两种插图方式,不明白论文中为什么用第二种

本文通过图表对比了不同批量大小下梯度下降法的批量学习效果,展示了向量化对于批量学习效率的影响,并比较了几种优化算法在相同任务上的表现。
\iffalse
\begin{figure}
\begin{floatrow}[3]
\ffigbox{ \caption{Vectorisation comparison for batch learning (batch size 100, samples of 10 time steps)} \label{fig:batchcmp}}{\includegraphics[width=4.7cm,height=4.5cm]{picture//batchcomp_bold2}}
\ffigbox{ \caption{Batch learning with Gradient Descent} \label{fig:batch} }{\includegraphics[width=4.7cm,height=4.5cm]{picture//batchsize_bold2}}
\ffigbox{\caption{Comparison with Optimization Algorithms} \label{fig:lbfgs}}
{\includegraphics[width= 4.7cm,height=4.5cm]{picture//lbfgs2_bold}}
\end{floatrow}
\end{figure}
\fi
\begin{figure*}\vspace{-.2cm}
\begin{centering}
\subfigure[]{\includegraphics[width=4.6cm,height=3.5cm]{picture//batchsize_bold2}\label{fig:batch}}
\subfigure[]{\includegraphics[width=4.6cm,height=3.5cm]{picture//batchcomp_bold2}\label{fig:batchcmp}}
\subfigure[]{\includegraphics[width= 4.6cm,height=3.5cm]{picture//lbfgs2_bold}\label{fig:lbfgs}}
\end{centering}\vspace{-.2cm}
\caption{(a) batch learning with gradient descent;  (b) vectorization comparison for (mini-)batch learning, where the batch size is 100 and samples of 10 time steps; and (c) comparison with optimization algorithms.}
\end{figure*}

 

转载于:https://www.cnblogs.com/huashiyiqike/p/3771021.html

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