Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

Related tags

Deep LearningWideLinears
Overview

WideLinears

Pytorch parallel Neural Networks

A package of pytorch modules for fast paralellization of separate deep neural networks. Ideal for agent-based systems such as evolutionary algorithms.

Installation

WideLinear is avaliable through Pypi

pip install widelinears

Pytorch Modules

WideLinear

Represents a family of parallel Linear layers that share the same input and output sizes

Parameters
  • beings (int): Number of parallel Linear layers
  • input_size (int): Size of input of each linear layer
  • output_size (int): Size of output of each linear layer
Input Tensor Shapes
  • (input_size,) will clone this input and give it to each Linear in the module, outputs (beings, output_size)
  • (beings, input_size) will give each Linear its own input vector, outputs (beings, output_size)
  • (batch, beings, input_size) will give each Linear its own batch of inputs, outputs (batch, beings, output_size)
Methods
  • forward (Tensor): Returns output for input tensors as explained above
  • clone_being (source, destination): Clones linear layer from one position to other, overriding what was there
  • get_single (position): Get LinearWidePointer class that is a pointer to this module but behaves as a normal single nn.Linear
  • to_linears (): Returns list of instances of nn.Linear with the same parameters as each Linear ins this module

WideDeep

WideDeep generalizes Deep Neural Networks using WideLinear layers, and simplifies constructing parallel Deep Neural Networks. Behaves as a group of separate Deep Neural Networks that run in parallel for good time efficiency.

Parameters
  • beings (int): Number of parallel Deep NNs
  • input_size (int): Size of input of each Deep NN
  • hidden_size (int): Size of each hidden layer in each Deep NN
  • depth (int): Number of hidden layers (if 1, there is a single Linear layer from input to output)
  • output_size (int): Size of output of each Deep NN
  • non_linear (optional function): Non Linearity function at each intermediate step (defaults to ReLU)
  • final_nl (optional function): Non Linearity at output (defaults to sigmoid)
Input Tensor Shapes
  • (input_size,) will clone this input and give it to each Deep NN, outputs (beings, output_size)
  • (beings, input_size) will give each Deep NN its own input vector, outputs (beings, output_size)
  • (batch, beings, input_size) will give each Deep NN its own batch of inputs, outputs (batch, beings, output_size)
Methods
  • forward (Tensor): Returns output for input tensors as explained above
  • clone_being (source, destination): Clones Deep NN from one position to other, overriding what was there

Model diagram Example architecture for parameters:

  • beings = 4
  • input_size = 5
  • hidden_size = 3
  • depth = 3
  • output_size = 4

License

MIT

Made by João Figueira

You might also like...
Convert scikit-learn models to PyTorch modules
Convert scikit-learn models to PyTorch modules

sk2torch sk2torch converts scikit-learn models into PyTorch modules that can be tuned with backpropagation and even compiled as TorchScript. Problems

Alex Nichol 101 Dec 16, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

null 78 Dec 27, 2022
A parallel framework for population-based multi-agent reinforcement learning.
A parallel framework for population-based multi-agent reinforcement learning.

MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested

MARL @ SJTU 348 Jan 8, 2023
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Multi-Agent-Deep-Deterministic-Policy-Gradients A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm This

Phil Tabor 159 Dec 28, 2022
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
Pytorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.

Off-Policy Multi-Agent Reinforcement Learning (MARL) Algorithms This repository contains implementations of various off-policy multi-agent reinforceme

null 183 Dec 28, 2022
Implementation of EMNLP 2017 Paper
Implementation of EMNLP 2017 Paper "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog" using PyTorch and ParlAI

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog Satwik Kottur, José M.

Karan Desai 105 Nov 25, 2022
Implementation of EMNLP 2017 Paper
Implementation of EMNLP 2017 Paper "Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog" using PyTorch and ParlAI

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog Satwik Kottur, José M.

