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私は入力テンソルとして文字列プレースホルダーをとるsavedModelをエクスポートしています。この文字列テンソルを前処理してモデルに渡すことができるようにグラフを挿入しました。しかし、私はpy_funcを使用して、テンソルでのPython文字列操作を実行しています。不明：KeyError： 'pyfunc_0'
ここでinput_textはsavedModelシグネチャの入力テンソルです。デフォルトのinput_intsの別のプレースホルダを作成しました。これはpy_funcの結果をinput_textに初期化しています。私は最初にinput_textを操作（input_ints =tf.py_func(preprocess, [input_text], tf.int64)）として持っていましたが、tf.nn.dynamic_rnnはテンソルを指定していませんでした。
# 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)
、私は適切にモデルをエクスポートすることができますが、モデルを復元するとき、私は次のエラーを取得：
2017-11-23 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)]]
を私はGitHubのに掲示このissue見てきましたが、私はこれを実装する方法は不明です。また、私はモデルを読み込んで、入力用に文字列を渡していますが、 'freeze_graph'を使用していません。モデルを保存するための
マイコード：モデルをロードするための
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")
私のコード。ない関数を定義するか、プレースホルダは「pyfunc_0」はエラーにつながる：
#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)
更新：
をsavedModelが動作しているようロード時に機能し、プレースホルダを定義します。しかし、モデルを保存するためにビルダーを使用する前にグラフに追加されていない理由はわかりません。
'SavedModelBuilder'を作成し、メタデータを追加するのに使用したコードを確認するのに役立ちます。 – MatthewScarpino
'' pyfunc_0''は、あなたが思うところには存在しません。それを追跡し、それがなぜ存在しないのかを調べる。 –
@MatthewScarpinoモデルの保存方法について詳しく説明しました。 – skbrhmn