From cc791f59c4027fb047469ea0854e450d23d8f1c1 Mon Sep 17 00:00:00 2001
From: feihugis <hufei68@gmail.com>
Date: Tue, 7 Apr 2020 22:07:17 -0500
Subject: [PATCH 1/2] Make keras model load compatible with old version of
 models

---
 tensorflow/python/keras/utils/generic_utils.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/python/keras/utils/generic_utils.py b/tensorflow/python/keras/utils/generic_utils.py
index 27015cbc8f2..f86bc30bcde 100644
--- a/tensorflow/python/keras/utils/generic_utils.py
+++ b/tensorflow/python/keras/utils/generic_utils.py
@@ -296,6 +296,15 @@ def class_and_config_for_serialized_keras_object(
     raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
 
   cls_config = config['config']
+  # Check if `cls_config` is a list. If it is a list, return the class and the
+  # associated class configs for recursively deserialization. This case will
+  # happen on the old version of sequential model (e.g. `keras_version` ==
+  # "2.0.6"), which is serialized in a different structure, for example 
+  # "{'class_name': 'Sequential',
+  #   'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}".
+  if isinstance(cls_config, list):
+    return (cls, cls_config)
+
   deserialized_objects = {}
   for key, item in cls_config.items():
     if isinstance(item, dict) and '__passive_serialization__' in item:

From 49b07e664590f73942bcbaf1c378e59ccab9f04b Mon Sep 17 00:00:00 2001
From: feihugis <hufei68@gmail.com>
Date: Mon, 13 Apr 2020 22:59:16 -0500
Subject: [PATCH 2/2] Add the test case

---
 .../python/keras/utils/generic_utils_test.py  | 49 +++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/tensorflow/python/keras/utils/generic_utils_test.py b/tensorflow/python/keras/utils/generic_utils_test.py
index 334758871fa..3850591e453 100644
--- a/tensorflow/python/keras/utils/generic_utils_test.py
+++ b/tensorflow/python/keras/utils/generic_utils_test.py
@@ -18,6 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+
 from tensorflow.python import keras
 from tensorflow.python.platform import test
 
@@ -298,6 +300,53 @@ class SerializeKerasObjectTest(test.TestCase):
     self.assertEqual(new_layer.units, 3)
     self.assertIs(new_layer.units.fn, serializable_fn)
 
+  def test_serializable_with_old_config(self):
+    # model config generated by tf-1.2.1
+    old_model_config = {
+        'class_name': 'Sequential',
+        'config': [
+            {
+                'class_name': 'Dense',
+                'config': {
+                    'name': 'dense_1',
+                    'trainable': True,
+                    'batch_input_shape': [
+                        None,
+                        784
+                    ],
+                    'dtype': 'float32',
+                    'units': 32,
+                    'activation': 'linear',
+                    'use_bias': True,
+                    'kernel_initializer': {
+                        'class_name': 'Ones',
+                        'config': {
+                            'dtype': 'float32'
+                        }
+                    },
+                    'bias_initializer': {
+                        'class_name': 'Zeros',
+                        'config': {
+                            'dtype': 'float32'
+                        }
+                    },
+                    'kernel_regularizer': None,
+                    'bias_regularizer': None,
+                    'activity_regularizer': None,
+                    'kernel_constraint': None,
+                    'bias_constraint': None
+                }
+            }
+        ]
+    }
+    old_model = keras.utils.generic_utils.deserialize_keras_object(
+        old_model_config, module_objects={'Sequential': keras.Sequential})
+    new_model = keras.Sequential(
+        [keras.layers.Dense(32, input_dim=784, kernel_initializer='Ones'),])
+    input_data = np.random.normal(2, 1, (5, 784))
+    output = old_model.predict(input_data)
+    expected_output = new_model.predict(input_data)
+    self.assertAllEqual(output, expected_output)
 
 class SliceArraysTest(test.TestCase):