An optimizer that trains as fast as Adam and as good as SGD.

Related tags

Pytorch UtilitiesAdaBound
Overview

AdaBound

PyPI - Version PyPI - Python Version PyPI - Wheel GitHub - LICENSE

An optimizer that trains as fast as Adam and as good as SGD, for developing state-of-the-art deep learning models on a wide variety of popular tasks in the field of CV, NLP, and etc.

Based on Luo et al. (2019). Adaptive Gradient Methods with Dynamic Bound of Learning Rate. In Proc. of ICLR 2019.

Quick Links

Installation

AdaBound requires Python 3.6.0 or later. We currently provide PyTorch version and AdaBound for TensorFlow is coming soon.

Installing via pip

The preferred way to install AdaBound is via pip with a virtual environment. Just run

pip install adabound

in your Python environment and you are ready to go!

Using source code

As AdaBound is a Python class with only 100+ lines, an alternative way is directly downloading adabound.py and copying it to your project.

Usage

You can use AdaBound just like any other PyTorch optimizers.

optimizer = adabound.AdaBound(model.parameters(), lr=1e-3, final_lr=0.1)

As described in the paper, AdaBound is an optimizer that behaves like Adam at the beginning of training, and gradually transforms to SGD at the end. The final_lr parameter indicates AdaBound would transforms to an SGD with this learning rate. In common cases, a default final learning rate of 0.1 can achieve relatively good and stable results on unseen data. It is not very sensitive to its hyperparameters. See Appendix G of the paper for more details.

Despite of its robust performance, we still have to state that, there is no silver bullet. It does not mean that you will be free from tuning hyperparameters once using AdaBound. The performance of a model depends on so many things including the task, the model structure, the distribution of data, and etc. You still need to decide what hyperparameters to use based on your specific situation, but you may probably use much less time than before!

Demos

Thanks to the awesome work by the GitHub team and the Jupyter team, the Jupyter notebook (.ipynb) files can render directly on GitHub. We provide several notebooks (like this one) for better visualization. We hope to illustrate the robust performance of AdaBound through these examples.

For the full list of demos, please refer to this page.

Citing

If you use AdaBound in your research, please cite Adaptive Gradient Methods with Dynamic Bound of Learning Rate.

@inproceedings{Luo2019AdaBound,
  author = {Luo, Liangchen and Xiong, Yuanhao and Liu, Yan and Sun, Xu},
  title = {Adaptive Gradient Methods with Dynamic Bound of Learning Rate},
  booktitle = {Proceedings of the 7th International Conference on Learning Representations},
  month = {May},
  year = {2019},
  address = {New Orleans, Louisiana}
}

Contributors

@kayuksel

License

Apache 2.0

Comments
  • What is up with Epoch 150

    What is up with Epoch 150

    I'm wondering what is happening at epoch 150 in all visualizations? I would like to introduce that into all my models ;-)

    https://github.com/Luolc/AdaBound/blob/master/demos/cifar10/visualization.ipynb

    opened by kootenpv 8
  • AdaBoundW

    AdaBoundW

    An AdaBound version with decoupled weight decay, which has been implemented to the code as an additional class, as it has been discussed in the recent issue #13.

    opened by kayuksel 3
  • Question about the code

    Question about the code

    IIRC, because group['lr'] will never be changed, so finalr_lr will always be the same as group['final_lr']. Is this intended? https://github.com/Luolc/AdaBound/blob/6fa826003f41a57501bde3e2baab1488410fe2da/adabound/adabound.py#L110

    opened by crcrpar 2
  • Don't work properly with higher lr

    Don't work properly with higher lr

    I'm new in deep learning and I found the project works well with SGD but turns to be sth wrong with adabound.

    When I start with lr=1e-3, it shows as below and break down: invalid argument 2: non-empty 3D or 4D (batch mode) tensor expected for input, but got: [1 x 64 x 0 x 27] at /pytorch/aten/src/THCUNN/generic/SpatialAdaptiveMaxPooling.cu:24

    But seems to work right if I set lr to 1e-4 or lower. It confused me a lot. Any ideas?

    python=3.6 pytorch=1.0.1 / 0.4

    opened by Ocelot7777 0
  • Can this deal with complex numbers?

    Can this deal with complex numbers?

    Hi authors,

    I intended to use this method on complex numbers and it turned out with a error message like:

    File "optimizer.py", line 701, in step step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_( RuntimeError: "clamp_scalar_cpu" not implemented for 'ComplexFloat'

    I'm wondering if it's possible to improve this for complex numbers? Thanks.

