diff --git a/src/TensorFlowNET.Core/Binding.cs b/src/TensorFlowNET.Core/Binding.cs
index e8355e32..e3136f83 100644
--- a/src/TensorFlowNET.Core/Binding.cs
+++ b/src/TensorFlowNET.Core/Binding.cs
@@ -10,7 +10,13 @@ namespace Tensorflow
 
         /// <summary>
         ///     Alias to null, similar to python's None.
+        ///     For TensorShape, please use Unknown
         /// </summary>
         public static readonly object None = null;
+
+        /// <summary>
+        /// Used for TensorShape None
+        /// </summary>
+        public static readonly int Unknown = -1;
     }
 }
diff --git a/src/TensorFlowNET.Core/Tensors/TensorShape.cs b/src/TensorFlowNET.Core/Tensors/TensorShape.cs
index 1edd634f..853255cb 100644
--- a/src/TensorFlowNET.Core/Tensors/TensorShape.cs
+++ b/src/TensorFlowNET.Core/Tensors/TensorShape.cs
@@ -71,61 +71,32 @@ namespace Tensorflow
             }
         }
 
-        public TensorShape(params object[] dims)
+        public TensorShape(params int[] dims)
         {
-            Array arr;
+            switch (dims.Length)
+            {
+                case 0: shape = new Shape(new int[0]); break;
+                case 1: shape = Shape.Vector((int)dims[0]); break;
+                case 2: shape = Shape.Matrix(dims[0], dims[1]); break;
+                default: shape = new Shape(dims); break;
+            }
+        }
 
-            if (dims.Length == 1)
+        public TensorShape(int[][] dims)
+        {
+            if(dims.Length == 1)
             {
-                switch (dims[0])
+                switch (dims[0].Length)
                 {
-                    case int[] intarr:
-                        arr = intarr;
-                        break;
-                    case long[] longarr:
-                        arr = longarr;
-                        break;
-                    case object[] objarr:
-                        arr = objarr;
-                        break;
-                    case int _:
-                    case long _:
-                        arr = dims;
-                        break;
-                    case null: //==Binding.None
-                        arr = dims;
-                        break;
-                    default:
-                        Binding.print(dims);
-                        throw new ArgumentException(nameof(dims));
+                    case 0: shape = new Shape(new int[0]); break;
+                    case 1: shape = Shape.Vector((int)dims[0][0]); break;
+                    case 2: shape = Shape.Matrix(dims[0][0], dims[1][2]); break;
+                    default: shape = new Shape(dims[0]); break;
                 }
-            } else
-                arr = dims;
-
-            var intdims = new int[arr.Length];
-            for (int i = 0; i < arr.Length; i++)
-            {
-                var val = arr.GetValue(i);
-                if (val == Binding.None)
-                    intdims[i] = -1;
-                else
-                    intdims[i] = Converts.ToInt32(val);
             }
-
-            switch (intdims.Length)
+            else
             {
-                case 0:
-                    shape = new Shape(new int[0]);
-                    break;
-                case 1:
-                    shape = Shape.Vector((int) intdims[0]);
-                    break;
-                case 2:
-                    shape = Shape.Matrix(intdims[0], intdims[1]);
-                    break;
-                default:
-                    shape = new Shape(intdims);
-                    break;
+                throw new NotImplementedException("TensorShape int[][] dims");
             }
         }
 
@@ -232,8 +203,6 @@ namespace Tensorflow
         public static implicit operator TensorShape(Shape shape) => new TensorShape((int[]) shape.Dimensions.Clone());
         public static implicit operator Shape(TensorShape shape) => new Shape((int[]) shape.dims.Clone());
         
-        public static implicit operator TensorShape(object[] dims) => new TensorShape(dims);        
-
         public static implicit operator int[](TensorShape shape) => (int[])shape.dims.Clone(); //we clone to avoid any changes
         public static implicit operator TensorShape(int[] dims) => new TensorShape(dims);
 
