Fix cloning of Sequential models w. input_tensors argument (#20550)

* Fix cloning for Sequential w. input tensor

* Add missing test for input_tensor argument

* Add Sequential wo. Input to test, build model to ensure defined inputs

Author: Carbyne

Diff for: keras/src/models/cloning.py

@@ -298,7 +298,7 @@ def _clone_sequential_model(model, clone_function, input_tensors=None):
         input_dtype = None
         input_batch_shape = None
 
-    if input_tensors:
+    if input_tensors is not None:
         if isinstance(input_tensors, (list, tuple)):
             if len(input_tensors) != 1:
                 raise ValueError(
@@ -310,7 +310,12 @@ def _clone_sequential_model(model, clone_function, input_tensors=None):
                 "Argument `input_tensors` must be a KerasTensor. "
                 f"Received invalid value: input_tensors={input_tensors}"
             )
-        inputs = Input(tensor=input_tensors, name=input_name)
+        inputs = Input(
+            tensor=input_tensors,
+            batch_shape=input_tensors.shape,
+            dtype=input_tensors.dtype,
+            name=input_name,
+        )
         new_layers = [inputs] + new_layers
     else:
         if input_batch_shape is not None:

Diff for: keras/src/models/cloning_test.py

@@ -61,6 +61,15 @@ def get_sequential_model(explicit_input=True):
     return model
 
 
+def get_cnn_sequential_model(explicit_input=True):
+    model = models.Sequential()
+    if explicit_input:
+        model.add(layers.Input(shape=(7, 3)))
+    model.add(layers.Conv1D(2, 2, padding="same"))
+    model.add(layers.Conv1D(2, 2, padding="same"))
+    return model
+
+
 def get_subclassed_model():
     class ExampleModel(models.Model):
         def __init__(self, **kwargs):
@@ -124,6 +133,23 @@ def clone_function(layer):
             if not isinstance(l1, layers.InputLayer):
                 self.assertEqual(l2.name, l1.name + "_custom")
 
+    @parameterized.named_parameters(
+        ("cnn_functional", get_cnn_functional_model),
+        ("cnn_sequential", get_cnn_sequential_model),
+        (
+            "cnn_sequential_noinputlayer",
+            lambda: get_cnn_sequential_model(explicit_input=False),
+        ),
+    )
+    def test_input_tensors(self, model_fn):
+        ref_input = np.random.random((2, 7, 3))
+        model = model_fn()
+        model(ref_input)  # Maybe needed to get model inputs if no Input layer
+        input_tensor = model.inputs[0]
+        new_model = clone_model(model, input_tensors=input_tensor)
+        tree.assert_same_structure(model.inputs, new_model.inputs)
+        tree.assert_same_structure(model.outputs, new_model.outputs)
+
     def test_shared_layers_cloning(self):
         model = get_mlp_functional_model(shared_layers=True)
         new_model = clone_model(model)