墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
1.1k累计阅读
1.1k平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
启动服务时，控制台打印大量如下信息
Generating unique operation named: insertUsingPOST_1
Generating unique operation named: delUsingDELETE_1
多个controller类中的方法名相同，我这里是自己生成的多个controller使用了相同的模板代码，而他们的CRUD方法名没有加上模块名的前缀，从而导致swaggerr扫描时相同的POST方法有相同的方法名从而提示POST重复，但是并不影响正常业务，只是不够规范
直接修改每个controller中的方法名不重复即可，这里我直接加上各个模块的前缀即可。
springboot
Microchip Studio 7.0 从下载到项目调试：AVR开发者的完整入门指南（附常见问题解决）
本文为AVR开发者提供了Microchip Studio 7.0从下载安装到项目调试的完整入门指南。详细介绍了如何获取安装包、创建第一个AVR工程项目，并配置编译与调试环境。同时，文章重点解决了开发过程中常见的编译错误和仿真问题，帮助开发者快速上手并高效使用这一官方集成开发环境。
springboot启动日志：Generating unique operation named
使用swagger，启动项目打印类似如下信息： Generating unique operation named: changeUsingPUT_1。原因： spring 在加载controller 的时候，发现这些类里面的方法名称有重复的，所以给你自动生成了一些名称，用以区分这些重复的方法。2.修改日志级别：DEBUG < INFO < WARN < ERROR，使用warn及以上日志级别即可不打印该信息。1.修改controller中方法名，保证各个controller类中，没有重名的方法；
Clion+platformIO开发ESP32时找不到lib文件中引入第三方库的头文件(头文件下有波浪线)，实际编译是成功的解决方法
Clion+platformIO开发ESP引入第三方库文件编译器无法找到.h文件但编译成功
Generating unique operation named: xxxxxUsingGET_1解决记录
我设置了"/"，之前理解错了，以为是选根路径，这里应该上一级路径，比如我不过滤，就直接不填全放行就行，改成.paths(PathSelectors.ant(""))，就不再提示了，就是"/"重复了。当然，如果有需求，可以填写一级路径，比如/user/getUser,想只看user的就填.paths(PathSelectors.ant("/user"))@GetMapping("/")根路径的方法名还是会提示重复不唯一，全局搜索，确认是唯一方法名，修改成每个接口方法的名字都不同样以后，
ROS入门必学导航算法——Navigation
该平台集成了多款无人车底盘，包括履带式、四轮差速、阿克曼、麦克纳姆轮底盘等，以满足不同形式和配置的需求，并搭载了RTK定位系统、双目相机、深度相机和激光雷达等多种传感器，以满足开发者的科研需求。amcl节点接收里程计数据（通过"odom"话题）和激光雷达数据（通常通过"scan"话题），结合已知的地图，实时地估计出机器人在该地图上的位置。在ROS中，这是通过"tf库"变换实现的，通过将传感器数据转换到共同的参考系中，系统可以更容易地集成来自不同传感器的数据，并确保它们在相同的空间和时间范围内可用。
FileZilla Server 1.2.0 TLS证书过期问题
FileZilla Server TLS证书过期后重新生成
解决: Generating unique operation named
解决 Generating unique operation named 问题
【硬件相关】IB网与以太网核心技术解析及高性能网络部署指南
本文深入解析了IB网与以太网的核心技术差异，包括协议栈、流量控制机制及硬件架构，并提供了高性能网络部署的实战指南。通过对比测试和调优案例，展示了IB网在低延迟和高负载下的优势，以及混合组网中的创新应用，助力超算集群和高效网络部署。
使用Swagger2时启动控制台输出大量 Generating unique operation named: ***
在springboot 2 项目中使用Swagger2生成在线文档，项目启动时，出现大量类似以下日志 出现这种日志的主要原因是，两个不同的类 使用@ApiOperation注解，且不同的类中存在相同的方法名导致，如 // 类1，有以下代码 @RestController @RequestMapping("/api/c1") @Api(tags = "接口1") public class C1Controller { @ApiOperation(value = "启用记录", notes = ""
YOLO11的持续学习：如何在不遗忘旧数据的情况下学习新类别
类增量学习（Class-Incremental Learning, CIL）是持续学习（Continual Learning）的一个重要分支，它指的是模型在不遗忘已学旧类别知识的前提下，逐步学习新类别的能力。与传统机器学习假设所有数据一次可用不同，类增量学习更贴近现实世界应用场景——新的类别数据往往会随着时间推移逐步出现。 举个例子，假设我们已经训练好一个YOLOv11模型用于检测"猫"和"狗"两种动物，现在需要让模型新增识别"兔子"的能力。传统方法需要收集所有类别（猫、狗、兔子）的数据重新训练，而类增量
高创伺服的龙门调试经历2
摘要：本文介绍了高创伺服在龙门控制中的配置方法。首先通过ServoStudio2连接驱动器并命名区分主从轴（MASTER/DIFF），然后设置接口模式、23位编码器参数及极限位置。重点说明龙门模式的三项关键参数设置（gantryfbenmode=1、gantrymode=1/2）和连线状态确认。建议单轴自动整定后调整从轴增益（如0.2），并强调带刹车电机的同步控制要点：刹车线需接同一继电器由主轴控制，避免位置偏差。
使用Swagger2时启动日志中出现大面积的日志内容：Generating unique operation named: xxx
