python - Tensorflow FailedPreconditionError，但所有变量都已初始化

Question

编辑:在尝试了几件事之后，我将以下内容添加到我的代码中:

with tf.Session(graph=self.graph) as session:
    session.run(tf.initialize_all_variables())
    try:
        session.run(tf.assert_variables_initialized())
    except tf.errors.FailedPreconditionError:
        raise RuntimeError("Not all variables initialized!")

现在，偶尔会失败，即 tf.assert_variables_initialized() 会引发 FailedPreconditionError，即使紧接在它之前执行了 tf.initialize_all_variables()。有谁知道这是怎么发生的？

---

原始问题:

背景

我正在使用 GradientDescentOptimizer 在通过 Tensorflow 创建的基本神经网络上运行交叉验证 (CV) 超参数搜索。在看似随机的时刻，我收到不同变量的 FailedPreconditionError。例如(帖子末尾的完整堆栈跟踪):

FailedPreconditionError: Attempting to use uninitialized value Variable_5
     [[Node: Variable_5/read = Identity[T=DT_FLOAT, _class=["loc:@Variable_5"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_5)]]

一些运行失败的速度相当快，而另一些则不然——其中一个已经运行了 15 个小时，没有出现任何问题。我在多个 GPU 上并行运行它 - 不是优化本身，而是每个 CV 折叠。

我检查过的内容

来自 this和 this据我了解，尝试使用尚未使用 tf.initialize_all_variables() 初始化的变量时会发生此错误。但是，我 99% 确定我正在这样做(如果没有，我希望它总是失败)- 我将在下面发布代码。

API doc说是

This exception is most commonly raised when running an operation that reads a tf.Variable before it has been initialized.

“最常见”表示在不同的场景下也可以提出。所以，现在的主要问题是:

问题: 是否还有可能引发此异常的其他情况，它们是什么？

代码

MLP 类:

class MLP(object):
    def __init__(self, n_in, hidden_config, n_out, optimizer, f_transfer=tf.nn.tanh, f_loss=mean_squared_error,
                 f_out=tf.identity, seed=None, global_step=None, graph=None, dropout_keep_ratio=1):

        self.graph = tf.Graph() if graph is None else graph           
        # all variables defined below
        with self.graph.as_default():
            self.X = tf.placeholder(tf.float32, shape=(None, n_in))
            self.y = tf.placeholder(tf.float32, shape=(None, n_out))
            self._init_weights(n_in, hidden_config, n_out, seed)
            self._init_computations(f_transfer, f_loss, f_out)
            self._init_optimizer(optimizer, global_step)

     def fit_validate(self, X, y, val_X, val_y, val_f, iters=100, val_step=1):
            [snip]
            with tf.Session(graph=self.graph) as session:
VAR INIT HERE-->tf.initialize_all_variables().run() #<-- VAR INIT HERE
                for i in xrange(iters):
                    [snip: get minibatch here]    
                    _, l = session.run([self.optimizer, self.loss], feed_dict={self.X:X_batch, self.y:y_batch})
                    # validate
                    if i % val_step == 0:
                        val_yhat = self.validation_yhat.eval(feed_dict=val_feed_dict, session=session)

如您所见，tf.init_all_variables().run() 总是在其他任何事情完成之前被调用。网络初始化为:

def estimator_getter(params):
    [snip]    
    graph = tf.Graph()
    with graph.as_default():
        global_step = tf.Variable(0, trainable=False)
        learning_rate = tf.train.exponential_decay(params.get('learning_rate',0.1), global_step, decay_steps, decay_rate)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    net = MLP(config_num_inputs[config_id], hidden, 1, optimizer, seed=params.get('seed',100), global_step=global_step, graph=graph, dropout_keep_ratio=dropout)

完整示例堆栈跟踪:

FailedPreconditionError: Attempting to use uninitialized value Variable_5
     [[Node: Variable_5/read = Identity[T=DT_FLOAT, _class=["loc:@Variable_5"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable_5)]]
Caused by op u'Variable_5/read', defined at:
  File "tf_paramsearch.py", line 373, in <module>
    randomized_search_params(int(sys.argv[1]))
  File "tf_paramsearch.py", line 356, in randomized_search_params
    hypersearch.fit()
  File "/home/centos/ODQ/main/python/odq/cv.py", line 430, in fit
    return self._fit(sampled_params)
  File "/home/centos/ODQ/main/python/odq/cv.py", line 190, in _fit
    for train_key, test_key in self.cv)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 766, in __call__
    n_jobs = self._initialize_pool()
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 537, in _initialize_pool
    self._pool = MemmapingPool(n_jobs, **poolargs)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py", line 580, in __init__
    super(MemmapingPool, self).__init__(**poolargs)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/pool.py", line 418, in __init__
    super(PicklingPool, self).__init__(**poolargs)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/pool.py", line 159, in __init__
    self._repopulate_pool()
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/pool.py", line 223, in _repopulate_pool
    w.start()
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/process.py", line 130, in start
    self._popen = Popen(self)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/forking.py", line 126, in __init__
    code = process_obj._bootstrap()
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/multiprocessing/pool.py", line 113, in worker
    result = (True, func(*args, **kwds))
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 130, in __call__
    return self.func(*args, **kwargs)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "/home/centos/ODQ/main/python/odq/cv.py", line 131, in _fold_runner
    estimator = estimator_getter(parameters)
  File "tf_paramsearch.py", line 264, in estimator_getter
    net = MLP(config_num_inputs[config_id], hidden, 1, optimizer, seed=params.get('seed',100), global_step=global_step, graph=graph, dropout_keep_ratio=dropout)
  File "tf_paramsearch.py", line 86, in __init__
    self._init_weights(n_in, hidden_config, n_out, seed)
  File "tf_paramsearch.py", line 105, in _init_weights
    self.out_weights = tf.Variable(tf.truncated_normal([hidden_config[-1], n_out], stddev=stdev))
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 206, in __init__
    dtype=dtype)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 275, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 523, in identity
    return _op_def_lib.apply_op("Identity", input=input, name=name)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
    op_def=op_def)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2117, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/centos/miniconda2/envs/tensorflow/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

Answer 1

好的，我找到问题了。在我的代码中有一个罕见的情况导致其中一个隐藏层被创建为形状 (0, N)，即没有输入。在这种情况下，Tensorflow 显然无法初始化属于该层的变量。

虽然这是有道理的，但在这种情况下，Tensorflow 记录一条警告消息可能很有用(顺便说一句，我也尝试将 Tensorflow 日志记录设置为 Debug模式，但找不到方法 -- tf.logging .set_verbosity() 似乎没有效果)。