哇塞，好久么有跟进mxnet啦，python改版了好多好多啊，突然发现C++用起来才是最爽的. 贴一个mxnet中的C++Example中的mlp网络和实现，感觉和python对接毫无违和感。真是一级棒呐.
//
// Created by xijun1 on 2017/12/8.
// #include <iostream>
#include <vector>
#include <string>
#include <mxnet/mxnet-cpp/MxNetCpp.h>
#include <mxnet/mxnet-cpp/op.h> namespace mlp{ template < typename T , typename U >
class MLP{
public:
static mx_float OutputAccuracy(mx_float* pred, mx_float* target) {
int right = ;
for (int i = ; i < ; ++i) {
float mx_p = pred[i * + ];
float p_y = ;
for (int j = ; j < ; ++j) {
if (pred[i * + j] > mx_p) {
mx_p = pred[i * + j];
p_y = j;
}
}
if (p_y == target[i]) right++;
}
return right / 128.0;
}
static bool train(T x , U y);
static bool predict(T x);
static bool net() {
using mxnet::cpp::Symbol;
using mxnet::cpp::NDArray; Symbol x = Symbol::Variable("X");
Symbol y = Symbol::Variable("label"); std::vector<std::int32_t> shapes({ , });
//定义一个两层的网络. wx + b
Symbol weight_0 = Symbol::Variable("weight_0");
Symbol biases_0 = Symbol::Variable("biases_0"); Symbol fc_0 = mxnet::cpp::FullyConnected("fc_0",x,weight_0,biases_0
,); Symbol output_0 = mxnet::cpp::LeakyReLU("relu_0",fc_0,mxnet::cpp::LeakyReLUActType::kLeaky); Symbol weight_1 = Symbol::Variable("weight_1");
Symbol biases_1 = Symbol::Variable("biases_1");
Symbol fc_1 = mxnet::cpp::FullyConnected("fc_1",output_0,weight_1,biases_1,);
Symbol output_1 = mxnet::cpp::LeakyReLU("relu_1",fc_1,mxnet::cpp::LeakyReLUActType::kLeaky);
Symbol pred = mxnet::cpp::SoftmaxOutput("softmax",output_1,y); //目标函数，loss函数 //定义使用计算驱动
mxnet::cpp::Context ctx = mxnet::cpp::Context::cpu( );
NDArray arr_x(mxnet::cpp::Shape( , ) , ctx , false);
NDArray arr_y(mxnet::cpp::Shape() , ctx , false ); //定义输入数据
std::shared_ptr< mx_float > aptr_x(new mx_float[*] , [](mx_float* aptr_x){ delete [] aptr_x ;});
std::shared_ptr< mx_float > aptr_y(new mx_float[] , [](mx_float * aptr_y){ delete [] aptr_y ;}); //初始化数据
for(int i= ; i< ; i++){
for(int j=;j< ; j++){
//定义x
aptr_x.get()[i*+j]= i % +0.1f;
} //定义y
aptr_y.get()[i]= i % ;
} //将数据转换到NDArray中
arr_x.SyncCopyFromCPU(aptr_x.get(),*);
arr_x.WaitToRead(); arr_y.SyncCopyFromCPU(aptr_y.get(),);
arr_y.WaitToRead(); //定义各个层参数的数组
NDArray arr_w_0(mxnet::cpp::Shape(,),ctx, false);
NDArray arr_b_0(mxnet::cpp::Shape( ),ctx,false);
NDArray arr_w_1(mxnet::cpp::Shape( , ) , ctx , false);
NDArray arr_b_1(mxnet::cpp::Shape( ) , ctx , false); //初始化权重参数
arr_w_0 = 0.01f;
arr_b_1 = 0.01f;
arr_w_1 = 0.01f;
arr_b_1 = 0.01f; //求解梯度 NDArray arr_w_0_g(mxnet::cpp::Shape( , ),ctx, false);
NDArray arr_b_0_g(mxnet::cpp::Shape( ) , ctx , false);
NDArray arr_w_1_g(mxnet::cpp::Shape( , ) , ctx , false);
NDArray arr_b_1_g(mxnet::cpp::Shape( ) , ctx , false); //将数据绑定到网络图中. //输入数据参数
std::vector< NDArray > bind_data;
bind_data.push_back( arr_x );
bind_data.push_back( arr_w_0 );
bind_data.push_back( arr_b_0 );
bind_data.push_back( arr_w_1 );
bind_data.push_back( arr_b_1 );
bind_data.push_back( arr_y ); //所有的梯度参数
std::vector< NDArray > arg_grad_store;
arg_grad_store.push_back( NDArray() ); //不需要输入的梯度
arg_grad_store.push_back( arr_w_0_g );
arg_grad_store.push_back( arr_b_0_g );
arg_grad_store.push_back( arr_w_1_g );
arg_grad_store.push_back( arr_b_1_g );
arg_grad_store.push_back( NDArray() ); //不需要输出 loss 的梯度 //如何操作梯度.
std::vector< mxnet::cpp::OpReqType > grad_req_type; grad_req_type.push_back(mxnet::cpp::kNullOp);
grad_req_type.push_back(mxnet::cpp::kWriteTo);
grad_req_type.push_back(mxnet::cpp::kWriteTo);
grad_req_type.push_back(mxnet::cpp::kWriteTo);
grad_req_type.push_back(mxnet::cpp::kWriteTo);
grad_req_type.push_back(mxnet::cpp::kNullOp); //定义一个状态数组
std::vector< NDArray > aux_states; std::cout<<" make the Executor"<<std::endl; std::shared_ptr<mxnet::cpp::Executor > executor
= std::make_shared<mxnet::cpp::Executor>(
pred,
ctx,
bind_data,
arg_grad_store,
grad_req_type,
aux_states );
//训练
std::cout<<" Training "<<std::endl; int max_iters = ; //最大迭代次数
mx_float learning_rate = 0.0001; //学习率 for (int iter = ; iter < max_iters ; ++iter) {
executor->Forward(true);
if(iter % == ){
std::vector<NDArray> & out = executor->outputs;
std::shared_ptr<mx_float> tp_x( new mx_float[*] ,
[](mx_float * tp_x){ delete [] tp_x ;});
out[].SyncCopyToCPU(tp_x.get(),*);
NDArray::WaitAll();
std::cout<<"epoch "<<iter<<" "<<"Accuracy: "<< OutputAccuracy(tp_x.get() , aptr_y.get())<<std::endl;
}
//依据梯度更新参数
executor->Backward();
for (int i = ; i < ; ++i) {
bind_data[i] -= arg_grad_store[i]*learning_rate;
}
NDArray::WaitAll();
} }
static bool SetDriver();
}; template <typename T , typename U >
bool MLP<T,U>::SetDriver() {
return true;
}
template <typename T , typename U >
bool MLP<T,U>::train(T x, U y) {
return true;
}
template <typename T , typename U >
bool MLP<T,U>::predict(T x) {
return true;
} } int main(int argc , char * argv[]){
mlp::MLP<mx_float ,mx_uint>::net();
MXNotifyShutdown();
return ;
}
结果：
poch 18900 Accuracy: 0.703125
epoch 19000 Accuracy: 0.703125
epoch 19100 Accuracy: 0.703125
epoch 19200 Accuracy: 0.703125
epoch 19300 Accuracy: 0.703125
epoch 19400 Accuracy: 0.703125
epoch 19500 Accuracy: 0.703125
epoch 19600 Accuracy: 0.703125
epoch 19700 Accuracy: 0.703125
epoch 19800 Accuracy: 0.703125
epoch 19900 Accuracy: 0.703125