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Js optimization works for IE6.0 (detailed)

= doc. getElementById ("but2 ");Var inputs = doc. getElementsByTagName ("input ");}}5. Avoid Double interpretation: Do not call functions or methods repeatedly. 1. String concatenation String concatenation is often encountered in our development, so I put it in the first place. We are often used to directly concatenate strings using the + = method, in fact, this splicing method is very inefficient. We can use a clever method to concatenate strings, t

Javascript checks whether the form data on a page has changed

This article is transferred from:Http://info.codepub.com/2008/09/info-22582.html The original article is as follows: Usage: When data on a page is modified, some operations need to be performed.Add initfileds () to the page body loading event (onload) to record the initial data of the page.Call the checkmodification () method to determine whether the page data is changed.If the returned value is true, it indicates that the value has been changed.If the returned value is false, it is not chang

AJAX Junior chat room code

;Line-height: 19px;}# Loadifo {Position: absolute;Top: 100px;Z-index: 1;Right: 10px;Line-height: 21px;}. Header {Height: 60px;Background-color: #000;Text-align: center;Color: # FFF;Font-weight: bold;Line-height: 60px;Font-family: Tahoma;Font-size: 12pt;Float: left;Width: 100%;Margin-bottom: 20px;Filter: Alpha (Opacity = 50 );Opacity: 0.5;}/* Effect */. Btn {Border: 1px solid # AAA;Position: absolute;Margin-top: 2px;}. Inputs {Font-size: 9pt;Background

JS optimization for IE6.0 function (detailed finishing) _javascript skills

follows: var doc = document; for (var i = 0; i var but1 = Doc.getelementbyid ("but1"); var but2 = Doc.getelementbyid ("But2"); var inputs = Doc.getelementsbytagname ("input"); } } Five, avoid double interpretation: do not call functions or methods repeatedly 1, String stitching The concatenation of strings is often encountered in our development, so I put it in the first place, we are often accustomed directly to

automatically identify if all controls on the page have been changed

In the page development of new or modified, sometimes to all the controls on the page after the assignment, click Save, this page does not close, then the problem, if all the values on the page does not change, then if you continue to save the operation, it is a bit inappropriate, then you need to determine whether all the controls on the page have been changed, The following methods are as follows:1, JS as follows:var inputsdata;var textareasdata;var selectsdata;$ (function () {Inputload ();});

--convlstm principle and TensorFlow realization of spatial deep learning

section, and then we are going to implement a convlstm. But before we do that, let's take a look at the code design for the common Rnncell in TensorFlow, TensorFlow Rnncell Basicrnncell,grucell and Lstmcell, which are inherited from Rnncell , and all need to implement a common method called call (), which is called to indicate what relationship the input, state, and output are in each step of the loop. As far as Basicrnncell is concerned, its call method simply accepts input and state, outputs

Full select/cancel all select control (checkbox) of the DataGrid)

DataGrid Control: JavaScript Functions: VaR checkflag = true;Function chooseall (){// If (! Document. All ("checkall"). Checked) // select allIf (checkflag) // select all{VaR inputs = Document. All. Tags ("input ");For (VAR I = 0; I {If (inputs [I]. type = "checkbox" inputs [I]. ID! = "Checkall "){Inputs [I]. Checke

Test generation based on software testing

1. IntroductionDesigning test inputs and corresponding expected outputs is one of the most basic technical activities of any testing organization. Both the test input data and the corresponding expected output are written to the test case. A collection of test cases is a test set. Currently, a number of guidelines, techniques, and support tools exist to generate test cases. The next step is to introduce a lot of guidance and techniques based on test g

Talking about the tensorflow1.0 layer (pooling) and the fully connected layer (dense)

This article mainly introduces the tensorflow1.0 pool layer (pooling) and the full connection layer (dense), now share to everyone, but also to make a reference. Come and see it together. The pooling layer is defined in tensorflow/python/layers/pooling.py. There is a maximum pooling and pooling of mean values. 1, Tf.layers.max_pooling2d Max_pooling2d ( inputs, pool_size, strides, padding= ' valid ', data_format= ' Channels_last ', Name=none)

[Leng Feng] Full select/cancel all select control of DataGrid (CheckBox)

Author: Leng Feng Source: CSDN DataGrid Control: JavaScript Functions: Var checkFlag = true;Function ChooseAll (){// If (! Document. all ("CheckAll"). Checked) // select allIf (checkFlag) // select all{Var inputs = document. all. tags ("INPUT ");For (var I = 0; I {If (inputs [I]. type = "checkbox" inputs [I]. id! = "CheckAll "){

Select/deselect all control of DataGrid (CheckBox)

datagrid| Control DataGrid control: JavaScript functions: var checkflag = true; Function Chooseall () { //if (!document.all ("Checkall"). Checked)//Select all if (checkflag)//Select all { var inputs = Document.all.tags ("INPUT" ); for (var i=0 i { if (Inputs[i].type = "checkbox" inputs[i].id!= "Checkall") {

