Fix some typo of readme.

Author: AsakusaRinne

README.md

@@ -9,7 +9,7 @@
 [![Badge](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu/#/en_US)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/javiercp/BinderTF.NET/master?urlpath=lab)
 
-English | [中文](docs/Readme-CN.md)
+English | [中文](docs/README-CN.md)
 
 *master branch is corresponding to tensorflow v2.10, v0.6x branch is from tensorflow v2.6, v0.15-tensorflow1.15 is from tensorflow1.15.*
 
@@ -20,13 +20,13 @@ English | [中文](docs/Readme-CN.md)
 
 `SciSharp STACK`'s mission is to bring popular data science technology into the .NET world and to provide .NET developers with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET.
 
-![pythn vs csharp](docs/assets/syntax-comparision.png)
+![python vs csharp](docs/assets/syntax-comparision.png)
 
 SciSharp's philosophy allows a large number of machine learning code written in Python to be quickly migrated to .NET, enabling .NET developers to use cutting edge machine learning models and access a vast number of TensorFlow resources which would not be possible without this project.
 
 In comparison to other projects, like for instance [TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/) which only provide TensorFlow's low-level C++ API and can only run models that were built using Python, Tensorflow.NET makes it possible to build the pipeline of training and inference with pure C# and F#. Besides, Tensorflow.NET provides binding of Tensorflow.Keras to make it easy to transfer your code from python to .NET.
 
-[ML.NET](https://github.com/dotnet/machinelearning) also support using tensorflow as backend to train and infer your model, which provides better integration with .NET.
+[ML.NET](https://github.com/dotnet/machinelearning) also take Tensorflow.NET as one of the backends to train and infer your model, which provides better integration with .NET.
 
 ## Documention
 
@@ -42,7 +42,7 @@ Troubleshooting of running example or installation：[Tensorflow.NET FAQ](tensor
 
 ### Installation
 
-You can search the package name in NuGet Manager, or use the commands below in pckage manager console.
+You can search the package name in NuGet Manager, or use the commands below in package manager console.
 
 The installation contains two parts, the first is the main body:
 
@@ -57,13 +57,13 @@ PM> Install-Package TensorFlow.Keras
 The second part is the computing support part. Only one of the following packages is needed, depending on your device and system.
 
 ```
-### Cpu version for Windows, Linux and Mac
+### CPU version for Windows, Linux and Mac
 PM> Install-Package SciSharp.TensorFlow.Redist
 
-### Gpu version for Windows (CUDA and CUDNN are required)
+### GPU version for Windows (CUDA and cuDNN are required)
 PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 
-### Gpu version for Linux (CUDA and CUDNN are required)
+### GPU version for Linux (CUDA and cuDNN are required)
 PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU
 ```
 

docs/README-CN.md

@@ -24,7 +24,7 @@ Tensorflow.NET并非对于Python的简单封装，而是基于C API的pure C#实
 
 `SciSharp STACK`开源社区的目标是构建.NET平台下易用的科学计算库，而Tensorflow.NET就是其中最具代表性的仓库之一。在深度学习领域Python是主流，无论是初学者还是资深开发者，模型的搭建和训练都常常使用Python写就的AI框架，比如tensorflow。但在实际应用深度学习模型的时候，又可能希望用到.NET生态，亦或只是因为.NET是自己最熟悉的领域，这时候Tensorflow.NET就有显著的优点，因为它不仅可以和.NET生态很好地贴合，其API还使得开发者很容易将Python代码迁移过来。下面的对比就是很好的例子，Python代码和C#代码有着高度相似的API，这会使得迁移的时候无需做过多修改。
 
-![pythn vs csharp](assets/syntax-comparision.png)
+![python vs csharp](assets/syntax-comparision.png)
 
 除了高度相似的API外，Tensorflow.NET与tensorflow也已经打通数据通道，tensorflow训练并保存的模型可以在Tensorflow.NET中直接读取并继续训练或推理，反之Tensorflow.NET保存的模型也可以在tensorflow中读取，这大大方便了模型的训练和部署。
 
@@ -50,20 +50,21 @@ Tensorflow.NET并非对于Python的简单封装，而是基于C API的pure C#实
 ```sh
 ### 安装Tensorflow.NET
 PM> Install-Package TensorFlow.NET
+
 ### 安装Tensorflow.Keras
 PM> Install-Package TensorFlow.Keras
 ```
 
 第二部分是计算支持部分，只需要根据自己的设备和系统选择下面之一即可：
 
 ```
-### CPU版本
+### CPU版本，支持Windows、Linux和Mac
 PM> Install-Package SciSharp.TensorFlow.Redist
 
-### Windows下的GPU版本（需要安装CUDA和CUDNN）
+### Windows下的GPU版本（需要安装CUDA和cuDNN）
 PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 
-### Linux下的GPU版本（需要安装CUDA和CUDNN）
+### Linux下的GPU版本（需要安装CUDA和cuDNN）
 PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU
 ```
 
@@ -173,14 +174,24 @@ model.save("./toy_resnet_model");
 
 ## Tensorflow.NET版本对应关系
 
-| TensorFlow.NET Versions                 | tensorflow 1.14, cuda 10.0 | tensorflow 1.15, cuda 10.0 | tensorflow 2.3, cuda 10.1 | tensorflow 2.4, cuda 11 | tensorflow 2.10, cuda 11 |
-| -------------------------- | ------------- | -------------- | ------------- | ------------- | ------------ |
-| tf.net 0.7+, tf.keras 0.7+ |  |  |  |  | x |
-| tf.net 0.4x, tf.keras 0.5 |  |  |  | x |  |
-| tf.net 0.3x, tf.keras 0.4 |               |                | x |  |  |
-| tf.net 0.2x          |               | x | x |              |  |
-| tf.net 0.15          | x | x |               |               |  |
-| tf.net 0.14          | x |                |               |               |  |
+| TensorFlow.NET Versions                 | tensorflow 1.14, cuda 10.0 | tensorflow 1.15, cuda 10.0 | tensorflow 2.3, cuda 10.1 | tensorflow 2.4, cuda 11 | tensorflow 2.7, cuda 11 |tensorflow 2.10, cuda 11 |
+| -------------------------- | ------------- | -------------- | ------------- | ------------- | ------------ | ------------ |
+| tf.net 0.10x, tf.keras 0.10 |  |  |  |  |  | x |
+| tf.net 0.7x, tf.keras 0.7   |  |  |  |  | x |  |
+| tf.net 0.4x, tf.keras 0.5   |  |  |  | x |  |  |
+| tf.net 0.3x, tf.keras 0.4   |  |  | x |  |  |  |
+| tf.net 0.2x                 |  | x | x |  |  |  |
+| tf.net 0.15                 | x | x |  |  |  |  |
+| tf.net 0.14                 | x |  |  |  |  |  |
+
+
+```
+tf.net 0.4x -> tf native 2.4 
+tf.net 0.6x -> tf native 2.6      
+tf.net 0.7x -> tf native 2.7
+tf.net 0.10x -> tf native 2.10
+...
+```
 
 如果使用过程中发现有缺失的版本，请告知我们，谢谢！