HTML颜色参考

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HTML颜色参考

在HTML里,颜色有两种表示方式。一种是用颜色名称表示,比如blue表示蓝色。另外一种是用16进制的数值表示RGB的颜色值。RGB是Red,Green,Blue意思,RGB每个原色的最小值是0,最大值是255,如果换算成16进制表示,就是(#00),(#FF)。比如白色的RGB(255,255,255),就用#FFFFFF表示;还有黑色的RGB(0,0,0),就用#000000表示。

注:在W3C制定的HTML 4.0标准中,只有16种颜色可以用颜色名称表示(aqua, black, blue, fuchsia, gray, green, lime, maroon, navy, olive, purple, red, silver, teal, white, and yellow),其它的颜色都要用16进制RGB颜色值表示。

当然,现在的浏览器支持更多的颜色名称。不过为保险起见,建议你还是采用16进制RGB颜色值来表示颜色。并且在值前加上#这个符号。

为了方便大家找到合适的颜色,我们在这里列了一些颜色说明,以供大家参考,不妨作为一种速查工具。

Web安全颜色

以前,很多电脑显示器最多支持256色,因此出现了216种Web安全颜色,以保证网页的颜色能够正确显示。

那为什么不是256种Web安全颜色呢?因为Microsoft和Mac操作系统有40种不同的系统保留颜色。

以下就是216种Web安全颜色:

#000000 #000033 #000066 #000099 #0000CC #0000FF
#003300 #003333 #003366 #003399 #0033CC #0033FF
#006600 #006633 #006666 #006699 #0066CC #0066FF
#009900 #009933 #009966 #009999 #0099CC #0099FF
#00CC00 #00CC33 #00CC66 #00CC99 #00CCCC #00CCFF
#00FF00 #00FF33 #00FF66 #00FF99 #00FFCC #00FFFF
#330000 #330033 #330066 #330099 #3300CC #3300FF
#333300 #333333 #333366 #333399 #3333CC #3333FF
#336600 #336633 #336666 #336699 #3366CC #3366FF
#339900 #339933 #339966 #339999 #3399CC #3399FF
#33CC00 #33CC33 #33CC66 #33CC99 #33CCCC #33CCFF
#33FF00 #33FF33 #33FF66 #33FF99 #33FFCC #33FFFF
#660000 #660033 #660066 #660099 #6600CC #6600FF
#663300 #663333 #663366 #663399 #6633CC #6633FF
#666600 #666633 #666666 #666699 #6666CC #6666FF
#669900 #669933 #669966 #669999 #6699CC #6699FF
#66CC00 #66CC33 #66CC66 #66CC99 #66CCCC #66CCFF
#66FF00 #66FF33 #66FF66 #66FF99 #66FFCC #66FFFF
#990000 #990033 #990066 #990099 #9900CC #9900FF
#993300 #993333 #993366 #993399 #9933CC #9933FF
#996600 #996633 #996666 #996699 #9966CC #9966FF
#999900 #999933 #999966 #999999 #9999CC #9999FF
#99CC00 #99CC33 #99CC66 #99CC99 #99CCCC #99CCFF
#99FF00 #99FF33 #99FF66 #99FF99 #99FFCC #99FFFF
#CC0000 #CC0033 #CC0066 #CC0099 #CC00CC #CC00FF
#CC3300 #CC3333 #CC3366 #CC3399 #CC33CC #CC33FF
#CC6600 #CC6633 #CC6666 #CC6699 #CC66CC #CC66FF
#CC9900 #CC9933 #CC9966 #CC9999 #CC99CC #CC99FF
#CCCC00 #CCCC33 #CCCC66 #CCCC99 #CCCCCC #CCCCFF
#CCFF00 #CCFF33 #CCFF66 #CCFF99 #CCFFCC #CCFFFF
#FF0000 #FF0033 #FF0066 #FF0099 #FF00CC #FF00FF
#FF3300 #FF3333 #FF3366 #FF3399 #FF33CC #FF33FF
#FF6600 #FF6633 #FF6666 #FF6699 #FF66CC #FF66FF
#FF9900 #FF9933 #FF9966 #FF9999 #FF99CC #FF99FF
#FFCC00 #FFCC33 #FFCC66 #FFCC99 #FFCCCC #FFCCFF
#FFFF00 #FFFF33 #FFFF66 #FFFF99 #FFFFCC #FFFFFF

