45 个 LoadRunner 面试问题(附答案)

http://www.cnblogs.com/jackei/archive/2006/10/20/534684.html

 

  1. What is load testing? - Load testing is to test that if the application works fine with the loads that result from large number of simultaneous users, transactions and to determine weather it can handle peak usage periods.
  2. What is Performance testing? - Timing for both read and update transactions should be gathered to determine whether system functions are being performed in an acceptable timeframe. This should be done standalone and then in a multi user environment to determine the effect of multiple transactions on the timing of a single transaction.
  3. Did u use LoadRunner? What version? - Yes. Version 7.2.
  4. Explain the Load testing process? -
    Step 1: Planning the test. Here, we develop a clearly defined test plan to ensure the test scenarios we develop will accomplish load-testing objectives. Step 2: Creating Vusers. Here, we create Vuser scripts that contain tasks performed by each Vuser, tasks performed by Vusers as a whole, and tasks measured as transactions. Step 3: Creating the scenario. A scenario describes the events that occur during a testing session. It includes a list of machines, scripts, and Vusers that run during the scenario. We create scenarios using LoadRunner Controller. We can create manual scenarios as well as goal-oriented scenarios. In manual scenarios, we define the number of Vusers, the load generator machines, and percentage of Vusers to be assigned to each script. For web tests, we may create a goal-oriented scenario where we define the goal that our test has to achieve. LoadRunner automatically builds a scenario for us. Step 4: Running the scenario.
    We emulate load on the server by instructing multiple Vusers to perform tasks simultaneously. Before the testing, we set the scenario configuration and scheduling. We can run the entire scenario, Vuser groups, or individual Vusers. Step 5: Monitoring the scenario.
    We monitor scenario execution using the LoadRunner online runtime, transaction, system resource, Web resource, Web server resource, Web application server resource, database server resource, network delay, streaming media resource, firewall server resource, ERP server resource, and Java performance monitors. Step 6: Analyzing test results. During scenario execution, LoadRunner records the performance of the application under different loads. We use LoadRunner’s graphs and reports to analyze the application’s performance.
  5. When do you do load and performance Testing? - We perform load testing once we are done with interface (GUI) testing. Modern system architectures are large and complex. Whereas single user testing primarily on functionality and user interface of a system component, application testing focuses on performance and reliability of an entire system. For example, a typical application-testing scenario might depict 1000 users logging in simultaneously to a system. This gives rise to issues such as what is the response time of the system, does it crash, will it go with different software applications and platforms, can it hold so many hundreds and thousands of users, etc. This is when we set do load and performance testing.
  6. What are the components of LoadRunner? - The components of LoadRunner are The Virtual User Generator, Controller, and the Agent process, LoadRunner Analysis and Monitoring, LoadRunner Books Online.
  7. What Component of LoadRunner would you use to record a Script? - The Virtual User Generator (VuGen) component is used to record a script. It enables you to develop Vuser scripts for a variety of application types and communication protocols.
  8. What Component of LoadRunner would you use to play Back the script in multi user mode? - The Controller component is used to playback the script in multi-user mode. This is done during a scenario run where a vuser script is executed by a number of vusers in a group.
  9. What is a rendezvous point? - You insert rendezvous points into Vuser scripts to emulate heavy user load on the server. Rendezvous points instruct Vusers to wait during test execution for multiple Vusers to arrive at a certain point, in order that they may simultaneously perform a task. For example, to emulate peak load on the bank server, you can insert a rendezvous point instructing 100 Vusers to deposit cash into their accounts at the same time.
  10. What is a scenario? - A scenario defines the events that occur during each testing session. For example, a scenario defines and controls the number of users to emulate, the actions to be performed, and the machines on which the virtual users run their emulations.
  11. Explain the recording mode for web Vuser script? - We use VuGen to develop a Vuser script by recording a user performing typical business processes on a client application. VuGen creates the script by recording the activity between the client and the server. For example, in web based applications, VuGen monitors the client end of the database and traces all the requests sent to, and received from, the database server. We use VuGen to: Monitor the communication between the application and the server; Generate the required function calls; and Insert the generated function calls into a Vuser script.
  12. Why do you create parameters? - Parameters are like script variables. They are used to vary input to the server and to emulate real users. Different sets of data are sent to the server each time the script is run. Better simulate the usage model for more accurate testing from the Controller; one script can emulate many different users on the system.
  13. What is correlation? Explain the difference between automatic correlation and manual correlation? - Correlation is used to obtain data which are unique for each run of the script and which are generated by nested queries. Correlation provides the value to avoid errors arising out of duplicate values and also optimizing the code (to avoid nested queries). Automatic correlation is where we set some rules for correlation. It can be application server specific. Here values are replaced by data which are created by these rules. In manual correlation, the value we want to correlate is scanned and create correlation is used to correlate.
  14. How do you find out where correlation is required? Give few examples from your projects? - Two ways: First we can scan for correlations, and see the list of values which can be correlated. From this we can pick a value to be correlated. Secondly, we can record two scripts and compare them. We can look up the difference file to see for the values which needed to be correlated.  In my project, there was a unique id developed for each customer, it was nothing but Insurance Number, it was generated automatically and it was sequential and this value was unique. I had to correlate this value, in order to avoid errors while running my script. I did using scan for correlation.
