C++从零实现简单深度神经网络（基于OpenCV）

代码地址如下：
http://www.demodashi.com/demo/11138.html

一、准备工作

####需要准备什么环境需要安装有Visual Studio并且配置了OpenCV。能够使用OpenCV的core模块。使用者需要有基本的C++编程基础。
本例子实现什么功能

本例实现了简单的深度神经网络，基于OpenCV的矩阵类Mat。程序实现了BP算法，支持创建和训练多层神经网络，支持loss可视化。支持模型的保存和加载。

二、示例代码

新建和初始化一个神经网络的过程非常简单，像下面这样：

    //Set neuron number of every layer
    vector<int> layer_neuron_num = { 784,100,10 };

    // Initialise Net and weights
    Net net;
    net.initNet(layer_neuron_num);
    net.initWeights(0, 0., 0.01);
    net.initBias(Scalar(0.5));

训练神经网络也很容易，下面是一个例子。训练完之后可以保存模型

#include"../include/Net.h"
//<opencv2\opencv.hpp>

using namespace std;
using namespace cv;
using namespace liu;

int main(int argc, char *argv[])
{
    //Set neuron number of every layer
    vector<int> layer_neuron_num = { 784,100,10 };

    // Initialise Net and weights
    Net net;
    net.initNet(layer_neuron_num);
    net.initWeights(0, 0., 0.01);
    net.initBias(Scalar(0.5));

    //Get test samples and test samples 
    Mat input, label, test_input, test_label;
    int sample_number = 800;
    get_input_label("data/input_label_1000.xml", input, label, sample_number);
    get_input_label("data/input_label_1000.xml", test_input, test_label, 200, 800);

    //Set loss threshold,learning rate and activation function
    float loss_threshold = 0.5;
    net.learning_rate = 0.3;
    net.output_interval = 2;
    net.activation_function = "sigmoid";

    //Train,and draw the loss curve(cause the last parameter is ture) and test the trained net
    net.train(input, label, loss_threshold, true);
    net.test(test_input, test_label);

    //Save the model
    net.save("models/model_sigmoid_800_200.xml");

    getchar();
    return 0;
}

加载训练过的模型然后直接使用就更加方便了。

#include"../include/Net.h"
//<opencv2\opencv.hpp>

using namespace std;
using namespace cv;
using namespace liu;

int main(int argc, char *argv[])
{
    //Get test samples and the label is 0--1
    Mat test_input, test_label;
    int sample_number = 200;
    int start_position = 800;
    get_input_label("data/input_label_1000.xml", test_input, test_label, sample_number, start_position);

    //Load the trained net and test.
    Net net;
    net.load("models/model_sigmoid_800_200.xml");
    net.test(test_input, test_label);

    getchar();
    return 0;
}