我有一张包含 5 张图表的表格，表格移到了页面之外。如何防止这种情况发生？以下是重现该问题的代码
\documentclass{article}
\usepackage[utf8]{inputenc} % allow utf-8 input
\usepackage[T1]{fontenc} % use 8-bit T1 fonts
\usepackage{hyperref} % hyperlinks
\usepackage{url} % simple URL typesetting
\usepackage{booktabs} % professional-quality tables
\usepackage{amsfonts} % blackboard math symbols
\usepackage{nicefrac} % compact symbols for 1/2, etc.
\usepackage{microtype} % microtypography
\usepackage{hyperref}
\usepackage{multirow}
\usepackage{multicol}
\usepackage{color}
\usepackage{enumitem}% http://ctan.org/pkg/enumitem
% For figures
\usepackage{graphicx} % more modern
%\usepackage{epsfig} % less modern
\usepackage{subfigure}
\usepackage{wrapfig}
\usepackage{amsmath, amssymb}
\usepackage[font=small,labelfont=bf]{caption}
\usepackage{setspace}
\usepackage{tikz}
\usepackage{amsmath, amssymb,bm,color}
\usetikzlibrary{positioning,arrows}
\def\layersep{2.5cm}
\def\layersepp{3cm}
\begin{document}
\begin{table}
\begin{tabular}{|c|c|c|}
\hline
\begin{tabular}{c}
VAE\\
\begin{tikzpicture}
\begin{scope}[xshift=-7.5cm,yshift=-5cm,thick,
node distance=1.6cm,on grid,>=stealth',
comp/.style={circle,draw=black}]
\node [comp] (input) {$x$};
\node [comp] (latent) [right=of input] {$z$} edge [<-, draw=blue,very thick] (input);
\node [comp] (copy) [right=of latent] {$\hat{x}$} edge [<-, draw=brown, very thick] (latent);
\end{scope}
\end{tikzpicture}\\\hline
RNN\\
\begin{tikzpicture}[shorten >=1pt,->,draw=black, node distance=\layersep,transform shape,rotate=90] %<-- rotate the NN
\tikzstyle{every pin edge}=[<-,shorten <=1pt]
\tikzstyle{neuron}=[circle,thick,draw,fill=white,minimum size=17pt,inner sep=0pt]
\tikzstyle{output neuron}=[neuron];
\tikzstyle{hoz}=[rotate=-90];
% hidden states
\foreach \x / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (I-\name) at (0,-\x) {$h_\name$};
\node[hoz] (I-4) at (0,-4) {$\dots$};
% connect hidden states
\begin{scope}[
node/.style={circle,draw=black},
every edge/.style={draw=red, very thick}
]
\path (I-1.east) edge (I-2.west);
\path (I-2.east) edge (I-3.west);
\path (I-3.east) edge (I-4.west);
\path (I-4.east) edge (I-t.west);
% inputs states
\foreach \x / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (X-\name) at (-1,-\x) {$x_\name$};
% connect inputs to hidden states
\foreach \name in {1,2,3,t}
\path (X-\name.north) edge (I-\name.south);
\end{scope}
\end{tikzpicture}\\\hline
DANN\\
\begin{tikzpicture}
\begin{scope}[xshift=-7.5cm,yshift=-5cm,thick,
node distance=1.6cm,on grid,>=stealth',
comp/.style={circle,draw=black}]
\node [comp] (input) {$x$};
\node [comp] (latent) [right=of input] {$z$} edge [<-,very thick,draw=blue] (input);
\node [comp] (label) [right=of latent,yshift=-0.8cm] {$L_y$} edge [<-, very thick] (latent);
\node [comp] (domain) [above=of label] {$L_D$} edge [<-, very thick] (latent);
\end{scope}
\end{tikzpicture}
\end{tabular}
&
\begin{tabular}{c}
VRNN\\
\begin{tikzpicture}[shorten >=1pt,->,draw=black, node distance=\layersep,transform shape,rotate=90] %<-- rotate the NN
\tikzstyle{every pin edge}=[<-,shorten <=1pt]
\tikzstyle{recurrence}=[<-,color=green]
\tikzstyle{neuron}=[circle,thick,draw,fill=white,minimum size=17pt,inner sep=0pt]