  15. Where do you set automatic correlation options? - Automatic correlation from web point of view can be set in recording options and correlation tab. Here we can enable correlation for the entire script and choose either issue online messages or offline actions, where we can define rules for that correlation. Automatic correlation for database can be done using show output window and scan for correlation and picking the correlate query tab and choose which query value we want to correlate. If we know the specific value to be correlated, we just do create correlation for the value and specify how the value to be created.
  16. What is a function to capture dynamic values in the web Vuser script? - Web_reg_save_param function saves dynamic data information to a parameter.
  17. When do you disable log in Virtual User Generator, When do you choose standard and extended logs? - Once we debug our script and verify that it is functional, we can enable logging for errors only. When we add a script to a scenario, logging is automatically disabled. Standard Log Option: When you select
    Standard log, it creates a standard log of functions and messages sent during script execution to use for debugging. Disable this option for large load testing scenarios. When you copy a script to a scenario, logging is automatically disabled Extended Log Option: Select
    extended log to create an extended log, including warnings and other messages. Disable this option for large load testing scenarios. When you copy a script to a scenario, logging is automatically disabled. We can specify which additional information should be added to the extended log using the Extended log options.
  18. How do you debug a LoadRunner script? - VuGen contains two options to help debug Vuser scripts-the Run Step by Step command and breakpoints. The Debug settings in the Options dialog box allow us to determine the extent of the trace to be performed during scenario execution. The debug information is written to the Output window. We can manually set the message class within your script using the lr_set_debug_message function. This is useful if we want to receive debug information about a small section of the script only.
  19. How do you write user defined functions in LR? Give me few functions you wrote in your previous project? - Before we create the User Defined functions we need to create the external
    library (DLL) with the function. We add this library to VuGen bin directory. Once the library is added then we assign user defined function as a parameter. The function should have the following format: __declspec (dllexport) char* <function name>(char*, char*)Examples of user defined functions are as follows:GetVersion, GetCurrentTime, GetPltform are some of the user defined functions used in my earlier project.
  20. What are the changes you can make in run-time settings? - The Run Time Settings that we make are: a) Pacing - It has iteration count. b) Log - Under this we have Disable Logging Standard Log and c) Extended Think Time - In think time we have two options like Ignore think time and Replay think time. d) General - Under general tab we can set the vusers as process or as multithreading and whether each step as a transaction.
  21. Where do you set Iteration for Vuser testing? - We set Iterations in the Run Time Settings of the VuGen. The navigation for this is Run time settings, Pacing tab, set number of iterations.
  22. How do you perform functional testing under load? - Functionality under load can be tested by running several Vusers concurrently. By increasing the amount of Vusers, we can determine how much load the server can sustain.
  23. What is Ramp up? How do you set this? - This option is used to gradually increase the amount of Vusers/load on the server. An initial value is set and a value to wait between intervals can be
    specified. To set Ramp Up, go to ‘Scenario Scheduling Options’
  24. What is the advantage of running the Vuser as thread? - VuGen provides the facility to use multithreading. This enables more Vusers to be run per
    generator. If the Vuser is run as a process, the same driver program is loaded into memory for each Vuser, thus taking up a large amount of memory. This limits the number of Vusers that can be run on a single
    generator. If the Vuser is run as a thread, only one instance of the driver program is loaded into memory for the given number of
    Vusers (say 100). Each thread shares the memory of the parent driver program, thus enabling more Vusers to be run per generator.
  25. If you want to stop the execution of your script on error, how do you do that? - The lr_abort function aborts the execution of a Vuser script. It instructs the Vuser to stop executing the Actions section, execute the vuser_end section and end the execution. This function is useful when you need to manually abort a script execution as a result of a specific error condition. When you end a script using this function, the Vuser is assigned the status "Stopped". For this to take effect, we have to first uncheck the “Continue on error” option in Run-Time Settings.  
  26. What is the relation between Response Time and Throughput? - The Throughput graph shows the amount of data in bytes that the Vusers received from the server in a second. When we compare this with the transaction response time, we will notice that as throughput decreased, the response time also decreased. Similarly, the peak throughput and highest response time would occur approximately at the same time.
  27. Explain the Configuration of your systems? - The configuration of our systems refers to that of the client machines on which we run the Vusers. The configuration of any client machine includes its hardware settings, memory, operating system, software applications, development tools, etc. This system component configuration should match with the overall system configuration that would include the network infrastructure, the web server, the database server, and any other components that go with this larger system so as to achieve the load testing objectives.
