我是TensorFlow的新手，我正在尝试实现本文中的一些算法，这些算法需要偶尔从全局共享模型复制到本地线程特定模型。
完成上述任务的最佳/正确方法是什么？我在下面提供了一个虚拟示例，说明了我目前执行此操作的方式以及我遇到的错误。有人能解释为什么会出现错误吗？
import tensorflow as tf
import threading
class ExampleModel(object):
def __init__(self, graph):
with graph.as_default():
self.w = tf.Variable(tf.constant(1, shape=[1,2]))
sess = tf.InteractiveSession()
graph = tf.get_default_graph()
global_network = ExampleModel(graph)
sess.run(tf.initialize_all_variables())
def example(i):
global global_network, graph
local_network = ExampleModel(graph)
sess.run(local_network.w.assign(global_network.w))
threads = []
for i in range(5):
t = threading.Thread(target=example, args=(i,))
threads.append(t)
for t in threads:
t.start()
Exception in thread Thread-3:
Traceback (most recent call last):
File "/Users/kennyhsu5/anaconda/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/Users/kennyhsu5/anaconda/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "tmp.py", line 16, in example
local_network = ExampleModel(graph)
File "tmp.py", line 7, in __init__
self.w = tf.Variable(tf.constant(1, shape=[1,2]))
File "/Users/kennyhsu5/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 211, in __init__
dtype=dtype)
File "/Users/kennyhsu5/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 319, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "/Users/kennyhsu5/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2976, in __exit__
self._graph._pop_control_dependencies_controller(self)
File "/Users/kennyhsu5/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2996, in _pop_control_dependencies_controller
assert self._control_dependencies_stack[-1] is controller
AssertionError
关于Tensorflow中的tf. Graph类：
重要提示：此类对于图构造不是线程安全的。所有操作都应该从单个线程创建，或者必须提供外部同步。除非另有说明，否则所有方法都不是线程安全的。
self. w=…声明和local_network.w.赋值（…）操作导致错误。
我知道它基本上扼杀了您的多线程实现，但您可以通过将这些声明移动到主线程来修复上述代码。然后，您可以使用线程实际运行您规定的操作。例如：
import tensorflow as tf
import threading
class ExampleModel(object):
def __init__(self, graph):
with graph.as_default():
self.w = tf.Variable(tf.constant(1, shape=[1,2]))
sess = tf.InteractiveSession()
graph = tf.get_default_graph()
global_network = ExampleModel(graph)
sess.run(tf.global_variables_initializer())
def example(sess, assign_w):
sess.run(assign_w)
threads = []
for i in range(5):
local_network = ExampleModel(graph)
assign_w = local_network.w.assign(global_network.w)
t = threading.Thread(target=example, args=(sess, assign_w))
threads.append(t)
for t in threads:
t.start()
我还建议您通过args参数而不是使用global将变量传递给线程。
最后，考虑使用global_variables_initializer而不是弃用的initialize_all_variables。
