linear regression 2

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

@@ -17,7 +17,7 @@ namespace Tensorflow
         /// A `Tensor` with the same data as `input`, but its shape has an additional
         /// dimension of size 1 added.
         /// </returns>
-        public static Tensor expand_dims(Tensor input, int axis = -1, string name = "", int dim = -1)
+        public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
             => array_ops.expand_dims(input, axis, name, dim);
     }
 }

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

@@ -6,14 +6,14 @@ namespace Tensorflow
 {
     public partial class tf
     {
-        public static Tensor read_file(string filename, string name = "") => gen_io_ops.read_file(filename, name);
+        public static Tensor read_file(string filename, string name = null) => gen_io_ops.read_file(filename, name);
 
         public static gen_image_ops image => new gen_image_ops();
 
         public static void import_graph_def(GraphDef graph_def,
             Dictionary<string, Tensor> input_map = null,
             string[] return_elements = null,
-            string name = "",
+            string name = null,
             OpList producer_op_list = null) => importer.import_graph_def(graph_def, input_map, return_elements, name, producer_op_list);
     }
 }

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

@@ -10,12 +10,12 @@ namespace Tensorflow
 
         public static Tensor sub(Tensor a, Tensor b) => gen_math_ops.sub(a, b);
 
-        public static Tensor subtract<T>(Tensor x, T[] y, string name = "") where T : struct
+        public static Tensor subtract<T>(Tensor x, T[] y, string name = null) where T : struct
             => gen_math_ops.sub(x, ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y"), name);
 
         public static Tensor multiply(Tensor x, Tensor y) => gen_math_ops.mul(x, y);
 
-        public static Tensor divide<T>(Tensor x, T[] y, string name = "") where T : struct
+        public static Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
             => x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
 
         public static Tensor pow<T1, T2>(T1 x, T2 y) => gen_math_ops.pow(x, y);
@@ -28,7 +28,7 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor reduce_sum(Tensor input, int[] axis = null) => math_ops.reduce_sum(input);
 
-        public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = "") 
+        public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null) 
             => math_ops.cast(x, dtype, name);
     }
 }

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

@@ -21,6 +21,6 @@ namespace Tensorflow
             float stddev = 1.0f,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
-            string name = "") => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
+            string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
     }
 }

src/TensorFlowNET.Core/Eager/Execute.cs

@@ -6,7 +6,7 @@ namespace Tensorflow.Eager
 {
     public class Execute
     {
-        public void record_gradient(string op_name, InputList inputs, Dictionary<string, object> attrs, Tensor[] results, string name = "")
+        public void record_gradient(string op_name, InputList inputs, Dictionary<string, object> attrs, Tensor[] results, string name = null)
         {
             pywrap_tfe_src.RecordGradient(op_name, inputs._inputs, attrs, results, name);
         }

src/TensorFlowNET.Core/Eager/pywrap_tfe_src.cs

@@ -10,7 +10,7 @@ namespace Tensorflow.Eager
     /// </summary>
     public class pywrap_tfe_src
     {
-        public static void RecordGradient(string op_name, Tensor[] inputs, Dictionary<string, object> attrs, Tensor[] results, string name = "")
+        public static void RecordGradient(string op_name, Tensor[] inputs, Dictionary<string, object> attrs, Tensor[] results, string name = null)
         {
             var input_ids = inputs.Select(x => x.Id).ToArray();
             var input_dtypes = inputs.Select(x => x.dtype).ToArray();

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

@@ -12,7 +12,7 @@ namespace Tensorflow
         public static ITensorOrOperation[] import_graph_def(GraphDef graph_def,
             Dictionary<string, Tensor> input_map = null,
             string[] return_elements = null,
-            string name = "",
+            string name = null,
             OpList producer_op_list = null)
         {
             var op_dict = op_def_registry.get_registered_ops();

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

@@ -123,7 +123,7 @@ namespace Tensorflow
         /// <param name="strip_default_attrs"></param>
         /// <param name="meta_info_def"></param>
         /// <returns></returns>
-        public static MetaGraphDef export_scoped_meta_graph(string filename = "",
+        public static (MetaGraphDef, Dictionary<string, RefVariable>) export_scoped_meta_graph(string filename = "",
             GraphDef graph_def = null,
             bool as_text = false,
             string unbound_inputs_col_name = "unbound_inputs",
@@ -138,7 +138,7 @@ namespace Tensorflow
             var var_list = new Dictionary<string, RefVariable>();
             var variables = graph.get_collection(ops.GraphKeys.GLOBAL_VARIABLES);
 
-            foreach(var v in variables as RefVariable[])
+            foreach(var v in variables as List<RefVariable>)
             {
                 var_list[v.name] = v;
             }
@@ -151,7 +151,10 @@ namespace Tensorflow
                 saver_def: saver_def,
                 strip_default_attrs: strip_default_attrs);
 
-            throw new NotImplementedException("meta_graph.export_scoped_meta_graph");
+            if (!string.IsNullOrEmpty(filename))
+                graph_io.write_graph(scoped_meta_graph_def, "", filename, as_text: as_text);
+
+            return (scoped_meta_graph_def, var_list);
         }
 
         private static bool _should_include_node()
@@ -159,7 +162,7 @@ namespace Tensorflow
             return true;
         }
 
-        private static byte[] create_meta_graph_def(MetaInfoDef meta_info_def = null,
+        private static MetaGraphDef create_meta_graph_def(MetaInfoDef meta_info_def = null,
             GraphDef graph_def = null,
             string export_scope = "",
             string exclude_nodes = "",
@@ -168,7 +171,7 @@ namespace Tensorflow
             bool strip_default_attrs = false)
         {
             // Sets graph to default graph if it's not passed in.
-            var graph = ops.get_default_graph();
+            var graph = ops.get_default_graph().as_default();
             // Creates a MetaGraphDef proto.
             var meta_graph_def = new MetaGraphDef();
             if (meta_info_def == null)
@@ -186,10 +189,55 @@ namespace Tensorflow
                 meta_graph_def.GraphDef = graph_def;
 
             // Fills in meta_info_def.stripped_op_list using the ops from graph_def.
-            if (meta_graph_def.MetaInfoDef.StrippedOpList.Op.Count == 0)
+            if (meta_graph_def.MetaInfoDef.StrippedOpList == null || 
+                meta_graph_def.MetaInfoDef.StrippedOpList.Op.Count == 0)
                 meta_graph_def.MetaInfoDef.StrippedOpList = stripped_op_list_for_graph(meta_graph_def.GraphDef);
 
