Fix cloning with compiled sequential model

keras/src/models/cloning.py

@@ -323,7 +323,15 @@ def _clone_sequential_model(model, clone_function, input_tensors=None):
                 name=input_name,
             )
             new_layers = [inputs] + new_layers
-    return Sequential(new_layers, name=model.name, trainable=model.trainable)
+    cloned_model = Sequential(
+        new_layers, name=model.name, trainable=model.trainable
+    )
+
+    # If model compiled already then set same to cloned model
+    if model.compiled:
+        compiled_config = model.get_compile_config()
+        cloned_model.compile_from_config(compiled_config)
+    return cloned_model
 
 
 def _clone_functional_model(
@@ -405,5 +413,7 @@ def operation_fn(layer):
         # class than the original. However various existing models rely
         # on this behavior, so we keep it.
         new_model = Functional(input_tensors, output_tensors, name=model.name)
-
+    if model.compiled:
+        compiled_config = model.get_compile_config()
+        new_model.compile_from_config(compiled_config)
     return new_model

keras/src/models/cloning_test.py

@@ -242,3 +242,12 @@ def clone_function(layer):
             if isinstance(l2, layers.Dense):
                 self.assertFalse(hasattr(l1, "flag"))
                 self.assertTrue(hasattr(l2, "flag"))
+
+    def test_compiled_model_cloning(self):
+        model = models.Sequential()
+        model.add(layers.Input((3,)))
+        model.add(layers.Dense(5, activation="relu"))
+        model.add(layers.Dense(1, activation="sigmoid"))
+        model.compile(optimizer="adam", loss="binary_crossentropy")
+        cloned_model = clone_model(model)
+        self.assertEqual(model.compiled, cloned_model.compiled)