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Tensorflow/tutoriel32/model.py

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+from tensorflow.keras import layers, models
+
+# Fonction d'activation à tester: sigmoid, tanh, relu, 
+
+def model(nbr_sortie, nbr_cc):
+    entree=layers.Input(shape=(75, 100, 3), dtype='float32')
+
+    result=layers.Conv2D(nbr_cc, 5, activation='relu', padding='same')(entree)
+    result=layers.Conv2D(nbr_cc, 5, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(2*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.Conv2D(4*nbr_cc, 3, activation='relu', padding='same')(result)
+    result=layers.BatchNormalization()(result)
+    result=layers.MaxPool2D()(result)
+    
+    result=layers.Flatten()(result)
+    result=layers.Dense(1024, activation='relu')(result)
+    result=layers.Dense(1024, activation='relu')(result)
+    result=layers.BatchNormalization()(result)
+    sortie=layers.Dense(nbr_sortie, activation='softmax')(result)
+
+    model=models.Model(inputs=entree, outputs=sortie)
+    return model
+