gen_math_ops.mul(x, y)

TensorFlow.NET.sln

@@ -9,8 +9,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\T
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "test\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{1FE60088-157C-4140-91AB-E96B915E4BAE}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{EC622ADF-8DAE-474B-B18E-9598A4F91BA2}"
-EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -29,10 +27,6 @@ Global
 		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Release|Any CPU.Build.0 = Release|Any CPU
-		{EC622ADF-8DAE-474B-B18E-9598A4F91BA2}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{EC622ADF-8DAE-474B-B18E-9598A4F91BA2}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{EC622ADF-8DAE-474B-B18E-9598A4F91BA2}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{EC622ADF-8DAE-474B-B18E-9598A4F91BA2}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

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

@@ -15,5 +15,10 @@ namespace Tensorflow
         {
             return gen_math_ops.mul(x, y);
         }
+
+        public static unsafe Tensor multiply(Tensor x, RefVariable y)
+        {
+            return gen_math_ops.mul(x, y);
+        }
     }
 }

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -24,6 +24,7 @@ namespace Tensorflow
         private List<String> _unfetchable_ops = new List<string>();
 
         private string _name_stack;
+        public string _graph_key;
         public Status Status { get; }
 
         public Graph()

src/TensorFlowNET.Core/Operations/OpDefLibrary.cs

@@ -36,24 +36,50 @@ namespace Tensorflow
             foreach (var input_arg in op_def.InputArg)
             {
                 var input_name = input_arg.Name;
-                if (keywords.ContainsKey(input_name))
+                switch (keywords[input_name])
                 {
-                    inputs.Add(keywords[input_name] as Tensor);
-                }
-
-                if (!String.IsNullOrEmpty(input_arg.TypeAttr))
-                {
-                    attrs[input_arg.TypeAttr] = (keywords[input_name] as Tensor).dtype;
+                    case Tensor value:
+                        if (keywords.ContainsKey(input_name))
+                        {
+                            inputs.Add(value);
+                        }
+
+                        if (!String.IsNullOrEmpty(input_arg.TypeAttr))
+                        {
+                            attrs[input_arg.TypeAttr] = value.dtype;
+                        }
+
+                        if (input_arg.IsRef)
+                        {
+
+                        }
+                        else
+                        {
+                            input_types.Add(value.dtype);
+                        }
+                        break;
+                    case RefVariable value:
+                        if (keywords.ContainsKey(input_name))
+                        {
+                            inputs.Add(value._initial_value);
+                        }
+
+                        if (!String.IsNullOrEmpty(input_arg.TypeAttr))
+                        {
+                            attrs[input_arg.TypeAttr] = value._initial_value.dtype;
+                        }
+
+                        if (input_arg.IsRef)
+                        {
+
+                        }
+                        else
+                        {
+                            input_types.Add(value._initial_value.dtype);
+                        }
+                        break;
                 }
 
-                if (input_arg.IsRef)
-                {
-
-                }
-                else
-                {
-                    input_types.Add((keywords[input_name] as Tensor).dtype);
-                }
             }
 
             // Process remaining attrs

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -31,6 +31,17 @@ namespace Tensorflow
             return new Tensor(_op, 0, _op.OutputType(0));
         }
 
+        public static Tensor mul(Tensor x, RefVariable y)
+        {
+            var keywords = new Dictionary<string, object>();
+            keywords.Add("x", x);
+            keywords.Add("y", y);
+
+            var _op = _op_def_lib._apply_op_helper("Mul", name: "mul", keywords: keywords);
+
+            return new Tensor(_op, 0, _op.OutputType(0));
+        }
+
         public static Tensor mat_mul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false)
         {
             var keywords = new Dictionary<string, object>();

