Tensorflow Session&Graph学习笔记

tensorflow的核心部分是Graph。Graph可以看成由两部分组成的：Node和Edge。 Node是操作，其中包括生成数据的操作以及数据运算的操作，edge可以看成流动的数据

Graph中tensor的命名有三种方式：1. default是通过Operation命名，如<OP_NAME>:<i>，其中<OP_NAME>是操作的名字，<i>是操作的索引

2. 通过name来命名，如tf.constant(42.0, name="answer")

3. 通过命名域来命名，如with tf.name_scope("answer"): ,会添加answer/到变量名前

Programmers‘ Guide上给的例子：

c_0 = tf.constant(0, name="c")  # => operation named "c"

# Already-used names will be "uniquified".
c_1 = tf.constant(2, name="c")  # => operation named "c_1"

# Name scopes add a prefix to all operations created in the same context.
with tf.name_scope("outer"):
  c_2 = tf.constant(2, name="c")  # => operation named "outer/c"

  # Name scopes nest like paths in a hierarchical file system.
  with tf.name_scope("inner"):
    c_3 = tf.constant(3, name="c")  # => operation named "outer/inner/c"

  # Exiting a name scope context will return to the previous prefix.
  c_4 = tf.constant(4, name="c")  # => operation named "outer/c_1"

  # Already-used name scopes will be "uniquified".
  with tf.name_scope("inner"):
    c_5 = tf.constant(5, name="c")  # => operation named "outer/inner_1/c"print(vars())

使用vars() 可以查看所有变量的名称

Session 是连接客户端和C++实际执行后台的工具

sess.run()可以执行Graph中的运算，一般有两步。1、变量的初始化，2、相关变量的操作

x = tf.constant([[37.0, -23.0], [1.0, 4.0]])
w = tf.Variable(tf.random_uniform([2, 2]))
y = tf.matmul(x, w)
output = tf.nn.softmax(y)
init_op = w.initializer

with tf.Session() as sess:
  # Run the initializer on `w`.
  sess.run(init_op)

  # Evaluate `output`. `sess.run(output)` will return a NumPy array containing
  # the result of the computation.
  print(sess.run(output))

执行结果：