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Projet IA
Reconnaissance de chiffres manuscrits

                     29/01/2013

     Encadré par Laurent BOUGRAIN




Nicolas DELSAUT, Alexis FOUCHE, Julien GUÉPIN, Tristan STEF
                           (IL)
Perceptron multicouches
             Entrées :                   Couche de sortie :
                               Couches
            valeur de                    discrimination =>
                               cachées
           chaque pixel                  classe majoritaire

                          w1


                          w2      .
               .                  .
                          .       .
               .
                          .
               .
                          .
                          .
                          .
                          .       .
                          .       .
               .          .       .
               .          .
               .          wn




2/7
Activation


             w1
                  .
     .            .
     .            .
     .
                      Sortie = activation( sum{i} (Wi * entrée i) )
             w2

             .
             .    .
             .    .
     .
                  .
     .
     .       wn
Rétropropagation du gradient de
l'erreur




 derivate_activation( sum{i}( wi * erreur i) )           wi, erreur i


 derivate_activation( sum{i}( goal - last output ) )

                                                       wj, erreur j
Stratégie adoptée
Réseau de neurones à simple couche ou
couche(s) cachée(s)

Apprentissage avec 500 itérations sur 60 000
échantillons

Coefficient d'apprentissage et momentum
paramètrables
Implémentation
Projet développé en Java

Réseau de neurone :
Entrées = valeur de chaque pixel d'une image
Sorties = chiffre représenté

Interface graphique, apprentissage dans un
thread, dessin de caractères, ...
Conclusion
Perceptron une couche cachée plus performant
mais apprentissage plus long que perceptron
simple couche


Améliorations possibles :
● Batterie de tests pour trouver les valeurs
  optimales, enregistrement des valeurs
● Reconnaissance de tous les caractères

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Présentation IA

  • 1. Projet IA Reconnaissance de chiffres manuscrits 29/01/2013 Encadré par Laurent BOUGRAIN Nicolas DELSAUT, Alexis FOUCHE, Julien GUÉPIN, Tristan STEF (IL)
  • 2. Perceptron multicouches Entrées : Couche de sortie : Couches valeur de discrimination => cachées chaque pixel classe majoritaire w1 w2 . . . . . . . . . . . . . . . . . . . . . wn 2/7
  • 3. Activation w1 . . . . . . Sortie = activation( sum{i} (Wi * entrée i) ) w2 . . . . . . . . . wn
  • 4. Rétropropagation du gradient de l'erreur derivate_activation( sum{i}( wi * erreur i) ) wi, erreur i derivate_activation( sum{i}( goal - last output ) ) wj, erreur j
  • 5. Stratégie adoptée Réseau de neurones à simple couche ou couche(s) cachée(s) Apprentissage avec 500 itérations sur 60 000 échantillons Coefficient d'apprentissage et momentum paramètrables
  • 6. Implémentation Projet développé en Java Réseau de neurone : Entrées = valeur de chaque pixel d'une image Sorties = chiffre représenté Interface graphique, apprentissage dans un thread, dessin de caractères, ...
  • 7. Conclusion Perceptron une couche cachée plus performant mais apprentissage plus long que perceptron simple couche Améliorations possibles : ● Batterie de tests pour trouver les valeurs optimales, enregistrement des valeurs ● Reconnaissance de tous les caractères