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
🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

3D Object Reconstruction from a Single Depth View with Adversarial Learning Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni

Bo Yang 125 Nov 26, 2022
Label-Free Model Evaluation with Semi-Structured Dataset Representations

Label-Free Model Evaluation with Semi-Structured Dataset Representations Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch

8 Oct 06, 2022
Generating Band-Limited Adversarial Surfaces Using Neural Networks

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

3 Jul 26, 2022
Relative Uncertainty Learning for Facial Expression Recognition

Relative Uncertainty Learning for Facial Expression Recognition The official implementation of the following paper at NeurIPS2021: Title: Relative Unc

35 Dec 28, 2022
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202

sangho.lee 28 Nov 08, 2022
Codebase for testing whether hidden states of neural networks encode discrete structures.

structural-probes Codebase for testing whether hidden states of neural networks encode discrete structures. Based on the paper A Structural Probe for

John Hewitt 349 Dec 17, 2022
Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

Monopol Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV. Background This applicat

Jayaku Briliantio 3 Apr 07, 2022
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Rex Cheng 106 Jan 03, 2023
Sound-guided Semantic Image Manipulation - Official Pytorch Code (CVPR 2022)

🔉 Sound-guided Semantic Image Manipulation (CVPR2022) Official Pytorch Implementation Sound-guided Semantic Image Manipulation IEEE/CVF Conference on

CVLAB 58 Dec 28, 2022
Code for "Multi-Compound Transformer for Accurate Biomedical Image Segmentation"

News The code of MCTrans has been released. if you are interested in contributing to the standardization of the medical image analysis community, plea

97 Jan 05, 2023
OMLT: Optimization and Machine Learning Toolkit

OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.

C⚙G - Imperial College London 179 Jan 02, 2023
Reporting and Visualization for Hazardous Events

Reporting and Visualization for Hazardous Events

Jv Kyle Eclarin 2 Oct 03, 2021
A novel pipeline framework for multi-hop complex KGQA task. About the paper title: Improving Multi-hop Embedded Knowledge Graph Question Answering by Introducing Relational Chain Reasoning

Rce-KGQA A novel pipeline framework for multi-hop complex KGQA task. This framework mainly contains two modules, answering_filtering_module and relati

金伟强 -上海大学人工智能小渣渣~ 16 Nov 18, 2022
Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

Implicit Neural Representations with Periodic Activation Functions Project Page | Paper | Data Vincent Sitzmann*, Julien N. P. Martel*, Alexander W. B

Vincent Sitzmann 1.4k Jan 06, 2023
Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

APR The repo for the paper Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study. Environment setu

ielab 8 Nov 26, 2022
Implementation of Google Brain's WaveGrad high-fidelity vocoder

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Ivan Vovk 363 Dec 27, 2022
September-Assistant - Open-source Windows Voice Assistant

September - Windows Assistant September is an open-source Windows personal assis

The Nithin Balaji 9 Nov 22, 2022
A sequence of Jupyter notebooks featuring the 12 Steps to Navier-Stokes

CFD Python Please cite as: Barba, Lorena A., and Forsyth, Gilbert F. (2018). CFD Python: the 12 steps to Navier-Stokes equations. Journal of Open Sour

Barba group 2.6k Dec 30, 2022
deep learning model that learns to code with drawing in the Processing language

sketchnet sketchnet - processing code generator can we teach a computer to draw pictures with code. We use Processing and java/jruby code paired with

41 Dec 12, 2022
The final project of "Applying AI to 2D Medical Imaging Data" of "AI for Healthcare" nanodegree - Udacity.

Pneumonia Detection from X-Rays Project Overview In this project, you will apply the skills that you have acquired in this 2D medical imaging course t

Omar Laham 1 Jan 14, 2022