new pages

Section05-Neural-Networks-and-Language-Modeling/section05-test.tex

@@ -124,12 +124,58 @@
 \begin{frame}{循环单元}
 
 \begin{itemize}
-\item 假设有输入序列$(\textbf{x}_0,\textbf{x}_1,...,\textbf{x}_t,...)$，这里$\textbf{x}_t$表示序列中第$t$个元素，也被称作时刻$t$所对应的输入。它所对应的输出序列是$(\textbf{y}_0,\textbf{y}_1,...,\textbf{y}_t,...)$。 循环神经网络的核心是`` 记忆''任意长时间的历史
+\item 有输入序列$(\textbf{x}_0,\textbf{x}_1,...,\textbf{x}_t,...)$，其中$\textbf{x}_t$表示序列中第$t$个元素，也被称作\alert{时刻$t$}输入。它所对应的输出序列是$(\textbf{y}_0,\textbf{y}_1,...,\textbf{y}_t,...)$。 在循环神经网络中，每个时刻的输出都可以用同一个\alert{循环单元}来描述。\visible<2->{对于语言模型，一种简单的结构：}
+
+\visible<2->{
+{\small
+\begin{tcolorbox}
+[bicolor,sidebyside,righthand width=4.3cm,size=title,frame engine=empty,
+ colback=blue!10!white,colbacklower=black!5!white]
+ 
+\begin{eqnarray}
+\textbf{y}_t & = & \textrm{Softmax}(\textbf{h}_t \textbf{V}) \nonumber \\
+\textbf{h}_t & = & \textrm{TanH}(\textbf{x}_t \textbf{U} + \textbf{h}_{t-1} \textbf{W}) \nonumber
+\end{eqnarray}
+\footnotesize{$\textbf{h}_t$: $t$时刻的隐层状态\\
+$\textbf{h}_{t-1}$: $t-1$时刻的隐层状态\\
+$\textbf{V}, \textbf{U}, \textbf{W}$: 参数
+}
+\tcblower
+\begin{center}
+\begin{tikzpicture}
+\begin{scope}
+\node [anchor=west,inner sep=3pt,minimum width=8em] (h) at (0,0) {\tiny{$\textbf{h}_t  =  \textrm{TanH}(\textbf{x}_t \textbf{U} + \textbf{h}_{t-1} \textbf{W})$}};
+\node [anchor=south west,inner sep=3pt] (r) at ([yshift=-0.2em]h.north west) {\tiny{循环单元:}};
+\begin{pgfonlayer}{background}
+\node [rectangle,draw,inner sep=0em,fill=green!20!white] [fit = (r) (h)] (rbox) {};
+\end{pgfonlayer}
+\node [anchor=south,draw,minimum width=8em,fill=green!20!white] (y) at ([yshift=1.5em]rbox.north) {\tiny{$\textbf{y}_t = \textrm{Softmax}(\textbf{h}_t \textbf{V})$}};
+\node [anchor=south,inner sep=2pt] (output) at ([yshift=1em]y.north) {\scriptsize{$\textbf{y}_t$}};
+\node [anchor=north,inner sep=2pt] (input) at ([yshift=-1em]h.south) {\scriptsize{$\textbf{x}_t$}};
+\draw [->,thick] (input.north) -- ([yshift=-0.1em]rbox.south);
+\draw [->,thick] ([yshift=0.1em]rbox.north) -- ([yshift=-0.1em]y.south) node [pos=0.5,left] {\tiny{$\textbf{h}_t$}};
+\draw [->,thick] ([yshift=0.1em]y.north) -- (output.south);
+\draw [->,thick] ([xshift=0.1em]rbox.east) -- ([xshift=1em]rbox.east) node [pos=1,above] {\tiny{$\textbf{h}_t$}};
+\draw [->,thick] ([xshift=-1em]rbox.west) -- ([xshift=-0.1em]rbox.west) node [pos=0,above] {\tiny{$\textbf{h}_{t-1}$}};
+
+\end{scope}
+\end{tikzpicture}
+\end{center}
+\end{tcolorbox}
+}
+}
+
+\item<3-> \textbf{如何体现循环？}$t$时刻的状态是$t-1$时刻状态的函数，这个过程可以不断被执行
 \end{itemize}
 
