Naive Bayes Classifier running but prob is not correct.

TensorFlow.NET.sln

@@ -11,6 +11,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\T
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "src\TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{0254BFF9-453C-4FE0-9609-3644559A79CE}"
 EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{647E7590-9B50-48D9-B9E2-47743E9EAA56}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -33,6 +35,10 @@ Global
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{0254BFF9-453C-4FE0-9609-3644559A79CE}.Release|Any CPU.Build.0 = Release|Any CPU
+		{647E7590-9B50-48D9-B9E2-47743E9EAA56}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{647E7590-9B50-48D9-B9E2-47743E9EAA56}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{647E7590-9B50-48D9-B9E2-47743E9EAA56}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{647E7590-9B50-48D9-B9E2-47743E9EAA56}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -252,6 +252,11 @@ namespace Tensorflow
             return math_ops.reduce_sum(input);
         }
 
+        public static Tensor reduce_sum(Tensor input, int axis, int? reduction_indices = null)
+        {
+            return math_ops.reduce_sum(input, axis);
+        }
+
         public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
             => math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name, reduction_indices: reduction_indices);
 

src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs

@@ -83,7 +83,7 @@ namespace Tensorflow
         {
             var log_prob = _log_unnormalized_prob(_z(x));
             var log_norm = _log_normalization();
-            return log_prob - log_norm;
+            return tf.sub(log_prob, log_norm);
         }
 
         private Tensor _log_unnormalized_prob (Tensor x)

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -222,14 +222,14 @@ namespace Tensorflow
         /// <returns> The reduced tensor.</returns>
         public static Tensor reduce_logsumexp(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
         {
-            with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
+            return with(ops.name_scope(name, "ReduceLogSumExp", new { input_tensor }), scope =>
             {
                 var raw_max = reduce_max(input_tensor, axis, true);
                 var my_max = array_ops.stop_gradient(array_ops.where(gen_math_ops.is_finite(raw_max), raw_max, array_ops.zeros_like(raw_max)));
                 var result = gen_math_ops.log(
                 reduce_sum(
                     gen_math_ops.exp(gen_math_ops.sub(input_tensor, my_max)),
-                    new Tensor(axis),
+                    axis[0],
                     keepdims));
                 if (!keepdims)
                 {
@@ -238,7 +238,6 @@ namespace Tensorflow
                 result = gen_math_ops.add(result, my_max);
                 return _may_reduce_to_scalar(keepdims, axis, result);
             });
-            return null;
         }
 
         public static Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
@@ -295,13 +294,17 @@ namespace Tensorflow
             if (!common_shapes.has_fully_defined_shape(output) &&
                 !keepdims &&
                 axis == null)
+                // We want set_shape to be reflected in the C API graph for when we run it.
                 output.shape = new long[0];
             return output;
         }
 
         private static Tensor _may_reduce_to_scalar(bool keepdims, int[] axis, Tensor output)
         {
-            output.shape = new long[0];
+            if (!common_shapes.has_fully_defined_shape(output) &&
+                !keepdims &&
+                axis == null)
+                output.shape = new long[0];
             return output;
         }
 
@@ -323,7 +326,7 @@ namespace Tensorflow
             if (axis != null)
             {
                 // should return axis. or check before.
-                return null;
+                return ops.convert_to_tensor(axis, TF_DataType.TF_INT32);
             }
             else
             {

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -58,4 +58,8 @@ Bug memory leak issue when allocating Tensor.</PackageReleaseNotes>
     <Folder Include="Keras\Initializers\" />
   </ItemGroup>
 
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
+  </ItemGroup>
+
 </Project>

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -12,7 +12,7 @@ namespace TensorFlowNET.Examples
 {
     public class ImageRecognition : Python, IExample
     {
-        public int Priority => 6;
+        public int Priority => 7;
         public bool Enabled => true;
         public string Name => "Image Recognition";
 

test/TensorFlowNET.Examples/KMeansClustering.cs

@@ -15,8 +15,8 @@ namespace TensorFlowNET.Examples
     /// </summary>
     public class KMeansClustering : Python, IExample
     {
-        public int Priority => 7;
-        public bool Enabled => true;
+        public int Priority => 8;
+        public bool Enabled => false;
         public string Name => "K-means Clustering";
 
         Datasets mnist;

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -12,7 +12,7 @@ namespace TensorFlowNET.Examples
     /// </summary>
     public class NaiveBayesClassifier : Python, IExample
     {
-        public int Priority => 100;
+        public int Priority => 6;
         public bool Enabled => true;
         public string Name => "Naive Bayes Classifier";
 
@@ -88,7 +88,7 @@ namespace TensorFlowNET.Examples
             var s = tf.Session();
             if (xx.dtype ==  typeof(float))
             {
-                var samples = np.vstack <float>(xx.ravel(), yy.ravel());
+                var samples = np.hstack<float>(xx.ravel().reshape(-1,1), yy.ravel().reshape(-1,1));
                 var Z = s.run(predict(samples));
             }
 
@@ -179,7 +179,7 @@ namespace TensorFlowNET.Examples
             var joint_likelihood = tf.add(ops.convert_to_tensor(priors, TF_DataType.TF_FLOAT), cond_probs);
             // normalize to get (log)-probabilities
 
-            var norm_factor = tf.reduce_logsumexp(joint_likelihood, new int[] { 1 }, true);
+            var norm_factor = tf.reduce_logsumexp(joint_likelihood, new int[] { 1 }, keepdims: true);
             var log_prob = joint_likelihood - norm_factor;
             // exp to get the actual probabilities
             return tf.exp(log_prob);

test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs

@@ -11,7 +11,7 @@ namespace TensorFlowNET.Examples
 {
     public class TextClassificationWithMovieReviews : Python, IExample
     {
-        public int Priority => 7;
+        public int Priority => 9;
         public bool Enabled => false;
         public string Name => "Movie Reviews";