我是TensorFlow Serving的新手。我使用估算器训练了一个广泛而深入的模型。现在我要为模型服务。创建我的服务输入接收器功能并保存模型。当我尝试使用保存的模型进行预测时，我总是会收到InternalError: Unable to get element as bytes。我不太了解该服务功能应该包含什么内容或应该发送哪种格式类型。有人可以先解释一下服务功能的概念，以及如何正确创建功能。
可复制的示例：https://github.com/dangz90/wide_and_deep_debugging/blob/master/wide%20and%20deep%20debug.ipynb
服务功能：
def serving_input_receiver_fn():
serialized_tf_example = tf.placeholder(dtype=tf.string,
shape=[],
name='input_tensor')
receiver_tensors = {'inputs': serialized_tf_example}
parsed_features = tf.parse_single_example(
serialized_tf_example,
# Defaults are not specified since both keys are required.
features={
'var1': tf.FixedLenFeature(shape=[1], dtype=tf.string),
'var2': tf.FixedLenFeature(shape=[1], dtype=tf.string),
'var3': tf.FixedLenFeature(shape=[1], dtype=tf.string),
'var4': tf.VarLenFeature(dtype=tf.string),
})
return tf.estimator.export.ServingInputReceiver(parsed_features, receiver_tensors)
estimator_predictor = tf.contrib.predictor.from_estimator(m, serving_input_receiver_fn)
estimator_predictor({ 'inputs': examples.SerializeToString() })
我曾尝试发送熊猫，示例等。但实际上我不知道数据应该是哪种格式。对于我的训练，输入的数据保存为tfrecord，然后加载为数据集。 注意：如果我直接使用模型m.predict()，我就能正确获得预测结果
完整错误：
--------------------------------------------------------------------------- InternalError Traceback (most recent call last) /databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 1333 try:
-> 1334 return fn(*args) 1335 except errors.OpError as e:
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata) 1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata) 1320
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata) 1406 self._session, options, feed_dict, fetch_list, target_list,
-> 1407 run_metadata) 1408
InternalError: Unable to get element as bytes.
During handling of the above exception, another exception occurred:
InternalError Traceback (most recent call last) <command-364073753108128> in <module>()
3
4 estimator_predictor = tf.contrib.predictor.from_estimator(m, serving_input_receiver_fn)
----> 5 estimator_predictor({ 'inputs': examples_ })
/databricks/python/lib/python3.6/site-packages/tensorflow/contrib/predictor/predictor.py in __call__(self, input_dict)
75 if value is not None:
76 feed_dict[self.feed_tensors[key]] = value
---> 77 return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata)
674 feed_dict=feed_dict,
675 options=options,
--> 676 run_metadata=run_metadata)
677
678 def run_step_fn(self, step_fn):
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata) 1169 feed_dict=feed_dict, 1170 options=options,
-> 1171 run_metadata=run_metadata) 1172 except _PREEMPTION_ERRORS as e: 1173 logging.info('An error was raised. This may be due to a preemption in '
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, *args, **kwargs) 1268 raise six.reraise(*original_exc_info) 1269 else:
-> 1270 raise six.reraise(*original_exc_info) 1271 1272
/databricks/python/lib/python3.6/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
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, *args, **kwargs) 1253 def run(self, *args,
**kwargs): 1254 try:
-> 1255 return self._sess.run(*args, **kwargs) 1256 except _PREEMPTION_ERRORS: 1257 raise
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, fetches, feed_dict, options, run_metadata) 1325 feed_dict=feed_dict, 1326 options=options,
-> 1327 run_metadata=run_metadata) 1328 1329 for hook in self._hooks:
/databricks/python/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py in run(self, *args, **kwargs) 1089 1090 def run(self, *args,
**kwargs):
-> 1091 return self._sess.run(*args, **kwargs) 1092 1093 def run_step_fn(self, step_fn, raw_session, run_with_hooks):
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1150 if final_fetches or final_targets or (handle and feed_dict_tensor): 1151 results = self._do_run(handle, final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata) 1153 else: 1154 results = []
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1326 if handle is None: 1327 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata) 1329 else: 1330 return self._do_call(_prun_fn, handle, feeds, fetches)
/databricks/python/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) 1346 pass 1347 message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message) 1349 1350 def _extend_graph(self):
InternalError: Unable to get element as bytes.