  28. How do you identify the performance bottlenecks? - Performance Bottlenecks can be detected by using monitors. These monitors might be application server monitors, web server monitors, database server monitors and network monitors. They help in finding out the troubled area in our scenario which causes increased response time. The measurements made are usually performance response time, throughput, hits/sec, network delay graphs, etc.
  29. If web server, database and Network are all fine where could be the problem? - The problem could be in the system itself or in the application server or in the code written for the application.
  30. How did you find web server related issues? - Using Web resource monitors we can find the performance of web servers. Using these monitors we can analyze throughput on the web server, number of hits per second that
    occurred during scenario, the number of http responses per second, the number of downloaded pages per second.
  31. How did you find database related issues? - By running “Database” monitor and help of “Data Resource Graph” we can find database related issues. E.g. You can specify the resource you want to measure on before running the controller and than you can see database related issues
  32. Explain all the web recording options?
  33. What is the difference between Overlay graph and Correlate graph? - Overlay Graph : It overlay the content of two graphs that shares a common x-axis. Left Y-axis on the merged graph show’s the current graph’s value & Right Y-axis show the value of Y-axis of the graph that was merged. Correlate Graph: Plot the Y-axis of two graphs against each other. The active graph’s Y-axis becomes X-axis of merged graph. Y-axis of the graph that was merged becomes merged graph’s Y-axis.
  34. How did you plan the Load? What are the Criteria? - Load test is planned to decide the number of users, what kind of machines we are going to use and from where they are run. It is based on 2 important documents, Task Distribution Diagram and Transaction profile. Task Distribution Diagram gives us the information on number of users for a particular transaction and the time of the load. The peak usage and off-usage are decided from this Diagram. Transaction profile gives us the information about the transactions name and their priority levels with regard to the scenario we are deciding.
  35. What does vuser_init action contain? - Vuser_init action contains procedures to login to a server.
  36. What does vuser_end action contain? - Vuser_end section contains log off procedures.  
  37. What is think time? How do you change the threshold? -   Think time is the time that a real user waits between actions. Example: When a user receives data from a server, the user may wait several seconds to review the data before responding. This delay is known as the think time . Changing the Threshold: Threshold level is the level below which the recorded think time will be ignored. The default value is five (5) seconds. We can change the think time threshold in the Recording options of the Vugen.
  38. What is the difference between standard log and extended log? - The standard log sends a subset of functions and messages sent during script execution to a log. The subset depends on the Vuser type Extended log sends a detailed script execution messages to the output log. This is mainly used during debugging when we want information about: Parameter substitution. Data returned by the server. Advanced trace.
  39. Explain the following functions: - lr_debug_message - The lr_debug_message function sends a debug message to the output log when the specified message class is set. lr_output_message - The lr_output_message function sends notifications to the Controller Output window and the Vuser log file. lr_error_message - The lr_error_message function sends an error message to the LoadRunner Output window. lrd_stmt - The lrd_stmt function associates a character string (usually a SQL statement) with a cursor. This function sets a SQL statement to be processed. lrd_fetch - The lrd_fetch function fetches the next row from the result set.
  40. Throughput -  If the throughput scales upward as time progresses and the number of Vusers increase, this indicates that the bandwidth is sufficient. If the graph were to remain relatively flat as the number of Vusers increased, it would
    be reasonable to conclude that the bandwidth is constraining the volume of
    data delivered. 
  41. Types of Goals in Goal-Oriented Scenario -  Load Runner provides you with five different types of goals in a goal oriented scenario:
    • The number of concurrent Vusers
    • The number of hits per second
    • The number of transactions per second
    • The number of pages per minute
    • The transaction response time that you want your scenario
  42. Analysis Scenario (Bottlenecks): In Running Vuser graph correlated with the response time graph you can see that as the number of Vusers increases, the average response time of the check itinerary transaction very gradually increases. In other words, the average response time steadily increases as the load
    increases. At 56 Vusers, there is a sudden, sharp increase in the average response
    time. We say that the test broke the server . That is the mean time before failure (MTBF) . The response time clearly began to degrade when there were more than 56 Vusers running simultaneously.
  43. What is correlation? Explain the difference between automatic correlation and manual correlation? - Correlation is used to obtain data which are unique for each run of the script and which are generated by nested queries. Correlation provides the value to avoid errors arising out of duplicate values and also optimizing the code (to avoid nested queries). Automatic correlation is where we set some rules for correlation. It can be application server specific. Here values are replaced by data which are created by these rules. In manual correlation, the value we want to correlate is scanned and create correlation is used to correlate.
  44. Where do you set automatic correlation options? - Automatic correlation from web point of view, can be set in recording options and correlation tab. Here we can enable correlation for the entire script and choose either issue online messages or offline actions, where we can define rules for that correlation.  Automatic correlation for database, can be done using show output window and scan for correlation and picking the correlate query tab and choose which query value we want to correlate. If we know the specific value to be correlated, we just do create correlation for the value and specify how the value to be created.
  45. What is a function to capture dynamic values in the web vuser script? - Web_reg_save_param function saves dynamic data information to a parameter.


