update docs of constant.

docs/source/Constant.md

@@ -44,7 +44,7 @@ Let's continue using the last examples, we're going to initialize a tensor in an
 
 ##### NDArray
 
-The first thing we need to know is about `ndarray`'s memory model. The ndarray memory model is a very important data structure, and almost all underlying computation are inseparable from this datb a structure. One fundamental aspect of the ndarray is that an array is seen as a "chunk" of memory starting at some location. The interpretation of this memory depends on the stride information.
+The first thing we need to know is about `ndarray`'s memory model. The ndarray memory model is a very important data structure, and almost all underlying computation are inseparable from this datb a structure. One fundamental aspect of the ndarray is that an array is seen as a "chunk" of memory starting at some location. The interpretation of this memory depends on the stride information. A segment of memory is inherently 1-dimensional, and there are many different schemes for arranging the items of an N-dimensional array in a 1-dimensional block. `ndarray` objects can accommodate any strided indexing scheme. In a strided scheme, the N-dimensional index <img src="_static\constant\n-index-formula.svg"/> corresponds to the offset (in bytes) : <img src="_static\constant\n-index-formula-offset.svg" />.
 
 <img src="_static\contiguous-block-of-memory.png"  />
 
@@ -60,11 +60,21 @@ Through the above diagram, we know how the data is stored in memory, and then we
 
 If you don't understand very well what `Tensor` is, you can go back to the chapter `Tensor` there is pretty much explanation if you skipped that chapter. Tensor is actually an NDArray that is with more than 2 dimensions.
 
-TensorFlow will decide whether to copy the data or use the same pointer. Normally speaking, it's more safe whenever you copy data for the following process, especially in interoperating between .NET runtime and C++ runtime that they all have their own garbage collection (GC) mechanism, application will crash if someone access a block of destroyed memory.
+TensorFlow will decide whether to copy the data or use the same pointer. Normally speaking, it's more safe whenever you copy data for the following process, especially in interoperating between .NET runtime and C++ runtime that they all have their own garbage collection (GC) mechanism, application will crash if someone access a block of destroyed memory. `TF_STRING` and `TF_RESOURCE` tensors have a different representation in `TF_Tensor` than they do in `tensorflow::Tensor`. Other types have the same representation, so copy only if it is safe to do so.
 
 <img src="_static\tensor-constant-ndarray.png" />
 
+Before tensorflow is creating the `TF_Tensor`, it checks the shape and data size. If the size doesn't match, it will return `nullptr` pointer.
 
+##### Get the data of Tensor
+
+For `eager` mode, it's pretty simple to view the actual value in a `tensor`.
+
+```csharp
+var data = tensor.numpy()
+```
+
+The `data` will be a `ndarray` variable.
 
 ##### Other functions to create a Constant
 

docs/source/EagerMode.md

@@ -1,2 +1,3 @@
-# Chapter. Eager Mode
+# Chapter 4. Eager Mode
 
+TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs: operations return concrete values instead of constructing a computational graph to run later. This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well.

docs/source/Graph.md

@@ -1,4 +1,4 @@
-# Chapter. Graph
+# Chapter 3. Graph
 
 TensorFlow uses a **dataflow graph** to represent your computation in terms of the dependencies between individual operations. A graph defines the computation. It doesn't compute anything, it doesn't hold any values, it just defines the operations that you specified in your code.
 

docs/source/Tensor.md

@@ -6,13 +6,13 @@
 
 ##### What is Tensor?
 
-Tensor holds a multi-dimensional array of elements of a single data type which is very similar with numpy's ndarray. When the dimension is zero, it can be called a scalar. When the dimension is 2, it can be called a matrix. When the dimension is greater than 2, it is usually called a tensor. If you are very familiar with numpy, then understanding Tensor will be quite easy.
+Tensor holds a multi-dimensional array of elements of a single data type which is very similar with `NumPy`'s `ndarray`. When the dimension is zero, it can be called a scalar. When the dimension is 2, it can be called a matrix. When the dimension is greater than 2, it is usually called a tensor. If you are very familiar with `NumPy`, then understanding Tensor will be quite easy.
 
 <img src="_static\tensor-naming.png">
 
 ##### How to create a Tensor?
 
-There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor. But the best way to create a Tensor is using high level APIs like `tf.constant`, `tf.zeros` and `tf.ones`. We'll talk about constant more in next chapter.
+There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor. But the best way to create a Tensor is using high level APIs like `tf.constant`, `tf.zeros` and `tf.ones`. We'll talk about constant more detail in next chapter.
 
 ```csharp
 // Create a tensor holds a scalar value
@@ -45,3 +45,8 @@ var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
 ![column-major order](_static/column-major-order.png)
 
 ![row-major order](_static/row-major-order.png)
+
+##### Index/ Slice of Tensor
+
+Tensor element can be accessed by `index` and `slice` related operations. Through some high level APIs, we can easily access specific dimension's data.
+

docs/source/_static/constant/n-index-formula-offset.svg

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