noccn is a collection of wrappers around Alex Krizhevsky's 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.

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.

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.

Use pip to install noccn in a virtualenv:

#!shell
virtualenv noccn --system-site-packages
cd noccn
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

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.

Be sure to specify the location of the cuda-convnet repository.

export CUDA_CONVNET=$VIRTUAL_ENV/opt/cuda-convnet

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

#!shell
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.

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
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.

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

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

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
ccn-make-batches models/01/options.cfg