\tikzstyle{output neuron}=[neuron];
\tikzstyle{hoz}=[rotate=-90];
\begin{scope}[every path/.style={very thick}]
% hidden states
\foreach \y / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (H-\name) at (0,-\y) {$h_\name$};
\node[hoz] (H-4) at (0,-4) {$\dots$};
% connect hidden states
\path[color=red] (H-1.east) edge (H-2.west);
\path[color=red] (H-2.east) edge (H-3.west);
\path[color=red] (H-3.east) edge (H-4.west);
\path[color=red] (H-4.east) edge (H-t.west);
% inputs states
\foreach \y / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (X-\name) at (-\layersep,-\y) {$x_\name$};
% connect inputs to hidden states
\foreach \name in {1,2,3,t}
\path[color=red] (X-\name.north) edge (H-\name.south);
% latent states
\foreach \y / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (Z-\name) at (\layersep,-\y) {$z_\name$};
% connect inputs to hidden states
\foreach \name in {1,2,3,t}
\path[color=red] (Z-\name.south) edge (H-\name.north);
% draw inference connections
\foreach \name in {1,2,3,t}
\path[color=brown] (Z-\name.south) edge [bend left=70] (X-\name.north);
% draw generation connections
\foreach \name in {1,2,3,t}
\path[color=blue] (X-\name.north) edge [bend left=70] (Z-\name.south);
\end{scope}
\end{tikzpicture}
\end{tabular}
&
\begin{tabular}{c}
VADA\\
\begin{tikzpicture}[shorten >=1pt,->,draw=black, node distance=\layersep,transform shape,rotate=90] %<-- rotate the NN
\tikzstyle{every pin edge}=[<-,shorten <=1pt]
\tikzstyle{recurrence}=[<-,color=green]
\tikzstyle{neuron}=[circle,thick,draw,fill=white,minimum size=17pt,inner sep=0pt]
\tikzstyle{output neuron}=[neuron];
\tikzstyle{hoz}=[rotate=-90];
\begin{scope}[every path/.style={very thick}]
% hidden states
\foreach \x / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (H-\name) at (0,-\x) {$h_\name$};
\node[hoz] (H-4) at (0,-4) {$\dots$};
% connect hidden states
\path[color=red] (H-1.east) edge (H-2.west);
\path[color=red] (H-2.east) edge (H-3.west);
\path[color=red] (H-3.east) edge (H-4.west);
\path[color=red] (H-4.east) edge (H-t.west);
% inputs states
\foreach \x / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (X-\name) at (-\layersep,-\x) {$x_\name$};
% connect inputs to hidden states
\foreach \name in {1,2,3,t}
\path[color=red] (X-\name.north) edge (H-\name.south);
% latent states
\foreach \x / \name in {0/1, 1.5/2, 3/3, 5/t}
\node[neuron, hoz] (Z-\name) at (\layersep,-\x) {$z_\name$};
% connect inputs to hidden states
\foreach \name in {1,2,3,t}
\path[color=red] (Z-\name.south) edge (H-\name.north);
% draw inference connections
\foreach \name in {1,2,3,t}
\path[color=brown] (Z-\name.south) edge [bend left=70] (X-\name.north);
% draw generation connections
\foreach \name in {1,2,3,t}
\path[color=blue] (X-\name.north) edge [bend left=70] (Z-\name.south);
% draw label classifiers
\foreach \x / \name in {-.5/1, 1/2, 2.5/3, 4.5/t}
\node[neuron, hoz] (X-\name) at (\layersepp,-\x) {$L_y^\name$};
% draw domain classifiers
\foreach \x / \name in {.5/1, 2/2, 3.5/3, 5.5/t}
\node[neuron, hoz] (X-\name) at (\layersepp,-\x) {$L_D^\name$};
\end{scope}
\end{tikzpicture}
\end{tabular}
\\\hline
\end{tabular}
\end{table}
\end{document}
