implemented nn class

README.md

@@ -66,13 +66,14 @@ using(var sess = tf.Session())
 
 Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
 
-More examples:
+### More examples:
 
 * [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs)
 * [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)
 * [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs)
 * [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs)
 * [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs)
+* [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs)
 * [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs)
 * [Named Entity Recognition](test/TensorFlowNET.Examples/NamedEntityRecognition.cs)
 

TensorFlow.NET.sln

@@ -9,11 +9,11 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "t
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\TensorFlowNET.Core\TensorFlowNET.Core.csproj", "{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Utility", "src\TensorFlowNET.Utility\TensorFlowNET.Utility.csproj", "{00D9085C-0FC7-453C-A0CC-BAD98F44FEA0}"
-EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{268BF0B6-0AA9-4FD3-A245-7AF336F1E3E9}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{E8340C61-12C1-4BEE-A340-403E7C1ACD82}"
+EndProject
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "scikit-learn", "..\scikit-learn.net\src\scikit-learn\scikit-learn.csproj", "{199DDAD8-4A6F-43B3-A560-C0393619E304}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -33,18 +33,18 @@ Global
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.Build.0 = Release|Any CPU
-		{00D9085C-0FC7-453C-A0CC-BAD98F44FEA0}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{00D9085C-0FC7-453C-A0CC-BAD98F44FEA0}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{00D9085C-0FC7-453C-A0CC-BAD98F44FEA0}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{00D9085C-0FC7-453C-A0CC-BAD98F44FEA0}.Release|Any CPU.Build.0 = Release|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.Build.0 = Release|Any CPU
-		{268BF0B6-0AA9-4FD3-A245-7AF336F1E3E9}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{268BF0B6-0AA9-4FD3-A245-7AF336F1E3E9}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{268BF0B6-0AA9-4FD3-A245-7AF336F1E3E9}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{268BF0B6-0AA9-4FD3-A245-7AF336F1E3E9}.Release|Any CPU.Build.0 = Release|Any CPU
+		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Release|Any CPU.Build.0 = Release|Any CPU
+		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

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

@@ -7,7 +7,24 @@ namespace Tensorflow
     public static partial class tf
     {
         public static IInitializer zeros_initializer => new Zeros();
+        public static IInitializer glorot_uniform_initializer => new GlorotUniform();
         
+        public static variable_scope variable_scope(string name,
+               string default_name = null,
+               object values = null,
+               bool auxiliary_name_scope = true) => new variable_scope(name, 
+                   default_name, 
+                   values,
+                   auxiliary_name_scope);
+
+        public static variable_scope variable_scope(VariableScope scope,
+              string default_name = null,
+              object values = null,
+              bool auxiliary_name_scope = true) => new variable_scope(scope,
+                  default_name,
+                  values,
+                  auxiliary_name_scope);
+
         public class Zeros : IInitializer
         {
             private TF_DataType dtype;
@@ -30,5 +47,105 @@ namespace Tensorflow
                 return new { dtype = dtype.name() };
             }
         }
+
+        /// <summary>
+        /// Initializer capable of adapting its scale to the shape of weights tensors.
+        /// </summary>
+        public class VarianceScaling : IInitializer
+        {
+            protected float _scale;
+            protected string _mode;
+            protected string _distribution;
+            protected int? _seed;
+            protected TF_DataType _dtype;
+
+            public VarianceScaling(float scale = 1.0f,
+                string mode = "fan_in",
+                string distribution= "truncated_normal",
+                int? seed = null,
+                TF_DataType dtype = TF_DataType.TF_FLOAT)
+            {
+                if (scale < 0)
+                    throw new ValueError("`scale` must be positive float.");
+                _scale = scale;
+                _mode = mode;
+                _distribution = distribution;
+                _seed = seed;
+                _dtype = dtype;
+            }
+
+            public Tensor call(TensorShape shape, TF_DataType dtype)
+            {
+                var (fan_in, fan_out) = _compute_fans(shape);
+                if (_mode == "fan_in")
+                    _scale /= Math.Max(1, fan_in);
+                else if (_mode == "fan_out")
+                    _scale /= Math.Max(1, fan_out);
+                else
+                    _scale /= Math.Max(1, (fan_in + fan_out) / 2);
+
+                if (_distribution == "normal" || _distribution == "truncated_normal")
+                {
+                    throw new NotImplementedException("truncated_normal");
+                }
+                else if(_distribution == "untruncated_normal")
+                {
+                    throw new NotImplementedException("truncated_normal");
+                }
+                else
+                {
+                    var limit = Math.Sqrt(3.0f * _scale);
+                    return random_ops.random_uniform(shape, (float)-limit, (float)limit, dtype, seed: _seed);
+                }
+            }
+
+            private (int, int) _compute_fans(int[] shape)
+            {
+                if (shape.Length < 1)
+                    return (1, 1);
+                if (shape.Length == 1)
+                    return (shape[0], shape[0]);
+                if (shape.Length == 2)
+                    return (shape[0], shape[1]);
+                else
+                    throw new NotImplementedException("VarianceScaling._compute_fans");
+            }
+
+            public virtual object get_config()
+            {
+                return new
+                {
+                    scale = _scale,
+                    mode = _mode,
+                    distribution = _distribution,
+                    seed = _seed,
+                    dtype = _dtype
+                };
+            }
+        }
+
+        public class GlorotUniform : VarianceScaling
+        {
+            public GlorotUniform(float scale = 1.0f,
+                string mode = "fan_avg",
+                string distribution = "uniform",
+                int? seed = null,
+                TF_DataType dtype = TF_DataType.TF_FLOAT) : base(scale, mode, distribution, seed, dtype)
+            {
+
+            }
+
+            public object get_config()
+            {
+                return new
+                {
+                    scale = _scale,
+                    mode = _mode,
+                    distribution = _distribution,
+                    seed = _seed,
+                    dtype = _dtype
+                };
+            }
+        }
     }
 }

src/TensorFlowNET.Core/Contrib/Learn/Preprocessing/VocabularyProcessor.cs

@@ -0,0 +1,19 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Contrib.Learn.Preprocessing
+{
+    public class VocabularyProcessor
+    {
+        private int _max_document_length;
+        private int _min_frequency;
+
+        public VocabularyProcessor(int max_document_length,
+            int min_frequency)
+        {
+            _max_document_length = max_document_length;
+            _min_frequency = min_frequency;
+        }
+    }
+}

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

@@ -7,7 +7,7 @@ using static Tensorflow.OpDef.Types;
 
 namespace Tensorflow
 {
-    public class importer
+    public class importer : Python
     {
         public static ITensorOrOperation[] import_graph_def(GraphDef graph_def,
             Dictionary<string, Tensor> input_map = null,
@@ -26,7 +26,7 @@ namespace Tensorflow
 
             string prefix = "";
             var graph = ops.get_default_graph();
-            Python.with<ops.name_scope>(new ops.name_scope(name, "import", input_map.Values), scope =>
+            with(new ops.name_scope(name, "import", input_map.Values), scope =>
             {
                 prefix = scope;
                 /*if (!string.IsNullOrEmpty(prefix))

src/TensorFlowNET.Core/Gradients/gradients_impl.py.cs

@@ -7,7 +7,7 @@ using System.Threading;
 
 namespace Tensorflow
 {
-    public class gradients_impl
+    public class gradients_impl : Python
     {
         public static Tensor[] gradients(Tensor[] ys,
             Tensor[] xs, 
@@ -58,7 +58,7 @@ namespace Tensorflow
              **/
             var grads = new Dictionary<string, Tensor[][]>();
 
