그런 다음 나는 동일한 그래프를 다시 재현하는 모델을 복원하려고 초기화되지 않은 변수 값 오류 메시지

Question

나는 훈련 한 나의 모델을 사용하려고, tensorflow에서 모델을 복원하고

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

를 사용하여 그것을 저장 내 결과, 복원 작동하지만 네트워크 매개 변수 또는 작업의 모든 값에 액세스하려고하면 그것은 초기화되지 않은 변수를 사용하려고하면 오류가 발생합니다.

그래프를 재건 후, 복원하는 데 사용하는 코드는 다음과 같습니다 그러나

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

는 나에게 초기화되지 않은 변수

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

어떤 생각을 사용하려고 시도의 오류를주고 그 중 하나를 다음 ? 결과 복원 및 재생

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)  

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})) 

오류 : 대한

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 [ ]: 

같은 오류 간단한 예를 들어

, 여기에 교육 및 모델을 저장 :

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

Answer 1

마지막으로 액세스 할 모든 변수 나 연산이 나중에 이름 지정이 필요하고 자리 표시자인 것처럼 보이면 그래프를 다시 정의 할 필요가 없습니다. 다음 작업이 마침내 진행되고 있습니다. 더 간단한 방법이 있다면 나에게 더 많은 힌트를주세요.

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)) 

Answer 2

최근에 TF v1.5와 비슷한 문제가 발생했습니다. 변수가 제대로 복원되기 위해서는 전역 컬렉션에 ops를 추가해야합니다. 다음은 모델을 훈련, 저장 및 복원하는 MNIST 데이터 세트에 대한 두 가지 발췌 문장입니다.

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)) 

복원하고 여기에

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)) 

을 평가

기차와 지속이 더 나은 기준으로 관련 TF documentation