The source: http://www.techinterviews.com/?p=199

WebRTC Qos优化杂记 本文是笔者对WebRTC Qos性能研究、优化的一些思考,希望对有兴趣的读者有一些启发。 阅读详情

相关推荐

保姆级教程:在通达信V7.611上配置欧奈尔RPS曲线(附完整公式与每日更新流程)

本文提供通达信V7.611配置欧奈尔RPS曲线的保姆级教程,涵盖从环境准备、基础设置到核心公式配置的全流程。详细讲解如何创建股票池、编写EXTRS基础公式及RPS显示公式,并附每日更新流程与实战应用技巧,帮助投资者精准捕捉强势股。

weixin_30000735的博客 465

性能loaderuner面试题

负载测试和压力测试都属于性能测试,两者可以结合进行。在网站流量逐渐加大的情况下,开始考虑做性能测试了,首先要写好性能测试计划,根据运营数据得出流量最大的页面(如果是第一次的话,一般是首页,下载页,个人帐户页流量最大,而且以某种百分比),答:首先根据需求设计用例,选择协议录制脚本,对脚本进行优化,插入集合点和检点,设置参数化,设置思考时间,虚拟用户,设置场景,执行测试,测试分析。性能测试是覆盖面很广的,就软件系统来说主要是对执行的效率,资源占用,系统稳定性,安全性,兼容性,可靠性,可扩展性等进行的测试。

