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非常有帮助，因此以下介绍下tiny-cnn在windows7 64bit vs2013的编译及使用。

1.      从下载源代码：

$ git clone   版本为77d80a8，更新日期2016.01.22

2.      源文件里已经包括了vs2013project，vc/tiny-cnn.sln，默认是win32的，examples/main.cpp须要OpenCV的支持。这里新建一个x64的控制台projecttiny-cnn。

3.      仿照源project，将对应.h文件加入到新控制台project中。新加一个test_tiny-cnn.cpp文件；

4.      将examples/mnist中test.cpp和train.cpp文件里的代码拷贝到test_tiny-cnn.cpp文件里；

#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include 
          
           using namespace tiny_cnn;using namespace tiny_cnn::activation;// rescale output to 0-100template 
           
            double rescale(double x){ Activation a; return 100.0 * (x - a.scale().first) / (a.scale().second - a.scale().first);}void construct_net(network
            
             & nn);void train_lenet(std::string data_dir_path);// convert tiny_cnn::image to cv::Mat and resizecv::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
             
               nn; construct_net(nn); std::cout << "load models..." << std::endl; // load MNIST dataset std::vector
              
                train_labels, test_labels; std::vector
               
                 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
                
                 & 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
                 
                  (32, 32, 5, 1, 6) // C1, 1@32x32-in, 6@28x28-out << average_pooling_layer
                  
                   (28, 28, 6, 2) // S2, 6@28x28-in, 6@14x14-out << convolutional_layer
                   
                    (14, 14, 5, 6, 16, connection_table(tbl, 6, 16)) // C3, 6@14x14-in, 16@10x10-in << average_pooling_layer
                    
                     (10, 10, 16, 2) // S4, 16@10x10-in, 16@5x5-out << convolutional_layer
                     
                      (5, 5, 5, 16, 120) // C5, 16@5x5-in, 120@1x1-out << fully_connected_layer
                      
                       (120, 10); // F6, 120-in, 10-out}void recognize(const std::string& dictionary, const std::string& filename, int target) { network
                       
                         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
                        
                         
                           > scores; // sort & print top-3 for (int i = 0; i < 10; i++) scores.emplace_back(rescale
                          
                           (res[i]), i); std::sort(scores.begin(), scores.end(), std::greater
                           
                            
                             >()); 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: "<
                             
                              <
                              
                               output_to_image(); // cv::imshow("layer:" + std::to_string(i), image2mat(out_img)); //} visualize filter shape of first convolutional layer //auto weight = nn.at
                               
                                
                                 >(0).weight_to_image(); //cv::imshow("weights:", image2mat(weight)); //cv::waitKey(0);}// convert tiny_cnn::image to cv::Mat and resizecv::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_
                                 
                                   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加入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_中的第三个參数强制转化为size_t类型；

6.      执行程序，train时，执行结果例如以下图所看到的：

7.      对生成的model进行測试，通过绘图工具，每一个数字生成一张图像，共10幅，例如以下图：

通过导入train时生成的model。对这10张图像进行识别，识别结果例如以下图，当中6和9被误识为5和1：

GitHub：