How to optimize your JS code

, which is obviously time-consuming, and let's look at the following example: function Func1 () {var start = new Date (). GetTime ();for (var i = 0; i var but1 = document.getElementById ("but1");var but2 = document.getElementById ("But2");var inputs = document.getElementsByTagName ("input");var divs = document.getelementsbytagname ("div");var but1 = document.getElementById ("but1");var but2 = document.getElementById ("But2");var

Vindicate on Bitcoin-use Golang to place vows on Bitcoin blockchain

/v1/btc/test3/addrs/mt4p3rZpJE5fXEqvGzNBk9HxYXcWKpPJSd/full A total of two, the first 0.65 second 1.3 Total the balance of this address is 1.95 {"Address": "Mt4p3rzpje5fxeqvgznbk9hxyxcwkppjsd", "total_received": 195000000, "total_sent": 0, "balance": 195000000, "Unconfirmed_balance": 0, "final_balance": 195000000, "N_tx": 2, "Unconfirmed_n_tx": 0, "Final_n_tx": 2, "TXs": [ The second pen {"Block_hash": "00000000000004149feebc41cfeb5a66df052f989aec60faec711caee4f93b3c", "Block_height": 125

Mixer structural Analysis [Uavcan for example]_php tutorial

Mixer structural Analysis [Uavcan for example] The mixer instruction is a system app command, located under the Firmware/src/systemcmds/mixer directory, where the function is to load the contents of the mix file into a specific device, and then resolve these definitions by mixergroups in the specific device. This example is taking Uvacan as an example, after the system runs, the name of the device is:/dev/uavcan/esc. There are mixergroup instances in the definition of Uavcan, and an output insta

Mixer structure analysis [uavcan as an example] _ PHP Tutorial

Mixer structure analysis [uavcan]. Mixer structure analysis [uavcan as an example] mixer commands are system app commands located in the Firmwaresrcsystemcmdsmixer directory, its function is to load valid content in the mix file to the specific device mixer structure analysis [uavcan as an example] Mixer commands are system app commands located in the Firmware/src/systemcmds/mixer Directory. the function is to load valid content in the mix file to a specific device, then, the specific device's M

Deep Learning Notes (iv): Cyclic neural network concept, structure and code annotation _ Neural network

= # size of hidden layer of neurons seq_length = # number of S Teps to unroll the RNN for learning_rate = 1e-1 # model Parameters WxH = NP.RANDOM.RANDN (hidden_size, vocab_size) *0.01 # Input to hidden whh = Np.random.randn (hidden_size, hidden_size) *0.01 # hidden to hidden Why = Np.random.randn (Vocab_size, hidden_size) *0.01 # hidden to Output BH = Np.zeros ((hidden_size, 1)) # hidden bias by = Np.zeros (VOcab_size, 1)) # output bias def lossfun (inputs

How to optimize php code structure

After each method call, you must verify the return value to determine whether to return or continue execution. How can I adjust the following code? {Code...} after each method call, you must verify the return value to determine whether to return or continue execution. How can we adjust the following code? /*** Execute business logic ** @ param $ action execution Method * @ param $ allParams * @ return array | bool */public static function parseMore ($ action, $ allParams) {// user login request

Detailed instructions for parsing Python select Epoll poll

socketimport sysimport queue# Create a TCP/IP Process Server = Socket.socket (socket.af_inet, Socket. SOCK_STREAM) server.setblocking (0) #连接地址和端口server_address = (' localhost ', 10000) print >>sys.stderr, ' starting up ' On%s prot%s '% Server_addressserver.bind (server_address) #最大允许链接数server. Listen (5) inputs = [Server]outputs = []message_ Queues = {}while inputs:print >>sys.stderr, ' \nwaiting for the next event ' readable,writable,exceptional =

How to optimize PHP code structure

After each call to the method, you need to validate the return value to decide whether to return or continue execution, and how does the following code adjust better? /** * 执行业务逻辑 * @param $action 执行方法 * @param $allParams * @return array|bool */public static function parseMore($action, $allParams){ // 用户登录请求数据解析 $inputs = self::userLoginParse($allParams); //验证 if(self::$_errorNo != StatusCode::STATUS_TRUE) return array(); if($

Python implementation of deep neural network framework

(self, num_neuron_inputs, num_neuron_outputs): Self.num_neuron_inputs = Num_neuron_inputs self.num_neuron_outputs = num_neuron_outputs self.inputs = Np.zeros (Batch_size, Num_neur on_inputs)) Self.outputs = Np.zeros ((batch_size, num_neuron_outputs)) Self.weights = Np.zeros ((num_neuron_in Puts, num_neuron_outputs)) Self.bias = Np.zeros (num_neuron_outputs) self.weights_previous_direction = Np.zer Os ((Num_neuron_inputs, num_neuron_outputs)) self.bias_previous_direction = Np.zeros (num_neuron_o

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