-            throw new NotImplementedException("create_meta_graph_def");
+            var clist = graph.get_all_collection_keys();
+            foreach(var ctype in clist)
+            {
+                if (clear_extraneous_savers)
+                {
+                    throw new NotImplementedException("create_meta_graph_def clear_extraneous_savers");
+                }
+                else
+                {
+                    add_collection_def(meta_graph_def, ctype, graph);
+                }
+            }
+
+            return meta_graph_def;
+        }
+
+        private static void add_collection_def(MetaGraphDef meta_graph_def, 
+            string key, 
+            Graph graph = null,
+            string export_scope = "")
+        {
+            if (!meta_graph_def.CollectionDef.ContainsKey(key))
+                meta_graph_def.CollectionDef[key] = new CollectionDef();
+            var col_def = meta_graph_def.CollectionDef[key];
+
+            switch (graph.get_collection(key))
+            {
+                case List<RefVariable> collection_list:
+                    col_def.BytesList = new Types.BytesList();
+                    foreach (var x in collection_list)
+                    {
+                        var proto = x.to_proto(export_scope);
+                        col_def.BytesList.Value.Add(proto.ToByteString());
+                    }
+                    
+                    break;
+                case List<object> collection_list:
+                    col_def.NodeList = new Types.NodeList();
+                    foreach (var x in collection_list)
+                        if (x is ITensorOrOperation x2)
+                            col_def.NodeList.Value.Add(ops.strip_name_scope(x2.name, export_scope));
+                    break;
+                case List<Operation> collection_list:
+                    break;
+            }
         }
 
         private static OpList stripped_op_list_for_graph(GraphDef graph_def)

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -118,7 +118,7 @@ namespace Tensorflow
 
             if (obj is Tensor tensor && allow_tensor)
             {
-                if (tensor.Graph.Equals(this))
+                if (tensor.graph.Equals(this))
                 {
                     return tensor;
                 }
@@ -164,7 +164,7 @@ namespace Tensorflow
         }
 
         public unsafe Operation create_op(string op_type, Tensor[] inputs, TF_DataType[] dtypes, 
-            TF_DataType[] input_types = null, string name = "", 
+            TF_DataType[] input_types = null, string name = null, 
             Dictionary<string, AttrValue> attrs = null, OpDef op_def = null)
         {
             if (inputs == null)

src/TensorFlowNET.Core/Graphs/graph_io.py.cs

@@ -1,4 +1,5 @@
-using System;
+using Google.Protobuf;
+using System;
 using System.Collections.Generic;
 using System.IO;
 using System.Text;
@@ -9,12 +10,22 @@ namespace Tensorflow
     {
         public static string write_graph(Graph graph, string logdir, string name, bool as_text = true)
         {
-            var def = graph._as_graph_def();
+            var graph_def = graph._as_graph_def();
             string path = Path.Combine(logdir, name);
-            string text = def.ToString();
             if (as_text)
-                File.WriteAllText(path, text);
+                File.WriteAllText(path, graph_def.ToString());
+            else
+                File.WriteAllBytes(path, graph_def.ToByteArray());
+            return path;
+        }
 
+        public static string write_graph(MetaGraphDef graph_def, string logdir, string name, bool as_text = true)
+        {
+            string path = Path.Combine(logdir, name);
+            if (as_text)
+                File.WriteAllText(path, graph_def.ToString());
+            else
+                File.WriteAllBytes(path, graph_def.ToByteArray());
             return path;
         }
     }

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -12,7 +12,7 @@ namespace Tensorflow
 {
     public class OpDefLibrary
     {
-        public Operation _apply_op_helper(string op_type_name, string name = "", dynamic args = null)
+        public Operation _apply_op_helper(string op_type_name, string name = null, dynamic args = null)
         {
             Dictionary<string, object> keywords = ConvertToDict(args);
             var g = ops.get_default_graph();

src/TensorFlowNET.Core/Operations/Operation.Output.cs

@@ -1,5 +1,7 @@
-using System;
+using Newtonsoft.Json;
+using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Runtime.InteropServices;
 using System.Text;
 
@@ -13,6 +15,8 @@ namespace Tensorflow
 
         private Tensor[] _outputs;
         public Tensor[] outputs => _outputs;
+        [JsonIgnore]
+        public Tensor output => _outputs.FirstOrDefault();
 
         public int NumControlOutputs => c_api.TF_OperationNumControlOutputs(_handle);
         public int OutputNumConsumers(int index) => c_api.TF_OperationOutputNumConsumers(new TF_Output(_handle, index));

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -1,4 +1,5 @@
 using Google.Protobuf.Collections;
+using Newtonsoft.Json;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -11,11 +12,16 @@ namespace Tensorflow
     {
         private readonly IntPtr _handle; // _c_op in python
 
-        public Graph graph { get; }
+        private Graph _graph;
+        [JsonIgnore]
+        public Graph graph => _graph;
+        [JsonIgnore]
         public int _id => _id_value;
+        [JsonIgnore]
         public int _id_value;
 
         public string type => OpType;
+        [JsonIgnore]
         public Operation op => this;
         public TF_DataType dtype => TF_DataType.DtInvalid;
         private Status status = new Status();
@@ -42,7 +48,7 @@ namespace Tensorflow
                 return;
 
             _handle = handle;
-            this.graph = ops.get_default_graph();
+            _graph = ops.get_default_graph();
             _outputs = new Tensor[NumOutputs];
             for (int i = 0; i < NumOutputs; i++)
                 _outputs[i] = new Tensor(this, i, OutputType(i));
@@ -50,7 +56,7 @@ namespace Tensorflow
 
         public Operation(Graph g, string opType, string oper_name)
         {
-            graph = g;
+            _graph = g;
 
             var desc = c_api.TF_NewOperation(g, opType, oper_name);
             c_api.TF_SetAttrType(desc, "dtype", TF_DataType.TF_INT32);
@@ -78,7 +84,7 @@ namespace Tensorflow
         /// <param name="op_def"></param>
         public Operation(NodeDef node_def, Graph g, Tensor[] inputs = null, TF_DataType[] output_types = null, ITensorOrOperation[] control_inputs = null, TF_DataType[] input_types = null, string original_op = "", OpDef op_def = null)
         {
-            graph = g;
+            _graph = g;
 