在项目中使用Swagger2生成在线文档时，在项目的启动的日志中出现了大面积的Generating unique operation named: xxx日志信息，看的很是不爽 出现这种日志的情况是因为在Controller定义接口的时候是如下定义的： @ResponseBody @RequestMapping(value = "/createOrder") @ApiOperation(value = "创建订单", notes = "创建订单", tags = { "订单模块" }, httpMetho
Codex 100个真实案例 - 用AI画业务流程图Mermaid（产品经理都馋哭了）
还在用 Visio、ProcessOn 拖拖拽拽画流程图？改一个箭头方向就要调半天？本案例教你用 Codex 自然语言描述业务流程，AI 自动转为 Mermaid 代码，一秒生成专业流程图、时序图、甘特图、饼图！还能搭建在线渲染工具、批量导出 PNG/SVG、集成到文档系统。产品经理、架构师、项目经理都能用，从此画图再也不是体力活！
控制台输出大量 Generating unique operation named: ***
在springboot 2 项目中使用Swagger2生成在线文档，项目启动时，出现大量类似以下日志 出现这种日志的主要原因是，两个不同的类 使用@ApiOperation注解，且不同的类中存在相同的方法名(enable)导致，如 // 类1，有以下代码 @RestController @RequestMapping("/api/c1") @Api(tags = "接口1") public class C1Controller { @ApiOperation(value = "启用记录", no...
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")
IDEA的使用指南
IDEA基于java的使用手册,可以最快速的上手,在使用中不断的丰富自己的使用技巧.
【Java】SpringBoot 项目启动问题、代码优化、疑难处理链接学习【不定时更新】
SpringBoot 项目启动问题解决链接学习
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
Springboot 启动信息：Generating
Springboot 启动信息：Generating unique operation named 1.springboot 项目启动时，显示Generating unique operation named 2.这是因为spring 在加载controller 的时候，发现这些类里面的方法名称有重复的，所以给你自动生成了一些名称，用以区分这些方法 3.关闭该信息提示： application-dev.yml配置文件中添加如下配置 logging: level: springfox: e
温度PID控制PLC程序
温度PID稳定控制程序，适用于欧姆龙PLC，对其他PLC也具有很大参考价值
springboot禁用swagger后还是出现读日志时间过长情况
日常罢课springboot禁用swagger后还是出现读日志时间过长情况解决方法 springboot禁用swagger后还是出现读日志时间过长情况 2019-11-26 10:46:35.873 INFO 12888 --- [ restartedMain] .d.s.w.r.o.CachingOperationNameGenerator : Generating unique opera...
springboot+swagger（含token）
首先将swagger引入到pom <dependency> <groupId>io.springfox</groupId> <artifactId>springfox-swagger2</artifactId> <version>2.9.2</version> <exclusions> <exclusion>
后台启动信息：Generating unique operation named
程序启动，去除无用信息。
java.lang.IllegalArgumentException: invalid comparison: java.util.Date and java.lang.String
"C:\Program Files\Java\jdk1.8.0_251\bin\java.exe" -XX:TieredStopAtLevel=1 -noverify -Dspring.output.ansi.enabled=always -Dcom.sun.management.jmxremote -Dspring.jmx.enabled=true -Dspring.liveBeansView.mbeanDomain -Dspring.application.admin.enabled=true "-j.
.d.s.w.r.o.CachingOperationNameGenerator : Generating unique operation named
1.出现原因之一: 在controller上加了@RequestMapping注解后,在方法上mapping注解,必须是POST或GET,不然后启动项目的时候就会出现 2.解决 方法上的mapping注解指定请求类型,即可!!! ...
swagger启动卡住Generating unique operation named 。Scanning for api listing references
https://blog.youkuaiyun.com/YardStrong/article/details/100118822
java 修改包名_修改包名后运行报错
2019-09-23@Mark 2019-09-23 00:22:07.658 INFO 20082 --- [ main] com.shine.AdminApplication : Starting AdminApplication on MacBook.local with PID 20082 (/Users/mac/Documents/shi...
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
332
扫一扫
