我正在尝试使用 TensorFlow 对一些包含分类和数字数据混合的日志数据运行 DNNClassifier。我已经创建了特征列来指定和存储/散列 tensorflow 的数据。当我运行代码时，我收到“无法将元素作为字节获取”内部错误。注意:我不想删除此 article 中所述的 Nan 值所以我使用此代码将它们转换为 0 train = train.fillna(0, axis=0) 所以我不确定为什么我仍然收到此错误。如果我删除 Nan，那么它会起作用，但我不想删除 Nan，因为我觉得模型需要它们进行训练。
def create_train_input_fn():
return tf.estimator.inputs.pandas_input_fn(
x=train,
y=train_label,
batch_size=32,
num_epochs=None,
shuffle=True)
def create_test_input_fn():
return tf.estimator.inputs.pandas_input_fn(
x=valid,
y=valid_label,
num_epochs=1,
shuffle=False)
feature_columns = []
end_time = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('end_time', 1000), 10)
feature_columns.append(end_time)
device = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device', 1000), 10)
feature_columns.append(device)
device_os = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_os', 1000), 10)
feature_columns.append(device_os)
device_os_version = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_os_version', 1000), 10)
feature_columns.append(device_os_version)
Latency = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column('Latency'),
boundaries=[.000000, .000010, .000100, .001000, .010000, .100000])
feature_columns.append(Latency)
Megacycles = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column('Megacycles'),
boundaries=[0, 50, 100, 200, 300])
feature_columns.append(Megacycles)
Cost = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column('Cost'),
boundaries=[0.000001e-08, 1.000000e-08, 5.000000e-08, 10.000000e-08, 15.000000e-08 ])
feature_columns.append(Cost)
device_brand = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_brand', 1000), 10)
feature_columns.append(device_brand)
device_family = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('device_family', 1000), 10)
feature_columns.append(device_family)
browser_version = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('browser_version', 1000), 10)
feature_columns.append(browser_version)
app = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('app', 1000), 10)
feature_columns.append(app)
ua_parse = tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_hash_bucket('ua_parse', 1000), 10)
feature_columns.append(ua_parse)
estimator = tf.estimator.DNNClassifier(hidden_units=[256, 128, 64],
feature_columns=feature_columns,
n_classes=2,
model_dir='graphs/dnn')
train_input_fn = create_train_input_fn()
estimator.train(train_input_fn, steps=2000)
然后我收到这个错误:
InternalErrorTraceback (most recent call last)
<ipython-input-67-6abd6f1afc3a> in <module>()
1 train_input_fn = create_train_input_fn()
----> 2 estimator.train(train_input_fn, steps=2000)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
312
313 saving_listeners = _check_listeners_type(saving_listeners)
--> 314 loss = self._train_model(input_fn, hooks, saving_listeners)
315 logging.info('Loss for final step: %s.', loss)
316 return self
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners)
813 loss = None
814 while not mon_sess.should_stop():
--> 815 _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss])
816 return loss
817
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in __exit__(self, exception_type, exception_value, traceback)
649 if exception_type in [errors.OutOfRangeError, StopIteration]:
650 exception_type = None
--> 651 self._close_internal(exception_type)
652 # __exit__ should return True to suppress an exception.
653 return exception_type is None
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in _close_internal(self, exception_type)
686 if self._sess is None:
687 raise RuntimeError('Session is already closed.')
--> 688 self._sess.close()
689 finally:
690 self._sess = None
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in close(self)
932 if self._sess:
933 try:
--> 934 self._sess.close()
935 except _PREEMPTION_ERRORS:
936 pass
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in close(self)
1076 self._coord.join(
1077 stop_grace_period_secs=self._stop_grace_period_secs,
-> 1078 ignore_live_threads=True)
1079 finally:
1080 try:
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/training/coordinator.pyc in join(self, threads, stop_grace_period_secs, ignore_live_threads)
385 self._registered_threads = set()
386 if self._exc_info_to_raise:
--> 387 six.reraise(*self._exc_info_to_raise)
388 elif stragglers:
389 if ignore_live_threads:
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/estimator/inputs/queues/feeding_queue_runner.pyc 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.
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc 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)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
1126 if final_fetches or final_targets or (handle and feed_dict_tensor):
1127 results = self._do_run(handle, final_targets, final_fetches,
-> 1128 feed_dict_tensor, options, run_metadata)
1129 else:
1130 results = []
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1342 if handle is None:
1343 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1344 options, run_metadata)
1345 else:
1346 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
/usr/local/envs/py2env/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1361 except KeyError:
1362 pass
-> 1363 raise type(e)(node_def, op, message)
1364
1365 def _extend_graph(self):
InternalError: Unable to get element as bytes.
最佳答案
我同意 Thomas Decaux 的观点。我遇到了完全相同的问题。我检查了我的标签是否表示为字符串("is"和“否”)而不是整数 (1,0)。将标签转换为 int64 后，没有出现此类错误。
关于python - TensorFlow 内部错误 : Unable to get element as bytes，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49500495/
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