diff --git a/test/TensorFlowNET.UnitTest/Training/GradientDescentOptimizerTests.cs b/test/TensorFlowNET.UnitTest/Training/GradientDescentOptimizerTests.cs
index d766890b..f7062f00 100644
--- a/test/TensorFlowNET.UnitTest/Training/GradientDescentOptimizerTests.cs
+++ b/test/TensorFlowNET.UnitTest/Training/GradientDescentOptimizerTests.cs
@@ -1,5 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
+using System.Linq;
 using Tensorflow;
 using Tensorflow.NumPy;
 using static Tensorflow.Binding;
@@ -67,6 +68,51 @@ namespace TensorFlowNET.UnitTest.Training
             TestBasic<double>();
         }
 
+        private void TestMinimizeResourceVariable<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[,] { { 1.0f, 2.0f } }, dtype: dtype);
+                var var1 = tf.Variable(new[] { 3.0 }, dtype: dtype);
+                var x = tf.constant(new[,] { { 4.0f }, { 5.0f } }, dtype: dtype);
+
+                var pred = math_ops.matmul(var0, x) + var1;
+                var loss = pred * pred;
+                var sgd_op = tf.train.GradientDescentOptimizer(3.0f).minimize(loss);
+
+                var global_variables = tf.global_variables_initializer();
+                sess.run(global_variables);
+
+                sess.run(new[] { var0, var1 });
+                // Fetch params to validate initial values
+                self.assertAllCloseAccordingToType<T>(new[,] { { 1.0, 2.0 } }, self.evaluate<T[,]>(var0));
+                self.assertAllCloseAccordingToType(new[] { 3.0 }, self.evaluate<T[]>(var1));
+                // Run 1 step of sgd
+                sgd_op.run();
+                // Validate updated params
+                var np_pred = 1.0 * 4.0 + 2.0 * 5.0 + 3.0;
+                var np_grad = 2 * np_pred;
+                self.assertAllCloseAccordingToType(
+                    new[,] { { 1.0 - np_grad * 4.0, 2.0 - np_grad * 5.0 } },
+                    self.evaluate<T[,]>(var0));
+                self.assertAllCloseAccordingToType(
+                    new[] { 3.0 - np_grad },
+                    self.evaluate<T[]>(var1));
+            }
+        }
+
+        [TestMethod]
+        public void TestMinimizeResourceVariable()
+        {
+            //TODO: add np.half
+            TestMinimizeResourceVariable<float>();
+            TestMinimizeResourceVariable<double>();
+        }
+
         private void TestTensorLearningRate<T>() where T : struct
         {
             var dtype = GetTypeForNumericType<T>();
@@ -115,5 +161,72 @@ namespace TensorFlowNET.UnitTest.Training
             TestTensorLearningRate<float>();
             TestTensorLearningRate<double>();
         }
+
+        public void TestGradWrtRef<T>() where T : struct
+        {
+            var dtype = GetTypeForNumericType<T>();
+
+            var graph = tf.Graph().as_default();
+            using (var sess = self.cached_session())
+            {
+                var opt = tf.train.GradientDescentOptimizer(3.0f);
+                var values = new[] { 1.0, 3.0 };
+                var vars_ = values.Select(
+                        v => tf.Variable(new[] { v }, dtype: dtype) as IVariableV1
+                    ).ToList();
+                var grads_and_vars = opt.compute_gradients(tf.add(vars_[0], vars_[1]), vars_);
+                sess.run(tf.global_variables_initializer());
+                foreach (var (grad, _) in grads_and_vars)
+                    self.assertAllCloseAccordingToType(new[] { 1.0 }, self.evaluate<T[]>(grad));
+
+            }
+        }
+
+        [TestMethod]
+        public void TestGradWrtRef()
+        {
+            TestGradWrtRef<float>();
+            TestGradWrtRef<double>();
+        }
+
+        public void TestWithGlobalStep<T>() where T : struct
+        {
+            var dtype = GetTypeForNumericType<T>();
+
+            tf.Graph().as_default();
+            using (var sess = self.cached_session())
+            {
+                var global_step = tf.Variable(0, trainable: false);
+                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 grads_and_vars = new[] {
+                    Tuple.Create(grads0, var0 as IVariableV1),
+                    Tuple.Create(grads1, var1 as IVariableV1)
+                };
+                var sgd_op = tf.train.GradientDescentOptimizer(3.0f)
+                    .apply_gradients(grads_and_vars, global_step: global_step);
+
+                sess.run(tf.global_variables_initializer());
+                // 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 and global_step
+                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));
+                Assert.AreEqual(1, self.evaluate<int>(global_step));
+            }
+
+        }
+
+        [TestMethod]
+        public void TestWithGlobalStep()
+        {
+            TestWithGlobalStep<float>();
+            TestWithGlobalStep<double>();
+        }
     }
 }
diff --git a/test/Tensorflow.UnitTest/PythonTest.cs b/test/Tensorflow.UnitTest/PythonTest.cs
index dff65293..1ccd39f0 100644
--- a/test/Tensorflow.UnitTest/PythonTest.cs
+++ b/test/Tensorflow.UnitTest/PythonTest.cs
@@ -175,8 +175,8 @@ namespace TensorFlowNET.UnitTest
                     return 1;
                 }
 
-                var a = (double)x;
-                var b = (double)y;
+                var a = Convert.ToDouble(x);
+                var b = Convert.ToDouble(y);
 
                 double delta = Math.Abs(a - b);
                 if (delta < _epsilon)
@@ -187,6 +187,19 @@ namespace TensorFlowNET.UnitTest
             }
         }
 
+        public void assertAllCloseAccordingToType<T>(
+            double[,] expected,
+            T[,] given,
+            double eps = 1e-6,
+            float float_eps = 1e-6f)
+        {
+            Assert.AreEqual(expected.GetLength(0), given.GetLength(0));
+            Assert.AreEqual(expected.GetLength(1), given.GetLength(1));
+
+            var flattenGiven = given.Cast<T>().ToArray();
+            assertAllCloseAccordingToType(expected, flattenGiven, eps, float_eps);
+        }
+
         public void assertAllCloseAccordingToType<T>(
             ICollection expected,
             ICollection<T> given,
@@ -267,21 +280,35 @@ namespace TensorFlowNET.UnitTest
             {
                 var sess = tf.get_default_session();
                 var ndarray = tensor.eval(sess);
-                if (typeof(T) == typeof(double)
-                    || typeof(T) == typeof(float)
-                    || typeof(T) == typeof(int))
+
+                if (typeof(T) == typeof(int))
+                {
+                    int i = ndarray;
+                    result = i;
+                }
+                else if (typeof(T) == typeof(float))
+                {
+                    float f = ndarray;
+                    result = f;
+                }
+                else if (typeof(T) == typeof(double))
                 {
-                    result = Convert.ChangeType(ndarray, typeof(T));
+                    double d = ndarray;
+                    result = d;
                 }
-                else if (typeof(T) == typeof(double[]))
+                else if (
+                    typeof(T) == typeof(double[])
+                    || typeof(T) == typeof(double[,]))
                 {
                     result = ndarray.ToMultiDimArray<double>();
                 }
-                else if (typeof(T) == typeof(float[]))
+                else if (typeof(T) == typeof(float[])
+                    || typeof(T) == typeof(float[,]))
                 {
                     result = ndarray.ToMultiDimArray<float>();
                 }
-                else if (typeof(T) == typeof(int[]))
+                else if (typeof(T) == typeof(int[]) 
+                    || typeof(T) == typeof(int[,]))
                 {
                     result = ndarray.ToMultiDimArray<int>();
                 }