CARIA.2.0

Precedent repo CARIA:
Trainer pour CARIA-INTELLIGENT modeles

server-trainer/RoadSigns-ModelTraining_MobileNetV2.py

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+import os
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.layers import AveragePooling2D, Dropout, Flatten, Dense, Input
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from imutils import paths
+import numpy as np
+
+# Définir les chemins des données d'entraînement et de validation
+train_data_dir = "server-trainer/images/road_sign_trainers/train_speed"
+valid_data_dir = "server-trainer/images/road_sign_trainers/test_speed"
+
+# Initialiser les hyperparamètres
+INIT_LR = 1e-4
+EPOCHS = 1
+BS = 32
+
+# Charger et prétraiter les images
+train_datagen = ImageDataGenerator(
+    rescale=1.0 / 255,
+    rotation_range=20,
+    zoom_range=0.15,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.15,
+    horizontal_flip=True,
+    fill_mode="nearest"
+)
+
+valid_datagen = ImageDataGenerator(rescale=1.0 / 255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_data_dir,
+    target_size=(224, 224),
+    batch_size=BS,
+    class_mode='categorical'
+)
+
+valid_generator = valid_datagen.flow_from_directory(
+    valid_data_dir,
+    target_size=(224, 224),
+    batch_size=BS,
+    class_mode='categorical'
+)
+
+# Générer le fichier class_names.txt
+class_names_file = os.path.join("server-ia/data/modeles/RoadSign", "class_names.txt")
+with open(class_names_file, "w") as f:
+    # Utiliser le générateur class_indices pour récupérer les noms de classe et les écrire dans le fichier
+    for class_name, class_index in train_generator.class_indices.items():
+        f.write(f"{class_name}\n")
+
+# Charger le modèle pré-entraîné MobileNetV2 sans la couche supérieure
+base_model = MobileNetV2(
+    weights="imagenet",
+    include_top=False,
+    input_tensor=Input(shape=(224, 224, 3))
+)
+
+# Construire le modèle de tête qui sera placé au-dessus du modèle de base
+head_model = base_model.output
+head_model = AveragePooling2D(pool_size=(7, 7))(head_model)
+head_model = Flatten(name="flatten")(head_model)
+head_model = Dense(128, activation="relu")(head_model)
+head_model = Dropout(0.5)(head_model)
+head_model = Dense(len(train_generator.class_indices), activation="softmax")(head_model)
+
+# Combiner le modèle de base avec le modèle de tête
+model = Model(inputs=base_model.input, outputs=head_model)
+
+# Geler les couches du modèle de base
+for layer in base_model.layers:
+    layer.trainable = False
+
+# Compiler le modèle
+opt = Adam(learning_rate=INIT_LR)
+model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
+
+# Entraîner le modèle
+history = model.fit(
+    train_generator,
+    steps_per_epoch=len(train_generator),
+    validation_data=valid_generator,
+    validation_steps=len(valid_generator),
+    epochs=EPOCHS
+)
+
+# Sauvegarder le modèle
+model.save("server-ia/data/modeles/RoadSign/modele_signaux_routiers.h5")
+
+# Évaluer le modèle
+print("[INFO] Évaluation du modèle...")
+predictions = model.predict(valid_generator, steps=len(valid_generator), verbose=1)
+predictions = np.argmax(predictions, axis=1)
+print(classification_report(valid_generator.classes, predictions, target_names=valid_generator.class_indices.keys()))