diff --git a/README.md b/README.md index 66b00d62f..84dd7bb6e 100644 --- a/README.md +++ b/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 ``` diff --git a/docs/README-CN.md b/docs/README-CN.md index 06be13d2a..9776b0fb8 100644 --- a/docs/README-CN.md +++ b/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。 @@ -50,6 +50,7 @@ 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 ``` @@ -57,13 +58,13 @@ 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！