diff --git a/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs b/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
index 94085605..5e257417 100644
--- a/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
+++ b/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
@@ -30,6 +30,15 @@ namespace Tensorflow.NumPy
         [AutoNumPy]
         public static NDArray stack(params NDArray[] arrays) => new NDArray(array_ops.stack(arrays));
 
+        [AutoNumPy]
+        public static NDArray stack(NDArray[] arrays, int axis = 0) => new NDArray(array_ops.stack(arrays, axis));
+        
+        [AutoNumPy]
+        public static NDArray stack((NDArray, NDArray) tuple, int axis = 0) => new NDArray(array_ops.stack(new[] { tuple.Item1, tuple.Item2 }, axis));
+
+        [AutoNumPy]
+        public static NDArray stack((NDArray, NDArray, NDArray) tuple, int axis = 0) => new NDArray(array_ops.stack(new[] { tuple.Item1, tuple.Item2, tuple.Item3 }, axis));
+
         [AutoNumPy]
         public static NDArray moveaxis(NDArray array, Axis source, Axis destination) => new NDArray(array_ops.moveaxis(array, source, destination));
     }
diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Merging.Test.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Merging.Test.cs
index 36e44e48..9bc2fa76 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Merging.Test.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Merging.Test.cs
@@ -1,4 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System.Collections.Generic;
 using Tensorflow.NumPy;
 using static Tensorflow.KerasApi;
 
@@ -8,12 +9,16 @@ namespace Tensorflow.Keras.UnitTest.Layers
     public class LayersMergingTest : EagerModeTestBase
     {
         [TestMethod]
-        public void Concatenate()
+        [DataRow(1, 4, 1, 5)]
+        [DataRow(2, 2, 2, 5)]
+        [DataRow(3, 2, 1, 10)]
+        public void Concatenate(int axis, int shapeA, int shapeB, int shapeC)
         {
-            var x = np.arange(20).reshape((2, 2, 5));
-            var y = np.arange(20, 30).reshape((2, 1, 5));
-            var z = keras.layers.Concatenate(axis: 1).Apply(new Tensors(x, y));
-            Assert.AreEqual((2, 3, 5), z.shape);
+            var x = np.arange(10).reshape((1, 2, 1, 5));
+            var y = np.arange(10, 20).reshape((1, 2, 1, 5));
+            var z = keras.layers.Concatenate(axis: axis).Apply(new Tensors(x, y));
+            Assert.AreEqual((1, shapeA, shapeB, shapeC), z.shape);
         }
+
     }
 }
diff --git a/test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs b/test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs
index cb570fc0..53a67cbf 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs
@@ -1,10 +1,13 @@
 using Microsoft.VisualStudio.TestPlatform.Utilities;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Newtonsoft.Json.Linq;
 using System.Linq;
+using System.Xml.Linq;
 using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.UnitTest.Helpers;
 using Tensorflow.NumPy;
+using static HDF.PInvoke.H5Z;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 
@@ -124,4 +127,44 @@ public class ModelLoadTest
         var model = tf.saved_model.load(@"D:\development\temp\saved_model") as Tensorflow.Keras.Engine.Model;
         model.summary();
     }
+
+
+
+    [TestMethod]
+    public void CreateConcatenateModelSaveAndLoad()
+    {
+        // a small demo model that is just here to see if the axis value for the concatenate method is saved and loaded.
+        var input_layer = tf.keras.layers.Input((8, 8, 5));
+
+        var conv1 = tf.keras.layers.Conv2D(2, kernel_size: 3, activation: "relu", padding: "same"/*, data_format: "_conv_1"*/).Apply(input_layer);
+        conv1.Name = "conv1";
+
+        var conv2 = tf.keras.layers.Conv2D(2, kernel_size: 3, activation: "relu", padding: "same"/*, data_format: "_conv_2"*/).Apply(input_layer);
+        conv2.Name = "conv2";
+
+        var concat1 = tf.keras.layers.Concatenate(axis: 3).Apply((conv1, conv2));
+        concat1.Name = "concat1";
+
+        var model = tf.keras.Model(input_layer, concat1);
+        model.compile(tf.keras.optimizers.Adam(), tf.keras.losses.CategoricalCrossentropy());
+
+        model.save(@"Assets/concat_axis3_model");
+
+        
+        var tensorInput = np.arange(320).reshape((1, 8, 8, 5)).astype(TF_DataType.TF_FLOAT);
+
+        var tensors1 = model.predict(tensorInput);
+
+        Assert.AreEqual((1, 8, 8, 4), tensors1.shape);
+
+        model = null;
+        keras.backend.clear_session();
+
+        var model2 = tf.keras.models.load_model(@"Assets/concat_axis3_model");
+
+        var tensors2 = model2.predict(tensorInput);
+
+        Assert.AreEqual(tensors1.shape, tensors2.shape);
+    }
+
 }