learning rate test

test/TensorFlowNET.UnitTest/Training/GradientDescentOptimizerTests.cs

@@ -1,6 +1,7 @@
 using Microsoft.VisualStudio.TestPlatform.Utilities;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
+using System.Diagnostics;
 using System.Linq;
 using Tensorflow.NumPy;
 using TensorFlowNET.UnitTest;
@@ -69,5 +70,53 @@ namespace Tensorflow.Keras.UnitTest.Optimizers
             TestBasic<double>();
         }
 
+        private void TestTensorLearningRate<T>() where T : struct
+        {
+            var dtype = GetTypeForNumericType<T>();
+
+            // train.GradientDescentOptimizer is V1 only API.
+            tf.Graph().as_default();
+            using (var sess = self.cached_session())
+            {
+                var var0 = tf.Variable(new[] { 1.0, 2.0 }, dtype: dtype);
+                var var1 = tf.Variable(new[] { 3.0, 4.0 }, dtype: dtype);
+                var grads0 = tf.constant(new[] { 0.1, 0.1 }, dtype: dtype);
+                var grads1 = tf.constant(new[] { 0.01, 0.01 }, dtype: dtype);
+                var lrate = constant_op.constant(3.0);
+                var grads_and_vars = new[] {
+                    Tuple.Create(grads0, var0 as IVariableV1),
+                    Tuple.Create(grads1, var1 as IVariableV1)
+                };
+                var sgd_op = tf.train.GradientDescentOptimizer(lrate)
+                    .apply_gradients(grads_and_vars);
+
+                var global_variables = tf.global_variables_initializer();
+                sess.run(global_variables);
+
+                var initialVar0 = sess.run(var0);
+                var initialVar1 = sess.run(var1);
+                // Fetch params to validate initial values
+                self.assertAllCloseAccordingToType(new[] { 1.0, 2.0 }, self.evaluate<T[]>(var0));
+                self.assertAllCloseAccordingToType(new[] { 3.0, 4.0 }, self.evaluate<T[]>(var1));
+                // Run 1 step of sgd
+                sgd_op.run();
+                // Validate updated params
+                self.assertAllCloseAccordingToType(
+                    new[] { 1.0 - 3.0 * 0.1, 2.0 - 3.0 * 0.1 },
+                    self.evaluate<T[]>(var0));
+                self.assertAllCloseAccordingToType(
+                    new[] { 3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01 },
+                    self.evaluate<T[]>(var1));
+                // TODO: self.assertEqual(0, len(optimizer.variables()));
+            }
+        }
+
+        [TestMethod]
+        public void TestTensorLearningRate()
+        {
+            //TODO: add np.half
+            TestTensorLearningRate<float>();
+            TestTensorLearningRate<double>();
+        }
     }
 }