QQ在线客服代码 飘浮在右侧的QQ客服js特效演示

分享40个JS特效在线客服,总有一款适合您 js 阅读详情

 转自:http://www.cnblogs.com/strivers/archive/2011/01/28/1947160.html

 

制作在线QQ的具体步骤

1、首先登录到http://is.qq.com/webpresence/code.shtml 网站
2、选择风格
3、填写相关数据
4、生成网页代码
5、复制代码到“写字板”,另存文件名为001.html
6、创建自建栏目,将写字板里的图案样式部分复制到“自建栏目”中

7、全部完成以后,将图标设置链接,地址为:http://wpa.qq.com/msgrd?v=1&uin=你的QQ号码&site=240zt
保存发布就OK了!

                       http://wpa.qq.com/msgrd?V=1&Uin= **        

                       tencent://message/?uin=***&Site=im.qq.com&Menu=yes

 

 

QQ在线代码生成:http://imis.qq.com/webpresence/code.shtml

http://club.china.alibaba.com/club/block/alitalk/alitalkfire.html

http://www.taobao.com/wangwang/tese/index.php

 

 

飘浮在右侧的QQ客服代码:

<html>

<head>
<meta http-equiv="Content-Type" content="text/html; charset=gb2312">
<title>阿里西西网页特效代码演示中心-QQ在线客服代码演示</title>

</head>
<body>
<DIV id=divStay style="POSITION: absolute">
<TABLE cellSpacing=0 cellPadding=0 width="100%" border=0>
<TBODY>
<TR>
    <TD vAlign=top width="7%"> </TD></TR></TBODY></TABLE></DIV>
<SCRIPT language=javascript>
function picsize(obj,MaxWidth){
img=new Image();
img.src=obj.src;
if (img.width>MaxWidth)
{
    return MaxWidth;
}
else
{
    return img.width;
}
}
function CloseQQ()
{
divStayTopleft.style.display="none";
return true;
}
var online= new Array();
</SCRIPT>
<div id="divStayTopleft" style="POSITION: absolute">
<table cellspacing="0" cellpadding="0" width="109" border="0">
<tr>
   <td colspan="3">
   <a onclick="CloseQQ()" href="javascript:;" shape="circle" coords="91,16,12">
   <img height="34" src="
http://js.alixixi.com/texiaotu/qq_top.gif" width="109" usemap="#Map" border="0"></a></td>
</tr>
<tr>
   <td width="6"><img height="100" src="
http://js.alixixi.com/texiaotu/qq_life.gif" width="6"></td>
   <td valign="top" width="96" background="sto/qq_bg.gif">
   <table cellspacing="0" cellpadding="0" width="90" align="center" border="0">
    <tr>
     <td height="30">
     <table cellspacing="0" cellpadding="0" width="90" border="0">
      <tr>
       <td>
       <img height="13" src="
http://js.alixixi.com/texiaotu/qq_ico1.gif" width="16"><span
       <span style="font-size: 9pt">web风服饰</span></span></td>
      </tr>
     </table>
     </td>
    </tr>
    <tr>
     <td>
     <table border="0" width="100%" cellpadding="2" id="table47">
      <tr>
       <td width="15" valign="top" height="23">
       <img border="0" src="
http://js.alixixi.com/texiaotu/qq_v01.gif" width="16" height="16"></td>
       <td valign="bottom"><script>document.write("<a target=blank href=tencent://message/?uin=409889622&Site=阿里西西&Menu=yes><img border=0 SRC=http://wpa.qq.com/pa?p=1:615050000:10 alt=[阿里西西]></a>");</script></td>
      </tr>
     </table>
     </td>
    </tr>
    <tr>
     <td></td>
    </tr>
    <tr>
     <td>
     <table border="0" width="100%" cellpadding="2" id="table47">
      <tr>
       <td width="15" valign="top" height="23">
       <img border="0" src="
http://js.alixixi.com/texiaotu/qq_n01.gif" width="16" height="16"></td>
       <td valign="bottom"><script>document.write("<a target=blank href=tencent://message/?uin=409889622&Site=网页特效&Menu=yes><img border=0 SRC=http://wpa.qq.com/pa?p=1:43486831:10 alt=[QQQQQ]></a>");</script></td>
      </tr>
     </table>
     </td>
    </tr>
    <tr>
     <td></td>
    </tr>
   </table>
   </td>
   <td width="7"><img height="100" src="
http://js.alixixi.com/texiaotu/qq_right.gif" width="7"></td>
</tr>
<tr>
   <td colspan="3">
   <img height="30" src="
http://js.alixixi.com/texiaotu/qq_bottom1.gif" width="109"></td>
</tr>
<tr>
   <td colspan="3">
   <img height="33" src="
http://js.alixixi.com/texiaotu/qq_logo.gif" width="109"></td>
</tr>
</table>
</div>
<script type="text/javascript">
function FloatTop()
{
var startX1 =document.body.offsetWidth-125 ,startY1 = 5;
var startX2 =0,startY2 = 95;
var ns = (navigator.appName.indexOf("Netscape") != -1);
var d = document;
function ml(id,startX,startY)
{
var el=d.getElementById?d.getElementById(id):d.all?d.all[id]:d.layers[id];
if(d.layers)el.style=el;
el.sP=function(x,y){this.style.left=x;this.style.top=y;};
el.x = startX;
el.y = startY;
return el;
}
window.stayTopLeft=function()
{
var pY = ns ? pageYOffset : document.body.scrollTop;
ftlObj.y += (pY + startY1 - ftlObj.y)/8;
ftlObj1.y += (pY + startY2 - ftlObj1.y)/8;
ftlObj.sP(document.body.scrollLeft+document.body.offsetWidth-125, ftlObj.y);
ftlObj1.sP(ftlObj1.x, ftlObj1.y);
setTimeout("stayTopLeft()", 30);
}
// ftlObj = ml("divStay",document.body.scrollLeft+document.body.offsetWidth-125,0);
// ftlObj1 = ml("divStayTopLeft",0,30);
ftlObj = ml("divStay",(document.body.scrollLeft+document.body.offsetWidth)/2+379,0);
ftlObj1 = ml("divStayTopLeft",(document.body.scrollLeft+document.body.offsetWidth)/2+379,30);
stayTopLeft();
}
FloatTop();
</script>
<!--End: QQ在线客服 -->

<p> </p>

</body>

</html>

js响应式网站右侧悬浮在线客服插件 js响应式网站右侧悬浮在线客服插件,本款在线客服代码可自适应PC电脑端跟手机移动端。 立即下载

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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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