             // Build the list of control inputs.
             var control_input_ops = new List<Operation>();

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

@@ -7,9 +7,9 @@ namespace Tensorflow
 {
     public class array_ops
     {
-        public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = "") => gen_array_ops.placeholder_with_default(input, shape, name);
+        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 = "")
+        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 =>
@@ -46,11 +46,11 @@ namespace Tensorflow
             }
         }
 
-        public static Tensor expand_dims(Tensor input, int axis = -1, string name = "", int dim = -1) => expand_dims_v2(input, axis, name);
+        public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1) => expand_dims_v2(input, axis, name);
 
-        private static Tensor expand_dims_v2(Tensor input, int axis, string name = "") => gen_array_ops.expand_dims(input, axis, name);
+        private static Tensor expand_dims_v2(Tensor input, int axis, string name = null) => gen_array_ops.expand_dims(input, axis, name);
 
-        public static Tensor rank(Tensor input, string name = "")
+        public static Tensor rank(Tensor input, string name = null)
         {
             return math_ops.rank_internal(input, name, optimize: true);
         }
@@ -63,7 +63,7 @@ namespace Tensorflow
         /// <param name="name"></param>
         /// <param name="optimize"></param>
         /// <returns></returns>
-        public static Tensor ones_like<T>(T tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool optimize = true)
+        public static Tensor ones_like<T>(T tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
             => ones_like_impl(tensor, dtype, name, optimize);
 
         private static Tensor ones_like_impl<T>(T tensor, TF_DataType dtype, string name, bool optimize = true)
@@ -81,7 +81,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor ones(Tensor shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "")
+        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 =>
@@ -92,7 +92,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor where(Tensor condition, Tensor x = null, Tensor y = null, string name = "")
+        public static Tensor where(Tensor condition, Tensor x = null, Tensor y = null, string name = null)
         {
             if( x == null && y == null)
             {
@@ -118,19 +118,19 @@ namespace Tensorflow
         /// (`int32` or `int64`). Defaults to `tf.int32`.
         /// </param>
         /// <returns>A `Tensor` of type `out_type`.</returns>
-        public static Tensor shape(Tensor input, string name = "", TF_DataType out_type = TF_DataType.TF_INT32)
+        public static Tensor shape(Tensor input, string name = null, TF_DataType out_type = TF_DataType.TF_INT32)
         {
             return shape_internal(input, name, optimize: true, out_type: out_type);
         }
 
-        public static Tensor size(Tensor input, string name = "", bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
+        public static Tensor size(Tensor input, string name = null, bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
         {
             return size_internal(input, name, optimize: optimize, out_type: out_type);
         }
 
-        private static Tensor shape_internal(Tensor input, string name = "", bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
+        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 Tensor[] { input }), scope =>
+            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "Shape", new { input }), scope =>
             {
                 name = scope;
 
@@ -138,18 +138,18 @@ namespace Tensorflow
                 {
                     var input_tensor = ops.convert_to_tensor(input);
                     var input_shape = tensor_util.to_shape(input_tensor.shape);
-                    if (optimize && input_shape.is_fully_defined())
+                    if (optimize && input_tensor.NDims > -1 && input_shape.is_fully_defined())
                     {
                         var nd = np.array(input_tensor.shape, out_type.as_numpy_datatype());
                         return constant_op.constant(nd, name: name);
                     }
                 }
 
-                return gen_array_ops.shape(input);
+                return gen_array_ops.shape(input, name: name, out_type: out_type);
             });
         }
 
-        private static Tensor size_internal(Tensor input, string name = "", bool optimize = true, TF_DataType out_type = TF_DataType.TF_INT32)
+        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 =>
             {
@@ -180,7 +180,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool optimize = true)
+        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 =>
             {

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

@@ -9,7 +9,7 @@ namespace Tensorflow
         public static (Tensor, Tensor, float) remove_squeezable_dimensions(Tensor labels,
             Tensor predictions,
             int expected_rank_diff = 0,
-            string name = "")
+            string name = null)
         {
             throw new NotImplementedException("remove_squeezable_dimensions");
         }

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

@@ -5,11 +5,11 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public class control_flow_ops
+    public class control_flow_ops : Python
     {
-        public static Operation group<T>(T[] inputs, string name = "") where T : ITensorOrOperation
+        public static Operation group<T>(T[] inputs, string name = null) where T : ITensorOrOperation
         {
-            return Python.with<ops.name_scope, Operation>(new ops.name_scope(name, "group_deps", inputs), scope =>
+            return with<ops.name_scope, Operation>(new ops.name_scope(name, "group_deps", inputs), scope =>
             {
                 name = scope;
 
@@ -37,7 +37,7 @@ namespace Tensorflow
             });
         }
 
-        private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = "")
+        private static Operation _GroupControlDeps(string dev, Operation[] deps, string name = null)
         {
             return Python.with<_ControlDependenciesController, Operation>(ops.control_dependencies(deps), ctl =>
             {
@@ -81,7 +81,7 @@ namespace Tensorflow
             return op.OpType == "Exit" || op.OpType == "RefExit";
         }
 
-        public static Tensor[] tuple(Tensor[] tensors, string name = "", Operation[] control_inputs = null)
+        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 =>
             {
@@ -109,7 +109,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor with_dependencies(Operation[] dependencies, Tensor output_tensor, string name = "")
+        public static Tensor with_dependencies(Operation[] dependencies, Tensor output_tensor, string name = null)
         {
             var values = new List<object>();
             values.AddRange(dependencies);
@@ -127,7 +127,7 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor _Identity(Tensor data, string name = "")
+        public static Tensor _Identity(Tensor data, string name = null)
         {
             data = ops.internal_convert_to_tensor_or_composite(data, as_ref: true);
             if ((int)data.dtype > 100)

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -12,28 +12,28 @@ namespace Tensorflow
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
         public static Execute _execute = new Execute();
 