这是示例在序列化之前的样子。
features {
feature {
key: "var4"
value {
bytes_list {
value: "43"
value: "65"
value: "89"
value: "02"
}
}
}
feature {
key: "var3"
value {
bytes_list {
value: "0123194"
}
}
}
feature {
key: "var2"
value {
bytes_list {
value: "1243"
}
}
}
feature {
key: "var1"
value {
bytes_list {
value: "54"
}
}
}
}
要获取示例，我运行以下脚本：
def serialise_input(data):
dict_feature = {}
for e in data.items():
if e[0] == "var4":
dict_feature.update({e[0]: Feature(bytes_list=BytesList(value=[m.encode('utf-8') for m in e[1]]))})
else:
dict_feature.update({e[0]: Feature(bytes_list=BytesList( value=[e[1].encode()] ))})
example = Example(features=Features(feature=dict_feature))
return example.SerializeToString()
# Serialize Input
raw_data = test.toPandas().dropna().iloc[0,:-1]
examples_ = serialise_input(raw_data)
谢谢。
答案 0 :(得分：2)
您的serialized_tf_example占位符似乎有shape=[]，这是一个示例。您应该传递单个tf.train.Example，该字符串序列化为字符串：
# I assume here examples_ is a list of tf.train.Examples
example = examples_[0].SerializeToString()
estimator_predictor({ 'inputs': example })
如果要提供一批示例而不是单个序列化示例，则需要使用shape=[None]和tf.io.parse_example。
此外，您提供的示例应具有在其上定义的功能，以供您在功能字典中引用（例如var1），以便可以对其进行正确解析。
服务功能指定了模型在预测时应如何接收输入（当您将训练有素的TensorFlow图形/模型导出为SavedModel时）。而不是像在训练中那样通常使用占位符或tf.data.Dataset并需要有状态python / TF运行时执行的情况下进行活动的TF会话，而是希望SavedModel能够序列化并写入磁盘，以便可以使用TF进行部署在移动设备等上服务或运行它。
因此，您导出此SavedModel-服务输入是其中的一部分，它定义了如何解析发送的请求，然后将其连接到模型中。您可能希望将其发送的方式为tf.train.Examples协议缓冲区（序列化为字符串，以便可以在RPC中发送）。然后，您将它们解析为一个特征字典，以便您的估算者可以像理解您的训练数据一样理解它们。
答案 1 :(得分：1)
问题在于我如何构造输入和输入服务功能。解决方法如下：
feat_name_type = {'var1':str, 'var2':str, 'var3':str, 'var4':list}
def input_fn(df):
examples = [None] * len(df)
for i, sample in df.iterrows():
ex = tf.train.Example()
for feat_name, feat_type in feat_name_type.items():
feat = ex.features.feature[feat_name]
if feat_type == int:
feat.int64_list.value.extend([sample[feat_name]])
elif feat_type == float:
feat.float_list.value.extend([sample[feat_name]])
elif feat_type == str:
feat.bytes_list.value.extend([sample[feat_name].encode()])
elif feat_type == list:
feat.bytes_list.value.extend([s.encode() for s in sample[feat_name]])
examples[i] = ex.SerializeToString()
return {"inputs": examples}
'''Service input function'''
tf_feat_cols = deep_columns + wide_columns #The feature columns created for feeding into the model
serve_rcvr_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
tf.feature_column.make_parse_example_spec(tf_feat_cols)
)
rcvr_fn_map = {
tf.estimator.ModeKeys.PREDICT: serve_rcvr_fn,
}