-            Python.with<ops.name_scope>(new ops.name_scope(name, "gradients", values: all), scope =>
+            with(new ops.name_scope(name, "gradients", values: all), scope =>
             {
                 string grad_scope = scope;
                 // Get a uid for this call to gradients that can be used to help
@@ -131,7 +131,7 @@ namespace Tensorflow
                             // for ops that do not have gradients.
                             var grad_fn = ops.get_gradient_function(op);
 
-                            Python.with<ops.name_scope>(new ops.name_scope(op.name + "_grad"), scope1 =>
+                            with(new ops.name_scope(op.name + "_grad"), scope1 =>
                             {
                                 string name1 = scope1;
                                 if (grad_fn != null)

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -196,11 +196,11 @@ namespace Tensorflow
 
             _create_op_helper(op, true);
 
-            Console.Write($"create_op: {op_type} '{node_def.Name}'");
+            /*Console.Write($"create_op: {op_type} '{node_def.Name}'");
             Console.Write($", inputs: {(inputs.Length == 0 ? "empty" : String.Join(", ", inputs.Select(x => x.name)))}");
             Console.Write($", control_inputs: {(control_inputs.Length == 0 ? "empty" : String.Join(", ", control_inputs.Select(x => x.name)))}");
             Console.Write($", outputs: {(op.outputs.Length == 0 ? "empty" : String.Join(", ", op.outputs.Select(x => x.name)))}");
-            Console.WriteLine();
+            Console.WriteLine();*/
 
             return op;
         }

src/TensorFlowNET.Core/Operations/Losses/losses_impl.py.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
             string scope = "",
             string loss_collection= "losses")
         {
-            with<ops.name_scope>(new ops.name_scope(scope,
+            with(new ops.name_scope(scope,
                 "sparse_softmax_cross_entropy_loss",
                 (logits, labels, weights)),
                 namescope =>

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -10,7 +10,7 @@ using static Tensorflow.OpDef.Types;
 
 namespace Tensorflow
 {
-    public class OpDefLibrary
+    public class OpDefLibrary : Python
     {
         public Operation _apply_op_helper(string op_type_name, string name = null, dynamic args = null)
         {
@@ -44,7 +44,7 @@ namespace Tensorflow
             var input_types = new List<TF_DataType>();
             dynamic values = null;
 
-            return Python.with<ops.name_scope, Operation>(new ops.name_scope(name), scope =>
+            return with(new ops.name_scope(name), scope =>
             {
                 var inferred_from = new Dictionary<string, object>();
                 var base_types = new List<TF_DataType>();

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

@@ -5,14 +5,14 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class array_ops
+    public class array_ops : Python
     {
         public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = null) => gen_array_ops.placeholder_with_default(input, shape, name);
 
         public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "zeros", shape), scope =>
+            return with(new ops.name_scope(name, "zeros", shape), scope =>
             {
                 name = scope;
                 switch (dtype)
@@ -68,7 +68,7 @@ namespace Tensorflow
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "ones_like", new { tensor }), scope =>
+            return with(new ops.name_scope(name, "ones_like", new { tensor }), scope =>
             {
                 name = scope;
                 var tensor1 = ops.convert_to_tensor(tensor, name: "tensor");
@@ -84,7 +84,7 @@ namespace Tensorflow
         public static Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null)
         {
             dtype = dtype.as_base_dtype();
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "ones", new { shape }), scope =>
+            return with(new ops.name_scope(name, "ones", new { shape }), scope =>
             {
                 name = scope;
                 var output = gen_array_ops.fill(shape, constant_op.constant(1.0f, dtype: dtype), name: name);
@@ -130,7 +130,7 @@ namespace Tensorflow
 
         private static Tensor shape_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Shape", new { input }), scope =>
+            return with(new ops.name_scope(name, "Shape", new { input }), scope =>
             {
                 name = scope;
 
@@ -151,7 +151,7 @@ namespace Tensorflow
 
         private static Tensor size_internal(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Size", new Tensor[] { input }), scope =>
+            return with(new ops.name_scope(name, "Size", new Tensor[] { input }), scope =>
             {
                 name = scope;
 
@@ -182,7 +182,7 @@ namespace Tensorflow
 
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
+            return with(new ops.name_scope(name, "zeros_like", new Tensor[] { tensor }), scope =>
             {
                 name = scope;
                 tensor = ops.convert_to_tensor(tensor, name: "tensor");

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

@@ -9,7 +9,7 @@ namespace Tensorflow
     {
         public static Operation group<T>(T[] inputs, string name = null) where T : ITensorOrOperation
         {
-            return with<ops.name_scope, Operation>(new ops.name_scope(name, "group_deps", inputs), scope =>
+            return with(new ops.name_scope(name, "group_deps", inputs), scope =>
             {
                 name = scope;
 
@@ -39,7 +39,7 @@ namespace Tensorflow
 
         private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = null)
         {
-            return Python.with<_ControlDependenciesController, Operation>(ops.control_dependencies(deps), ctl =>
+            return with(ops.control_dependencies(deps), ctl =>
             {
                 if (dev == null)
                 {
@@ -83,7 +83,7 @@ namespace Tensorflow
 
         public static Tensor[] tuple(Tensor[] tensors, string name = null, Operation[] control_inputs = null)
         {
-            return Python.with<ops.name_scope, Tensor[]>(new ops.name_scope(name, "tuple", tensors), scope =>
+            return with(new ops.name_scope(name, "tuple", tensors), scope =>
             {
                 name = scope;
                 var gating_ops = tensors.Select(x => x.op).ToList();
@@ -115,11 +115,11 @@ namespace Tensorflow
             values.AddRange(dependencies);
             values.Add(output_tensor);
 
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "control_dependency", values), scope =>
+            return with(new ops.name_scope(name, "control_dependency", values), scope =>
             {
                 name = scope;
 
-                return Python.with<_ControlDependenciesController, Tensor>(ops.control_dependencies(dependencies), ctl =>
+                return with(ops.control_dependencies(dependencies), ctl =>
                 {
                     output_tensor = ops.convert_to_tensor_or_composite(output_tensor);
                     return _Identity(output_tensor, name: name);

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

@@ -24,10 +24,34 @@ namespace Tensorflow
             if (!seed2.HasValue)
                 seed2 = 0;
 
-            var _op = _op_def_lib._apply_op_helper("RandomStandardNormal", name: name,
+            var _op = _op_def_lib._apply_op_helper("RandomStandardNormal", 
+                name: name,
                 args: new { shape, dtype, seed, seed2 });
 
             return _op.outputs[0];
         }
+
+        /// <summary>
+        /// Outputs random values from a uniform distribution.
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="seed"></param>
+        /// <param name="seed2"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor random_uniform(Tensor shape, TF_DataType dtype, int? seed = 0, int? seed2 = 0, string name = null)
+        {
+            if (!seed.HasValue)
+                seed = 0;
+            if (!seed2.HasValue)
+                seed2 = 0;
+
+            var _op = _op_def_lib._apply_op_helper("RandomUniform",
+                name: name,
+                args: new { shape, dtype, seed, seed2});
+
+            return _op.outputs[0];
+        }
     }
 }

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

@@ -14,7 +14,7 @@ namespace Tensorflow
             if(base_type == x.dtype)
                 return x;
 
