我一直在尝试在Tensorflow中恢复模型，但是我一直在
尝试导入元数据时遇到一些问题:
这是我导入元图的代码:
#Create a clean graph and import MetaGraphDef nodes
new_graph = tf.Graph()
with tf.Session(graph=new_graph) as sess:
# Import the previously exported metagraph
saver = tf.train.import_meta_graph('/tmp/saver-model.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
在我的模型类中，我指定了占位符和集合，如下所示:
"""Place Holders"""
self.input = tf.placeholder(tf.float32, [None, sl], name = 'input')
self.labels = tf.placeholder(tf.int64, [None], name = 'labels')
self.keep_prob = tf.placeholder("float", name= 'Drop_out_keep_prob')
tf.add_to_collection('vars', self.input)
tf.add_to_collection('vars', self.labels)
tf.add_to_collection('vars', self.keep_prob)
我按以下方式训练模型:
saver = tf.train.Saver(tf.global_variables())
# Session time
sess = tf.Session() # without context manager, close the session later.
writer = tf.summary.FileWriter("/tmp/model/log_tb", sess.graph) # Writer for tensorboard
sess.run(model.init_op)
并使用这三个不同的选项导出，包括未记录的
export_scoped_meta_graph:# Export the model to /tmp/my-model.meta.
scoped_meta = meta_graph.export_scoped_meta_graph(filename='/tmp/scoped.meta')
meta_graph_def = tf.train.export_meta_graph(filename='/tmp/my-model.meta')
saver.save(sess, '/tmp/saver-model')
这是我尝试在Windows 10下运行时遇到的错误:
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "BestSplits" device_type: "CPU"') for unknown op: BestSplits
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "CountExtremelyRandomStats" device_type: "CPU"') for unknown op: CountExtremelyRandomStats
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "FinishedNodes" device_type: "CPU"') for unknown op: FinishedNodes
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "GrowTree" device_type: "CPU"') for unknown op: GrowTree
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ReinterpretStringToFloat" device_type: "CPU"') for unknown op: ReinterpretStringToFloat
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "SampleInputs" device_type: "CPU"') for unknown op: SampleInputs
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "ScatterAddNdim" device_type: "CPU"') for unknown op: ScatterAddNdim
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNInsert" device_type: "CPU"') for unknown op: TopNInsert
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TopNRemove" device_type: "CPU"') for unknown op: TopNRemove
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "TreePredictions" device_type: "CPU"') for unknown op: TreePredictions
E c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\framework\op_kernel.cc:943] OpKernel ('op: "UpdateFertileSlots" device_type: "CPU"') for unknown op: UpdateFertileSlots
TypeError: expected bytes, NoneType found
During handling of the above exception, another exception occurred:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: expected bytes, NoneType found
During handling of the above exception, another exception occurred:
SystemError Traceback (most recent call last)
<ipython-input-37-60792895b01c> in <module>()
6 #saver = tf.train.import_meta_graph('/tmp/saver-model.meta')
7 saver = tf.train.import_meta_graph('/tmp/my-model.meta')
----> 8 saver.restore(sess, tf.train.latest_checkpoint('./'))
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\training\saver.py in restore(self, sess, save_path)
1437 return
1438 sess.run(self.saver_def.restore_op_name,
-> 1439 {self.saver_def.filename_tensor_name: save_path})
1440
1441 @staticmethod
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
765 try:
766 result = self._run(None, fetches, feed_dict, options_ptr,
--> 767 run_metadata_ptr)
768 if run_metadata:
769 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
963 if final_fetches or final_targets:
964 results = self._do_run(handle, final_targets, final_fetches,
--> 965 feed_dict_string, options, run_metadata)
966 else:
967 results = []
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1013 if handle is None:
1014 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015 target_list, options, run_metadata)
1016 else:
1017 return self._do_call(_prun_fn, self._session, handle, feed_dict,
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1020 def _do_call(self, fn, *args):
1021 try:
-> 1022 return fn(*args)
1023 except errors.OpError as e:
1024 message = compat.as_text(e.message)
c:\users\carlos\anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1002 return tf_session.TF_Run(session, options,
1003 feed_dict, fetch_list, target_list,
-> 1004 status, run_metadata)
1005
1006 def _prun_fn(session, handle, feed_dict, fetch_list):
SystemError: <built-in function TF_Run> returned a result with an error set
尝试在debian下运行此命令时:
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 1
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 1: Y Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0)
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 1022, in _do_call
return fn(*args)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 1004, in _run_fn
status, run_metadata)
File "/usr/lib/python3.4/contextlib.py", line 66, in __exit__
next(self.gen)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InternalError: Unable to get element from the feed as bytes.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/training/saver.py", line 1439, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unable to get element from the feed as bytes.
最佳答案
我设法解决了这个问题，并决定与他人分享，以防将来有人遇到这种情况:
将所有占位符添加到集合中:
tf.add_to_collection('vars', input)
tf.add_to_collection('vars', labels)
tf.add_to_collection('vars', keep_prob)
独立合并和初始化变量(避免使用
tf.global_variables_initializer()):merged = tf.summary.merge([loss_summ, cost_summ, tloss_summ, acc_summ])
在训练期间保存模型:
if i%100 == 0:
saver.save(sess, save_dir + 'model.ckpt', global_step=i+100)
初始化新的元图，在将元图导入新的图元之前先包含保护程序
session :
这将防止
saver.saver_def.filename_tensor_name错误这是因为:
* The default name scope for a tf.train.Saver is "save/" and the placeholder
is actually a tf.constant() whose name defaults to "Const:0", which explains
why the flag defaults to "save/Const:0".
saver = tf.train.Saver()
sess = tf.Session()
sess.run(init_op)
使用
tf.train.get_checkpoint_state()获取检查点:sess =tf.Session()
ckpt = tf.train.get_checkpoint_state(save_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
关于python - Tensorflow:保存/恢复 session ，检查点，元图，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/42832083/