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
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022
Pure python implementation reverse-mode automatic differentiation

MiniGrad A minimal implementation of reverse-mode automatic differentiation (a.k.a. autograd / backpropagation) in pure Python. Inspired by Andrej Kar

Kenny Song 76 Sep 12, 2022
The Generic Manipulation Driver Package - Implements a ROS Interface over the robotics toolbox for Python

Armer Driver Armer aims to provide an interface layer between the hardware drivers of a robotic arm giving the user control in several ways: Joint vel

QUT Centre for Robotics (QCR) 13 Nov 26, 2022
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching（CVPR2021）

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

106 Dec 28, 2022
High performance distributed framework for training deep learning recommendation models based on PyTorch.

PERSIA (Parallel rEcommendation tRaining System with hybrId Acceleration) is developed by AI 340 Dec 30, 2022

Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

tflite2tensorflow Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite. 1. Supported Layers No. TFLite Layer TF

Katsuya Hyodo 214 Dec 29, 2022
[NeurIPS 2021] "Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems"

Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems Introduction Multi-agent control i

VITA 6 May 05, 2022
This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

MPMAB_BEACON This is code used for the paper "Decentralized Multi-player Multi-armed Bandits: Beyond Linear Reward Functions", Neurips 2021. Requireme

Cong Shen Research Group 0 Oct 26, 2021
Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

One-Shot Free-View Neural Talking Head Synthesis Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Vide

ZLH 406 Dec 23, 2022
Keep CALM and Improve Visual Feature Attribution

Keep CALM and Improve Visual Feature Attribution Jae Myung Kim1*, Junsuk Choe1*, Zeynep Akata2, Seong Joon Oh1† * Equal contribution † Corresponding a

NAVER AI 90 Dec 07, 2022
Weakly Supervised 3D Object Detection from Point Cloud with Only Image Level Annotation

SCCKTIM Weakly Supervised 3D Object Detection from Point Cloud with Only Image-Level Annotation Our code will be available soon. The class knowledge t

1 Nov 12, 2021
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 08, 2023
Python Blood Vessel Topology Analysis

Python Blood Vessel Topology Analysis This repository is not being updated anymore. The new version of PyVesTo is called PyVaNe and is available at ht

6 Nov 15, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.

DeepProbLog DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predic

KU Leuven Machine Learning Research Group 94 Dec 18, 2022
PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

SelfReg PyTorch official implementation of Self-supervised Contrastive Regularization for Domain Generalization (SelfReg, https://arxiv.org/abs/2104.0

64 Dec 16, 2022
Random Forests for Regression with Missing Entries

Random Forests for Regression with Missing Entries These are specific codes used in the article: On the Consistency of a Random Forest Algorithm in th

Irving Gómez-Méndez 1 Nov 15, 2021
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

Video-Captioning - A machine Learning project to generate captions for video frames indicating the relationship between the objects in the video

1 Jan 23, 2022
A tool to prepare websites grabbed with wget for local viewing.

makelocal A tool to prepare websites grabbed with wget for local viewing. exapmples After fetching xkcd.com with: wget -r -no-remove-listing -r -N --p

5 Apr 23, 2022