我在Jupyter笔记本中尝试训练模型时遇到了以下错误：
INFO:tensorflow:Create CheckpointSaverHook.INFO:tensorflow:Error reported to Coordinator: <class 'SystemError'>, <built-in function TF_Run> returned a result with an error setINFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpodutz9be/model.ckpt.---------------------------------------------------------------------------TypeError Traceback (most recent call last)TypeError: expected bytes, float foundDuring 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我尝试了指定或不指定特征列的数据类型，排除分类列，重启内核，改变批量大小和训练轮数。我可能错过了什么非常简单的事情，但已经花了几个小时试图找出问题所在 🙁
这是代码本身，提前感谢您查看这个问题：
train = pd.read_csv('train.csv')test = pd.read_csv('test.csv')test.columnstrain.info()<class 'pandas.core.frame.DataFrame'>RangeIndex: 891 entries, 0 to 890Data columns (total 12 columns):PassengerId 891 non-null int64Survived 891 non-null int64Pclass 891 non-null int64Name 891 non-null objectSex 891 non-null objectAge 714 non-null float64SibSp 891 non-null int64Parch 891 non-null int64Ticket 891 non-null objectFare 891 non-null float64Cabin 204 non-null objectEmbarked 889 non-null objectdtypes: float64(2), int64(5), object(5)memory usage: 83.6+ KBy = train['Survived']X = train.drop(['Name','Survived','Ticket','PassengerId'],axis=1)X_test = testcols_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)顺便说一下，当TF崩溃时，我在终端中看到了这个错误：
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)回答：
天哪，解决方案竟然如此简单 🙂
我手动将整数列的dtype设置为int8，这些列包含的数字在字节大小的范围内：
pclass = tf.feature_column.numeric_column(‘Pclass’, dtype=tf.int8)
parch = tf.feature_column.numeric_column(‘Parch’, dtype=tf.int8)
缩小数据大小确实是合理的，尽管我仍然想知道为什么我之前在处理其他仅包含字节大小数字的数据集时没有遇到这个问题。
