13、15章编辑反馈后修改

Chapter15/Figures/figure-encoder-structure-of-transformer-model-optimized-by-nas.tex

@@ -19,7 +19,7 @@
 	\node[unit,fill=red!30] at (0,\d) (conv_\x) {卷积$1 \times 1$：2048};
 
 \foreach \x/\d in {1/12em}
-	\node[unit,fill=blue!30] at (0,\d) (relu_\x) {RELU};
+	\node[unit,fill=blue!30] at (0,\d) (relu_\x) {ReLU};
 
 \draw[->,thick] ([yshift=-1.4em]ln_1.-90) -- ([yshift=-0.1em]ln_1.-90);
 \draw[->,thick] ([yshift=0.1em]ln_1.90) -- ([yshift=-0.1em]sa_1.-90);
@@ -52,8 +52,8 @@
 \node[unit,fill=red!30] at (0,4em) (glu_1) {门控线性单元：512};
 \node[unit,fill=red!30] at (-3em,10em) (conv_1) {卷积$1 \times 1$：2048};
 \node[unit,fill=cyan!30] at (3em,10em) (conv_2) {卷积$3 \times 1$：256};
-\node[unit,fill=blue!30] at (-3em,12em) (relu_1) {RELU};
-\node[unit,fill=blue!30] at (3em,12em) (relu_2) {RELU};
+\node[unit,fill=blue!30] at (-3em,12em) (relu_1) {ReLU};
+\node[unit,fill=blue!30] at (3em,12em) (relu_2) {ReLU};
 \node[unit,fill=cyan!30] at (0em,18em) (conv_3) {Sep卷积$9 \times 1$：256};
 
 

Chapter15/Figures/figure-evolution-and-change-of-ml-methods.tex

@@ -41,7 +41,7 @@
 \node[anchor=south,cnode,fill=white] (cl1) at ([xshift=-4em,yshift=1.5em]m1.south){};
 \node[anchor=north,cnode,fill=white] (cl2) at ([xshift=0em,yshift=-1em]m1.north){};
 
-\node[anchor=south west,wnode,align=left,font=\tiny] (wl7) at ([xshift=0.5em,yshift=0em]cl1.east){使用{\color{ugreen}\bfnew{特征}}对{\color{blue}\bfnew{数据}}\\中信息进行提取};
+\node[anchor=south west,wnode,align=right,font=\tiny] (wl7) at ([xshift=0.5em,yshift=-1em]cl1.east){使用{\color{ugreen}\bfnew{特征}}对{\color{blue}\bfnew{数据}}\\中的信息进行\\提取};
 \node[anchor=west,wnode,align=right,font=\tiny] (wl8) at ([xshift=0.5em,yshift=0em]cl2.east){使用提取的信息对\\{\color{red!50}\bfnew{模型}}中的参数\\进行训练};
 
 \draw [-,thick,dashed] ([xshift=0em,yshift=0em]ml1.west) -- ([xshift=0em,yshift=0em]ml1.east);
@@ -65,7 +65,7 @@
 \node[anchor=south,cnode,fill=white] (cc1) at ([xshift=-4em,yshift=1.5em]m2.south){};
 \node[anchor=north,cnode,fill=white] (cc2) at ([xshift=0em,yshift=-1em]m2.north){};
 
-\node[anchor=south west,wnode,align=left,font=\tiny] (wl7) at ([xshift=0.5em,yshift=0em]cc1.east){使用{\color{red!50} \bfnew{模型}}对{\color{blue} \bfnew{数据}}\\中信息进行提取};
+\node[anchor=south west,wnode,align=right,font=\tiny] (wl7) at ([xshift=0.5em,yshift=-0.5em]cc1.east){使用{\color{red!50} \bfnew{模型}}对{\color{blue} \bfnew{数据}}\\中的信息进行\\提取};
 \node[anchor=west,wnode,align=right,font=\tiny] (wl8) at ([xshift=0.5em,yshift=0em]cc2.east){使用提取的信息对\\{\color{red!50} \bfnew{模型}}中的参数\\进行训练};
 
 \draw [-,thick,dashed] ([xshift=0em,yshift=0em]mc1.west) -- ([xshift=0em,yshift=0em]mc1.east);

Chapter15/Figures/figure-learning-rate.tex

@@ -31,7 +31,7 @@
       \addplot[blue,line width=1.25pt] coordinates {(2.9706,2) (3.1706,1.79) (3.3706,1.63) (3.4656,1.572) (3.6706,1.4602) (3.7136,1.44)};
       \addplot[blue,dashed,line width=1.25pt] coordinates {(3.7136,1.44) (3.7136,2)};
       \addplot[blue,line width=1.25pt] coordinates {(3.7136,2) (3.9136,1.79) (4.1136,1.63) (4.2086,1.572) (4.4136,1.4602) (4.4566,1.44) (4.7000,1.3574) (5.0000,1.2531)};
-      \addlegendentry{\scriptsize 调整后的学习率}
+      \addlegendentry{\scriptsize 重置后的学习率}
 
       \end{axis}
      }

Chapter15/Figures/figure-structure-search-based-on-gradient-method.tex

@@ -4,8 +4,8 @@
 \begin{tikzpicture}
 \tikzstyle{node}=[minimum height=6em,inner sep=4pt,align=left,draw,font=\footnotesize,rounded corners=4pt,thick,drop shadow]
 
-\node[node,fill=orange!30] (n1) at (0,0){\scriptsize\bfnew{超网络}： \\ [1ex] 模型结构参数 \\[0.4ex] 网络参数};
-\node[anchor=west,node,fill=yellow!30] (n2) at ([xshift=4em]n1.east){\scriptsize\bfnew{优化后的超网络}： \\ [1ex]模型{\color{red}结构参数}（已优化） \\ [0.4ex]网络参数（已优化）};
+\node[node,fill=orange!30] (n1) at (0,0){\scriptsize\bfnew{超网络} \\ [1ex] 模型结构参数 \\[0.4ex] 网络参数};
+\node[anchor=west,node,fill=yellow!30] (n2) at ([xshift=4em]n1.east){\scriptsize\bfnew{优化后的超网络} \\ [1ex]模型{\color{red}结构参数}（已优化） \\ [0.4ex]网络参数（已优化）};
 \node[anchor=west,node,fill=red!30] (n3) at ([xshift=6em]n2.east){\scriptsize\bfnew{找到的模型结构}};
 
 \draw[-latex,thick] (n1.0) -- node[above,align=center,font=\scriptsize]{优化后的\\超网络}(n2.180);

Chapter15/Figures/figure-wmt16.tex

@@ -48,6 +48,9 @@
 \node [anchor=north,rotate=90] (n2) at (5.4cm,1cm) {\scriptsize 训练集\ PPL};
 \node [anchor=north,rotate=90] (n3) at (4.2cm,1cm) {\scriptsize 校验集\ PPL};
 \node [anchor=north,rotate=90] (n4) at (10.7cm,1cm) {\scriptsize 校验集\ PPL};
+
+\node [anchor=north] (label1) at (1.6cm,-1.2cm) {\small (a)浅层模型};
+\node [anchor=north] (label2) at (8.4cm,-1.2cm) {\small (b)深层模型};
 \end{tikzpicture}
 
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