一个菜鸟前端的成长日记之 我的button跳转页面为何不生效

自制JS某课脚本 根据实际需求自制网页讯网刷题脚本,绕过人脸,弹窗,答题,实现自动化运行 阅读详情

从大连来到深圳,充满了期待和梦想,希望能进一个高大上的大公司,想体验大都市的豪华编码环境。but,现实给了我重重一击,来深圳找了好久工作,总算找到一家着急用人公司。于是,我打算在这家公司好好干,慢慢修行。

所谓路漫漫其修远兮,吾将上下而求索。来到深圳让我知道,任何的新技术不是我们追求的最终目标,面试官不会只关注我会多少新的技术,而是关注我对基础知识的了解。所以,我觉得在新的一个篇章里,放下对新技术的追求,潜心修炼内功。

<script>
    function login() {
    	window.location.href="inside.html";
    }
</script>
	
<form action="">
	<p>帐号:<input type="text"></p>
	<p>密码:<input type="text"></p>
	<button onclick="login()">登录</button>
</form>

前几天随便写登陆页,点击button发现页面并不跳转,这是什么原因呢。

如何通过常规测井曲线识别砂岩、泥岩、碳酸盐岩和煤层 常规测井曲线通过不同岩性的物理性质差异识别砂岩、泥岩、碳酸盐岩和煤层。砂岩表现为低自然伽马(GR)、中等密度(RHOB)和声波时差(DT);泥岩以高GR、高中子(NPHI)和高DT为特征;碳酸盐岩显示低GR、高RHOB和低DT;煤层则具有极低RHOB、极高NPHI和DT的独特组合。综合GR与三孔隙度曲线(RHOB、NPHI、DT)可有效区分岩性,其中密度测井对煤层识别最可靠。电阻率(RT)曲线辅助判断流体性质,但需结合其他参数综合分析。 阅读详情

相关推荐

手把手教你自制Type-C OTG转换器(附电路图及避坑指南)

本文详细指导如何自制Type-C OTG转换器,包括核心电路设计、元器件选型及焊接技巧,特别强调硬件检测电路的关键作用。通过实测数据和避坑指南,帮助开发者解决兼容性问题,实现高效数据传输。

weixin_27038245的博客 712

UE游戏引擎材质简单入门

什么是物体的材质,材质属性定义了物体的某些特性,从而决定了物体表面是如何与光线相作用的。UE中物理基础渲染(Physically Based Rendering OR PBR)几乎可以让我们模拟显示世界中的任何材质。那么如何在UE中制作出真实的PBR材质,基于我们提供的游戏引擎插件,如何将这些材质资源导出,并应用到 SuperMap iClient3D for Cesium/WebGL 平台上,提升模型的显示效果呢,下面小编将详细讲解一下。

m0_71055362的博客 2510

docker安装的 n8n提示Your n8n server is configured to use a secure cookie, however you are either visiting

将原来的docker n8n进行删除后即可通过一下方式重新安装,如安装完n8n,提示。

满地找牙的博客 3366

Lane、PHY和Preset Value Bit

每个Lane支持不同的传输速度和带宽,例如PCIe 3.0中,一个Lane的传输速度可以达到8 GT/s(Giga Transfers per second),也就是1 GB/s(GigaBytes per second)。Preset Value Bit(预置值位)是在PCIe 3.0的Lane上新引入的一种机制,用于优化信号传输质量。PCIe设备可以包含多个Lane,例如x1、x4、x8、x16等,表示该设备同时使用的Lane数目,比如x16表示该设备采用了16个Lane,具有更高的传输速度和带宽。

weixin_58083606的博客 1296

基于555定时器的呼吸灯电路

呼吸灯是指LED灯在芯片的控制下完成由亮到暗的逐渐变化,感觉就像人在呼吸,其广泛使用在手机上,起到一个通知提醒的作用。呼吸灯在许多灯光装饰上也有大量使用,如音响、汽车广告牌等,在生活中较为常见。...

qq1074237849的博客 9492

北航论文模板:解决XeLaTeX中Font shape'TU/SimSun(1)/b/n' undefined(font) using 'TU/SimSun(1)/m/n' instead

北航论文模板:解决XeLaTeX中Font shape ‘TU/SimSun(1)/b/n’ undefined(font) using ‘TU/SimSun(1)/m/n’ instead 问题 问题描述: 在使用北航论文模板时,产生了warning:Font shape 'TU/SimSun(1)/b/n' undefined(font) using 'TU/SimSun(1)/m/n' i...

qq_39560620的博客 1万+

ChatGPT充值,银行卡被拒绝,图文教程

ChatGPT充值,银行卡被拒绝

weixin_46713695的博客 2万+

【EMC专题】为什么芯片规格书ESD性能中看不到MM(机器放电模型)

器件级ESD包括三种类型,HBM人体放电模型、MM机械放电模型和CDM元件充电模型。但在芯片规格书中为什么只见到HBM模型与CDM模型,却没有看到MM模型的数据?

一点硬件 450

老版QQ原生表情gif动态

这是老版的QQ表情,不过全是动态的的,Windows照片查看器是不会动的,可以右击用浏览器打开查看,再说一遍是老版的,只是分享给宁愿用老版也要动态的人。

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

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

C# WinForm 基于DirectShow.dll编写的程序,可边预览边保存视频,音频和图片

基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择(默认选中第一个)2.可录制wmv和avi两种格式的视频并且都带有音频,avi可以录制的分辨率和帧数进行选择,wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照,也可边录制视频边拍照4.添加了视频和音频的压缩功能(酌情使用)这次二次开发发现C#对DirectShow的相关信息太少,这次做完也不敢藏私共享出来,还望大家指正

上一篇: 文章收藏
ccfalling
博客等级 码龄10年 1粉丝 1原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值