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<div class="goNextBtn fireworks" @click="goNext">立即测试</div>
css
<style>
.fireworks {
-webkit-animation: free_download 1s linear alternate infinite;
animation: free_download 1s linear alternate infinite;
}
@-webkit-keyframes free_download {
0% {
-webkit-transform: scale(0.9);
}
100% {
-webkit-transform: scale(1);
}
}
@keyframes free_download {
0% {
transform: scale(0.9);
}
100% {
transform: scale(1);
}
}
</style>
超炫酷且实用的CSS3按钮动画
通过应用CSS3动画，我们可以为按钮增添一些炫酷的效果，提升用户体验并吸引他们的注意力。本文将介绍一些令人印象深刻的CSS3按钮动画效果，并提供相应的源代码供您参考和使用。通过应用这些CSS3按钮动画效果，您可以为您的网页增添一些令人印象深刻的交互效果。当用户将鼠标悬停在按钮上时，按钮会平滑地放大，释放鼠标时则恢复原始大小。这种效果通过使用CSS3的渐变背景色和过渡效果实现。当用户将鼠标悬停在按钮上时，按钮会沿顺时针方向平滑地旋转一定角度。这种效果通过CSS3的关键帧动画和过渡效果实现。
css图片放大缩小动画
css图片放大缩小动画
CSS3实现鼠标悬停背景放大移出缩小动态效果
伪类选择器以冒号（）开头，hover是其中之一。其基本语法如下：/* 悬停样式 */例如，为一个按钮添加悬停效果：.button {CSS3 还支持使用自定义缓动函数曲线。它接受四个参数，代表贝塞尔曲线的两个控制点。代码解析：表示一个自定义的缓动函数，类似于，但更柔和。开发者可以借助在线工具（如）可视化调整曲线，获取理想的动画效果。
css动画，放大缩小；元素开始隐藏，延迟后显示动画
2.右淡入，先隐藏，0.1秒后显示动画。1.动画放大缩小，循环。
CSS3实现图片动态渐变放大缩小的性感效果
鼠标悬停，图片动态放大缩小。IE 下效果不佳，建议在支持CSS3浏览器的浏览，例如FF等
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css按钮循环放大缩小动画动效
首先给需要加动效的盒子，加上类名fireworks，然后针对这个类名，写一个样式循环，使用transform来缩放对应的盒子，里面的内容会跟着一起适配。
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css动画放大缩小效果
html： <div class="xing_one my_xing"><img src="~@/static/images/maskscroll/maskscroll_xing.png" class="img100"></div> css： @keyframes xing { 0% { transform: scale(1); } 25% { transform: scale(1.8); }
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在 Windows 10 中通过 WSL2 安装 vLLM 部署本地大模型的方法和步骤
通过 WSL2 在 Windows 10 中部署 vLLM 的核心优势是兼顾 Windows 生态与 Linux 环境的 GPU 加速能力，适合需要在 Windows 下开发但依赖 vLLM 高性能推理的场景。相比原生 Windows 部署（Ollama 等工具），该方案性能更优（接近原生 Ubuntu），且支持更大模型和量化优化。若追求极致性能，建议直接使用 Ubuntu 系统；若必须依赖 Windows 工具链，WSL2 + vLLM 是当前最优选择。
前端（二）：CSS实现缩放动画效果
【代码】前端（二）：CSS实现缩放动画效果。
统信UOS系统盘扩容保姆级教程：用GParten-分区编辑器5分钟搞定
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css按钮动画 之放大缩小
css按钮动画 之放大缩小
【x265编码器】章节1——x265的lookahead模块分析
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css按钮放大缩小动画效果
.btn { animation: scaleDrew 2.5s ease-in-out infinite; } @keyframes scaleDrew { 0% { transform: scale(1); } 25% { transform: scale(1.05); } 50% { transform: scale(1); } 75% { transform: scale(1.05);
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
css动画之按钮放大缩小动态效果
/* 按钮动画效果 */ .按钮的class名{ -webkit-animation: free_download 1s linear alternate infinite; animation: free_download 1s linear alternate infinite; } @-webkit-keyframes free_download{ 0%{-webki...
SIMATIC S7-1200, S7-1500 PID 控制[手册]
SIMATIC S7-1200, S7-1500 PID 控制[手册]
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
css动画之按钮放大缩小动态效果——animation
<button class="btn">点我</button> .btn { animation: scaleDrew 2.5s ease-in-out infinite; } @keyframes scaleDrew { /* 定义关键帧、scaleDrew是需要绑定到选择器的关键帧名称 */ 0% { transform: scale(1); ...
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');```[](https://bitdeli.com/free "Bitdeli Badge")
html点击按钮放大缩小,一个CSS+jQuery实现的放大缩小动画效果
今天帮朋友写了一些代码,自己觉得写着写着，好几个版本以后，有点满意，于是就贴出来。都是定死了的。因为需求就只有4个元素。如果是要用CSS的class来处理，那就需要用到CSS3动画了。功能 : 在上方的按钮上滑动，可以切换各个page,点击下方的各个page，也可以切换收缩还是展开状态。初始效果预览复制代码 代码如下: CSS+jquery动画效果 body{z-index: 0;width: ...
js 实现圆的放大和缩小
<!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>amplification</title> <style type="text/css"> .circle { border-radius: 50%; back...
一些常用的css样式 超出显示省略号 放大缩小
1.动画之按钮放大缩小效果 .按钮的class名{ -webkit-animation: free_download 1s linear alternate infinite; animation: free_download 1s linear alternate infinite; } @-webkit-keyframes free_download{ 0%{-webkit-transform:scale(0.9);} 100%{-webkit-transform:sca
界面加载完成开始动画：扭一扭按钮放大缩小的动画
按钮开始放大缩小动画。
按钮呼吸动态实现
按钮大小大小的动作，像呼吸一样！ <button class="prize_push_btn" >立刻充值</button> <style> .prize_push_btn{ width: 320upx; height: 80upx; line-height: 80upx; background: #FDCD45; text-align...
js+css实现简单的弹框动画
让下次可以继续指定放大的动画的样式类,来展示动画 *//* 用于标记box1是否被打开过, 如果之前被打开过的话, 再次点击打开无效 *//* 元素如果要显示, 必须通过添加该类的方式(约定) *//* 动画完成后, 执行指定的函数 *//* 动画完成后, 移除监听 *//* body占满整个页面 *//* 添加元素显示时的动画 *//* 定义元素放大的动画 *//* 添加让元素显示的类 *//* 定义元素放大的类 *//* 定义元素缩小的类 *//* 元素缩小的动画 *//* 标记打开过 */
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