freemarker语法完整版
Freemarker页面语法
A 概念
最常用的 3 个概念
sequence 序列，对应java 里的list 、数组等非键值对的集合
hash 键值对的集合
namespace 对一个ftl 文件的引用, 利用这个名字可以访问到该ftl 文件的资源
B 指令
if, else, elseif
语法
Java代码
...
...
...
...
...
...
...
...
...
...
用例
Freemarker代码
x is 1
x is 1
x is not 1
x is 1
x is 1
x is not 1
switch, case, default, break
语法
Freemarker代码
...
...
...
...
...
...
...
...
...
...
用例
字符串
Freemarker代码
This will be processed if it is small
This will be processed if it is medium
This will be processed if it is large
This will be processed if it is neither
This will be processed if it is small
This will be processed if it is medium
This will be processed if it is large
This will be processed if it is neither
数字
Freemarker代码
1
2
d
1
2
d
如果x=1 输出 1 2, x=2 输出 2, x=3 输出d
list, break
语法
Freemarker代码
...
...
...
...
关键字
item_index:是list当前值的下标
item_has_next:判断list是否还有值
用例
Freemarker代码
${x_index + 1}. ${x},
${x_index + 1}. ${x},
输出：
1.winter,
2.spring,
3.summer,
4.autumn
include
语法
Freemarker代码
或则
Java代码
options包含两个属性
encoding="GBK" 编码格式
parse=true 是否作为ftl语法解析,默认是true，false就是以文本方式引入.注意在ftl文件里布尔值都是直接赋值
的如parse=true,而不是parse="true"
用例
/common/copyright.ftl 包含内容
Ftl代码
Copyright 2001-2002 ${me}
All rights reserved.
Copyright 2001-2002 ${me}
All rights reserved.
模板文件
Java代码
Some test
Yeah
___________________________________________________________________________
Some test
Yeah
___________________________________________________________________________
输出结果:
Some test
Yeah.
Copyright 2001-2002 Juila Smith
All rights reserved.
Import
语法
Freemarker代码
类似于java里的import,它导入文件，然后就可以在当前文件里使用被导入文件里的宏组件
用例
假设mylib.ftl 里定义了宏copyright 那么我们在其他模板页面里可以这样使用
Freemarker代码
<#-- "my"在freemarker里被称作namespace -->
<#-- "my"在freemarker里被称作namespace -->
compress
语法
Freemarker代码
...
...
用来压缩空白空间和空白的行
escape, noescape
语法
Freemarker代码
...
...
...
...
...
...
用例
主要使用在相似的字符串变量输出，比如某一个模块的所有字符串输出都必须是html安全的，这个时候就可以使用
该表达式
Freemarker代码
First name: ${firstName}
Last name: ${lastName}
Maiden name: ${maidenName}
First name: ${firstName}
Last name: ${lastName}
Maiden name: ${maidenName}
相同表达式
Ftl代码
First name: ${firstName?html}
Last name: ${lastName }
Maiden name: ${maidenName?html}
First name: ${firstName?html}
Last name: ${lastName }
Maiden name: ${maidenName?html}
assign
语法
Freemarker代码
<#-- 或则 -->
<#-- 或则 -->
<#-- 或则 -->
capture this
<#-- 或则 -->
capture this
<#-- 或则 -->
<#-- 或则 -->
<#-- 或则 -->
capture this
<#-- 或则 -->
capture this
用例
生成变量,并且给变量赋值
给seasons赋予序列值
Ftl代码
给变量test加1
Ftl代码
给my namespage 赋予一个变量bgColor,下面可以通过my.bgColor来访问这个变量
Ftl代码
将一段输出的文本作为变量保存在x里
Ftl代码
${n}
Number of words: ${x?word_list?size}
${x}
Hello ${user}! error
true
${n}
Number of words: ${x?word_list?size}
${x}
Hello ${user}! error
true
同时也支持中文赋值，如：
Ftl代码
java
${语法}
java
${语法}
打印输出:
java
global
语法
Freemarker代码
<#--或则-->
<#--或则-->
capture this
<#--或则-->
<#--或则-->
capture this
全局赋值语法，利用这个语法给变量赋值，那么这个变量在所有的namespace [A1] 中是可见的, 如果这个变量被当前的assign 语法覆盖 如 在当前页面里x=2 将被隐藏，或者通过${.global.x} 来访问
setting
语法
Freemarker代码
用来设置整个系统的一个环境
locale
number_format
boolean_format
date_format , time_format , datetime_format
time_zone
classic_compatible
用例
假如当前是匈牙利的设置，然后修改成美国
Ftl代码
${1.2}
${1.2}
${1.2}
${1.2}
输出
1,2
1.2
因为匈牙利是采用", "作为十进制的分隔符，美国是用". "
macro, nested, return
语法
Freemarker代码
...
