Implements Gradient Centralization and allows it to use as a Python package in TensorFlow

Overview

Gradient Centralization TensorFlow Twitter

PyPI Upload Python Package Flake8 Lint Python Version

Binder Open In Colab

GitHub license PEP8 GitHub stars GitHub forks GitHub watchers

This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique for Deep Neural Networks as suggested by Yong et al. in the paper Gradient Centralization: A New Optimization Technique for Deep Neural Networks. It can both speedup training process and improve the final generalization performance of DNNs.

Installation

Run the following to install:

pip install gradient-centralization-tf

Usage

gctf.centralized_gradients_for_optimizer

Create a centralized gradients functions for a specified optimizer.

Arguments:

  • optimizer: a tf.keras.optimizers.Optimizer object. The optimizer you are using.

Example:

>>> opt = tf.keras.optimizers.Adam(learning_rate=0.1)
>>> optimizer.get_gradients = gctf.centralized_gradients_for_optimizer(opt)
>>> model.compile(optimizer = opt, ...)

gctf.get_centralized_gradients

Computes the centralized gradients.

This function is ideally not meant to be used directly unless you are building a custom optimizer, in which case you could point get_gradients to this function. This is a modified version of tf.keras.optimizers.Optimizer.get_gradients.

Arguments:

  • optimizer: a tf.keras.optimizers.Optimizer object. The optimizer you are using.
  • loss: Scalar tensor to minimize.
  • params: List of variables.

Returns:

A gradients tensor.

gctf.optimizers

Pre built updated optimizers implementing GC.

This module is speciially built for testing out GC and in most cases you would be using gctf.centralized_gradients_for_optimizer though this module implements gctf.centralized_gradients_for_optimizer. You can directly use all optimizers with tf.keras.optimizers updated for GC.

Example:

>>> model.compile(optimizer = gctf.optimizers.adam(learning_rate = 0.01), ...)
>>> model.compile(optimizer = gctf.optimizers.rmsprop(learning_rate = 0.01, rho = 0.91), ...)
>>> model.compile(optimizer = gctf.optimizers.sgd(), ...)

Returns:

A tf.keras.optimizers.Optimizer object.

Developing gctf

To install gradient-centralization-tf, along with tools you need to develop and test, run the following in your virtualenv:

git clone [email protected]:Rishit-dagli/Gradient-Centralization-TensorFlow
# or clone your own fork

pip install -e .[dev]

License

Copyright 2020 Rishit Dagli

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Comments
  • On windows Tensorflow 2.5 it gives error

    On windows Tensorflow 2.5 it gives error

    On windows 10 with miniconda enviroment tensorflow 2.5 gives error on centralized_gradients.py file.

    the solution is change import keras.backend as K with import tensorflow.keras.backend as K

    bug 
    opened by mgezer 5
  • The results in the mnist example are wrong/misleading

    The results in the mnist example are wrong/misleading

    Describe the bug The results in your colab ipython notebook are misleading: https://colab.research.google.com/github/Rishit-dagli/Gradient-Centralization-TensorFlow/blob/main/examples/gctf_mnist.ipynb

    In this example, the model is first trained with a normal Adam optimizer:

    model.compile(optimizer = tf.keras.optimizers.Adam(),
                  loss = 'sparse_categorical_crossentropy',
                  metrics = ['accuracy'])
    
    history_no_gctf = model.fit(training_images, training_labels, epochs=5, callbacks = [time_callback_no_gctf])
    

    And afterwards the same model is recompiled with the gctf.optimizers.adam(). However, recompiling a keras model does not reset the weights. This means that in the first fit call the model is trained and then in the second fit call with the new optimizer the same model is used and of course then the results are better.

    This can be fixed, by recreating the model for the second run, by just adding these few lines:

    import gctf #import gctf
    
    time_callback_gctf = TimeHistory()
    
    # Model architecture
    model = tf.keras.models.Sequential([
                                        tf.keras.layers.Flatten(), 
                                        tf.keras.layers.Dense(512, activation=tf.nn.relu),
                                        tf.keras.layers.Dense(256, activation=tf.nn.relu),
                                        tf.keras.layers.Dense(64, activation=tf.nn.relu),
                                        tf.keras.layers.Dense(512, activation=tf.nn.relu),
                                        tf.keras.layers.Dense(256, activation=tf.nn.relu),
                                        tf.keras.layers.Dense(64, activation=tf.nn.relu), 
                                        tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
    
    model.compile(optimizer = gctf.optimizers.adam(),
                  loss = 'sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    
    history_gctf = model.fit(training_images, training_labels, epochs=5, callbacks=[time_callback_gctf])
    

    However, then the results are not better than without gctf:

    Type                   Execution time    Accuracy      Loss
    -------------------  ----------------  ----------  --------
    Model without gctf:           24.7659    0.88825   0.305801
    Model with gctf               24.7881    0.889567  0.30812
    

    Could you please clarify what happens here. I tried this gctf.optimizers.adam() optimizer in my own research and it didn't change the results at all and now after seeing it doesn't work in the example which was constructed here. Makes me question the results of this paper.