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -124,25 +124,25 @@ namespace Tensorflow
                             // wired, don't know why we have to start from offset 9.
                             var bytes = tensor.Data();
                             var output = UTF8Encoding.Default.GetString(bytes, 9, bytes.Length - 9);
-                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                            result[i] = fetchValue(tensor, ndims, output);
                         }
                         break;
                     case TF_DataType.TF_FLOAT:
                         {
                             var output = *(float*)c_api.TF_TensorData(output_values[i]);
-                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                            result[i] = fetchValue(tensor, ndims, output);
                         }
                         break;
                     case TF_DataType.TF_INT16:
                         {
                             var output = *(short*)c_api.TF_TensorData(output_values[i]);
-                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                            result[i] = fetchValue(tensor, ndims, output);
                         }
                         break;
                     case TF_DataType.TF_INT32:
                         {
                             var output = *(int*)c_api.TF_TensorData(output_values[i]);
-                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                            result[i] = fetchValue(tensor, ndims, output);
                         }
                         break;
                     default:
@@ -153,6 +153,18 @@ namespace Tensorflow
             return result;
         }
 
+        private object fetchValue<T>(Tensor tensor, int[] ndims, T output)
+        {
+            if (tensor.NDims == 0)
+            {
+                return output;
+            }
+            else
+            {
+                return np.array(output).reshape(ndims);
+            }
+        }
+
         /// <summary>
         /// If a tensor handle that is fed to a device incompatible placeholder, 
         /// we move the tensor to the right device, generate a new tensor handle, 

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -36,15 +36,11 @@ Docs: https://tensorflownet.readthedocs.io</Description>
 
   <ItemGroup>
     <PackageReference Include="Google.Protobuf" Version="3.6.1" />
-    <PackageReference Include="NumSharp" Version="0.6.3" />
+    <PackageReference Include="NumSharp" Version="0.6.4" />
   </ItemGroup>
 
   <ItemGroup>
     <Content CopyToOutputDirectory="PreserveNewest" Include="./runtimes/win-x64/native/tensorflow.dll" Link="tensorflow.dll" Pack="true" PackagePath="runtimes/win-x64/native/tensorflow.dll" />
   </ItemGroup>
 
-  <ItemGroup>
-    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
-  </ItemGroup>
-
 </Project>

src/TensorFlowNET.Core/Tensors/RefVariable.cs

@@ -10,17 +10,19 @@ namespace Tensorflow
         public Tensor _initial_value;
 
         public RefVariable(object initial_value, 
-            TF_DataType trainable, 
+            string name = "",
+            TF_DataType trainable = TF_DataType.DtInvalid,
             bool validate_shape = true) : 
-            base(initial_value, trainable, validate_shape)
+            base(initial_value, name, trainable, validate_shape)
         {
-            _init_from_args(initial_value, trainable);
+            _init_from_args(initial_value, name, trainable);
         }
 
         private void _init_from_args(object initial_value,
-            TF_DataType trainable)
+            string name = "",
+            TF_DataType trainable = TF_DataType.DtInvalid)
         {
-            var name = ops.name_scope("", "Variable", initial_value);
+            name = ops.name_scope("", "Variable", initial_value);
             _initial_value = ops.convert_to_tensor(initial_value, name: "initial_value");
         }
     }

src/TensorFlowNET.Core/Tensors/Variable.cs

@@ -16,7 +16,7 @@ namespace Tensorflow
     /// </summary>
     public class Variable
     {
-        public Variable(object initial_value, TF_DataType trainable, bool validate_shape = true)
+        public Variable(object initial_value, string name = "", TF_DataType trainable = TF_DataType.DtInvalid, bool validate_shape = true)
         {
 
         }

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -12,6 +12,14 @@ namespace Tensorflow
             {
                 case TF_DataType.TF_INT32:
                     return typeof(int);
+                case TF_DataType.TF_INT16:
+                    return typeof(short);
+                case TF_DataType.TF_FLOAT:
+                    return typeof(float);
+                case TF_DataType.TF_DOUBLE:
+                    return typeof(double);
+                case TF_DataType.TF_STRING:
+                    return typeof(string);
                 default:
                     throw new NotImplementedException("as_numpy_datatype failed");
             }

src/TensorFlowNET.Core/tf.cs

@@ -11,16 +11,18 @@ namespace Tensorflow
     public static partial class tf
     {
         public static TF_DataType int16 = TF_DataType.TF_INT16;
+        public static TF_DataType float16 = TF_DataType.TF_HALF;
         public static TF_DataType float32 = TF_DataType.TF_FLOAT;
+        public static TF_DataType float64 = TF_DataType.TF_DOUBLE;
         public static TF_DataType chars = TF_DataType.TF_STRING;
 
         public static Context context = new Context();
 
         public static Graph g = new Graph(c_api.TF_NewGraph());
 
