我正在尝试对包含混合分类数据和数字数据的某些日志数据使用TensorFlow运行DNNClassifier。我创建了特征列来指定和存储/散列张量流的数据。运行代码时，我收到“无法将元素作为字节获取”内部错误。
import pandas as pd
import tensorflow as tf
import matplotlib as plt
%matplotlib inline
diabetes = pd.read_csv('Crimes_-_2001_to_present.csv')
diabetes.head()
diabetes.columns
id= tf.feature_column.numeric_column('ID')
date_time = tf.feature_column.numeric_column('Date') # grabing feature
column as numeric values, rest should be done in same way
dist = tf.feature_column.numeric_column('District')
#x_coord = tf.feature_column.numeric_column(key='X Coordinate',dtype=tf.float64)
#y_coord = tf.feature_column.numeric_column('Y Coordiante')
year = tf.feature_column.numeric_column('Year')
lat = tf.feature_column.numeric_column('Latitude')
long = tf.feature_column.numeric_column('Longitude')
loc = tf.feature_column.numeric_column('Location')
arrest = tf.feature_column.categorical_column_with_vocabulary_list('Arrest',['True', 'False']) # grabing feature column as vocabulary according to the defined list
#crime = tf.feature_column.categorical_column_with_hash_bucket('Primary Type', hash_bucket_size=40) # grabing feature column as vocabulary according to the hash bucket size and will automatically create list in background
Dist_bucket = tf.feature_column.bucketized_column(dist, boundaries =[1,10,20,30,40] ) #bucketized column split feature column value into different categories based on numerical ranges given in boundaries
feat_column = [id, date_time, Dist_bucket, year, lat, long, loc] #gathering all feature columns
x_data = diabetes.drop('Arrest', axis=1)
labels = diabetes['Arrest']
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x_data, labels,test_size = 0.3, random_state = 101 )
input_func = tf.estimator.inputs.pandas_input_fn(x=x_train, y=y_train,
batch_size=10, num_epochs=1000, shuffle=True)
model=tf.estimator.LinearClassifier(feature_columns=feat_column, n_classes=2)
model.train(input_fn = input_func, steps = 1000)
eval_input_func = tf.estimator.inputs.pandas_input_fn(x = x_test, y=y_test, batch_size=10, num_epochs = 1, shuffle=False)
results = model.evaluate(eval_input_func)
results
然后我收到此错误
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
~\Anaconda3\envs\TensorFlow\lib\site-
packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1291 try:
-> 1292 return fn(*args)
1293 except errors.OpError as e:
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1276 return self._call_tf_sessionrun(
-> 1277 options, feed_dict, fetch_list, target_list, run_metadata)
1278
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1366 self._session, options, feed_dict, fetch_list, target_list,
-> 1367 run_metadata)
1368
InternalError: Unable to get element as bytes.
During handling of the above exception, another exception occurred:
InternalError Traceback (most recent call last)
<ipython-input-8-9c9a6ee5b5b0> in <module>
7
8
----> 9 model.train(input_fn = input_func, steps = 1000)
10
11
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
354
355 saving_listeners = _check_listeners_type(saving_listeners)
--> 356 loss = self._train_model(input_fn, hooks, saving_listeners)
357 logging.info('Loss for final step: %s.', loss)
358 return self
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1179 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1180 else:
-> 1181 return self._train_model_default(input_fn, hooks, saving_listeners)
1182
1183 def _train_model_default(self, input_fn, hooks, saving_listeners):
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1213 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
1214 hooks, global_step_tensor,
-> 1215 saving_listeners)
1216
1217 def _train_model_distributed(self, input_fn, hooks, saving_listeners):
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
1407 loss = None
1408 while not mon_sess.should_stop():
-> 1409 _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
1410 return loss
1411
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\training\monitored_session.py in __exit__(self, exception_type, exception_value, traceback)
781 if exception_type in [errors.OutOfRangeError, StopIteration]:
782 exception_type = None
--> 783 self._close_internal(exception_type)
784 # __exit__ should return True to suppress an exception.
785 return exception_type is None
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\training\monitored_session.py in _close_internal(self, exception_type)
819 if self._sess is None:
820 raise RuntimeError('Session is already closed.')
--> 821 self._sess.close()
822 finally:
823 self._sess = None
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\training\monitored_session.py in close(self)
1067 if self._sess:
1068 try:
-> 1069 self._sess.close()
1070 except _PREEMPTION_ERRORS:
1071 pass
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\training\monitored_session.py in close(self)
1211 self._coord.join(
1212 stop_grace_period_secs=self._stop_grace_period_secs,
-> 1213 ignore_live_threads=True)
1214 finally:
1215 try:
~\Anaconda3\envs\TensorFlow\lib\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:
~\Anaconda3\envs\TensorFlow\lib\site-packages\six.py in reraise(tp, value, tb)
690 value = tp()
691 if value.__traceback__ is not tb:
--> 692 raise value.with_traceback(tb)
693 raise value
694 finally:
~\Anaconda3\envs\TensorFlow\lib\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\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
885 try:
886 result = self._run(None, fetches, feed_dict, options_ptr,
--> 887 run_metadata_ptr)
888 if run_metadata:
889 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1108 if final_fetches or final_targets or (handle and feed_dict_tensor):
1109 results = self._do_run(handle, final_targets, final_fetches,
-> 1110 feed_dict_tensor, options, run_metadata)
1111 else:
1112 results = []
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1284 if handle is None:
1285 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1286 run_metadata)
1287 else:
1288 return self._do_call(_prun_fn, handle, feeds, fetches)
~\Anaconda3\envs\TensorFlow\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1306 self._config.experimental.client_handles_error_formatting):
1307 message = error_interpolation.interpolate(message, self._graph)
-> 1308 raise type(e)(node_def, op, message)
1309
1310 def _extend_graph(self):
InternalError: Unable to get element as bytes.