implemented NB classifier fit API

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

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.APIs
+{
+    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");
+        }
+    }
+}

src/TensorFlowNET.Core/Framework/sparse_tensor.py.cs

@@ -0,0 +1,12 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Framework
+{
+    public static class SparseTensor
+    {
+        private static Tensor _dense_shape { get; set; }
+
+    }
+}

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

@@ -4,9 +4,10 @@ using System.Collections.Generic;
 using System.Text;
 
 
+
 namespace Tensorflow
 {
-    abstract class _BaseDistribution : Python
+    class _BaseDistribution : Python
     {
         // Abstract base class needed for resolving subclass hierarchy.
     }
@@ -19,7 +20,7 @@ namespace Tensorflow
     class Distribution : _BaseDistribution
     {
         public TF_DataType _dtype {get;set;}
-        public ReparameterizationType _reparameterization_type {get;set;}
+        public static 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

@@ -1,4 +1,5 @@
 using System.Collections.Generic;
+using Tensorflow;
 
 namespace Tensorflow
 {
@@ -7,7 +8,7 @@ namespace Tensorflow
         public Tensor _loc { get; set; }
         public Tensor _scale { get; set; }
 
-        Dictionary<string, object> parameters = new Dictionary<string, object>();
+        public Dictionary<string, object> parameters = new Dictionary<string, object>();
         /// <summary>
         /// The Normal distribution with location `loc` and `scale` parameters.
         /// Mathematical details
@@ -24,7 +25,7 @@ namespace Tensorflow
         /// <param name="validate_args"></param>
         /// <param name="allow_nan_stats"></param>
         /// <param name="name"></param>
-        public Normal (Tensor loc, Tensor scale, bool validate_args=false, bool allow_nan_stats=true, string name="Normal") 
+        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);
@@ -39,7 +40,7 @@ namespace Tensorflow
                     this._loc = array_ops.identity(loc, name);
                     this._scale = array_ops.identity(scale, name);
                     base._dtype = this._scale.dtype;
-                    base._reparameterization_type = new ReparameterizationType("FULLY_REPARAMETERIZED");
+                    // base._reparameterization_type = new ReparameterizationType("FULLY_REPARAMETERIZED");
                     base._validate_args = validate_args;
                     base._allow_nan_stats = allow_nan_stats;
                     base._parameters = parameters;
@@ -56,7 +57,7 @@ namespace Tensorflow
         /// <returns></returns>
         public Tensor loc()
         {
-            return this._loc;
+            return _loc;
         }
         /// <summary>
         /// Distribution parameter for standard deviation."
@@ -64,17 +65,17 @@ namespace Tensorflow
         /// <returns></returns>
         public Tensor scale()
         {
-            return this._scale;
+            return _scale;
         }
 
         public Tensor _batch_shape_tensor()
         {
-            return array_ops.broadcast_dynamic_shape(array_ops.shape(this._loc), array_ops.shape(this._scale));
+            return array_ops.broadcast_dynamic_shape(array_ops.shape(_loc), array_ops.shape(_scale));
         }
 
         public Tensor _batch_shape()
         {
-            return array_ops.broadcast_static_shape(new Tensor(this._loc.shape), new Tensor(this._scale.shape));
+            return array_ops.broadcast_static_shape(new Tensor(_loc.shape), new Tensor(_scale.shape));
         }
 
     }

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -305,6 +305,9 @@ namespace Tensorflow
                 case "list(type)":
                     attr_value.List.Type.AddRange((value as IList<TF_DataType>).Select(x => _MakeType(x, attr_def)));
                     break;
+                case "list(int)":
+                    attr_value.List.I.AddRange((value as int[]).Select(x => Convert.ToInt64(x)));
+                    break;
                 case "bool":
                     attr_value.B = (bool)value;
                     break;

