Update model_tensorflow.py

model_tensorflow.py

@@ -8,6 +8,77 @@ 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