The implementation of this code is heavily borrowed from Caffe, runs only on cpu and the efficiency should be extremely low(when it's completed), help me with it if you're interested 😉

CaffeBean
Eigen -- A C++ template library for linear algebra
googletest -- for unit test
jsoncpp -- A C++ library for interacting with JSON
Boost.Gil -- Generic Image Library
Note that all these 3rd-party libraries except Boost.Gil had been contained in lib folder, after you have Boost.Gil correctly installed, clone the repo by

git clone https://github.com/SiriusKY/CaffeBean.git

then use cmake tool like

cmake -DCMAKE_BUILD_TYPE=Debug -G "CodeBlocks - Unix Makefiles" /path/to/CaffeBean
cmake --build /path/to/CaffeBean/cmake-build-debug --target CaffeBean -- -j 4

Now you are ready to go!

/path/to/CaffeBean/cmake-build-debug/CaffeBean

• Input Layer
• Fully Connected Layer
• L1 Loss Layer
• Relu Layer
• Pooling Layer(not fully completed)
• Softmax Loss Layer
• Convolution Layer
• Reshape Layer
• Accuracy Layer

• Finish as more layers as possible (important:Conv Layer)
• A python API

• Write the code
• Add unit test in CaffeBean/lib/tests.cpp
• PR

• It's a toy, don't be serious