ssh(Struts2+spring2+hibernate) java.lang.OutOfMemoryError: PermGen space及其解决方法

nnDetection实战:手把手教你用Python在自家电脑上跑通第一个肺结节检测模型 本文详细介绍了如何使用nnDetection框架在普通电脑上实现肺结节检测模型的搭建与运行。从环境配置、数据预处理到模型训练与推理,手把手教你完成医学图像检测的全流程,特别适合初学者入门医学AI领域。 阅读详情

修改tomcat bin目录下的 catalina.bat

 

@echo off
rem Licensed to the Apache Software Foundation (ASF) under one or more
rem contributor license agreements.  See the NOTICE file distributed with
rem this work for additional information regarding copyright ownership.
rem The ASF licenses this file to You under the Apache License, Version 2.0
rem (the "License"); you may not use this file except in compliance with
rem the License.  You may obtain a copy of the License at
rem
rem     http://www.apache.org/licenses/LICENSE-2.0
rem
rem Unless required by applicable law or agreed to in writing, software
rem distributed under the License is distributed on an "AS IS" BASIS,
rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
rem See the License for the specific language governing permissions and
rem limitations under the License.

if "%OS%" == "Windows_NT" setlocal
rem ---------------------------------------------------------------------------
rem Start/Stop Script for the CATALINA Server
rem
rem Environment Variable Prequisites
rem
rem   CATALINA_HOME   May point at your Catalina "build" directory.
rem
rem   CATALINA_BASE   (Optional) Base directory for resolving dynamic portions
rem                   of a Catalina installation.  If not present, resolves to
rem                   the same directory that CATALINA_HOME points to.
rem
rem   CATALINA_OPTS   (Optional) Java runtime options used when the "start",
rem                   or "run" command is executed.
rem
rem   CATALINA_TMPDIR (Optional) Directory path location of temporary directory
rem                   the JVM should use (java.io.tmpdir).  Defaults to
rem                   %CATALINA_BASE%/temp.
rem
rem   JAVA_HOME       Must point at your Java Development Kit installation.
rem                   Required to run the with the "debug" argument.
rem
rem   JRE_HOME        Must point at your Java Runtime installation.
rem                   Defaults to JAVA_HOME if empty.
rem
rem   JAVA_OPTS       (Optional) Java runtime options used when the "start",
rem                   "stop", or "run" command is executed.
rem
rem   JSSE_HOME       (Optional) May point at your Java Secure Sockets Extension
rem                   (JSSE) installation, whose JAR files will be added to the
rem                   system class path used to start Tomcat.
rem
rem   JPDA_TRANSPORT  (Optional) JPDA transport used when the "jpda start"
rem                   command is executed. The default is "dt_shmem".
rem
rem   JPDA_ADDRESS    (Optional) Java runtime options used when the "jpda start"
rem                   command is executed. The default is "jdbconn".
rem
rem   JPDA_SUSPEND    (Optional) Java runtime options used when the "jpda start"
rem                   command is executed. Specifies whether JVM should suspend
rem                   execution immediately after startup. Default is "n".
rem
rem   JPDA_OPTS       (Optional) Java runtime options used when the "jpda start"
rem                   command is executed. If used, JPDA_TRANSPORT, JPDA_ADDRESS,
rem                   and JPDA_SUSPEND are ignored. Thus, all required jpda
rem                   options MUST be specified. The default is:
rem
rem                   -Xdebug -Xrunjdwp:transport=%JPDA_TRANSPORT%,
rem                       address=%JPDA_ADDRESS%,server=y,suspend=%JPDA_SUSPEND%
rem
rem $Id: catalina.bat 615987 2008-01-28 18:48:10Z rjung $
rem ---------------------------------------------------------------------------

rem Guess CATALINA_HOME if not defined


rem 冷漠大神自定义配置 love
set JAVA_OPTS=-Xms1024m -Xmx1024m -Xverify:none -XX:PermSize=128M -XX:MaxPermSize=256m -XX:+UseAdaptiveSizePolicy -XX:+UseParallelGC -XX:+UseParallelOldGC -XX:NewSize=200m -Xmn200m

rem 调整PermGen区域的内存大小


set CURRENT_DIR=%cd%
if not "%CATALINA_HOME%" == "" goto gotHome
set CATALINA_HOME=%CURRENT_DIR%
if exist "%CATALINA_HOME%/bin/catalina.bat" goto okHome
cd ..
set CATALINA_HOME=%cd%
cd %CURRENT_DIR%
:gotHome
if exist "%CATALINA_HOME%/bin/catalina.bat" goto okHome
echo The CATALINA_HOME environment variable is not defined correctly
echo This environment variable is needed to run this program
goto end
:okHome

