深度学习开源库tiny-dnn的使用(MNIST)
2024-09-03 18:15:05
tiny-dnn是一个基于DNN的深度学习开源库,它的License是BSD 3-Clause。之前名字是tiny-cnn是基于CNN的,tiny-dnn与tiny-cnn相关又增加了些新层。此开源库很活跃,几乎每天都有新的提交,因此下面详细介绍下tiny-dnn在windows7 64bit vs2013的编译及使用。
1. 从https://github.com/tiny-dnn/tiny-dnn 下载源码:
$ git clone https://github.com/tiny-dnn/tiny-dnn.git 版本号为6281c1b,更新日期2016.12.03
2. 源文件中已经包含了vs2013工程,vc/vc12/tiny-dnn.sln,默认是win32的,这里新建一个x64的控制台工程tiny-dnn;
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-dnn.cpp文件;
4. 仿照examples/mnist中test.cpp和train.cpp文件中的代码添加测试代码;
#include "funset.hpp" #include <string> #include <algorithm> #include "tiny_dnn/tiny_dnn.h" static void construct_net(tiny_dnn::network<tiny_dnn::sequential>& 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 // by default will use backend_t::tiny_dnn unless you compiled // with -DUSE_AVX=ON and your device supports AVX intrinsics tiny_dnn::core::backend_t backend_type = tiny_dnn::core::default_engine(); // construct nets: C: convolution; S: sub-sampling; F: fully connected nn << tiny_dnn::convolutional_layer<tiny_dnn::activation::tan_h>(32, 32, 5, 1, 6, // C1, 1@32x32-in, 6@28x28-out tiny_dnn::padding::valid, true, 1, 1, backend_type) << tiny_dnn::average_pooling_layer<tiny_dnn::activation::tan_h>(28, 28, 6, 2) // S2, 6@28x28-in, 6@14x14-out << tiny_dnn::convolutional_layer<tiny_dnn::activation::tan_h>(14, 14, 5, 6, 16, // C3, 6@14x14-in, 16@10x10-out connection_table(tbl, 6, 16), tiny_dnn::padding::valid, true, 1, 1, backend_type) << tiny_dnn::average_pooling_layer<tiny_dnn::activation::tan_h>(10, 10, 16, 2) // S4, 16@10x10-in, 16@5x5-out << tiny_dnn::convolutional_layer<tiny_dnn::activation::tan_h>(5, 5, 5, 16, 120, // C5, 16@5x5-in, 120@1x1-out tiny_dnn::padding::valid, true, 1, 1, backend_type) << tiny_dnn::fully_connected_layer<tiny_dnn::activation::tan_h>(120, 10, // F6, 120-in, 10-out true, backend_type); } static void train_lenet(const std::string& data_dir_path) { // specify loss-function and learning strategy tiny_dnn::network<tiny_dnn::sequential> nn; tiny_dnn::adagrad optimizer; construct_net(nn); std::cout << "load models..." << std::endl; // load MNIST dataset std::vector<tiny_dnn::label_t> train_labels, test_labels; std::vector<tiny_dnn::vec_t> train_images, test_images; tiny_dnn::parse_mnist_labels(data_dir_path + "/train-labels.idx1-ubyte", &train_labels); tiny_dnn::parse_mnist_images(data_dir_path + "/train-images.idx3-ubyte", &train_images, -1.0, 1.0, 2, 2); tiny_dnn::parse_mnist_labels(data_dir_path + "/t10k-labels.idx1-ubyte", &test_labels); tiny_dnn::parse_mnist_images(data_dir_path + "/t10k-images.idx3-ubyte", &test_images, -1.0, 1.0, 2, 2); std::cout << "start training" << std::endl; tiny_dnn::progress_display disp(static_cast<unsigned long>(train_images.size())); tiny_dnn::timer t; int minibatch_size = 10; int num_epochs = 30; optimizer.alpha *= static_cast<tiny_dnn::float_t>(std::sqrt(minibatch_size)); // create callback auto on_enumerate_epoch = [&](){ std::cout << t.elapsed() << "s elapsed." << std::endl; tiny_dnn::result res = nn.test(test_images, test_labels); std::cout << res.num_success << "/" << res.num_total << std::endl; disp.restart(static_cast<unsigned long>(train_images.size())); t.restart(); }; auto on_enumerate_minibatch = [&](){ disp += minibatch_size; }; // training nn.train<tiny_dnn::mse>(optimizer, 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 network model & trained weights nn.save(data_dir_path + "/LeNet-model"); } // rescale output to 0-100 template <typename Activation> static double rescale(double x) { Activation a; return 100.0 * (x - a.scale().first) / (a.scale().second - a.scale().first); } static void convert_image(const std::string& imagefilename, double minv, double maxv, int w, int h, tiny_dnn::vec_t& data) { tiny_dnn::image<> img(imagefilename, tiny_dnn::image_type::grayscale); tiny_dnn::image<> resized = resize_image(img, 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; }); } int test_dnn_mnist_train() { std::string data_dir_path = "E:/GitCode/NN_Test/data"; train_lenet(data_dir_path); return 0; } int test_dnn_mnist_predict() { std::string model { "E:/GitCode/NN_Test/data/LeNet-model" }; std::string image_path { "E:/GitCode/NN_Test/data/images/"}; int target[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 }; tiny_dnn::network<tiny_dnn::sequential> nn; nn.load(model); for (int i = 0; i < 10; i++) { std::string str = std::to_string(i); str += ".png"; str = image_path + str; // convert imagefile to vec_t tiny_dnn::vec_t data; convert_image(str, -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 j = 0; j < 10; j++) scores.emplace_back(rescale<tiny_dnn::tan_h>(res[j]), j); std::sort(scores.begin(), scores.end(), std::greater<std::pair<double, int>>()); for (int j = 0; j < 3; j++) fprintf(stdout, "%d: %f; ", scores[j].second, scores[j].first); fprintf(stderr, "\n"); // save outputs of each layer for (size_t j = 0; j < nn.depth(); j++) { auto out_img = nn[j]->output_to_image(); auto filename = image_path + std::to_string(i) + "_layer_" + std::to_string(j) + ".png"; out_img.save(filename); } // save filter shape of first convolutional layer auto weight = nn.at<tiny_dnn::convolutional_layer<tiny_dnn::tan_h>>(0).weight_to_image(); auto filename = image_path + std::to_string(i) + "_weights.png"; weight.save(filename); fprintf(stdout, "the actual digit is: %d, correct digit is: %d \n\n", scores[0].second, target[i]); } return 0; }
5. 运行程序,train时,运行结果如下图所示,准确率达到99%以上:
6. 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:
7. 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0,8,9被误识别为2,2,1.
GitHub:https://github.com/fengbingchun/NN_Test
最新文章
- 玩转spring boot——MVC应用
- gdb的可视化工具安装
- android JSON获取值String无法转换成JSONObject
- jquery1.9+获取append后的动态元素
- Java静态同步方法和非静态同步方法
- go 函数
- c++取小数整数部分
- “非常PHP学习网”(www.veryphp.cn)一期上线
- Asp.Net部分面试题
- Android Toast和Notification
- SqlServer数据库设计,纠结的问题,有胆你就来!
- riot.js教程【二】组件撰写准则、预处理器、标签样式和装配方法
- Java集合系列[1]----ArrayList源码分析
- 目标文件去除header一行开头的#号
- SQL 序列-DML-DML-数据类型-用户管理、权限-事务-视图
- Excel常用公式
- tensorflow读取数据的方式
- js、css、img等浏览器缓存问题的2种解决方案
- Android的环境搭建
- 关于GPL协议的理解(开源与商用、免费与收费的理解)