CUDA-CONVNET

I’ve released an update to cuda-convnet, called cuda-convnet2. The two main new features are faster training on Kepler-generation GPUs and support for multi-GPU training.

This 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.

Fermi-generation GPU (GTX 4xx, GTX 5xx, or Tesla equivalent) required. Documentation

Compiling -- how to check out and compile this code.
Data -- what kind of data this net can train on.
LayerParams -- how to specify an architecture for the net.
NeuronTypes -- types of hidden unit nonlinearities.
TrainingNet -- how to train the net.
Options -- the command-line arguments that the net takes.
ViewingNet -- how to look inside the checkpoints saved by the net.
CheckingGradients -- how to numerically test the gradients for correctness.

Fast results

11% error on CIFAR-10 in 75 minutes, with image translations and horizontal reflections (def, params).
13% error on CIFAR-10 in 25 minutes, with image translations and horizontal reflections (def, params).
    See Methodology for details of training.

        Filters learned by this net:

           https://github.com/JBAhire/Cuda-ConvNet2/blob/master/80-second-filters2.png

18% error on CIFAR-10 in 20 minutes, without any image translations/transformations/preprocessing (def, params).
26% error on CIFAR-10 in 80 seconds, without any image translations/transformations/preprocessing (def, params).