May 2020
Intermediate to advanced
576 pages
26h 55m
French
Content preview from Deep Learning avec Keras et TensorFlow
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174
Chapitre 4. Modèles personnalisés et entraînement avec TensorFlow
la perte : elle est égale à la perte principale plus les autres pertes (ce modèle
comprend une perte de régularisation par couche). Puisque la fonction
mean_squared_error() retourne une perte par instance, nous calculons
la moyenne sur le lot à l’aide de tf.reduce_mean() (si nous voulons
appliquer différents poids à chaque instance, c’est là qu’il faut le faire). Les
pertes de régularisation étant déjà réduites à un seul scalaire chacune, il nous
faut simplement les additionner (avec tf.add_n(), qui effectue la somme de
plusieurs tenseurs de mêmes forme et type de données). ...
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