fix name_scope stack can't restore when name is empty.

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -197,7 +197,9 @@ namespace Tensorflow
         {
             string new_stack = "";
 
-            if (name.EndsWith("/"))
+            if (string.IsNullOrEmpty(name))
+                new_stack = "";
+            else if (name.EndsWith("/"))
                 new_stack = ops._name_from_scope_name(name);
             else
                 new_stack = unique_name(name);

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

@@ -9,32 +9,24 @@ namespace Tensorflow
     {
         public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "")
         {
-            Tensor output = null;
-
             dtype = dtype.as_base_dtype();
-            Python.with(new ops.name_scope(name, "zeros", shape), self =>
+            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "zeros", shape), scope =>
             {
-                name = self as ops.name_scope;
+                name = scope;
                 switch (dtype)
                 {
                     case TF_DataType.TF_BOOL:
-                        output = _constant_if_small(false, shape, dtype, name);
-                        break;
+                        return _constant_if_small(false, shape, dtype, name);
                     case TF_DataType.TF_DOUBLE:
-                        output = _constant_if_small(0.0D, shape, dtype, name);
-                        break;
+                        return _constant_if_small(0.0D, shape, dtype, name);
                     case TF_DataType.TF_FLOAT:
-                        output = _constant_if_small(0.0F, shape, dtype, name);
-                        break;
+                        return _constant_if_small(0.0F, shape, dtype, name);
                     case TF_DataType.TF_INT32:
-                        output = _constant_if_small(0, shape, dtype, name);
-                        break;
+                        return _constant_if_small(0, shape, dtype, name);
                     default:
-                        break;
+                        throw new TypeError("can't find type for zeros");
                 }
             });
-
-            return output;
         }
 
         private static Tensor _constant_if_small<T>(T value, Shape shape, TF_DataType dtype, string name)

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -31,6 +31,12 @@ TensorFlow 1.13 RC.</PackageReleaseNotes>
     <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
   </PropertyGroup>
 
+  <ItemGroup>
+    <Compile Remove="runtimes\**" />
+    <EmbeddedResource Remove="runtimes\**" />
+    <None Remove="runtimes\**" />
+  </ItemGroup>
+
   <ItemGroup>
     <None Remove="Protobuf\README.md" />
   </ItemGroup>

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -13,28 +13,35 @@ namespace Tensorflow
         private TF_DataType _dtype;
         public string name { get; set; }
 
-        public VariableScope()
+        public VariableScope(TF_DataType dtype = TF_DataType.TF_FLOAT)
         {
             _reuse = _ReuseMode.AUTO_REUSE;
+            _dtype = dtype;
         }
 
         public RefVariable get_variable(_VariableStore var_store, 
             string name, 
             TensorShape shape = null, 
             TF_DataType dtype = TF_DataType.DtInvalid,
+            IInitializer initializer = null,
+            bool? trainable = null,
             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, Tensor>(new ops.name_scope(""), scope =>
+            return Python.with<ops.name_scope, RefVariable>(new ops.name_scope(""), scope =>
             {
                 if (dtype == TF_DataType.DtInvalid)
                     dtype = _dtype;
 
-                return var_store.get_variable(full_name);
-
+                return var_store.get_variable(full_name, 
+                    shape: shape, 
+                    dtype: dtype,
+                    initializer: initializer,
+                    trainable: trainable,
+                    synchronization: synchronization,
+                    aggregation: aggregation);
             });
-
         }
     }
 }

