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DROP PROCEDURE IF EXISTS mycursor;
CREATE PROCEDURE mycursor()
BEGIN
DECLARE v_done INT DEFAULT 0;
DECLARE v_a INT(6);
DECLARE v_b VARCHAR(20);
DECLARE cur_bak CURSOR FOR SELECT id, name FROM info;
DECLARE CONTINUE HANDLER FOR SQLSTATE '02000' SET v_done = 1;
OPEN cur_bak;
REPEAT
FETCH cur_bak INTO v_a, v_b;
IF NOT v_done THEN
INSERT INTO infobak(id, name) VALUES (v_a, v_b);
END IF;
UNTIL v_done
END REPEAT;
CLOSE cur_bak;
END;
CALL mycursor;
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rc522作为使用非常多的读写卡芯片，使用SPI芯片读写应用已经非常广了。但有些时候我们只有串口可以使用，或者我们觉得串口使用更加方便，那么就必须要编写串口的驱动程序。不过很难找到可以直接使用的串口程序。所以在此将我这边写好的串口驱动写好如下： 直接上代码 下面是RC522.C的驱动文件。支持读写卡号8位数据。rc522用的是stm32 的串口3，调试显示是串口1； #include "rc522.h" #include "delay.h" /////////////////////////////////
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游标（CURSOR）是用来存储查询结果集的数据类型 , 在存储过程和函数中可以使用游标对结果集进行循环的处理。游标的使用包括游标的声明、OPEN、FETCH 和 CLOSE，其语法分别如下。
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游标可以被视为结果集的指针，它允许用户逐行（或一小部分）地访问和操作大型结果集。
STM32 超声波模块输入捕获测距（标准库）
通道２设置为下降沿触发，触发瞬间会将CNT的记录到CCR２中，由于上升沿触发时已经将CNT置零所以此时的CNT的值就是高电平的持续时间。通道１设置为上升沿触发，触发的瞬间会将CNT的值记录到CCR１里面，同时设置通道１触发带有从模式，并把从模式设置为置零CNT。超声波传感器模块上面通常有两个超声波元器件，一个用于发射，一个用于接收。电路板上有四个引脚：VCC、GND、Trig（触发）、Echo（回应）。输入捕获原理可以看看这位大佬，感觉写的很清楚，工作电压与电流：5V，15mA。感测角度：不小于15度。
【AI大模型】核心基石：Transformer架构入门通俗完整解读
文章摘要： Transformer架构是AI大模型（如GPT、Llama等）的核心基石，彻底颠覆了传统RNN/LSTM的逐字计算模式，通过自注意力机制实现全局并行运算和长文本语义理解。本文用通俗语言拆解Transformer的核心模块：位置编码保留语序、多头注意力捕捉上下文关联、掩码机制确保生成逻辑、残差连接稳定训练，并搭配极简PyTorch代码演示Decoder架构的逐字生成过程。Transformer的并行性、可堆叠性和全局注意力优势，使其成为千亿参数大模型的基础。理解Transformer是掌握大模型
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利用存储过程实现sql代码中的逻辑判断与循环遍历
《多模态融合改进》目录一览 | 专栏介绍 ：全网 第一份 完整的多模态改进教程，提供《多模态模型改进完整项目包》-开箱即用
在大家订阅专栏后，便可获得多模态模型改进完整项目包-开箱即用，方便简单
mysql游标事例,mysql 存储过程 游标的例子
mysql 存储过程 游标的例子create procedure getInfor2()BEGINdeclare _abc varchar(200);declare stopFlag int;DECLARE cursor_name CURSORFORselect zhuanyuan from server;DECLARE CONTINUE HANDLER FOR NOT FOUND set sto...
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
mysql多个游标存储过程例子
功能：从personinfo 和 employee表中得到各自主键成为personConnect表的主键插入 CREATE PROCEDURE insertPersonConnet() begin declare personConnetId varchar(20); declare emplyeeConnetId varchar(20); declare flag i...
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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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create procedure getInfor2()BEGIN declare _abc varchar(200);declare stopFlag int; DECLARE cursor_name CURSOR FOR select zhuanyuan from server;DECLARE CONTINUE HANDLER FOR NOT FOUND set stopFlag=1; OPE
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