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<body class="doc-content-wrap"> <body>
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<div id="context" class="container markdown-body"> <div id="context" class="container markdown-body">
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...@@ -241,15 +235,11 @@ python predict.py -m output/pass-00020 -t data/housing.test.npy ...@@ -241,15 +235,11 @@ python predict.py -m output/pass-00020 -t data/housing.test.npy
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gan/index.html
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...@@ -53,15 +47,11 @@ TODO: Write about https://github.com/PaddlePaddle/Paddle/tree/develop/demo/gan ...@@ -53,15 +47,11 @@ TODO: Write about https://github.com/PaddlePaddle/Paddle/tree/develop/demo/gan
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image_classification/README.md
浏览文件 @ 8e08141c
...@@ -247,7 +247,6 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$ ...@@ -247,7 +247,6 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
网络输入定义为 `data_layer` (数据层)，在图像分类中即为图像像素信息。CIFRAR10是RGB 3通道32x32大小的彩色图，因此输入数据大小为3072(3x32x32)，类别大小为10，即10分类。 网络输入定义为 `data_layer` (数据层)，在图像分类中即为图像像素信息。CIFRAR10是RGB 3通道32x32大小的彩色图，因此输入数据大小为3072(3x32x32)，类别大小为10，即10分类。
```python ```python
datadim = 3 * 32 * 32 datadim = 3 * 32 * 32
classdim = 10 classdim = 10
data = data_layer(name='image', size=datadim) data = data_layer(name='image', size=datadim)
...@@ -299,7 +298,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$ ...@@ -299,7 +298,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
3. 定义分类器 3. 定义分类器
通过上面VGG网络提取高层特征，然后经过全连接层映射到类别维度大小的向量，再通过Softmax归一化得到每个类别的概率，也可称作分类器。 通过上面VGG网络提取高层特征，然后经过全连接层映射到类别维度大小的向量，再通过Softmax归一化得到每个类别的概率，也可称作分类器。
```python ```python
out = fc_layer(input=net, size=class_num, act=SoftmaxActivation()) out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
``` ```
......
image_classification/index.html
浏览文件 @ 8e08141c
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...@@ -294,7 +288,6 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$ ...@@ -294,7 +288,6 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
网络输入定义为 `data_layer` (数据层)，在图像分类中即为图像像素信息。CIFRAR10是RGB 3通道32x32大小的彩色图，因此输入数据大小为3072(3x32x32)，类别大小为10，即10分类。 网络输入定义为 `data_layer` (数据层)，在图像分类中即为图像像素信息。CIFRAR10是RGB 3通道32x32大小的彩色图，因此输入数据大小为3072(3x32x32)，类别大小为10，即10分类。
```python ```python
datadim = 3 * 32 * 32 datadim = 3 * 32 * 32
classdim = 10 classdim = 10
data = data_layer(name='image', size=datadim) data = data_layer(name='image', size=datadim)
...@@ -346,7 +339,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$ ...@@ -346,7 +339,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
3. 定义分类器 3. 定义分类器
通过上面VGG网络提取高层特征，然后经过全连接层映射到类别维度大小的向量，再通过Softmax归一化得到每个类别的概率，也可称作分类器。 通过上面VGG网络提取高层特征，然后经过全连接层映射到类别维度大小的向量，再通过Softmax归一化得到每个类别的概率，也可称作分类器。
```python ```python
out = fc_layer(input=net, size=class_num, act=SoftmaxActivation()) out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
``` ```
...@@ -598,15 +591,11 @@ python classify.py --job=extract --model=output/pass-00299 --data=image/dog.png ...@@ -598,15 +591,11 @@ python classify.py --job=extract --model=output/pass-00299 --data=image/dog.png
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image_detection/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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<style type="text/css" > <style type="text/css" >
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margin-left:0;
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box-sizing: border-box; box-sizing: border-box;
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image_qa/index.html
浏览文件 @ 8e08141c
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margin-left:0;
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box-sizing: border-box; box-sizing: border-box;
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......
label_semantic_roles/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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<script type="text/javascript" src="../.tmpl/marked.js"> <script type="text/javascript" src="../.tmpl/marked.js">
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margin-left:0;
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box-sizing: border-box; box-sizing: border-box;
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machine_translation/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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tex2jax: { tex2jax: {
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<link href="../.tmpl/theme.css" rel="stylesheet">
<link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet"> <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
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<link href="../.tmpl/github-markdown.css" rel='stylesheet'> <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
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margin-left:0;
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box-sizing: border-box; box-sizing: border-box;
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<div id="context" class="container markdown-body"> <div id="context" class="container markdown-body">
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......
query_relationship/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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tex2jax: { tex2jax: {
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<script src="http://cdn.bootcss.com/mathjax/2.7.0/MathJax.js" async></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
<script type="text/javascript" src="../.tmpl/marked.js"> <script type="text/javascript" src="../.tmpl/marked.js">
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<link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet"> <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
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margin-left:0;
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<div id="context" class="container markdown-body"> <div id="context" class="container markdown-body">
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......
