来自： http://blog.csdn.net/fengbingchun/article/details/50573841

tiny-cnn是一个基于CNN的开源库，它的License是BSD 3-Clause。作者也一直在维护更新，对进一步掌握CNN很有帮助，因此下面介绍下tiny-cnn在windows7 64bit vs2013的编译及使用。

1.      从https://github.com/nyanp/tiny-cnn下载源码：

$ git clone https://github.com/nyanp/tiny-cnn.git  版本号为77d80a8，更新日期2016.01.22

2.      源文件中已经包含了vs2013工程，vc/tiny-cnn.sln，默认是win32的，examples/main.cpp需要OpenCV的支持，这里新建一个x64的控制台工程tiny-cnn；

3.      仿照源工程，将相应.h文件加入到新控制台工程中，新加一个test_tiny-cnn.cpp文件；

4.      将examples/mnist中test.cpp和train.cpp文件中的代码复制到test_tiny-cnn.cpp文件中；

#include <iostream>  #include <string>  #include <vector>  #include <algorithm>  #include <tiny_cnn/tiny_cnn.h>  #include <opencv2/opencv.hpp>    using namespace tiny_cnn;  using namespace tiny_cnn::activation;    // rescale output to 0-100  template <typename Activation>  double rescale(double x)  {   Activation a;   return 100.0 * (x - a.scale().first) / (a.scale().second - a.scale().first);  }    void construct_net(network<mse, adagrad>& nn);  void train_lenet(std::string data_dir_path);  // convert tiny_cnn::image to cv::Mat and resize  cv::Mat image2mat(image<>& img);  void convert_image(const std::string& imagefilename, double minv, double maxv, int w, int h, vec_t& data);  void recognize(const std::string& dictionary, const std::string& filename, int target);    int main()  {   //train   std::string data_path = "D:/Download/MNIST";   train_lenet(data_path);     //test   std::string model_path = "D:/Download/MNIST/LeNet-weights";   std::string image_path = "D:/Download/MNIST/";   int target[10] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };     for (int i = 0; i < 10; i++) {    char ch[15];    sprintf(ch, "%d", i);    std::string str;    str = std::string(ch);    str += ".png";    str = image_path + str;      recognize(model_path, str, target[i]);   }     std::cout << "ok!" << std::endl;   return 0;  }    void train_lenet(std::string data_dir_path) {   // specify loss-function and learning strategy   network<mse, adagrad> nn;     construct_net(nn);     std::cout << "load models..." << std::endl;     // load MNIST dataset   std::vector<label_t> train_labels, test_labels;   std::vector<vec_t> train_images, test_images;     parse_mnist_labels(data_dir_path + "/train-labels.idx1-ubyte",    &train_labels);   parse_mnist_images(data_dir_path + "/train-images.idx3-ubyte",    &train_images, -1.0, 1.0, 2, 2);   parse_mnist_labels(data_dir_path + "/t10k-labels.idx1-ubyte",    &test_labels);   parse_mnist_images(data_dir_path + "/t10k-images.idx3-ubyte",    &test_images, -1.0, 1.0, 2, 2);     std::cout << "start training" << std::endl;     progress_display disp(train_images.size());   timer t;   int minibatch_size = 10;   int num_epochs = 30;     nn.optimizer().alpha *= std::sqrt(minibatch_size);     // create callback   auto on_enumerate_epoch = [&](){    std::cout << t.elapsed() << "s elapsed." << std::endl;    tiny_cnn::result res = nn.test(test_images, test_labels);    std::cout << res.num_success << "/" << res.num_total << std::endl;      disp.restart(train_images.size());    t.restart();   };     auto on_enumerate_minibatch = [&](){    disp += minibatch_size;   };     // training   nn.train(train_images, train_labels, minibatch_size, num_epochs,    on_enumerate_minibatch, on_enumerate_epoch);     std::cout << "end training." << std::endl;     // test and show results   nn.test(test_images, test_labels).print_detail(std::cout);     // save