合并分支 'zengxin' 到 'caorunzhe'

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Book/Chapter1/chapter1.tex

@@ -45,7 +45,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter1/Figures/figure-Required-parts-of-MT}
+\input{./Chapter1/Figures/figure-required-parts-of-mt}
     \caption{机器翻译系统的组成}
     \label{fig:1-2}
 \end{figure}
@@ -220,7 +220,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter1/Figures/figure-Example-RBMT}
+\input{./Chapter1/Figures/figure-example-rbmt}
 \setlength{\belowcaptionskip}{-1.5em}
     \caption{基于规则的机器翻译的示例图（左：规则库；右：规则匹配结果）}
     \label{fig:1-8}
@@ -290,7 +290,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter1/Figures/figure-Example-SMT}
+\input{./Chapter1/Figures/figure-example-smt}
     \caption{统计机器翻译的示例图（左：语料资源；中：翻译模型与语言模型；右：翻译假设与翻译引擎）}
     \label{fig:1-11}
 \end{figure}
@@ -311,7 +311,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter1/Figures/figure-Example-NMT}
+\input{./Chapter1/Figures/figure-example-nmt}
     \caption{神经机器翻译的示例图（左：编码器-解码器网络；右：编码器示例网络）}
     \label{fig:1-12}
 \end{figure}

Book/Chapter2/chapter2.tex

@@ -35,8 +35,8 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
- 	\subfigure[机器翻译系统被看作一个黑盒] {\input{./Chapter2/Figures/figure-MT-system-as-a-black-box}  }
- 	\subfigure[机器翻系统 = 前/后处理 + 翻译引擎] {\input{./Chapter2/Figures/figure-MT=language-analysis+translation-engine}}
+ 	\subfigure[机器翻译系统被看作一个黑盒] {\input{./Chapter2/Figures/figure-mt-system-as-a-black-box}  }
+ 	\subfigure[机器翻系统 = 前/后处理 + 翻译引擎] {\input{./Chapter2/Figures/figure-mt=language-analysis+translation-engine}}
 	\caption{机器翻译系统的结构}
     \label{fig:2-1}
 \end{figure}
@@ -125,7 +125,7 @@ F(X)=\int_{-\infty}^x f(x)dx
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
- \input{./Chapter2/Figures/figure-Probability-density-function&Distribution-function}
+ \input{./Chapter2/Figures/figure-probability-density-function&distribution-function}
 \caption{一个概率密度函数(左)与其对应的分布函数(右)}
 \label{fig:2-3}
 \end{figure}
@@ -310,7 +310,7 @@ F(X)=\int_{-\infty}^x f(x)dx
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter2/Figures/figure-Self-information-function}
+\input{./Chapter2/Figures/figure-self-information-function}
 \caption{自信息函数$\textrm{I}(x)$关于$\textrm{P}(x)$的曲线}
 \label{fig:2-6}
 \end{figure}
@@ -429,7 +429,7 @@ F(X)=\int_{-\infty}^x f(x)dx
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter2/Figures/figure-Example-of-word-segmentation-based-on-dictionary}
+\input{./Chapter2/Figures/figure-example-of-word-segmentation-based-on-dictionary}
 \caption{基于词典进行分词的实例}
 \label{fig:2-8}
 \end{figure}
@@ -638,7 +638,7 @@ F(X)=\int_{-\infty}^x f(x)dx
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter2/Figures/figure-examples-of-Chinese-word-segmentation-based-on-1-gram-model}
+\input{./Chapter2/Figures/figure-examples-of-chinese-word-segmentation-based-on-1-gram-model}
 \caption{基于1-gram语言模型的中文分词实例}
 \label{fig:2-17}
 \end{figure}

Book/Chapter3/Chapter3.tex

@@ -170,7 +170,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter3/Figures/figure-processes-SMT}
+\input{./Chapter3/Figures/figure-processes-smt}
     \caption{简单的统计机器翻译流程}
     \label{fig:3-5}
 \end{figure}
@@ -472,7 +472,7 @@ g(\mathbf{s},\mathbf{t}) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)}
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\input{./Chapter3/Figures/figure-greedy-MT-decoding-pseudo-code}
+\input{./Chapter3/Figures/figure-greedy-mt-decoding-pseudo-code}
     \caption{贪婪的机器翻译解码算法的伪代码}
     \label{fig:3-10}
 \end{figure}
@@ -483,8 +483,8 @@ g(\mathbf{s},\mathbf{t}) \equiv \prod_{j,i \in \widehat{A}}{\textrm{P}(s_j,t_i)}
 %----------------------------------------------
 \begin{figure}[htp]
     \centering
-\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-1}}
-\subfigure{\input{./Chapter3/Figures/greedy-MT-decoding-process-3}}
+\subfigure{\input{./Chapter3/Figures/greedy-mt-decoding-process-1}}
+\subfigure{\input{./Chapter3/Figures/greedy-mt-decoding-process-3}}
 \setlength{\belowcaptionskip}{14.0em}
     \caption{贪婪的机器翻译解码过程实例}
     \label{fig:3-11}