-        public static Tensor expand_dims(Tensor input, int axis, string name = "")
+        public static Tensor expand_dims(Tensor input, int axis, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("ExpandDims", name: name, args: new { input, dim = axis });
 
             return _op.outputs[0];
         }
 
-        public static Tensor greater<Tx, Ty>(Tx x, Ty y, string name = "")
+        public static Tensor greater<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Greater", name: name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = "")
+        public static Tensor less<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = "")
+        public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Placeholder", args: new { dtype, shape });
             var _result = _op.outputs;
@@ -53,21 +53,21 @@ namespace Tensorflow
         /// </summary>
         /// <param name="input"></param>
         /// <param name="name"></param>
-        public static Tensor identity(Tensor input, string name = "")
+        public static Tensor identity(Tensor input, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Identity", name, new { input });
 
             return _op.outputs[0];
         }
 
-        public static Tensor log(Tensor x, string name = "")
+        public static Tensor log(Tensor x, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Log", name: name, args: new { x });
 
             return _op.outputs[0];
         }
 
-        public static Tensor rank(Tensor input, string name = "")
+        public static Tensor rank(Tensor input, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Rank", name: name, args: new { input });
 
@@ -81,7 +81,7 @@ namespace Tensorflow
         /// <param name="value">A `Tensor`. 0-D (scalar). Value to fill the returned tensor.</param>
         /// <param name="name">A name for the operation (optional).</param>
         /// <returns>A `Tensor`. Has the same type as `value`.</returns>
-        public static Tensor fill<T>(Tensor dims, T value, string name = "")
+        public static Tensor fill<T>(Tensor dims, T value, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Fill", name, new { dims, value });
 
@@ -102,7 +102,7 @@ namespace Tensorflow
             return (_op.outputs[0], _op.outputs[1]);
         }
 
-        public static Tensor reshape(Tensor tensor, Tensor shape, string name = "")
+        public static Tensor reshape(Tensor tensor, Tensor shape, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Reshape", name, new { tensor, shape });
             return _op.outputs[0];
@@ -120,37 +120,37 @@ namespace Tensorflow
         /// <param name="shape"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = "")
+        public static Tensor placeholder_with_default<T>(T input, int[] shape, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("PlaceholderWithDefault", name, new { input, shape, name });
             return _op.outputs[0];
         }
 
-        public static Tensor select(Tensor condition, Tensor t, Tensor e, string name = "")
+        public static Tensor select(Tensor condition, Tensor t, Tensor e, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Select", name, new { condition, t, e });
             return _op.outputs[0];
         }
 
-        public static Tensor shape(Tensor input, TF_DataType out_type = TF_DataType.TF_INT32, string name = "")
+        public static Tensor shape(Tensor input, TF_DataType out_type = TF_DataType.TF_INT32, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Shape", name, new { input, out_type });
             return _op.outputs[0];
         }
 
-        public static Tensor size(Tensor input, TF_DataType out_type = TF_DataType.TF_INT32, string name = "")
+        public static Tensor size(Tensor input, TF_DataType out_type = TF_DataType.TF_INT32, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Size", name, new { input, out_type });
             return _op.outputs[0];
         }
 
-        public static Tensor tile(Tensor input, Tensor multiples, string name = "")
+        public static Tensor tile(Tensor input, Tensor multiples, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Tile", name, new { input, multiples });
             return _op.outputs[0];
         }
 
-        public static Tensor zeros_like(Tensor x, string name = "")
+        public static Tensor zeros_like(Tensor x, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("ZerosLike", name, new { x });
             return _op.outputs[0];

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

@@ -8,7 +8,7 @@ namespace Tensorflow
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
 
-        public static Operation no_op(string name = "")
+        public static Operation no_op(string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("NoOp", name, null);
 

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

@@ -8,7 +8,7 @@ namespace Tensorflow
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
 
-        public static Tensor dynamic_stitch(Tensor[] indices, Tensor[] data, string name = "")
+        public static Tensor dynamic_stitch(Tensor[] indices, Tensor[] data, string name = null)
         {
             var _attr_N = indices.Length;
             var _op = _op_def_lib._apply_op_helper("DynamicStitch", name, new { indices, data });

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

@@ -15,7 +15,7 @@ namespace Tensorflow
             bool try_recover_truncated = false,
             float acceptable_fraction = 1,
             string dct_method = "",
-            string name = "")
+            string name = null)
         {
             // Add nodes to the TensorFlow graph.
             if (tf.context.executing_eagerly())
@@ -39,7 +39,7 @@ namespace Tensorflow
             }
         }
 
-        public Tensor resize_bilinear(Tensor images, Tensor size, bool align_corners = false, string name = "")
+        public Tensor resize_bilinear(Tensor images, Tensor size, bool align_corners = false, string name = null)
         {
             if (tf.context.executing_eagerly())
             {

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

@@ -8,21 +8,21 @@ namespace Tensorflow
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
 
-        public static Operation save_v2(Tensor prefix, string[] tensor_names, string[] shape_and_slices, Tensor[] tensors, string name = "")
+        public static Operation save_v2(Tensor prefix, string[] tensor_names, string[] shape_and_slices, Tensor[] tensors, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("SaveV2", name: name, args: new { prefix, tensor_names, shape_and_slices, tensors });
 
             return _op;
         }
 
-        public static Tensor[] restore_v2(Tensor prefix, string[] tensor_names, string[] shape_and_slices, TF_DataType[] dtypes, string name = "")
+        public static Tensor[] restore_v2(Tensor prefix, string[] tensor_names, string[] shape_and_slices, TF_DataType[] dtypes, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("RestoreV2", name: name, args: new { prefix, tensor_names, shape_and_slices, dtypes });
 
             return _op.outputs;
         }
 
-        public static Tensor read_file(string filename, string name = "")
+        public static Tensor read_file(string filename, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("ReadFile", name: name, args: new { filename });
 

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -10,7 +10,7 @@ namespace Tensorflow
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
 
-        public static Tensor add(Tensor x, Tensor y, string name = "")
+        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 });
 
@@ -24,42 +24,42 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
-        public static Tensor neg(Tensor x, string name = "")
+        public static Tensor neg(Tensor x, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Neg", name, args: new { x });
 
             return _op.outputs[0];
         }
 
-        public static Tensor sub<Tx, Ty>(Tx x, Ty y, string name = "")
+        public static Tensor sub<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Sub", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor mul(Tensor x, Tensor y, string name = "")
+        public static Tensor mul(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Mul", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor real_div(Tensor x, Tensor y, string name = "")
+        public static Tensor real_div(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("RealDiv", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor floor_mod(Tensor x, Tensor y, string name = "")
+        public static Tensor floor_mod(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("FloorMod", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor floor_div(Tensor x, Tensor y, string name = "")
+        public static Tensor floor_div(Tensor x, Tensor y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("FloorDiv", name, args: new { x, y });
 
