diff --git a/src/TensorFlowNET.Core/Sessions/Session.cs b/src/TensorFlowNET.Core/Sessions/Session.cs
index e43dd913..a610f4e7 100644
--- a/src/TensorFlowNET.Core/Sessions/Session.cs
+++ b/src/TensorFlowNET.Core/Sessions/Session.cs
@@ -38,6 +38,12 @@ namespace Tensorflow
             Status.Check(true);
         }
 
+        public Session as_default()
+        {
+            tf.defaultSession = this;
+            return this;
+        }
+
         public static Session LoadFromSavedModel(string path)
         {
             var graph = c_api.TF_NewGraph();
diff --git a/test/TensorFlowNET.UnitTest/PythonTest.cs b/test/TensorFlowNET.UnitTest/PythonTest.cs
index 5d9bb374..508a0a81 100644
--- a/test/TensorFlowNET.UnitTest/PythonTest.cs
+++ b/test/TensorFlowNET.UnitTest/PythonTest.cs
@@ -132,23 +132,66 @@ namespace TensorFlowNET.UnitTest
         }
 
         /// <summary>
-        /// Evaluates tensors and returns numpy values.
+        /// Evaluates tensors and returns a dictionary of {name:result, ...}.
         /// <param name="tensors">A Tensor or a nested list/tuple of Tensors.</param>
         /// </summary>
-        /// <returns> tensors numpy values.</returns>
-        public object evaluate(params Tensor[] tensors)
+        public Dictionary<string, NDArray> evaluate(params Tensor[] tensors)
         {
+            var results = new Dictionary<string, NDArray>();
             //  if context.executing_eagerly():
             //    return self._eval_helper(tensors)
             //  else:
             {
                 var sess = ops.get_default_session();
-                if (sess == None)
-                    with(self.session(), s => sess = s);
-                return sess.run(tensors);
+                if (sess == null)
+                    sess = self.session();
+
+                with<Session>(sess, s =>
+                {
+                    foreach (var t in tensors)
+                        results[t.name] = t.eval();
+                });
+                return results;
+            }
+        }
+
+        public NDArray evaluate(Tensor tensor)
+        {
+            NDArray result = null;
+            //  if context.executing_eagerly():
+            //    return self._eval_helper(tensors)
+            //  else:
+            {
+                var sess = ops.get_default_session();
+                if (sess == null)
+                    sess = self.session();
+                with<Session>(sess, s =>
+                {
+                    result = tensor.eval();
+                });
+                return result;
+            }
+        }
+
+        public object eval_scalar(Tensor tensor)
+        {
+            NDArray result = null;
+            //  if context.executing_eagerly():
+            //    return self._eval_helper(tensors)
+            //  else:
+            {
+                var sess = ops.get_default_session();
+                if (sess == null)
+                    sess = self.session();
+                with<Session>(sess, s =>
+                {
+                    result = tensor.eval();
+                });
+                return result.Array.GetValue(0);
             }
         }
 
+
         //Returns a TensorFlow Session for use in executing tests.
         public Session session(Graph graph = null, object config = null, bool use_gpu = false, bool force_gpu = false)
         {
@@ -188,16 +231,11 @@ namespace TensorFlowNET.UnitTest
             //if (context.executing_eagerly())
             //  yield None
             //else 
-            {
-                with<Session>(self._create_session(graph, config, force_gpu), sess =>
-                {
-                    with(self._constrain_devices_and_set_default(sess, use_gpu, force_gpu), (x) =>
-                    {
-                        s = sess;
-                    });
-                });
-            }
-            return s;
+            //{
+            s = self._create_session(graph, config, force_gpu);
+            self._constrain_devices_and_set_default(s, use_gpu, force_gpu);
+            //}
+            return s.as_default();
         }
 
         private IPython _constrain_devices_and_set_default(Session sess, bool useGpu, bool forceGpu)
diff --git a/test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs b/test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs
index 85908baf..0b5f0879 100644
--- a/test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs
+++ b/test/TensorFlowNET.UnitTest/control_flow_ops_test/CondTestCases.cs
@@ -13,53 +13,29 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         [TestMethod]
         public void testCondTrue()
         {
-            var x = tf.constant(2);
-            var y = tf.constant(5);
-            var z = control_flow_ops.cond(tf.less(x, y), () => tf.multiply(x, tf.constant(17)),
-                () => tf.add(y, tf.constant(23)));
-            self.assertEquals(self.evaluate(z), 34);
+            with(tf.Graph().as_default(), g =>
+            {
+                var x = tf.constant(2);
+                var y = tf.constant(5);
+                var z = control_flow_ops.cond(tf.less(x, y), () => tf.multiply(x, tf.constant(17)),
+                    () => tf.add(y, tf.constant(23)));
+                //tf.train.export_meta_graph(@"D:\dev\tensorboard\logdir\sharp.meta", as_text: false);
+                self.assertEquals(eval_scalar(z), 34);
+            });
         }
 
-        [Ignore("Todo")]
+        [Ignore("This Test Fails due to missing edges in the graph!")]
         [TestMethod]
         public void testCondFalse()
         {
-            //  def testCondFalse(self):
-            //    x = constant_op.constant(2)
-            //    y = constant_op.constant(1)
-            //    z = control_flow_ops.cond(
-            //        math_ops.less(
-            //            x,
-            //            y), lambda: math_ops.multiply(x, 17), lambda: math_ops.add(y, 23))
-            //    self.assertEquals(self.evaluate(z), 24)
-        }
-
-        [Ignore("Todo")]
-        [TestMethod]
-        public void testCondTrueLegacy()
-        {
-            //  def testCondTrueLegacy(self):
-            //    x = constant_op.constant(2)
-            //    y = constant_op.constant(5)
-            //    z = control_flow_ops.cond(
-            //        math_ops.less(x, y),
-            //        fn1=lambda: math_ops.multiply(x, 17),
-            //        fn2=lambda: math_ops.add(y, 23))
-            //    self.assertEquals(self.evaluate(z), 34)
-        }
-
-        [Ignore("Todo")]
-        [TestMethod]
-        public void testCondFalseLegacy()
-        {
-            //  def testCondFalseLegacy(self):
-            //    x = constant_op.constant(2)
-            //    y = constant_op.constant(1)
-            //    z = control_flow_ops.cond(
-            //        math_ops.less(x, y),
-            //        fn1=lambda: math_ops.multiply(x, 17),
-            //        fn2=lambda: math_ops.add(y, 23))
-            //    self.assertEquals(self.evaluate(z), 24)
+            with(tf.Graph().as_default(), g =>
+            {
+                var x = tf.constant(2);
+                var y = tf.constant(1);
+                var z = control_flow_ops.cond(tf.less(x, y), () => tf.multiply(x, tf.constant(17)),
+                    () => tf.add(y, tf.constant(23)));
+                self.assertEquals(eval_scalar(z), 24);
+            });
         }
 
         [Ignore("Todo")]