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https://github.com/asmith26/wide_resnets_keras

Keras implementation + pretrained weights for "Wide Residual Networks"
https://github.com/asmith26/wide_resnets_keras

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Keras implementation + pretrained weights for "Wide Residual Networks"

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# 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:
- `pip install -r requirements.txt`
- To plot the architecture of the model used (like the plot of the WRN-16-2 architecture plotted [below](#example-plot)), you need to install `pydot` and `graphviz`. I recommend installing with `conda install -c conda-forge python-graphviz`:

## Training Details:
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](https://github.com/asmith26/wide_resnets_keras/blob/master/main.py#L34-48): Containing data details.
- [NETWORK/TRAINING CONFIGURATION](https://github.com/asmith26/wide_resnets_keras/blob/master/main.py#L50-87): Includes the main parameters the authors experimented with.
- [OUTPUT CONFIGURATION](https://github.com/asmith26/wide_resnets_keras/blob/master/main.py#L89-97): Defines paths regarding where to save model/checkpoint weights and plots.

## Results and Trained models:
- ***WRN-28-10 no dropout***:
- Using these values in **main.py**, I obtained a **test loss = 0.31** and **test 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-28-10.h5**, whilst **[models/test.py](https://github.com/asmith26/wide_resnets_keras/blob/master/models/test.py)** provides an example of running these weights against the test set.

**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](https://github.com/szagoruyko/wide-residual-networks/blob/8b166cc15fa8a598490ce0ae66365bf165dffb75/augmentation.lua).

Ideally, we will add such methods directly to the [Keras image preprocessing script](https://github.com/fchollet/keras/blob/master/keras/preprocessing/image.py).

## WRN-16-2 Architecture
![WRN-16-2 Architecture](models/WRN-16-2.png?raw=true "WRN-16-2 Architecture")