Finished BasicOperations example.

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -114,22 +114,36 @@ namespace Tensorflow
             for (int i = 0; i < fetch_list.Length; i++)
             {
                 var tensor = new Tensor(output_values[i]);
-                
+                Type type = tensor.dtype.as_numpy_datatype();
+                var ndims = tensor.shape.Select(x => (int)x).ToArray();
+
                 switch (tensor.dtype)
                 {
                     case TF_DataType.TF_STRING:
-                        // wired, don't know why we have to start from offset 9.
-                        var bytes = tensor.Data();
-                        result[i] = UTF8Encoding.Default.GetString(bytes, 9, bytes.Length - 9);
+                        {
+                            // 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);
+                        }
                         break;
                     case TF_DataType.TF_FLOAT:
-                        result[i] = *(float*)c_api.TF_TensorData(output_values[i]);
+                        {
+                            var output = *(float*)c_api.TF_TensorData(output_values[i]);
+                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                        }
                         break;
                     case TF_DataType.TF_INT16:
-                        result[i] = *(short*)c_api.TF_TensorData(output_values[i]);
+                        {
+                            var output = *(short*)c_api.TF_TensorData(output_values[i]);
+                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                        }
                         break;
                     case TF_DataType.TF_INT32:
-                        result[i] = *(int*)c_api.TF_TensorData(output_values[i]);
+                        {
+                            var output = *(int*)c_api.TF_TensorData(output_values[i]);
+                            result[i] = tensor.NDims == 0 ? output : np.array(output).reshape(ndims);
+                        }
                         break;
                     default:
                         throw new NotImplementedException("can't get output");

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -22,6 +22,8 @@ namespace Tensorflow
         public object value;
         public int value_index { get; }
 
+        private Status status = new Status();
+
         private TF_DataType _dtype = TF_DataType.DtInvalid;
         public TF_DataType dtype => _handle == IntPtr.Zero ? _dtype : c_api.TF_TensorType(_handle);
         public ulong bytesize => _handle == IntPtr.Zero ? 0 : c_api.TF_TensorByteSize(_handle);
@@ -33,8 +35,17 @@ namespace Tensorflow
             get
             {
                 var dims = new long[rank];
-                for (int i = 0; i < rank; i++)
-                    dims[i] = c_api.TF_Dim(_handle, i);
+
+                if (_handle == IntPtr.Zero)
+                {
+                    c_api.TF_GraphGetTensorShape(op.Graph, _as_tf_output(), dims, rank, status);
+                    status.Check();
+                }
+                else
+                {
+                    for (int i = 0; i < rank; i++)
+                        dims[i] = c_api.TF_Dim(_handle, i);
+                }
 
                 return dims;
             }
@@ -48,7 +59,22 @@ namespace Tensorflow
         /// 3	3-Tensor (cube of numbers)
         /// n	n-Tensor (you get the idea)
         /// </summary>
-        public int rank => _handle == IntPtr.Zero ? 0 : c_api.TF_NumDims(_handle);
+        public int rank
+        {
+            get
+            {
+                if (_handle == IntPtr.Zero)
+                {
+                    var output = _as_tf_output();
+                    return c_api.TF_GraphGetTensorNumDims(op.Graph, output, status);
+                }
+                else
+                {
+                    return c_api.TF_NumDims(_handle);
+                }
+            }
+        }
+
         public int NDims => rank;
 
         /// <summary>
@@ -182,6 +208,7 @@ namespace Tensorflow
         public void Dispose()
         {
             c_api.TF_DeleteTensor(_handle);
+            status.Dispose();
         }
 
         public static implicit operator IntPtr(Tensor tensor)

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -6,6 +6,16 @@ namespace Tensorflow
 {
     public static class dtypes
     {
+        public static Type as_numpy_datatype(this TF_DataType type)
+        {
+            switch (type)
+            {
+                case TF_DataType.TF_INT32:
+                    return typeof(int);
+                default:
+                    throw new NotImplementedException("as_numpy_datatype failed");
+            }
+        }
         public static TF_DataType as_dtype(Type type)
         {
             TF_DataType dtype = TF_DataType.DtInvalid;

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:
@@ -61,7 +61,39 @@ namespace TensorFlowNET.Examples
             //
             // The value returned by the constructor represents the output
             // of the Constant op.
+            var nd1 = np.array(3, 3).reshape(1, 2);
+            var matrix1 = tf.constant(nd1);
+
+            // Create another Constant that produces a 2x1 matrix.
+            var nd2 = np.array(2, 2).reshape(2, 1);
+            var matrix2 = tf.constant(nd2);
+
+            // Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
+            // The returned value, 'product', represents the result of the matrix
+            // multiplication.
+            var product = tf.matmul(matrix1, matrix2);
 
+            // To run the matmul op we call the session 'run()' method, passing 'product'
+            // which represents the output of the matmul op.  This indicates to the call
+            // that we want to get the output of the matmul op back.
+            //
+            // All inputs needed by the op are run automatically by the session.  They
+            // typically are run in parallel.
+            //
+            // The call 'run(product)' thus causes the execution of threes ops in the
+            // graph: the two constants and matmul.
+            //
+            // The output of the op is returned in 'result' as a numpy `ndarray` object.
+            using (sess = tf.Session())
+            {
+                var result = sess.run(product);
+                Console.WriteLine(result);
+                if((result as NDArray).Data<int>()[0] != 12)
+                {
+                    throw new Exception("BasicOperations error");
+                }
+                // ==> [[ 12.]]
+            }
         }
     }
 }