Wide Residual Networks implemented in TensorLayer and TensorFlow.
Branch: master
Clone or download
Latest commit 72922f9 Oct 31, 2016
Type Name Latest commit message Commit time
Failed to load latest commit information.
LICENSE chore: license Oct 29, 2016
README.md chore: README updates Oct 30, 2016
cifar_wide_resnet_keras.py feat: working tensorlayer and keras scripts Oct 29, 2016
cifar_wide_resnet_tl.py refactor: change from kwargs to fn Oct 30, 2016


Wide ResNet implemented with TensorLayer

Original Paper's Abstract

The paper on Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 is by Sergey Zagoruyko and Nikos Komodakis.

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train.

To tackle these problems, in this work we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts.

For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks. We further show that WRNs achieve incredibly good results (e.g., achieving new state-of-the-art results on CIFAR-10, CIFAR-100 and SVHN) and train several times faster than pre-activation ResNets.

The performance over original ResNets is substantial and this should be used over the original ResNets.

Original Implementation in Lua and Torch

Lua and Torch Repository Link

Installation Instructions for TensorLayer

[stable version] pip install tensorlayer
[master version] pip install git+https://github.com/zsdonghao/tensorlayer.git

TensorLayer Implementation Credits

I would like to give credits to Hao Dong for helping out with this new package as I was originally unfamiliar with the API. You are able to get up to speed quickly if you have experience with Keras.


I have included two files.

  1. cifar_wide_resnet_tl.py
    • This allows you to run each iteration manually with an external call.
    • It illustrates how TensorLayer allows for more complex use.
    • It runs for 10 iterations, you can easily increase the number of iterations.
  2. cifar_wide_resnet_keras.py
    • This is a similar implementation with Keras.
    • However extending this further is challenging.