Standardized Testing for All Optimizers (tensorflow#2208)

* Added standardized optimizer serialization and minimize tests

tensorflow_addons/optimizers/tests/standard_test.py

@@ -0,0 +1,77 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import pytest
+import numpy as np
+import tensorflow as tf
+
+from tensorflow_addons import optimizers
+import inspect
+
+class_exceptions = [
+    "MultiOptimizer",  # is wrapper
+    "SGDW",  # is wrapper
+    "AdamW",  # is wrapper
+    "SWA",  # is wrapper
+    "AveragedOptimizerWrapper",  # is wrapper
+    "ConditionalGradient",  # is wrapper
+    "Lookahead",  # is wrapper
+    "MovingAverage",  # is wrapper
+]
+
+
+def discover_classes(module, parent):
+
+    classes = [
+        class_info[1]
+        for class_info in inspect.getmembers(module, inspect.isclass)
+        if issubclass(class_info[1], parent) and not class_info[0] in class_exceptions
+    ]
+
+    return classes
+
+
+classes_to_test = discover_classes(optimizers, tf.keras.optimizers.Optimizer)
+
+
+@pytest.mark.parametrize("optimizer", classes_to_test)
+@pytest.mark.parametrize("serialize", [True, False])
+def test_optimizer_minimize_serialize(optimizer, serialize, tmpdir):
+    """
+    Purpose of this test is to confirm that the optimizer can minimize the loss in toy conditions.
+    It also tests for serialization as a parameter.
+    """
+    model = tf.keras.Sequential([tf.keras.Input(shape=[1]), tf.keras.layers.Dense(1)])
+
+    x = np.array(np.ones([1]))
+    y = np.array(np.zeros([1]))
+
+    opt = optimizer()
+    loss = tf.keras.losses.MSE
+
+    model.compile(optimizer=opt, loss=loss)
+
+    # serialize whole model including optimizer, clear the session, then reload the whole model.
+    # successfully serialized optimizers should not require a compile before training
+    if serialize:
+        model.save(str(tmpdir), save_format="tf")
+        tf.keras.backend.clear_session()
+        model = tf.keras.models.load_model(str(tmpdir))
+
+    history = model.fit(x, y, batch_size=1, epochs=10)
+
+    loss_values = history.history["loss"]
+
+    np.testing.assert_array_less(loss_values[-1], loss_values[0])