MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming to maximize efficiency and productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.
MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning system, and interesting insights of DL systems for hackers.
这是我自己移植编译的Windows版本，只是为了自我学习和与大家交流。如果有什么问题欢迎告知。
- CSDN:http://blog.csdn.net/sunshine_in_moon
- 我写的Mxnet系列教程，还在更新中......
- Email:[email protected]
github使用的还不是很熟悉，我发现Windows文件夹下的一些东西并没有上传上去，在这里做个补充：
- 3rdparty:http://pan.baidu.com/s/1eSOFiaI 下载后解压，然后直接替
换掉windows中3rdparty即可。 - x64: http://pan.baidu.com/s/1jIhn88I 编译成功的tools里的工具，下载后直接
替换掉windows中x64 - Release http://pan.baidu.com/s/1kUXcmKR 编译成功的动态库，下载解压后直接
替换掉windows中Release
下面的这三个文件我觉得是CMake的附带产物,可能并不一定需要 - CMakeFiles http://pan.baidu.com/s/1bFdDIA
- dmlc-core http://pan.baidu.com/s/1c2iLM3Q
- mxnet.dir http://pan.baidu.com/s/1nvyIzVz
以上三个文件下载解压后直接放到windows中即可。
1、Compile im2rec.cpp to Tools.exe in Mxnet-windows\windows\x64\Release
Usage:<image.lst> <image_root_dir> <output.rec> [additional parameters]
e.g. Tools.exe E:\lfw\image_train.lst E:\lfw\ image.rec
2、Modify tools\im2rec.py to im2rec_Linux.py for Linux and im2rec_Windows.py for Windows
python im2rec_*****.py prefix root
e.g. python im2rec_Windows.py E:\lfw\image E:\lfw