IntellIJ IDEA 关于Maven的配置

IDEA如何配置 Maven 及 Maven 安装过程(详细版) IDEA 如何配置Maven 阅读详情

IntelliJ IDEA下载地址:https://www.jetbrains.com/idea/download/#section=windows

IntelliJ IDEA 是Java开发利器,用社区版不爽,干催就用旗舰版,这个是收费的,需要licence。 

想必你现在已经不需要上面的连接了  ps: 斜眼

一.配置Maven环境

1.下载apache-maven文件,选择自己需要的版本,地址: http://mirror.bit.edu.cn/apache/maven/maven-3/3.5.0/binaries/apache-maven-3.5.0-bin.zip

2.解压1所下载文件,本人解压到:D:\server\maven\apache-maven-3.5.0

3.配置Maven环境变量

a. MAVEN_HOME : D:\server\maven\apache-maven-3.5.0

b.PATH : %MAVEN_HOME%\bin;

c. MAVEN_OPTS : -Xms128m -Xmx512m -Duser.language=zh -Dfile.encoding=UTF-8

4.在CMD中输入mvn -v,如出现下列信息,表示配置成功。

二.修改Maven仓库路径

1.个人根据自身情况而定,新建文件,如下:

       

2.配置D:\server\maven\中setting.xml文件,内容如下:

<?xml version="1.0" encoding="UTF-8"?>

<!--
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.  See the NOTICE file
distributed with this work for additional information
regarding copyright ownership.  The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License.  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied.  See the License for the
specific language governing permissions and limitations
under the License.
-->

<!--
 | This is the configuration file for Maven. It can be specified at two levels:
 |
 |  1. User Level. This settings.xml file provides configuration for a single user,
 |                 and is normally provided in ${user.home}/.m2/settings.xml.
 |
 |                 NOTE: This location can be overridden with the CLI option:
 |
 |                 -s /path/to/user/settings.xml
 |
 |  2. Global Level. This settings.xml file provides configuration for all Maven
 |                 users on a machine (assuming they're all using the same Maven
 |                 installation). It's normally provided in
 |                 ${maven.conf}/settings.xml.
 |
 |                 NOTE: This location can be overridden with the CLI option:
 |
 |                 -gs /path/to/global/settings.xml
 |
 | The sections in this sample file are intended to give you a running start at
 | getting the most out of your Maven installation. Where appropriate, the default
 | values (values used when the setting is not specified) are provided.
 |
 |-->
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 http://maven.apache.org/xsd/settings-1.0.0.xsd"> 
  <!--   <settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 http://maven.apache.org/xsd/settings-1.0.0.xsd">-->

  <!-- localRepository
   | The path to the local repository maven will use to store artifacts.
   |
   | Default: ${user.home}/.m2/repository  -->
  

  <!-- interactiveMode
   | This will determine whether maven prompts you when it needs input. If set to false,
   | maven will use a sensible default value, perhaps based on some other setting, for
   | the parameter in question.
   |
   | Default: true
  <interactiveMode>true</interactiveMode>
  -->

  <!-- offline
   | Determines whether maven should attempt to connect to the network when executing a build.
   | This will have an effect on artifact downloads, artifact deployment, and others.
   |
   | Default: false
  <offline>false</offline>
  -->

  <!-- pluginGroups
   | This is a list of additional group identifiers that will be searched when resolving plugins by their prefix, i.e.
   | when invoking a command line like "mvn prefix:goal". Maven will automatically add the group identifiers
   | "org.apache.maven.plugins" and "org.codehaus.mojo" if these are not already contained in the list.
   |-->
  <pluginGroups>
    <!-- pluginGroup
     | Specifies a further group identifier to use for plugin lookup.
    <pluginGroup>com.your.plugins</pluginGroup>
    -->
  </pluginGroups>