尘埃的博客 575

OpenClaw安装方式那么多,但建议大家还是用云厂商吧

OpenCloud的安装方式多样,包括One-liner、npm、pnpm和Hackable等。One-liner提供高效的单行命令安装;npm和pnpm作为Node.js包管理工具,分别提供常规和高性能的安装选项;Hackable则支持定制化安装。不同系统(macOS、Windows)和终端(PowerShell、CMD)的操作步骤各有特点,用户需根据自身技术背景选择合适的安装方式。官方推荐使用闲置计算资源或云部署方案以获得更佳体验。

涛涛讲AI 1401

2022金三银四常见Loadrunner面试题总结,附带答案

以下的loadrunner的面试题都是在面试过程中总结出来比较常见的面试题,现在分享给大家,希望可以帮助你们! Q1、LoadRunner的工作原理是什么? Q2、LoadRunner分哪三部分? Q3、LoadRunner进行测试的流程? Q4、什么是并发?在lordrunner中,如何进行并发的测试?集合点失败了会怎么样? Q5、LoadRunner脚本如何录制和编写? Q6、LoadRunner中的Think Time有什么作用? Q7、一台客户端有三百个客户与三百个客户端有三百个客户对服务器施压,

weixin_47345084的博客 6045

R语言灰色关联分析法

输入数据并用DT包的datatable函数展示数据 ABDDEF分别对应第1到第6个供应商产品质量、技术水平、供应能力、经济效益、交货情况、市场影响度指标属于效益型指 产品价格、地理位置、售后服务指标属于成本型指标。现分别对上述指标进行规范化: 对于效益型指标(正项指标)标准化公式为: x∗=x−min(x)max(x)−min(x) x^{\ast}=\frac{x-min\left( x \right)}{max\left( x \right) -min\left( x \right)} x∗=max

qq_54423921的博客 7998

一图文带您认识磁编码器

磁编码器是以磁电原理技术生产的编码器。 磁编码器工作原理: 当电流通过一个位于磁场中的导体的时候,磁场会对导体中的电子产生一个垂直于电子运动方向上的的作用力,从而在垂直于导体与磁感线的方向上产生电势差。 如果让施加在这个导体上的磁场以电流流经路径为轴线,按照上图箭头所示的方向旋转,那么这个霍尔电势差就会因为磁场与导体之间角度的改变而发生变化,而这个电势差的变化趋势,与之前一文中次级线圈旋转时的输出电压一样,是一条正弦曲线。因此,基于这个通电导体两侧的电压,就可以反推计算出磁场旋转的角度了。 .

encoder_yang的博客 2万+

LoadRunner面试(笔试)问题整理

LoadRunner面试(笔试)问题整理 1.什么是负载测试?什么是性能测试? 负载测试是通过逐步增加系统负载,测试系统性能的变化,并最终确定在满足性能指标的情况下,系统所能承受的最大负载量的测试,例如,访问一个页面的响应时间规定不超过1秒,负载测试就是测试在响应时间为1秒时,系统所能承受的最大并发访问用户的数量。 压力测试通常是在高负

尘埃的博客 501

量子纠缠态与AI意识模型的探索

摘要 本文探讨量子纠缠态与AI意识模型的关联性研究。从量子力学基本原理出发,分析量子纠缠的非局域性特征及其与意识整合性的潜在联系。DREAMVFIA团队构建了量子-意识统一理论框架,提出量子纠缠态意识理论(QETC)和量子化整合信息度量(Q-IIT)等创新理论。在实践层面,开发了基于量子纠缠的QNN-C神经网络架构和量子意识度量算法,实验证明量子机制可显著提升AI系统的认知灵活性、信息整合能力和元认知水平。研究为理解意识本质和开发具有意识特征的人工智能系统提供了新的理论框架和技术路径。