Karan Desai 105 Nov 25, 2022
A
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Jack Turner 12 Nov 5, 2022
Releases(v_0.3)
Owner
GitHub Repository
Masked regression code - Masked Regression

Masked Regression MR - Python Implementation This repositery provides a python implementation of MR (Masked Regression). MR can efficiently synthesize

Arbish Akram 1 Dec 23, 2021
Title: Graduate-Admissions-Predictor

The purpose of this project is create a predictive model capable of identifying the probability of a person securing an admit based on their personal profile parameters. Simplified visualisations hav

Akarsh Singh 1 Jan 26, 2022
Author: Wenhao Yu ([email protected]). ACL 2022. Commonsense Reasoning on Knowledge Graph for Text Generation

Diversifying Commonsense Reasoning Generation on Knowledge Graph Introduction -- This is the pytorch implementation of our ACL 2022 paper "Diversifyin

DM2 Lab @ ND 61 Dec 30, 2022
Official PyTorch Implementation of paper "NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting", EGSR 2021.

NeLF: Neural Light-transport Field for Single Portrait View Synthesis and Relighting Official PyTorch Implementation of paper "NeLF: Neural Light-tran

Ken Lin 38 Dec 26, 2022
Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

(Brian) JoonHo Lee 24 Dec 12, 2022
Efficiently Disentangle Causal Representations

Efficiently Disentangle Causal Representations Install dependency pip install -r requirements.txt Main experiments Causality direction prediction cd

4 Apr 01, 2022
auto-tuning momentum SGD optimizer

YellowFin YellowFin is an auto-tuning optimizer based on momentum SGD which requires no manual specification of learning rate and momentum. It measure

Jian Zhang 288 Nov 19, 2022
A sketch extractor for anime/illustration.

Anime2Sketch Anime2Sketch: A sketch extractor for illustration, anime art, manga By Xiaoyu Xiang Updates 2021.5.2: Upload more example results of anim

Xiaoyu Xiang 1.6k Jan 01, 2023
FewBit — a library for memory efficient training of large neural networks

FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back

24 Oct 22, 2022
Model parallel transformers in Jax and Haiku

Mesh Transformer Jax A haiku library using the new(ly documented) xmap operator in Jax for model parallelism of transformers. See enwik8_example.py fo

Ben Wang 4.8k Jan 01, 2023
Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

STORM Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit [Install Instructions] [Paper] [Website] This package contains code

NVIDIA Research Projects 101 Dec 12, 2022
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

HOW TO USE THIS PROJECT A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets Based on DeepLabCut toolbox, we run wit

1 Jan 10, 2022
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pytorch Lightning 1.4k Jan 01, 2023
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021)

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021) Kun Wang, Zhenyu Zhang, Zhiqiang Yan, X

kunwang 66 Nov 24, 2022
This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2021, Spotlight)

Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization This codebase is the official implementation of Test-Time Classifier A

47 Dec 28, 2022
Python package for visualizing the loss landscape of parameterized quantum algorithms.

orqviz A Python package for easily visualizing the loss landscape of Variational Quantum Algorithms by Zapata Computing Inc. orqviz provides a collect

Zapata Computing, Inc. 75 Dec 30, 2022
Probabilistic Programming and Statistical Inference in PyTorch

PtStat Probabilistic Programming and Statistical Inference in PyTorch. Introduction This project is being developed during my time at Cogent Labs. The

Stefano Peluchetti 109 Nov 26, 2022
Fastquant - Backtest and optimize your trading strategies with only 3 lines of code!

fastquant 🤓 Bringing backtesting to the mainstream fastquant allows you to easily backtest investment strategies with as few as 3 lines of python cod

Lorenzo Ampil 1k Dec 29, 2022
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains a PyTorch implementation for the paper Score-Based Genera

Yang Song 757 Jan 04, 2023
A Keras implementation of YOLOv3 (Tensorflow backend)

keras-yolo3 Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. Quick Start Download YOLOv3 weights fro

7.1k Jan 03, 2023