无法恢复预先训练网络Tensorflow

我坚持着修复与Tensorflow预训练的网络....无法恢复预先训练网络Tensorflow

import tensorflow as tf 
import os 
os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 

sess=tf.Session() 
saver = tf.train.import_meta_graph('./model/20170512-110547/model-20170512-110547.meta') 
saver.restore(sess,'./model/20170512-110547/')

我想用这是训练前训练的网络人脸识别，然后想添加一些图层进行转移学习。（我从这里下载的模型。https://github.com/davidsandberg/facenet）

当我执行上面的代码，它显示了错误，

WARNING:tensorflow:The saved meta_graph is possibly from an older release: 
'model_variables' collection should be of type 'byte_list', but instead is of type 'node_list'. 
Traceback (most recent call last): 
    File "/Users/user/Desktop/desktop/Python/HCR/Transfer_face/test.py", line 7, in <module> 
    saver.restore(sess,'./model/20170512-110547/') 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1560, in restore 
    {self.saver_def.filename_tensor_name: save_path}) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run 
    run_metadata_ptr) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1124, in _run 
    feed_dict_tensor, options, run_metadata) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run 
    options, run_metadata) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call 
    raise type(e)(node_def, op, message) 
tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./model/20170512-110547/ 
    [[Node: save/RestoreV2_491 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_491/tensor_names, save/RestoreV2_491/shape_and_slices)]] 

Caused by op u'save/RestoreV2_491', defined at: 
    File "/Users/user/Desktop/desktop/Python/HCR/Transfer_face/test.py", line 6, in <module> 
    saver = tf.train.import_meta_graph('./model/20170512-110547/model-20170512-110547.meta') 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1698, in import_meta_graph 
    **kwargs) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/meta_graph.py", line 656, in import_scoped_meta_graph 
    producer_op_list=producer_op_list) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def 
    op_def=op_def) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/Users/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__ 
    self._traceback = self._graph._extract_stack() # pylint: disable=protected-access 

NotFoundError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to find any matching files for ./model/20170512-110547/ 
    [[Node: save/RestoreV2_491 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_491/tensor_names, save/RestoreV2_491/shape_and_slices)]]

我不明白，为什么系统无法找到预先训练数据... 并且目录结构如下

USER-NO-的MacBook-PRO：Transfer_face用户$ LS -R

模型test.py

./model：

20170512-110547

./model/20170512-110547：

20170512-110547.pb

模型20170512-110547.ckpt-250000.index

模型20170512-110547.ckpt-250000.data-00000-的-00001

模型20170512-110547.meta

来源

2017-10-14 soshi shimada

尝试使用tensorflow的旧版本：'保存的meta_graph可能来自旧版本'。该模型使用r0.12 – Maxim

构建，谢谢。我试过版本0.12和1.2.0（这是写在要求）。但仍然显示相同的错误.... –

当你调用'saver.restore（）'（而不是相对路径''。/ model/20170512-110547 /'''''）时，尝试传递完整的绝对路径到模型目录。。旧版本的TensorFlow（包括0.12，我认为）有一个错误，他们不接受某些API中的相对路径，但应该在最新版本中修复这个错误。 – mrry

导入.pb文件。

import tensorflow as tf 
from tensorflow.python.framework import tensor_util 

with tf.gfile.GFile('20170512-110547.pb', "rb") as f: 
    graph_def = tf.GraphDef() 
    graph_def.ParseFromString(f.read()) 

#import into default graph 
tf.import_graph_def(graph_def) 

#print some data 
wts = [n for n in graph_def.node if n.op == 'Const'] 

for n in wts: 
    print(tensor_util.MakeNdarray(n.attr['value'].tensor))

链接问题：

Import a simple Tensorflow frozen_model.pb file and make prediction in C++

get the value weights from .pb file by Tensorflow

无法恢复预先训练网络Tensorflow

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