在TensorFlow中恢复模型时，尝试使用未初始化的变量错误消息。

Question

在TensorFlow中恢复模型时，尝试使用未初始化的变量错误消息。

3

我已经训练好了模型，并使用了保存方法。

    saver = tf.train.Saver()
    saver.save(sess, '/final_model', global_step = i)

然后我重新构建了完全相同的图，并尝试恢复模型以重现我的结果，恢复工作正常，但只要尝试访问网络参数或操作的任何值，它就会给出一个错误，指出正在尝试使用未初始化的变量。

在重新构建图之后，我用以下代码进行恢复：

    sess=tf.Session() 
    new_saver = tf.train.import_meta_graph('final_model-699.meta')
    new_saver.restore(sess, tf.train.latest_checkpoint('./'))

然而，尝试使用未初始化的变量会导致以下任何一个错误。

    print(sess.run(weights['hidden1']))
    print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y}))

有什么想法吗？

以一个简单例子为例，以下是训练和保存模型的过程：

train_x = np.random.rand(200,2)
w= np.array([2,3])
train_y = np.dot(train_x, w)
train_y = np.reshape(train_y, [200,1])
feature_dim = 2
output_dim = 1

x = tf.placeholder(tf.float32, [None, feature_dim])
y_ = tf.placeholder(tf.float32, [None, output_dim])
weights = {
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1 / np.sqrt(feature_dim)))
}    

def network1(data):
    output = tf.matmul(x, weights['hidden1'])
    return output
y = network1(x)

loss_f = output_dim * tf.reduce_mean(tf.squared_difference(y, y_))
optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(10000):
    batch_x = train_x
    batch_y = train_y
    sess.run(optimizer_f, feed_dict={x: batch_x, y_: batch_y})
    print(sess.run(loss_f, feed_dict={x: batch_x, y_: batch_y}))
saver.save(sess,'./savedmodel/', global_step = i)

恢复和重现结果

import scipy.io
import numpy as np
import tensorflow as tf
import random
train_x = np.random.rand(200,2)
w= np.array([2,3])
train_y = np.dot(train_x, w)
train_y = np.reshape(train_y, [200,1])
feature_dim = 2
output_dim = 1
x = tf.placeholder(tf.float32, [None, feature_dim])
y_ = tf.placeholder(tf.float32, [None, output_dim])


weights = {
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1 / np.sqrt(feature_dim)))
}

def network1(data):
    output = tf.matmul(x, weights['hidden1'])
    return output

y = network1(x)
loss_f = tf.reduce_mean(tf.squared_difference(y, y_))
optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f)
sess = tf.Session()
saver = tf.train.import_meta_graph('./savedmodel/-9999.meta')
saver.restore(sess, tf.train.latest_checkpoint('./savedmodel/'))

print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y}))

错误信息:

FailedPreconditionErrorTraceback (most recent call last)
<ipython-input-5-17910473afab> in <module>()
----> 1 print(sess.run(loss_f, feed_dict={x: train_x, y_: train_y}))

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    776     try:
    777       result = self._run(None, fetches, feed_dict, options_ptr,
--> 778                          run_metadata_ptr)
    779       if run_metadata:
    780         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    980     if final_fetches or final_targets:
    981       results = self._do_run(handle, final_targets, final_fetches,
--> 982                              feed_dict_string, options, run_metadata)
    983     else:
    984       results = []

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1030     if handle is None:
   1031       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1032                            target_list, options, run_metadata)
   1033     else:
   1034       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
   1050         except KeyError:
   1051           pass
-> 1052       raise type(e)(node_def, op, message)
   1053 
   1054   def _extend_graph(self):

FailedPreconditionError: Attempting to use uninitialized value Variable
     [[Node: Variable/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable)]]
     [[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_7_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op u'Variable/read', defined at:
  File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main
    "__main__", fname, loader, pkg_name)
  File "/usr/lib/python2.7/runpy.py", line 72, in _run_code
    exec code in run_globals
  File "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/usr/local/lib/python2.7/dist-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelapp.py", line 477, in start
    ioloop.IOLoop.instance().start()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/usr/local/lib/python2.7/dist-packages/tornado/ioloop.py", line 888, in start
    handler_func(fd_obj, events)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/usr/local/lib/python2.7/dist-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
    handler(stream, idents, msg)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/usr/local/lib/python2.7/dist-packages/ipykernel/zmqshell.py", line 533, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
    if self.run_code(code, result):
  File "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-3-7d229041d9bb>", line 6, in <module>
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1 / np.sqrt(feature_dim)))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 197, in __init__
    expected_shape=expected_shape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 316, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1338, in identity
    result = _op_def_lib.apply_op("Identity", input=input, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
    self._traceback = _extract_stack()

