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
98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Python中，有许多强大的HTTP请求方法库可供使用，它们为开发人员提供了与Web服务器进行通信的便捷方式。无论您需要发送HTTP请求、接收响应、处理Cookie或者进行身份验证，这些库都能满足您的需求。在本文中，我们将介绍几个常用的HTTP请求方法库，并提供相应的源代码示例。
Requests是Python中最受欢迎的HTTP请求库之一。它提供了简单而优雅的API，使得发送HTTP请求变得非常容易。使用Requests，您可以发送GET、POST、PUT、DELETE等各种类型的请求，并处理响应数据。
下面是一个使用Requests库发送GET请求的示例：
import requests
response = requests.get("http://api.example.com/data")
print(response.ESP32-C5 Wi-Fi CSI感知技术原理与工程实践
Wi-Fi信道状态信息（CSI）是OFDM系统中反映多径信道特性的复数频域响应，源于导频子载波的信道估计过程，本质属于物理层原始观测数据。其高维度（如56子载波）、高时间分辨率（百赫兹级采样）及零硬件增益的特性，使其成为无感人体感知的理想信号源。相比UWB或毫米波雷达，CSI方案依托现有Wi-Fi基础设施，兼具成本优势与部署灵活性，广泛应用于呼吸监测、跌倒检测、手势识别等边缘智能场景。乐鑫ESP32-C5通过5.8GHz低噪接收、亚纳秒级多天线同步与固件级CSI增强，显著提升微动感知鲁棒性，结合AGC锁定、
头歌 JAVA 桥接模式实验
这样，通过客户端代码配置不同的 FileConvertor 子类和 DataHandler 子类（如 OracleHandler 或 SQLServerHandle），即可实现从不同数据库读取数据并转换成不同格式的功能，而无需修改已有代码。
从CCD数据上报看wxHOOK的封号风险与规避
本文深入分析了微信CCD数据上报机制对wxHOOK的检测逻辑与封号风险，揭示了调试器路径暴露、非白名单DLL加载和内存校验失败等关键风险点。通过实战案例和规避方案，帮助开发者理解如何安全使用wxHOOK技术，避免封号风险，包括内存镜像欺骗、内核级隐蔽和硬件辅助方案等高级技巧。
JetLinks社区版VS企业版：手把手教你用PostgreSQL+Redis搭建物联网中台（Win7实测）
本文详细对比了JetLinks社区版与企业版的核心差异，并提供了在Windows 7系统上，使用PostgreSQL和Redis搭建物联网中台的完整部署教程。文章深入解析了从环境准备、后端服务构建到前端部署的每一步，为开发者在资源有限环境下构建高性价比物联网平台提供了清晰的实战指南。
Qt绘图实战：用QGraphicsItem实现可拖拽箭头连接（附完整代码）
本文详细介绍了如何使用Qt的QGraphicsItem框架实现一个功能完备的可拖拽箭头连接组件。通过核心代码示例，讲解了如何创建自定义图形项、实现智能吸附、碰撞检测以及实时拖拽编辑路径，为构建流程图、拓扑图等专业图形编辑器提供了完整的解决方案。
高通Camx架构调试不求人：手把手教你开UMD/KMD Log与Dump图像（附离线Log合成脚本）
本文详细介绍了高通Camx架构的调试方法，包括UMD/KMD Log的精准配置与图像Dump技术，帮助工程师快速定位移动影像系统中的问题。通过实战案例和自动化脚本，提升调试效率，适用于图像色彩异常、帧率波动等常见问题的诊断与解决。
Qt 侧边菜单栏+堆叠窗口stackedWidget
效果如下：环境：Qt 6.6.2 MSVC2019 64bit。
libVLC 视频抓图
libVLC 中通过libvlc_video_take_snapshot核心接口来截图。以下是VLC播放器中使用的截图界面，我们仿照这个做一个界面。首先，你需要初始化 libVLC 实例和媒体播放器对象。1. 初始化 libVLC 和媒体播放器。菜单添加action，并响应槽函数。生成目录下snap.png。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
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，
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
win10 跳过U8环境检测的IE WebControls.rar
win10 安装u8的时候 提示IE WebControls 不通过，这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后，重新检测通过。
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
346
扫一扫
