X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/presentation_reservoir.git/blobdiff_plain/c420ac31a3a51a6f1a318ba4e979fe02d7dcdb6d..4196be5e022044fd7b16f0dd92a7337472a24c1f:/reservoir.tex diff --git a/reservoir.tex b/reservoir.tex index d3ca368..950da0f 100644 --- a/reservoir.tex +++ b/reservoir.tex @@ -16,7 +16,7 @@ of the neuromorphic simulation code. Application on the MNIST problem} -\author{Rapha\"el Couturier, {\bf Michel Salomon}} +\author{Rapha\"el Couturier, Michel Salomon} \institute[FEMTO-ST Institute]{\textit{FEMTO-ST - DISC Department - AND Team}} @@ -69,7 +69,7 @@ Application on the MNIST problem} \begin{itemize} \item Study the concept of Reservoir Computing \item Design a faster simulation code - \item Apply it to a new problem + \item Apply it to new problems \end{itemize} \end{femtoBlock} \end{frame} @@ -88,7 +88,7 @@ Application on the MNIST problem} \centering \includegraphics[width=7.5cm]{ntc.png} \begin{itemize} - \item $\delta_t \rightarrow \mbox{temporal spacing}; \tau_D \rightarrow \mbox{time delay}$ + \item $\delta \tau \rightarrow \mbox{temporal spacing}; \tau_D \rightarrow \mbox{time delay}$ \item $f(x) \rightarrow \mbox{nonlinear transformation}; h(t) \rightarrow \mbox{impulse response}$ \end{itemize} \end{femtoBlock} @@ -173,53 +173,199 @@ Application on the MNIST problem} % Grandes lignes a partir du pdf de Laurent \begin{femtoBlock} {Main lines\\} + \begin{enumerate} + \item Pre-processing + \begin{itemize} + \item Input data formatting (1D~vector; sampling period $\rightarrow \delta \tau$) + \item $W^I$ initialization (randomly; normalization) + \end{itemize} + \item Concatenation of 1D~vectors $\rightarrow$ batch processing + \item Nonlinear transient computation + \begin{itemize} + \item Numerical integration using a Runge-Kutta C routine + \item Computation of matrices $A$ and $B$ + \end{itemize} + \item Training of the Read-out $\rightarrow$ More-Penrose matrix inversion + \item Testing of the solution (cross-validation) + \end{enumerate} \end{femtoBlock} + \smallskip % Inconvenient de ce code => temps de calcul \begin{femtoBlock} - {Computation time\\} + {Computation time\\} + \vspace{0.125cm} + \centering + 12 min for 306~``neurons'' on a quad-core i7 1,8~GHz (2013) \end{femtoBlock} \end{frame} \section{Parallelization and optimization} \begin{frame}{Parallelization Scheme} % Slide 8 - % 1 - Comment le paralleliser - % 2 - Langage et bibliotheque -\end{frame} - -\begin{frame}{Finding the Optimal Parameters} % Slide 9 - % 1 - Quels parametres et pourquoi ? + \begin{femtoBlock} + {Guidelines\\} + \begin{itemize} + \item Reservoir response is independent, whatever the data \\ + $\rightarrow$ computation of matrices A and B can be parallelized + \item Different regression tests are also independent + \end{itemize} + \end{femtoBlock} \end{frame} -\begin{frame}{Finding the Optimal Parameters} % Slide 10 - % 2 - Optimisation par recuit simule +\begin{frame}{Parallelization Scheme} % Slide 9 + \begin{femtoBlock} + {In practice\\} + \begin{itemize} + \item Simulation code rewritten in C++ + \item Eigen C++ library for linear algebra operations + \item InterProcess~Communication \\ + $\rightarrow$ {\bf M}essage~{\bf P}assing~{\bf I}nterface + \end{itemize} + \end{femtoBlock} + \smallskip + \begin{femtoBlock} + {Performance on speech recognition problem\\} + \begin{itemize} + \item Same WER $\rightarrow$ similar classification accuracy + \item Reduced computation time + \end{itemize} + \centering + \vspace{0.125cm} + {\bf We can study problems with huge Matlab computation time} + \end{femtoBlock} + \medskip + \begin{femtoBlock} + {Testing a new idea?\\} + \vspace{0.25cm} + \centering + First test with Matlab and then adapt to C++ with MPI + \end{femtoBlock} \end{frame} -\begin{frame}{Performances} % Slide 11 - % 1 - Taux d'erreur en terme de classification - % 2 - Gain en temps d'execution / speedup curve +\begin{frame}{Finding Optimal Parameters} % Slide 10 + % 1 - Quels parametres et pourquoi ? + \begin{femtoBlock} + {What parameters can be optimized?