diff --git a/src/TensorFlowNET.Core/APIs/tf.array.cs b/src/TensorFlowNET.Core/APIs/tf.array.cs
index 6a186a63..1c28f124 100644
--- a/src/TensorFlowNET.Core/APIs/tf.array.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.array.cs
@@ -29,6 +29,10 @@ namespace Tensorflow
         /// A convenient alias for None, useful for indexing arrays.
         /// </summary>
         public Slice newaxis = Slice.NewAxis;
+        /// <summary>
+        /// A convenient alias for ...
+        /// </summary>
+        public Slice ellipsis = Slice.Ellipsis;
 
         /// <summary>
         /// BatchToSpace for N-D tensors of type T.
diff --git a/src/TensorFlowNET.Core/APIs/tf.image.cs b/src/TensorFlowNET.Core/APIs/tf.image.cs
index 92013c13..9d836560 100644
--- a/src/TensorFlowNET.Core/APIs/tf.image.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.image.cs
@@ -209,7 +209,7 @@ namespace Tensorflow
                     name, sorted_input, canonicalized_coordinates, tile_size);
 
             public Tensor resize(Tensor image, TensorShape size)
-                => image_ops_impl.resize_images(image, tf.constant(size));
+                => image_ops_impl.resize_images_v2(image, size);
 
             public Tensor resize_bilinear(Tensor images, Tensor size, bool align_corners = false, bool half_pixel_centers = false, string name = null)
                 => gen_image_ops.resize_bilinear(images, size, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name);
diff --git a/src/TensorFlowNET.Core/Eager/EagerTensor.cs b/src/TensorFlowNET.Core/Eager/EagerTensor.cs
index cf66ca48..084a8cb3 100644
--- a/src/TensorFlowNET.Core/Eager/EagerTensor.cs
+++ b/src/TensorFlowNET.Core/Eager/EagerTensor.cs
@@ -19,6 +19,12 @@ namespace Tensorflow.Eager
 
         public override int rank => c_api.TFE_TensorHandleNumDims(EagerTensorHandle, tf.Status.Handle);
 
+        public override void set_shape(TensorShape shape)
+        {
+            if (!shape.is_compatible_with(this.shape))
+                throw new ValueError($"Tensor's shape is not compatible.");
+        }
+
         public static int GetRank(IntPtr handle)
         {
             var tfe_tensor_handle = c_api.TFE_EagerTensorHandle(handle);
diff --git a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
index 86434bfc..947a7b2e 100644
--- a/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
+++ b/src/TensorFlowNET.Core/Operations/image_ops_impl.cs
@@ -672,10 +672,8 @@ or rank = 4. Had rank = {0}", rank));
         internal static Tensor _resize_images_common(Tensor images, Func<Tensor, Tensor, Tensor> resizer_fn,
             Tensor size, bool preserve_aspect_ratio, string name, bool skip_resize_if_same)
         {
-            using (ops.name_scope(name, "resize", new [] {images, size}))
-            return tf_with(ops.name_scope(name, "resize", new [] {images, size}), delegate
+            return tf_with(ops.name_scope(name, "resize", new[] {images, size}), delegate
             {
-                images = ops.convert_to_tensor(images, name: "images");
                 if (images.TensorShape.ndim == Unknown)
                     throw new ValueError("\'images\' contains no shape.");
                 bool is_batch = true;
@@ -688,18 +686,6 @@ or rank = 4. Had rank = {0}", rank));
 
                 var (height, width) = (images.dims[1], images.dims[2]);
 
-                try
-                {
-                    size = ops.convert_to_tensor(size, dtypes.int32, name: "size");
-                }
-                catch (Exception ex)
-                {
-                    if (ex is TypeError || ex is ValueError)
-                        throw new ValueError("\'size\' must be a 1-D int32 Tensor");
-                    else
-                        throw;
-                }
-
                 if (!size.TensorShape.is_compatible_with(new [] {2}))
                     throw new ValueError(@"\'size\' must be a 1-D Tensor of 2 elements:
 new_height, new_width");
@@ -756,7 +742,7 @@ new_height, new_width");
                 
                 images = resizer_fn(images, size);
 