  <!-- proxies
   | This is a list of proxies which can be used on this machine to connect to the network.
   | Unless otherwise specified (by system property or command-line switch), the first proxy
   | specification in this list marked as active will be used.
   |-->
  <proxies>
    <!-- proxy
     | Specification for one proxy, to be used in connecting to the network.
     |
    <proxy>
      <id>optional</id>
      <active>true</active>
      <protocol>http</protocol>
      <username>proxyuser</username>
      <password>proxypass</password>
      <host>proxy.host.net</host>
      <port>80</port>
      <nonProxyHosts>local.net|some.host.com</nonProxyHosts>
    </proxy>
    -->
  </proxies>

  <!-- servers
   | This is a list of authentication profiles, keyed by the server-id used within the system.
   | Authentication profiles can be used whenever maven must make a connection to a remote server.
   |-->
  <servers>
    <!-- server
     | Specifies the authentication information to use when connecting to a particular server, identified by
     | a unique name within the system (referred to by the 'id' attribute below).
     |
     | NOTE: You should either specify username/password OR privateKey/passphrase, since these pairings are
     |       used together.
     |
    <server>
      <id>deploymentRepo</id>
      <username>repouser</username>
      <password>repopwd</password>
    </server>
    -->

    <!-- Another sample, using keys to authenticate.
    <server>
      <id>siteServer</id>
      <privateKey>/path/to/private/key</privateKey>
      <passphrase>optional; leave empty if not used.</passphrase>
    </server>
    -->
  </servers>
	<localRepository>E:/apache-maven-3.5.4/repository</localRepository>
<mirrors>
        <mirror>
            <id>alimaven</id>
            <mirrorOf>central</mirrorOf>
            <name>aliyun maven</name>
            <url>http://maven.aliyun.com/nexus/content/repositories/central/</url>
        </mirror>
        <mirror>
            <id>alimaven</id>
            <name>aliyun maven</name>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
            <mirrorOf>central</mirrorOf>
        </mirror>
        <mirror>
            <id>central</id>
            <name>Maven Repository Switchboard</name>
            <url>http://repo1.maven.org/maven2/</url>
            <mirrorOf>central</mirrorOf>
        </mirror>
        <mirror>
            <id>repo2</id>
            <mirrorOf>central</mirrorOf>
            <name>Human Readable Name for this Mirror.</name>
            <url>http://repo2.maven.org/maven2/</url>
        </mirror>
        <mirror>
            <id>ibiblio</id>
            <mirrorOf>central</mirrorOf>
            <name>Human Readable Name for this Mirror.</name>
            <url>http://mirrors.ibiblio.org/pub/mirrors/maven2/</url>
        </mirror>
        <mirror>
            <id>jboss-public-repository-group</id>
            <mirrorOf>central</mirrorOf>
            <name>JBoss Public Repository Group</name>
            <url>http://repository.jboss.org/nexus/content/groups/public</url>
        </mirror>
        <mirror>
            <id>google-maven-central</id>
            <name>Google Maven Central</name>
            <url>https://maven-central.storage.googleapis.com
            </url>
            <mirrorOf>central</mirrorOf>
        </mirror>
        <!-- 中央仓库在中国的镜像 -->
        <mirror>
            <id>maven.net.cn</id>
            <name>oneof the central mirrors in china</name>
            <url>http://maven.net.cn/content/groups/public/</url>
            <mirrorOf>central</mirrorOf>
        </mirror>
    </mirrors>
    
</settings>

三.IntellIJ IDEA 中配置Maven

Idea 自带了apache maven,默认使用的是内置maven,所以我们可以配置全局setting,来调整一下配置,比如远程仓库地址,本地编译环境变量等。

1.打开Settings,在输入框输入maven,如图

2.如果本地设置了MAVEN_OPTS 系统环境变量,这个步骤可以忽略。

3.如果配置了本地apache-maven setting.xml 中的软件源,这步骤可以忽略。

在Mac下的IntelliJ IDEA配置Maven 前提:在Tomcat/Java/Idea编译器环境变量都配好的情况下配置 一.在maven官网下载相应的安装包并解压 二.将文件命名为apache-maven并拷贝到/library下,请开启你的终端旅行(含泪提示:我当时拷贝到/usr/local下发现,无论怎么样都测试不成功,后来改成/library就ok了,具体原因我不清楚,排除了编辑文件出错、保存出错) 配置环境变量->vi... 阅读详情

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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; 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