\\} + \begin{itemize} + \item Currently + \begin{itemize} + \item Pitch of the Read-Out + \item Amplitude parameters $\rightarrow \delta; \beta; \phi_0$ + \item Regression parameter $\rightarrow \lambda$ + \end{itemize} + \item Next + \begin{itemize} + \item Number of nodes significantly improving the solution (threshold) + \item Input data filter (convolution filter for images) + \end{itemize} + \end{itemize} + \centering + Potentially any parameter can be optimized + \end{femtoBlock} + \smallskip + \begin{femtoBlock} + {Optimization heuristics\\} + \begin{itemize} + \item Currently $\rightarrow$ simulated annealing \\ + {\small (probabilistic global search controlled by a cooling schedule)} + \item Next $\rightarrow$ other metaheuristics like evolutionary algorithms + \end{itemize} + \end{femtoBlock} \end{frame} \section{Performances on the MNIST problem} -\begin{frame}{Application on the MNIST problem} % Slide 12 +\begin{frame}{Application on the MNIST problem} % Slide 11 + \begin{femtoBlock} + {Task of handwritten digits recognition\\} + \centering + \vspace{0.125cm} + National Institute of Standards and Technology database + \begin{itemize} + \item Training dataset $\rightarrow$ american census bureau employees + \item Test dataset $\rightarrow$ american high school students + \end{itemize} + \end{femtoBlock} + \smallskip + \begin{femtoBlock} + {Mixed-NIST database is widely used in machine learning\\} + \centering + \vspace{0.125cm} + Mixing of both datasets and improved images + \begin{columns} + \begin{column}{7.5cm} + \begin{itemize} + \item Datasets + \begin{itemize} + \item Training $\rightarrow$ 60,000 samples + \item Test $\rightarrow$ 10,000 samples + \end{itemize} + \item Grayscale Images + \begin{itemize} + \item Normalized to fit into a $20\times20$ pixel bounding box + \item Centered and anti-aliased + \end{itemize} + \end{itemize} + \end{column} + \begin{column}{3cm} + \centering + \includegraphics[width=3cm]{mnist.png} + \end{column} + \end{columns} + \end{femtoBlock} % 1 - Decrire ce qu'est le MNIST % 2 - Setup de notre reservoir \end{frame} -\begin{frame}{Comparison with other approaches} % Slide 13 +\begin{frame}{Performances of the parallel code} % Slide 12 + % To be completed + 10000 images:\\ + 1000 reservoirs of 2 neurons, error : 3.85\%\\ + 1 reservoir of 2000 neurons, error : 7.14\% + \includegraphics[width=7.5cm]{speedup.pdf} +\end{frame} + +\begin{frame}{Exploring ways to improve the results} % Slide 13 + % Tableau recapitulant les performances +\end{frame} + +\begin{frame}{Comparison with other approaches} % Slide 14 % 1 - Convolutional Neural Networks % 2 - Reservoir en pipeline (papier de 2015) \end{frame} -\begin{frame}{Comparison with other approaches} % Slide 14 +\begin{frame}{Comparison with other approaches} % Slide 15 % Tableau recapitulant les performances \end{frame} \section{Conclusion and perspectives} -\begin{frame}{Conclusion and perspectives} % Slide 15 - +\begin{frame}{Conclusion and perspectives} % Slide 16 + \begin{femtoBlock} + {Results\\} + \begin{itemize} + \item A parallel code allowing fast simulations + \item An evaluation on the MNIST problem + \end{itemize} + \end{femtoBlock} + \begin{femtoBlock} + {Future works\\} + \begin{itemize} + \item Further code improvement + \item Use of several reservoirs + \begin{itemize} + \item Committees + \item Correct errors of a reservoir by another one + \end{itemize} + \end{itemize} + \end{femtoBlock} + %Many perspectives (we are just beginning) + %Improvement of the code\\ + %Test of many ideas : number of comities\\ + %One reservoir to learn and another one to learn error and correct + %them\\ + %Test other large problems in simulation before in real\\ + %=> Try to test many configuration and to find optimal parameters \end{frame} \begin{frame}{Thank you for your attention}