Keras implementation of "Wide Residual Networks"

This repo contains the code to run Wide Residual Networks using Keras.

Paper (v1): http://arxiv.org/abs/1605.07146v1 (the authors have since published a v2 of the paper, which introduces slightly different preprocessing and improves the accuracy a little).
Original code: https://github.com/szagoruyko/wide-residual-networks

Dependencies:

numpy, Keras and it's dependencies (including the default tensorflow backend) can be installed with:
- sudo apt-get install python-pip python-dev gfortran libblas-dev liblapack-dev libhdf5-serial-dev libatlas-base-dev
  - Note BLAS/LAPACK/ATLAS make linear algebra/numpy operations much much faster (check numpy was installed against numpy with import numpy as np; np.__config__.show() ), and HDF5 dependencies allow saving/loading of trained models.
- sudo pip install -r requirements.txt which includes TensorFlow backend (now the Keras default); alternatively install the Theano backend with sudo pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps
To plot the architecture of the model used (like the plot of the WRN-16-2 architecture plotted below), you need to install pydot and graphviz. I had to install the C-version for graphviz first (following comments in this issue):

$ sudo apt-get install graphviz
$ sudo pip install -I pydot==1.1.0
$ sudo pip install -I graphviz==0.5.2

Run the default configuration (i.e. best configuration for CIFAR10 from original paper/code, WRN-28-10 without dropout) with:

$ python main.py

There are three configuration sections at the top of main.py:

DATA CONFIGURATION: Containing data details.
NETWORK/TRAINING CONFIGURATION: Includes the main parameters the authors experimented with.
OUTPUT CONFIGURATION: Defines paths regarding where to save model/checkpoint weights and plots.

WRN-40-4 no dropout:
- Using the same values in main.py except depth=40 and k:=widen-factor=4, I obtained a test loss = 0.37 and accuracy = 0.93. This test error (i.e. 1 - 0.93 = 7%) is a little higher than the reported result (Table 4 states the same model obtains a test error of 4.97%); see the note below for a likely explanation.
- You can find the trained weights for this model at models/WRN-40-4.h5, whilst models/test.py provides an example of running these weights against the test set.
  - WARNING: These weights were obtained using the Theano backend - I am currently unable to reproduce these results using these trained weights with the TensorFlow backend.

Note: I have not followed the exact same preprocessing and data augmentation steps used in the paper, in particular:

"global contrast normalization", and
"random crops from image padded by 4 pixels on each side, filling missing pixels with reflections of original image", which appears to be implemented in this file.

Ideally, we will add such methods directly to the Keras image preprocessing script.