Note: The original cxxnet can be found here.
Requirements in order to build the code:
- MS Visual Studio 2013
- CUDA toolkit 6.5
- OpenCV
Other dependencies like mshadow, OpenBLAS and zlib can be found in the directory ./extern
MSVC solution file can be found in the directory ./prjvs2013
CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow(https://github.com/tqchen/mshadow). It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance.
Creater: Tianqi Chen and Bing Xu
Documentation and Tutorial: https://github.com/antinucleon/cxxnet/wiki
- Small but sharp knife: the core part of the implementation is less than 2000 lines, and easily extendible.
- cxxnet is build with mshadow, a tensor template library for unified CPU/GPU computation. All the functions are only implemented once, as a result.
- Speed: On Bing Xu’s EVGA GeForce 780 GTX with 2304 CUDA cores, cxxnet archived 211 images per second in training on ImageNet data with Alex Krizhevsky’s deep network structure. The prediction speed is 400 pic / second on the same card.
- Common Requirement: NVIDIA CUDA with cuBLAS, cuRAND and cudaRT; OpenCV; mshadow (will be downloaded by using build.sh)
- MKL version: Intel MKL directly run
build.sh - If you don’t have MKL, using
build.sh blas=1to build with CBLAS- Depending your version of CBLAS(ATLAS, etc.), you may need to change -lblas to -lcblas in Makefile