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Cast", new { x }), scope =>
+            return with(new ops.name_scope(name, "Cast", new { x }), scope =>
             {
                 x = ops.convert_to_tensor(x, name: "x");
                 if (x.dtype.as_base_dtype() != base_type)
@@ -165,7 +165,7 @@ namespace Tensorflow
             if (delta == null)
                 delta = 1;
 
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Range", new object[] { start, limit, delta }), scope =>
+            return with(new ops.name_scope(name, "Range", new object[] { start, limit, delta }), scope =>
             {
                 name = scope;
                 var start1 = ops.convert_to_tensor(start, name: "start");
@@ -178,7 +178,7 @@ namespace Tensorflow
 
         public static Tensor floordiv(Tensor x, Tensor y, string name = null)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "floordiv", new { x, y }), scope =>
+            return with(new ops.name_scope(name, "floordiv", new { x, y }), scope =>
             {
                 return gen_math_ops.floor_div(x, y, scope);
             });
@@ -186,7 +186,7 @@ namespace Tensorflow
 
         public static Tensor rank_internal(Tensor input, string name = null, bool optimize = true)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
+            return with(new ops.name_scope(name, "Rank", new List<Tensor> { input }), scope =>
             {
                 name = scope;
                 var input_tensor = ops.convert_to_tensor(input);
@@ -206,7 +206,7 @@ namespace Tensorflow
         {
             Tensor result = null;
 
-            Python.with<ops.name_scope>(new ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
+            with(new ops.name_scope(name, "MatMul", new Tensor[] { a, b }), scope =>
             {
                 name = scope;
 
@@ -236,7 +236,7 @@ namespace Tensorflow
             if (dt.is_floating() || dt.is_integer())
                 return x;
 
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
+            return with(new ops.name_scope(name, "Conj", new List<Tensor> { x }), scope =>
             {
 
                 return x;

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

@@ -4,8 +4,18 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class random_ops
+    public class random_ops : Python
     {
+        /// <summary>
+        /// 
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="mean"></param>
+        /// <param name="stddev"></param>
+        /// <param name="dtype"></param>
+        /// <param name="seed"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
         public static Tensor random_normal(int[] shape, 
             float mean = 0.0f, 
             float stddev = 1.0f, 
@@ -13,7 +23,7 @@ namespace Tensorflow
             int? seed = null, 
             string name = null)
         {
-            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "random_normal", new object[] { shape, mean, stddev }), scope =>
+            return with(new ops.name_scope(name, "random_normal", new { shape, mean, stddev }), scope =>
             {
                 var shape_tensor = _ShapeTensor(shape);
                 var mean_tensor = ops.convert_to_tensor(mean, dtype: dtype, name: "mean");
@@ -26,6 +36,34 @@ namespace Tensorflow
             });
         }
 
+        /// <summary>
+        /// Outputs random values from a uniform distribution.
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="minval"></param>
+        /// <param name="maxval"></param>
+        /// <param name="dtype">The type of the output</param>
+        /// <param name="seed">Used to create a random seed for the distribution.</param>
+        /// <param name="name">A name for the operation</param>
+        /// <returns>A tensor of the specified shape filled with random uniform values.</returns>
+        public static Tensor random_uniform(int[] shape, 
+            float minval = 0,
+            float? maxval = null,
+            TF_DataType dtype = TF_DataType.TF_FLOAT, 
+            int? seed = null, 
+            string name = null)
+        {
+            return with(new ops.name_scope(name, "random_uniform", new { shape, minval, maxval }), scope =>
+            {
+                name = scope;
+                var tensorShape = _ShapeTensor(shape);
+                var minTensor = ops.convert_to_tensor(minval, dtype: dtype, name: "min");
+                var maxTensor = ops.convert_to_tensor(maxval, dtype: dtype, name: "max");
+                var rnd = gen_random_ops.random_uniform(tensorShape, dtype);
+                return math_ops.add(rnd * (maxTensor - minTensor), minTensor, name: name);
+            });
+        }
+
         private static Tensor _ShapeTensor(int[] shape)
         {
             return ops.convert_to_tensor(shape, name: "shape");

src/TensorFlowNET.Core/Python.cs

@@ -43,12 +43,12 @@ namespace Tensorflow
             }
         }
 
-        public static void with<T>(IPython py, Action<T> action) where T : IPython
+        public static void with<T>(T py, Action<T> action) where T : IPython
         {
             try
             {
                 py.__enter__();
-                action((T)py);
+                action(py);
             }
             catch (Exception ex)
             {
@@ -62,12 +62,12 @@ namespace Tensorflow
             }
         }
 
-        public static TOut with<TIn, TOut>(IPython py, Func<TIn, TOut> action) where TIn : IPython
+        public static TOut with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IPython
         {
             try
             {
                 py.__enter__();
-                return action((TIn)py);
+                return action(py);
             }
             catch (Exception ex)
             {

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -62,26 +62,29 @@ namespace Tensorflow
 
                         switch (subfeed_val)
                         {
-                            case IntPtr pointer:
-                                feed_dict_tensor[subfeed_t] = pointer;
+                            case IntPtr val:
+                                feed_dict_tensor[subfeed_t] = val;
                                 break;
-                            case NDArray nd:
-                                feed_dict_tensor[subfeed_t] = nd;
+                            case NDArray val:
+                                feed_dict_tensor[subfeed_t] = val;
                                 break;
-                            case float floatVal:
-                                feed_dict_tensor[subfeed_t] = (NDArray)floatVal;
+                            case float val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
                                 break;
-                            case double doubleVal:
-                                feed_dict_tensor[subfeed_t] = (NDArray)doubleVal;
+                            case double val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
                                 break;
-                            case int intVal:
-                                feed_dict_tensor[subfeed_t] = (NDArray)intVal;
+                            case short val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
                                 break;
-                            case string str:
-                                feed_dict_tensor[subfeed_t] = (NDArray)str;
+                            case int val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
                                 break;
-                            case byte[] bytes:
-                                feed_dict_tensor[subfeed_t] = (NDArray)bytes;
+                            case string val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
+                                break;
+                            case byte[] val:
+                                feed_dict_tensor[subfeed_t] = (NDArray)val;
                                 break;
                             default:
                                 Console.WriteLine($"can't handle data type of subfeed_val");

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -4,7 +4,7 @@
     <TargetFramework>netstandard2.0</TargetFramework>
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
-    <Version>0.4.0</Version>
+    <Version>0.4.2</Version>
     <Authors>Haiping Chen</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -16,11 +16,11 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET</PackageTags>
     <Description>Google's TensorFlow binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.4.0.0</AssemblyVersion>
-    <PackageReleaseNotes>Added Linear Regression example.
-</PackageReleaseNotes>
+    <AssemblyVersion>0.4.2.0</AssemblyVersion>
+    <PackageReleaseNotes>Added ConfigProto to control CPU and GPU resource.
+Fixed import name scope issue.</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.4.0.0</FileVersion>
+    <FileVersion>0.4.2.0</FileVersion>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">

src/TensorFlowNET.Core/Tensors/Tensor.Operators.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
                 dtype = tr.dtype.as_base_dtype();
             
             var namescope = new ops.name_scope(null, name, new { x, y });
-            return Python.with<ops.name_scope, Tensor>(namescope, scope =>
+            return with(namescope, scope =>
             {
                 Tensor result = null;
                 var x1 = ops.convert_to_tensor(x, dtype: dtype, name: "x");

