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						<p>当我们训练自己的神经网络的时候，无一例外的就是都会加上一句 <code>sess.run(tf.global_variables_initializer())</code> ，这行代码的官方解释是 初始化模型的参数。那么，它到底做了些什么？</p>

一步步看源代码：（代码在后面）

• global_variables_initializer 返回一个用来初始化 计算图中 所有global variable的 op。
  
  • 这个op 到底是啥，还不清楚。
  • 函数中调用了 variable_initializer() 和 global_variables()
• global_variables() 返回一个 Variable list ，里面保存的是 gloabal variables。
• variable_initializer() 将 Variable list 中的所有 Variable 取出来，将其 variable.initializer 属性做成一个 op group。
• 然后看 Variable 类的源码可以发现， variable.initializer 就是一个 assign op。

所以： sess.run(tf.global_variables_initializer()) 就是 run了 所有global Variable 的 assign op，这就是初始化参数的本来面目。

def global_variables_initializer():
  """Returns an Op that initializes global variables.
  Returns:
    An Op that initializes global variables in the graph.
  """
  return variables_initializer(global_variables())

def global_variables():
  """Returns global variables.
  Returns:
    A list of `Variable` objects.
  """
  return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)

def variables_initializer(var_list, name="init"):
  """Returns an Op that initializes a list of variables.
  Args:
    var_list: List of `Variable` objects to initialize.
    name: Optional name for the returned operation.

  Returns:
    An Op that run the initializers of all the specified variables.
  """
  if var_list:
    return control_flow_ops.group(*[v.initializer for v in var_list], name=name)
  return control_flow_ops.no_op(name=name)
 
 
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class Variable(object):
    def _init_from_args(self, ...):
        self._initializer_op = state_ops.assign(
            self._variable, self._initial_value,
            validate_shape=validate_shape).op
    @property
    def initializer(self):
        """The initializer operation for this variable."""
        return self._initializer_op
 
 
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