Pyramid addon for OpenAPI3 validation of requests and responses.

You Only Look Once for Panopitic Driving Perception