璀璨星空特效展示,前后端源码附效果截图

安卓动画壁纸实战:制作一个星空动态壁纸(带随机流星动画) 通过上面的代码可以看到,其实安卓的动态壁纸并没有想象中的那么困难,无非就是自定义绘制这一套,如果熟悉自定义绘制的话,写起来还是非常容易的。不过我们这里只展示了使用 Canvas 的绘制,事实上,由 SurfaceHolder 我们可以有更多的"骚操作",例如调用第三方成熟的动画库直接刷新 Surface 等,感兴趣的可以去搜一搜。 阅读详情

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效果展示图:
在这里插入图片描述
html文件

<!DOCTYPE html>
<html lang="en" >
<head>
<meta charset="UTF-8">
<title>IT'S FULL OF STARS</title>

<link rel="stylesheet" href="css/style.css">

</head>
<body>

  <canvas id="canvas" width="1400" height="600"></canvas>

  <script  src="js/script.js"></script>

</body>
</html>

css文件:
body { background-color: #000; margin: 0; overflow: hidden; background-repeat: no-repeat; }
JS文件:

var canvas = document.getElementById("canvas");

const mobile = ( navigator.userAgent.match(/Android/i)
      || navigator.userAgent.match(/webOS/i)
      || navigator.userAgent.match(/iPhone/i)
      || navigator.userAgent.match(/BlackBerry/i)
      || navigator.userAgent.match(/Windows Phone/i)
);

canvas.width = window.innerWidth;
canvas.height = window.innerHeight;

// Initialize the GL context
var gl = canvas.getContext('webgl');
if(!gl){
   
   
  console.error("Unable to initialize WebGL.");
}

var layers_ = 10;

if(mobile){
   
   
	layers_ = 6;
}

//Time step
var dt = 0.015;
//Time
var time = 0.0;

//************** Shader sources **************

var vertexSource = `

a
Python模拟星空(完整代码) Python模拟3D星空动画的完整代码。 阅读详情

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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")

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