@@ -75,7 +75,7 @@ namespace Tensorflow
         /// <param name="transpose_b"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor mat_mul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false, string name = "")
+        public static Tensor mat_mul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("MatMul", name, args: new { a, b, transpose_a, transpose_b });
 
@@ -89,21 +89,21 @@ namespace Tensorflow
         /// <param name="y"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = "")
+        public static Tensor maximum<T1, T2>(T1 x, T2 y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Maximum", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = "")
+        public static Tensor pow<Tx, Ty>(Tx x, Ty y, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Pow", name, args: new { x, y });
 
             return _op.outputs[0];
         }
 
-        public static Tensor sum(Tensor input, Tensor axis = null, bool keep_dims = false, string name = "")
+        public static Tensor sum(Tensor input, Tensor axis = null, bool keep_dims = false, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Sum", name, args: new { input, reduction_indices = axis, keep_dims });
 
@@ -118,7 +118,7 @@ namespace Tensorflow
         /// <param name="delta"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor range(Tensor start, Tensor limit, Tensor delta, string name = "")
+        public static Tensor range(Tensor start, Tensor limit, Tensor delta, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Range", name, new { start, limit, delta });
 

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

@@ -17,7 +17,7 @@ namespace Tensorflow
         /// <param name="seed2"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor random_standard_normal(Tensor shape, TF_DataType dtype = TF_DataType.DtInvalid, int? seed = null, int? seed2 = null, string name = "")
+        public static Tensor random_standard_normal(Tensor shape, TF_DataType dtype = TF_DataType.DtInvalid, int? seed = null, int? seed2 = null, string name = null)
         {
             if (!seed.HasValue)
                 seed = 0;

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

@@ -6,9 +6,9 @@ namespace Tensorflow
 {
     public class math_ops : Python
     {
-        public static Tensor add(Tensor x, Tensor y, string name = "") => gen_math_ops.add(x, y, name);
+        public static Tensor add(Tensor x, Tensor y, string name = null) => gen_math_ops.add(x, y, name);
 
-        public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
+        public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             var base_type = dtype.as_base_dtype();
             if(base_type == x.dtype)
@@ -64,7 +64,7 @@ namespace Tensorflow
         /// <param name="dtype"></param>
         /// <param name="name"></param>
         /// <returns>A `Tensor` or `SparseTensor` or `IndexedSlices` with same shape as `x` and same type as `dtype`.</returns>
-        public static Tensor __case__(Tensor x, TF_DataType dtype, string name = "")
+        public static Tensor __case__(Tensor x, TF_DataType dtype, string name = null)
         {
             var base_type = dtype.as_base_dtype();
             if (x is Tensor && base_type == x.dtype)
@@ -122,15 +122,15 @@ namespace Tensorflow
             });
         }
 
-        public static Tensor floordiv(Tensor x, Tensor y, string name = "")
+        public static Tensor floordiv(Tensor x, Tensor y, string name = null)
         {
-            return with<ops.name_scope, Tensor>(new ops.name_scope("", "floordiv", new { x, y }), scope =>
+            return with<ops.name_scope, Tensor>(new ops.name_scope(name, "floordiv", new { x, y }), scope =>
             {
                 return gen_math_ops.floor_div(x, y, scope);
             });
         }
 
-        public static Tensor rank_internal(Tensor input, string name = "", bool optimize = true)
+        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 =>
             {
@@ -148,7 +148,7 @@ namespace Tensorflow
             bool transpose_a = false, bool transpose_b = false,
             bool adjoint_a = false, bool adjoint_b = false,
             bool a_is_sparse = false, bool b_is_sparse = false,
-            string name = "")
+            string name = null)
         {
             Tensor result = null;
 
@@ -176,7 +176,7 @@ namespace Tensorflow
         /// <param name="x">`Tensor` to conjugate.  Must have numeric or variant type.</param>
         /// <param name="name">A name for the operation (optional).</param>
         /// <returns>A `Tensor` that is the conjugate of `x` (with the same type).</returns>
-        public static Tensor conj(Tensor x, string name = "")
+        public static Tensor conj(Tensor x, string name = null)
         {
             var dt = x.dtype;
             if (dt.is_floating() || dt.is_integer())

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

@@ -11,7 +11,7 @@ namespace Tensorflow
             float stddev = 1.0f, 
             TF_DataType dtype = TF_DataType.TF_FLOAT, 
             int? seed = null, 
-            string name = "")
+            string name = null)
         {
             return Python.with<ops.name_scope, Tensor>(new ops.name_scope(name, "random_normal", new object[] { shape, mean, stddev }), scope =>
             {

src/TensorFlowNET.Core/Protobuf/IProtoBuf.cs

@@ -0,0 +1,31 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    /// <summary>
+    /// In order for a object to be serialized to and from MetaGraphDef, 
+    /// the class must implement to_proto() and from_proto() methods
+    /// </summary>
+    public interface IProtoBuf
+    {
+        string name { get; }
+
+        /// <summary>
+        /// Converts a `Variable` to a `VariableDef` protocol buffer.
+        /// </summary>
+        /// <param name="export_scope"></param>
+        /// <returns></returns>
+        VariableDef to_proto(string export_scope);
+
+        /// <summary>
+        /// Returns a `Variable` object created from `variable_def`.
+        /// </summary>
+        /// <typeparam name="T"></typeparam>
+        /// <param name="variable_def"></param>
+        /// <param name="import_scope"></param>
+        /// <returns></returns>
+        T from_proto<T>(VariableDef variable_def, string import_scope);
+    }
+}

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -52,4 +52,10 @@ Upgraded to TensorFlow 1.13 RC2.
     <Content CopyToOutputDirectory="PreserveNewest" Include="./runtimes/win-x64/native/tensorflow.dll" Link="tensorflow.dll" Pack="true" PackagePath="runtimes/win-x64/native/tensorflow.dll" />
   </ItemGroup>
 
+  <ItemGroup>
+    <Reference Include="Newtonsoft.Json">
+      <HintPath>C:\Program Files\dotnet\sdk\NuGetFallbackFolder\newtonsoft.json\9.0.1\lib\netstandard1.0\Newtonsoft.Json.dll</HintPath>
+    </Reference>
+  </ItemGroup>
+
 </Project>

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

@@ -41,7 +41,7 @@ namespace Tensorflow
             if( y is Tensor tr)
                 dtype = tr.dtype.as_base_dtype();
             
-            var namescope = new ops.name_scope("", name, new { x, y });
+            var namescope = new ops.name_scope(null, name, new { x, y });
             return Python.with<ops.name_scope, Tensor>(namescope, scope =>
             {
                 Tensor result = null;