networks   std::ofstream ofs("D:/Download/MNIST/LeNet-weights");   ofs << nn;  }    void construct_net(network<mse, adagrad>& nn) {   // connection table [Y.Lecun, 1998 Table.1]  #define O true  #define X false   static const bool tbl[] = {    O, X, X, X, O, O, O, X, X, O, O, O, O, X, O, O,    O, O, X, X, X, O, O, O, X, X, O, O, O, O, X, O,    O, O, O, X, X, X, O, O, O, X, X, O, X, O, O, O,    X, O, O, O, X, X, O, O, O, O, X, X, O, X, O, O,    X, X, O, O, O, X, X, O, O, O, O, X, O, O, X, O,    X, X, X, O, O, O, X, X, O, O, O, O, X, O, O, O   };  #undef O  #undef X     // construct nets   nn << convolutional_layer<tan_h>(32, 32, 5, 1, 6)  // C1, 1@32x32-in, 6@28x28-out    << average_pooling_layer<tan_h>(28, 28, 6, 2)   // S2, 6@28x28-in, 6@14x14-out    << convolutional_layer<tan_h>(14, 14, 5, 6, 16,    connection_table(tbl, 6, 16))              // C3, 6@14x14-in, 16@10x10-in    << average_pooling_layer<tan_h>(10, 10, 16, 2)  // S4, 16@10x10-in, 16@5x5-out    << convolutional_layer<tan_h>(5, 5, 5, 16, 120) // C5, 16@5x5-in, 120@1x1-out    << fully_connected_layer<tan_h>(120, 10);       // F6, 120-in, 10-out  }    void recognize(const std::string& dictionary, const std::string& filename, int target) {   network<mse, adagrad> nn;     construct_net(nn);     // load nets   std::ifstream ifs(dictionary.c_str());   ifs >> nn;     // convert imagefile to vec_t   vec_t data;   convert_image(filename, -1.0, 1.0, 32, 32, data);     // recognize   auto res = nn.predict(data);   std::vector<std::pair<double, int> > scores;     // sort & print top-3   for (int i = 0; i < 10; i++)    scores.emplace_back(rescale<tan_h>(res[i]), i);     std::sort(scores.begin(), scores.end(), std::greater<std::pair<double, int>>());     for (int i = 0; i < 3; i++)    std::cout << scores[i].second << "," << scores[i].first << std::endl;     std::cout << "the actual digit is: " << scores[0].second << ", correct digit is: "<<target<<std::endl;     // visualize outputs of each layer   //for (size_t i = 0; i < nn.depth(); i++) {   // auto out_img = nn[i]->output_to_image();   // cv::imshow("layer:" + std::to_string(i), image2mat(out_img));   //}   //// visualize filter shape of first convolutional layer   //auto weight = nn.at<convolutional_layer<tan_h>>(0).weight_to_image();   //cv::imshow("weights:", image2mat(weight));     //cv::waitKey(0);  }    // convert tiny_cnn::image to cv::Mat and resize  cv::Mat image2mat(image<>& img) {   cv::Mat ori(img.height(), img.width(), CV_8U, &img.at(0, 0));   cv::Mat resized;   cv::resize(ori, resized, cv::Size(), 3, 3, cv::INTER_AREA);   return resized;  }    void convert_image(const std::string& imagefilename,   double minv,   double maxv,   int w,   int h,   vec_t& data) {   auto img = cv::imread(imagefilename, cv::IMREAD_GRAYSCALE);   if (img.data == nullptr) return; // cannot open, or it's not an image     cv::Mat_<uint8_t> resized;   cv::resize(img, resized, cv::Size(w, h));     // mnist dataset is "white on black", so negate required   std::transform(resized.begin(), resized.end(), std::back_inserter(data),    [=](uint8_t c) { return (255 - c) * (maxv - minv) / 255.0 + minv; });  }

5.      编译时会提示几个错误，解决方法是：

(1)、error C4996，解决方法：将宏_SCL_SECURE_NO_WARNINGS添加到属性的预处理器定义中；

(2)、调用for_函数时，error C2668，对重载函数的调用不明教，解决方法：将for_中的第三个参数强制转化为size_t类型；

6.      运行程序，train时，运行结果如下图所示：

7.      对生成的model进行测试，通过画图工具，每个数字生成一张图像，共10幅，如下图：

通过导入train时生成的model，对这10张图像进行识别，识别结果如下图，其中6和9被误识为5和1：