您必须使用 dtype 字符串和形状 [1] 为占位符张量 'input_example_tensor' 提供一个值
You must feed a value for placeholder tensor 'input_example_tensor' with dtype string and shape [1]
我正在使用 chatbot-retrieval 项目开发一个 tensorflow 服务 client/server 应用程序。
我的代码有两部分，即服务部分和客户端部分。
下面是服务部分的代码片段。
def get_features(context, utterance):
context_len = 50
utterance_len = 50
features = {
"context": context,
"context_len": tf.constant(context_len, shape=[1,1], dtype=tf.int64),
"utterance": utterance,
"utterance_len": tf.constant(utterance_len, shape=[1,1], dtype=tf.int64),
}
return features
def my_input_fn(estimator, input_example_tensor ):
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
context = tf.identity(tf_example['context'], name='context')
utterance = tf.identity(tf_example['utterance'], name='utterance')
features = get_features(context, utterance)
return features
def my_signature_fn(input_example_tensor, features, predictions):
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
tf_context = tf.identity(tf_example['context'], name='tf_context_utterance')
tf_utterance = tf.identity(tf_example['utterance'], name='tf_utterance')
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=tf.identity(predictions)
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':tf_context,
'utterance':tf_utterance
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
use_deprecated_input_fn = True,
signature_fn = my_signature_fn,
exports_to_keep = 1
)
下面是客户端部分的代码片段。
def tokenizer_fn(iterator):
return (x.split(" ") for x in iterator)
vp = tf.contrib.learn.preprocessing.VocabularyProcessor.restore(FLAGS.vocab_processor_file)
input_context = "biz banka kart farkli bir banka atmsinde para"
input_utterance = "farkli banka kart biz banka atmsinde para"
context_feature = np.array(list(vp.transform([input_context])))
utterance_feature = np.array(list(vp.transform([input_utterance])))
context_tensor = tf.contrib.util.make_tensor_proto(context_feature, shape=[1, context_feature.size])
utterance_tensor = tf.contrib.util.make_tensor_proto(context_feature, shape=[1, context_feature.size])
request.inputs['context'].CopyFrom(context_tensor)
request.inputs['utterance'].CopyFrom(utterance_tensor)
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
服务部分和客户端部分均已正确构建。在 运行 服务应用程序和客户端应用程序之后，当 rpc 调用完成时，我收到以下传播到客户端应用程序的奇怪错误。
以下是 rpc 调用完成时出现的错误
AbortionError(code=StatusCode.INVALID_ARGUMENT, details="You must feed a value for placeholder tensor 'input_example_tensor' with dtype string and shape [1]
[[Node: input_example_tensor = Placeholder[_output_shapes=[[1]], dtype=DT_STRING, shape=[1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]")
错误很奇怪，因为似乎无法从客户端应用程序提供占位符。
如果我通过 tensorflow 服务访问模型，如何为占位符 'input_example_tensor' 提供数据？
答案：
（我 post 在这里编辑了我的答案，因为由于缺少 Whosebug 徽章我无法 post 它作为答案。任何自愿将其作为 his/her 问题答案提交的人是非常欢迎。我会批准它作为答案。）
我可以通过在 estimator.export 函数中使用选项 use_deprecated_input_fn = False 并相应地更改输入签名来解决问题。
下面是最终代码，运行没问题。
def get_features(input_example_tensor, context, utterance):
context_len = 50
utterance_len = 50
features = {
"my_input_example_tensor": input_example_tensor,
"context": context,
"context_len": tf.constant(context_len, shape=[1,1], dtype=tf.int64),
"utterance": utterance,
"utterance_len": tf.constant(utterance_len, shape=[1,1], dtype=tf.int64),
}
return features
def my_input_fn():
input_example_tensor = tf.placeholder(tf.string, name='tf_example_placeholder')
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
context = tf.identity(tf_example['context'], name='context')
utterance = tf.identity(tf_example['utterance'], name='utterance')
features = get_features(input_example_tensor, context, utterance)
return features, None
def my_signature_fn(input_example_tensor, features, predictions):
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=predictions
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':features['context'],
'utterance':features['utterance']
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator._targets_info = tf.contrib.learn.estimators.tensor_signature.TensorSignature(tf.constant(0, shape=[1,1]))
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
input_feature_key ="my_input_example_tensor",
use_deprecated_input_fn = False,
signature_fn = my_signature_fn,
exports_to_keep = 1
)
OP 自行解决但无法自行回答，所以这是他们的答案：
问题已通过在 estimator.export 函数中使用选项 use_deprecated_input_fn = False 并相应地更改输入签名得到解决：
def my_signature_fn(input_example_tensor, features, predictions):
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=predictions
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':features['context'],
'utterance':features['utterance']
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator._targets_info = tf.contrib.learn.estimators.tensor_signature.TensorSignature(tf.constant(0, shape=[1,1]))
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
input_feature_key ="my_input_example_tensor",
use_deprecated_input_fn = False,
signature_fn = my_signature_fn,
exports_to_keep = 1
)
我正在使用 chatbot-retrieval 项目开发一个 tensorflow 服务 client/server 应用程序。
我的代码有两部分，即服务部分和客户端部分。
下面是服务部分的代码片段。
def get_features(context, utterance):
