TF_NewTensor pass.

README.md

@@ -2,6 +2,7 @@
 TensorFlow.NET provides .NET Standard binding for [TensorFlow](https://www.tensorflow.org/).
 
 [![Join the chat at https://gitter.im/publiclab/publiclab](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/Tensorflow-NET)
+![Tensorflow.NET](https://ci.appveyor.com/api/projects/status/tensorflow-net-p7kmsjyo10ey?svg=true)
 
 TensorFlow.NET is a member project of SciSharp stack.
 
@@ -10,11 +11,12 @@ TensorFlow.NET is a member project of SciSharp stack.
 ### How to use
 Download the pre-compiled dll [here](tensorflowlib) and place it in the bin folder.
 
+Import tensorflow.net.
 ```cs
-// import tensorflow.net
-using using Tensorflow;
+using Tensorflow;
 ```
 
+Add two constants.
 ```cs
 // Create a Constant op
 var a = tf.constant(4.0f);
@@ -27,6 +29,7 @@ using (var sess = tf.Session())
 }
 ```
 
+Feed placeholder.
 ```cs
 // Create a placeholder op
 var a = tf.placeholder(tf.float32);

The Definitive Guide to Tensorflow.NET/CH_1 Tensor.md

@@ -4,3 +4,28 @@
 
 ### 表示一个操作的输出
 
+
+
+##### What is Tensor?
+
+##### Tensor 是什么?
+
+
+
+
+
+TF uses column major order.
+
+TF 采用的是按列存储模式，如果我们用NumSharp产生一个2 X 3的矩阵，如果按顺序从0到5访问数据的话，是不会得到1-6的数字的，而是得到1，4， 2， 5， 3， 6这个顺序的一组数字。
+
+```cs
+// generate a matrix:[[1, 2, 3], [4, 5, 6]]
+var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
+// the index will be   0  2  4    1  3  5, it's column-major order.
+```
+
+
+
+![1545631998951](assets/column-major order.png)
+
+![1545631998951](assets/row-major order.png)

The Definitive Guide to Tensorflow.NET/INTRO.md

@@ -22,4 +22,12 @@ TensorFlow is an open source project for machine learning especially for deep le
 
 
 
-为了避免混淆，本书中对TensorFlow中定义的特有类不进行翻译，比如Tensor, Graph, Shape这些词都会保留英文名称。
+为了避免混淆，本书中对TensorFlow中定义的特有类不进行翻译，比如Tensor, Graph, Shape这些词都会保留英文名称。
+
+
+
+术语简称：
+
+TF: Google TensorFlow
+
+TF.NET: Tensorflow.NET

src/TensorFlowNET.Core/Tensor/Tensor.cs

@@ -7,50 +7,60 @@ namespace Tensorflow
 {
     public class Tensor
     {
-        private readonly Operation _op;
-        public Operation op => _op;
-        private readonly int _value_index;
-        public int value_index => _value_index;
-        private TF_DataType _dtype;
-        public TF_DataType dtype => _dtype;
+        public Operation op { get; }
+        public int value_index { get; }
+        public TF_DataType dtype { get; }
 
-        public Graph graph => _op.graph;
+        public Graph graph => op.graph;
 
         public string name;
-
-        private readonly IntPtr _handle;
-        public IntPtr handle => _handle;
-
-        private readonly int _ndim;
-        public int ndim => _ndim;
+        public IntPtr handle { get; }
+        public int ndim { get; }
+        public ulong bytesize { get; }
+        public ulong dataTypeSize { get;}
+        public ulong size => bytesize / dataTypeSize;
+        public IntPtr buffer { get; }
 
         public Tensor(IntPtr handle)
         {
-            _handle = handle;
-            _dtype = c_api.TF_TensorType(_handle);
-            _ndim = c_api.TF_NumDims(_handle);
+            this.handle = handle;
+            dtype = c_api.TF_TensorType(handle);
+            ndim = c_api.TF_NumDims(handle);
+            bytesize = c_api.TF_TensorByteSize(handle);
+            buffer = c_api.TF_TensorData(handle);
+            dataTypeSize = c_api.TF_DataTypeSize(dtype);
