python - 训练 Tensorflow LinearClassifier 时出现类型错误

Question

尝试在 Jupyter 笔记本中训练模型时遇到以下错误:

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

我尝试指定或不指定特征列的数据类型，排除分类列，重新启动内核，更改批量大小和纪元。我可能错过了一些非常愚蠢的东西，但花了几个小时试图找出问题所在:(

这是代码本身，提前感谢您的研究:

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)

顺便说一句，当 TF 崩溃时，我在终端中看到此错误:

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

天哪，解决方案有点太简单了:)

对于包含字节大小范围内的数字的整数列，我手动将 dtype 设置为 int8:

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

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

缩小数据大小绝对是合理的，尽管我仍然想知道为什么我之前在其他也只有字节大小的数字列的数据集上没有遇到这个问题。