Update to v1.1.6

Author: ameritusweb

examples/gnn/ParallelReverseAutoDiff.GnnExample/GraphAttentionPaths/Transformer/TransformerNeuralNetwork.cs

@@ -14,7 +14,7 @@ namespace ParallelReverseAutoDiff.Test.GraphAttentionPaths.Transformer
     /// </summary>
     public class TransformerNeuralNetwork : NeuralNetwork
     {
-        private const string NAMESPACE = "ParallelReverseAutoDiff.Test.GraphAttentionPaths.Transformer.Architecture";
+        private const string NAMESPACE = "ParallelReverseAutoDiff.GnnExample.GraphAttentionPaths.Transformer.Architecture";
         private const string ARCHITECTURE = "Transformer";
 
         private readonly IModelLayer embeddingLayer;

examples/gnn/ParallelReverseAutoDiff.GnnExample/ParallelReverseAutoDiff.GnnExample.xml

@@ -2,7 +2,7 @@
 <package xmlns="http://schemas.microsoft.com/packaging/2013/05/nuspec.xsd">
   <metadata>
     <id>ParallelReverseAutoDiff</id>
-    <version>1.1.5</version>
+    <version>1.1.6</version>
     <authors>ameritusweb</authors>
     <owners>ameritusweb</owners>
     <license type="expression">LGPL-2.1-only</license>
@@ -11,7 +11,7 @@
     <requireLicenseAcceptance>false</requireLicenseAcceptance>
     <description>A library for parallelized reverse mode automatic differentiation in C# for custom neural network development.</description>
 	<repository type="git" url="https://github.com/ameritusweb/ParallelReverseAutoDiff.git" commit="0a9bbd18f45c4f4434160a7c064539f29f3a3c67" />
-    <releaseNotes>Enhance the matrix class.</releaseNotes>
+    <releaseNotes>Add batch matrix multiply.</releaseNotes>
     <copyright>ameritusweb, 2023</copyright>
     <tags>autodiff automatic-differentiation parallel reverse-mode differentiation C# neural network</tags>
     <dependencies>

src/ParallelReverseAutoDiff.1.1.5.nupkg renamed to src/ParallelReverseAutoDiff.1.1.6.nupkg

@@ -894,6 +894,9 @@ In this example, the Forward method calculates the average of the features for e
 
 This level of customization allows PRAD to be a versatile tool in the field of machine learning, capable of being tailored to a wide range of tasks, datasets, and innovative architectures.
 
+## Support developer
+[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/ameritusweb)
+
 ## Like the project?
 
 Give it a :star: Star!