diff --git a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj b/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
index 51b9fd8e..3fd562c6 100644
--- a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
+++ b/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
@@ -5,7 +5,7 @@
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
     <TargetTensorFlow>1.14.0</TargetTensorFlow>
-    <Version>0.8.3</Version>
+    <Version>0.9.0</Version>
     <Authors>Haiping Chen, Meinrad Recheis</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -17,16 +17,14 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET, C#</PackageTags>
     <Description>Google's TensorFlow full binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.8.3.0</AssemblyVersion>
-    <PackageReleaseNotes>Changes since v0.8:
+    <AssemblyVersion>0.9.0.0</AssemblyVersion>
+    <PackageReleaseNotes>Changes since v0.8.3:
 
-1. Remove global static graph instance.
-2. Provide custom gradient function.
-3. Add gradient function for Conv2D.
-4. Fix bug for Transfer Learning example.
-5. Overload Graph.Import(byte[] bytes)</PackageReleaseNotes>
+1. Added full connected Neural Network example.
+2. Added word CNN Text Classification example.
+3. Fixed AdaOptimizer issue.</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.8.3.0</FileVersion>
+    <FileVersion>0.9.0.0</FileVersion>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
diff --git a/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs b/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs
index d57cda11..0e8216fd 100644
--- a/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs
+++ b/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs
@@ -40,15 +40,19 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public bool Run()
         {
+            bool successful = false;
+
             PrepareData();
             BuildGraph();
-            Train();
+            successful = Train();
 
-            return true;
+            return successful;
         }
 
         public Graph BuildGraph()
         {
+            var graph = new Graph().as_default();
+
             // Placeholders for inputs (x) and outputs(y)
             x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X");
             y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
@@ -67,7 +71,7 @@ namespace TensorFlowNET.Examples.ImageProcess
             // Network predictions
             var cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
 
-            return tf.get_default_graph();
+            return graph;
         }
 
         private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
diff --git a/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs b/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
index 3339840f..0f88b869 100644
--- a/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
+++ b/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
@@ -298,7 +298,7 @@ namespace TensorFlowNET.Examples
                 }
             }
 
-            return max_accuracy > 0.8;
+            return max_accuracy > 0.9;
         }
 
         public bool Train()