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
     /// A tensor is a generalization of vectors and matrices to potentially higher dimensions. 
     /// Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes.
     /// </summary>
-    public partial class Tensor : IDisposable, ITensorOrOperation
+    public partial class Tensor : Python, IDisposable, ITensorOrOperation
     {
         private readonly IntPtr _handle;
 
@@ -77,6 +77,11 @@ namespace Tensorflow
             return null;
         }
 
+        public TensorShape getShape()
+        {
+            return tensor_util.to_shape(shape);
+        }
+
         /// <summary>
         /// number of dimensions
         /// 0	Scalar (magnitude only)

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
     /// class directly, but instead instantiate one of its subclasses such as
     /// `GradientDescentOptimizer`, `AdagradOptimizer`, or `MomentumOptimizer`.
     /// </summary>
-    public abstract class Optimizer
+    public abstract class Optimizer : Python
     {
         // Values for gate_gradients.
         public static int GATE_NONE = 0;
@@ -87,7 +87,7 @@ namespace Tensorflow
             _create_slots(var_list);
 
             var update_ops = new List<Operation>();
-            return Python.with<ops.name_scope, Operation>(new ops.name_scope(name, Name), scope =>
+            return with(new ops.name_scope(name, Name), scope =>
             {
                 name = scope;
                 _prepare();
@@ -98,7 +98,7 @@ namespace Tensorflow
                         continue;
 
                     var scope_name = var.op.name;
-                    Python.with<ops.name_scope>(new ops.name_scope("update_" + scope_name), scope2 =>
+                    with(new ops.name_scope("update_" + scope_name), scope2 =>
                     {
                         update_ops.Add(processor.update_op(this, grad));
                     });

src/TensorFlowNET.Core/Train/Saving/BaseSaverBuilder.cs

@@ -5,7 +5,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class BaseSaverBuilder
+    public class BaseSaverBuilder : Python
     {
         protected SaverDef.Types.CheckpointFormatVersion _write_version;
 
@@ -79,7 +79,7 @@ namespace Tensorflow
             Tensor save_tensor = null;
             Operation restore_op = null;
 
-            return Python.with<ops.name_scope, SaverDef>(new ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
+            return with(new ops.name_scope(name, "save", saveables.Select(x => x.op).ToArray()), scope =>
             {
                 name = scope;
 

src/TensorFlowNET.Core/Variables/PureVariableScope.cs

@@ -0,0 +1,86 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class PureVariableScope : IPython
+    {
+        private string _name;
+        private VariableScope _scope;
+        private string _new_name;
+        private string _old_name_scope;
+        private bool _reuse;
+        private _VariableStore _var_store;
+        private VariableScope _old;
+        private _VariableScopeStore _var_scope_store;
+        private VariableScope variable_scope_object;
+        private VariableScope _cached_variable_scope_object;
+
+        public PureVariableScope(string name, 
+            string old_name_scope = null, 
+            TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            _name = name;
+            _old_name_scope = old_name_scope;
+            _var_store = variable_scope._get_default_variable_store();
+            _var_scope_store = variable_scope.get_variable_scope_store();
+        }
+
+        public PureVariableScope(VariableScope scope,
+            string old_name_scope = null,
+            TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            _scope = scope;
+            _old_name_scope = old_name_scope;
+            _var_store = variable_scope._get_default_variable_store();
+            _var_scope_store = variable_scope.get_variable_scope_store();
+            _new_name = _scope._name;
+
+            string name_scope = _scope._name_scope;
+            variable_scope_object = new VariableScope(_reuse,
+                name: _new_name,
+                name_scope: name_scope);
+
+            _cached_variable_scope_object = variable_scope_object;
+        }
+
+        public void __enter__()
+        {
+            _old = _var_scope_store.current_scope;
+            if(_scope != null)
+            {
+                _var_scope_store.open_variable_scope(_new_name);
+                variable_scope_object = _cached_variable_scope_object;
+            }
+            else
+            {
+                _new_name = string.IsNullOrEmpty(_old._name) ? _name : _old._name + "/" + _name;
+                _reuse = _reuse || _old.resue;
+                string name_scope = _old_name_scope == null ? _name : _old_name_scope;
+
+                variable_scope_object = new VariableScope(_reuse,
+                    name: _new_name,
+                    name_scope: name_scope);
+
+                _var_scope_store.open_variable_scope(_new_name);
+            }
+            _var_scope_store.current_scope = variable_scope_object;
+        }
+
+        public void Dispose()
+        {
+            
+        }
+
+        public void __exit__()
+        {
+            
+        }
+
+        public static implicit operator VariableScope(PureVariableScope scope)
+        {
+            return scope.variable_scope_object;
+        }
+    }
+}

src/TensorFlowNET.Core/Variables/RefVariable.Operators.cs

@@ -17,7 +17,7 @@ namespace Tensorflow
         private static Tensor op_helper<T>(string default_name, RefVariable x, T y)
         {
             var tensor1 = x.value();
-            return with<ops.name_scope, Tensor>(new ops.name_scope(null, default_name, new { tensor1, y }), scope => {
+            return with(new ops.name_scope(null, default_name, new { tensor1, y }), scope => {
                 var tensor2 = ops.convert_to_tensor(y, tensor1.dtype.as_base_dtype(), "y");
                 return gen_math_ops.add(tensor1, tensor2, scope);
             });

src/TensorFlowNET.Core/Variables/RefVariable.cs

@@ -1,4 +1,6 @@
-using System;
+using Google.Protobuf;
+using Google.Protobuf.Collections;
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
@@ -99,7 +101,7 @@ namespace Tensorflow
             if (initial_value is null)
                 throw new ValueError("initial_value must be specified.");
 
-            var init_from_fn = false;
+            var init_from_fn = initial_value.GetType().Name == "Func`1";
 
             if(collections == null)
             {
@@ -115,12 +117,27 @@ namespace Tensorflow
                 collections.Add(ops.GraphKeys.TRAINABLE_VARIABLES);
 
             ops.init_scope();
-            var values = init_from_fn ? new List<object>() : new List<object> { initial_value };
-            Python.with<ops.name_scope>(new ops.name_scope(name, "Variable", values), scope =>
+            var values = init_from_fn ? new object[0] : new object[] { initial_value };
+            with(new ops.name_scope(name, "Variable", values), scope =>
             {
+                name = scope;
                 if (init_from_fn)
                 {
-
+                    // Use attr_scope and device(None) to simulate the behavior of
+                    // colocate_with when the variable we want to colocate with doesn't
+                    // yet exist.
+                    string true_name = ops._name_from_scope_name(name);
+                    var attr = new AttrValue
+                    {
+                        List = new AttrValue.Types.ListValue()
+                    };
+                    attr.List.S.Add(ByteString.CopyFromUtf8($"loc:{true_name}"));
+                    with(new ops.name_scope("Initializer"), scope2 =>
+                    {
+                        _initial_value = (initial_value as Func<Tensor>)();
+                        _initial_value = ops.convert_to_tensor(_initial_value, name: "initial_value", dtype: dtype);
+                        _variable = state_ops.variable_op_v2(_initial_value.shape, _initial_value.dtype.as_base_dtype(), name: name);
+                    });
                 }
                 // Or get the initial value from a Tensor or Python object.
                 else
@@ -135,7 +152,9 @@ namespace Tensorflow
                 // Manually overrides the variable's shape with the initial value's.
                 if (validate_shape)
                 {
-                    var initial_value_shape = _initial_value.shape;
+                    var initial_value_shape = _initial_value.getShape();
+                    if (!initial_value_shape.is_fully_defined())
+                        throw new ValueError($"initial_value must have a shape specified: {_initial_value}");
                 }
 