 \end{frame}
 
 %%%------------------------------------------------------------------------------------------------------------
+%%% 循环神经网络的“记忆”
+\begin{frame}{循环神经网络的``记忆''}
+\end{frame}
+
+%%%------------------------------------------------------------------------------------------------------------
 %%% 深度学习带来的问题及思考 - 并不是无所不能
 
 

Section05-Neural-Networks-and-Language-Modeling/section05.tex

@@ -4111,6 +4111,58 @@ NLP问题的\alert{隐含结构}假设 & 无隐含结构假设，\alert{端到
 \end{frame}
 
 %%%------------------------------------------------------------------------------------------------------------
+%%% 循环神经网络的结构
+\begin{frame}{循环单元}
+
+\begin{itemize}
+\item 有输入序列$(\textbf{x}_0,\textbf{x}_1,...,\textbf{x}_t,...)$，其中$\textbf{x}_t$表示序列中第$t$个元素，也被称作\alert{时刻$t$}输入。它所对应的输出序列是$(\textbf{y}_0,\textbf{y}_1,...,\textbf{y}_t,...)$。 在循环神经网络中，每个时刻的输出都可以用同一个\alert{循环单元}来描述。\visible<2->{对于语言模型，一种简单的结构：}
+
+\visible<2->{
+{\small
+\begin{tcolorbox}
+[bicolor,sidebyside,righthand width=4.3cm,size=title,frame engine=empty,
+ colback=blue!10!white,colbacklower=black!5!white]
+ 
+\begin{eqnarray}
+\textbf{y}_t & = & \textrm{Softmax}(\textbf{h}_t \textbf{V}) \nonumber \\
+\textbf{h}_t & = & \textrm{TanH}(\textbf{x}_t \textbf{U} + \textbf{h}_{t-1} \textbf{W}) \nonumber
+\end{eqnarray}
+\footnotesize{$\textbf{h}_t$: $t$时刻的隐层状态\\
+$\textbf{h}_{t-1}$: $t-1$时刻的隐层状态\\
+$\textbf{V}, \textbf{U}, \textbf{W}$: 参数
+}
+\tcblower
+\begin{center}
+\begin{tikzpicture}
+\begin{scope}
+\node [anchor=west,inner sep=3pt,minimum width=8em] (h) at (0,0) {\tiny{$\textbf{h}_t  =  \textrm{TanH}(\textbf{x}_t \textbf{U} + \textbf{h}_{t-1} \textbf{W})$}};
+\node [anchor=south west,inner sep=3pt] (r) at ([yshift=-0.2em]h.north west) {\tiny{循环单元:}};
+\begin{pgfonlayer}{background}
+\node [rectangle,draw,inner sep=0em,fill=green!20!white] [fit = (r) (h)] (rbox) {};
+\end{pgfonlayer}
+\node [anchor=south,draw,minimum width=8em,fill=green!20!white] (y) at ([yshift=1.5em]rbox.north) {\tiny{$\textbf{y}_t = \textrm{Softmax}(\textbf{h}_t \textbf{V})$}};
+\node [anchor=south,inner sep=2pt] (output) at ([yshift=1em]y.north) {\scriptsize{$\textbf{y}_t$}};
+\node [anchor=north,inner sep=2pt] (input) at ([yshift=-1em]h.south) {\scriptsize{$\textbf{x}_t$}};
+\draw [->,thick] (input.north) -- ([yshift=-0.1em]rbox.south);
+\draw [->,thick] ([yshift=0.1em]rbox.north) -- ([yshift=-0.1em]y.south) node [pos=0.5,left] {\tiny{$\textbf{h}_t$}};
+\draw [->,thick] ([yshift=0.1em]y.north) -- (output.south);
+\draw [->,thick] ([xshift=0.1em]rbox.east) -- ([xshift=1em]rbox.east) node [pos=1,above] {\tiny{$\textbf{h}_t$}};
+\draw [->,thick] ([xshift=-1em]rbox.west) -- ([xshift=-0.1em]rbox.west) node [pos=0,above] {\tiny{$\textbf{h}_{t-1}$}};
+
+\end{scope}
+\end{tikzpicture}
+\end{center}
+\end{tcolorbox}
+}
+}
+
+\item<3-> \textbf{如何体现循环？}$t$时刻的状态是$t-1$时刻状态的函数，这个过程可以不断被执行
+\end{itemize}
+
+\end{frame}
+
+
+%%%------------------------------------------------------------------------------------------------------------
 \subsection{词嵌入}
 
 %%%------------------------------------------------------------------------------------------------------------