This is an update to cuda-convnet.

This project has three major new features relative to cuda-convnet:

1. Improved training times on Kepler-generation Nvidia GPUs (Geforce Titan, K20, K40).
2. Multi-GPU training support implementing data parallelism, model parallelism, and the hybrid approach described in One weird trick for parallelizing convolutional neural networks.
3. Less-polished code and incomplete (but improving) documentation.

Documentation

Usage

• Compiling -- how to compile the code
• Data -- how to generate training data
• TrainingExample -- how to train an example network
• LayerParams -- how to specify a custom network
• MultiGPU -- how to train multi-GPU networks
• ShowNet -- how to look inside trained networks

Reference

• Arguments -- listing of command-line arguments
• NeuronTypes -- listing of supported neuron activation functions
• LearningRates -- listing of supported learning rate schedules

Contact

• My email