    Ni

    opened by ni-chen 0
  • When did the optimizer switch to SGD？

    When did the optimizer switch to SGD？

    I set the initial lr=0.0001, final_lr=0.1, but I still don't know when the optimizer will become SGD. Do I need to improve my learning rate to the final learning rate manually? thanks！

    opened by yunbujian 0
  • Pytorch 1.6 warning

    Pytorch 1.6 warning 

    /home/xxxx/.local/lib/python3.7/site-packages/adabound/adabound.py:94: UserWarning: This overload of add_ is deprecated:
        add_(Number alpha, Tensor other)
Consider using one of the following signatures instead:
        add_(Tensor other, *, Number alpha) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)
  exp_avg.mul_(beta1).add_(1 - beta1, grad)
    opened by MichaelMonashev 1
  • Learning rate changing

    Learning rate changing

    Hi， thanks a lot for sharing your excellent work.

    I wonder if I want to change learning rate with epoch increasing, how do I set parameter lr and final_lr in adamnboound ? Or is there any need changing learining rate with epoch increasing?

    Looking for your reply, thanks a lot.

    opened by EddieEduardo 0
  • LSTM hyparameters for language modeling

    LSTM hyparameters for language modeling

    Greetings,

    Thanks for your great paper. I am wondering about the hyperparameters you used for language modeling experiments. Could you provide information about that?

    Thank you!

    opened by hoangcuong2011 0
Releases(v0.0.5)

  • v0.0.5(Mar 6, 2019)

    Bug Fixes

    • Fix wrong assertion of final_lr 02e11bae10c82f6b5365f7925c8cf71252adcd52
    • Fix .gitignore in CIFAR-10 demo to include the learning curve data 54ef9aa6c133caf0d9c82198d46979cfdbbb12f6
    Source code(tar.gz)
    Source code(zip)
Owner
LoLo
A fool living in the amazing world.
LoLo
GitHub Repository https://www.luolc.com/publications/adabound/
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

PyTorch Implementation of Differentiable ODE Solvers This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpr

Ricky Chen 4.4k Jan 04, 2023
ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.

Accelerate PyTorch models with ONNX Runtime

270 Dec 24, 2022
An optimizer that trains as fast as Adam and as good as SGD.

AdaBound An optimizer that trains as fast as Adam and as good as SGD, for developing state-of-the-art deep learning models on a wide variety of popula

LoLo 2.9k Dec 27, 2022
higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

higher is a library providing support for higher-order optimization, e.g. through unrolled first-order optimization loops, of "meta" aspects of these

Facebook Research 1.5k Jan 03, 2023
The goal of this library is to generate more helpful exception messages for numpy/pytorch matrix algebra expressions.

Tensor Sensor See article Clarifying exceptions and visualizing tensor operations in deep learning code. One of the biggest challenges when writing co

Terence Parr 704 Dec 14, 2022
Training PyTorch models with differential privacy

Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the cli

1.3k Dec 29, 2022
A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

A PyTorch repo for data loading and utilities to be shared by the PyTorch domain libraries.

878 Dec 30, 2022
A code copied from google-research which named motion-imitation was rewrited with PyTorch

motor-system Introduction A code copied from google-research which named motion-imitation was rewrited with PyTorch. More details can get from this pr

NewEra 6 Jan 08, 2022
A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions

Kim Seonghyeon 433 Dec 27, 2022
PyTorch extensions for fast R&D prototyping and Kaggle farming

Pytorch-toolbelt A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming: What

Eugene Khvedchenya 1.3k Jan 05, 2023
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
A tiny package to compare two neural networks in PyTorch

Compare neural networks by their feature similarity

Anand Krishnamoorthy 180 Dec 30, 2022
Model summary in PyTorch similar to `model.summary()` in Keras

Keras style model.summary() in PyTorch Keras has a neat API to view the visualization of the model which is very helpful while debugging your network.

Shubham Chandel 3.7k Dec 29, 2022
A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16.

Hugging Face 3.5k Jan 08, 2023
Bunch of optimizer implementations in PyTorch

Bunch of optimizer implementations in PyTorch

Hyeongchan Kim 76 Jan 03, 2023
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS

(Generic) EfficientNets for PyTorch A 'generic' implementation of EfficientNet, MixNet, MobileNetV3, etc. that covers most of the compute/parameter ef

Ross Wightman 1.5k Jan 01, 2023
A few Windows specific scripts for PyTorch

It is a repo that contains scripts that makes using PyTorch on Windows easier. Easy Installation Update: Starting from 0.4.0, you can go to the offici

408 Dec 15, 2022
PyTorch implementations of normalizing flow and its variants.

PyTorch implementations of normalizing flow and its variants.

Tatsuya Yatagawa 55 Dec 01, 2022