Shake-Shake regularization

This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake regularization of 3-branch residual networks which was accepted as a workshop contribution at ICLR 2017.

The code is based on fb.resnet.torch.

Table of Contents

1. Introduction
2. Results
3. Usage
4. Contact

Introduction

The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%.

Figure 1: Left: Forward training pass. Center: Backward training pass. Right: At test time.

Bibtex:

@article{Gastaldi17ShakeShake,
   title = {Shake-Shake regularization},
   author = {Xavier Gastaldi},
   journal = {arXiv preprint arXiv:1705.07485},
   year = 2017,
}

Results on CIFAR-10

The base network is a 26 2x32d ResNet (i.e. the network has a depth of 26, 2 residual branches and the first residual block has a width of 32). “Shake” means that all scaling coefficients are overwritten with new random numbers before the pass. “Even” means that all scaling coefficients are set to 0.5 before the pass. “Keep” means that we keep, for the backward pass, the scaling coefficients used during the forward pass. “Batch” means that, for each residual block, we apply the same scaling coefficient for all the images in the mini-batch. “Image” means that, for each residual block, we apply a different scaling coefficient for each image in the mini-batch. The numbers in the Table below represent the average of 3 runs except for the 96d models which were run 5 times.

Table 1: Error rates (%) on CIFAR-10 (Top 1 of the last epoch)

Other results

Cifar-100:
29 2x4x64d: 15.85%

Reduced CIFAR-10:
26 2x96d: 17.05%¹

SVHN:
26 2x96d: 1.4%¹

Reduced SVHN:
26 2x96d: 12.32%¹

Usage

0. Install fb.resnet.torch, optnet and lua-stdlib.
1. Download Shake-Shake

git clone https://github.com/xgastaldi/shake-shake.git

2. Copy the elements in the shake-shake folder and paste them in the fb.resnet.torch folder. This will overwrite 5 files (main.lua, train.lua, opts.lua, checkpoints.lua and models/init.lua) and add 4 new files (models/shakeshake.lua, models/shakeshakeblock.lua, models/mulconstantslices.lua and models/shakeshaketable.lua).
3. To reproduce CIFAR-10 results (e.g. 26 2x32d “Shake-Shake-Image” ResNet) on 2 GPUs:

CUDA_VISIBLE_DEVICES=0,1 th main.lua -dataset cifar10 -nGPU 2 -batchSize 128 -depth 26 -shareGradInput false -optnet true -nEpochs 1800 -netType shakeshake -lrShape cosine -baseWidth 32 -LR 0.2 -forwardShake true -backwardShake true -shakeImage true

To get comparable results using 1 GPU, please change the batch size and the corresponding learning rate:

CUDA_VISIBLE_DEVICES=0 th main.lua -dataset cifar10 -nGPU 1 -batchSize 64 -depth 26 -shareGradInput false -optnet true -nEpochs 1800 -netType shakeshake -lrShape cosine -baseWidth 32 -LR 0.1 -forwardShake true -backwardShake true -shakeImage true

A 26 2x96d “Shake-Shake-Image” ResNet can be trained on 2 GPUs using:

CUDA_VISIBLE_DEVICES=0,1 th main.lua -dataset cifar10 -nGPU 2 -batchSize 128 -depth 26 -shareGradInput false -optnet true -nEpochs 1800 -netType shakeshake -lrShape cosine -baseWidth 96 -LR 0.2 -forwardShake true -backwardShake true -shakeImage true

4. To reproduce CIFAR-100 results (e.g. 29 2x4x64d “Shake-Even-Image” ResNeXt) on 2 GPUs:

CUDA_VISIBLE_DEVICES=0,1 th main.lua -dataset cifar100 -depth 29 -baseWidth 64 -groups 4 -weightDecay 5e-4 -batchSize 32 -netType shakeshake -nGPU 2 -LR 0.025 -nThreads 8 -shareGradInput true -nEpochs 1800 -lrShape cosine -forwardShake true -backwardShake false -shakeImage true

Note

Changes made to fb.resnet.torch files:

main.lua
Ln 17, 54-59, 81-100: Adds a log

train.lua
Ln 36-38 58-60 206-213: Adds the cosine learning rate function
Ln 88-89: Adds the learning rate to the elements printed on screen

opts.lua
Ln 21-64: Adds Shake-Shake options

checkpoints.lua
Ln 15-16: Adds require ‘models/shakeshakeblock’, ‘models/shakeshaketable’ and require ‘std’
Ln 60-61: Avoids using the fb.resnet.torch deepcopy (it doesn’t seem to be compatible with the BN in shakeshakeblock) and replaces it with the deepcopy from stdlib
Ln 67-86: Saves only the last model

models/init.lua
Ln 91-92: Adds require ‘models/mulconstantslices’, require ‘models/shakeshakeblock’ and require ‘models/shakeshaketable’

The main model is in shakeshake.lua. The residual block model is in shakeshakeblock.lua. mulconstantslices.lua is just an extension of nn.mulconstant that multiplies elements of a vector with image slices of a mini-batch tensor. shakeshaketable.lua contains the method used for CIFAR-100 since the ResNeXt code uses a table implementation instead of a module version.

Reimplementations

Pytorch
https://github.com/hysts/pytorch_shake_shake

Tensorflow
https://github.com/tensorflow/models/blob/master/research/autoaugment/
https://github.com/tensorflow/tensor2tensor

Contact

xgastaldi.mba2011 at london.edu
Any discussions, suggestions and questions are welcome!

References

(1) Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le. AutoAugment: Learning Augmentation Policies from Data. In arXiv:1805.09501, May 2018.

GitHub

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