recognize_digits/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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tex2jax: { tex2jax: {
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<script type="text/javascript" src="../.tmpl/marked.js"> <script type="text/javascript" src="../.tmpl/marked.js">
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margin-left:0;
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......
recommender_system/index.html
浏览文件 @ 8e08141c
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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......
skip_thought/index.html
浏览文件 @ 8e08141c
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word2vec/README.md
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...@@ -36,6 +36,7 @@ $$X = USV^T$$ ...@@ -36,6 +36,7 @@ $$X = USV^T$$
</p> </p>
另一方面，我们知道两个向量的余弦值在$[-1,1]$的区间内：两个完全相同的向量余弦值为1, 两个相互垂直的向量之间余弦值为0，两个方向完全相反的向量余弦值为-1，即相关性和余弦值大小成正比。因此我们还可以计算两个词向量的余弦相似度: 另一方面，我们知道两个向量的余弦值在$[-1,1]$的区间内：两个完全相同的向量余弦值为1, 两个相互垂直的向量之间余弦值为0，两个方向完全相反的向量余弦值为-1，即相关性和余弦值大小成正比。因此我们还可以计算两个词向量的余弦相似度:
``` ```
similarity: 0.899180685161 similarity: 0.899180685161
please input two words: big huge please input two words: big huge
...@@ -82,6 +83,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$ ...@@ -82,6 +83,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$
$$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
其中$f(w_t, w_{t-1}, ..., w_{t-n+1})$表示根据历史n-1个词得到当前词$w_t$的条件概率，$R(\theta)$表示参数正则项。 其中$f(w_t, w_{t-1}, ..., w_{t-n+1})$表示根据历史n-1个词得到当前词$w_t$的条件概率，$R(\theta)$表示参数正则项。
<p align="center"> <p align="center">
<img src="image/nnlm.png" width=500><br/> <img src="image/nnlm.png" width=500><br/>
图2. N-gram神经网络模型 图2. N-gram神经网络模型
...@@ -113,6 +115,7 @@ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$ ...@@ -113,6 +115,7 @@ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
### Continuous Bag-of-Words model(CBOW) ### Continuous Bag-of-Words model(CBOW)
CBOW模型通过一个词的上下文（各N个词）预测当前词。当N=2时，模型如下图所示： CBOW模型通过一个词的上下文（各N个词）预测当前词。当N=2时，模型如下图所示：
<p align="center"> <p align="center">
<img src="image/cbow.png" width=250><br/> <img src="image/cbow.png" width=250><br/>
图3. CBOW模型 图3. CBOW模型
...@@ -127,16 +130,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$ ...@@ -127,16 +130,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$
### Skip-gram model ### Skip-gram model
CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去掉了噪声，因此在小数据集上很有效。而Skip-gram的方法中，用一个词预测其上下文，得到了当前词上下文的很多样本，因此可用于更大的数据集。 CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去掉了噪声，因此在小数据集上很有效。而Skip-gram的方法中，用一个词预测其上下文，得到了当前词上下文的很多样本，因此可用于更大的数据集。
<p align="center"> <p align="center">
<img src="image/skipgram.png" width=250><br/> <img src="image/skipgram.png" width=250><br/>
图4. Skip-gram模型 图4. Skip-gram模型
</p> </p>
如上图所示，Skip-gram模型的具体做法是，将一个词的词向量映射到$2n$个词的词向量（$2n$表示当前输入词的前后各$n$个词），然后分别通过softmax得到这$2n$个词的分类损失值之和。
如上图所示，Skip-gram模型的具体做法是，将一个词的词向量映射到$2n$个词的词向量（$2n$表示当前输入词的前后各$n$个词），然后分别通过softmax得到这$2n$个词的分类损失值之和。
## 数据准备 ## 数据准备
### 数据介绍与下载 ### 数据介绍与下载
本教程使用Penn Tree Bank (PTB)数据集。PTB数据集较小，训练速度快，应用于Mikolov的公开语言模型训练工具\[[2](#参考文献)\]中。其统计情况如下： 本教程使用Penn Tree Bank (PTB)数据集。PTB数据集较小，训练速度快，应用于Mikolov的公开语言模型训练工具\[[2](#参考文献)\]中。其统计情况如下：
...@@ -225,13 +229,12 @@ CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去 ...@@ -225,13 +229,12 @@ CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去
``` ```
如"I have a dream" 一句提供了5条数据: 如"I have a dream" 一句提供了5条数据:
> `<s> <s> <s> <s> I `<br/>
> `<s> <s> <s> I have`<br/>
> `<s> <s> I have a `<br/>
> `<s> I have a dream`<br/>
> `I have a dream <e>`<br/>
> `<s> <s> <s> <s> I` <br>
> `<s> <s> <s> I have` <br>
> `<s> <s> I have a` <br>
> `<s> I have a dream` <br>
> `I have a dream <e>` <br>
## 模型配置说明 ## 模型配置说明
......
