TensorFlow源代码学习--1 Session API reference

学习TensorFlow源代码，先把API文档扒出来研究一下整体结构： 一下是文档内容的整理，简单翻译一下

原文地址：http://www.tcvpr.com/archives/181

TensorFlow C++ Session API reference documentation

TensorFlow’s public C++ API includes only the API for executing graphs, as of version 0.5. To control the execution of a graph from C++: TensorFlow的C++ API只包含执行图（graphs）的操作，像V0.5一样，控制执行图如下：

1. Build the computation graph using the [Python API].

• 使用Python API建立一个图

1. Use [tf.train.write_graph()] to write the graph to a file.

• 使用[tf.train.write_graph()]把图写入文件

Env

[tensorflow::Env]

• An interface used by the tensorflow implementation to access operating system functionality like the filesystem etc.Callers may wish to provide a custom Env object to get fine grain control.All Env implementations are safe for concurrent access from multiple threads without any external synchronization.
• TensorFlow使用此接口接入操作系统功能，例如文件系统等。调用者可以提供自定义的ENV对象来得到细粒度的控制（fine grain control），ENV所有的执行对于并发多线程完全支持，不需要外部同步！

[tensorflow::RandomAccessFile]

• A file abstraction for randomly reading the contents of a file.
• 一个文件的抽象，为了从文件中随机读取内容

[tensorflow::WritableFile]

• A file abstraction for sequential writing.The implementation must provide buffering since callers may append small fragments at a time to the file.
• 一个文件的抽象，用于按照序列存储文件，此执行必须提供Buffer，因为调用者对于一个文件一次可能只增加小的片段。

[tensorflow::EnvWrapper]

• An implementation of Env that forwards all calls to another Env .May be useful to clients who wish to override just part of the functionality of another Env .
• ENV的一个执行，将ENV的所有调用链接到其他ENV。对于想要重载（override）其他ENV部分功能的用户，此类可能有用。

Session

[tensorflow::Session]

• A Session instance lets a caller drive a TensorFlow graph computation.When a Session is created with a given target, a new Session object is bound to the universe of resources specified by that target. Those resources are available to this session to perform computation described in the GraphDef. After extending the session with a graph, the caller uses the Run() API to perform the computation and potentially fetch outputs as Tensors.

• 一个Session的实例，可以调用者启动一个TensorFlow图（graph）的计算功能。当一个有目标的Session建立时，一个新的Session对象一定是所有目标（target）指定的资源的总体。那些资源对于此Session是可用的，用来执行在图中定义（GraphDef）的计算。

• Example:

• 例子

  //c++ tensorflow::GraphDef graph;

  // … Create or load graph into “graph”. // This example uses the default options which connects // to a local runtime.

  tensorflow::SessionOptions options; std::unique_ptrtensorflow::Session session(tensorflow::NewSession(options));

  // Create the session with this graph.

  tensorflow::Status s = session->Create(graph); if (!s.ok()) { … } // Run the graph and fetch the first output of the “output” // operation, and also run to but do not return anything // for the “update_state” operation.

  std::vectortensorflow::Tensor outputs; s = session->Run({}, {“output:0”}, {“update_state”}, &outputs); if (!s.ok()) { … } // Map the output as a flattened float tensor, and do something // with it.

  auto output_tensor = outputs[0].flat(); if (output_tensor(0) > 0.5) { … } // Close the session to release the resources associated with // this session.

  session->Close();

• A Session allows concurrent calls to Run() , though a Session must be created / extended by a single thread.Only one thread must call Close() , and Close() must only be called after all other calls to Run() have returned.

• Session允许多个线程执行Run，但是Session必须由一个线程创建/扩展。只有一个线程能调用Close，并且必须在其他线程中的所有Run调用完成后。

[tensorflow::SessionOptions]

• Configuration information for a Session
• Session的配置信息

Status

[tensorflow::Status]

• No description
• 状态信息，文档无描述

[tensorflow::Status::State]

• No description
• 状态信息，文档无描述

Tensor

[tensorflow::Tensor]

• Represents an n-dimensional array of values.
• 表示一个N维值的数组

[tensorflow::TensorShape]

• No description
• Tensor形状，文档无描述

[tensorflow::TensorShapeDim]

• No description
• Tensor形状维数，文档无描述

[tensorflow::TensorShapeUtils]

• Static helper routines for TensorShape. Includes a few common predicates on a tensor shape.
• 对于TensorShape静态辅助例子，包括一些对于tensor形状的常见限定

[tensorflow::PartialTensorShape]

• Manages the partially known dimensions of a Tensor and their sizes.
• 管理一个Tensor部分已知的规模以及大小

[tensorflow::PartialTensorShapeUtils]

• Static helper routines for PartialTensorShape. Includes a few common predicates on a partially known tensor shape.
• 对于PartialTensorShape静态辅助例子，包括一些对于部分tensor形状的常见限定

[TF_Buffer]

• No description
• TensorFlow的Buffer，文档无描述

Thread

[tensorflow::Thread]

• No Description
• 线程操作，文档无描述

[tensorflow::ThreadOptions]

• Options to configure a Thread .Note that the options are all hints, and the underlying implementation may choose to ignore it.
• 线程配置，注意：这些描述都是隐藏的，底层实现可以忽略