How does the SHOGUN Toolbox convolutional neural network compare to Caffe and Theano?

Question

I'm interested in implementing a convolutional neural network in my C++ program where I'm tracking tagged insects (I'm also using OpenCV). I see people mention Caffe, Torch and Theano a lot but I haven't heard the CNN in the SHOGUN Toolbox discussed. Does this CNN work well and would anyone recommend it if you're working in C++? I've used Theano via scikit-neuralnetwork in Python to test out some images and that worked really well, except unfortunately Theano is Python-only.

Answer 1

The difference lies in the speed. cnn is computationally expensive, so a GPU implementation is at least 10 times faster than CPU. caffe and theano provide seamless integration of calling either CPU or GPU, which may not be easy for you to implement without much GPU programming experience.

Other factors may exist including a unified interface for multiplayer, stochastic gradient descent, and etc. but I think speed issue is most crucial among all these factors.

Answer 2

Shogun also has GPU support of some of the operations used in the NN code. This is work in progress though. At this point in time, other libraries might be faster. We mostly built these networks in there in order to be able to easily compare them to the other algorithms in the toolbox.

The advantage, however, is that you can use it from a large number of languages (while internally, C++ code is executed) -- useful if you don't want to use python.

Here are some IPython notebooks that you could use as a basis to compare:

• autoencoders for denoising and classification
• (convolution) networks for digit classification

We appreciate any experience to be shared. Shogun is in constant development and especially the NNs attract a lot of people to work on them, so expect things to change. If you are interested in helping GPU-fying Shogun, please let us know.