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there are three files: T.h , T.cpp and main.cpp .I wanna get a executive file named demo,I can vi this Makefile as follows:
CPP Programming
linux Pytorch C++ 环境搭建
Pytorch C++ 环境搭建 0，阅读Pytorch C++官网文档，在虚拟机上安装Ubuntu16.04的Linux操作系统搭建环境，并使用makefile进行编译，代码简洁易读。 sudo apt-get update sudo apt-get install make cmake libnss3 tree 1，下载pytorch c++ cpu 库文件，并...
分享一个嵌入式中Makefile的通用Demo
国庆在家事情比较少，之前因为稍微学了点Makefile的基本语法，所以就想着自己写个Makefile，一来以后自己作参考，二来也是可以复习下之前的一些命令，三来就是分享啦~，哈哈。
Makefile 多目录编译Demo
多目录编译、Makefile 三种用法 一定要会的
makefile demo
demo用一个简单的冒泡排序做为 被调函数。
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C++工程的makefile文件编写demo
makefile文件的编写demo，在linux下运行
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Linux下C语言编程基础(Makefile)
假设我们有下面这样的一个程序，源代码如下： /* main.c */ #include "mytool1.h" #include "mytool2.h" int main(int argc，char **argv) { mytool1_print("hello")； mytool2_print("hello")； }
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把入库交货单的 GOS 附件自动带进审批 Work Item：用 SOFM 对象打通 SAP Workflow 附件链路
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makefile编译c++程序（用上opencv库）demo
c++程序 #include <iostream> #include <opencv2/opencv.hpp> using namespace std; using namespace cv; int main() { Mat srcImg, srcHSV; srcImg=imread("2.jpg"); namedWindow("1",WINDOW_KEEPRATIO); imshow("1",srcImg); waitKey();
SolidWorks 小车底盘 装配体
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C++ffmpeg（5）-4.3-ubuntu-环境搭建-x264-x265
1.X264 2.X265 3.fdk-acc 4.ffmpeg-4.3 5.demo 1.X264 #windows \n\t linux \n 不一样 #库都是在环境变量中 #安装前要先安装 sudo apt-get install build-essential sudo apt-get install nasm sudo apt-get insall yasm sudo apt-get insall make cmake sudo apt-get insall di......
CDR插件开发之CPG插件017 - 在VS2019中实现CPG插件调试（自动写入CPG、自动启动CDR）
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【嵌入式DIY实例-ESP32篇】-热成像仪
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C++ Makefile写法小demo
============================================================================================= #include "mytool1.h" #include "mytool2.h" int main(int argc,char **argv) { mytool1_pri...
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
Makefile demo of C++ project , include jni
VOICE_PAIR_HEAD = -I /usr/lib/jvm/java-6-sun/include -I /usr/lib/jvm/java-6-sun/include/linux TARGETS = output/ test:CoreAlgorithm.cpp Device.cpp DeviceManager.cpp Preprocessor.cpp SpeechLog.cp...
复分析 阿尔福斯(Ahlfors) 第三版答案 英文
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Statistics and Machine Learning Toolbox
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C程序调用C++函数并使用gcc和g++编译链接
最近遇到这样一个场景：将dynamorio内置到QEMU模拟器中，能直接分析QEMU模拟出来的指令等信息。QEMU是用C语言写的，而dynamorio使用C++实现的，这就要涉及到用C程序(QEMU)调用C++(dynamorio)的函数。我们知道，C和C++的命名空间不同，函数编译出来的名字都不同，怎么去写程序和编译呢？ 文章目录1. 编写demo2. 编写Makefile3. 编译执行4. 原理 1. 编写demo 一共三个文件testFunc.h、testFunc.cpp和main.c，用main.c
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光步前台收银系统系统功能：商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传，基本资料下载，开钱箱、小票打印、收款结算，支持多币种付款方式（现金、储值卡、礼券、微信等）,支持多种促销。运行环境：.net 4.0登陆信息用户名:1001 密码:123456内置商品信息，商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开
C++在Linux上生成动态库并调用接口测试
C++在Linux上生成动态库并调用接口测试
ubuntu下makefile和cpp编译生成可执行文件
大坑年年有，今年格外多 刚入坑c++，啥也不是，啥也不会，拿到一份代码，啥都有，都跑步起来 刚开始入手了一份只有cpp的，需要自己写makefile，失败告终 后来找了有cpp和makefle的，还是跑不起来。 感谢大佬 https://blog.youkuaiyun.com/bruceoxl/article/details/78241874 终于摸索出来了 由于库一直链接不上，对命令的含义也不熟悉，各种问题 下面这句命令见了好多次了，不知道在哪里用。 通过pkg-config –cflags opencv 获得ope
linux下的基本的makefile的制作
CC=g++#选的编译器是g++的 EXEC=MyApp SRCS=demo.a\ tset.a#后缀名为.cpp的源文件 OBJS=$(SRCS:.cpp=.o)#把.cpp文件替换为.o start:$(OBJS) $(CC) -o $(EXEC) -c $(SRCS) .cpp.o: $(CC) -o $@ -c $< cl...
Extending Python with C or C++ - Demo/embed/demo.c
Extending Python with C or C++ - Demo/embed/demo.c Python 2.7.16rc1 - 2019-02-17 - Python-2.7.16rc1.tgz https://www.python.org/downloads/source/ Extending Python with C or C++ An example may be found ...
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