相关推荐
C# 进程间 双内存共享 高帧率图像传输
演示了两个进程之间通过 双内存共享 的方式, 来实时处理图像,并以高帧率(40~60FPS)传输的基本示例。 进程间内存共享的优点:高性能、低延迟、资源效率高、简单可靠。 适用场景:实时图像处理、大数据传输、多进程协作、实时监控系统,等等。
mysql 存储过程 嵌套查询_mysql 存储过程和 之间的嵌套
DELIMITER $$USE `mydatabase`$$DROP PROCEDURE IF EXISTS `updateA`$$CREATE DEFINER=`root`@`localhost` PROCEDURE `updateA`()BEGINDECLARE id INT ;DECLARE STOP INT DEFAULT 0;DECLARE cur CURSOR FOR SELECT a...
SAP中贸易伙伴的应用原理简析
公司也称为贸易伙伴,是出具合并报表的组织单位,可以为客户和供应商分配公司(贸易伙伴),用于识别集团内关闻公司间的业务往来,在编制合并报表时进行往来账务的计算处理。 如下图某贸易伙伴公司(关联方名称G0059)在我方公司的SAP系统中有多重身份(即是供应商,又是客户)。那么在我方公司SAP系统中建立起公司(贸易伙伴),并关联到供应商和客户主数据中。之后,就可以通过G0059这个贸易伙伴名称来识别与该贸易伙伴间的往来业务。 相应的配置设定如下: 定义公司(贸易伙伴或关联方) 会计凭证中会
mysql 存储过程中双重循环嵌套的应用
话不多直接上代码,注释很详细,下面代码确认运行无误,自己去感悟吧朋友CREATE PROCEDURE SYC_FILE_DOC_INFO() BEGIN DECLARE A_PK_FILEDOC char(24); DECLARE A_PK_CORP char(6); DECLARE A_VCODE varchar(30) D...
Linux运维人,该醒醒了,2019年要变天了
外界都说运维只是处于被动低效率手工救火的状态,企业对其重视程度不高,提起运维很多人能联想到的字眼就有“苦逼”、“辛苦”、“加班”、“背锅”。在大数据、人工智能、容器技术的强力加持下,运维这个传统的技术工种已经从“机械劳动”这样的刻板印象中蜕变出来,成为了任何一家技术公司所必须依赖和大力投入的核心技术能力。 现在,运维人员的发展前景也开始让很多程序员眼羡,作为Linux运维人员的你:这一年,你的L...
Blender软件导出地形到Gazebo中
Blender软件导出地形到Gazebo中,并配置纹理、刚度、高度。
mysql 存储过程 嵌套调用_MySQL存储过程例子(包含事务,输出参数,嵌套调用)
MySQL存储过程例子(包含事务,输出参数,嵌套调用)MySQL存储过程例子,包含事务,输出参数,嵌套调用,学习mysql存储过程的朋友可以参考下。drop procedure if exists pro_rep_shadow_rs;delimiter |------------------------------------ rep_shadow_rs-- 用来处理信息的增加,更新和删除-- 每...
Windows系统中 Xmanager 6 企业版下载安装激活教程
Xmanager 6 企业版安装激活教程 1.Xmanager 6 下载网址:https://yunpan.360.cn/surl_yrJrWq3GmjN 2. 双击 Xmanager-6.0.0169r 安装 3. 点击运行按钮 4. 点击下一步 5. 选择我接受许可证协议中的条款(A), 点击下一步。 6. 输入用户名:byg,公司名称:microsoft,序...
MySQL存储过程例子,包含事务,输出参数,嵌套调用,游标,循环等等
drop procedure if exists pro_rep_shadow_rs; delimiter | ---------------------------------- -- rep_shadow_rs -- 用来处理信息的增加,更新和删除 -- 每次只更新上次以来没有做过的数据 -- 根据不同的标志位 -- 需要一个输出的参数, -- 如果返回为0,则调用失败,事务回滚 -- 如果返
一种白平衡增益计算中白点权重计算简易实现方式
一种白平衡白点距离权重简易实现方式
OpenClaw人人养虾:发现与传输
客户端需要先"发现"Gateway 的地址和端口,才能建立连接。OpenClaw 支持多种发现(Discovery)和传输(Transport)机制。
mysql 存储过程 嵌套调用_MySQL存储过程例子,包含事务,参数,嵌套调用,游标,循环等...
drop procedure if exists pro_rep_shadow_rs;delimiter |------------------------------------ rep_shadow_rs-- 用来处理信息的增加,更新和删除-- 每次只更新上次以来没有做过的数据-- 根据不同的标志位-- 需要一个输出的参数,-- 如果返回为0,则调用失败,事务回滚-- 如果返回为1,调用成功,...
JavaFX 概述
要从 JavaFX 中充分受益,了解 JavaFX 的设计方式以及对 JavaFX 包含的功能有一个很好的概述是很有用的。本文的目的是为您提供 JavaFX 概述。本文将首先介绍一般的 JavaFX 设计,然后介绍 JavaFX 中的各种特性。如果您熟悉 Flash/Flex,您会发现 JavaFX 在某种程度上受到了 Flash/Flex 的启发。JavaFX 中也有一些相同的想法。一般来说,JavaFX 应用程序包含一个或多个对应于窗口的阶段。每个阶段都有一个场景。
(转)MySQL存储过程例子,包含事务,参数,嵌套调用,游标,循环等
MySQL存储过程例子,包含事务,参数,嵌套调用,游标,循环等,阅读MySQL存储过程例子,包含事务,参数,嵌套调用,游标,循环等,view plaincopy to clipboardprint?drop procedure if exists pro_rep_shadow_rs; delimiter | ------------------------------- view ...
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
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")
1997



被折叠的 条评论
为什么被折叠?