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -1,4 +1,5 @@
-using NumSharp.Core;
+using Newtonsoft.Json;
+using NumSharp.Core;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -17,10 +18,13 @@ namespace Tensorflow
         private readonly IntPtr _handle;
 
         private int _id;
+        [JsonIgnore]
         public int Id => _id;
-
-        public Graph Graph => op?.graph;
+        [JsonIgnore]
+        public Graph graph => op?.graph;
+        [JsonIgnore]
         public Operation op { get; }
+        [JsonIgnore]
         public Tensor[] outputs => op.outputs;
 
         /// <summary>
@@ -63,9 +67,9 @@ namespace Tensorflow
             set
             {
                 if (value == null)
-                    c_api.TF_GraphSetTensorShape(this.Graph, this._as_tf_output(), null, -1, status);
+                    c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), null, -1, status);
                 else
-                    c_api.TF_GraphSetTensorShape(this.Graph, this._as_tf_output(), value, value.Length, status);
+                    c_api.TF_GraphSetTensorShape(this.graph, this._as_tf_output(), value, value.Length, status);
             }
         }
 
@@ -100,6 +104,7 @@ namespace Tensorflow
 
         public int NDims => rank;
 
+        [JsonIgnore]
         public Operation[] Consumers => consumers();
 
         public string Device => op.Device;
@@ -108,7 +113,7 @@ namespace Tensorflow
         {
             var output = _as_tf_output();
             var consumer_names = c_api.TF_OperationOutputConsumers_wrapper(output);
-            return consumer_names.Select(x => Graph.OperationByName(x)).ToArray();
+            return consumer_names.Select(x => graph.OperationByName(x)).ToArray();
         }
 
         public TF_Output _as_tf_output()
@@ -154,7 +159,7 @@ namespace Tensorflow
         /// <returns></returns>
         public NDArray eval(FeedItem[] feed_dict = null, Session session = null)
         {
-            return ops._eval_using_default_session(this, feed_dict, Graph, session);
+            return ops._eval_using_default_session(this, feed_dict, graph, session);
         }
 
         public TF_DataType ToTFDataType(Type type)

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -65,7 +65,7 @@ namespace Tensorflow
         /// <param name="name"></param>
         /// <param name="as_ref"></param>
         /// <returns></returns>
-        public static Tensor _tensor_shape_tensor_conversion_function(TensorShape s, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool as_ref = false)
+        public static Tensor _tensor_shape_tensor_conversion_function(TensorShape s, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)
         {
             var s_list = s.Dimensions;
             var int64_value = 0;

src/TensorFlowNET.Core/Tensors/tensor_util.cs

@@ -105,10 +105,16 @@ namespace Tensorflow
                                 nparray = Convert.ToSingle(values);
                             break;
                         case "Double":
-                            nparray = Convert.ToDouble(values);
+                            if (values.GetType().IsArray)
+                                nparray = np.array((double[])values, np_dt);
+                            else
+                                nparray = Convert.ToDouble(values);
                             break;
                         case "String":
-                            nparray = Convert.ToString(values);
+                            if (values.GetType().IsArray)
+                                nparray = np.array((string[])values, np_dt);
+                            else
+                                nparray = Convert.ToString(values);
                             break;
                         default:
                             throw new NotImplementedException("make_tensor_proto Not Implemented");

src/TensorFlowNET.Core/Tensors/tf.constant.cs

@@ -20,10 +20,10 @@ namespace Tensorflow
                 verify_shape: verify_shape,
                 allow_broadcast: false);
 
-        public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = "") => array_ops.zeros(shape, dtype, name);
+        public static Tensor zeros(Shape shape, TF_DataType dtype = TF_DataType.TF_FLOAT, string name = null) => array_ops.zeros(shape, dtype, name);
 
         public static Tensor size(Tensor input,
-            string name = "",
+            string name = null,
             TF_DataType out_type = TF_DataType.TF_INT32) => array_ops.size(input,
                 name,
                 optimize: true,

src/TensorFlowNET.Core/Train/Optimizer.cs

@@ -27,7 +27,7 @@ namespace Tensorflow
         public Dictionary<string, object> _non_slot_dict;
         public Dictionary<string, object> _deferred_slot_restorations;
 
-        public Optimizer(float learning_rate, bool use_locking, string name = "")
+        public Optimizer(float learning_rate, bool use_locking, string name = null)
         {
             if (String.IsNullOrEmpty(name))
                 throw new NotImplementedException("Must specify the optimizer name");
@@ -64,7 +64,7 @@ namespace Tensorflow
             return apply_gradients(grads_and_vars);
         }
 
-        public Operation apply_gradients(Tuple<Tensor, RefVariable>[] grads_and_vars, Tensor global_step = null, string name = "")
+        public Operation apply_gradients(Tuple<Tensor, RefVariable>[] grads_and_vars, Tensor global_step = null, string name = null)
         {
             // No DistributionStrategy case.
             var converted_grads_and_vars = new List<Tuple<Tensor, RefVariable, _OptimizableVariable>>();

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

@@ -15,7 +15,7 @@ namespace Tensorflow
             bool sharded = false, 
             int max_to_keep = 5,
             float keep_checkpoint_every_n_hours = 10000,
-            string name = "",
+            string name = null,
             bool restore_sequentially = false,
             string filename = "model",
             bool build_save = true,

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

@@ -282,7 +282,7 @@ namespace Tensorflow
                 clear_devices: clear_devices,
                 clear_extraneous_savers: clear_extraneous_savers,
                 strip_default_attrs: strip_default_attrs);
-            return meta_graph_def;
+            return meta_graph_def.Item1;
         }
 
         /// <summary>

src/TensorFlowNET.Core/Train/Saving/saveable_object_util.py.cs

@@ -75,7 +75,7 @@ namespace Tensorflow
                     }
                     else
                     {
-                        string name = "";
+                        string name = null;
                         Tensor tensor = null;
 
                         if (convert_variable_to_tensor)

src/TensorFlowNET.Core/Train/gen_training_ops.py.cs

@@ -8,7 +8,7 @@ namespace Tensorflow
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
 