                 // If 'initial_value' makes use of other variables, make sure we don't

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -4,17 +4,27 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class VariableScope
+    /// <summary>
+    /// Variable scope object to carry defaults to provide to `get_variable`
+    /// </summary>
+    public class VariableScope : Python
     {
         public bool use_resource { get; set; }
-        private _ReuseMode _reuse { get; set; }
+        private _ReuseMode _reuse;
+        public bool resue;
 
-        private object _regularizer;
         private TF_DataType _dtype;
-        public string name { get; set; }
+        public string _name { get; set; }
+        public string _name_scope { get; set; }
+        public string original_name_scope => _name_scope;
 
-        public VariableScope(TF_DataType dtype = TF_DataType.TF_FLOAT)
+        public VariableScope(bool reuse, 
+            string name = "", 
+            string name_scope = "",
+            TF_DataType dtype = TF_DataType.TF_FLOAT)
         {
+            _name = name;
+            _name_scope = name_scope;
             _reuse = _ReuseMode.AUTO_REUSE;
             _dtype = dtype;
         }
@@ -28,8 +38,8 @@ namespace Tensorflow
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation= VariableAggregation.NONE)
         {
-            string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
-            return Python.with<ops.name_scope, RefVariable>(new ops.name_scope(""), scope =>
+            string full_name = !string.IsNullOrEmpty(this._name) ? this._name + "/" + name : name;
+            return with(new ops.name_scope(null), scope =>
             {
                 if (dtype == TF_DataType.DtInvalid)
                     dtype = _dtype;

src/TensorFlowNET.Core/Variables/_VariableScopeStore.cs

@@ -7,10 +7,20 @@ namespace Tensorflow
     public class _VariableScopeStore
     {
         public VariableScope current_scope { get; set; }
+        private Dictionary<string, int> variable_scopes_count;
 
         public _VariableScopeStore()
         {
-            current_scope = new VariableScope();
+            current_scope = new VariableScope(false);
+            variable_scopes_count = new Dictionary<string, int>();
+        }
+
+        public void open_variable_scope(string scope_name)
+        {
+            if (variable_scopes_count.ContainsKey(scope_name))
+                variable_scopes_count[scope_name] += 1;
+            else
+                variable_scopes_count[scope_name] = 1;
         }
     }
 }

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -74,7 +74,9 @@ namespace Tensorflow
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
-            bool initializing_from_value = false;
+            bool initializing_from_value = true;
+            if (use_resource == null)
+                use_resource = false;
 
             if (_vars.ContainsKey(name))
             {
@@ -86,7 +88,18 @@ namespace Tensorflow
                 throw new NotImplementedException("_get_single_variable");
             }
 
-            Tensor init_val = null;
+            RefVariable v = null;
+            // Create the tensor to initialize the variable with default value.
+            if (initializer == null)
+            {
+                if (dtype.is_floating())
+                {
+                    initializer = tf.glorot_uniform_initializer;
+                    initializing_from_value = false;
+                }
+            }
+
+            // Create the variable.
             ops.init_scope();
             {
                 if (initializing_from_value)
@@ -95,23 +108,19 @@ namespace Tensorflow
                 }
                 else
                 {
-                    init_val = initializer.call(shape, dtype);
+                    Func<Tensor> init_val = () => initializer.call(shape, dtype);
                     var variable_dtype = dtype.as_base_dtype();
+
+                    v = variable_scope.default_variable_creator(init_val,
+                        name: name,
+                        trainable: trainable,
+                        dtype: TF_DataType.DtInvalid,
+                        validate_shape: validate_shape,
+                        synchronization: synchronization,
+                        aggregation: aggregation);
                 }
             }
 
-            // Create the variable.
-            if (use_resource == null)
-                use_resource = false;
-
-            var v = variable_scope.default_variable_creator(init_val, 
-                name: name, 
-                trainable: trainable, 
-                dtype: TF_DataType.DtInvalid,
-                validate_shape: validate_shape,
-                synchronization: synchronization,
-                aggregation: aggregation);
-
             _vars[name] = v;
 
             return v;

src/TensorFlowNET.Core/Variables/state_ops.cs

@@ -0,0 +1,28 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class state_ops
+    {
+        /// <summary>
+        /// Create a variable Operation.
+        /// </summary>
+        /// <param name="shape"></param>
+        /// <param name="dtype"></param>
+        /// <param name="name"></param>
+        /// <param name="container"></param>
+        /// <param name="shared_name"></param>
+        /// <returns></returns>
+        public static Tensor variable_op_v2(long[] shape,
+            TF_DataType dtype,
+            string name = "Variable",
+            string container = "",
+            string shared_name = "") => gen_state_ops.variable_v2(shape, 
+                dtype, 
+                name: name, 
+                container: container, 
+                shared_name: shared_name);
+    }
+}

src/TensorFlowNET.Core/Variables/tf.variable.cs

@@ -20,8 +20,8 @@ namespace Tensorflow
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
-            var scope = variable_scope.get_variable_scope();
-            var store = variable_scope._get_default_variable_store();
+            var scope = Tensorflow.variable_scope.get_variable_scope();
+            var store = Tensorflow.variable_scope._get_default_variable_store();
             return scope.get_variable(store, 
                 name, 
                 shape: shape, 

src/TensorFlowNET.Core/Variables/variable_scope.py.cs

@@ -1,15 +1,104 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 
 namespace Tensorflow
 {
-    public class variable_scope
+    public class variable_scope : IPython
     {
         public static string _VARSTORE_KEY = "__variable_store";
         public static string _VARSCOPESTORE_KEY = "__varscope";
         public static bool _DEFAULT_USE_RESOURCE = false;
 
+        private bool _use_resource;
+        public bool UseResource => _use_resource;
+        private string _name;
+        private VariableScope _scope;
+        private string _default_name;
+        private object _values;
+        private ops.name_scope _current_name_scope;
+        private bool _auxiliary_name_scope;
+        private PureVariableScope _cached_pure_variable_scope;
+
+        public variable_scope(string name, 
+            string default_name = "", 
+            object values = null,
+            bool auxiliary_name_scope = true)
+        {
+            _name = name;
+            _default_name = default_name;
+            _values = values;
+            _current_name_scope = null;
+
+            _use_resource = false;
+            if (_default_name == null && _name == null)
+                throw new TypeError("If default_name is None then name is required");
+
+            _auxiliary_name_scope = auxiliary_name_scope;
+        }
+
+        public variable_scope(VariableScope scope,
+            string default_name = "",
+            object values = null,
+            bool auxiliary_name_scope = true)
+        {
+            _scope = scope;
+            _default_name = default_name;
+            _values = values;
+            _current_name_scope = null;
+
+            _use_resource = false;
+            if (_default_name == null && _scope == null)
+                throw new TypeError("If default_name is None then scope is required");
+
+            _auxiliary_name_scope = auxiliary_name_scope;
+        }
+
+        public void __enter__()
+        {
+            _scope = _enter_scope_uncached();
+        }
+
+        private VariableScope _enter_scope_uncached()
+        {
+            ops.name_scope current_name_scope;
+            if (_auxiliary_name_scope)
+                // Create a new name scope later
+                current_name_scope = null;
+            else
+            {
+                // Reenter the current name scope
+                string name_scope = ops.get_name_scope();
+                if(!string.IsNullOrEmpty(name_scope))
+                    // Hack to reenter
+                    name_scope += "/";
+                current_name_scope = new ops.name_scope(name_scope);
+            }
+
+            if (_name != null || _scope != null)
+            {
+                var name_scope = _name == null ? _scope._name.Split('/').Last() : _name;
+                if (name_scope != null || current_name_scope != null)
+                    current_name_scope = new ops.name_scope(name_scope);
+                current_name_scope.__enter__();
+                var current_name_scope_name = current_name_scope;
+                _current_name_scope = current_name_scope;
+                string old_name_scope = current_name_scope_name;
+                PureVariableScope pure_variable_scope = null;
+                if(_scope == null)
+                    pure_variable_scope = new PureVariableScope(_name, old_name_scope: old_name_scope);
+                else
+                    pure_variable_scope = new PureVariableScope(_scope, old_name_scope: old_name_scope);
+                pure_variable_scope.__enter__();
+                VariableScope entered_pure_variable_scope = pure_variable_scope;
+                _cached_pure_variable_scope = pure_variable_scope;
+                return entered_pure_variable_scope;
+            }
+
+            throw new NotImplementedException("_enter_scope_uncached");
+        }
+
         public static RefVariable default_variable_creator(object initial_value,
             string name = null,
             bool? trainable = null,
@@ -101,5 +190,22 @@ namespace Tensorflow
             