    To Reproduce Execute the colab file given in the repository: https://colab.research.google.com/github/Rishit-dagli/Gradient-Centralization-TensorFlow/blob/main/examples/gctf_mnist.ipynb

    Expected behavior The right comparison would be if both models start from a random initialization, not that the second model can start with the already pre-trained weights.

    Looking forward to a fast a swift explanation.

    Best, Max

    question 
    opened by themasterlink 2
  • Wider dependency requirements

    Wider dependency requirements

    The package as of now to be installed requires tensorflow ~= 2.4.0 and keras ~= 2.4.0. It turns out that this is sometimes problematic for folks who have custom installations of TensorFlow and a winder requirement could be set up.

    enhancement 
    opened by Rishit-dagli 1
  • Release 0.0.3

    Release 0.0.3

    This release includes some fixes and improvements

    ✅ Bug Fixes / Improvements

    • Allow wider versions for TensorFlow and Keras while installing the package (#14 )
    • Fixed incorrect usage example in docstrings and description for centralized_gradients_for_optimizer (#13 )
    • Add clear aims for each of the examples of using gctf (#15 )
    • Updates PyPi classifiers to clearly show the aims of this project. This should have no changes in the way you use this package (#18 )
    • Add clear instructions for using this with custom optimizers i.e. directly use get_centralized_gradients however a complete example has not been pushed due to the reasons mentioned in the issue (#16 )
    opened by Rishit-dagli 0
  • Add an

    Add an "About The Examples" section

    Add an "About The Examples" section which contains a summary of the usage example notebooks and links to run it on Binder and Colab.


    Close #15

    opened by Rishit-dagli 0
  • Update relevant pypi classifiers

    Update relevant pypi classifiers

    Add PyPI classifiers for:

    • Development status
    • Intended Audience
    • Topic

    Further also added the Programming Language :: Python :: 3 :: Only classifer


    Closes #18

    opened by Rishit-dagli 0
  • Update pypi classifiers

    Update pypi classifiers

    I am specifically thinking of adding three more categories of pypi classifiers:

    • Development status
    • Intended Audience
    • Topic

    Apart from this I also think it would be great to add the Programming Language :: Python :: 3 :: Only to make sure the audience to know that this package is intended for Python 3 only.

    opened by Rishit-dagli 0
  • Add an

    Add an "About the examples" section

    It would be great to write an "About the example" section which could demonstrate in short what the example notebooks aim to achieve and show.

    documentation 
    opened by Rishit-dagli 0
  • Error in usage example for gctf.centralized_gradients_for_optimizer

    Error in usage example for gctf.centralized_gradients_for_optimizer

    I noticed that the docstrings for gctf.centralized_gradients_for_optimizer have an error in the example usage section. The example creates an Adam optimizer instance and saves it to opt however the centralized_gradients_for_optimizer is applied on optimizer which ideally does not exist and running the example would result in an error.

    documentation 
    opened by Rishit-dagli 0
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    opened by imgbot[bot] 0
  • [ImgBot] Optimize images

    [ImgBot] Optimize images

    opened by imgbot[bot] 0
Releases(v0.0.3)
  • v0.0.3(Mar 11, 2021)

    This release includes some fixes and improvements

    ✅ Bug Fixes / Improvements

    • Allow wider versions for TensorFlow and Keras while installing the package (#14 )
    • Fixed incorrect usage example in docstrings and description for centralized_gradients_for_optimizer (#13 )
    • Add clear aims for each of the examples of using gctf (#15 )
    • Updates PyPi classifiers to clearly show the aims of this project. This should have no changes in the way you use this package (#18 )
    • Add clear instructions for using this with custom optimizers i.e. directly use get_centralized_gradients however a complete example has not been pushed due to the reasons mentioned in the issue (#16 )
    Source code(tar.gz)
    Source code(zip)
  • v0.0.2(Feb 21, 2021)

