diff --git a/src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs b/src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs
index f36b14df..a5a9b674 100644
--- a/src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs
+++ b/src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs
@@ -82,10 +82,19 @@ namespace Tensorflow
 
         public static Tensor operator %(Tensor x, Tensor y) => BinaryOpWrapper("mod", x, y);
 
+        public static Tensor operator >(double x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(float x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(int x, Tensor y) => gen_math_ops.greater(x, y);
+        public static Tensor operator >(Tensor x, Tensor y) => gen_math_ops.greater(x, y);
         public static Tensor operator >(Tensor x, int y) => gen_math_ops.greater(x, y);
         public static Tensor operator >=(Tensor x, Tensor y) => gen_math_ops.greater_equal(x, y);
         public static Tensor operator >(Tensor x, float y) => gen_math_ops.greater(x, y);
         public static Tensor operator >(Tensor x, double y) => gen_math_ops.greater(x, y);
+
+        public static Tensor operator <(double x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(float x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(int x, Tensor y) => gen_math_ops.less(x, y);
+        public static Tensor operator <(Tensor x, Tensor y) => gen_math_ops.less(x, y);
         public static Tensor operator <(Tensor x, int y) => gen_math_ops.less(x, y);
         public static Tensor operator <=(Tensor x, Tensor y) => gen_math_ops.less_equal(x, y);
         public static Tensor operator <(Tensor x, float y) => gen_math_ops.less(x, y);
diff --git a/test/TensorFlowNET.UnitTest/OperationsTest.cs b/test/TensorFlowNET.UnitTest/OperationsTest.cs
index aafb6e56..358f3fb9 100644
--- a/test/TensorFlowNET.UnitTest/OperationsTest.cs
+++ b/test/TensorFlowNET.UnitTest/OperationsTest.cs
@@ -833,5 +833,313 @@ namespace TensorFlowNET.UnitTest
             }
             #endregion
         }
+
+        [TestMethod]
+        public void greaterThanOpTests()
+        {
+            const int rows = 2; // to avoid broadcasting effect
+            const int cols = 10;
+
+            #region intTest
+            const int intThreshold = 10;
+
+            var firstIntFeed = Enumerable.Range(0, rows * cols).ToArray();
+            var secondIntFeed = Enumerable.Repeat(intThreshold, rows * cols).ToArray();
+            var intResult = firstIntFeed.Count(elem => elem > intThreshold);
+            var intResultTwo = firstIntFeed.Count(elem => elem < intThreshold);
+
+            var a = tf.placeholder(tf.int32, shape: new TensorShape(rows, cols));
+            var b = tf.placeholder(tf.int32, shape: new TensorShape(rows, cols));
+            var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator >(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator >(Tensor x, int y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > intThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator >(int x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold > a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResultTwo);
+            }
+            #endregion
+
+            #region floatTest
+            const float floatThreshold = 10.0f;
+
+            var firstFloatFeed = Enumerable.Range(0, rows * cols).Select(elem => (float)elem).ToArray();
+            var secondFloatFeed = Enumerable.Repeat(floatThreshold, rows * cols).ToArray();
+            var floatResult = firstFloatFeed.Count(elem => elem > floatThreshold);
+            var floatResultTwo = firstFloatFeed.Count(elem => elem < floatThreshold);
+
+            a = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
+            b = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator >(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator >(Tensor x, float y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > floatThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator >(float x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold > a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResultTwo);
+            }
+            #endregion
+
+            #region doubleTest
+            const double doubleThreshold = 10.0;
+
+            var firstDoubleFeed = Enumerable.Repeat(0, rows * cols).Select(elem => (double)elem).ToArray();
+            var secondDoubleFeed = Enumerable.Repeat(doubleThreshold, rows * cols).ToArray();
+            var doubleResult = firstDoubleFeed.Count(elem => elem > doubleThreshold);
+            var doubleResultTwo = firstDoubleFeed.Count(elem => elem < doubleThreshold);
+
+            a = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));
+            b = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.greater(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator >(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator >(Tensor x, double y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a > doubleThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator >(double x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold > a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResultTwo);
+            }
+            #endregion
+        }
+
+        [TestMethod]
+        public void lessThanOpTests()
+        {
+            const int rows = 2; // to avoid broadcasting effect
+            const int cols = 10;
+
+            #region intTest
+            const int intThreshold = 10;
+
+            var firstIntFeed = Enumerable.Range(0, rows * cols).ToArray();
+            var secondIntFeed = Enumerable.Repeat(intThreshold, rows * cols).ToArray();
+            var intResult = firstIntFeed.Count(elem => elem < intThreshold);
+            var intResultTwo = firstIntFeed.Count(elem => elem > intThreshold);
+
+            var a = tf.placeholder(tf.int32, shape: new TensorShape(rows, cols));
+            var b = tf.placeholder(tf.int32, shape: new TensorShape(rows, cols));
+            var c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator <(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator <(Tensor x, int y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < intThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResult);
+            }
+
+            // Testing `operator <(int x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(intThreshold < a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstIntFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, intResultTwo);
+            }
+            #endregion
+
+            #region floatTest
+            const float floatThreshold = 10.0f;
+
+            var firstFloatFeed = Enumerable.Range(0, rows * cols).Select(elem => (float)elem).ToArray();
+            var secondFloatFeed = Enumerable.Repeat(floatThreshold, rows * cols).ToArray();
+            var floatResult = firstFloatFeed.Count(elem => elem < floatThreshold);
+            var floatResultTwo = firstFloatFeed.Count(elem => elem > floatThreshold);
+
+            a = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
+            b = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator <(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator <(Tensor x, float y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < floatThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResult);
+            }
+
+            // Testing `operator <(float x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(floatThreshold < a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstFloatFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, floatResultTwo);
+            }
+            #endregion
+
+            #region doubleTest
+            const double doubleThreshold = 10.0;
+
+            var firstDoubleFeed = Enumerable.Repeat(0, rows * cols).Select(elem => (double)elem).ToArray();
+            var secondDoubleFeed = Enumerable.Repeat(doubleThreshold, rows * cols).ToArray();
+            var doubleResult = firstDoubleFeed.Count(elem => elem < doubleThreshold);
+            var doubleResultTwo = firstDoubleFeed.Count(elem => elem > doubleThreshold);
+
+            a = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));
+            b = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(tf.less(a, b), tf.int32), 1));
+
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator <(Tensor x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < b, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))),
+                    new FeedItem(b, new NDArray(secondDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator <(Tensor x, double y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(a < doubleThreshold, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResult);
+            }
+
+            // Testing `operator <(double x, Tensor y)
+            c = tf.reduce_sum(tf.reduce_sum(tf.cast(doubleThreshold < a, tf.int32), 1));
+            using (var sess = tf.Session())
+            {
+                var o = sess.run(c,
+                    new FeedItem(a, new NDArray(firstDoubleFeed, new Shape(rows, cols))));
+                Assert.AreEqual((int)o, doubleResultTwo);
+            }
+            #endregion
+        }
     }
 }