Tensorflow中Graph的概念

一、英文文档深入解读

class tf.Graph

A TensorFlow computation, represented as a dataflow graph.

Graph实际上是一个由数据流图表示的计算过程

A Graph contains a set of Operation objects, which represent units of computation; and Tensorobjects, which represent the units of data that flow between operations.

Graph表示了数据流图中的操作流水，而Tensor表示的是其中流动的数据

底下这几段讲述了系统默认的Graph获取方式，以及他的“线程不安全”特性。

A default Graph is always registered, and accessible by calling tf.get_default_graph(). To add an operation to the default graph, simply call one of the functions that defines a new Operation:

c = tf.constant(4.0)assert c.graph is tf.get_default_graph()

Another typical usage involves the Graph.as_default() context manager, which overrides the current default graph for the lifetime of the context:

g = tf.Graph()with g.as_default():  # Define operations and tensors in `g`.  c = tf.constant(30.0)  assert c.graph is g

Important note: This class is not thread-safe for graph construction. All operations should be created from a single thread, or external synchronization must be provided. Unless otherwise specified, all methods are not thread-safe.

二、关于Tensorflow的图计算过程

我们通过下面的代码来看一下Tensorflow的图计算过程：

import tensorflow as tfa = tf.constant(1)b = tf.constant(2)c = tf.constant(3)d = tf.constant(4)add1 = tf.add(a, b)mul1 = tf.mul(b, c)add2 = tf.add(c, d)output = tf.add(add1, mul1)with tf.Session() as sess:    print sess.run(output)# result: 9

上面的代码构成的Graph如下图所示，

 

当Session加载Graph的时候，Graph里面的计算节点都不会被触发执行。当运行sess.run(output)的时候，会沿着指定的Tensor output来进图路径往回触发相对应的节点进行计算（图中红色线表示的那部分）。当我们需要output的值时，触发Operation tf.add(add1, mul1)被执行，而该节点则需要Tensor add1和Tensor mul1的值，则往回触发Operation tf.add(a, b)和Operation tf.mul(b, c)。以此类推。

所以在计算Graph时，并不一定是Graph中的所有节点都被计算了，而是指定的计算节点或者该节点的输出结果被需要时。