QA Official

ajax post Request Method

https://qaofficial.com/post/2019/04/24/68537-ajax-post-request-method.html 2019-04-24
<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <script> function f1(){ var username=document.getElementById('username').value; //对传递的特殊的特殊符号进行编码处理 这步必须放到请求字符串之前 username=encodeURIComponent(username); //Change user name information into " request string" var info="name="+username+"&age=23"; //1.创建ajax对象 if(typeof ActiveXObject!="undefined") {

js native and ajax get and post methods and jsonp native writing

https://qaofficial.com/post/2019/04/24/68560-js-native-and-ajax-get-and-post-methods-and-jsonp-native-writing.html 2019-04-24
login.onclick = function(){var xhr = new XMLHttpRequest();xhr.open("get","http://localhost/ajax2/test2.php?username="+username.value+"&pwd="+pwd2.value,true);xhr.send();xhr.onreadystatechange = function(){if (xhr.readyState == 4) {if (xhr.status>=200 && xhr.status<300) {alert(xhr.responseText);};};} } ajax method btn.onclick = function(){ajax( "GET", "http://localhost/ajax2/my02.php", {xingming:xingming.value,pwd:pwd.value}, function(data){console.log(data);},function(errCode){console.log(errCode);}) post Method Pass Parameters It differs from a get method: 01 safety type.Post is safer.The speed of get is 0.3 order of magnitude faster.Post is of a larger order of magnitude. Specific Implementation: var xhr = new XMLHttpRequest();

tp5 how to judge the request method, tp5 request method summary judgment ajax submission

https://qaofficial.com/post/2019/04/24/68568-tp5-how-to-judge-the-request-method-tp5-request-method-summary-judgment-ajax-submission.html 2019-04-24
说明 代码 是否为 GET 请求 if (Request::instance()->isGet()) 是否为 POST 请求 if (Request::instance()->isPost()) 是否为 PUT 请求 if (Request::instance()->isPut()) 是否为 DELETE 请求 if (Request::instance()->isDelete()) 是否为 Ajax 请求 if (Request::instance()->isAjax()) 是否为 Pjax 请求 if (Request::instance()->isPjax()) 是否为手机访问 if (Request::instance()->isMobile()) 是否为 HEAD 请求 if (Request::instance()->isHead()) 是

upper_bound () and low_bound functions

https://qaofficial.com/post/2019/04/24/68629-upper_bound-and-low_bound-functions.html 2019-04-24
prerequisite: a non-descending sequence!!!!!! lower_bound () function uses: Its parameters are: 1. Address of an array element (or array name to represent the first address of the array, to represent the address of the element to be compared at the beginning of the array, not necessarily the first address, but only the " first" address to be compared), 2. The address of an array element (corresponding to the address of

yii2 ajax post submission problem

https://qaofficial.com/post/2019/04/24/68556-yii2-ajax-post-submission-problem.html 2019-04-24
The first solution is to turn off Csrf 1, partially closed: publicfunctioninit () { $ this-> enablecsrfvalidation = false;} 2, global shutdown: in the configuration file (main-local.php or web.php), set to " enablecsrfvalidation" = > false,//true to turn on csrf validation  The second solution is to add hidden fields to the form form;Name="_csrf " is the default configuration for the framework <input name="_csrf" type="hidden" id="_csrf" value="<?= Yii::$app->request->csrfToken ?

Add BN Layer to deeplabV2

https://qaofficial.com/post/2019/04/24/68620-add-bn-layer-to-deeplabv2.html 2019-04-24
Deeplabv2 Project The caffe version in this project is older, and there are many different writing styles that need to be changed greatly.、 The new version of caffe is called A and the deeplabV2 version is called B. 1. Add. cpp,.cu and. hpp files 2.修改caffe.proto,添加

BN layer of caffe for deep learning

https://qaofficial.com/post/2019/04/24/68634-bn-layer-of-caffe-for-deep-learning.html 2019-04-24
1. Introduction If googlenet is called the google inception v1, its Batch Normalization (http://arxiv.org/pdf/1502.03167v3.pdf) article is about BN-inception v1. It is not the essential content modification of the network itself, but to normalize the output of the conv layer so that the update of the next layer can be faster and more accurate. 2. Network Analysis caffe officials split the BN layer into two layers for experiments.

Change hyperlink request method from get request to post request

https://qaofficial.com/post/2019/04/24/68542-change-hyperlink-request-method-from-get-request-to-post-request.html 2019-04-24
There are four RequestMaping requests in SpringMVC: get, post, put and delete. However, the browser itself can only accept requests in get and post. If the request is based on hyperlink, the hyperlink defaults to get.However, if the hyperlink request method can be changed from get method to post method, it will be very good. With post, there will be put and delete request methods. Here is a record of

Code of Several Sorting Algorithms

https://qaofficial.com/post/2019/04/24/68596-code-of-several-sorting-algorithms.html 2019-04-24
Select Sort void select(int[] a) { for(int i = 0; i < a.length;i ++) { for(int j = i; j < a.length; j ++) { int ai = a[i]; int aj = a[j]; if(a[i] > a[j]) { a[i] = aj a[j] = ai; } } } bubble sort void bubble(int[] a) { BOOL exchanged = YES; for (int i = 0; i < a.length - 1 && exchanged; i ++) { exchanged = NO; for (int j = 0; j < (a.

Deep Learning Common Network Structure

https://qaofficial.com/post/2019/04/24/68590-deep-learning-common-network-structure.html 2019-04-24
LeNet: Gradient-based Learning Applied to Document Recognition AlexNet: ImageNet Classification with Deep Convolution Neural Networks ZFNet: visualizing and understanding convolutional networks VGGNet: Very Deep Convolution Network for Large Scale Image Recognition NiN: Network in Network GoogLeNet: Involved FurtherInception-v3: Rethinking the Initial Architecture of Computer Vision ResNet: Depth Residual Learning for Image RecognitionStochastic_Depth: deep network with random depthWResNet: weighted residuals for very deep networksinception-resnet: INCEPTION-V4, INCEPTION-RESNET and the Influence of Remaining Connections on LearningFractalnet: ultra-deep neural network without residual error WRN: Wide Residual Network ResNeXt: aggregate residual transformation of deep neural networks DenseNet: Densely Connected Convolution Network PyramidNet: Deep Pyramid Residual Network DPN: Dual Path NetworkSqueezeNet:AlexNet Level Accuracy, 50-fold Reduction of Parameters, Model Size Less than 0.