implemented 1)tf.sqrt 2) tf.distributions.normal

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

@@ -2,13 +2,17 @@
 using System.Collections.Generic;
 using System.Text;
 
-namespace Tensorflow.APIs
+namespace Tensorflow
 {
     public static partial class tf
     {
         public static class distributions
         {
-            public static Normal(Tensor loc, Tensor scale, bool validate_args = false, bool allow_nan_stats = true, string name = "Normal") => Normal(loc, scale, validate_args = false, allow_nan_stats = true, "Normal");
+            public static Normal Normal(Tensor loc,
+                Tensor scale,
+                bool validate_args = false,
+                bool allow_nan_stats = true,
+                string name = "Normal") => new Normal(loc, scale, validate_args = false, allow_nan_stats = true, "Normal");
         }
     }
 }

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

@@ -1,7 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
-using Tensorflow.Layers;
+using Tensorflow.Keras.Layers;
 using Tensorflow.Operations.Activation;
 
 namespace Tensorflow

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

@@ -10,6 +10,8 @@ namespace Tensorflow
 
         public static Tensor sub(Tensor a, Tensor b) => gen_math_ops.sub(a, b);
 
+        public static Tensor sqrt(Tensor a, string name = null) => gen_math_ops.sqrt(a, name);
+
         public static Tensor subtract<T>(Tensor x, T[] y, string name = null) where T : struct
             => gen_math_ops.sub(x, ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y"), name);
 

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

@@ -7,7 +7,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    class _BaseDistribution : Python
+    public class _BaseDistribution : Python
     {
         // Abstract base class needed for resolving subclass hierarchy.
     }
@@ -17,10 +17,10 @@ namespace Tensorflow
     /// Distribution is a base class for constructing and organizing properties
     /// (e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian). 
     /// </summary>
-    class Distribution : _BaseDistribution
+    public class Distribution : _BaseDistribution
     {
         public TF_DataType _dtype {get;set;}
-        public static ReparameterizationType _reparameterization_type {get;set;}
+        //public ReparameterizationType _reparameterization_type {get;set;}
         public bool _validate_args {get;set;}
         public bool _allow_nan_stats {get;set;}
         public Dictionary<string, object> _parameters  {get;set;}

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

@@ -3,7 +3,7 @@ using Tensorflow;
 
 namespace Tensorflow
 {
-    class Normal : Distribution
+    public class Normal : Distribution
     {
         public Tensor _loc { get; set; }
         public Tensor _scale { get; set; }
@@ -25,7 +25,7 @@ namespace Tensorflow
         /// <param name="validate_args"></param>
         /// <param name="allow_nan_stats"></param>
         /// <param name="name"></param>
-        Normal (Tensor loc, Tensor scale, bool validate_args=false, bool allow_nan_stats=true, string name="Normal") 
+        public Normal (Tensor loc, Tensor scale, bool validate_args=false, bool allow_nan_stats=true, string name="Normal") 
         {
             parameters.Add("name", name);
             parameters.Add("loc", loc);

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -62,6 +62,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor sqrt(Tensor x, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Sqrt", name, args: new { x });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor sub<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Sub", name, args: new { x, y });

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -74,7 +74,7 @@ namespace TensorFlowNET.Examples
 
             // Create a 3x2 univariate normal distribution with the 
             // Known mean and variance           
-            var dist = tf.distributions.Normal(loc=mean, scale=tf.sqrt(variance));
+            var dist = tf.distributions.Normal(mean, tf.sqrt(variance));
 
         }