Rational Activation Functions - Replacing Padé Activation Units

Overview

ArXiv Badge PWC

Logo

Rational Activations - Learnable Rational Activation Functions

First introduce as PAU in Padé Activation Units: End-to-end Learning of Activation Functions in Deep Neural Network.

1. About Rational Activation Functions

Rational Activations are a novel learnable activation functions. Rationals encode activation functions as rational functions, trainable in an end-to-end fashion using backpropagation and can be seemingless integrated into any neural network in the same way as common activation functions (e.g. ReLU).

Rationals: Beyond known Activation Functions

Rational can approximate any known activation function arbitrarily well (cf. Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks): rational_approx (*the dashed lines represent the rational approximation of every function)

Rational are made to be optimized by the gradient descent, and can discover good properties of activation functions after learning (cf Recurrent Rational Networks): rational_properties

Rationals evaluation on different tasks

Rational matches or outperforms common activations in terms of predictive performance and training time. And, therefore relieves the network designer of having to commit to a potentially underperforming choice.

  • Recurrent Rational Functions have then been introduced in Recurrent Rational Networks, and both Rational and Recurrent Rational Networks are evaluated on RL Tasks. rl_scores :octocat: See rational_rl github repo

2. Dependencies

We support MxNet, Keras, and PyTorch. Instructions for MxNet can be found here. Instructions for Keras here. The following README instructions assume that you want to use rational activations in PyTorch.

PyTorch>=1.4.0
CUDA>=10.2

3. Installation

To install the rational_activations module, you can use pip, but:

‼️ rational_activations is currently compatible with torch==1.9.0 by default ‼️

For non TensorFlow and MXNet users, or if the command bellow don't work the package listed bellow don't work on your machine:

TensorFlow or MXNet (and torch==1.9.0)

 pip3 install -U pip wheel
 pip3 install torch rational_activations

Other CUDA/Pytorch

For any other torch version, please install from source: Modify requirements.txt to your corresponding torch version

 pip3 install airspeed  # to compile the CUDA templates
 git clone https://github.com/ml-research/rational_activations.git
 cd rational_activations
 pip3 install -r requirements.txt --user
 python3 setup.py install --user

If you encounter any trouble installing rational, please contact this person.

4. Using Rational in Neural Networks

Rational can be integrated in the same way as any other common activation function.

import torch
from rational.torch import Rational

model = torch.nn.Sequential(
    torch.nn.Linear(D_in, H),
    Rational(), # e.g. instead of torch.nn.ReLU()
    torch.nn.Linear(H, D_out),
)

Please also check the documentation 📔

5. Cite Us in your paper

@inproceedings{molina2019pade,
  title={Pad{\'e} Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks},
  author={Molina, Alejandro and Schramowski, Patrick and Kersting, Kristian},
  booktitle={International Conference on Learning Representations},
  year={2019}
}

@article{delfosse2021recurrent,
  title={Recurrent Rational Networks},
  author={Delfosse, Quentin and Schramowski, Patrick and Molina, Alejandro and Kersting, Kristian},
  journal={arXiv preprint arXiv:2102.09407},
  year={2021}
}

@misc{delfosse2020rationals,
  author = {Delfosse, Quentin and Schramowski, Patrick and Molina, Alejandro and Beck, Nils and Hsu, Ting-Yu and Kashef, Yasien and Rüling-Cachay, Salva and Zimmermann, Julius},
  title = {Rational Activation functions},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished={\url{https://github.com/ml-research/rational_activations}}
}
Owner
[email protected]
Machine Learning Group at TU Darmstadt
<a href=[email protected]">
[NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Code for NeurIPS 2021 Paper "Exploring Architectural Ingredients of A

Hanxun Huang 26 Dec 01, 2022
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

2.8k Dec 28, 2022
Code for the paper "Adversarial Generator-Encoder Networks"

This repository contains code for the paper "Adversarial Generator-Encoder Networks" (AAAI'18) by Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky. Pr

Dmitry Ulyanov 279 Jun 26, 2022
The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

Dr. Sander Ali Khowaja 1 May 08, 2022
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

Joseph K J 37 Jan 03, 2023
Bare bones use-case for deploying a containerized web app (built in streamlit) on AWS.

Containerized Streamlit web app This repository is featured in a 3-part series on Deploying web apps with Streamlit, Docker, and AWS. Checkout the blo

Collin Prather 62 Jan 02, 2023
Node Editor Plug for Blender

NodeEditor Blender的程序化建模插件 Show Current 基本框架:自定义的tree-node-socket、tree中的node与socket采用字典查询、基于socket入度的拓扑排序 数据传递和处理依靠Tree中的字典,socket传递字典key TODO 增加更多的节点

Cuimi 11 Dec 03, 2022
Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads-Tutorial-3 Testing the Facial Emotion Recognition (FER) algorithm on animations

PegHeads Inc 2 Jan 03, 2022
Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.

Streamlit - Drawable Canvas Streamlit component which provides a sketching canvas using Fabric.js. Features Draw freely, lines, circles, boxes and pol

Fanilo Andrianasolo 325 Dec 28, 2022
MacroTools provides a library of tools for working with Julia code and expressions.

MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an

FluxML 278 Dec 11, 2022
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Codes accompanying the paper "Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning" (NeurIPS 2021 Spotlight

Implicit Constraint Q-Learning This is a pytorch implementation of ICQ on Datasets for Deep Data-Driven Reinforcement Learning (D4RL) and ICQ-MA on SM

42 Dec 23, 2022
Equivariant GNN for the prediction of atomic multipoles up to quadrupoles.

Equivariant Graph Neural Network for Atomic Multipoles Description Repository for the Model used in the publication 'Learning Atomic Multipoles: Predi

16 Nov 22, 2022
Semi-automated OpenVINO benchmark_app with variable parameters

Semi-automated OpenVINO benchmark_app with variable parameters. User can specify multiple options for any parameters in the benchmark_app and the progam runs the benchmark with all combinations of gi

Yasunori Shimura 8 Apr 11, 2022
Exploration of some patients clinical variables.

Answer_ALS_clinical_data Exploration of some patients clinical variables. All the clinical / metadata data is available here: https://data.answerals.o

1 Jan 20, 2022
Official Implementation of PCT

Official Implementation of PCT Prerequisites python == 3.8.5 Please make sure you have the following libraries installed: numpy torch=1.4.0 torchvisi

32 Nov 21, 2022
Pytorch implementation of few-shot semantic image synthesis

Few-shot Semantic Image Synthesis Using StyleGAN Prior Our method can synthesize photorealistic images from dense or sparse semantic annotations using

40 Sep 26, 2022
Saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

Saliency Methods 🔴 Now framework-agnostic! (Example core notebook) 🔴 🔗 For further explanation of the methods and more examples of the resulting ma

PAIR code 849 Dec 27, 2022
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023