1 files changed, 26 insertions, 4 deletions
diff --git a/tensorflow/python/keras/engine/sequential.py b/tensorflow/python/keras/engine/sequential.py
index cd76f08a32..41cdfda660 100644
--- a/tensorflow/python/keras/engine/sequential.py
+++ b/tensorflow/python/keras/engine/sequential.py
@@ -29,6 +29,7 @@ from tensorflow.python.keras.engine.input_layer import InputLayer
 from tensorflow.python.keras.engine.training import Model
 from tensorflow.python.keras.utils import layer_utils
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training.checkpointable import base as checkpointable
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -108,6 +109,7 @@ class Sequential(Model):
       return self._layers[1:]
     return self._layers
 
+  @checkpointable.no_automatic_dependency_tracking
   def add(self, layer):
     """Adds a layer instance on top of the layer stack.
 
@@ -191,6 +193,7 @@ class Sequential(Model):
     else:
       self._layers.append(layer)
 
+  @checkpointable.no_automatic_dependency_tracking
   def pop(self):
     """Removes the last layer in the model.
 
@@ -211,11 +214,30 @@ class Sequential(Model):
       self.build()
 
   def build(self, input_shape=None):
-    if input_shape and not self.inputs:
-      batch_shape = tuple(input_shape)
+    self._set_inputs_and_outputs(input_shape=input_shape)
+
+  def symbolic_set_inputs(self, inputs):
+    self._set_inputs_and_outputs(tensor=inputs)
+
+  @checkpointable.no_automatic_dependency_tracking
+  def _set_inputs_and_outputs(self, input_shape=None, tensor=None):
+    """Set model's input and output specs based on the input received.
+
+    If `tensor` is provided, `input_shape` is not required.
+
+    Args:
+      input_shape: Optional shape of input.
+      tensor: Optional existing tensor to wrap into the `Input` layer.
+    """
+    if not self.inputs:
       dtype = K.floatx()
-      x = Input(
-          batch_shape=batch_shape, dtype=dtype, name=self.name + '_input')
+      if tensor is not None:
+        batch_shape = (None,) + tuple(tensor.get_shape().as_list()[1:])
+        x = Input(dtype=dtype, name=self.name + '_input', tensor=tensor)
+      elif input_shape is not None:
+        batch_shape = tuple(input_shape)
+        x = Input(
+            batch_shape=batch_shape, dtype=dtype, name=self.name + '_input')
       self.inputs = [x]
       for layer in self._layers:
         x = layer(x)