Meta-programming neural network 是一个基于C++14实现的元编程神经网络库 Compile-time matrix constructions, headonly, no dependency, limitless layers, limitless nodes
- 支持任意深度和超大结点数
- 矩阵运算（CNN采用张量运算）
- 循环类网络输入输出支持多对单、单对多、多对多
- 源码Head-only并且无依赖
- 使用方法极其简单，适合程序局部应用ANN以及用来学习研究
#include "BPNN.hpp" int main() { /// 1. Create a 4 layers NN each layer nodes are 20, 30, 20 and 2 /// The first 20 is input layer and the last 2 is output typedef mtl::BPNN<20, 30, 20, 2> MyNN; MyNN bpnn; /// 2. Initialize, setup parameters and activate functions bpnn.init() .set_aberration(0.0001) .set_learnrate(0.8) .set_sigfunc(mtl::logsig) .set_dsigfunc(mtl::dlogsig); /// 3. Create input output matrixs, and then enter matrix datas your self MyNN::InMatrix inMx; MyNN::OutMatrix outMx; MyNN::OutMatrix expectMx; /// enter matrix datas ... /// 4. Training, call train in your own way bpnn.train(inMx, outMx, 100); /// 5. Simulate bpnn.simulate(inMx, outMx, expectMx); }#include "RNN.hpp" int main() { /// 1. Create a 4 layers NN each layer nodes are 20, 30, 20 and 2 /// The first 20 is input layer and the last 2 is output typedef mtl::RNN<20, 30, 20, 2> MyRnn; MyRnn rnn; /// 2. Initialize, setup parameters and activate functions rnn.init() .set_aberration(0.0001) .set_learnrate(0.8) .set_sigfunc(mtl::logsig) .set_dsigfunc(mtl::dlogsig); /// 3. Create input output matrixs, and then enter matrix datas your self /// RNN suport multi-in-out like M:1, 1:M and M:M also 1:1 which is meaningless MyRnn::InMatrix<10> inMx; /// 10 input a group, you can change it each training MyRnn::OutMatrix<2> outMx; /// 2 ouput a group MyRnn::OutMatrix<2> expectMx; /// enter matrix datas ... /// 4. Training, call train in your own way rnn.train(inMx, outMx, 100); /// 5. Simulate rnn.simulate(inMx, outMx，expectMx); }#include "LSTM.hpp" int main() { /// 1. Create a 4 layers NN each layer nodes are 20, 30, 20 and 2 /// The first 20 is input layer and the last 2 is output typedef mtl::LSTM<20, 30, 20, 2> MyLSTM; MyLSTM lstm; /// 2. Initialize, setup parameters, LSTM wouldn't setup activate functions lstm.init() .set_aberration(0.0001) .set_learnrate(0.8); /// 3. Create input output matrixs, and then enter matrix datas your self /// RNN suport multi-in-out like M:1, 1:M and M:M also 1:1 which is meaningless MyLSTM::InMatrix<10> inMx; MyLSTM::OutMatrix<2> outMx; MyLSTM::OutMatrix<2> expectMx; /// enter matrix datas ... /// 4. Training, call train in your own way lstm.train(inMx, outMx, 100); /// 5. Simulate lstm.simulate(inMx, outMx，expectMx); }