如何在Tensorflow中使用多個型號

Question

我有兩個型號分別訓練m1和m2。現在我想保留m1固定並根據m2的輸出微調m1。 m1的所有變量都在變量範圍"m1/"之下，而m2的變量在"m2/"之下。這基本上就是我所做的：

# 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() 
... 

但現在存在的最後一行代碼的錯誤：

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... 

你能告訴我爲什麼這個錯誤發生時，我該如何解決？提前致謝！

Answer 1

對單獨的模型使用單獨的圖表;在這種情況下，主管定義爲m1_vars，但它與默認圖形一起工作，其中m2_vars也駐留，當嘗試初始化m2_vars時會導致問題。由於m2_vars被另一個會話初始化。

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)