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我也要
虽然说很多浏览器提供了这个工具，但是我感觉还是没有自己的用着方便 ，因为好些人还是不会合作的，
那么今天我写一个简单的，而且获取是最全的工具给大家使用，
本工具和其它类与工具一样会不断更新，希望大家关注，
下载工具
http://www.sufeinet.com/thread-1967-1-1.html
本工具的源码暂时不提供，等到正式版时会把代码发布给大家。
我们一起来看看工具吧
只要你输入一个URL就会自动获取他的cookie
下面我就以本站为例子大家可以看看效果
.net 获取浏览器Cookie(包括HttpOnly)实例分享
介绍了.net 获取浏览器Cookie(包括HttpOnly)实例，有需要的朋友可以参考一下
c#中获取cookie
string cookieName = "BJ_UserName"; string CookieValue = ""; if (HttpContext.Current.Request.Cookies[cookieName] != null) {
SBM与超效率SBM模型实战：从原始面板数据到MATLAB高效实现
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获取当前网站的所有Cookie
得到本网站写的所有cookie 请参考 HttpCookie c; string subKeyName; string subKeyValue; for(int i = 0; i < Request.Cookies.Count;i++) { c = Request.Cookies[i]; if (c.HasKeys) { for...
arduino uno spi驱动12864
arduino uno spi驱动12864
Windows下MMdetection3D保姆级安装指南（含CUDA和PyTorch版本避坑）
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C#cookie操作
//写入 protected void Button1_Click(object sender, EventArgs e) { HttpCookie cookie=new HttpCookie("MyCook");//初使化并设置Cookie的名称 DateTime dt=DateTime.Now; TimeSpan ts = new TimeSpan(0
【YOLO 系列】基于YOLOv8的工业零件表面缺陷智能检测系统【python源码+Pyqt5界面+数据集+训练代码】
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c#cookie读取写入操作《转》
转：https://www.cnblogs.com/yuanzijian-ruiec/p/9709848.html public static void SetCookie(string cname, string value, int effective) { HttpCookie cookie = new HttpCookie(cname); ...
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c#Cookie的使用
创建 HttpCookie cookie = new HttpCookie("name"); cookie.Value = HttpUtility.UrlDecode("猪刚烈", Encoding.GetEncoding("UTF-8")); //HttpUtility 这个是用来编码 解码的 cookie.Expires = Da
基于 Vivado HLS 的 AC97 音频系统设计
介绍一种基于 FPGA 的 AC97 音频系统设计。系统的核心部件采用 Xilinx Atlys 的板载 Spartan6 XCSLX45 芯片，使用的工具为 Xilinx 的高层 次 综 合 技 术 Vivado HLS。首先使用 C /C + + 代码编写的算法描述系统的 FIＲ 滤波器; 然后按 照 Vivado HLS 编译工具的规范，将程序代码转换为 ＲTL 模型，快速、直接地生成对 应左右声道的 FIＲ IP 核和综合结果; 再结合互连的 MicroBlaze 处理器 IP 核，处理 一段立体声
C#Cookie处理源代码
#region Cookie /// /// 添加Cookie 贾世义 /// /// 名称 /// 键 /// 值 /// 保存天数 public static void CookieWrite(string name, string key, st
YOLOv11【第二章：核心模块进阶篇·第28节】DynamicHead动态检测头设计！
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VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
C#cookie操作类
/*原贴地址：http://www.cnblogs.com/prolifes/articles/1235546.html*/using System;using System.Data;using System.Configuration;using System.Web;using System.Web.Security;using System.Web.UI;using Syste
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
c#cookie读取写入操作
public static void SetCookie(string cname, string value, int effective) { HttpCookie cookie = new HttpCookie(cname); cookie.Value = value; cookie...
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")
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
[转]C#cookie操作类
摘自：http://kb.cnblogs.com/a/1235546/
C#Cookie操作类，删除Cookie，给Cookie赋值
usingSystem;usingSystem.Data;usingSystem.Configuration;usingSystem.Web;usingSystem.Web.Security;usingSystem.Web.UI;usingSystem.Web.UI.WebControls;usingSystem.Web.UI.WebControls.WebParts;using...
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