FailedPreconditionError:尝试在Tensorflow中使用未初始化的
classification
pandas
python
tensorflow
4
0

我正在研究TensorFlow教程 ,该教程使用“怪异”格式上载数据。我想对数据使用NumPy或pandas格式,以便可以将其与scikit学习结果进行比较。

我从Kaggle获得了数字识别数据: https ://www.kaggle.com/c/digit-recognizer/data。

这是TensorFlow教程中的代码(效果很好):

# Stuff from tensorflow tutorial 
import tensorflow as tf

sess = tf.InteractiveSession()

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

在这里,我读取了数据,去除了目标变量,然后将数据拆分为测试和训练数据集(一切正常):

# Read dataframe from training data
csvfile='train.csv'
from pandas import DataFrame, read_csv
df = read_csv(csvfile)

# Strip off the target data and make it a separate dataframe.
Target = df.label
del df["label"]

# Split data into training and testing sets
msk = np.random.rand(len(df)) < 0.8
dfTest = df[~msk]
TargetTest = Target[~msk]
df = df[msk]
Target = Target[msk]

# One hot encode the target
OHTarget=pd.get_dummies(Target)
OHTargetTest=pd.get_dummies(TargetTest)

现在,当我尝试运行训练步骤时,出现FailedPreconditionError

for i in range(100):
    batch = np.array(df[i*50:i*50+50].values)
    batch = np.multiply(batch, 1.0 / 255.0)
    Target_batch = np.array(OHTarget[i*50:i*50+50].values)
    Target_batch = np.multiply(Target_batch, 1.0 / 255.0)
    train_step.run(feed_dict={x: batch, y_: Target_batch})

这是完整的错误:

---------------------------------------------------------------------------
FailedPreconditionError                   Traceback (most recent call last)
<ipython-input-82-967faab7d494> in <module>()
      4     Target_batch = np.array(OHTarget[i*50:i*50+50].values)
      5     Target_batch = np.multiply(Target_batch, 1.0 / 255.0)
----> 6     train_step.run(feed_dict={x: batch, y_: Target_batch})

/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in run(self, feed_dict, session)
   1265         none, the default session will be used.
   1266     """
-> 1267     _run_using_default_session(self, feed_dict, self.graph, session)
   1268
   1269

/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc in _run_using_default_session(operation, feed_dict, graph, session)
   2761                        "the operation's graph is different from the session's "
   2762                        "graph.")
-> 2763   session.run(operation, feed_dict)
   2764
   2765

/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict)
    343
    344     # Run request and get response.
--> 345     results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
    346
    347     # User may have fetched the same tensor multiple times, but we

/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, target_list, fetch_list, feed_dict)
    417         # pylint: disable=protected-access
    418         raise errors._make_specific_exception(node_def, op, e.error_message,
--> 419                                               e.code)
    420         # pylint: enable=protected-access
    421       raise e_type, e_value, e_traceback

FailedPreconditionError: Attempting to use uninitialized value Variable_1
     [[Node: gradients/add_grad/Shape_1 = Shape[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Variable_1)]]
Caused by op u'gradients/add_grad/Shape_1', defined at:
  File "/Users/user32/anaconda/lib/python2.7/runpy.py", line 162, in _run_module_as_main
    ...........

...which was originally created as op u'add', defined at:
  File "/Users/user32/anaconda/lib/python2.7/runpy.py", line 162, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
[elided 17 identical lines from previous traceback]
  File "/Users/user32/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-45-59183d86e462>", line 1, in <module>
    y = tf.nn.softmax(tf.matmul(x,W) + b)
  File "/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 403, in binary_op_wrapper
    return func(x, y, name=name)
  File "/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 44, in add
    return _op_def_lib.apply_op("Add", x=x, y=y, name=name)
  File "/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
    op_def=op_def)
  File "/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/Users/user32/anaconda/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 988, in __init__
    self._traceback = _extract_stack()

关于如何解决此问题的任何想法?

参考资料:
Stack Overflow
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共 6 个回答
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根据官方文档FailedPreconditionError

当运行一个在初始化之前读取tf.Variable的操作时,通常会引发此异常。

在您的情况下,该错误甚至说明了未初始化的变量: Attempting to use uninitialized value Variable_1 。 TF教程之一解释了很多有关变量的知识,包括变量的创建/初始化/保存/加载。

基本上,初始化变量有3个选项:

我几乎总是使用第一种方法。请记住,您应该将其放入会话运行中。因此,您将获得如下内容:

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

如果您对有关变量的更多信息感到好奇,请阅读此文档以了解如何report_uninitialized_variables并检查is_variable_initialized

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出现FailedPreconditionError原因是程序在初始化之前试图读取变量(名为"Variable_1" )。在TensorFlow中,必须通过运行变量的“初始化程序”来显式初始化所有变量。为了方便起见,您可以通过在训练循环之前执行以下语句来在当前会话中运行所有变量初始化器:

tf.initialize_all_variables().run()

请注意,该答案假定与问题一样,您正在使用tf.InteractiveSession ,它允许您在不指定会话的情况下运行操作。对于非交互式用途,更常见的是使用tf.Session ,并按如下所示进行初始化:

init_op = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init_op)
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不推荐使用tf.initialize_all_variables() 。而是使用以下命令初始化tensorflow变量:

tf.global_variables_initializer()

一个常见的示例用法是:

with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
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不同的用例,但是将您的会话设置为默认会话对我有用:

with sess.as_default():
    result = compute_fn([seed_input,1])

一旦解决,这就是很明显的错误之一。

我的用例如下:
1)将keras VGG16存储为张量流图
2)加载kers VGG16作为图表
3)在图形上运行tf函数并获取:

FailedPreconditionError: Attempting to use uninitialized value block1_conv2/bias
     [[Node: block1_conv2/bias/read = Identity[T=DT_FLOAT, _class=["loc:@block1_conv2/bias"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](block1_conv2/bias)]]
     [[Node: predictions/Softmax/_7 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_168_predictions/Softmax", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
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您必须在使用变量之前对其进行初始化。

如果尝试在初始化变量之前对变量求值,则会遇到以下问题: FailedPreconditionError: Attempting to use uninitialized value tensor.

最简单的方法是使用以下tf.global_variables_initializer()一次初始化所有变量: tf.global_variables_initializer()

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

您可以使用sess.run(init)来运行初始化程序,而无需获取任何值。

要仅初始化变量的子集,请使用tf.variables_initializer()列出变量:

var_ab = tf.variables_initializer([a, b], name="a_and_b")
with tf.Session() as sess:
    sess.run(var_ab)

您还可以使用tf.Variable.initializer分别初始化每个变量

# create variable W as 784 x 10 tensor, filled with zeros
W = tf.Variable(tf.zeros([784,10])) with tf.Session() as sess:
    sess.run(W.initializer)
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我从完全不同的情况下收到此错误消息。似乎在tensorflow中的异常处理程序将其引发。您可以检查回溯中的每一行。在我的情况下,它发生在tensorflow/python/lib/io/file_io.py ,因为此文件包含另一个错误,未初始化self.__modeself.__name ,并且需要调用self._FileIO__mode ,并且self_FileIO__name代替。

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