rem Get standard environment variables
if "%CATALINA_BASE%" == "" goto gotSetenvHome
if exist "%CATALINA_BASE%/bin/setenv.bat" call "%CATALINA_BASE%/bin/setenv.bat"
goto gotSetenvBase
:gotSetenvHome
if exist "%CATALINA_HOME%/bin/setenv.bat" call "%CATALINA_HOME%/bin/setenv.bat"
:gotSetenvBase

rem Get standard Java environment variables
if exist "%CATALINA_HOME%/bin/setclasspath.bat" goto okSetclasspath
echo Cannot find %CATALINA_HOME%/bin/setclasspath.bat
echo This file is needed to run this program
goto end
:okSetclasspath
set BASEDIR=%CATALINA_HOME%
call "%CATALINA_HOME%/bin/setclasspath.bat" %1
if errorlevel 1 goto end

rem Add on extra jar files to CLASSPATH
if "%JSSE_HOME%" == "" goto noJsse
set CLASSPATH=%CLASSPATH%;%JSSE_HOME%/lib/jcert.jar;%JSSE_HOME%/lib/jnet.jar;%JSSE_HOME%/lib/jsse.jar
:noJsse
set CLASSPATH=%CLASSPATH%;%CATALINA_HOME%/bin/bootstrap.jar

if not "%CATALINA_BASE%" == "" goto gotBase
set CATALINA_BASE=%CATALINA_HOME%
:gotBase

if not "%CATALINA_TMPDIR%" == "" goto gotTmpdir
set CATALINA_TMPDIR=%CATALINA_BASE%/temp
:gotTmpdir

if not exist "%CATALINA_BASE%/conf/logging.properties" goto noJuli
set JAVA_OPTS=%JAVA_OPTS% -Djava.util.logging.manager=org.apache.juli.ClassLoaderLogManager -Djava.util.logging.config.file="%CATALINA_BASE%/conf/logging.properties"
:noJuli

rem ----- Execute The Requested Command ---------------------------------------

echo Using CATALINA_BASE:   %CATALINA_BASE%
echo Using CATALINA_HOME:   %CATALINA_HOME%
echo Using CATALINA_TMPDIR: %CATALINA_TMPDIR%
if ""%1"" == ""debug"" goto use_jdk
echo Using JRE_HOME:        %JRE_HOME%
goto java_dir_displayed
:use_jdk
echo Using JAVA_HOME:       %JAVA_HOME%
:java_dir_displayed

set _EXECJAVA=%_RUNJAVA%
set MAINCLASS=org.apache.catalina.startup.Bootstrap
set ACTION=start
set SECURITY_POLICY_FILE=
set DEBUG_OPTS=
set JPDA=

if not ""%1"" == ""jpda"" goto noJpda
set JPDA=jpda
if not "%JPDA_TRANSPORT%" == "" goto gotJpdaTransport
set JPDA_TRANSPORT=dt_shmem
:gotJpdaTransport
if not "%JPDA_ADDRESS%" == "" goto gotJpdaAddress
set JPDA_ADDRESS=jdbconn
:gotJpdaAddress
if not "%JPDA_SUSPEND%" == "" goto gotJpdaSuspend
set JPDA_SUSPEND=n
:gotJpdaSuspend
if not "%JPDA_OPTS%" == "" goto gotJpdaOpts
set JPDA_OPTS=-Xdebug -Xrunjdwp:transport=%JPDA_TRANSPORT%,address=%JPDA_ADDRESS%,server=y,suspend=%JPDA_SUSPEND%
:gotJpdaOpts
shift
:noJpda

if ""%1"" == ""debug"" goto doDebug
if ""%1"" == ""run"" goto doRun
if ""%1"" == ""start"" goto doStart
if ""%1"" == ""stop"" goto doStop
if ""%1"" == ""version"" goto doVersion

echo Usage:  catalina ( commands ... )
echo commands:
echo   debug             Start Catalina in a debugger
echo   debug -security   Debug Catalina with a security manager
echo   jpda start        Start Catalina under JPDA debugger
echo   run               Start Catalina in the current window
echo   run -security     Start in the current window with security manager
echo   start             Start Catalina in a separate window
echo   start -security   Start in a separate window with security manager
echo   stop              Stop Catalina
echo   version           What version of tomcat are you running?
goto end

:doDebug
shift
set _EXECJAVA=%_RUNJDB%
set DEBUG_OPTS=-sourcepath "%CATALINA_HOME%/../../java"
if not ""%1"" == ""-security"" goto execCmd
shift
echo Using Security Manager
set SECURITY_POLICY_FILE=%CATALINA_BASE%/conf/catalina.policy
goto execCmd

