Commit ca239bf0 by xiaotong

new pages

parent 6f46964c
...@@ -113,152 +113,66 @@ ...@@ -113,152 +113,66 @@
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
%%% 张量矩阵乘法 %%% 张量的单元操作
\begin{frame}{张量的矩阵乘法} \begin{frame}{张量的单元操作}
\begin{itemize} \begin{itemize}
\item 对于神经网络$\textbf{y}=f(\textbf{x}\cdot \textbf{w} + \textbf{b})$$\textbf{x} \cdot \textbf{w}$$\textbf{x} \times \textbf{w}$是线性变换,其中$\textbf{x}$是输入张量,$\textbf{w}$是一个矩阵 \item 神经网络$\textbf{y}=f(\textbf{x}\cdot \textbf{w} + \textbf{b})$也包括一些张量的单元操作(element-wise opertation)
\begin{itemize} \begin{itemize}
\item $\textbf{x} \cdot \textbf{w}$表示的是矩阵乘法(或记为$\times$ \item 加法:$\textbf{s}+\textbf{b}$,其中$\textbf{s} = \textbf{x}\cdot \textbf{w}$
\item 注意这里不是张量乘法,它还有其它定义 \item 激活函数:$f(\cdot)$
\item $\textbf{w}$$n \times m$的矩阵,$\textbf{x}$的形状是$... \times n$,即$\textbf{x}$的第一维度需要和$\textbf{w}$的行数\\ \end{itemize}
\vspace{0.5em} \item<2-> \textbf{单元加}就是对张量中的每个位置都进行加法
$\textbf{x}(1:2,1:2,\alert{1:3}) \times \textbf{w}(\alert{1:3},1:2) = \textbf{s}(1:2,1:2,1:2)$ \begin{itemize}
\item 一般要求两个张量的形状是一样的
\item<3-> 不过,这里可以使用加法的\textbf{广播},重复利用一个张量进行加法,并不要求两个张量形状相同
\end{itemize} \end{itemize}
\end{itemize} \end{itemize}
\vspace{-1.5em}
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\end{center} \end{center}
\begin{itemize}
\item<4-> 类似的,我们可以对所有位置做加法、乘法等等一系列算数操作,也包括激活函数
\end{itemize}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
......
...@@ -1942,6 +1942,218 @@ cycle} ...@@ -1942,6 +1942,218 @@ cycle}
\end{frame} \end{frame}
%%%------------------------------------------------------------------------------------------------------------ %%%------------------------------------------------------------------------------------------------------------
%%% 张量矩阵乘法
\begin{frame}{张量的矩阵乘法}
\begin{itemize}
\item 对于神经网络$\textbf{y}=f(\textbf{x}\cdot \textbf{w} + \textbf{b})$$\textbf{x} \cdot \textbf{w}$$\textbf{x} \times \textbf{w}$是线性变换,其中$\textbf{x}$是输入张量,$\textbf{w}$是一个矩阵
\begin{itemize}
\item $\textbf{x} \cdot \textbf{w}$表示的是矩阵乘法(或记为$\times$
\item 注意,这里不是张量乘法,因为张量乘法还有其它定义
\item $\textbf{w}$$n \times m$的矩阵,$\textbf{x}$的形状是$... \times n$,即$\textbf{x}$的第一维度需要和$\textbf{w}$的行数\\
\vspace{0.5em}
$\textbf{x}(1:2,1:2,\alert{1:3}) \times \textbf{w}(\alert{1:3},1:2) = \textbf{s}(1:2,1:2,1:2)$
\end{itemize}
\end{itemize}
\begin{center}
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\end{center}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
%%% 张量的单元操作
\begin{frame}{张量的单元操作}
\begin{itemize}
\item 神经网络$\textbf{y}=f(\textbf{x}\cdot \textbf{w} + \textbf{b})$也包括一些张量的单元操作(element-wise opertation)
\begin{itemize}
\item 加法:$\textbf{s}+\textbf{b}$,其中$\textbf{s} = \textbf{x}\cdot \textbf{w}$
\item 激活函数:$f(\cdot)$
\end{itemize}
\item<2-> \textbf{单元加}就是对张量中的每个位置都进行加法
\begin{itemize}
\item 一般要求两个张量的形状是一样的
\item<3-> 不过,这里可以使用加法的\textbf{广播},重复利用一个张量进行加法,并不要求两个张量形状相同
\end{itemize}
\end{itemize}
\vspace{-1.5em}
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\end{center}
\begin{itemize}
\item<4-> 类似的,我们可以对所有位置做加法、乘法等等一系列算数操作,也包括激活函数
\end{itemize}
\end{frame}
%%%------------------------------------------------------------------------------------------------------------
\subsection{参数学习 - 反向传播} \subsection{参数学习 - 反向传播}
\end{CJK} \end{CJK}
......
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