...
...
...
...
...
用例
Ftl代码
Test text, and the params: ${foo}, ${bar}, ${baaz}
Test text, and the params: ${foo}, ${bar}, ${baaz}
输出
Test text, and the params: a, b, 23
Test text, and the params: a, b, -1
Test text, and the params: a, Bar, 23
Test text, and the params: a, Bar, -1
定义循环输出的宏
Ftl代码
${title?cap_first}:
*${x?cap_first}
${title?cap_first}:
*${x?cap_first}
输出结果:
Animals:
*Mouse
*Elephant
*Python
包含body 的宏
Ftl代码
${c}. ${halfc} Last!
${c}. ${halfc} Last!
输出
1. 0.5
2. 1
3. 1.5
4. 2 Last!
t, lt, rt
语法
Freemarkder代码
去掉左右空白和回车换行
去掉左边空白和回车换行
去掉右边空白和回车换行
取消上面的效果
去掉左右空白和回车换行
去掉左边空白和回车换行
去掉右边空白和回车换行
取消上面的效果
C 一些常用方法或注意事项
表达式转换类
${expression} 计算expression 并输出
#{ expression } 数字计算#{ expression ;format} 安格式输出数字format 为M 和m
M 表示小数点后最多的位数,m 表示小数点后最少的位数如#{121.2322;m2M2} 输出121.23
数字循环
1..5 表示从1 到5 ，原型number..number
对浮点取整数
${123.23?int} 输出 123
给变量默认值
${var?default("hello world")?html} 如果var is null 那么将会被hello world 替代
判断对象是不是 null
Ftl代码
Mouse found
Mouse found
也可以直接${mouse?if_exists})输出布尔形
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
（1）解决输出中文乱码问题：
freemarker乱码的原因：
没有使用正确的编码格式读取模版文件,表现为模版中的中文为乱码
解决方法：在classpath上放置一个文件freemarker.properties，在里面写上模版文件的编码方式，比如
default_encoding=UTF-8
locale=zh_CN
注意：eclipse中除了xml文件、java文件外，默认的文件格式iso8859-1
数据插入模版时，没有使用正确的编码，表现出模版中的新插入数据为乱码
解决方法：在result的配置中，指定charset，s2的FreemarkerResult.java会将charset传递freemarker
/pages/Person/view.ftl
text/html;charset=UTF-8
（2）提高freemarker的性能
在freemarker.properties中设置：
template_update_delay=60000
避免每次请求都重新载入模版，即充分利用cached的模版
（3）尽量使用freemarker本身的提供的tag，使用S2 tags 的标签会在性能上有所损失
（4）freemarker的标签种类：
${..}:FreeMarker will replace it in the output with the actual value of the thing in the curly brackets. They are called interpolation s.
# ,代表是FTL tags（FreeMarker Template Language tags） ，hey are instructions to FreeMarker and will not be printed to the output
...
