noccn is a collection of wrappers around Alex Krizhevsky's cuda-convnet <http://code.google.com/p/cuda-convnet/>_.

What is cuda-convnet?

According to its website, cuda-convnet is "a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm."

cuda-convnet has really nice docs on its homepage <http://code.google.com/p/cuda-convnet/>_.

What is noccn for then?

noccn helps you deal with cuda-convnet's many command-line parameters by allowing you to put them into a configuration file (usually options.cfg). noccn also allows you to specify in your configuration file how you're building your data batches. This way, you'll easily remember how exactly you ran your experiments, and how you got your results.

There is support for turning a list of folders containing images into batches. The batch creation code can be extended with your own batch creator.

noccn is fairly stable -- I use it quite a lot -- but it's still underdocumented. A lot of the options will however just map to cuda-convnet's own.

The options.cfg file

Here's an example of an options.cfg file::

#!ini [DEFAULT] data-provider = convdata.CIFARDataProvider include = $HERE/../defaults.cfg

[train] layer-def = $HERE/layers.cfg layer-params = $HERE/layer-params.cfg data-path = $HERE/../../batches/ train-range = 1-29 test-range = 30-40 save-path = $HERE/tmp give-up-epochs = 200

[show] test-range = 41-44

[predict-test] train-range = 1 test-range = 30-40 report = 1

[predict-valid] train-range = 1 test-range = 41-44 report = 1

[predict-train] train-range = 1 test-range = 1-8 report = 1

write-preds = $HERE/preds/preds-train.csv

write-preds-cols = 1

[dataset] input-path = $HERE/../../images/ pattern = *.jpg output-path = $HERE/../../batches/

The path to this options.cfg file is the first argument to every script in noccn. options.cfg and arguments on the command-line can be combined, where arguments on the command-line will overrule those in the config file.

The section [train] contains all the parameters for training (ccn-train). Similarly, [show] has all the parameters for the ccn-show script and so on. We can define multiple sections for the ccn-predict script.

The section [DEFAULT] defines variables that are used for all other sections. The data-provider is a dotted path to the data provider implementation that you want to use. The default section may have an include parameter to include shared parameters from another file.

Installation in a virtualenv

Use pip to install noccn in a virtualenv::

#!shell virtualenv noccn --system-site-packages cd noccn bin/pip install path/to/noccn

If you're on Debian or Ubuntu, you can install the required numpy and scipy like this::

#!shell apt-get install python-numpy python-scipy

Scripts

A few of the scripts included in noccn wrap those found in cuda-convnet itself. These are ccn-train and ccn-show. Scripts that noccn itself adds are ccn-predict and ccn-make-batches.

Some scripts require that you point them to a model snapshot or a snapshot directory, using the -f argument.

ccn-train

Using `ccn-train` is simple; just pass the path to the `options.cfg`
file as defined above::

  #!shell
  bin/ccn-train models/01/options.cfg

noccn's train script will only save a snapshot if there was an
improvement in the test score.  If you want to store snapshots
regardless of whether or not the test score improved, you can pass
`always-save = 1`.

The `convnet.give_up_epochs` argument defines after how many epochs
without an improvement on the test score should we automatically stop
the learning.  This is useful if you want to run a few parameters
unattended.

ccn-show
~~~~~~~~

During training, you can take a look at the network's performance, at
random test samples and their predictions, and at the activations of
the first layer in your network using the `ccn-show` script::

  #!shell
  bin/ccn-show models/01/options.cfg -f models/01/tmp/ConvNet__*/

If you want to view a different convolutional layer, pass
e.g. `--show-filters=conv2`.

ccn-predict

The ccn-predict script prints out a classification report and a confusion matrix. This gives you numbers to evaluate your network's performance::

#!shell bin/ccn-predict models/01/options.cfg -f models/01/tmp/ConvNet__*/

ccn-make-batches

The `ccn-make-batches` script is a handy way to create input batches
for use with `cuda-convnet` from a folder with images.  Within the
folder that you point `ccn-make-batches` to (through the
configuration's `[dataset]` section), you should have one folder per
category, with JPEG images belonging to that category inside.  The way
`ccn-make-batches` collects images can be configured through the
`collector` argument (default:
`noccn.dataset._collect_filenames_and_labels`).  The way input files
are converted to data vectors can be overridden by passing in a
different `creator` (default: `noccn.dataset.BatchCreator`).

An example::

  #!shell
  bin/ccn-make-batches models/01/options.cfg