as报错: By not providing “Findncnn.cmake” in CMAKE_MODULE_PATH this project has
asked CMake to find a package configuration file provided by “ncnn”, but
CMake did not find one.
set(ncnn_DIR ${CMAKE_SOURCE_DIR}/ncnn-20221128-android-vulkan/${ANDROID_ABI}/lib/cmake/ncnn)
grade同步一下，然后再撤销删除grade同步就行。
By not providing "FindOpenCV.cmake" in CMAKE_MODULE_PATH this project
问题： CMake Error at CMakeLists.txt:3 (find_package): By not providing "FindOpenCV.cmake" in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by "OpenCV", ...
clion 点击run按钮 By not providing “Findcatkin.cmake“ in CMAKE_MODULE_PATH this project has asked CMake
clion 点击run按钮 By not providing “Findcatkin.cmake” in CMAKE_MODULE_PATH this project has。如果你还遇到其他问题，请提供更多的详细信息，比如编译器版本、操作系统、ROS 版本等，这样我才能更好地帮助你。然后重新运行 CLion，点击 Run 按钮，应该就可以顺利运行程序了。要解决这个问题，你需要设置。是你当前安装的 ROS 版本号，比如。设置为 ROS 的安装目录，将。的 CMake 模块路径。
ros noetic 编译realsense2_camera功能包时出错解决办法
分析： 缺少librealsense SDK库和依赖； 安装librealsense2： 构建librealsense2 SDK： 编译完成后可以在路径下看到文件但是当我们再次编译工作空间下的功能包时会出现如下error： 分析： 这是文件的配置路径不正确导致的，在文件中找到下面这两句并修改相应路径 （上面我在编译时将生成的路径改为了，因此这里在这个路径下寻找）：
By not providing “Findcatkin.cmake“ in CMAKE_MODULE_PATH this project has asked CMake to find a pa
See also "/tmp/QtCreator-XzPzFL/qtc-cmake-iCElsmaa/CMakeFiles/CMakeOutput.log". CMake Project parsing failed. Starting to parse CMake project, using: "-DCMAKE_BUILD_TYPE:STRING=Debug", "-DCMAKE_CXX_COMPILER:STRING=/usr/bin/g++", "-DCMAKE_C_COMPILER:STRING=
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SDL2 cmake配置
SDL2 cmake配置内容提要环境配置问题浮现坑1坑2结语 好久没有上来写博客了，经历了股市的牛熊转换，一不小心就被割了韭菜。今天总算可以冷静下来写篇博客了。 内容提要 SDL2是用C语言写的跨平台的多媒体库，而且支持多种语言（话说支持go语言，真是难得啊，改天可以玩玩）。 今天主要吐槽下Windows+mingw+sdl2开发库的cmake配置。 环境配置 下载windows的mingw安装包，官网地址：http://www.mingw-w64.org 下载SDL2 选择mingw版本 https:/
By not providing “FindOpenCV.cmake“ in CMAKE_MODULE_PATH this project has asked CMake to find a pack
OpenCV错误1. 错误描述2. 错误分析3. 解决方案 1. 错误描述 CMake Error at CMakeLists.txt:10 (find_package): By not providing “FindOpenCV.cmake” in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by “OpenCV”, but CMake did not find o
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ROS2 colcon build出现Could not find a package configuration file provided by “ament_cmake“
在编译ROS2时，采用colcon build编译，出现：Could not find a package configuration file provided by "ament_cmake" with any of the following names:
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LTE信令流程及业务流程
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By not providing “FindCUDA.cmake“ in CMAKE_MODULE_PATH this project has asked CMake to find a pac
修改完Cmakelists之后要，建议把build文件删除掉，再重新Cmake ..出现这个的原因是因为我更换了cmake的版本，从旧版本的3.16更换到了3.27。这是因为新版本中查找cuda的语句变了，你需要修改你的Cmakelists。
系规备考论文：论IT服务规划设计
系规备考论文：论IT服务规划设计
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在cmake中常见以下错误： By not providing "FindPython3.cmake" in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by "Python3", but CMake did not find one. Could not find a package configuration file provided by "
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k8s infiniband 和 RoCE(RDMA over Converged Ethernet)
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[ros2 foxy] colcon build 编译报错 ament_cmake
解决方案：修改工作空间的读写权限。
NTscan扫描工具
ntscan是一个扫描器，可以扫描没有密码和弱密码的共享，先到配置文件中填写猜测的用户名和密码，扫描过程中右侧出现的是扫描日志。
ubuntu By not providing “Findcatkin.cmake“ in CMAKE_MODULE_PATH this project has asked CMake to fi
ubuntu By not providing "Findcatkin.cmake" in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by "catkin", but CMake did not find one.使用CLion编译工程时报错解决新的改变三级标题功能快捷键合理的创建标题，有助于目录的生成如何改变文本的样式插入链接与图片如何插入一段漂亮的代码片
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")
STM32F407中文手册
STM32f407中文手册 本参考手册面向应用开发人员，提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列，各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性，请参见数据手册。有关 ARM Cortex™-M4F 内核的信息，请参见《Cortex™-M4F 技术参考手册》。
catkin cmake
By not providing “Findcatkin.cmake” in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by “catkin”, but CMake did not find one. Could not find a package configuration file provided by “catkin” with any of the fo
大中型超市收银系统(可直接运行)
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
By not providing FindCUDA.cmake
By not providing "FindCUDA.cmake" in CMAKE
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