微软支持的颜色名称

下面是微软IE4.0以上都支持的颜色名称:

aliceblue
(#F0F8FF)
antiquewhite
(#FAEBD7)
aqua
(#00FFFF)
aquamarine
(#7FFFD4)
azure
(#F0FFFF)
beige
(#F5F5DC)
bisque
(#FFE4C4)
black
(#000000)
blanchedalmond
(#FFEBCD)
blue
(#0000FF)
blueviolet
(#8A2BE2)
brown
(#A52A2A)
burlywood
(#DEB887)
cadetblue
(#5F9EA0)
chartreuse
(#7FFF00)
chocolate
(#D2691E)
coral
(#FF7F50)
cornflowerblue
(#6495ED)
cornsilk
(#FFF8DC)
crimson
(#DC143C)
cyan
(#00FFFF)
darkblue
(#00008B)
darkcyan
(#008B8B)
darkgoldenrod
(#B8860B)
darkgray
(#A9A9A9)
darkgreen
(#006400)
darkkhaki
(#BDB76B)
darkmagenta
(#8B008B)
darkolivegreen
(#556B2F)
darkorange
(#FF8C00)
darkorchid
(#9932CC)
darkred
(#8B0000)
darksalmon
(#E9967A)
darkseagreen
(#8FBC8B)
darkslateblue
(#483D8B)
darkslategray
(#2F4F4F)
darkturquoise
(#00CED1)
darkviolet
(#9400D3)
deeppink
(#FF1493)
deepskyblue
(#00BFFF)
dimgray
(#696969)
dodgerblue
(#1E90FF)
firebrick
(#B22222)
floralwhite
(#FFFAF0)
forestgreen
(#228B22)
fuchsia
(#FF00FF)
gainsboro
(#DCDCDC)
ghostwhite
(#F8F8FF)
gold
(#FFD700)
goldenrod
(#DAA520)
gray
(#808080)
green
(#008000)
greenyellow
(#ADFF2F)
honeydew
(#F0FFF0)
hotpink
(#FF69B4)
indianred
(#CD5C5C)
indigo
(#4B0082)
ivory
(#FFFFF0)
khaki
(#F0E68C)
lavender
(#E6E6FA)
lavenderblush
(#FFF0F5)
lawngreen
(#7CFC00)
lemonchiffon
(#FFFACD)
lightblue
(#ADD8E6)
lightcoral
(#F08080)
lightcyan
(#E0FFFF)
lightgoldenrodyellow
(#FAFAD2)
lightgreen
(#90EE90)
lightgrey
(#D3D3D3)
lightpink
(#FFB6C1)
lightsalmon
(#FFA07A)
lightseagreen
(#20B2AA)
lightskyblue
(#87CEFA)
lightslategray
(#778899)
lightsteelblue
(#B0C4DE)
lightyellow
(#FFFFE0)
lime
(#00FF00)
limegreen
(#32CD32)
linen
(#FAF0E6)
magenta
(#FF00FF)
maroon
(#800000)
mediumaquamarine
(#66CDAA)
mediumblue
(#0000CD)
mediumorchid
(#BA55D3)
mediumpurple
(#9370DB)
mediumseagreen
(#3CB371)
mediumslateblue
(#7B68EE)
mediumspringgreen
(#00FA9A)
mediumturquoise
(#48D1CC)
mediumvioletred
(#C71585)
midnightblue
(#191970)
mintcream
(#F5FFFA)
mistyrose
(#FFE4E1)
moccasin
(#FFE4B5)
navajowhite
(#FFDEAD)
navy
(#000080)
oldlace
(#FDF5E6)
olive
(#808000)
olivedrab
(#6B8E23)
orange
(#FFA500)
orangered
(#FF4500)
orchid
(#DA70D6)
palegoldenrod
(#EEE8AA)
palegreen
(#98FB98)
paleturquoise
(#AFEEEE)
palevioletred
(#DB7093)
papayawhip
(#FFEFD5)
peachpuff
(#FFDAB9)
peru
(#CD853F)
pink
(#FFC0CB)
plum
(#DDA0DD)
powderblue
(#B0E0E6)
purple
(#800080)
red
(#FF0000)
rosybrown
(#BC8F8F)
royalblue
(#4169E1)
saddlebrown
(#8B4513)
salmon
(#FA8072)
sandybrown
(#F4A460)
seagreen
(#2E8B57)
seashell
(#FFF5EE)
sienna
(#A0522D)
silver
(#C0C0C0)
skyblue
(#87CEEB)
slateblue
(#6A5ACD)
slategray
(#708090)
snow
(#FFFAFA)
springgreen
(#00FF7F)
steelblue
(#4682B4)
tan
(#D2B48C)
teal
(#008080)
thistle
(#D8BFD8)
tomato
(#FF6347)
turquoise
(#40E0D0)
violet
(#EE82EE)
wheat
(#F5DEB3)
white
(#FFFFFF)
whitesmoke
(#F5F5F5)
yellow
(#FFFF00)
yellowgreen
(#9ACD32)

系规备考论文:论IT服务规划设计 阅读详情

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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")

在html里ff3d3d是什么颜色,HTML颜色参考

在HTML里,颜色有两种表示方式。一种是用颜色名称表示,比如blue表示蓝色。另外一种是用16进制的数值表示RGB的颜色值。RGB是 Red,Green,Blue意思,RGB每个原色的最小值是0,最大值是255,如果换算成16进制表示,就是(#00),(#FF)。比如白色的 RGB(255,255,255),就用#FFFFFF表示;还有黑色的RGB(0,0,0),就用#000000表示。注:在W3...

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Statistics and Machine Learning Toolbox

Statistics and Machine Learning Toolbox(带书签),MATLAB统计学习工具

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