我有两个模型m1和m2分别训练.现在我想保持m1固定并根据m2的输出微调m1. m1的所有变量都在变量范围“m1 /”下,m2的变量在“m2 /”下.这基本上就是我做的:
# build m1 and m2
with tf.device("/cpu:0"):
m1.build_graph()
m2.build_graph()
# indicate the variables of m1 and m2
allvars = tf.global_variables()
m1_vars = [v for v in allvars if v.name.startswith('m1')]
m2_vars = [v for v in allvars if v.name.startswith('m2')]
# construct the saver
m1_saver = tf.train.Saver(m1_vars)
m2_saver = tf.train.Saver(m2_vars)
# Load m2 variables
m2_ckpt_state = tf.train.get_checkpoint_state(FLAGS.m2_log_root)
m2_sess = tf.Session()
m2_saver.restore(m2_sess, m2_ckpt_state.model_checkpoint_path)
# construct a train supervisor for m1
m1_sv = tf.train.Supervisor(is_chief=True, saver=m1_saver)
# construct a session for m1
m1_sess = m1_sv.prepare_or_wait_for_session()
...
但是现在最后一行代码中出现了错误:
Traceback (most recent call last):
File "run_summarization.py", line 407, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "run_summarization.py", line 401, in main run_fine_tune(model, ranker, batcher, vocab)
File "run_summarization.py", line 232, in run_fine_tune sess_context_manager = sv.prepare_or_wait_for_session(config=config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 719, in prepare_or_wait_for_session
init_feed_dict=self._init_feed_dict, init_fn=self._init_fn)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 280, in prepare_session
self._local_init_op, msg))
RuntimeError: Init operations did not make model ready. Init op: init,
init fn: None, local_init_op: name: "group_deps"
op: "NoOp"
input: "^init_1"
input: "^init_all_tables", error: Variables not initialized: m2/var1, m2/var2, m2/var3...
你能否告诉我为什么会出现这种错误?我该如何解决?提前致谢!
最佳答案 对单独的模型使用单独的图表;在这种情况下,supervisor是使用m1_vars定义的,但它适用于m2_vars也驻留的默认图,当尝试初始化m2_vars时会导致问题.由于m2_vars是用另一个会话初始化的.
function build_graph() should be defined as
gi = tf.Graph()
with gi.as_default():
...
rest of the code
return gi
with tf.device("/cpu:0"):
g1 = m1.build_graph()
g2 = m2.build_graph()
...
m2_sess = tf.Session(graph=g2)
...
init_op = tf.variables_initializer(m2_vars)
m1_sv = tf.train.Supervisor(graph=g1, is_chief=True, init_op=init_op, saver=m1_saver)