    This release includes some fixes and improvements

    ✅ Bug Fixes / Improvements

    • Fix the issue of supporting multiple modules
    • Fix multiple typos.
    Source code(tar.gz)
    Source code(zip)
  • v0.0.1(Feb 20, 2021)

Owner
Rishit Dagli
High School, Ted-X, Ted-Ed speaker|Mentor, TFUG Mumbai|International Speaker|Microsoft Student Ambassador|#ExploreML Facilitator
Rishit Dagli
Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease

Heart_Disease_Classification Based on the given clinical dataset, Predict whether the patient having Heart Disease or Not having Heart Disease Dataset

Ashish 1 Jan 30, 2022
An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Retina Blood Vessels Segmentation This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional

Srijarko Roy 23 Aug 20, 2022
Measures input lag without dedicated hardware, performing motion detection on recorded or live video

What is InputLagTimer? This tool can measure input lag by analyzing a video where both the game controller and the game screen can be seen on a webcam

Bruno Gonzalez 4 Aug 18, 2022
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

Autoregressive Image Generation using Residual Quantization (CVPR 2022) The official implementation of "Autoregressive Image Generation using Residual

Kakao Brain 529 Dec 30, 2022
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation (CVPR 2020)

Super-BPD for Fast Image Segmentation (CVPR 2020) Introduction We propose direction-based super-BPD, an alternative to superpixel, for fast generic im

189 Dec 07, 2022
A pure PyTorch batched computation implementation of "CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition"

A pure PyTorch batched computation implementation of "CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition"

張致強 14 Dec 02, 2022
A Machine Teaching Framework for Scalable Recognition

MEMORABLE This repository contains the source code accompanying our ICCV 2021 paper. A Machine Teaching Framework for Scalable Recognition Pei Wang, N

2 Dec 08, 2021
Dataset for the Research2Clinics @ NeurIPS 2021 Paper: What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

Behavioral Testing of Clinical NLP Models This repository contains code for testing the behavior of clinical prediction models based on patient letter

Betty van Aken 2 Sep 20, 2022
Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors

SSL_OSC Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors

zaixizhang 2 May 14, 2022
TICC is a python solver for efficiently segmenting and clustering a multivariate time series

TICC TICC is a python solver for efficiently segmenting and clustering a multivariate time series. It takes as input a T-by-n data matrix, a regulariz

406 Dec 12, 2022
Warning: This project does not have any current developer. See bellow.

Pylearn2: A machine learning research library Warning : This project does not have any current developer. We will continue to review pull requests and

Laboratoire d’Informatique des Systèmes Adaptatifs 2.7k Dec 26, 2022
This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

2 Mar 21, 2022
BMVC 2021 Oral: code for BI-GCN: Boundary-Aware Input-Dependent Graph Convolution for Biomedical Image Segmentation

BMVC 2021 BI-GConv: Boundary-Aware Input-Dependent Graph Convolution for Biomedical Image Segmentation Necassary Dependencies: PyTorch 1.2.0 Python 3.

Yanda Meng 15 Nov 08, 2022
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques. arXiv: Colossal-AI: A Unified Deep Learning Syst

HPC-AI Tech 7.9k Jan 08, 2023
CPU inference engine that delivers unprecedented performance for sparse models

The DeepSparse Engine is a CPU runtime that delivers unprecedented performance by taking advantage of natural sparsity within neural networks to reduce compute required as well as accelerate memory b

Neural Magic 1.2k Jan 09, 2023
Illuminated3D This project participates in the Nasa Space Apps Challenge 2021.

Illuminated3D This project participates in the Nasa Space Apps Challenge 2021.

Eleftheriadis Emmanouil 1 Oct 09, 2021
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Epistasis Lab at UPenn 8.9k Dec 30, 2022
Official code for paper "Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight"

Demysitifing Local Vision Transformer, arxiv This is the official PyTorch implementation of our paper. We simply replace local self attention by (dyna

138 Dec 28, 2022
基于Paddle框架的fcanet复现

fcanet-Paddle 基于Paddle框架的fcanet复现 fcanet 本项目基于paddlepaddle框架复现fcanet,并参加百度第三届论文复现赛,将在2021年5月15日比赛完后提供AIStudio链接~敬请期待 参考项目: frazerlin-fcanet 数据准备 本项目已挂

QuanHao Guo 7 Mar 07, 2022
Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

Adaptive Methods for Aggregated Domain Generalization (AdaClust) Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalizat

Xavier Thomas 15 Sep 20, 2022