-        public static object Variable<T>(T data, TF_DataType dtype = TF_DataType.DtInvalid)
+        public static RefVariable Variable<T>(T data, string name = "", TF_DataType dtype = TF_DataType.DtInvalid)
         {
-            return new RefVariable(data, dtype);
+            return new RefVariable(data, name, dtype);
         }
 
         public static unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null)

test/TensorFlowNET.Examples/BasicEagerApi.cs

@@ -0,0 +1,38 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// Basic introduction to TensorFlow's Eager API.
+    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py
+    /// </summary>
+    public class BasicEagerApi : IExample
+    {
+        private Tensor a, b, c, d;
+        public void Run()
+        {
+            // Set Eager API
+            Console.WriteLine("Setting Eager mode...");
+            tf.enable_eager_execution();
+
+            // Define constant tensors
+            Console.WriteLine("Define constant tensors");
+            a = tf.constant(2);
+            Console.WriteLine($"a = {a}");
+            b = tf.constant(3);
+            Console.WriteLine($"b = {b}");
+
+            // Run the operation without the need for tf.Session
+            Console.WriteLine("Running operations, without tf.Session");
+            c = a + b;
+            Console.WriteLine($"a + b = {c}");
+            d = a * b;
+            Console.WriteLine($"a * b = {d}");
+
+            // Full compatibility with Numpy
+        }
+    }
+}

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -19,7 +19,7 @@ namespace TensorFlowNET.Examples
             // Basic constant operations
             // The value returned by the constructor represents the output
             // of the Constant op.
-            /*var a = tf.constant(2);
+            var a = tf.constant(2);
             var b = tf.constant(3);
             
             // Launch the default graph.
@@ -50,7 +50,7 @@ namespace TensorFlowNET.Examples
                 // Run every operation with variable input
                 Console.WriteLine($"Addition with variables: {sess.run(add, feed_dict)}");
                 Console.WriteLine($"Multiplication with variables: {sess.run(mul, feed_dict)}");
-            }*/
+            }
 
             // ----------------
             // More in details:

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -0,0 +1,42 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// A linear regression learning algorithm example using TensorFlow library.
+    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py
+    /// </summary>
+    public class LinearRegression : IExample
+    {
+        private NumPyRandom rng = np.random;
+
+        public void Run()
+        {
+            // Parameters
+            double learning_rate = 0.01;
+            int training_epochs = 1000;
+            int display_step = 50;
+
+            // Training Data
+            var train_X = np.array(3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
+                         7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1);
+            var train_Y = np.array(1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
+                         2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3);
+            var n_samples = train_X.shape[0];
+
+            // tf Graph Input
+            var X = tf.placeholder(tf.float64);
+            var Y = tf.placeholder(tf.float64);
+
+            // Set model weights
+            var W = tf.Variable(rng.randn<double>(), name: "weight");
+            var b = tf.Variable(rng.randn<double>(), name: "bias");
+
+            var aa = tf.multiply(X, W);
+        }
+    }
+}

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -6,11 +6,10 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="NumSharp" Version="0.6.3" />
+    <PackageReference Include="NumSharp" Version="0.6.4" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -19,11 +19,10 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="15.9.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
     <PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
-    <PackageReference Include="NumSharp" Version="0.6.3" />
+    <PackageReference Include="NumSharp" Version="0.6.4" />
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -12,7 +12,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void StringVar()
         {
-            var mammal1 = tf.Variable("Elephant", tf.chars);
+            var mammal1 = tf.Variable("Elephant", "var1", tf.chars);
             var mammal2 = tf.Variable("Tiger");
         }