墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
192平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
在进行AIX系统的Oracle备份过程中，有时候会遇到3D32B80D错误。这个错误通常与MySQL相关，但在这里我们将讨论如何解决这个问题，并提供一些示例代码帮助您进行备份操作。
首先，确保您的数据库连接是正确的。检查您的数据库连接字符串、用户名和密码，并确保它们与目标数据库实例匹配。这是一个常见的错误导致备份失败。
示例代码：
import mysql.connector
# 数据库连接配置
config = {
'user': 'your_username',
'password': 'your_【信息科学与工程学】计算机科学与自动化——第十篇 芯片设计30 芯片中的数学1
所有涉及货币流动的岗位都受到严格的法律法规和行业准则约束，包括中国人民银行的各项货币政策工具管理规定、银行间市场交易规则、支付清算系统管理办法以及企业司库管理的相关指导意见。：在货币流动链条中，距离资金源头（央行投放）或关键流转节点越近的岗位，对资金的控制力、经手规模和影响力越大。处于资金流转的“管道”中，虽不直接决定资金量，但负责海量资金交易的安全、准确、高效清算，是货币流通的“基础设施”维护者。《关于规范金融机构资产管理业务的指导意见》（资管新规），要求信托业务打破刚性兑付，向净值化、标准化转型。
【创作赢红包】Gurobi学术版证书更新与Python环境配置全攻略
本文详细解析了Gurobi学术版证书过期后的完整更新流程与Python环境配置方法。从彻底清理旧版残留、手把手注册获取新许可证，到软件安装、证书激活，最后重点讲解如何将gurobipy接口正确配置到Python的site-packages目录，并提供了常见问题排查方案，帮助用户高效解决证书与配置难题。
Android HDMI-CEC协议解析：从机顶盒到电视的无缝遥控联动
本文深入解析Android HDMI-CEC协议，实现机顶盒与电视间的无缝遥控联动。通过剖析设备角色、消息格式及核心操作码，并结合Android HdmiControlManager API实战，详细讲解如何实现电源同步、按键穿透及音量控制，最终解决多设备遥控繁琐的痛点，提升用户体验。
基于远程服务器 Tip_Adapter源码从零开始复现
从零一步步实现源代码的运行
C#与西门子PLC通讯——新手快速入门
本文基于C# .Net Core和西门子博图TIA Portal V17搭建。由于手边没有西门子PLC实物，所以采用S7-PLCSIM Advanced V4.0作为模拟PLC，以实现0成本完成通讯测试实例。这篇入门笔记解决了新手第一次上手实践时遇到的坑，并且有意忽略了实际项目中会遇到的逻辑交互和更多的数据格式问题。
从时序迷宫到稳定通信：FSMC配置中的时间参数深度解析与实战避坑指南
本文深入解析STM32 FSMC配置中的时间参数，特别是AddressSetupTime和DataSetupTime的实战配置技巧。通过STM32F407连接SRAM的实际案例，提供从理论计算到CubeMX配置的全流程指南，帮助开发者避免常见时序陷阱，确保稳定通信。
YOLO小目标检测优化：9种实用方案，准确率直接提升15%
小目标检测是个系统工程，没有银弹，但有明确的优化路径。优先搞数据。Copy-Paste + 提升分辨率，这俩加起来成本最低、收益最大，能解决80%的小目标问题。别一上来就改模型结构。调参走在改结构前面。锚框聚类、损失加权、增强优化，这些都是零成本的，先做满了再说改模型的事。P2层是大杀器，但不要轻易用。加了P2层，TensorRT部署、后处理、速度都要跟着调，工程化工作量翻倍。数据和训练做到位还不够，再加也不迟。滑动窗口是兜底的。能从训练端解决的问题，就不要拖到推理端。
Mybatis面试题（基础篇）
（1）mybatis是支持定制化SQL、存储过程以及高级映射的优秀的持久层框架。（2）mybatis避免了几乎所有的JDBC代码（说明了mybatis底层用的就是JDBC，只不过是封装了）和手动设置参数以及获取结果集。（3）mybatis可以使用简单的XML或注解用于配置和原始映射，将接口和Java的pojo映射成数据库中的记录。
[Dify实战] 品牌舆情速览：多渠道抓取、聚类情绪、生成日报-带绑定资源
摘要： 品牌舆情监测面临多渠道信息分散、手工处理效率低的痛点。Dify舆情速览助手通过自动化流程（数据抓取→情绪聚类→日报生成）帮助快速识别热点与风险。方案核心包括： 结构化处理：按情绪（正/中/负）聚类，分级标注风险事件； 日报输出：含热点摘要、趋势分析及处置建议（动作+团队+时间窗口）； 质量控制：数据去噪、黑名单过滤，结合人工复核确保合规性。 落地需明确数据源分级（如权威媒体优先）、设置情绪阈值，并建立"发现-响应-复盘"闭环机制，提升舆情应对效率。 （字数：150）
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
416
扫一扫