             return trainable.Value;
         }
+
+        public static implicit operator VariableScope(variable_scope scope)
+        {
+            return scope._scope;
+        }
+
+        public void __exit__()
+        {
+            if (_current_name_scope != null)
+                _current_name_scope.__exit__();
+        }
+
+        public void Dispose()
+        {
+            if (_current_name_scope != null)
+                _current_name_scope.Dispose();
+        }
     }
 }

src/TensorFlowNET.Core/ops.py.cs

@@ -12,7 +12,7 @@ using System.ComponentModel;
 
 namespace Tensorflow
 {
-    public partial class ops
+    public partial class ops : Python
     {
         public static void add_to_collection<T>(string name, T value)
         {
@@ -216,7 +216,7 @@ namespace Tensorflow
             // inner_device_stack = default_graph._device_function_stack
             // var outer_context = default_graph.as_default;
 
-            Python.with(ops.control_dependencies(null), delegate
+            with(ops.control_dependencies(null), delegate
             {
                 var outer_graph = get_default_graph();
                 // outer_device_stack = None
@@ -475,5 +475,11 @@ namespace Tensorflow
                 return name;
             }
         }
+
+        public static string get_name_scope()
+        {
+            var g = get_default_graph();
+            return g.get_name_scope();
+        }
     }
 }

src/TensorFlowNET.Core/tf.cs

@@ -20,7 +20,7 @@ namespace Tensorflow
 
         public static RefVariable Variable<T>(T data, string name = null, TF_DataType dtype = TF_DataType.DtInvalid)
         {
-            return variable_scope.default_variable_creator(data, name: name, dtype: TF_DataType.DtInvalid);
+            return Tensorflow.variable_scope.default_variable_creator(data, name: name, dtype: TF_DataType.DtInvalid);
         }
 
         public static unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null)

src/TensorFlowNET.Utility/TensorFlowNET.Utility.csproj

@@ -1,13 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <TargetFramework>netstandard2.0</TargetFramework>
-    <AssemblyName>TensorFlowNET.Utility</AssemblyName>
-    <RootNamespace>TensorFlowNET.Utility</RootNamespace>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="SharpZipLib" Version="1.1.0" />
-  </ItemGroup>
-
-</Project>

src/TensorFlowNET.Utility/Web.cs

@@ -1,35 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Net;
-using System.Text;
-using System.Threading;
-using System.Threading.Tasks;
-
-namespace TensorFlowNET.Utility
-{
-    public class Web
-    {
-        public static bool Download(string url, string file)
-        {
-            if (File.Exists(file))
-            {
-                Console.WriteLine($"{file} already exists.");
-                return false;
-            }
-
-            var wc = new WebClient();
-            Console.WriteLine($"Downloading {file}");
-            var download = Task.Run(() => wc.DownloadFile(url, file));
-            while (!download.IsCompleted)
-            {
-                Thread.Sleep(1000);
-                Console.Write(".");
-            }
-            Console.WriteLine("");
-            Console.WriteLine($"Downloaded {file}");
-
-            return true;
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -39,7 +39,7 @@ namespace TensorFlowNET.Examples
 
                 var idx = 0;
                 float propability = 0;
-                with<Session>(tf.Session(graph), sess =>
+                with(tf.Session(graph), sess =>
                 {
                     var results = sess.run(output_operation.outputs[0], new FeedItem(input_operation.outputs[0], tensor));
                     var probabilities = results.Data<float>();
@@ -63,7 +63,7 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 117,
                                 int input_std = 1)
         {
-            return with<Graph, NDArray>(tf.Graph().as_default(), graph =>
+            return with(tf.Graph().as_default(), graph =>
             {
                 var file_reader = tf.read_file(file_name, "file_reader");
                 var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
@@ -74,7 +74,7 @@ namespace TensorFlowNET.Examples
                 var sub = tf.subtract(bilinear, new float[] { input_mean });
                 var normalized = tf.divide(sub, new float[] { input_std });
 
-                return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));
+                return with(tf.Session(graph), sess => sess.run(normalized));
             });
         }
 
@@ -85,15 +85,14 @@ namespace TensorFlowNET.Examples
             // get model file
             string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";
 
-            string zipFile = Path.Join(dir, "inception5h.zip");
-            Utility.Web.Download(url, zipFile);
+            Utility.Web.Download(url, dir, "inception5h.zip");
 
-            Utility.Compress.UnZip(zipFile, dir);
+            Utility.Compress.UnZip(Path.Join(dir, "inception5h.zip"), dir);
 
             // download sample picture
-            string pic = Path.Join(dir, "img", "grace_hopper.jpg");
             Directory.CreateDirectory(Path.Join(dir, "img"));
-            Utility.Web.Download($"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg", pic);
+            url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg";
+            Utility.Web.Download(url, Path.Join(dir, "img"), "grace_hopper.jpg");
         }
     }
 }

test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs

@@ -46,7 +46,7 @@ namespace TensorFlowNET.Examples
             var input_operation = graph.get_operation_by_name(input_name);
             var output_operation = graph.get_operation_by_name(output_name);
 
-            var results = with<Session, NDArray>(tf.Session(graph),
+            var results = with(tf.Session(graph),
                 sess => sess.run(output_operation.outputs[0],
                     new FeedItem(input_operation.outputs[0], nd)));
 
@@ -68,7 +68,7 @@ namespace TensorFlowNET.Examples
                                 int input_mean = 0,
                                 int input_std = 255)
         {
-            return with<Graph, NDArray>(tf.Graph().as_default(), graph =>
+            return with(tf.Graph().as_default(), graph =>
             {
                 var file_reader = tf.read_file(file_name, "file_reader");
                 var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
@@ -79,7 +79,7 @@ namespace TensorFlowNET.Examples
                 var sub = tf.subtract(bilinear, new float[] { input_mean });
                 var normalized = tf.divide(sub, new float[] { input_std });
 
-                return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));
+                return with(tf.Session(graph), sess => sess.run(normalized));
             });
         }
 
@@ -90,14 +90,14 @@ namespace TensorFlowNET.Examples
             // get model file
             string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz";
             
-            string zipFile = Path.Join(dir, $"{pbFile}.tar.gz");
-            Utility.Web.Download(url, zipFile);
+            Utility.Web.Download(url, dir, $"{pbFile}.tar.gz");
 