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -157,7 +157,7 @@ namespace Tensorflow
         }
         public static Tensor stop_gradient(Tensor x, string name = null)
         {
-            var _op = _op_def_lib._apply_op_helper("StopGradient", name, args: new { x });
+            var _op = _op_def_lib._apply_op_helper("StopGradient", name, args: new { input = x, name });
 
             return _op.outputs[0];
         }
@@ -174,7 +174,7 @@ namespace Tensorflow
         /// <returns> A `Tensor`. Has the same type as `input`.</returns>
         public static Tensor squeeze(Tensor input, int[] axis = null, string name = null)
         {
-            var _op = _op_def_lib._apply_op_helper("Squeeze", name, args: new { input, axis, name });
+            var _op = _op_def_lib._apply_op_helper("Squeeze", name, args: new { input, squeeze_dims = axis });
 
             return _op.outputs[0];
         }

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -27,6 +27,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor mean(Tensor input, int[] axis, bool keep_dims = false, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Mean", name, args: new { input, reduction_indices = axis, keep_dims = keep_dims, name });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor add(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Add", name, args: new { x, y });
@@ -36,7 +43,7 @@ namespace Tensorflow
 
         public static Tensor squared_difference(Tensor x, Tensor y, string name = null)
         {
-            var _op = _op_def_lib._apply_op_helper("SquaredDifference", name, args: new { x, y });
+            var _op = _op_def_lib._apply_op_helper("SquaredDifference", name, args: new { x, y, name });
 
             return _op.outputs[0];
         }

src/TensorFlowNET.Core/Operations/math_ops.py.cs

@@ -1,4 +1,5 @@
-using System;
+using NumSharp.Core;
+using System;
 using System.Collections.Generic;
 using System.Text;
 
@@ -37,8 +38,8 @@ namespace Tensorflow
         /// <param name="name"> A name for the operation (optional).</param>
         public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
         {
-            var r = _ReductionDims(input_tensor, new Tensor(axis));
-            var m = gen_math_ops.mean(input_tensor, r);
+            var r = _ReductionDims(input_tensor, axis);
+            var m = gen_math_ops.mean(input_tensor, (int[]) r, keepdims, name);
             return _may_reduce_to_scalar(keepdims,axis, m);
         }
         /// <summary>
@@ -135,8 +136,8 @@ namespace Tensorflow
                 return range(0, rank, 1);
             }
         }
-
-        private static int[] _ReductionDims(Tensor x, int[] axis)
+        
+        private static object _ReductionDims(Tensor x, int[] axis)
         {
             if (axis != null)
             {
@@ -147,12 +148,12 @@ namespace Tensorflow
                 var rank = array_ops.rank(x);
                 if (rank != null)
                 {
-                   // return constant_op.constant();
+                   return constant_op.constant(np.arange(rank), TF_DataType.TF_INT32);
                 }
-                // return range(0, rank, 1);
-                throw new NotFiniteNumberException();
+                return range(0, rank, 1);
             }
         }
+        
 
         public static Tensor range(object start, object limit = null, object delta = null, TF_DataType dtype = TF_DataType.DtInvalid, string name = "range" )
         {

src/TensorFlowNET.Core/Operations/nn_impl.py.cs

@@ -26,19 +26,19 @@ namespace Tensorflow
                 // on 32-bit floats before converting the mean and variance back to fp16
                 var y = math_ops.cast(x, TF_DataType.TF_FLOAT);
                 // Compute true mean while keeping the dims for proper broadcasting.
-                var mean = math_ops.reduce_mean(y, axes, keep_dims = true, name = "mean");
+                var mean = math_ops.reduce_mean(y, axes, true, name = "mean");
                 // Sample variance, not unbiased variance
                 // Note: stop_gradient does not change the gradient that gets
                 // backpropagated to the mean from the variance calculation,
                 // because that gradient is zero
-                var variance = math_ops.reduce_mean(math_ops.reduce_mean(math_ops.square_difference(y, array_ops.stop_gradient(mean)), axes, keep_dims = true, name = "Variance"));
+                var variance = math_ops.reduce_mean(math_ops.square_difference(y, array_ops.stop_gradient(mean)), axes, true, name = "Variance");
                 if (!keep_dims)
                 {
                     mean = array_ops.squeeze(mean, axes);
                     variance = array_ops.squeeze(variance, axes);
                 }
                 // TODO: if x.dtype == dtypes.float16:
-                if (x.dtype == TF_DataType.TF_FLOAT)
+                if (x.dtype == TF_DataType.TF_HALF)
                     return (math_ops.cast(mean, x.dtype), math_ops.cast(variance, x.dtype));
                 else
                     return (mean, variance);

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -71,9 +71,10 @@ namespace TensorFlowNET.Examples
             var tup = tf.nn.moments(cons, new int[]{1});
             var mean = tup.Item1;
             var variance = tup.Item2;
+
             // 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(loc=mean, scale=tf.sqrt(variance));
 
         }