InternalError in TensorFlow wenn man versucht, einen Kontrollpunkt zu speichern

Question

Ich versuche, eine einfache DNNClassifier mit einigen Testdaten von Pandas zu trainieren. Wenn TensorFlow versucht, den Kontrollpunkt zu speichern, trifft es auf den folgenden Fehler.

Es ist ein interner Fehler - es gibt keine Informationen verfügbar überall im Handbuch.

INFO:tensorflow:Error reported to Coordinator: <class 'tensorflow.python.framework.errors_impl.InternalError'>, Unable to get element as bytes. 
INFO:tensorflow:Saving checkpoints for 0 into /tmp/bets_model/model.ckpt. 
--------------------------------------------------------------------------- 
NotFoundError        Traceback (most recent call last) 
/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 
    1322  try: 
-> 1323  return fn(*args) 
    1324  except errors.OpError as e: 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata) 
    1301         feed_dict, fetch_list, target_list, 
-> 1302         status, run_metadata) 
    1303 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg) 
    472    compat.as_text(c_api.TF_Message(self.status.status)), 
--> 473    c_api.TF_GetCode(self.status.status)) 
    474  # Delete the underlying status object from memory otherwise it stays alive 

NotFoundError: /tmp/bets_model/model.ckpt-0_temp_d361b25e9071477a9e4ccd44a49a241a; No such file or directory 
    [[Node: save/SaveV2 = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](save/ShardedFilename, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, dnn/hiddenlayer_0/bias/part_0/read, dnn/dnn/hiddenlayer_0/bias/part_0/Adagrad/read, dnn/hiddenlayer_0/kernel/part_0/read, dnn/dnn/hiddenlayer_0/kernel/part_0/Adagrad/read, dnn/hiddenlayer_1/bias/part_0/read, dnn/dnn/hiddenlayer_1/bias/part_0/Adagrad/read, dnn/hiddenlayer_1/kernel/part_0/read, dnn/dnn/hiddenlayer_1/kernel/part_0/Adagrad/read, dnn/hiddenlayer_2/bias/part_0/read, dnn/dnn/hiddenlayer_2/bias/part_0/Adagrad/read, dnn/hiddenlayer_2/kernel/part_0/read, dnn/dnn/hiddenlayer_2/kernel/part_0/Adagrad/read, dnn/logits/bias/part_0/read, dnn/dnn/logits/bias/part_0/Adagrad/read, dnn/logits/kernel/part_0/read, dnn/dnn/logits/kernel/part_0/Adagrad/read, global_step)]] 

During handling of the above exception, another exception occurred: 

NotFoundError        Traceback (most recent call last) 
/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/training/saver.py in save(self, sess, save_path, global_step, latest_filename, meta_graph_suffix, write_meta_graph, write_state) 
    1572    self.saver_def.save_tensor_name, 
-> 1573    {self.saver_def.filename_tensor_name: checkpoint_file}) 
    1574   else: 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata) 
    888  result = self._run(None, fetches, feed_dict, options_ptr, 
--> 889       run_metadata_ptr) 
    890  if run_metadata: 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 
    1119  results = self._do_run(handle, final_targets, final_fetches, 
-> 1120        feed_dict_tensor, options, run_metadata) 
    1121  else: 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 
    1316  return self._do_call(_run_fn, self._session, feeds, fetches, targets, 
-> 1317       options, run_metadata) 
    1318  else: 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 
    1335   pass 
-> 1336  raise type(e)(node_def, op, message) 
    1337 

NotFoundError: /tmp/bets_model/model.ckpt-0_temp_d361b25e9071477a9e4ccd44a49a241a; No such file or directory 
    [[Node: save/SaveV2 = SaveV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](save/ShardedFilename, save/SaveV2/tensor_names, save/SaveV2/shape_and_slices, dnn/hiddenlayer_0/bias/part_0/read, dnn/dnn/hiddenlayer_0/bias/part_0/Adagrad/read, dnn/hiddenlayer_0/kernel/part_0/read, dnn/dnn/hiddenlayer_0/kernel/part_0/Adagrad/read, dnn/hiddenlayer_1/bias/part_0/read, dnn/dnn/hiddenlayer_1/bias/part_0/Adagrad/read, dnn/hiddenlayer_1/kernel/part_0/read, dnn/dnn/hiddenlayer_1/kernel/part_0/Adagrad/read, dnn/hiddenlayer_2/bias/part_0/read, dnn/dnn/hiddenlayer_2/bias/part_0/Adagrad/read, dnn/hiddenlayer_2/kernel/part_0/read, dnn/dnn/hiddenlayer_2/kernel/part_0/Adagrad/read, dnn/logits/bias/part_0/read, dnn/dnn/logits/bias/part_0/Adagrad/read, dnn/logits/kernel/part_0/read, dnn/dnn/logits/kernel/part_0/Adagrad/read, global_step)]] 

Answer 1

Ich fand es auf eigene Faust. Anscheinend wird tensorflow diese Low-Level-Fehler werfen, wenn sie die Daten nicht wie in kommt - zum Beispiel, wenn es einen Wert sieht, die nicht auf der Vokabelliste oder ein nan ist. Ich habe erwartet, eine aussagekräftigere Fehlermeldung, aber anscheinend ist es mehr Low-Level, als ich dachte - ich brauche, um meine eigene Due Diligence auf den Daten zu tun, bevor es zu einem Klassifizierer für die Ausbildung Fütterung.