node.js非常简单实现登录注册功能-学习小demo

node学习 express + mysql + ejs实现注册登录 之前写过一个node登录验证,现在回头看看真是丢脸,竟然把数据库的表读出来在内存中进行比对,真是误人子弟。 没写过后台接口真的是硬伤啊,所以还需要继续学习学习,大家一起努力吧! 阅读详情

const http = require("http");
const fs = require("fs");
const querystring = require("querystring");
const urlutil = require("url");
  var user = {};

http.createServer(function(req,res){
  //解析数据
  var str = "";
  var base= "./www"
  console.log(base);
  req.on("data",function(data){
    str += data;
  });
  req.on("end",function(){
    var obj = urlutil.parse(req.url,true);
    var url = obj.pathname;
    console.log(url);
    var get = obj.query;
    var post = querystring.parse(str);
    //判断请求的是静态文件还是接口
    if(url == "/user"){//这是请求的接口
      var act = get.act;
      if(act == "login"){//登录功能
          //判断用户是否存在
          console.log(get.name+"***"+get.pass);
          console.log(user[get.name]);
          console.log(user);
          if(user[get.name] == null){
            res.write('{"ok":false,"msg":"此用户不存在"}');
          }else{
            if(user[get.name] == get.pass)
node之实现登录注册分页小案例(node+vue+elementUI+axios+mysql) 第一步项目的基本配置 安装node 的express 脚手架 npm install -g express-generator 创建不带视图模板的项目 express demo --no-view // demo 是项目名称 --no-view 代表生成的项目不带视图模板 安装项目的依赖 1.首先 cd到创建的目录 npm run start 启动项目 2.npm install 安装 npm install node-dev -D //项目 热更新的方式 然后在 `pa 阅读详情

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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');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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