-        public static Tensor apply_gradient_descent(RefVariable var, Tensor alpha, Tensor delta, bool use_locking = false, string name = "")
+        public static Tensor apply_gradient_descent(RefVariable var, Tensor alpha, Tensor delta, bool use_locking = false, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("ApplyGradientDescent", name, new
             {

src/TensorFlowNET.Core/Train/tf.optimizers.cs

@@ -20,6 +20,16 @@ namespace Tensorflow
                 string import_scope = "") => saver._import_meta_graph_with_return_elements(meta_graph_or_file,
                     clear_devices,
                     import_scope).Item1;
+
+            public static (MetaGraphDef, Dictionary<string, RefVariable>) export_meta_graph(string filename = "",
+                bool as_text = false,
+                bool clear_devices = false,
+                bool clear_extraneous_savers = false,
+                bool strip_default_attrs = false) => meta_graph.export_scoped_meta_graph(filename: filename,
+                    as_text: as_text,
+                    clear_devices: clear_devices,
+                    clear_extraneous_savers: clear_extraneous_savers,
+                    strip_default_attrs: strip_default_attrs);
         }
     }
 }

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 Python.with<ops.name_scope, Tensor>(new ops.name_scope("", default_name, new object[] { tensor1, y }), scope => {
+            return Python.with<ops.name_scope, Tensor>(new ops.name_scope(null, default_name, new object[] { 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

@@ -5,7 +5,7 @@ using System.Text;
 
 namespace Tensorflow
 {
-    public partial class RefVariable : VariableV1
+    public partial class RefVariable : VariableV1, IProtoBuf
     {
         public bool _in_graph_mode = true;
         public Tensor _initial_value;
@@ -13,11 +13,12 @@ namespace Tensorflow
         public bool _trainable;
         public Tensor _variable;
         public Tensor _snapshot;
+        public bool _save_slice_info;
 
         private Operation _initializer_op;
         public Operation initializer => _initializer_op;
         public Operation op => _variable.op;
-        public Graph graph => _variable.Graph;
+        public Graph graph => _variable.graph;
         public TF_DataType dtype => _variable.dtype;
         public TensorShape shape => tensor_util.to_shape(_variable.shape);
 
@@ -28,7 +29,7 @@ namespace Tensorflow
             List<string> collections = null,
             bool validate_shape = true,
             string caching_device = "",
-            string name = "",
+            string name = null,
             VariableDef variable_def = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
             string import_scope = "") : base(initial_value,
@@ -92,7 +93,7 @@ namespace Tensorflow
             List<string> collections = null,
             bool validate_shape = true,
             string caching_device = "",
-            string name = "",
+            string name = null,
             TF_DataType dtype = TF_DataType.DtInvalid)
         {
             if (initial_value is null)
@@ -235,7 +236,7 @@ namespace Tensorflow
         /// A `Tensor` that will hold the new value of this variable after
         /// the assignment has completed.
         /// </returns>
-        public ITensorOrOperation assign(object value, bool use_locking = false, string name = "", bool read_value = true)
+        public ITensorOrOperation assign(object value, bool use_locking = false, string name = null, bool read_value = true)
         {
             var assign = gen_state_ops.assign(_variable, value, use_locking: use_locking, name: name);
             if (read_value)
@@ -247,5 +248,30 @@ namespace Tensorflow
         {
             return $"tf.Variable '{name}' shape={shape} dtype={dtype}";
         }
+
+        public VariableDef to_proto(string export_scope)
+        {
+            if(string.IsNullOrEmpty(export_scope) || _variable.name.StartsWith(export_scope))
+            {
+                var var_def = new VariableDef();
+                var_def.VariableName = ops.strip_name_scope(_variable.name, export_scope);
+                if (_initial_value != null)
+                    var_def.InitialValueName = ops.strip_name_scope(_initial_value.name, export_scope);
+                var_def.Trainable = _trainable;
+                var_def.InitializerName = ops.strip_name_scope(initializer.name, export_scope);
+                var_def.SnapshotName = ops.strip_name_scope(_snapshot.name, export_scope);
+                if (_save_slice_info)
+                    throw new NotImplementedException("to_proto _save_slice_info");
+
+                return var_def;
+            }
+
+            throw new NotImplementedException("to_proto RefVariable");
+        }
+
+        public T from_proto<T>(VariableDef variable_def, string import_scope)
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Core/Variables/VariableV1.cs

@@ -21,7 +21,7 @@ namespace Tensorflow
             List<string> collections = null,
             bool validate_shape = true,
             string caching_device = "",
-            string name = "",
+            string name = null,
             TF_DataType dtype = TF_DataType.DtInvalid)
         {
 

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

@@ -21,7 +21,7 @@ namespace Tensorflow
         /// <param name="container"></param>
         /// <param name="shared_name"></param>
         /// <returns></returns>
-        public static Tensor variable_v2(long[] shape, TF_DataType dtype, string name = "", string container = "", string shared_name = "")
+        public static Tensor variable_v2(long[] shape, TF_DataType dtype, string name = null, string container = "", string shared_name = "")
         {
             var _op = _op_def_lib._apply_op_helper("VariableV2", name: name, args: new { dtype, shape, container, shared_name });
 
@@ -50,7 +50,7 @@ namespace Tensorflow
         public static Tensor assign(Tensor tensor, object value, 
             bool validate_shape = true, 
             bool use_locking = true,
-            string name = "")
+            string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Assign", name: name, args: new { _ref_ = tensor, value, validate_shape, use_locking });
 

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

@@ -11,7 +11,7 @@ namespace Tensorflow
         public static bool _DEFAULT_USE_RESOURCE = false;
 
         public static RefVariable default_variable_creator(object initial_value,
-            string name = "",
+            string name = null,
             bool? trainable = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
             bool validate_shape = false,

src/TensorFlowNET.Core/ops.py.cs

@@ -81,22 +81,22 @@ namespace Tensorflow
         /// <param name="dtype"></param>
         /// <param name="name"></param>
         /// <returns></returns>
-        public static Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid)
+        public static Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, TF_DataType preferred_dtype = TF_DataType.DtInvalid)
         {
             return convert_to_tensor_v2(value, dtype, preferred_dtype, name);
         }
 
-        public static Tensor convert_to_tensor_v2(object value, TF_DataType dtype = TF_DataType.DtInvalid, TF_DataType dtype_hint = TF_DataType.DtInvalid, string name = "")
+        public static Tensor convert_to_tensor_v2(object value, TF_DataType dtype = TF_DataType.DtInvalid, TF_DataType dtype_hint = TF_DataType.DtInvalid, string name = null)
         {
             return internal_convert_to_tensor(value, dtype: dtype, name: name, preferred_dtype: dtype_hint, as_ref: false);
         }
 
