Fix Operation.get_attr #115

Author: Oceania2018

README.md

@@ -12,15 +12,15 @@ TensorFlow.NET is a member project of [SciSharp](https://github.com/SciSharp) st
 ![tensors_flowing](docs/assets/tensors_flowing.gif)
 
 ### How to use
-Download the pre-compiled dll [here](tensorflow.so) and place it in the working folder. 
-This is only need for Linux and Mac OS, and already packed for Windows.
-
 Install TensorFlow.NET through NuGet.
 ```sh
 PM> Install-Package TensorFlow.NET
 ```
 
+If you are using Linux or Mac OS, please download the pre-compiled dll [here](tensorflow.so) and place it in the working folder. This is only need for Linux and Mac OS, and already packed into NuGet for Windows.
+
 Import tensorflow.net.
+
 ```cs
 using Tensorflow;
 ```

src/TensorFlowNET.Core/Eager/Execute.cs

@@ -0,0 +1,17 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Eager
+{
+    public class Execute
+    {
+        public void record_gradient(string op_name, Tensor[] inputs, Dictionary<string, object> attrs, Tensor[] results, string name = "")
+        {
+            if (inputs == null)
+                inputs = new Tensor[0];
+
+            pywrap_tfe_src.RecordGradient(op_name, inputs, attrs, results, name);
+        }
+    }
+}

src/TensorFlowNET.Core/Eager/pywrap_tfe_src.cs

@@ -0,0 +1,18 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace Tensorflow.Eager
+{
+    /// <summary>
+    /// python\eager\pywrap_tfe_src.cc
+    /// </summary>
+    public class pywrap_tfe_src
+    {
+        public static void RecordGradient(string op_name, Tensor[] inputs, Dictionary<string, object> attrs, Tensor[] results, string name = "")
+        {
+            
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -113,7 +113,7 @@ public Operation(NodeDef node_def, Graph g, List<Tensor> inputs = null, TF_DataT
                 op_def = g.GetOpDef(node_def.Op);
 
             _handle = ops._create_c_op(g, node_def, inputs);
-            
+
             _outputs = new Tensor[NumOutputs];
             output_types = new TF_DataType[NumOutputs];
 
@@ -128,21 +128,26 @@ public Operation(NodeDef node_def, Graph g, List<Tensor> inputs = null, TF_DataT
 
         public object get_attr(string name)
         {
-            object ret = null;
+            AttrValue x = null;
 
             var fields = new string[] { "s", "i", "f", "b", "type", "shape", "tensor", "func" };
 
+            using (var buf = new Buffer())
+            {
+                c_api.TF_OperationGetAttrValueProto(_handle, name, buf, status);
+                status.Check(true);
+                x = AttrValue.Parser.ParseFrom(buf);
+            }
+
             switch (name)
             {
                 case "dtype":
-                    ret = _outputs[0];
-                    break;
+                    return x.Type;
                 case "shape":
-                    ret = new TensorShapeProto();
-                    break;
+                    return x.Shape;
+                default:
+                    throw new NotImplementedException($"{name}");
             }
-
-            return ret;
         }
 
         public TF_AttrMetadata GetAttributeMetadata(string attr_name, Status s)

src/TensorFlowNET.Core/Operations/gen_array_ops.cs

@@ -3,14 +3,16 @@
 using System.IO;
 using System.Text;
 using Tensorflow;
+using Tensorflow.Eager;
 
 namespace Tensorflow
 {
     public static class gen_array_ops
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
+        public static Execute _execute = new Execute();
 
-        public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null)
+        public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = "")
         {
             var keywords = new Dictionary<string, object>();
             keywords.Add("dtype", dtype);
@@ -24,6 +26,7 @@ public static Tensor placeholder(TF_DataType dtype, TensorShape shape = null)
             _attrs["dtype"] = _op.get_attr("dtype");
             _attrs["shape"] = _op.get_attr("shape");
 
+            _execute.record_gradient("Placeholder", _inputs_flat, _attrs, _result, name);
             return new Tensor(_op, 0, dtype);
         }