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -24,7 +24,7 @@ namespace Tensorflow
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             IInitializer initializer = null,
-            bool trainable = false,
+            bool? trainable = null,
             bool validate_shape = true,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
@@ -44,14 +44,23 @@ namespace Tensorflow
 
         private RefVariable _true_getter(string name,
             TensorShape shape = null,
-            TF_DataType dtype = TF_DataType.DtInvalid,
+            TF_DataType dtype = TF_DataType.TF_FLOAT,
             IInitializer initializer = null,
-            bool trainable = false,
+            bool? trainable = null,
             bool validate_shape = true,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
-            return _get_single_variable(name: name);
+            bool is_scalar = shape.NDim == 0;
+
+            return _get_single_variable(name: name, 
+                shape: shape, 
+                dtype: dtype,
+                initializer: initializer,
+                trainable: trainable,
+                validate_shape: validate_shape,
+                synchronization: synchronization,
+                aggregation: aggregation);
         }
 
         private RefVariable _get_single_variable(string name,
@@ -59,11 +68,14 @@ namespace Tensorflow
             TF_DataType dtype = TF_DataType.DtInvalid,
             IInitializer initializer = null,
             bool reuse = false,
-            bool trainable = false,
+            bool? trainable = null,
             bool validate_shape = false,
+            bool? use_resource = null,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
+            bool initializing_from_value = false;
+
             if (_vars.ContainsKey(name))
             {
                 if (!reuse)
@@ -74,7 +86,35 @@ namespace Tensorflow
                 throw new NotImplementedException("_get_single_variable");
             }
 
-            throw new NotImplementedException("_get_single_variable");
+            Tensor init_val = null;
+            ops.init_scope();
+            {
+                if (initializing_from_value)
+                {
+
+                }
+                else
+                {
+                    init_val = initializer.call(shape, dtype);
+                    var variable_dtype = dtype.as_base_dtype();
+                }
+            }
+
+            // 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/tf.variable.cs

@@ -12,14 +12,22 @@ namespace Tensorflow
             return variables.variables_initializer(g.ToArray());
         }
 
-        public static RefVariable get_variable(string name, 
-            TensorShape shape = null, 
+        public static RefVariable get_variable(string name,
+            TensorShape shape = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
             IInitializer initializer = null,
+            bool? trainable = null,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)
         {
+            var scope = variable_scope.get_variable_scope();
             var store = variable_scope._get_default_variable_store();
-            return variable_scope.get_variable_scope().get_variable(store, name, shape: shape);
+            return scope.get_variable(store, 
+                name, 
+                shape: shape, 
+                dtype: dtype,
+                initializer: initializer,
+                trainable: trainable);
         }
     }
 }

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

@@ -10,9 +10,16 @@ namespace Tensorflow
         public static string _VARSCOPESTORE_KEY = "__varscope";
         public static bool _DEFAULT_USE_RESOURCE = false;
 
-        public static RefVariable default_variable_creator(object initial_value, string name = "", TF_DataType dtype = TF_DataType.DtInvalid, bool ? use_resource = null, VariableSynchronization synchronization = VariableSynchronization.AUTO)
+        public static RefVariable default_variable_creator(object initial_value,
+            string name = "",
+            bool? trainable = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            bool validate_shape = false,
+            bool ? use_resource = null, 
+            VariableSynchronization synchronization = VariableSynchronization.AUTO,
+            VariableAggregation aggregation = VariableAggregation.NONE)
         {
-            var trainable = _get_trainable_value(synchronization);
+            trainable = _get_trainable_value(synchronization, trainable);
             if (!use_resource.HasValue)
             {
                 use_resource = get_variable_scope().use_resource;
@@ -77,18 +84,22 @@ namespace Tensorflow
             return ret;
         }
 
-        public static bool _get_trainable_value(VariableSynchronization synchronization, bool trainable = true)
+        public static bool _get_trainable_value(VariableSynchronization synchronization, bool? trainable = true)
         {
             if (synchronization == VariableSynchronization.ON_READ)
             {
-                if (trainable)
+                if (trainable.Value)
                     throw new ValueError("Synchronization value can be set to " +
                         "VariableSynchronization.ON_READ only for non-trainable variables. " +
                         "You have specified trainable=True and " +
                         "synchronization=VariableSynchronization.ON_READ.");
             }
+            else if (!trainable.HasValue)
+            {
+                trainable = true;
+            }
             
-            return trainable;
+            return trainable.Value;
         }
     }
 }