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8e08141c
编写于
1月 23, 2017
作者:
Y
Yu Yang
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803 deletion
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-803
.tmpl/override.css
.tmpl/override.css
+0
-506
.tmpl/template.html
.tmpl/template.html
+7
-17
.tmpl/theme.css
.tmpl/theme.css
+0
-5
fit_a_line/index.html
fit_a_line/index.html
+7
-17
gan/index.html
gan/index.html
+7
-17
image_caption/index.html
image_caption/index.html
+7
-17
image_classification/README.md
image_classification/README.md
+1
-2
image_classification/index.html
image_classification/index.html
+8
-19
image_detection/index.html
image_detection/index.html
+7
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image_qa/index.html
image_qa/index.html
+7
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label_semantic_roles/index.html
label_semantic_roles/index.html
+7
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machine_translation/index.html
machine_translation/index.html
+7
-17
query_relationship/index.html
query_relationship/index.html
+7
-17
recognize_digits/index.html
recognize_digits/index.html
+7
-17
recommender_system/index.html
recommender_system/index.html
+7
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skip_thought/index.html
skip_thought/index.html
+7
-17
speech_recognition/index.html
speech_recognition/index.html
+7
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understand_sentiment/index.html
understand_sentiment/index.html
+7
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word2vec/README.md
word2vec/README.md
+11
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word2vec/index.html
word2vec/index.html
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未找到文件。
.tmpl/override.css
已删除
100644 → 0
浏览文件 @
9c9e4f96
此差异已折叠。
点击以展开。
.tmpl/template.html
浏览文件 @
8e08141c
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.tmpl/theme.css
已删除
100644 → 0
浏览文件 @
9c9e4f96
此差异已折叠。
点击以展开。
fit_a_line/index.html
浏览文件 @
8e08141c
...
...
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gan/index.html
浏览文件 @
8e08141c
...
...
@@ -2,7 +2,7 @@
<head>
<script
type=
"text/x-mathjax-config"
>
MathJax
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.
Config
({
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<link
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rel=
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rel=
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<style
type=
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image_caption/index.html
浏览文件 @
8e08141c
...
...
@@ -2,7 +2,7 @@
<head>
<script
type=
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MathJax
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...
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...
image_classification/README.md
浏览文件 @
8e08141c
...
...
@@ -247,7 +247,6 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
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
datadim = 3 * 32 * 32
classdim = 10
data = data_layer(name='image', size=datadim)
...
...
@@ -299,7 +298,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
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。
```python
out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
```
...
...
image_classification/index.html
浏览文件 @
8e08141c
...
...
@@ -2,7 +2,7 @@
<head>
<script
type=
"text/x-mathjax-config"
>
MathJax
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,
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...
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<script
src=
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async
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<script
src=
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s://cdnjs.cloudflare.com/ajax/libs
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type=
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src=
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href=
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rel=
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...
@@ -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。
```python
datadim = 3 * 32 * 32
classdim = 10
data = data_layer(name='image', size=datadim)
...
...
@@ -346,7 +339,7 @@ $$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
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。
```python
out = fc_layer(input=net, size=class_num, act=SoftmaxActivation())
```
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word2vec/README.md
浏览文件 @
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@@ -36,6 +36,7 @@ $$X = USV^T$$
</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:
```
similarity: 0.899180685161
please input two words: big huge
...
...
@@ -82,6 +83,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$
$$
\f
rac{1}{T}
\s
um_t f(w_t, w_{t-1}, ..., w_{t-n+1};
\t
heta) + R(
\t
heta)$$
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,$R(
\t
heta)$表示参数正则项。
<p
align=
"center"
>
<img
src=
"image/nnlm.png"
width=
500
><br/>
图2. N-gram神经网络模型
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@@ -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)
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"
>
<img
src=
"image/cbow.png"
width=
250
><br/>
图3. CBOW模型
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@@ -127,16 +130,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$
### 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。
<p
align=
"center"
>
<img
src=
"image/skipgram.png"
width=
250
><br/>
图4. Skip-gram模型
</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:
...
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@@ -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:
> `<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
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word2vec/index.html
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{
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{
box-sizing
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border-box
;
min-width
:
200px
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...
@@ -38,7 +32,7 @@
</style>
<body
class=
"doc-content-wrap"
>
<body>
<div
id=
"context"
class=
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...
...
@@ -83,6 +77,7 @@ $$X = USV^T$$
</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:
```
similarity: 0.899180685161
please input two words: big huge
...
...
@@ -129,6 +124,7 @@ $$P(w_1, ..., w_T) = \prod_{t=n}^TP(w_t|w_{t-1}, w_{t-2}, ..., w_{t-n+1})$$
$$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
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。
<p
align=
"center"
>
<img
src=
"image/nnlm.png"
width=
500
><br/>
图2. N-gram神经网络模型
...
...
@@ -160,6 +156,7 @@ $$\frac{1}{T}\sum_t f(w_t, w_{t-1}, ..., w_{t-n+1};\theta) + R(\theta)$$
### 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:
<p
align=
"center"
>
<img
src=
"image/cbow.png"
width=
250
><br/>
图3. CBOW模型
...
...
@@ -174,16 +171,17 @@ $$context = \frac{x_{t-1} + x_{t-2} + x_{t+1} + x_{t+2}}{4}$$
### 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。
<p
align=
"center"
>
<img
src=
"image/skipgram.png"
width=
250
><br/>
图4. Skip-gram模型
</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:
...
...
@@ -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:
> `
<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
...
...
@@ -518,15 +515,11 @@ python calculate_dis.py data/vocabulary.txt model/pass-00029/_proj.txt
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