Add layers of ZeroPadding2D and UpSampling2D.

src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs

@@ -0,0 +1,12 @@
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class UpSampling2DArgs : LayerArgs
+    {
+        public TensorShape Size { get; set; }
+        public string DataFormat { get; set; }
+        /// <summary>
+        /// 'nearest', 'bilinear'
+        /// </summary>
+        public string Interpolation { get; set; } = "nearest";
+    }
+}

src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs

@@ -0,0 +1,38 @@
+using NumSharp;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public partial class LayersApi
+    {
+        /// <summary>
+        /// Zero-padding layer for 2D input (e.g. picture).
+        /// </summary>
+        /// <param name="padding"></param>
+        /// <returns></returns>
+        public ZeroPadding2D ZeroPadding2D(NDArray padding)
+            => new ZeroPadding2D(new ZeroPadding2DArgs
+            {
+                Padding = padding
+            });
+
+        /// <summary>
+        /// Upsampling layer for 2D inputs.<br/>
+        /// Repeats the rows and columns of the data by size[0] and size[1] respectively.
+        /// </summary>
+        /// <param name="size"></param>
+        /// <param name="data_format"></param>
+        /// <param name="interpolation"></param>
+        /// <returns></returns>
+        public UpSampling2D UpSampling2D(TensorShape size = null,
+            string data_format = null,
+            string interpolation = "nearest")
+            => new UpSampling2D(new UpSampling2DArgs
+            {
+                Size = size ?? (2, 2)
+            });
+    }
+}

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -6,7 +6,7 @@ using static Tensorflow.KerasApi;
 
 namespace Tensorflow.Keras.Layers
 {
-    public class LayersApi
+    public partial class LayersApi
     {
         /// <summary>
         /// Functional interface for the batch normalization layer.
@@ -372,19 +372,8 @@ namespace Tensorflow.Keras.Layers
                 InputShape = input_shape
             });
 
-        /// <summary>
-        /// Zero-padding layer for 2D input (e.g. picture).
-        /// </summary>
-        /// <param name="padding"></param>
-        /// <returns></returns>
-        public ZeroPadding2D ZeroPadding2D(NDArray padding)
-            => new ZeroPadding2D(new ZeroPadding2DArgs
-            {
-                Padding = padding
-            });
-
-        public Tensor add(params Tensor[] inputs)
-            => new Add(new MergeArgs { Inputs = inputs }).Apply(inputs);
+        public Add Add(params Tensor[] inputs)
+            => new Add(new MergeArgs { Inputs = inputs });
 
         public GlobalAveragePooling2D GlobalAveragePooling2D()
             => new GlobalAveragePooling2D(new Pooling2DArgs { });

src/TensorFlowNET.Keras/Layers/Reshaping/UpSampling2D.cs

@@ -0,0 +1,35 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Utils;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class UpSampling2D : Layer
+    {
+        UpSampling2DArgs args;
+        int[] size;
+        string data_format;
+        string interpolation => args.Interpolation;
+
+        public UpSampling2D(UpSampling2DArgs args) : base(args)
+        {
+            this.args = args;
+            data_format = conv_utils.normalize_data_format(args.DataFormat);
+            size = conv_utils.normalize_tuple(args.Size, 2, "size");
+            inputSpec = new InputSpec(ndim: 4);
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool is_training = false)
+        {
+            return keras.backend.resize_images(inputs, 
+                size[0], size[1], 
+                data_format, 
+                interpolation: interpolation);
+        }
+    }
+}

test/TensorFlowNET.UnitTest/Keras/Layers.Reshaping.Test.cs

@@ -0,0 +1,30 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
+using Tensorflow;
+using static Tensorflow.KerasApi;
+
+namespace TensorFlowNET.UnitTest.Keras
+{
+    [TestClass]
+    public class LayersReshapingTest : EagerModeTestBase
+    {
+        [TestMethod]
+        public void ZeroPadding2D()
+        {
+            var input_shape = new[] { 1, 1, 2, 2 };
+            var x = np.arange(np.prod(input_shape)).reshape(input_shape);
+            var zero_padding_2d = keras.layers.ZeroPadding2D(new[,] { { 1, 0 }, { 1, 0 } });
+            var y = zero_padding_2d.Apply(x);
+            Assert.AreEqual((1, 2, 3, 2), y.shape);
+        }
+
+        [TestMethod]
+        public void UpSampling2D()
+        {
+            var input_shape = new[] { 2, 2, 1, 3 };
+            var x = np.arange(np.prod(input_shape)).reshape(input_shape);
+            var y = keras.layers.UpSampling2D(size: (1, 2)).Apply(x);
+            Assert.AreEqual((2, 2, 2, 3), y.shape);
+        }
+    }
+}

test/TensorFlowNET.UnitTest/Keras/LayersTest.cs

@@ -1,5 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
+using Tensorflow;
 using static Tensorflow.KerasApi;
 
 namespace TensorFlowNET.UnitTest.Keras