Caffe: create a simple sin layer

// test new_creat layer
#include<iostream>
#include<string>
#include<vector>
#include<opencv2/opencv.hpp>
#include<caffe/caffe.hpp>
#include"caffe_reg.h"
#include"sin_layer.hpp"

using namespace std;
using namespace cv;
using namespace caffe;

void wrapInputLayer(Net<float>* net, vector<Mat> * input_channels)
{
    Blob<float>* input_layer = net->input_blobs()[0];

    int width = input_layer->width();
    int height = input_layer->height();
    float *input_data = input_layer->mutable_cpu_data();
    for (int i = 0; i < input_layer->channels(); ++i)
    {
        Mat channel(height, width, CV_32FC1, input_data);
        input_channels->push_back(channel);
        input_data += width * height;
    }

}

int main(void)
{
    // for test

    string deploy_proto = "ZOO_VGG16/deploy_sinlayer.prototxt";
    //string pre_trained_weight = "ZOO_VGG16/vgg__iter_10000.caffemodel";
    Net<float> * net = new Net<float>(deploy_proto, caffe::Phase::TEST, 0);
    // use safe pointer 
    //shared_ptr<Net<float> > net; // warp Net<float> to a pointer 
    //net.reset(new Net<float>(deploy_proto, TEST));

    //net->CopyTrainedLayersFrom(pre_trained_weight);
    // create input_data of 1*3*6*6
    int rows = 6;
    int cols = 6;
    vector<Mat> input_channels;
    wrapInputLayer(net,&input_channels); // bind net->input_data to vector<mat>
    // 
    input_channels[0] = Mat::ones(rows, cols, CV_32FC1);
    input_channels[1] = Mat::ones(rows, cols, CV_32FC1) * 2;
    input_channels[2] = Mat::ones(rows, cols, CV_32FC1) * 3;

    net->Forward();

    const float * data = net->blob_by_name("data")->cpu_data();
    const float *sin_data = net->blob_by_name("sin")->cpu_data();

    int count_input = net->blob_by_name("data")->count();
    int count_output = net->blob_by_name("sin")->count();

    assert(count_input == count_output);

    for (int i = 0; i < count_input; i++)
    {
        cout << data[i] << " ";
    }
    cout << endl;
    for (int i = 0; i < count_output; i++)
    {
        cout << sin_data[i] << " ";
    }
    cout << endl;

    cout << "sin(1): sin(2): sing(3)" << endl;
    cout << sin(1) << ";" << sin(2) << ";" <<sin(3)<<endl;

    // output class probability
    cout << "The probabilites for each classs: " << endl;
    const float *prob = net->blob_by_name("prob")->cpu_data();
    int nclass = net->blob_by_name("prob")->channels();
    for (int i = 0; i < nclass; i++)
    {
        cout << prob[i] << " ";
    }
    cout << endl;

    //free Net

}

C++ caffe train

#include<iostream>
#include<string>
#include<vector>
#include<opencv2/opencv.hpp>
#include<caffe/caffe.hpp>
#include"caffe_reg.h"
#include"sin_layer.hpp"
#include<memory>

using namespace std;
using namespace cv;
using namespace caffe;

int main()
{
    // for  train
    Caffe::set_mode(Caffe::GPU);
    SolverParameter solver_param;
    string solver_file = "ZOO_VGG16/solver.prototxt";

    //string train_proto = "ZOO_VGG16/train_sinlayer.prototxt";

    ReadSolverParamsFromTextFileOrDie(solver_file, &solver_param);

    //boost::shared_ptr<Solver<float> > solver(
    //  SolverRegistry<float>::CreateSolver(solver_param));

    Solver<float> * solver = SolverRegistry<float>::CreateSolver(solver_param);//Sover<float> 是纯虚函数，所有不能实例化对象，通过指针的形式指向子类。


    boost::shared_ptr<Net<float> > net = solver->net(); //train net

    //net->CopyTrainedLayersFrom(pre_trained_weight);


    cout << "forward... " << endl;
    net->Forward(); // forward once
    cout << "backward..." << endl;
    net->Backward();// backward once

    const float * data = net->blob_by_name("data")->cpu_data();
    const float *sin_data = net->blob_by_name("sin")->cpu_data();


    cout << endl;


    return 0;
}

参考文献：
https://github.com/BVLC/caffe/wiki/Simple-Example:-Sin-Layer