TINY-CNN use of Open source libraries (MNIST)

TINY-CNN is a CNN-based open Source library whose license is the BSD 3-clause. The author has also been maintaining the update, which is helpful for further mastering CNN, so the following is the compilation and use of tiny-cnn in Windows7 64bit vs2013.

1. Download the source code from HTTPS://GITHUB.COM/NYANP/TINY-CNN:

$ git clone https://github.com/nyanp/tiny-cnn.git version number is 77d80a8, updated on 2016.01.22

2. The source file already contains the VS2013 project, Vc/tiny-cnn.sln, the default is Win32, Examples/main.cpp need OPENCV support, here a new x64 console project TINY-CNN;

3. Follow the source project and add the corresponding. h file to the new console project, adding a new Test_tiny-cnn.cpp file;

4. Copy the code from the Test.cpp and train.cpp files in the examples/mnist to the Test_tiny-cnn.cpp file;

#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 0-100template <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 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 () {//trainstd::string data_path = "D:/download/mnist"; train_lenet (Data_path);//teststd::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;recogniz E (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 Strategynetwork<mse, adagrad> Nn;construct_net (NN); Std::cout << "Load models ..." << std::endl;//load MNIST datasetstd::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);p arse_mnist_images (Data_dir_path + "/train-images.idx3-ubyte" , &train_images, -1.0, 1.0, 2, 2);p arse_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 Callbackauto 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;};/ /Trainingnn.train (Train_images, Train_labels, Minibatch_size, Num_epochs,on_enumerate_minibatch, On_enumerate_ Epoch), Std::cout << "end training." << std::endl;//test and show Resultsnn.test (Test_images, Test_labels). Print_detail (std::cout);//Save Networksstd::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 falsestatic 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 Netsnn < < Convolutional_layer<tan_h> (5, 1, 6)//C1, [email protected], [email protected]<< Ave Rage_pooling_layer<tan_h> (6, 2)//S2, [email protected], [email protected]<< Convolutional_layer<tan_h> (5, 6, 16,connection_table (TBL, 6,))//C3, [email protected] , [email protected]<< average_pooling_layer<tan_h> (Ten, 2)//S4, [email protected], [ email protected]<< convolutional_layer<tan_h> (5, 5, 5, +)//C5, [email protected], [email& nbsp;protected]<< fully_connected_layer&Lt;tan_h> (120, 10); F6, 120-in, 10-out}void recognize (const std::string& dictionary, const std::string& filename, int target) {NETW Ork<mse, adagrad> nn;construct_net (NN);//Load Netsstd::ifstream IFS (DICTIONARY.C_STR ()); IFS >> nn;// Convert ImageFile to vec_tvec_t data;convert_image (filename, -1.0, 1.0, +, +, data);//Recognizeauto res = nn.predict (da TA); STD::VECTOR&LT;STD::p air<double, int> > scores;//sort & print top-3for (int i = 0; i < ten; i++) scores . Emplace_back (Rescale<tan_h> (res[i]), i); Std::sort (Scores.begin (), Scores.end (), STD::GREATER&LT;STD::p Air <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 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 = C V::imread (Imagefilename, Cv::imread_grayscale); if (img.data = = nullptr) return; Cannot open, or it's not a imagecv::mat_<uint8_t> resized;cv::resize (IMG, resized, Cv::size (W, h));//Mnist data Set is ' White on black ', so negate requiredstd::transform (Resized.begin (), Resized.end (), Std::back_inserter (data), [=] ( uint8_t c) {RETurn (255-c) * (MAXV-MINV)/255.0 + MINV; });}

5. A few errors will be prompted at compile time, and the workaround is:

(1), Error C4996, workaround: Add the macro _scl_secure_no_warnings to the properties of the preprocessor definition;

(2), call for_ function, error C2668, call the overloaded function unknown teach, workaround: The third parameter in the For_ is forced into the size_t type;

6. Run the program, train the results as shown:

7. Test the generated model and generate an image with a total of 10 images for each number using the drawing tool, such as:

The 10 images are identified by the model generated when the train is imported, and the results are identified as 6 and 9 are mistaken for 5 and 1:

TINY-CNN use of Open source libraries (MNIST)