context_len = 50
utterance_len = 50
features = {
"context": context,
"context_len": tf.constant(context_len, shape=[1,1], dtype=tf.int64),
"utterance": utterance,
"utterance_len": tf.constant(utterance_len, shape=[1,1], dtype=tf.int64),
}
return features
def my_input_fn(estimator, input_example_tensor ):
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
context = tf.identity(tf_example['context'], name='context')
utterance = tf.identity(tf_example['utterance'], name='utterance')
features = get_features(context, utterance)
return features
def my_signature_fn(input_example_tensor, features, predictions):
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
tf_context = tf.identity(tf_example['context'], name='tf_context_utterance')
tf_utterance = tf.identity(tf_example['utterance'], name='tf_utterance')
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=tf.identity(predictions)
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':tf_context,
'utterance':tf_utterance
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
use_deprecated_input_fn = True,
signature_fn = my_signature_fn,
exports_to_keep = 1
)
下面是客户端部分的代码片段。
def tokenizer_fn(iterator):
return (x.split(" ") for x in iterator)
vp = tf.contrib.learn.preprocessing.VocabularyProcessor.restore(FLAGS.vocab_processor_file)
input_context = "biz banka kart farkli bir banka atmsinde para"
input_utterance = "farkli banka kart biz banka atmsinde para"
context_feature = np.array(list(vp.transform([input_context])))
utterance_feature = np.array(list(vp.transform([input_utterance])))
context_tensor = tf.contrib.util.make_tensor_proto(context_feature, shape=[1, context_feature.size])
utterance_tensor = tf.contrib.util.make_tensor_proto(context_feature, shape=[1, context_feature.size])
request.inputs['context'].CopyFrom(context_tensor)
request.inputs['utterance'].CopyFrom(utterance_tensor)
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
服务部分和客户端部分均已正确构建。在 运行 服务应用程序和客户端应用程序之后，当 rpc 调用完成时，我收到以下传播到客户端应用程序的奇怪错误。
以下是 rpc 调用完成时出现的错误
AbortionError(code=StatusCode.INVALID_ARGUMENT, details="You must feed a value for placeholder tensor 'input_example_tensor' with dtype string and shape [1]
[[Node: input_example_tensor = Placeholder[_output_shapes=[[1]], dtype=DT_STRING, shape=[1], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]")
错误很奇怪，因为似乎无法从客户端应用程序提供占位符。
如果我通过 tensorflow 服务访问模型，如何为占位符 'input_example_tensor' 提供数据？
答案： （我 post 在这里编辑了我的答案，因为由于缺少 Whosebug 徽章我无法 post 它作为答案。任何自愿将其作为 his/her 问题答案提交的人是非常欢迎。我会批准它作为答案。）
我可以通过在 estimator.export 函数中使用选项 use_deprecated_input_fn = False 并相应地更改输入签名来解决问题。
下面是最终代码，运行没问题。
def get_features(input_example_tensor, context, utterance):
context_len = 50
utterance_len = 50
features = {
"my_input_example_tensor": input_example_tensor,
"context": context,
"context_len": tf.constant(context_len, shape=[1,1], dtype=tf.int64),
"utterance": utterance,
"utterance_len": tf.constant(utterance_len, shape=[1,1], dtype=tf.int64),
}
return features
def my_input_fn():
input_example_tensor = tf.placeholder(tf.string, name='tf_example_placeholder')
feature_configs = {
'context':tf.FixedLenFeature(shape=[50], dtype=tf.int64),
'utterance':tf.FixedLenFeature(shape=[50], dtype=tf.int64)
}
tf_example = tf.parse_example(input_example_tensor, feature_configs)
context = tf.identity(tf_example['context'], name='context')
utterance = tf.identity(tf_example['utterance'], name='utterance')
features = get_features(input_example_tensor, context, utterance)
return features, None
def my_signature_fn(input_example_tensor, features, predictions):
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=predictions
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':features['context'],
'utterance':features['utterance']
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator._targets_info = tf.contrib.learn.estimators.tensor_signature.TensorSignature(tf.constant(0, shape=[1,1]))
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
input_feature_key ="my_input_example_tensor",
use_deprecated_input_fn = False,
signature_fn = my_signature_fn,
exports_to_keep = 1
)
OP 自行解决但无法自行回答，所以这是他们的答案：
问题已通过在 estimator.export 函数中使用选项 use_deprecated_input_fn = False 并相应地更改输入签名得到解决：
def my_signature_fn(input_example_tensor, features, predictions):
default_graph_signature = exporter.regression_signature(
input_tensor=input_example_tensor,
output_tensor=predictions
)
named_graph_signatures = {
'inputs':exporter.generic_signature(
{
'context':features['context'],
'utterance':features['utterance']
}
),
'outputs':exporter.generic_signature(
{
'scores':predictions
}
)
}
return default_graph_signature, named_graph_signatures
def main():
##preliminary codes here##
estimator.fit(input_fn=input_fn_train, steps=100, monitors=[eval_monitor])
estimator._targets_info = tf.contrib.learn.estimators.tensor_signature.TensorSignature(tf.constant(0, shape=[1,1]))
estimator.export(
export_dir = FLAGS.export_dir,
input_fn = my_input_fn,
input_feature_key ="my_input_example_tensor",
use_deprecated_input_fn = False,
signature_fn = my_signature_fn,
exports_to_keep = 1
)