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value Variable
     [[Node: Variable/read = Identity[T=DT_FLOAT, _class=["loc:@Variable"], _device="/job:localhost/replica:0/task:0/gpu:0"](Variable)]]
     [[Node: Mean/_15 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_7_Mean", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]


In [ ]:

print(sess.run(weights['hidden1']))
In [ ]:

同样的错误发生在以下情况：

print(sess.run(weights['hidden1']))

- amina mollaysa

2个回答

2

最终我发现，似乎我们想要稍后访问的每个变量或操作都需要被赋予一个名称，占位符也是如此，这样我们就不需要重新定义图表了。以下是最终工作的内容，请给我更多提示，如果有更简单的方法，请告诉我。

import scipy.io
import numpy as np
import tensorflow as tf
import random

train_x = np.random.rand(200,2)
w= np.array([2,3])
train_y = np.dot(train_x, w)
train_y = np.reshape(train_y, [200,1])
feature_dim = 2
output_dim = 1

x = tf.placeholder(tf.float32, [None, feature_dim], name="input")
y_ = tf.placeholder(tf.float32, [None, output_dim], name="output")
weights = {
    'hidden1': tf.Variable(tf.random_normal([feature_dim, output_dim], stddev=1 / np.sqrt(feature_dim)), name="weights")
}    

def network1(data):
    output = tf.matmul(data, weights['hidden1'])
    return output

y = network1(x)

loss_f = tf.reduce_mean(tf.squared_difference(y, y_), name="op_to_restore")

optimizer_f = tf.train.AdamOptimizer(1e-4).minimize(loss_f)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(100):
    batch_x = train_x
    batch_y = train_y
    sess.run(optimizer_f, feed_dict={x: batch_x, y_: batch_y})
    print(sess.run(loss_f, feed_dict={x: batch_x, y_: batch_y}))
saver.save(sess,'./savedmodel/', global_step = i)     


with tf.Session() as sess:
    saver = tf.train.import_meta_graph('./savedmodel/-99.meta')
    saver.restore(sess,tf.train.latest_checkpoint('./savedmodel/'))
    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("input:0")
    y_ = graph.get_tensor_by_name("output:0")
    feed_dict={x: train_x, y_: train_y}
    op_to_restore = graph.get_tensor_by_name("op_to_restore:0")
    print(sess.run(op_to_restore, feed_dict))

- amina mollaysa

作为更好的参考：链接 - amina mollaysa

3

你遇到的错误是因为在恢复图形之前创建了变量（weights），并且在运行图形之前没有对它们进行初始化。这不是因为你没有明确命名占位符。即使你不指定名称，TensorFlow也会自动选择一个名称。 - iga

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- singleton · Accepted Answer

我最近在使用TF v1.5时遇到了类似的问题。看起来，变量和操作应该添加到全局集合中，以便它们能够正确地恢复。以下是两个MNIST数据集的片段，用于训练、保存和恢复模型。

训练并持久化

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

train_dir = 'train'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784], name='x')
W = tf.Variable(tf.zeros([784, 10]), name='W')
b = tf.Variable(tf.zeros([10]), name='b')
y = tf.nn.softmax(tf.matmul(x, W) + b, name='y')

saver = tf.train.Saver(tf.trainable_variables())
sess = tf.InteractiveSession()

tf.add_to_collection('x', x)
tf.add_to_collection('yt', yt)
tf.add_to_collection('accuracy', accuracy)

with tf.Session() as sess:
    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    meta_path = '%s.meta' % latest_checkpoint

    saver = tf.train.import_meta_graph(meta_path)
    saver.restore(sess, latest_checkpoint)

    x = tf.get_collection('x')[0]
    yt = tf.get_collection('yt')[0]
    accuracy = tf.get_collection('accuracy')[0]

    feed_dict={x: mnist.test.images, yt: mnist.test.labels}

    print(sess.run(accuracy, feed_dict))

恢复和评估

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

train_dir = 'train'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

with tf.Session() as sess:
    latest_checkpoint = tf.train.latest_checkpoint(train_dir)
    meta_path = '%s.meta' % latest_checkpoint

    saver = tf.train.import_meta_graph(meta_path)
    saver.restore(sess, latest_checkpoint)

    x = tf.get_collection('x')[0]
    yt = tf.get_collection('yt')[0]
    accuracy = tf.get_collection('accuracy')[0]

    feed_dict={x: mnist.test.images, yt: mnist.test.labels}

    print(sess.run(accuracy, feed_dict))

这里是与之相关的TF文档链接。