Added reverse dependency back to Keras.

Also the original code also passes since the saving code has been updated recently.

PiperOrigin-RevId: 303782079
Change-Id: I0ec9ca84d6584aa30bb5fc3c51906afc7de4320d

tensorflow/python/util/serialization.py

@@ -23,7 +23,6 @@ import wrapt
 
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.util.compat import collections_abc
-from tensorflow.python.keras.utils import generic_utils
 
 
 def get_json_type(obj):
@@ -41,10 +40,7 @@ def get_json_type(obj):
   # if obj is a serializable Keras class instance
   # e.g. optimizer, layer
   if hasattr(obj, 'get_config'):
-    return {
+    return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
-        'class_name': generic_utils.get_registered_name(obj.__class__),
-        'config': obj.get_config()
-    }
 
   # if obj is any numpy type
   if type(obj).__module__ == np.__name__:

tensorflow/python/util/serialization_test.py

@@ -23,12 +23,10 @@ import json
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
-from tensorflow.python.keras import losses
 from tensorflow.python.keras.engine import input_layer
 from tensorflow.python.keras.engine import sequential
 from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers import core
-from tensorflow.python.keras.utils import losses_utils, generic_utils
 from tensorflow.python.platform import test
 from tensorflow.python.util import serialization
 
@@ -71,41 +69,5 @@ class SerializationTests(test.TestCase):
     self.assertEqual(
         10, model_round_trip["config"]["layers"][1]["config"]["units"])
 
-  @test_util.run_in_graph_and_eager_modes
-  def test_serialize_custom_model_compile(self):
-    with generic_utils.custom_object_scope():
-
-      @generic_utils.register_keras_serializable(package="dummy-package")
-      class DummySparseCategoricalCrossentropyLoss(losses.LossFunctionWrapper):
-        # This loss is identical equal to tf.keras.losses.SparseCategoricalCrossentropy
-        def __init__(
-            self,
-            from_logits=False,
-            reduction=losses_utils.ReductionV2.AUTO,
-            name="dummy_sparse_categorical_crossentropy_loss",
-        ):
-          super(DummySparseCategoricalCrossentropyLoss, self).__init__(
-              losses.sparse_categorical_crossentropy,
-              name=name,
-              reduction=reduction,
-              from_logits=from_logits,
-          )
-
-      x = input_layer.Input(shape=[3])
-      y = core.Dense(10)(x)
-      model = training.Model(x, y)
-      model.compile(
-          loss=DummySparseCategoricalCrossentropyLoss(from_logits=True))
-      model_round_trip = json.loads(
-          json.dumps(model.loss, default=serialization.get_json_type))
-
-      # check if class name with package scope
-      self.assertEqual("dummy-package>DummySparseCategoricalCrossentropyLoss",
-                       model_round_trip["class_name"])
-
-      # check if configure is correctly
-      self.assertEqual(True, model_round_trip["config"]["from_logits"])
-
-
 if __name__ == "__main__":
   test.main()