Make MultiOptimizer serializable (#2719)

Make MultiOptimizer serializable

tensorflow_addons/optimizers/discriminative_layer_training.py

@@ -145,7 +145,15 @@ def apply_gradients(self, grads_and_vars, **kwargs):
 
     def get_config(self):
         config = super(MultiOptimizer, self).get_config()
-        config.update({"optimizer_specs": self.optimizer_specs})
+        optimizer_specs_without_gv = []
+        for optimizer_spec in self.optimizer_specs:
+            optimizer_specs_without_gv.append(
+                {
+                    "optimizer": optimizer_spec["optimizer"],
+                    "weights": optimizer_spec["weights"],
+                }
+            )
+        config.update({"optimizer_specs": optimizer_specs_without_gv})
         return config
 
     @classmethod

tensorflow_addons/optimizers/tests/discriminative_layer_training_test.py

@@ -286,3 +286,38 @@ def test_serialization():
 
     new_optimizer = tf.keras.optimizers.deserialize(config)
     assert new_optimizer.get_config() == optimizer.get_config()
+
+
+def test_serialization_after_training(tmpdir):
+    x = np.array(np.ones([100]))
+    y = np.array(np.ones([100]))
+    model = tf.keras.Sequential(
+        [tf.keras.Input(shape=[1]), tf.keras.layers.Dense(1), tf.keras.layers.Dense(1)]
+    )
+
+    opt1 = tf.keras.optimizers.Adam(learning_rate=1e-3)
+    opt2 = tf.keras.optimizers.SGD(learning_rate=0)
+
+    opt_layer_pairs = [(opt1, model.layers[0]), (opt2, model.layers[1])]
+
+    optimizer = MultiOptimizer(opt_layer_pairs)
+
+    # Train the model for a few epochs.
+    model.compile(loss="categorical_crossentropy", optimizer=optimizer)
+    model.fit(x, y)
+
+    # Verify the optimizer can still be serialized (saved).
+    model.save(str(tmpdir))
+    loaded_model = tf.keras.models.load_model(str(tmpdir))
+    old_config = model.optimizer.get_config()
+    new_config = loaded_model.optimizer.get_config()
+    # Verify the loaded model has the same optimizer as before.
+    assert len(old_config["optimizer_specs"]) == len(new_config["optimizer_specs"])
+    for old_optimizer_spec, new_optimizer_spec in zip(
+        old_config["optimizer_specs"], new_config["optimizer_specs"]
+    ):
+        assert old_optimizer_spec["weights"] == new_optimizer_spec["weights"]
+        assert (
+            old_optimizer_spec["optimizer"].get_config()
+            == new_optimizer_spec["optimizer"].get_config()
+        )