iffalse egin{figure} egin{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 egin{figure*}vspace{-.2cm} egin{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*}