Implemented unittests for Concatenate layers and calls

The loading and saving of a simple model with a Concatenate layer is tested to check if the model is the same after reloading.
Implemented missing axis parameter for np.stack (added some handy tuple calls too like the np.concatenate example).

Author: Jucko13

src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs

@@ -30,6 +30,15 @@ public static NDArray concatenate((NDArray, NDArray) tuple, int axis = 0)
         [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));
     }

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);
         }
+
     }
 }

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 void TestModelBeforeTF2_5()
         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);
+    }
+
 }