Ошибка типа при обучении Tensorflow LinearClassifier

Question

Я получаю следующую ошибку при попытке обучить модель в ноутбуке Jupyter:

INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Error reported to Coordinator: <class 'SystemError'>, <built-in function TF_Run> returned a result with an error set
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpodutz9be/model.ckpt.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: expected bytes, float found

During handling of the above exception, another exception occurred:

SystemError                               Traceback (most recent call last)
<ipython-input-12-44daeaf784e5> in <module>()
----> 1 model.train(input_fn=input_func,steps=200)

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps)
    239       hooks.append(training.StopAtStepHook(steps, max_steps))
    240 
--> 241     loss = self._train_model(input_fn=input_fn, hooks=hooks)
    242     logging.info('Loss for final step: %s.', loss)
    243     return self

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks)
    684         loss = None
    685         while not mon_sess.should_stop():
--> 686           _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
    687       return loss
    688 

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py in __exit__(self, exception_type, exception_value, traceback)
    532     if exception_type in [errors.OutOfRangeError, StopIteration]:
    533       exception_type = None
--> 534     self._close_internal(exception_type)
    535     # __exit__ should return True to suppress an exception.
    536     return exception_type is None

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py in _close_internal(self, exception_type)
    567     finally:
    568       try:
--> 569         self._sess.close()
    570       finally:
    571         self._sess = None

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py in close(self)
    809     if self._sess:
    810       try:
--> 811         self._sess.close()
    812       except _PREEMPTION_ERRORS:
    813         pass

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/training/monitored_session.py in close(self)
    906       self._coord.join(
    907           stop_grace_period_secs=self._stop_grace_period_secs,
--> 908           ignore_live_threads=True)
    909     finally:
    910       try:

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/training/coordinator.py in join(self, threads, stop_grace_period_secs, ignore_live_threads)
    387       self._registered_threads = set()
    388       if self._exc_info_to_raise:
--> 389         six.reraise(*self._exc_info_to_raise)
    390       elif stragglers:
    391         if ignore_live_threads:

~/.local/lib/python3.5/site-packages/six.py in reraise(tp, value, tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/estimator/inputs/queues/feeding_queue_runner.py in _run(self, sess, enqueue_op, feed_fn, coord)
     92         try:
     93           feed_dict = None if feed_fn is None else feed_fn()
---> 94           sess.run(enqueue_op, feed_dict=feed_dict)
     95         except (errors.OutOfRangeError, errors.CancelledError):
     96           # This exception indicates that a queue was closed.

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    893     try:
    894       result = self._run(None, fetches, feed_dict, options_ptr,
--> 895                          run_metadata_ptr)
    896       if run_metadata:
    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1122     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1123       results = self._do_run(handle, final_targets, final_fetches,
-> 1124                              feed_dict_tensor, options, run_metadata)
   1125     else:
   1126       results = []

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1319     if handle is None:
   1320       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1321                            options, run_metadata)
   1322     else:
   1323       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1325   def _do_call(self, fn, *args):
   1326     try:
-> 1327       return fn(*args)
   1328     except errors.OpError as e:
   1329       message = compat.as_text(e.message)

~/anaconda3/envs/tfdeeplearning/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1304           return tf_session.TF_Run(session, options,
   1305                                    feed_dict, fetch_list, target_list,
-> 1306                                    status, run_metadata)
   1307 
   1308     def _prun_fn(session, handle, feed_dict, fetch_list):

SystemError: <built-in function TF_Run> returned a result with an error set

Я попытался указать или не указать тип данных для столбцов объектов, исключая категориальные столбцы, перезапуск ядра, изменение размера пакета и эпох. Я, наверное, упускаю что-то очень глупое, но потратил часы, пытаясь выяснить, в чем дело :(

Вот сам код, заранее благодарю за просмотр:

train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
test.columns

train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

y  = train['Survived']
X = train.drop(['Name','Survived','Ticket','PassengerId'],axis=1)
X_test = test

cols_to_norm = [ 'Fare']
X[cols_to_norm] = X[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))

pclass = tf.feature_column.numeric_column('Pclass', dtype=tf.int64)
sex = tf.feature_column.categorical_column_with_vocabulary_list(key="Sex", vocabulary_list=["male", "female"])
age = tf.feature_column.numeric_column('Age', dtype=tf.float64)
sibsp = tf.feature_column.numeric_column('SibSp', dtype=tf.int64)
fare = tf.feature_column.numeric_column('Fare', dtype=tf.float64)
parch = tf.feature_column.numeric_column('Parch', dtype=tf.int64)
embarked = tf.feature_column.categorical_column_with_hash_bucket('Embarked', hash_bucket_size=10000)
age_buckets = tf.feature_column.bucketized_column(age, boundaries=[0,10,20,30,40,50,60,70,80])

feat_cols = [pclass, age_buckets, sex, sibsp, parch, embarked]

input_func = tf.estimator.inputs.pandas_input_fn(x=X,y=y,batch_size=4,num_epochs=None,shuffle=True)

model = tf.estimator.LinearClassifier(feature_columns=feat_cols)

model.train(input_fn=input_func,steps=200)

Кстати, я вижу эту ошибку в терминале при сбое TF:

I 22:37:15.691 NotebookApp] Saving file at /kaggle/titanic/titanic_nb.ipynb

[2408:2444:0428/225656.234325:ERROR:upload_data_presenter.cc(73)] Not implemented reached in virtual void extensions::RawDataPresenter::FeedNext(const net::UploadElementReader &)
[2408:2444:0428/225656.322176:ERROR:upload_data_presenter.cc(73)] Not implemented reached in virtual void extensions::RawDataPresenter::FeedNext(const net::UploadElementReader &)
[2408:2444:0428/225656.442632:ERROR:upload_data_presenter.cc(73)] Not implemented reached in virtual void extensions::RawDataPresenter::FeedNext(const net::UploadElementReader &)
[2408:2444:0428/225656.831056:ERROR:upload_data_presenter.cc(73)] Not implemented reached in virtual void extensions::RawDataPresenter::FeedNext(const net::UploadElementReader &)
[2408:2408:0428/225656.895872:ERROR:CONSOLE(6)] "Uncaught ReferenceError: gbar is not defined", source: https://clients5.google.com/pagead/drt/dn/ (6)

Answer 1

Черт возьми, решение было слишком простым:)

Я вручную установил dtype в int8 для целочисленных столбцов, которые содержат числа в диапазоне размера байта:

pclass = tf.feature_column.numeric_column ('Pclass', dtype = tf.int8)

parch = tf.feature_column.numeric_column ('Parch', dtype = tf.int8)

Определенно разумно уменьшить размер данных, хотя я все еще удивляюсь, почему я не сталкивался с этой проблемой раньше с другими наборами данных, которые также имели столбцы только с байтовыми числами.