Add the test case

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):