diff --git a/src/TensorFlowNET.Core/IReturnTensorOrOperation.cs b/src/TensorFlowNET.Core/IReturnTensorOrOperation.cs
new file mode 100644
index 00000000..51c840ac
--- /dev/null
+++ b/src/TensorFlowNET.Core/IReturnTensorOrOperation.cs
@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    /// <summary>
+    /// in order to limit function return value 
+    /// is Tensor or Operation
+    /// </summary>
+    public interface IReturnTensorOrOperation
+    {
+    }
+}
diff --git a/src/TensorFlowNET.Core/Operations/Operation.cs b/src/TensorFlowNET.Core/Operations/Operation.cs
index 769c7fb3..4d268c6d 100644
--- a/src/TensorFlowNET.Core/Operations/Operation.cs
+++ b/src/TensorFlowNET.Core/Operations/Operation.cs
@@ -7,7 +7,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public partial class Operation
+    public partial class Operation : IReturnTensorOrOperation
     {
         private readonly IntPtr _handle; // _c_op in python
 
diff --git a/src/TensorFlowNET.Core/Tensors/Tensor.cs b/src/TensorFlowNET.Core/Tensors/Tensor.cs
index 90b7c2dc..ab7d3304 100644
--- a/src/TensorFlowNET.Core/Tensors/Tensor.cs
+++ b/src/TensorFlowNET.Core/Tensors/Tensor.cs
@@ -12,7 +12,7 @@ namespace Tensorflow
     /// A tensor is a generalization of vectors and matrices to potentially higher dimensions. 
     /// Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes.
     /// </summary>
-    public partial class Tensor : IDisposable
+    public partial class Tensor : IDisposable, IReturnTensorOrOperation
     {
         private readonly IntPtr _handle;
 
@@ -258,7 +258,7 @@ namespace Tensorflow
                 }
             }
 
-            return $"{name} shape=({string.Join(",", shape)}) dtype={dtype.ToString()}";
+            return $"tf.Tensor {name} shape=({string.Join(",", shape)}) dtype={dtype.ToString()}";
         }
 
         public void Dispose()
diff --git a/src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs b/src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs
index 85fb19f8..dc510fcc 100644
--- a/src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs
+++ b/src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs
@@ -6,11 +6,21 @@ namespace Tensorflow
 {
     public partial class RefVariable
     {
-        public static Tensor operator +(RefVariable t1, int t2)
+        public static Tensor operator +(RefVariable x, int y) => op_helper("add", x, y);
+        public static Tensor operator +(RefVariable x, float y) => op_helper("add", x, y);
+        public static Tensor operator +(RefVariable x, double y) => op_helper("add", x, y);
+        
+        public static Tensor operator -(RefVariable x, int y) => op_helper("sub", x, y);
+        public static Tensor operator -(RefVariable x, float y) => op_helper("sub", x, y);
+        public static Tensor operator -(RefVariable x, double y) => op_helper("sub", x, y);
+        
+        private static Tensor op_helper<T>(string default_name, RefVariable x, T y)
         {
-            var tensor1 = t1._AsTensor();
-            var tensor2 = ops.convert_to_tensor(t2, tensor1.dtype, "y");
-            return gen_math_ops.add(tensor1, tensor2);
+            var tensor1 = x.value();
+            return Python.with<ops.name_scope, Tensor>(new ops.name_scope("", default_name, new object[] { tensor1, y }), scope => {
+                var tensor2 = ops.convert_to_tensor(y, tensor1.dtype.as_base_dtype(), "y");
+                return gen_math_ops.add(tensor1, tensor2, scope);
+            });
         }
     }
 }
diff --git a/src/TensorFlowNET.Core/Variables/RefVariable.cs b/src/TensorFlowNET.Core/Variables/RefVariable.cs
index 52d5f056..39a8d909 100644
--- a/src/TensorFlowNET.Core/Variables/RefVariable.cs
+++ b/src/TensorFlowNET.Core/Variables/RefVariable.cs
@@ -171,6 +171,34 @@ namespace Tensorflow
             return op;
         }
 
+        /// <summary>
+        /// Assigns a new value to the variable.
+        /// </summary>
+        /// <param name="value">The new value for this variable.</param>
+        /// <param name="use_locking">If `True`, use locking during the assignment.</param>
+        /// <param name="name">The name of the operation to be created</param>
+        /// <param name="read_value">
+        /// if True, will return something which evaluates to the
+        /// new value of the variable; if False will return the assign op.
+        /// </param>
+        /// <returns>
+        /// A `Tensor` that will hold the new value of this variable after
+        /// the assignment has completed.
+        /// </returns>
+        public T assign<T>(Tensor value, bool use_locking = false, string name = "", bool read_value = true)
+            where T : IReturnTensorOrOperation
+        {
+            var assign = gen_state_ops.assign(_variable, value, use_locking: use_locking, name: name);
+            if (read_value)
+                return (T)Convert.ChangeType(assign, typeof(T));
+            return (T)Convert.ChangeType(assign.op, typeof(T));
+        }
+
+        public Tensor assign(Tensor value, bool use_locking = false, string name = "")
+        {
+            return gen_state_ops.assign(_variable, value, use_locking: use_locking, name: name);
+        }
+
         public override string ToString()
         {
             return $"tf.Variable '{name}' shape={shape} dtype={dtype}";
diff --git a/test/TensorFlowNET.UnitTest/TrainSaverTest.cs b/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
index 1bb5fc3d..99e7ee20 100644
--- a/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
+++ b/test/TensorFlowNET.UnitTest/TrainSaverTest.cs
@@ -15,7 +15,11 @@ namespace TensorFlowNET.UnitTest
             var v1 = tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
             var v2 = tf.get_variable("v2", shape: new TensorShape(5), initializer: tf.zeros_initializer);
 
+            var inc_v1 = v1.assign(v1 + 1.0f);
+            var dec_v2 = v2.assign(v2 - 1.0f);
 
+            // Add an op to initialize the variables.
+            var init_op = tf.global_variables_initializer();
         }
     }
 }