This repository is dedicated to developing and maintaining code for experiments with wide neural networks.

Overview

Wide-Networks

This repository contains the code of various experiments on wide neural networks. In particular, we implement classes for abc-parameterizations of NNs as defined by (Yang & Hu 2021). Although an equivalent description can be given using only ac-parameterizations, we keep the 3 scales (a, b and c) in the code to allow more flexibility depending on how we want to approach the problem of dealing with infinitely wide NNs.

Structure of the code

The BaseModel class

All the code related to neural networks is in the directory pytorch. The different models we have implemented are in this directory along with the base class found in the file base_model.py which implements the generic attributes and methods all our NNs classes will share.

The BaseModel class inherits from the Pytorch Lightning module, and essentially defines the necessary attributes for any NN to work properly, namely the architecture (which is defined in the _build_model() method), the activation function (we consider the same activation function at each layer), the loss function, the optimizer and the initializer for the parameters of the network.

Optionally, the BaseModel class can define attributes for the normalization (e.g. BatchNorm, LayerNorm, etc) and the scheduler, and any of the aforementioned attributes (optional or not) can be customized depending on the needs (see examples for the scheduler of ipllr and the initializer of abc_param).

The ModelConfig class

All the hyper-parameters which define the model (depth, width, activation function name, loss name, optimizer name, etc) have to be passed as argument to _init_() as an object of the class ModelConfig (pytorch/configs/model.py). This class reads from a yaml config file which defines all the necessary objects for a NN (see examples in pytorch/configs). Essentially, the class ModelConfig is here so that one only has to set the yaml config file properly and then the attributes are correctly populated in BaseModel via the class ModelConfig.

abc-parameterizations

The code for abc-parameterizations (Yang & Hu 2021) can be found in pytorch/abc_params. There we define the base class for abc-parameterizations, mainly setting the layer, init and lr scales from the values of a,b,c, as well as defining the initial parameters through Gaussians of appropriate variance depending on the value of b and the activation function.

Everything that is architecture specific (fully-connected, conv, residual, etc) is left out of this base class and has to be implemented in the _build_model() method of the child class (see examples in pytorch/abc_params/fully_connected). We also define there the base classes for the ntk, muP (Yang & Hu 2021), ip and ipllr parameterizations, and there fully-connected implementations in pytorch/abc_params/fully_connected.

Experiment runs

Setup

Before running any experiment, make sure you first install all the necessary packages:

pip3 install -r requirements.txt

You can optionally create a virtual environment through

python3 -m venv your_env_dir

then activate it with

source your_env_dir/bin/activate

and then install the requirements once the environment is activated. Now, if you haven't installed the wide-networks library in site-packages, before running the command for your experiment, make sure you first add the wide-networks library to the PYTHONPATH by running the command

export PYTHONPATH=$PYTHONPATH:"$PWD"

from the root directory (wide-networks/.) of where the wide-networks library is located.

Python jobs

We define python jobs which can be run with arguments from the command line in the directory jobs. Mainly, those jobs launch a training / val / test pipeline for a given model using the Lightning module, and the results are collected in a dictionary which is saved to a pickle file a the end of training for later examination. Additionally, metrics are logged in TensorBoard and can be visualized during training with the command

tensorboard --logdir=`your_experiment_dir`

We have written jobs to launch experiments on MNIST and CIFAR-10 with the fully connected version of different models such as muP (Yang & Hu 2021), IP-LLR, Naive-IP which can be found in jobs/abc_parameterizations. Arguments can be passed to those Python scripts through the command line, but they are optional and the default values will be used if the parameters of the script are not manually set. For example, the command

python3 jobs/abc_parameterizations/fc_muP_run.py --activation="relu" --n_steps=600 --dataset="mnist"

will launch a training / val / test pipeline with ReLU as the activation function, 600 SGD steps and the MNIST dataset. The other parameters of the run (e.g. the base learning rate and batch size) will have their default values. The jobs will automatically create a directory (and potentially subdirectories) for the experiment and save there the python logs, the tensorboard events and the results dictionary saved to a pickle file as well as the checkpoints saved for the network.

Visualizing results

To visualize the results after training for a given experiment, one can launch the notebook experiments-results.ipynb located in pytorch/notebooks/training/abc_parameterizations, and simply change the arguments in the "Set variables" cell to load the results from the corresponding experiment. Then running all the cells will produce (and save) some figures related to the training phase (e.g. loss vs. steps).

Owner
Karl Hajjar
PhD student at Laboratoire de Mathématiques d'Orsay
Karl Hajjar
The reference baseline of final exam for XMU machine learning course

Mini-NICO Baseline The baseline is a reference method for the final exam of machine learning course. Requirements Installation we use /python3.7 /torc

JoaquinChou 3 Dec 29, 2021
Learning Modified Indicator Functions for Surface Reconstruction

Learning Modified Indicator Functions for Surface Reconstruction In this work, we propose a learning-based approach for implicit surface reconstructio

4 Apr 18, 2022
Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

Medical-Transformer Pytorch Code for the paper "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" About this repo: This repo

Jeya Maria Jose 615 Dec 25, 2022
A modular application for performing anomaly detection in networks

Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model

Shivam Patel 1 Dec 09, 2021
UCSD Oasis platform

oasis UCSD Oasis platform Local project setup Install Docker Compose and make sure you have Pip installed Clone the project and go to the project fold

InSTEDD 4 Jun 16, 2021
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

Vítor Albiero 519 Dec 29, 2022
Complementary Patch for Weakly Supervised Semantic Segmentation, ICCV21 (poster)

CPN (ICCV2021) This is an implementation of Complementary Patch for Weakly Supervised Semantic Segmentation, which is accepted by ICCV2021 poster. Thi

Ferenas 20 Dec 12, 2022
Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)

Manga Filling with ScreenVAE SIGGRAPH ASIA 2020 | Project Website | BibTex This repository is for ScreenVAE introduced in the following paper "Manga F

30 Dec 24, 2022
[CVPR'22] Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast

wseg Overview The Pytorch implementation of Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast. [arXiv] Though image-level weakly

Ye Du 96 Dec 30, 2022
LineBoard - Python+React+MySQL-白板即時系統改善人群行為

LineBoard-白板即時系統改善人群行為 即時顯示實驗室的使用狀況,並遠端預約排隊,以此來改善人們的工作效率 程式架構 運作流程 使用者先至該實驗室網站預約

Bo-Jyun Huang 1 Feb 22, 2022
Galactic and gravitational dynamics in Python

Gala is a Python package for Galactic and gravitational dynamics. Documentation The documentation for Gala is hosted on Read the docs. Installation an

Adrian Price-Whelan 101 Dec 22, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
Embeddinghub is a database built for machine learning embeddings.

Embeddinghub is a database built for machine learning embeddings.

Featureform 1.2k Jan 01, 2023
This is an example of a reproducible modelling project

An example of a reproducible modelling project What are we doing? This example was created for the 2021 fall lecture series of Stanford's Center for O

Armin Thomas 2 Oct 26, 2021
Code of our paper "Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning"

CCOP Code of our paper Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning Requirement Install OpenSelfSup Install Detectron2

Chenhongyi Yang 21 Dec 13, 2022
IEEE Winter Conference on Applications of Computer Vision 2022 Accepted

SSKT(Accepted WACV2022) Concept map Dataset Image dataset CIFAR10 (torchvision) CIFAR100 (torchvision) STL10 (torchvision) Pascal VOC (torchvision) Im

1 Nov 17, 2022
DrNAS: Dirichlet Neural Architecture Search

This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing weight as random va

Xiangning Chen 37 Jan 03, 2023