@@ -260,16 +229,5 @@ namespace Tensorflow
                 
         public static explicit operator (int, int, int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 8 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5], shape.dims[6], shape.dims[7]) : (0, 0, 0, 0, 0, 0, 0, 0);
         public static implicit operator TensorShape((int, int, int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7, dims.Item8);
-        
-        public static implicit operator TensorShape(int?[] dims) => new TensorShape(dims);
-        public static implicit operator TensorShape(int? dim) => new TensorShape(dim);
-        public static implicit operator TensorShape((object, object) dims) => new TensorShape(dims.Item1, dims.Item2);
-        public static implicit operator TensorShape((object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3);
-        public static implicit operator TensorShape((object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4);
-        public static implicit operator TensorShape((object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5);
-        public static implicit operator TensorShape((object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6);
-        public static implicit operator TensorShape((object, object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7);
-        public static implicit operator TensorShape((object, object, object, object, object, object, object, object) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7, dims.Item8);
-
     }
 }
diff --git a/src/TensorFlowNET.Core/tensorflow.cs b/src/TensorFlowNET.Core/tensorflow.cs
index 494f2e89..bdb2f537 100644
--- a/src/TensorFlowNET.Core/tensorflow.cs
+++ b/src/TensorFlowNET.Core/tensorflow.cs
@@ -63,11 +63,6 @@ namespace Tensorflow
             return gen_array_ops.placeholder(dtype, shape, name);
         }
 
-        public unsafe Tensor placeholder(TF_DataType dtype, object[] shape, string name = null)
-        {
-            return placeholder(dtype, new TensorShape(shape), name);
-        }
-
         public void enable_eager_execution()
         {
             // contex = new Context();
diff --git a/test/TensorFlowNET.UnitTest/TensorShapeTest.cs b/test/TensorFlowNET.UnitTest/TensorShapeTest.cs
index 90048aa6..efa7def3 100644
--- a/test/TensorFlowNET.UnitTest/TensorShapeTest.cs
+++ b/test/TensorFlowNET.UnitTest/TensorShapeTest.cs
@@ -12,48 +12,48 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Case1()
         {
-            int? a = 2;
-            int? b = 3;
-            var dims = new object[] {(int?) None, a, b};
+            int a = 2;
+            int b = 3;
+            var dims = new [] { Unknown, a, b};
             new TensorShape(dims).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
         }
 
         [TestMethod]
         public void Case2()
         {
-            int? a = 2;
-            int? b = 3;
-            var dims = new object[] {(int?) None, a, b};
-            new TensorShape(new object[] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
+            int a = 2;
+            int b = 3;
+            var dims = new[] { Unknown, a, b};
+            new TensorShape(new [] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
         }
 
         [TestMethod]
         public void Case3()
         {
-            int? a = 2;
-            int? b = null;
-            var dims = new object[] {(int?) None, a, b};
-            new TensorShape(new object[] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, -1);
+            int a = 2;
+            int b = Unknown;
+            var dims = new [] { Unknown, a, b};
+            new TensorShape(new [] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, -1);
         }
 
         [TestMethod]
         public void Case4()
         {
-            TensorShape shape = (None, None);
+            TensorShape shape = (Unknown, Unknown);
             shape.GetPrivate<Shape>("shape").Should().BeShaped(-1, -1);
         }
 
         [TestMethod]
         public void Case5()
         {
-            TensorShape shape = (1, None, 3);
+            TensorShape shape = (1, Unknown, 3);
             shape.GetPrivate<Shape>("shape").Should().BeShaped(1, -1, 3);
         }
 
         [TestMethod]
         public void Case6()
         {
-            TensorShape shape = (None, 1, 2, 3, None);
+            TensorShape shape = (Unknown, 1, 2, 3, Unknown);
             shape.GetPrivate<Shape>("shape").Should().BeShaped(-1, 1, 2, 3, -1);
         }
     }
diff --git a/test/TensorFlowNET.UnitTest/layers_test/flatten.cs b/test/TensorFlowNET.UnitTest/layers_test/flatten.cs
index 8f97d5c2..fa8ec792 100644
--- a/test/TensorFlowNET.UnitTest/layers_test/flatten.cs
+++ b/test/TensorFlowNET.UnitTest/layers_test/flatten.cs
@@ -42,7 +42,7 @@ namespace TensorFlowNET.UnitTest.layers_test
         {
             var sess = tf.Session().as_default();
 
-            var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(3, 4, None, 1, 2));
+            var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(3, 4, Unknown, 1, 2));
             sess.run(tf.layers.flatten(input), (input, np.arange(3 * 4 * 3 * 1 * 2).reshape(3, 4, 3, 1, 2))).Should().BeShaped(3, 24);
         }
 
@@ -51,7 +51,7 @@ namespace TensorFlowNET.UnitTest.layers_test
         {
             var sess = tf.Session().as_default();
 
-            var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(None, 4, 3, 1, 2));
+            var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(Unknown, 4, 3, 1, 2));
             sess.run(tf.layers.flatten(input), (input, np.arange(3 * 4 * 3 * 1 * 2).reshape(3, 4, 3, 1, 2))).Should().BeShaped(3, 24);
         }
     }