adding back tf code + adding models comparison on SQuAD

tensorflow_code/optimization_test.py

@@ -0,0 +1,54 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow_code import optimization
+import tensorflow as tf
+
+
+class OptimizationTest(tf.test.TestCase):
+
+    def test_adam(self):
+        with self.test_session() as sess:
+            w = tf.get_variable(
+                "w",
+                shape=[3],
+                initializer=tf.constant_initializer([0.1, -0.2, -0.1]))
+            x = tf.constant([0.4, 0.2, -0.5])
+            loss = tf.reduce_mean(tf.square(x - w))
+            tvars = tf.trainable_variables()
+            grads = tf.gradients(loss, tvars)
+            global_step = tf.train.get_or_create_global_step()
+            optimizer = optimization.AdamWeightDecayOptimizer(learning_rate=0.2)
+            train_op = optimizer.apply_gradients(zip(grads, tvars), global_step)
+            init_op = tf.group(tf.global_variables_initializer(),
+                               tf.local_variables_initializer())
+            sess.run(init_op)
+            np_w = sess.run(w)
+            np_loss = sess.run(loss)
+            np_grad = sess.run(grads)[0]
+            for i in range(100):
+                print(i)
+                sess.run(train_op)
+                np_w = sess.run(w)
+                np_loss = sess.run(loss)
+                np_grad = sess.run(grads)[0]
+            self.assertAllClose(np_w.flat, [0.4, 0.2, -0.5], rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    tf.test.main()