Add keras.layers.Reshape.

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

@@ -0,0 +1,7 @@
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class ReshapeArgs : LayerArgs
+    {
+        public TensorShape TargetShape { get; set; }
+    }
+}

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

@@ -34,5 +34,16 @@ namespace Tensorflow.Keras.Layers
             {
                 Size = size ?? (2, 2)
             });
+
+        /// <summary>
+        /// Layer that reshapes inputs into the given shape.
+        /// </summary>
+        /// <param name="target_shape"></param>
+        /// <returns></returns>
+        public Reshape Reshape(TensorShape target_shape)
+            => new Reshape(new ReshapeArgs
+            {
+                TargetShape = target_shape
+            });
     }
 }

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -372,8 +372,8 @@ namespace Tensorflow.Keras.Layers
                 InputShape = input_shape
             });
 
-        public Add Add(params Tensor[] inputs)
-            => new Add(new MergeArgs { Inputs = inputs });
+        public Add Add()
+            => new Add(new MergeArgs { });
 
         public GlobalAveragePooling2D GlobalAveragePooling2D()
             => new GlobalAveragePooling2D(new Pooling2DArgs { });

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

@@ -0,0 +1,34 @@
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+using static Tensorflow.KerasApi;
+using static Tensorflow.Binding;
+using System.Collections.Generic;
+using System;
+
+namespace Tensorflow.Keras.Layers
+{
+    /// <summary>
+    /// Layer that reshapes inputs into the given shape.
+    /// </summary>
+    public class Reshape : Layer
+    {
+        ReshapeArgs args;
+        public Reshape(ReshapeArgs args)
+            : base(args)
+        {
+            this.args = args;
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensor state = null, bool is_training = false)
+        {
+            var shape = new List<int> { inputs.shape[0] };
+            shape.AddRange(args.TargetShape.dims);
+
+            var result = array_ops.reshape(inputs, shape.ToArray());
+            if (!tf.Context.executing_eagerly())
+                // result = result.set_shape(compute_output_shape(inputs.shape));
+                throw new NotImplementedException("");
+            return result;
+        }
+    }
+}

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

@@ -1,6 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
-using Tensorflow;
+using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 
 namespace TensorFlowNET.UnitTest.Keras
@@ -26,5 +26,14 @@ namespace TensorFlowNET.UnitTest.Keras
             var y = keras.layers.UpSampling2D(size: (1, 2)).Apply(x);
             Assert.AreEqual((2, 2, 2, 3), y.shape);
         }
+
+        [TestMethod]
+        public void Reshape()
+        {
+            var inputs = tf.zeros((10, 5, 20));
+            var outputs = keras.layers.LeakyReLU().Apply(inputs);
+            outputs = keras.layers.Reshape((20, 5)).Apply(outputs);
+            Assert.AreEqual((10, 20, 5), outputs.shape);
+        }
     }
 }