Update model_tensorflow.py

model_tensorflow.py

@@ -1,13 +1,3 @@
-"""
-Architecture summary:
-    Stem   : Conv(32, 3×3) → BN → ReLU
-    Stage 1: SepConv(64)  → BN → ReLU → MaxPool → Dropout
-    Stage 2: SepConv(128) → BN → ReLU → MaxPool → Dropout
-    Stage 3: SepConv(256) → BN → ReLU → MaxPool → Dropout
-    Head   : GlobalAvgPool → Dense(128) → BN → ReLU → Dropout → Dense(6, softmax)
-
-"""
-
 import tensorflow as tf
 from tensorflow.keras import layers, Model
 
@@ -24,14 +14,13 @@ def separable_block(x, filters: int, dropout_rate: float = 0.25):
     x = layers.Activation("relu")(x)
 
     x = layers.MaxPooling2D(pool_size=2)(x)
-
     x = layers.SpatialDropout2D(rate=dropout_rate)(x)
 
     return x
 
 
 def build_sara_tf_model(input_shape=(150, 150, 3), num_classes: int = 6) -> Model:
-   
+
     inputs = tf.keras.Input(shape=input_shape, name="image_input")
 
     x = layers.Conv2D(32, kernel_size=3, padding="same",
@@ -43,7 +32,6 @@ def build_sara_tf_model(input_shape=(150, 150, 3), num_classes: int = 6) -> Mode
     x = separable_block(x, filters=128, dropout_rate=0.25)
     x = separable_block(x, filters=256, dropout_rate=0.30)
 
-   
     x = layers.GlobalAveragePooling2D(name="gap")(x)
 
     x = layers.Dense(128, use_bias=False, name="fc1")(x)
@@ -67,4 +55,9 @@ def build_sara_tf_model(input_shape=(150, 150, 3), num_classes: int = 6) -> Mode
 
 if __name__ == "__main__":
     model = build_sara_tf_model()
+
+    model.build((None, 150, 150, 3))
+
     model.load_weights("ton_modele_weights.h5")
+
+    print("Modèle chargé correctement !")