-                // images.set_shape(new TensorShape(new int[] { -1, new_height_const, new_width_const, -1 }));
+                images.set_shape(new TensorShape(new int[] { Unknown, new_height_const, new_width_const, Unknown }));
             
                 if (!is_batch)
                     images = array_ops.squeeze(images, axis: new int[] {0});
@@ -2163,6 +2149,33 @@ new_height, new_width");
             });
         }
 
+        /// <summary>
+        /// Resize `images` to `size` using the specified `method`.
+        /// </summary>
+        /// <param name="images"></param>
+        /// <param name="size"></param>
+        /// <param name="method"></param>
+        /// <param name="preserve_aspect_ratio"></param>
+        /// <param name="antialias"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor resize_images_v2(Tensor images, TensorShape size, string method = ResizeMethod.BILINEAR,
+            bool preserve_aspect_ratio = false,
+            bool antialias = false,
+            string name = null)
+        {
+            Func<Tensor, Tensor, Tensor> resize_fn = (images, size) =>
+            {
+                if (method == ResizeMethod.BILINEAR)
+                    return gen_image_ops.resize_bilinear(images, size, half_pixel_centers: true);
+                throw new NotImplementedException("");
+            };
+            return _resize_images_common(images, resize_fn, ops.convert_to_tensor(size),
+                preserve_aspect_ratio: preserve_aspect_ratio,
+                skip_resize_if_same: false,
+                name: name);
+        }
+
         /// <summary>
         /// Resize `images` to `size` using nearest neighbor interpolation.
         /// </summary>
diff --git a/src/TensorFlowNET.Core/Tensors/Tensor.cs b/src/TensorFlowNET.Core/Tensors/Tensor.cs
index b1b6700d..926524e9 100644
--- a/src/TensorFlowNET.Core/Tensors/Tensor.cs
+++ b/src/TensorFlowNET.Core/Tensors/Tensor.cs
@@ -149,7 +149,7 @@ namespace Tensorflow
         /// <summary>
         ///     Updates the shape of this tensor.
         /// </summary>
-        public void set_shape(TensorShape shape) 
+        public virtual void set_shape(TensorShape shape) 
         {
             this.shape = shape.rank >= 0 ? shape.dims : null;
         }
diff --git a/test/TensorFlowNET.UnitTest/ImageTest.cs b/test/TensorFlowNET.UnitTest/ImageTest.cs
index 47ec6a7c..f056084d 100644
--- a/test/TensorFlowNET.UnitTest/ImageTest.cs
+++ b/test/TensorFlowNET.UnitTest/ImageTest.cs
@@ -4,6 +4,7 @@ using NumSharp;
 using System;
 using System.Collections.Generic;
 using System.IO;
+using System.Linq;
 using System.Reflection;
 using System.Text;
 using Tensorflow;
@@ -35,9 +36,10 @@ namespace TensorFlowNET.UnitTest.Basics
             Assert.AreEqual(img.name, "decode_image/cond_jpeg/Merge:0");
         }
 
-        [TestMethod, Ignore]
+        [TestMethod]
         public void resize_image()
         {
+            tf.enable_eager_execution();
             var image = tf.constant(new int[5, 5]
             {
                 {1, 0, 0, 0, 0 },
@@ -46,10 +48,16 @@ namespace TensorFlowNET.UnitTest.Basics
                 {0, 0, 0, 1, 0 },
                 {0, 0, 0, 0, 1 }
             });
-            //image = image[tf.newaxis, ..., tf.newaxis];
-
-            var img = tf.image.resize(contents, (3, 5));
-            Assert.AreEqual(img.name, "decode_image/cond_jpeg/Merge:0");
+            image = image[tf.newaxis, tf.ellipsis, tf.newaxis];
+            image = tf.image.resize(image, (3, 5));
+            image = image[0, tf.ellipsis, 0];
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0.6666667f, 0.3333333f, 0, 0, 0 }, 
+                image[0].ToArray<float>()));
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 1, 0, 0 }, 
+                image[1].ToArray<float>()));
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 0, 0.3333335f, 0.6666665f }, 
+                image[2].ToArray<float>()));
+            tf.compat.v1.disable_eager_execution();
         }
 
         [TestMethod]