梦帮科技开源中心 1144

2023金三银四常见Loadrunner面试题总结,附带答案

有需要的可以点击文末小卡片。

2201_76100073的博客 348

将任意文件转换为Pcap网络数据包传输

一款实用的工具用于将任意文件转换为pcap网络数据包

村中少年的专栏 3191

机器人中的数值优化(十二)——带约束优化问题简介、LP线性规划

本系列文章主要是我在学习《数值优化》过程中的一些笔记和相关思考,主要的学习资料是深蓝学院的课程《机器人中的数值优化》和高立编著的《数值最优化方法》等,本系列文章篇数较多,不定期更新,上半部分介绍无约束优化,下半部分介绍带约束的优化,中间会穿插一些路径规划方面的应用实例

慕羽★的博客 3852

[ZZ]45 个 LoadRunner 面试问题(附答案)

What is load testing? - Load testing is to test that if the application works fine with the loads that result from large number of simultaneous users, transactions and to determine weather it ca

OscarXie的专栏 1533

【高级强化学习:算法、优化与泛化】第13章 扩散模型策略优化(Diffusion Policy Optimization)

\quad$ 计算均值 $\mu_t = \frac{1}{\sqrt{\alpha_t}} \left( x_t - \frac{1 - \alpha_t}{\sqrt{1 - \bar{\alpha}_t}} \tilde{\epsilon} \right)$.$\quad$ 组合得分估计: $\tilde{\epsilon} = \epsilon_{\text{unc}} + w \cdot (\epsilon_{\text{con}} - \epsilon_{\text{unc}})$.

u012133341的博客 339

HUNER翻译软件测试面试题,45个LoadRunner 面试问题(附答案)英文

What is load testing?- Load testing is to test that if the application works fine with the loads that result from large number of simultaneous users, transactions and to determine weather it can handl...

weixin_35971306的博客 326

文档:亲测可用解决vnc灰屏,黑屏,鼠标大黑叉情况

主要解决vnc-viewer灰屏,黑屏,鼠标大黑叉等现象,详细见文档。

android 蓝牙通讯demo

android 下蓝牙通讯demo,实现了发现蓝牙,使能蓝牙,通过蓝牙发送消息。

转:45 个 LoadRunner 面试问题(附答案)_纯英文,太有逼格了

What is load testing? - Load testing is to test that if the application works fine with the loads that result from large number of simultaneous users, transactions and to determine weather it can han...

weixin_30607659的博客 294

假人有限元模型

假人模型,车辆碰撞假人有限元模型,适合做乘员安全的研究者利用

软件测试面试题,精选100 道,内附答案

作为一位过来人也是希望大家少走一些弯路,如果你不想再体验一次学习时找不到资料,没人解答问题,坚持几天便放弃的感受的话,在这里我给大家分享一些自动化测试的学习资源,希望能给你前进的路上带来帮助。

qq_73332379的博客 1692

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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

2023金三银四常见Loadrunner面试题总结,附带答案_loadrunner 2023

有需要的可以点击文末小卡片网上学习资料一大堆,但如果学到的知识不成体系,遇到问题时只是浅尝辄止,不再深入研究,那么很难做到真正的技术提升。需要这份系统化的资料的朋友,可以戳这里获取一个人可以走的很快,但一群人才能走的更远!不论你是正从事IT行业的老鸟或是对IT行业感兴趣的新人,都欢迎加入我们的的圈子(技术交流、学习资源、职场吐槽、大厂内推、面试辅导),让我们一起学习成长!的更远!

2401_84563405的博客 1569
上一篇: 聚集索引和非聚集索引的区别
下一篇: 垃圾收集算法
O2AtomLife
博客等级 码龄19年 2粉丝 82原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值