I have been trying to restore a model in tensorflow, however I have been encountering some issues when I try to import a metagraph:
This is my code for importing the metagraph:
#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('./')) In my Model class I have specified the placeholders and collection as follows:
"""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) I train my model as follows:
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) self.init_op = tf.global_variables_initializer()
And exported using these three different options, including the undocumented 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') This is the error I get when attempting to run under Windows 10:
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 When attempting to run this under debian:
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. 1 Answers
Answers 1
I managed to solve it and decided to share in case someone comes accross this in the future:
Add all the placeholder to collections:
tf.add_to_collection('vars', input) tf.add_to_collection('vars', labels) tf.add_to_collection('vars', keep_prob) merge and initialize variables independently (avoid using tf.global_variables_initializer()):
merged = tf.summary.merge([loss_summ, cost_summ, tloss_summ, acc_summ]) save the model during training:
if i%100 == 0: saver.save(sess, save_dir + 'model.ckpt', global_step=i+100) Initialize a new metagraph, include the saver prior to importing the metagraph into the new session:
this will prevent saver.saver_def.filename_tensor_name error
The name 'save/Const:0' refers to a Tensor which does not exist
This is because:
* 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) Get the checkpoint using tf.train.get_checkpoint_state():
sess =tf.Session() ckpt = tf.train.get_checkpoint_state(save_dir) saver.restore(sess, ckpt.model_checkpoint_path) 
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