@ ,代表用户自定义的标签
<#-- --> 注释标签，注意不是<!-- -->
（5）一些特殊的指令：
r代表原样输出：${r"C:\foo\bar"}
${x}
？引出内置指令
String处理指令：
html:特殊的html字符将会被转义，比如"<"，处理后的结果是<
cap_first 、lower_case 、upper_case
trim :除去字符串前后的空格
sequences处理指令
size ：返回sequences的大小
numbers处理指令
int:number的整数部分，(e.g. -1.9?int is -1)
（6）对于null，或者miss value，freemarker会报错
?exists:旧版本的用法
!：default value operator，语法结构为： unsafe_expr !default_expr，比如 ${mouse!"No mouse."} 当mouse不存在时，返回default value；
(product.color)!"red" 这种方式，能够处理product或者color为miss value的情况；
而product.color!"red"将只处理color为miss value的情况
??: Missing value test operator ,测试是否为missing value
unsafe_expr ?? ：product.color??将只测试color是否为null
(unsafe_expr )??：(product.color)??将测试product和color是否存在null
Ftl代码
<#if mouse??>
Mouse found
No mouse found
Creating mouse...
<#if mouse??>
Mouse found
No mouse found
<#if mouse??>
Mouse found
No mouse found
Creating mouse...
<#if mouse??>
Mouse found
No mouse found
（7）模版值插入方式 （interpolation）
通用方式 （ Universal interpolations）： ${expression }
对于字符串：只是简单输出
对于数值，会自动根据local确定格式，称为human audience，否则称为computer audience，可以"?c", 比如， Details...,因此这里的id是给浏览器使用的，不需要进行格式化，注意?c只对数值有效
对于日期，会使用默认的日期格式转换，因此需要事先设置好默认的转换格式，包括date_format , time_format ，atetime_format
对于布尔值，不能输出，会报错并停止模版的执行，比如${a = 2} 会出错，但是可以 string built-in来进行转换
数值处理，具体参考：Built-ins for numbers
http://freemarker.org/docs/ref_builtins_number.html#ref_builtin_string_for_number
数值处理的例子：
${answer}
${answer?string} <#-- the same as ${answer} -->
${answer?string.number}
${answer?string.currency}
${answer?string.percent}
除了使用内置的formate，可以使用任何用Java decimal number format syntax 书写的formate，比如
${1234}
${12345?string("0.####E0")}
更加方便的格式：
US people writes: ${12345678?string(",##0.00")}
Hungarian people writes: ${12345678?string(",##0.00")}
日期处理，参考Built-ins for dates
http://freemarker.org/docs/ref_builtins_date.html#ref_builtin_string_for_date
日期处理的例子：
${openingTime?string.short}
${openingTime?string.medium}
${openingTime?string.long}
${openingTime?string.full}
${nextDiscountDay?string.short}
${nextDiscountDay?string.medium}
${nextDiscountDay?string.long}
${nextDiscountDay?string.full}
${lastUpdated?string.short}
${lastUpdated?string.medium}
${lastUpdated?string.long}
${lastUpdated?string.full}
注意：
由于java语言中的Date类型的不足，freemarker不能根据Date变量判断出变量包含的部分（日期、时间还是全部），在这种情况下，freemarker
不能正确显示出${lastUpdated?string.short} 或者 simply ${lastUpdated}，因此，可以通过?date, ?time and ?datetime built-ins
来帮助freemarker来进行判断，比如${lastUpdated?datetime?string.short}
除了使用内置的日期转换格式外，可以自己指定日期的格式，使用的是Java date format syntax，比如：
${lastUpdated?string("yyyy-MM-dd HH:mm:ss zzzz")}
${lastUpdated?string("EEE, MMM d, ''yy")}
${lastUpdated?string("EEEE, MMMM dd, yyyy, hh:mm:ss a '('zzz')'")}
数值专用方式 （ Numerical interpolations）：#{expression } or #{expression ; format }，这是数值专用的输出方式，但是 最好使用通用方式的string built-in或者number_format 来完成转换，Numerical interpolations方式将会被停用
（8）创建自定义模版
Ftl代码
Hello Joe!
评论这张
转发至微博
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")
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