-            Utility.Compress.ExtractTGZ(zipFile, dir);
+            Utility.Compress.ExtractTGZ(Path.Join(dir, $"{pbFile}.tar.gz"), dir);
 
             // download sample picture
             string pic = "grace_hopper.jpg";
-            Utility.Web.Download($"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}", Path.Join(dir, pic));
+            url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}";
+            Utility.Web.Download(url, dir, pic);
         }
     }
 }

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples
 
         // Parameters
         float learning_rate = 0.01f;
-        int training_epochs = 10000;
+        int training_epochs = 1000;
         int display_step = 50;
 
         public void Run()
@@ -53,7 +53,7 @@ namespace TensorFlowNET.Examples
             var init = tf.global_variables_initializer();
 
             // Start training
-            with<Session>(tf.Session(), sess => 
+            with(tf.Session(), sess => 
             {
                 // Run the initializer
                 sess.run(init);

test/TensorFlowNET.Examples/MetaGraph.cs

@@ -16,7 +16,7 @@ namespace TensorFlowNET.Examples
 
         private void ImportMetaGraph(string dir)
         {
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta");
                 new_saver.restore(sess, dir + "my-model-10000");

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -5,10 +5,16 @@
     <TargetFramework>netcoreapp2.2</TargetFramework>
   </PropertyGroup>
 
+  <ItemGroup>
+    <PackageReference Include="NumSharp" Version="0.7.3" />
+    <PackageReference Include="SharpZipLib" Version="1.1.0" />
+    <PackageReference Include="TensorFlow.NET" Version="0.4.2" />
+  </ItemGroup>
+
   <ItemGroup>
     <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
+    <ProjectReference Include="..\..\..\scikit-learn.net\src\scikit-learn\scikit-learn.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-    <ProjectReference Include="..\..\src\TensorFlowNET.Utility\TensorFlowNET.Utility.csproj" />
   </ItemGroup>
 
   <ItemGroup>

test/TensorFlowNET.Examples/TextClassification/DataHelpers.cs

@@ -0,0 +1,91 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using System.Text.RegularExpressions;
+
+namespace TensorFlowNET.Examples.CnnTextClassification
+{
+    public class DataHelpers
+    {
+        private const string TRAIN_PATH = "text_classification/dbpedia_csv/train.csv";
+        private const string TEST_PATH = "text_classification/dbpedia_csv/test.csv";
+
+        public static (int[][], int[], int) build_char_dataset(string step, string model, int document_max_len)
+        {
+            string alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:’'\"/|_#$%ˆ&*˜‘+=<>()[]{} ";
+            /*if (step == "train")
+                df = pd.read_csv(TRAIN_PATH, names =["class", "title", "content"]);*/
+            var char_dict = new Dictionary<string, int>();
+            char_dict["<pad>"] = 0;
+            char_dict["<unk>"] = 1;
+            foreach (char c in alphabet)
+                char_dict[c.ToString()] = char_dict.Count;
+
+            var contents = File.ReadAllLines(TRAIN_PATH);
+            
+            var x = new int[contents.Length][];
+            var y = new int[contents.Length];
+            for (int i = 0; i < contents.Length; i++)
+            {
+                string[] parts = contents[i].ToLower().Split(",\"").ToArray();
+                string content = parts[2];
+                content = content.Substring(0, content.Length - 1);
+                x[i] = new int[document_max_len];
+                for (int j = 0; j < document_max_len; j++)
+                {
+                    if (j >= content.Length)
+                        x[i][j] = char_dict["<pad>"];
+                    else
+                        x[i][j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict["<unk>"];
+                }
+                    
+                y[i] = int.Parse(parts[0]);
+            }
+
+            return (x, y, alphabet.Length + 2);
+        }
+
+        /// <summary>
+        /// Loads MR polarity data from files, splits the data into words and generates labels.
+        /// Returns split sentences and labels.
+        /// </summary>
+        /// <param name="positive_data_file"></param>
+        /// <param name="negative_data_file"></param>
+        /// <returns></returns>
+        public static (string[], NDArray) load_data_and_labels(string positive_data_file, string negative_data_file)
+        {
+            Directory.CreateDirectory("CnnTextClassification");
+            Utility.Web.Download(positive_data_file, "CnnTextClassification", "rt -polarity.pos");
+            Utility.Web.Download(negative_data_file, "CnnTextClassification", "rt-polarity.neg");
+
+            // Load data from files
+            var positive_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.pos")
+                .Select(x => x.Trim())
+                .ToArray();
+
+            var negative_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.neg")
+                .Select(x => x.Trim())
+                .ToArray();
+
+            var x_text = new List<string>();
+            x_text.AddRange(positive_examples);
+            x_text.AddRange(negative_examples);
+            x_text = x_text.Select(x => clean_str(x)).ToList();
+
+            var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray();
+            var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray();
+            var y = np.array(1);// np.concatenate(new int[][][] { positive_labels, negative_labels });
+            return (x_text.ToArray(), y);
+        }
+
+        private static string clean_str(string str)
+        {
+            str = Regex.Replace(str, @"[^A-Za-z0-9(),!?\'\`]", " ");
+            str = Regex.Replace(str, @"\'s", " \'s");
+            return str;
+        }
+    }
+}

test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs

@@ -0,0 +1,37 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+using Tensorflow;
+using TensorFlowNET.Examples.Utility;
+
+namespace TensorFlowNET.Examples.CnnTextClassification
+{
+    /// <summary>
+    /// https://github.com/dongjun-Lee/text-classification-models-tf
+    /// </summary>
+    public class TextClassificationTrain : Python, IExample
+    {
+        private string dataDir = "text_classification";
+        private string dataFileName = "dbpedia_csv.tar.gz";
+
+        private const int CHAR_MAX_LEN = 1014;
+
+        public void Run()
+        {
+            download_dbpedia();
+            Console.WriteLine("Building dataset...");
+            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
+            //var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15);
+        }
+
+        public void download_dbpedia()
+        {
+            string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
+            Web.Download(url, dataDir, dataFileName);
+            Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
+        }
+    }
+}

test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs

@@ -46,9 +46,8 @@ namespace TensorFlowNET.Examples
             // get model file
             string url = $"https://github.com/SciSharp/TensorFlow.NET/raw/master/data/{dataFile}";
 
-            string zipFile = Path.Join(dir, $"imdb.zip");
-            Utility.Web.Download(url, zipFile);
-            Utility.Compress.UnZip(zipFile, dir);
+            Utility.Web.Download(url, dir, "imdb.zip");
+            Utility.Compress.UnZip(Path.Join(dir, $"imdb.zip"), dir);
 
             // prepare training dataset
             var x_train = ReadData(Path.Join(dir, "x_train.txt"));

src/TensorFlowNET.Utility/Compress.cs → test/TensorFlowNET.Examples/Utility/Compress.cs

@@ -7,7 +7,7 @@ using System.Linq;
 using System.Threading;
 using System.Threading.Tasks;
 