word2vec/index.html
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...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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...@@ -83,6 +77,7 @@ $$X = USV^T$$ ...@@ -83,6 +77,7 @@ $$X = USV^T$$
</p> </p>
另一方面，我们知道两个向量的余弦值在$[-1,1]$的区间内：两个完全相同的向量余弦值为1, 两个相互垂直的向量之间余弦值为0，两个方向完全相反的向量余弦值为-1，即相关性和余弦值大小成正比。因此我们还可以计算两个词向量的余弦相似度: 另一方面，我们知道两个向量的余弦值在$[-1,1]$的区间内：两个完全相同的向量余弦值为1, 两个相互垂直的向量之间余弦值为0，两个方向完全相反的向量余弦值为-1，即相关性和余弦值大小成正比。因此我们还可以计算两个词向量的余弦相似度:
``` ```
similarity: 0.899180685161 similarity: 0.899180685161
please input two words: big huge please input two words: big huge
...@@ -129,6 +124,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$ ...@@ -129,6 +124,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$
$$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
其中$f(w_t, w_{t-1}, ..., w_{t-n+1})$表示根据历史n-1个词得到当前词$w_t$的条件概率，$R(\theta)$表示参数正则项。 其中$f(w_t, w_{t-1}, ..., w_{t-n+1})$表示根据历史n-1个词得到当前词$w_t$的条件概率，$R(\theta)$表示参数正则项。
<p align="center"> <p align="center">
<img src="image/nnlm.png" width=500><br/> <img src="image/nnlm.png" width=500><br/>
图2. N-gram神经网络模型 图2. N-gram神经网络模型
...@@ -160,6 +156,7 @@ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$ ...@@ -160,6 +156,7 @@ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
### Continuous Bag-of-Words model(CBOW) ### Continuous Bag-of-Words model(CBOW)
CBOW模型通过一个词的上下文（各N个词）预测当前词。当N=2时，模型如下图所示： CBOW模型通过一个词的上下文（各N个词）预测当前词。当N=2时，模型如下图所示：
<p align="center"> <p align="center">
<img src="image/cbow.png" width=250><br/> <img src="image/cbow.png" width=250><br/>
图3. CBOW模型 图3. CBOW模型
...@@ -174,16 +171,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$ ...@@ -174,16 +171,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$
### Skip-gram model ### Skip-gram model
CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去掉了噪声，因此在小数据集上很有效。而Skip-gram的方法中，用一个词预测其上下文，得到了当前词上下文的很多样本，因此可用于更大的数据集。 CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去掉了噪声，因此在小数据集上很有效。而Skip-gram的方法中，用一个词预测其上下文，得到了当前词上下文的很多样本，因此可用于更大的数据集。
<p align="center"> <p align="center">
<img src="image/skipgram.png" width=250><br/> <img src="image/skipgram.png" width=250><br/>
图4. Skip-gram模型 图4. Skip-gram模型
</p> </p>
如上图所示，Skip-gram模型的具体做法是，将一个词的词向量映射到$2n$个词的词向量（$2n$表示当前输入词的前后各$n$个词），然后分别通过softmax得到这$2n$个词的分类损失值之和。
如上图所示，Skip-gram模型的具体做法是，将一个词的词向量映射到$2n$个词的词向量（$2n$表示当前输入词的前后各$n$个词），然后分别通过softmax得到这$2n$个词的分类损失值之和。
## 数据准备 ## 数据准备
### 数据介绍与下载 ### 数据介绍与下载
本教程使用Penn Tree Bank (PTB)数据集。PTB数据集较小，训练速度快，应用于Mikolov的公开语言模型训练工具\[[2](#参考文献)\]中。其统计情况如下： 本教程使用Penn Tree Bank (PTB)数据集。PTB数据集较小，训练速度快，应用于Mikolov的公开语言模型训练工具\[[2](#参考文献)\]中。其统计情况如下：
...@@ -272,13 +270,12 @@ CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去 ...@@ -272,13 +270,12 @@ CBOW的好处是对上下文词语的分布在词向量上进行了平滑，去
``` ```
如"I have a dream" 一句提供了5条数据: 如"I have a dream" 一句提供了5条数据:
> `<s> <s> <s> <s> I `<br/>
> `<s> <s> <s> I have`<br/>
> `<s> <s> I have a `<br/>
> `<s> I have a dream`<br/>
> `I have a dream <e>`<br/>
> `<s> <s> <s> <s> I` <br>
> `<s> <s> <s> I have` <br>
> `<s> <s> I have a` <br>
> `<s> I have a dream` <br>
> `I have a dream <e>` <br>
## 模型配置说明 ## 模型配置说明
...@@ -518,15 +515,11 @@ python calculate_dis.py data/vocabulary.txt model/pass-00029/_proj.txt ...@@ -518,15 +515,11 @@ python calculate_dis.py data/vocabulary.txt model/pass-00029/_proj.txt
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