将操作从一个图复制到另一个图tensorflow

这是我的代码。
我试图做的是将操作从graph1复制到graph2。
这就是我得到的。 Graph1已经是经过训练的模型，我想重新训练模型，所以在初始化CNN权重和偏差时，出现了这个问题

我试着不构建另一个graph2，这意味着使用graph1作为第一个训练，但需要向Word_embedded_向量（=graph.get_tensor_by_name（embedded/W:0））添加更多单词

如果有任何其他方法通过不使用新图形进行再培训

如果不是，我想解决以下错误消息

回溯（最近一次呼叫最后一次）：
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第1327行，在
返回fn（*args）
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第1306行，在
状态，运行（元数据）
文件“/usr/local/ceral/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py”，第88行，在退出时__
下一个（self.gen）
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/framework/errors\u impl.py”，第466行，处于引发异常打开状态
pywrap_tensorflow.TF_GetCode（状态））
tensorflow.python.framework.errors\u impl.failedPremissionError:尝试使用未初始化的值嵌入/W
[[Node:_retval_embedding/W_0_0=_retval[T=DT_FLOAT，index=0，_device=“/job:localhost/replica:0/task:0/cpu:0”]（嵌入/W）]]
在处理上述异常期间，发生了另一个异常：
回溯（最近一次呼叫最后一次）：
文件“/Users/jj/eclipseworkspace/qna_beta/e01/test.py”，第105行，在
打印（sess2.run（嵌入式_W2））
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第895行，正在运行
运行_元数据_ptr）
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第1124行，正在运行
feed_dict_tensor、options、run_元数据）
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第1321行，运行
选项，运行（元数据）
文件“/Users/jj/tensorflow3/lib/python3.6/site packages/tensorflow/python/client/session.py”，第1340行，在
提升类型（e）（节点定义、操作、消息）
tensorflow.python.framework.errors\u impl.failedPremissionError:尝试使用未初始化的值嵌入/W
[[Node:_retval_embedding/W_0_0=_retval[T=DT_FLOAT，index=0，_device=“/job:localhost/replica:0/task:0/cpu:0”]（嵌入/W）]]

Tensorflow的版本是1.3.0

可能与

import tensorflow as tf  
import numpy as np  
import os  
import data_helpers  
from tensorflow.contrib import learn

# Parameters
# ==================================================

# Data Parameters
tf.flags.DEFINE_string("eval_file", "./text/tokenizedSmallText.txt", "Data source for the positive data.")  

# Eval Parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")  
tf.flags.DEFINE_string("checkpoint_dir", "./runs/ThuOct121628262017/checkpoints", "Checkpoint directory from training run")  
tf.flags.DEFINE_boolean("eval_train", False, "Evaluate on all training data")  

# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")  
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")  



FLAGS = tf.flags.FLAGS  
FLAGS._parse_flags()  
print("\nParameters:")  
for attr, value in sorted(FLAGS.__flags.items()):  
    print("{}={}".format(attr.upper(), value))
    print("")`  

x_raw= data_helpers.load_data_and_labels(FLAGS.eval_file)

# Map data into vocabulary
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
x_test = np.array(list(vocab_processor.transform(x_raw)))

print("\nEvaluating...\n")

# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph1 = tf.Graph()
graph2 = tf.Graph()

with graph1.as_default():
    session_conf = tf.ConfigProto(
      allow_soft_placement=FLAGS.allow_soft_placement,
      log_device_placement=FLAGS.log_device_placement)
    sess = tf.Session(config=session_conf)

# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
saver.restore(sess, checkpoint_file)

embedded_W = graph1.get_tensor_by_name("embedding/W:0")
embedded_W2 = tf.contrib.copy_graph.copy_op_to_graph(embedded_W,graph2,[])


with graph2.as_default():
    session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement)
    sess2 = tf.Session(config=session_conf)
    with sess2.as_default():
        tf.global_variables_initializer().run(session=sess2)
        print(embedded_W2)
        print(sess2.run(embedded_W2))

Traceback (most recent call last):
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py", line 88, in __exit__
next(self.gen)
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value embedding/W
 [[Node: _retval_embedding/W_0_0 = _Retval[T=DT_FLOAT, index=0, _device="/job:localhost/replica:0/task:0/cpu:0"](embedding/W)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/Users/jj/eclipse-workspace/qna_beta/e01/test.py", line 105, in <module>
print(sess2.run(embedded_W2))
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/Users/jj/tensorflow3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value embedding/W
 [[Node: _retval_embedding/W_0_0 = _Retval[T=DT_FLOAT, index=0, _device="/job:localhost/replica:0/task:0/cpu:0"](embedding/W)]]