Update model_tensorflow.py

model_tensorflow.py

@@ -8,77 +8,6 @@ Architecture summary:
 
 """
 
-"""
-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 os
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
-import tensorflow as tf
-from tensorflow.keras import layers, Model
-
-
-def separable_block(x, filters: int, dropout_rate: float = 0.25):
-    x = layers.SeparableConv2D(
-        filters, kernel_size=3, padding="same", use_bias=False)(x)
-    x = layers.BatchNormalization()(x)
-    x = layers.Activation("relu")(x)
-
-    x = layers.SeparableConv2D(
-        filters, kernel_size=3, padding="same", use_bias=False)(x)
-    x = layers.BatchNormalization()(x)
-    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",
-                      use_bias=False, name="stem_conv")(inputs)
-    x = layers.BatchNormalization(name="stem_bn")(x)
-    x = layers.Activation("relu", name="stem_relu")(x)
-
-    x = separable_block(x, filters=64,  dropout_rate=0.25)
-    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)
-    x = layers.BatchNormalization(name="fc1_bn")(x)
-    x = layers.Activation("relu", name="fc1_relu")(x)
-    x = layers.Dropout(0.5, name="fc1_drop")(x)
-
-    outputs = layers.Dense(num_classes, activation="softmax",
-                           name="predictions")(x)
-
-    model = Model(inputs=inputs, outputs=outputs, name="SaraCNN_TF")
-
-    model.compile(
-        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
-        loss="categorical_crossentropy",
-        metrics=["accuracy"],
-    )
-
-    return model
-
-
-if __name__ == "__main__":
-    model = build_sara_tf_model()
-    model.summary()
 import tensorflow as tf
 from tensorflow.keras import layers, Model
 
@@ -138,4 +67,4 @@ def build_sara_tf_model(input_shape=(150, 150, 3), num_classes: int = 6) -> Mode
 
 if __name__ == "__main__":
     model = build_sara_tf_model()
-    model.summary()
+    model.load_weights("ton_modele_weights.h5")