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本文详述了如何在命令行中查看Java版本以及如何使用javac、java、javadoc等常用Java命令进行编译、运行、生成文档和调试。掌握这些基本操作对Java开发和管理至关重要。
如何查看Java版本及常用命令详解
Java是一种广泛使用的编程语言，具有跨平台性和可移植性的特点，因此在开发和运行Java应用程序时，了解Java版本以及常用命令是至关重要的。本文将介绍如何查看Java版本以及一些常用的Java命令，帮助您更好地使用Java编程语言。
一、查看Java版本
要查看Java版本，可以使用以下命令：
java -version
在命令行或终端中执行上述命令后，将显示Java的版本信息。例如：
java version "1.8.0_271"
Java(TM) SE Runtime Environment (build 1.8.0_271-b09)
Java HotSpotJava开发环境：检查JDK版本，使用java -version和JAVA_HOME环境变量设置
在本文中，我们学习了如何检查JDK版本，使用java -version命令来验证JDK的安装，并了解了如何正确设置JAVA_HOME环境变量。通过正确配置Java探索Java开发环境：了解JDK版本、java -version和JAVA_HOME环境变量设置的重要性。本文将详细介绍如何检查JDK版本、使用java -version命令验证JDK安装，并正确设置JAVA_HOME环境变量，以确保顺利开发和运行Java应用程序。如果输出与Java安装目录的路径相符，则表示JAVA_HOME环境变量已成功设置。
如何在Linux中检查Java版本
在Linux系统中，您可以使用java命令或javac命令来检查已安装的Java版本。这些方法都可以帮助您在Linux环境中轻松确定Java的版本，以满足您的需求。在Linux系统中，我们经常需要检查已安装的Java版本，无论是为了确保应用程序的兼容性，还是为了执行特定的Java代码。因此，您可以通过在终端中执行java命令来检查Java版本。您可以在输出中找到实际文件的路径，然后使用该路径来确定Java的版本。Java通常安装在系统的特定目录中，您可以找到并检查该目录以获取版本信息。
【多模态大模型】GLIP：零样本目标检测新范式与视觉语言理解
本文深入解析了多模态大模型GLIP在零样本目标检测和视觉语言理解领域的创新应用。GLIP通过统一目标检测与自然语言理解任务，实现了零样本学习能力，能够精准定位复杂文本描述对应的图像区域。文章详细介绍了GLIP的核心技术、数据策略及实战表现，并对比了与其他多模态模型的差异，为AI视觉理解提供了新范式。
Ubuntu22.04下3分钟搞定GitHub加速：SwitchHosts+小鱼工具保姆级配置指南
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采用MPU6050的手势遥控器
随着社会的发展与进步，电子产品日益更新，个人家庭迎来了越来越多的智能家电。然而，现在的智能电子设备产品的遥控器多数都是按键形的。操作繁琐不直观，学习成本大。并且对于有老人小孩的家庭，给他们的操作带来了巨大的障碍。手势遥控器是指利用简单的手势来指挥电子设备，获得便捷的操作体验。让家庭成员都可以享受科技发展带来的福利。MPU6050是一个运动传感器，内置陀螺仪，再由电子设备利用判断结果做出相应的响应即...
Win11下WSL2安装Ubuntu22.04避坑指南：从镜像源到C++环境配置
本文提供了一份详细的Win11下WSL2安装Ubuntu22.04的实战避坑指南。内容涵盖从WSL2核心安装、自定义安装路径，到更换国内镜像源以解决更新缓慢问题，并一站式配置完整的C++开发环境（包括GCC、CMake、vcpkg等），最后分享提升开发体验的进阶技巧，助你在Windows上打造高效的Linux开发工作站。
【win11 cuda12.0安装tensorflow-gpu】
win11 cuda 12.0 安装 tensorflow-gpu
4G LTE 与 5G 无线网络规划对比：从 BBU/RRU 到 CU/DU/AAU 的架构演进解析
本文深度解析4G LTE与5G无线网络架构的演进差异，从BBU/RRU分立式架构到CU/DU/AAU三级重构，揭示移动通信代际跃迁的技术本质。重点对比了网络规划中的覆盖能力、容量规划、传输网络等九大维度，为通信工程师提供实用的技术对照手册，助力5G网络高效部署。
【BlueDroid】【PBAP】蓝牙电话本协议之同步通讯录分析（超详细）
Header - Type = "x-bt/vcard-listing"：代表了该Request对应的Function为PullvCardListing；Profile：PBAP (4)，上层应用层协议为PBAP，OBEX协议的上层应用层协议除了PBAP，还有OPP、MAP协议；Response in Frame：该request frame-485对应的Response frame为489；在PBAP协议同步通讯录和通讯记录中，都是基于OBEX协议实现的，PBAP协议作为了应用层协议；
Autodesk Revit软件二次开发：Revit二次开发中的插件设计与实现
在 Revit 中，族是构成建筑模型的基本单元，可以是门窗、家具等。通过自定义族，可以创建符合特定需求的建筑构件。参数化设计允许族的尺寸、形状等属性根据参数变化而变化。使用 Revit API 创建自定义族涉及多个步骤，包括定义族类型、设置参数、创建几何形状等。// 创建族文档// 定义参数Parameter param = family.FamilyManager.AddParameter("Width", BuiltInParameterGroup.PG_LENGTH, "宽度");
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.
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")
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
锂电池充电器电路设计 里面有电路
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