Book/Chapter4/chapter4.tex

@@ -2162,7 +2162,7 @@ d_1 = {d'} \circ {r_5}
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter4/Figures/structure-of-Chart}
+\input{./Chapter4/Figures/structure-of-chart}
 \caption{Chart结构}
 \label{fig:4-65}
 \end{figure}
@@ -2252,7 +2252,7 @@ d_1 = {d'} \circ {r_5}
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter4/Figures/content-of-Chart-in-tree-based-decoding}
+\input{./Chapter4/Figures/content-of-chart-in-tree-based-decoding}
 \caption{基于树的解码中Chart的内容}
 \label{fig:4-68}
 \end{figure}

Book/Chapter7/Chapter7.tex

@@ -90,7 +90,7 @@
 %----------------------------------------------
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/figure-construction-steps-of-MT-system}
+\input{./Chapter7/Figures/figure-construction-steps-of-mt-system}
 \caption{构建神经机器翻译系统的主要步骤}
 \label{fig:7-2}
 \end{figure}
@@ -417,7 +417,7 @@ y = f(x)
 % 图7.
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/figure-Underfitting-vs-Overfitting}
+\input{./Chapter7/Figures/figure-underfitting-vs-overfitting}
 \caption{欠拟合 vs 过拟合}
 \label{fig:7-11}
 \end{figure}
@@ -1191,7 +1191,7 @@ b &=& \omega_{\textrm{high}}\cdot |\mathbf{x}|
 % 图7.5.1
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/Post-Norm-vs-Pre-Norm}
+\input{./Chapter7/Figures/figure-post-norm-vs-pre-norm}
 \caption{Post-Norm Transformer vs Pre-Norm Transformer}
 \label{fig:7-28}
 \end{figure}
@@ -1273,7 +1273,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
 % 图7.5.2
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/dynamic-linear-aggregation-network-structure}
+\input{./Chapter7/Figures/figure-dynamic-linear-aggregation-network-structure}
 \caption{动态线性层聚合网络结构图}
 \label{fig:7-29}
 \end{figure}
@@ -1299,7 +1299,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
 % 图7.5.3
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/progressive-training}
+\input{./Chapter7/Figures/figure-progressive-training}
 \caption{渐进式深层网络训练过程}
 \label{fig:7-30}
 \end{figure}
@@ -1316,7 +1316,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
 % 图7.5.4
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/sparse-connections-between-different-groups}
+\input{./Chapter7/Figures/figure-sparse-connections-between-different-groups}
 \caption{不同组之间的稀疏连接}
 \label{fig:7-31}
 \end{figure}
@@ -1335,7 +1335,7 @@ $g_l$会作为输入的一部分送入第$l+1$层。其网络的结构图\ref{fi
 % 图7.5.5
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/learning-rate}
+\input{./Chapter7/Figures/figure-learning-rate}
 \caption{学习率重置vs从头训练的学习率曲线}
 \label{fig:7-32}
 \end{figure}
@@ -1411,7 +1411,7 @@ p_l=\frac{l}{2L}\cdot \varphi
 % 图7.5.7
 \begin{figure}[htp]
 \centering
-\input{./Chapter7/Figures/expanded-residual-network}
+\input{./Chapter7/Figures/figure-expanded-residual-network}
 \caption{Layer Dropout中残差网络的展开图}
 \label{fig:7-34}
 \end{figure}

Book/mt-book-xelatex.tex

@@ -122,13 +122,13 @@
 %	CHAPTERS
 %----------------------------------------------------------------------------------------
 
-\include{Chapter1/chapter1}
+%\include{Chapter1/chapter1}
 %\include{Chapter2/chapter2}
 %\include{Chapter3/chapter3}
 %\include{Chapter4/chapter4}
 %\include{Chapter5/chapter5}
 %\include{Chapter6/chapter6}
-%\include{Chapter7/chapter7}
+\include{Chapter7/chapter7}
 %\include{ChapterAppend/chapterappend}