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문자열 쉼표를 입력 텐서로 사용하는 savedModel을 내보내고 있습니다. 이 문자열 텐서를 전처리하여 모델에 전달할 그래프를 삽입했습니다. 그러나, 나는 py_func을 사용하여 텐서에서 파이썬 문자열 연산을 수행합니다.알 수 없음 : KeyError : 'pyfunc_0'
여기서 input_text은 savedModel 서명의 입력 텐서입니다. 기본 위치가 input_ints 인 다른 자리 표시자를 만들고 py_func을 수행 한 결과로 input_text에 초기화되었습니다. 처음에는 input_text를 연산 (input_ints =tf.py_func(preprocess, [input_text], tf.int64))으로 사용했지만 tf.nn.dynamic_rnn은 지정되지 않은 텐서를 받아들이지 않았습니다.
# Create the graph object
with tf.name_scope('inputs'):
input_text = tf.placeholder(tf.string, name="input_text")
input_ints = tf.placeholder_with_default(
tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])
def lstm_cell():
# Your basic LSTM cell
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size, reuse=tf.get_variable_scope().reuse)
# Add dropout to the cell
return tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
# def create_rnn():
with tf.name_scope("Embeddings"):
embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))
embed = tf.nn.embedding_lookup(embedding, input_ints)
with tf.name_scope("RNN_layers"):
cell = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(lstm_layers)])
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.name_scope("RNN_forward"):
outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state)
with tf.name_scope('predictions'):
predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid)
이제 위의 구현을 사용
, 나는 제대로 모델을 내보낼 수 있지만 모델을 복원 할 때, 나는 다음과 같은 오류 얻을 : 내가 Github에서에 게시 된이 issue 살펴 보았다2017-11-23 17:29:14.600184: W tensorflow/core/framework/op_kernel.cc:1192] Unknown: KeyError: 'pyfunc_0'
Traceback (most recent call last):
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/Users/sakibarrahman/anaconda/lib/python3.6/contextlib.py", line 89, in __exit__
next(self.gen)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.UnknownError: KeyError: 'pyfunc_0'
[[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "neural_load_model.py", line 85, in <module>
result = sess.run(output_tensor, {input_tensor: "Charter Communications, Inc. (CHTR) Stock Rating Reaffirmed by Goldman Sachs Group, Inc. (The)"})
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: KeyError: 'pyfunc_0'
[[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]
Caused by op 'inputs/PyFunc', defined at:
File "neural_load_model.py", line 74, in <module>
model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], import_path)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/saved_model/loader_impl.py", line 216, in load
saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1698, in import_meta_graph
**kwargs)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/meta_graph.py", line 656, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def
op_def=op_def)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
UnknownError (see above for traceback): KeyError: 'pyfunc_0'
[[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]
을하지만, 나는 이 방법을 구현하는 방법에 대해서는 확실하지 않습니다. 또한 모델을로드하고 'freeze_graph'를 사용하지 않고 입력 용 문자열을 전달합니다. 모델을 저장하는
내 코드 : 모델을로드하기위한
saver = tf.train.Saver()
#Define new functions
def preprocess(text):
.
.
.
tf.reset_default_graph()
.
.
.
#Define new placeholder that was not in the original model graph
#Define new placeholder with default value initialized with py_func that was not in the original model graph
with tf.name_scope('inputs'):
input_text = tf.placeholder(tf.string, name="input_text")
input_ints = tf.placeholder_with_default(
tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])
.
.
.
#Define placeholders and ops that I need and were in the original graph
saver = tf.train.Saver()
#Serving the model
with tf.Session() as sess:
#Restore from old checkpoint
saver.restore(sess, import_path)
print ('Exporting trained model to %s'%(export_path))
builder = saved_model_builder.SavedModelBuilder(export_path)
original_assets_directory = export_path + '/assets'
original_assets_filename = "vocabulary.pickle"
original_assets_filepath = write_vocab(original_assets_directory,
original_assets_filename)
# Set up the assets collection.
assets_filepath = tf.constant(original_assets_filepath)
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, assets_filepath)
filename_tensor = tf.Variable(
original_assets_filename,
name="vocab_tensor",
trainable=False,
collections=[])
assign_filename_op = filename_tensor.assign(original_assets_filename)
# Build the signature_def_map.
classification_inputs = utils.build_tensor_info(input_text)
classification_outputs_classes = utils.build_tensor_info(predictions)
classification_signature = signature_def_utils.build_signature_def(
inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={
signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
},
method_name=signature_constants.CLASSIFY_METHOD_NAME)
legacy_init_op = tf.group(
tf.tables_initializer(), name='legacy_init_op')
#add the sigs to the servable
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature
},
assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS),
legacy_init_op=tf.group(assign_filename_op))
print ("added meta graph and variables")
builder.save()
print("model saved")
내 코드입니다. 아니 함수를 정의하거나 자리는 'pyfunc_0'오류로 연결 :
#Define preprocess function
def preprocess(text_bin):
#Define new placeholders
with tf.name_scope('inputs'):
input_text = tf.placeholder(tf.string, name="input_text")
input_ints = tf.placeholder_with_default(
tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])
with tf.Session(graph=tf.Graph()) as sess:
# restore save model
model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], import_path)
print("model restored")
loaded_graph = tf.get_default_graph()
# get necessary tensors by name
input_tensor_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].inputs[signature_constants.CLASSIFY_INPUTS].name
input_tensor = loaded_graph.get_tensor_by_name(input_tensor_name)
output_tensor_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES].name
output_tensor = loaded_graph.get_tensor_by_name(output_tensor_name)
result = sess.run(output_tensor, {input_tensor: "Some String"})
print (result)
업데이트 :
을 savedModel가 작동하는 것 같다로드 할 때 기능과 자리를 정의. 그러나 모델을 저장하기 위해 빌더를 사용하기 전에 왜 그래프에 추가되지 않는지 모르겠습니다.
'SavedModelBuilder'를 만들고 메타 데이터를 추가하는 데 사용한 코드를 보는 것이 도움이 될 것입니다. – MatthewScarpino
''pyfunc_0 ''은 당신이 생각하는 곳에 존재하지 않습니다. 그것을 추적하고 그것이 존재하지 않는 이유를 알아보십시오. –
@MatthewScarpino 모델을 저장하는 방법에 대한 자세한 내용을 추가했습니다. – skbrhmn