Fix the compatibility issues of Orthogonal and GRU

keras/src/initializers/__init__.py

@@ -49,6 +49,7 @@
         "uniform": RandomUniform,
         "normal": RandomNormal,
         "orthogonal": OrthogonalInitializer,
+        "Orthogonal": OrthogonalInitializer,  # Legacy
         "one": Ones,
         "zero": Zeros,
     }

keras/src/initializers/random_initializers_test.py

@@ -147,6 +147,10 @@ def test_orthogonal_initializer(self):
 
         self.run_class_serialization_test(initializer)
 
+        # Test legacy class_name
+        initializer = initializers.get("Orthogonal")
+        self.assertIsInstance(initializer, initializers.OrthogonalInitializer)
+
     def test_get_method(self):
         obj = initializers.get("glorot_normal")
         self.assertTrue(obj, initializers.GlorotNormal)

keras/src/layers/rnn/gru.py

@@ -500,6 +500,7 @@ def __init__(
             trainable=kwargs.get("trainable", True),
             name="gru_cell",
             seed=seed,
+            implementation=kwargs.pop("implementation", 2),
         )
         super().__init__(
             cell,

keras/src/layers/rnn/gru_test.py

@@ -286,3 +286,26 @@ def test_masking(self):
             np.array([[0.11669192, 0.11669192], [0.28380975, 0.28380975]]),
             output,
         )
+
+    def test_legacy_implementation_argument(self):
+        sequence = np.arange(72).reshape((3, 6, 4)).astype("float32")
+        layer = layers.GRU(
+            3,
+            kernel_initializer=initializers.Constant(0.01),
+            recurrent_initializer=initializers.Constant(0.02),
+            bias_initializer=initializers.Constant(0.03),
+        )
+        config = layer.get_config()
+        config["implementation"] = 0  # Add legacy argument
+        layer = layers.GRU.from_config(config)
+        output = layer(sequence)
+        self.assertAllClose(
+            np.array(
+                [
+                    [0.5217289, 0.5217289, 0.5217289],
+                    [0.6371659, 0.6371659, 0.6371659],
+                    [0.39384964, 0.39384964, 0.3938496],
+                ]
+            ),
+            output,
+        )