-namespace TensorFlowNET.Utility
+namespace TensorFlowNET.Examples.Utility
 {
     public class Compress
     {

test/TensorFlowNET.Examples/Utility/Web.cs

@@ -0,0 +1,43 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Net;
+using System.Text;
+using System.Threading;
+using System.Threading.Tasks;
+
+namespace TensorFlowNET.Examples.Utility
+{
+    public class Web
+    {
+        public static bool Download(string url, string destDir, string destFileName)
+        {
+            if (destFileName == null)
+                destFileName = url.Split(Path.DirectorySeparatorChar).Last();
+
+            Directory.CreateDirectory(destDir);
+
+            string relativeFilePath = Path.Combine(destDir, destFileName);
+
+            if (File.Exists(relativeFilePath))
+            {
+                Console.WriteLine($"{relativeFilePath} already exists.");
+                return false;
+            }
+
+            var wc = new WebClient();
+            Console.WriteLine($"Downloading {relativeFilePath}");
+            var download = Task.Run(() => wc.DownloadFile(url, relativeFilePath));
+            while (!download.IsCompleted)
+            {
+                Thread.Sleep(1000);
+                Console.Write(".");
+            }
+            Console.WriteLine("");
+            Console.WriteLine($"Downloaded {relativeFilePath}");
+
+            return true;
+        }
+    }
+}

test/TensorFlowNET.UnitTest/CApiTest.cs

@@ -6,7 +6,7 @@ using Tensorflow;
 
 namespace TensorFlowNET.UnitTest
 {
-    public class CApiTest
+    public class CApiTest : Python
     {
         protected TF_Code TF_OK = TF_Code.TF_OK;
         protected TF_DataType TF_FLOAT = TF_DataType.TF_FLOAT;

test/TensorFlowNET.UnitTest/ConstantTest.cs

@@ -10,7 +10,7 @@ using Tensorflow;
 namespace TensorFlowNET.UnitTest
 {
     [TestClass]
-    public class ConstantTest
+    public class ConstantTest : Python
     {
         Status status = new Status();
 
@@ -27,7 +27,7 @@ namespace TensorFlowNET.UnitTest
         {
             string str = "Hello, TensorFlow.NET!";
             var tensor = tf.constant(str);
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
                 Assert.IsTrue(result.Data<string>()[0] == str);
@@ -39,7 +39,7 @@ namespace TensorFlowNET.UnitTest
         {
             // small size
             var tensor = tf.zeros(new Shape(3, 2), TF_DataType.TF_INT32, "small");
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
 
@@ -50,7 +50,7 @@ namespace TensorFlowNET.UnitTest
 
             // big size
             tensor = tf.zeros(new Shape(200, 100), TF_DataType.TF_INT32, "big");
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
 
@@ -74,7 +74,7 @@ namespace TensorFlowNET.UnitTest
             });
 
             var tensor = tf.constant(nd);
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(tensor);
                 var data = result.Data<int>();

test/TensorFlowNET.UnitTest/NameScopeTest.cs

@@ -15,7 +15,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void NestedNameScope()
         {
-            with<ops.name_scope>(new ops.name_scope("scope1"), scope1 =>
+            with(new ops.name_scope("scope1"), scope1 =>
             {
                 name = scope1;
                 Assert.AreEqual("scope1", g._name_stack);
@@ -24,7 +24,7 @@ namespace TensorFlowNET.UnitTest
                 var const1 = tf.constant(1.0);
                 Assert.AreEqual("scope1/Const:0", const1.name);
 
-                with<ops.name_scope>(new ops.name_scope("scope2"), scope2 =>
+                with(new ops.name_scope("scope2"), scope2 =>
                 {
                     name = scope2;
                     Assert.AreEqual("scope1/scope2", g._name_stack);

test/TensorFlowNET.UnitTest/PlaceholderTest.cs

@@ -7,7 +7,7 @@ using Tensorflow;
 namespace TensorFlowNET.UnitTest
 {
     [TestClass]
-    public class PlaceholderTest
+    public class PlaceholderTest : Python
     {
         [TestMethod]
         public void placeholder()
@@ -15,7 +15,7 @@ namespace TensorFlowNET.UnitTest
             var x = tf.placeholder(tf.int32);
             var y = x * 3;
 
-            Python.with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var result = sess.run(y, 
                     new FeedItem(x, 2));

test/TensorFlowNET.UnitTest/SessionTest.cs

@@ -82,7 +82,7 @@ namespace TensorFlowNET.UnitTest
             var a = constant_op.constant(np.array(3.0).reshape(1, 1));
             var b = constant_op.constant(np.array(2.0).reshape(1, 1));
             var c = math_ops.matmul(a, b, name: "matmul");
-            Python.with(tf.Session(), delegate
+            with(tf.Session(), delegate
             {
                 var result = c.eval();
                 Assert.AreEqual(6, result.Data<double>()[0]);

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -1,7 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
-    <TargetFramework>netcoreapp2.1</TargetFramework>
+    <TargetFramework>netcoreapp2.2</TargetFramework>
 
     <IsPackable>false</IsPackable>
   </PropertyGroup>

test/TensorFlowNET.UnitTest/TrainSaverTest.cs

@@ -19,7 +19,7 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportGraph()
         {
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta");
             });
@@ -44,7 +44,7 @@ namespace TensorFlowNET.UnitTest
 
         public void ImportSavedModel()
         {
-            with<Session>(Session.LoadFromSavedModel("mobilenet"), sess =>
+            with(Session.LoadFromSavedModel("mobilenet"), sess =>
             {
                 
             });
@@ -65,7 +65,7 @@ namespace TensorFlowNET.UnitTest
             // Add ops to save and restore all the variables.
             var saver = tf.train.Saver();
 
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 sess.run(init_op);
 

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -30,6 +30,47 @@ namespace TensorFlowNET.UnitTest
             var mammal2 = tf.Variable("Tiger");
         }
 
+        /// <summary>
+        /// https://www.tensorflow.org/api_docs/python/tf/variable_scope
+        /// how to create a new variable
+        /// </summary>
+        [TestMethod]
+        public void VarCreation()
+        {
+            with(tf.variable_scope("foo"), delegate
+            {
+                with(tf.variable_scope("bar"), delegate
+                {
+                    var v = tf.get_variable("v", new TensorShape(1));
+                    Assert.AreEqual(v.name, "foo/bar/v:0");
+                });
+            });
+        }
+
+        /// <summary>
+        /// how to reenter a premade variable scope safely
+        /// </summary>
+        [TestMethod]
+        public void ReenterVariableScope()
+        {
+            variable_scope vs = null;
+            with(tf.variable_scope("foo"), v => vs = v);
+
+            // Re-enter the variable scope.
+            with(tf.variable_scope(vs, auxiliary_name_scope: false), v =>
+            {
+                var vs1 = (VariableScope)v;
+                // Restore the original name_scope.
+                with(tf.name_scope(vs1.original_name_scope), delegate
+                {
+                    var v1 = tf.get_variable("v", new TensorShape(1));
+                    Assert.AreEqual(v1.name, "foo/v:0");
+                    var c1 = tf.constant(new int[] { 1 }, name: "c");
+                    Assert.AreEqual(c1.name, "foo/c:0");
+                });
+            });
+        }
+
         [TestMethod]
         public void ScalarVar()
         {
@@ -49,7 +90,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Assign1()
         {
-            with<Graph>(tf.Graph().as_default(), graph =>
+            with(tf.Graph().as_default(), graph =>
             {
                 var variable = tf.Variable(31, name: "tree");
                 var init = tf.global_variables_initializer();
@@ -75,7 +116,7 @@ namespace TensorFlowNET.UnitTest
             // Add an op to initialize the variables.
             var init_op = tf.global_variables_initializer();
 
-            with<Session>(tf.Session(), sess =>
+            with(tf.Session(), sess =>
             {
                 sess.run(init_op);
                 // o some work with the model.