diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py
index 21df53ed59d..912c782deef 100644
--- a/tensorflow/python/keras/engine/network.py
+++ b/tensorflow/python/keras/engine/network.py
@@ -239,17 +239,12 @@ class Network(base_layer.Layer):
       outputs = outputs[0]
     self._nested_outputs = outputs
     self._nested_inputs = inputs
+    self._nested_inputs_are_flat_list = (
+        isinstance(self._nested_inputs, (list, tuple)) and
+        not any(nest.is_sequence(t) for t in self._nested_inputs))
     self.inputs = nest.flatten(inputs)
     self.outputs = nest.flatten(outputs)
 
-    # Models constructed with a single Tensor or list of Tensors can
-    # be called with a dict, where the keys of the dict are the names
-    # of the `Input` objects. Extra keys are ignored.
-    self._enable_dict_to_input_mapping = (
-        not nest.is_sequence(self._nested_inputs) or
-        (isinstance(self._nested_inputs, (list, tuple)) and
-         not any(nest.is_sequence(t) for t in self._nested_inputs)))
-
     if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
       base_layer_utils.create_keras_history(self._nested_outputs)
 
@@ -917,16 +912,16 @@ class Network(base_layer.Layer):
 
   def _flatten_to_reference_inputs(self, tensors):
     """Maps `tensors` to their respective `keras.Input`."""
-    if self._enable_dict_to_input_mapping and isinstance(tensors, dict):
-      ref_inputs = self._nested_inputs
-      if not nest.is_sequence(ref_inputs):
-        ref_inputs = [self._nested_inputs]
-
-      # Flatten in the order the `Input`s were passed during Model construction.
-      return [tensors[inp._keras_history.layer.name] for inp in ref_inputs]
-
-    # Otherwise both self.inputs and tensors will already be in same order.
-    return nest.flatten(tensors)
+    if self._nested_inputs_are_flat_list and isinstance(tensors, dict):
+      # Backwards compat: Allows passing a dict to a Model constructed with a
+      # list. Matches dict keys to input names.
+      tensors = [
+          tensors[inp._keras_history.layer.name] for inp in self._nested_inputs
+      ]
+    else:
+      # Otherwise both self.inputs and tensors will be flattened in same order.
+      tensors = nest.flatten(tensors)
+    return tensors
 
   def _conform_to_reference_input(self, tensor, ref_input):
     """Set shape and dtype based on `keras.Input`s."""
diff --git a/tensorflow/python/keras/engine/network_test.py b/tensorflow/python/keras/engine/network_test.py
index e227d08f595..46f4d951407 100644
--- a/tensorflow/python/keras/engine/network_test.py
+++ b/tensorflow/python/keras/engine/network_test.py
@@ -1576,20 +1576,6 @@ class NestedNetworkTest(keras_parameterized.TestCase):
     res = reversed_model({'a': a_val, 'b': b_val})
     self.assertAllClose(self.evaluate(res), self.evaluate(b_val))
 
-  def test_dict_mapping_single_input(self):
-    b = input_layer_lib.Input(shape=(1,), name='b')
-    outputs = b * 2
-    model = training_lib.Model(b, outputs)
-
-    b_val = array_ops.ones((1, 1))
-    extra_val = array_ops.ones((1, 10))
-
-    inputs = {'a': extra_val, 'b': b_val}
-    res = model(inputs)
-
-    # Check that 'b' was used and 'a' was ignored.
-    self.assertEqual(res.shape.as_list(), [1, 1])
-
 
 @combinations.generate(combinations.keras_mode_combinations())
 class AddLossTest(keras_parameterized.TestCase):