         }
 
         public Tensor(Operation op, int value_index, TF_DataType dtype)
         {
-            _op = op;
-            _value_index = value_index;
-            _dtype = dtype;
+            this.op = op;
+            this.value_index = value_index;
+            this.dtype = dtype;
         }
 
         public TF_Output _as_tf_output()
         {
-            return c_api_util.tf_output(_op._c_op, _value_index);
+            return c_api_util.tf_output(op._c_op, value_index);
+        }
+
+        public T[] Data<T>()
+        {
+            var data = new T[size];
+
+            for (ulong i = 0; i < size; i++)
+            {
+                data[i] = Marshal.PtrToStructure<T>(buffer + (int)(i * dataTypeSize));
+            }
+
+            return data;
         }
 
-        public T Data<T>()
+        public byte[] Data()
         {
-            /*var buffer = new byte[6 * sizeof(float)];
-            var h1 = c_api.TF_TensorData(handle);
-            var bytes = Marshal.PtrToStructure<float>(h1);
-            Marshal.Copy(h1, buffer, 0, 24);*/
+            var data = new byte[bytesize];
+            Marshal.Copy(buffer, data, 0, (int)bytesize);
 
-            return default(T);
+            return data;
         }
     }
 }

src/TensorFlowNET.Core/c_api.cs

@@ -17,6 +17,14 @@ namespace Tensorflow
         [DllImport(TensorFlowLibName)]
         public static unsafe extern void TF_AddInput(TF_OperationDescription desc, TF_Output input);
 
+        /// <summary>
+        /// returns the sizeof() for the underlying type corresponding to the given TF_DataType enum value.
+        /// </summary>
+        /// <param name="dt"></param>
+        /// <returns></returns>
+        [DllImport(TensorFlowLibName)]
+        public static unsafe extern ulong TF_DataTypeSize(TF_DataType dt);
+
         [DllImport(TensorFlowLibName)]
         public static unsafe extern void TF_DeleteSessionOptions(IntPtr opts);
 
@@ -104,6 +112,14 @@ namespace Tensorflow
         [DllImport(TensorFlowLibName)]
         public static extern unsafe void TF_SetAttrType(TF_OperationDescription desc, string attr_name, TF_DataType value);
 
+        /// <summary>
+        /// Return the size of the underlying data in bytes.
+        /// </summary>
+        /// <param name="tensor"></param>
+        /// <returns></returns>
+        [DllImport(TensorFlowLibName)]
+        public static extern unsafe ulong TF_TensorByteSize(IntPtr tensor);
+
         [DllImport(TensorFlowLibName)]
         public static extern unsafe IntPtr TF_TensorData(IntPtr tensor);
 

test/TensorFlowNET.UnitTest/TensorTest.cs

@@ -36,6 +36,15 @@ namespace TensorFlowNET.UnitTest
             Assert.AreEqual(tensor.ndim, nd.ndim);
             Assert.AreEqual(nd.shape[0], c_api.TF_Dim(handle, 0));
             Assert.AreEqual(nd.shape[1], c_api.TF_Dim(handle, 1));
+            Assert.AreEqual(tensor.bytesize, (uint)nd.size * sizeof(float));
+
+            // Column major order
+            // https://en.wikipedia.org/wiki/File:Row_and_column_major_order.svg
+            // matrix:[[1, 2, 3], [4, 5, 6]]
+            // index:   0  2  4    1  3  5
+            // result:  1  4  2    5  3  6
+            var array = tensor.Data<float>();
+            Assert.IsTrue(Enumerable.SequenceEqual(nd.Data<float>(), array));
         }
     }
 }