:doRun
shift
if not ""%1"" == ""-security"" goto execCmd
shift
echo Using Security Manager
set SECURITY_POLICY_FILE=%CATALINA_BASE%/conf/catalina.policy
goto execCmd

:doStart
shift
if not "%OS%" == "Windows_NT" goto noTitle
set _EXECJAVA=start "Tomcat" %_RUNJAVA%
goto gotTitle
:noTitle
set _EXECJAVA=start %_RUNJAVA%
:gotTitle
if not ""%1"" == ""-security"" goto execCmd
shift
echo Using Security Manager
set SECURITY_POLICY_FILE=%CATALINA_BASE%/conf/catalina.policy
goto execCmd

:doStop
shift
set ACTION=stop
set CATALINA_OPTS=
goto execCmd

:doVersion
%_EXECJAVA% -classpath "%CATALINA_HOME%/lib/catalina.jar" org.apache.catalina.util.ServerInfo
goto end


:execCmd
rem Get remaining unshifted command line arguments and save them in the
set CMD_LINE_ARGS=
:setArgs
if ""%1""=="""" goto doneSetArgs
set CMD_LINE_ARGS=%CMD_LINE_ARGS% %1
shift
goto setArgs
:doneSetArgs

rem Execute Java with the applicable properties
if not "%JPDA%" == "" goto doJpda
if not "%SECURITY_POLICY_FILE%" == "" goto doSecurity
%_EXECJAVA% %JAVA_OPTS% %CATALINA_OPTS% %DEBUG_OPTS% -Djava.endorsed.dirs="%JAVA_ENDORSED_DIRS%" -classpath "%CLASSPATH%" -Dcatalina.base="%CATALINA_BASE%" -Dcatalina.home="%CATALINA_HOME%" -Djava.io.tmpdir="%CATALINA_TMPDIR%" %MAINCLASS% %CMD_LINE_ARGS% %ACTION%
goto end
:doSecurity
%_EXECJAVA% %JAVA_OPTS% %CATALINA_OPTS% %DEBUG_OPTS% -Djava.endorsed.dirs="%JAVA_ENDORSED_DIRS%" -classpath "%CLASSPATH%" -Djava.security.manager -Djava.security.policy=="%SECURITY_POLICY_FILE%" -Dcatalina.base="%CATALINA_BASE%" -Dcatalina.home="%CATALINA_HOME%" -Djava.io.tmpdir="%CATALINA_TMPDIR%" %MAINCLASS% %CMD_LINE_ARGS% %ACTION%
goto end
:doJpda
if not "%SECURITY_POLICY_FILE%" == "" goto doSecurityJpda
%_EXECJAVA% %JAVA_OPTS% %CATALINA_OPTS% %JPDA_OPTS% %DEBUG_OPTS% -Djava.endorsed.dirs="%JAVA_ENDORSED_DIRS%" -classpath "%CLASSPATH%" -Dcatalina.base="%CATALINA_BASE%" -Dcatalina.home="%CATALINA_HOME%" -Djava.io.tmpdir="%CATALINA_TMPDIR%" %MAINCLASS% %CMD_LINE_ARGS% %ACTION%
goto end
:doSecurityJpda
%_EXECJAVA% %JAVA_OPTS% %CATALINA_OPTS% %JPDA_OPTS% %DEBUG_OPTS% -Djava.endorsed.dirs="%JAVA_ENDORSED_DIRS%" -classpath "%CLASSPATH%" -Djava.security.manager -Djava.security.policy=="%SECURITY_POLICY_FILE%" -Dcatalina.base="%CATALINA_BASE%" -Dcatalina.home="%CATALINA_HOME%" -Djava.io.tmpdir="%CATALINA_TMPDIR%" %MAINCLASS% %CMD_LINE_ARGS% %ACTION%
goto end

:end

会员管理小程序实战开发04-用户登录及注册 上一节我们介绍了用户信息数据源的创建及网格布局的使用,本节的话我们讲解用户的登录及注册功能任何开发。 在我们前述章节里介绍的,不论是管理员表还是用户表,都涉及到一个问题就是openid,那这个openid如何获取呢?如果我直接把代码贴给你,就缺失了一个思考的过程。既然这个是微信提出的概念,那么必然就会有官方文档作为对应。程序开发的基本功就是要会看官方文档,很多初学的开发同学可能会对官方文档颇有诟病,觉得不够基础。怎么说呢,当你进入到开发岗之后,自学能力是必要的,还是要试着可以看懂官方文档才好。 登录接口解读 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; % 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]); 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