-        public static Tensor convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
+        public static Tensor convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             return internal_convert_to_tensor_or_composite(value: value, dtype: dtype, name: name, as_ref: false);
         }
 
-        public static Tensor internal_convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool as_ref = false)
+        public static Tensor internal_convert_to_tensor_or_composite(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)
         {
             return internal_convert_to_tensor(value, dtype: dtype, name: name, as_ref: as_ref);
         }
@@ -382,22 +382,22 @@ namespace Tensorflow
             };
         }
 
-        public static Tensor[] convert_n_to_tensor_or_indexed_slices(Tensor[] values, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
+        public static Tensor[] convert_n_to_tensor_or_indexed_slices(Tensor[] values, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             return internal_convert_n_to_tensor_or_indexed_slices(values, dtype: dtype, name: name);
         }
 
-        public static Tensor convert_to_tensor_or_indexed_slices(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "")
+        public static Tensor convert_to_tensor_or_indexed_slices(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {
             return internal_convert_to_tensor_or_indexed_slices(value: value, dtype: dtype, name: name, as_ref: false);
         }
 
-        public static Tensor internal_convert_to_tensor_or_indexed_slices(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool as_ref = false)
+        public static Tensor internal_convert_to_tensor_or_indexed_slices(Tensor value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)
         {
             return value;
         }
 
-        public static Tensor[] internal_convert_n_to_tensor_or_indexed_slices(Tensor[] values, TF_DataType dtype = TF_DataType.DtInvalid, string name = "", bool as_ref = false)
+        public static Tensor[] internal_convert_n_to_tensor_or_indexed_slices(Tensor[] values, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)
         {
             var ret = new List<Tensor>();
 
@@ -418,7 +418,7 @@ namespace Tensorflow
         }
 
         public static Tensor[] internal_convert_n_to_tensor<T>(T[] values, TF_DataType dtype = TF_DataType.DtInvalid, 
-            string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid, 
+            string name = null, TF_DataType preferred_dtype = TF_DataType.DtInvalid, 
             bool as_ref = false)
         {
             var ret = new List<Tensor>();
@@ -433,7 +433,7 @@ namespace Tensorflow
         }
 
         public static Tensor internal_convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid,
-            string name = "", TF_DataType preferred_dtype = TF_DataType.DtInvalid,
+            string name = null, TF_DataType preferred_dtype = TF_DataType.DtInvalid,
             bool as_ref = false)
         {
             if (dtype == TF_DataType.DtInvalid)

src/TensorFlowNET.Core/tf.cs

@@ -18,7 +18,7 @@ namespace Tensorflow
 
         public static Session defaultSession;
 
-        public static RefVariable Variable<T>(T data, string name = "", TF_DataType dtype = TF_DataType.DtInvalid)
+        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);
         }

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -1,4 +1,5 @@
-using NumSharp.Core;
+using Newtonsoft.Json;
+using NumSharp.Core;
 using System;
 using System.Collections.Generic;
 using System.Text;
@@ -31,7 +32,7 @@ namespace TensorFlowNET.Examples
             var n_samples = train_X.shape[0];
 
             // tf Graph Input
-            /*var X = tf.placeholder(tf.float32);
+            var X = tf.placeholder(tf.float32);
             var Y = tf.placeholder(tf.float32);
 
             // Set model weights 
@@ -50,25 +51,32 @@ namespace TensorFlowNET.Examples
             var reduce = tf.reduce_sum(pow);
             var cost = reduce / (2.0f * n_samples);
 
-            // import graph
-
             // radient descent
             // Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
             var grad = tf.train.GradientDescentOptimizer(learning_rate);
-            var optimizer = grad.minimize(cost);*/
+            var optimizer = grad.minimize(cost);
 
-            var new_saver = tf.train.import_meta_graph("linear_regression.meta");
+            //tf.train.export_meta_graph(filename: "linear_regression.meta.bin");
+            // import meta
+            // var new_saver = tf.train.import_meta_graph("linear_regression.meta.bin");
+            var text = JsonConvert.SerializeObject(graph, new JsonSerializerSettings
+            {
+                Formatting = Formatting.Indented
+            });
 
-            var X = graph.OperationByName("Placeholder");
-            var Y = graph.OperationByName("Placeholder_1");
-            var W = graph.OperationByName("weight");
+            /*var cost = graph.OperationByName("truediv").output;
+            var pred = graph.OperationByName("Add").output;
             var optimizer = graph.OperationByName("GradientDescent");
+            var X = graph.OperationByName("Placeholder").output;
+            var Y = graph.OperationByName("Placeholder_1").output;
+            var W = graph.OperationByName("weight").output;
+            var b = graph.OperationByName("bias").output;*/
 
             // Initialize the variables (i.e. assign their default value)
             var init = tf.global_variables_initializer();
 
             // Start training
-            Python.with<Session>(tf.Session(graph), sess => 
+            with<Session>(tf.Session(graph), sess => 
             {
                 // Run the initializer
                 sess.run(init);
@@ -78,11 +86,10 @@ namespace TensorFlowNET.Examples
                 {
                     foreach (var (x, y) in zip<float>(train_X, train_Y))
                     {
-                        var w = sess.run(W);
-                        sess.run(optimizer,
+                        sess.run(optimizer, 
                             new FeedItem(X, x),
                             new FeedItem(Y, y));
-                        w = sess.run(W);
+                        var rW = sess.run(W);
                     }
 
                     // Display logs per epoch step

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -6,6 +6,7 @@
   </PropertyGroup>
 
   <ItemGroup>
+    <PackageReference Include="Newtonsoft.Json" Version="12.0.1" />
     <PackageReference Include="NumSharp" Version="0.7.3" />
     <PackageReference Include="TensorFlow.NET" Version="0.3.0" />
   </ItemGroup>

test/TensorFlowNET.Examples/python/linear_regression.py

@@ -49,7 +49,7 @@ if False:
     # tf.train.export_meta_graph(filename='save_model.meta');
 else:
     # tf Graph Input
-    new_saver = tf.train.import_meta_graph("save_model.meta")
+    new_saver = tf.train.import_meta_graph("linear_regression.meta")
     nodes = tf.get_default_graph()._nodes_by_name;
     optimizer = nodes["GradientDescent"]
     cost = nodes["truediv"].outputs[0]